2022/08/31 19:33:17 - 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: 480638974 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 19:33:18 - 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=False) wildreceipt_data_root = 'data/kie/wildreceipt/' wildreceipt_train = dict( type='WildReceiptDataset', data_root='data/kie/wildreceipt/', metainfo='data/kie/wildreceipt/class_list.txt', ann_file='train.txt', pipeline=[ dict(type='LoadKIEAnnotations'), dict(type='Resize', scale=(1024, 512), keep_ratio=True), dict(type='PackKIEInputs') ]) wildreceipt_test = dict( type='WildReceiptDataset', data_root='data/kie/wildreceipt/', metainfo='data/kie/wildreceipt/class_list.txt', ann_file='test.txt', test_mode=True, pipeline=[ dict(type='LoadKIEAnnotations'), 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=26, module_loss=dict(type='SDMGRModuleLoss'), postprocessor=dict(type='SDMGRPostProcessor')), 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'), dict(type='Resize', scale=(1024, 512), keep_ratio=True), dict(type='PackKIEInputs') ] val_evaluator = dict( type='F1Metric', mode='macro', num_classes=26, ignored_classes=[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 25]) test_evaluator = dict( type='F1Metric', mode='macro', num_classes=26, ignored_classes=[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 25]) 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='data/kie/wildreceipt/class_list.txt', ann_file='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='data/kie/wildreceipt/class_list.txt', ann_file='test.txt', test_mode=True, pipeline=[ dict(type='LoadKIEAnnotations'), 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='data/kie/wildreceipt/class_list.txt', ann_file='test.txt', test_mode=True, pipeline=[ dict(type='LoadKIEAnnotations'), dict(type='Resize', scale=(1024, 512), keep_ratio=True), dict(type='PackKIEInputs') ])) launcher = 'none' work_dir = './work_dirs/sdmgr_novisual_60e_wildreceipt' 2022/08/31 19:33:20 - 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([26, 256]): The value is the same before and after calling `init_weights` of SDMGR kie_head.node_cls.bias - torch.Size([26]): 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 19:33:27 - mmengine - INFO - Epoch(train) [1][100/317] lr: 1.0000e-03 eta: 0:08:08 time: 0.0214 data_time: 0.0027 memory: 680 loss_node: 1.2014 loss_edge: 0.0001 acc_node: 55.4795 acc_edge: 100.0000 loss: 1.2015 2022/08/31 19:33:30 - mmengine - INFO - Epoch(train) [1][200/317] lr: 1.0000e-03 eta: 0:08:00 time: 0.0254 data_time: 0.0026 memory: 527 loss_node: 0.7605 loss_edge: 0.0001 acc_node: 62.7586 acc_edge: 100.0000 loss: 0.7606 2022/08/31 19:33:33 - mmengine - INFO - Epoch(train) [1][300/317] lr: 1.0000e-03 eta: 0:08:06 time: 0.0242 data_time: 0.0027 memory: 968 loss_node: 0.6437 loss_edge: 0.0000 acc_node: 75.6098 acc_edge: 100.0000 loss: 0.6437 2022/08/31 19:33:33 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:33:33 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/08/31 19:33:34 - mmengine - INFO - Epoch(val) [1][100/472] eta: 0:00:01 time: 0.0037 data_time: 0.0008 memory: 485 2022/08/31 19:33:34 - mmengine - INFO - Epoch(val) [1][200/472] eta: 0:00:01 time: 0.0043 data_time: 0.0007 memory: 138 2022/08/31 19:33:35 - mmengine - INFO - Epoch(val) [1][300/472] eta: 0:00:00 time: 0.0046 data_time: 0.0009 memory: 329 2022/08/31 19:33:35 - mmengine - INFO - Epoch(val) [1][400/472] eta: 0:00:00 time: 0.0039 data_time: 0.0007 memory: 76 2022/08/31 19:33:35 - mmengine - INFO - Epoch(val) [1][472/472] kie/macro_f1: 0.6674 2022/08/31 19:33:38 - mmengine - INFO - Epoch(train) [2][100/317] lr: 1.0000e-03 eta: 0:07:46 time: 0.0229 data_time: 0.0027 memory: 438 loss_node: 0.5350 loss_edge: 0.0000 acc_node: 84.0000 acc_edge: 100.0000 loss: 0.5350 2022/08/31 19:33:40 - mmengine - INFO - Epoch(train) [2][200/317] lr: 1.0000e-03 eta: 0:07:50 time: 0.0318 data_time: 0.0027 memory: 928 loss_node: 0.5174 loss_edge: 0.0000 acc_node: 84.0000 acc_edge: 100.0000 loss: 0.5174 2022/08/31 19:33:43 - mmengine - INFO - Epoch(train) [2][300/317] lr: 1.0000e-03 eta: 0:07:49 time: 0.0234 data_time: 0.0027 memory: 639 loss_node: 0.5139 loss_edge: 0.0000 acc_node: 84.1880 acc_edge: 100.0000 loss: 0.5140 2022/08/31 19:33:43 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:33:43 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/08/31 19:33:44 - mmengine - INFO - Epoch(val) [2][100/472] eta: 0:00:01 time: 0.0035 data_time: 0.0007 memory: 523 2022/08/31 19:33:44 - mmengine - INFO - Epoch(val) [2][200/472] eta: 0:00:01 time: 0.0044 data_time: 0.0008 memory: 138 2022/08/31 19:33:45 - mmengine - INFO - Epoch(val) [2][300/472] eta: 0:00:00 time: 0.0049 data_time: 0.0009 memory: 329 2022/08/31 19:33:45 - mmengine - INFO - Epoch(val) [2][400/472] eta: 0:00:00 time: 0.0039 data_time: 0.0007 memory: 76 2022/08/31 19:33:46 - mmengine - INFO - Epoch(val) [2][472/472] kie/macro_f1: 0.7313 2022/08/31 19:33:48 - mmengine - INFO - Epoch(train) [3][100/317] lr: 1.0000e-03 eta: 0:07:45 time: 0.0250 data_time: 0.0028 memory: 866 loss_node: 0.4287 loss_edge: 0.0000 acc_node: 88.2629 acc_edge: 100.0000 loss: 0.4287 2022/08/31 19:33:51 - mmengine - INFO - Epoch(train) [3][200/317] lr: 1.0000e-03 eta: 0:07:47 time: 0.0250 data_time: 0.0028 memory: 919 loss_node: 0.4567 loss_edge: 0.0000 acc_node: 87.4074 acc_edge: 100.0000 loss: 0.4567 2022/08/31 19:33:54 - mmengine - INFO - Epoch(train) [3][300/317] lr: 1.0000e-03 eta: 0:07:48 time: 0.0284 data_time: 0.0026 memory: 566 loss_node: 0.3467 loss_edge: 0.0001 acc_node: 91.1243 acc_edge: 100.0000 loss: 0.3467 2022/08/31 19:33:54 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:33:54 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/08/31 19:33:55 - mmengine - INFO - Epoch(val) [3][100/472] eta: 0:00:01 time: 0.0037 data_time: 0.0008 memory: 290 2022/08/31 19:33:55 - mmengine - INFO - Epoch(val) [3][200/472] eta: 0:00:01 time: 0.0043 data_time: 0.0007 memory: 138 2022/08/31 19:33:56 - mmengine - INFO - Epoch(val) [3][300/472] eta: 0:00:00 time: 0.0048 data_time: 0.0008 memory: 329 2022/08/31 19:33:56 - mmengine - INFO - Epoch(val) [3][400/472] eta: 0:00:00 time: 0.0041 data_time: 0.0008 memory: 76 2022/08/31 19:33:56 - mmengine - INFO - Epoch(val) [3][472/472] kie/macro_f1: 0.7723 2022/08/31 19:33:58 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:33:59 - mmengine - INFO - Epoch(train) [4][100/317] lr: 1.0000e-03 eta: 0:07:42 time: 0.0293 data_time: 0.0028 memory: 704 loss_node: 0.4266 loss_edge: 0.0000 acc_node: 86.4253 acc_edge: 100.0000 loss: 0.4266 2022/08/31 19:34:02 - mmengine - INFO - Epoch(train) [4][200/317] lr: 1.0000e-03 eta: 0:07:40 time: 0.0268 data_time: 0.0029 memory: 941 loss_node: 0.2743 loss_edge: 0.0000 acc_node: 91.4286 acc_edge: 100.0000 loss: 0.2743 2022/08/31 19:34:04 - mmengine - INFO - Epoch(train) [4][300/317] lr: 1.0000e-03 eta: 0:07:36 time: 0.0261 data_time: 0.0027 memory: 588 loss_node: 0.3773 loss_edge: 0.0000 acc_node: 80.1242 acc_edge: 100.0000 loss: 0.3773 2022/08/31 19:34:05 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:34:05 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/08/31 19:34:05 - mmengine - INFO - Epoch(val) [4][100/472] eta: 0:00:01 time: 0.0038 data_time: 0.0009 memory: 855 2022/08/31 19:34:06 - mmengine - INFO - Epoch(val) [4][200/472] eta: 0:00:01 time: 0.0048 data_time: 0.0009 memory: 138 2022/08/31 19:34:06 - mmengine - INFO - Epoch(val) [4][300/472] eta: 0:00:00 time: 0.0044 data_time: 0.0008 memory: 329 2022/08/31 19:34:06 - mmengine - INFO - Epoch(val) [4][400/472] eta: 0:00:00 time: 0.0044 data_time: 0.0008 memory: 76 2022/08/31 19:34:07 - mmengine - INFO - Epoch(val) [4][472/472] kie/macro_f1: 0.7620 2022/08/31 19:34:09 - mmengine - INFO - Epoch(train) [5][100/317] lr: 1.0000e-03 eta: 0:07:31 time: 0.0244 data_time: 0.0029 memory: 416 loss_node: 0.3318 loss_edge: 0.0000 acc_node: 90.4040 acc_edge: 100.0000 loss: 0.3319 2022/08/31 19:34:12 - mmengine - INFO - Epoch(train) [5][200/317] lr: 1.0000e-03 eta: 0:07:32 time: 0.0258 data_time: 0.0030 memory: 875 loss_node: 0.2584 loss_edge: 0.0000 acc_node: 92.6380 acc_edge: 100.0000 loss: 0.2584 2022/08/31 19:34:15 - mmengine - INFO - Epoch(train) [5][300/317] lr: 1.0000e-03 eta: 0:07:31 time: 0.0278 data_time: 0.0030 memory: 938 loss_node: 0.3129 loss_edge: 0.0000 acc_node: 88.7218 acc_edge: 100.0000 loss: 0.3129 2022/08/31 19:34:15 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:34:15 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/08/31 19:34:16 - mmengine - INFO - Epoch(val) [5][100/472] eta: 0:00:01 time: 0.0036 data_time: 0.0007 memory: 502 2022/08/31 19:34:17 - mmengine - INFO - Epoch(val) [5][200/472] eta: 0:00:01 time: 0.0044 data_time: 0.0008 memory: 138 2022/08/31 19:34:17 - mmengine - INFO - Epoch(val) [5][300/472] eta: 0:00:00 time: 0.0045 data_time: 0.0008 memory: 329 2022/08/31 19:34:17 - mmengine - INFO - Epoch(val) [5][400/472] eta: 0:00:00 time: 0.0043 data_time: 0.0008 memory: 76 2022/08/31 19:34:18 - mmengine - INFO - Epoch(val) [5][472/472] kie/macro_f1: 0.7874 2022/08/31 19:34:20 - mmengine - INFO - Epoch(train) [6][100/317] lr: 1.0000e-03 eta: 0:07:28 time: 0.0263 data_time: 0.0028 memory: 713 loss_node: 0.2729 loss_edge: 0.0000 acc_node: 92.7536 acc_edge: 100.0000 loss: 0.2729 2022/08/31 19:34:23 - mmengine - INFO - Epoch(train) [6][200/317] lr: 1.0000e-03 eta: 0:07:26 time: 0.0253 data_time: 0.0029 memory: 948 loss_node: 0.2748 loss_edge: 0.0001 acc_node: 93.8462 acc_edge: 100.0000 loss: 0.2748 2022/08/31 19:34:26 - mmengine - INFO - Epoch(train) [6][300/317] lr: 1.0000e-03 eta: 0:07:24 time: 0.0261 data_time: 0.0027 memory: 822 loss_node: 0.3054 loss_edge: 0.0000 acc_node: 93.2432 acc_edge: 100.0000 loss: 0.3054 2022/08/31 19:34:26 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:34:26 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/08/31 19:34:27 - mmengine - INFO - Epoch(val) [6][100/472] eta: 0:00:01 time: 0.0037 data_time: 0.0007 memory: 248 2022/08/31 19:34:27 - mmengine - INFO - Epoch(val) [6][200/472] eta: 0:00:01 time: 0.0049 data_time: 0.0008 memory: 138 2022/08/31 19:34:28 - mmengine - INFO - Epoch(val) [6][300/472] eta: 0:00:00 time: 0.0053 data_time: 0.0009 memory: 329 2022/08/31 19:34:28 - mmengine - INFO - Epoch(val) [6][400/472] eta: 0:00:00 time: 0.0039 data_time: 0.0007 memory: 76 2022/08/31 19:34:28 - mmengine - INFO - Epoch(val) [6][472/472] kie/macro_f1: 0.7988 2022/08/31 19:34:31 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:34:31 - mmengine - INFO - Epoch(train) [7][100/317] lr: 1.0000e-03 eta: 0:07:19 time: 0.0212 data_time: 0.0027 memory: 692 loss_node: 0.2710 loss_edge: 0.0000 acc_node: 91.4110 acc_edge: 100.0000 loss: 0.2710 2022/08/31 19:34:34 - mmengine - INFO - Epoch(train) [7][200/317] lr: 1.0000e-03 eta: 0:07:18 time: 0.0281 data_time: 0.0030 memory: 832 loss_node: 0.3148 loss_edge: 0.0000 acc_node: 94.3005 acc_edge: 100.0000 loss: 0.3148 2022/08/31 19:34:37 - mmengine - INFO - Epoch(train) [7][300/317] lr: 1.0000e-03 eta: 0:07:17 time: 0.0270 data_time: 0.0027 memory: 912 loss_node: 0.2606 loss_edge: 0.0000 acc_node: 81.4103 acc_edge: 100.0000 loss: 0.2606 2022/08/31 19:34:37 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:34:37 - mmengine - INFO - Saving checkpoint at 7 epochs 2022/08/31 19:34:38 - mmengine - INFO - Epoch(val) [7][100/472] eta: 0:00:01 time: 0.0040 data_time: 0.0009 memory: 353 2022/08/31 19:34:38 - mmengine - INFO - Epoch(val) [7][200/472] eta: 0:00:01 time: 0.0057 data_time: 0.0010 memory: 138 2022/08/31 19:34:39 - mmengine - INFO - Epoch(val) [7][300/472] eta: 0:00:00 time: 0.0049 data_time: 0.0009 memory: 329 2022/08/31 19:34:39 - mmengine - INFO - Epoch(val) [7][400/472] eta: 0:00:00 time: 0.0043 data_time: 0.0009 memory: 76 2022/08/31 19:34:39 - mmengine - INFO - Epoch(val) [7][472/472] kie/macro_f1: 0.8007 2022/08/31 19:34:42 - mmengine - INFO - Epoch(train) [8][100/317] lr: 1.0000e-03 eta: 0:07:13 time: 0.0268 data_time: 0.0028 memory: 613 loss_node: 0.2270 loss_edge: 0.0000 acc_node: 94.5545 acc_edge: 100.0000 loss: 0.2271 2022/08/31 19:34:45 - mmengine - INFO - Epoch(train) [8][200/317] lr: 1.0000e-03 eta: 0:07:10 time: 0.0271 data_time: 0.0028 memory: 614 loss_node: 0.2513 loss_edge: 0.0000 acc_node: 94.1748 acc_edge: 100.0000 loss: 0.2513 2022/08/31 19:34:47 - mmengine - INFO - Epoch(train) [8][300/317] lr: 1.0000e-03 eta: 0:07:09 time: 0.0260 data_time: 0.0028 memory: 970 loss_node: 0.2614 loss_edge: 0.0000 acc_node: 94.3396 acc_edge: 100.0000 loss: 0.2614 2022/08/31 19:34:48 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:34:48 - mmengine - INFO - Saving checkpoint at 8 epochs 2022/08/31 19:34:49 - mmengine - INFO - Epoch(val) [8][100/472] eta: 0:00:01 time: 0.0045 data_time: 0.0008 memory: 283 2022/08/31 19:34:49 - mmengine - INFO - Epoch(val) [8][200/472] eta: 0:00:01 time: 0.0050 data_time: 0.0010 memory: 138 2022/08/31 19:34:50 - mmengine - INFO - Epoch(val) [8][300/472] eta: 0:00:00 time: 0.0047 data_time: 0.0009 memory: 329 2022/08/31 19:34:50 - mmengine - INFO - Epoch(val) [8][400/472] eta: 0:00:00 time: 0.0043 data_time: 0.0009 memory: 76 2022/08/31 19:34:50 - mmengine - INFO - Epoch(val) [8][472/472] kie/macro_f1: 0.7963 2022/08/31 19:34:53 - mmengine - INFO - Epoch(train) [9][100/317] lr: 1.0000e-03 eta: 0:07:05 time: 0.0253 data_time: 0.0027 memory: 963 loss_node: 0.1983 loss_edge: 0.0000 acc_node: 92.4419 acc_edge: 100.0000 loss: 0.1983 2022/08/31 19:34:56 - mmengine - INFO - Epoch(train) [9][200/317] lr: 1.0000e-03 eta: 0:07:04 time: 0.0281 data_time: 0.0028 memory: 586 loss_node: 0.2789 loss_edge: 0.0000 acc_node: 95.2055 acc_edge: 100.0000 loss: 0.2789 2022/08/31 19:34:58 - mmengine - INFO - Epoch(train) [9][300/317] lr: 1.0000e-03 eta: 0:07:01 time: 0.0217 data_time: 0.0028 memory: 850 loss_node: 0.2187 loss_edge: 0.0000 acc_node: 93.4959 acc_edge: 100.0000 loss: 0.2188 2022/08/31 19:34:59 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:34:59 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/08/31 19:35:00 - mmengine - INFO - Epoch(val) [9][100/472] eta: 0:00:01 time: 0.0041 data_time: 0.0008 memory: 578 2022/08/31 19:35:00 - mmengine - INFO - Epoch(val) [9][200/472] eta: 0:00:01 time: 0.0050 data_time: 0.0010 memory: 138 2022/08/31 19:35:00 - mmengine - INFO - Epoch(val) [9][300/472] eta: 0:00:00 time: 0.0050 data_time: 0.0009 memory: 329 2022/08/31 19:35:01 - mmengine - INFO - Epoch(val) [9][400/472] eta: 0:00:00 time: 0.0041 data_time: 0.0008 memory: 76 2022/08/31 19:35:01 - mmengine - INFO - Epoch(val) [9][472/472] kie/macro_f1: 0.8199 2022/08/31 19:35:04 - mmengine - INFO - Epoch(train) [10][100/317] lr: 1.0000e-03 eta: 0:06:58 time: 0.0256 data_time: 0.0029 memory: 935 loss_node: 0.1780 loss_edge: 0.0000 acc_node: 95.8763 acc_edge: 100.0000 loss: 0.1780 2022/08/31 19:35:05 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:35:07 - mmengine - INFO - Epoch(train) [10][200/317] lr: 1.0000e-03 eta: 0:06:57 time: 0.0256 data_time: 0.0030 memory: 712 loss_node: 0.2119 loss_edge: 0.0000 acc_node: 95.7447 acc_edge: 100.0000 loss: 0.2119 2022/08/31 19:35:10 - mmengine - INFO - Epoch(train) [10][300/317] lr: 1.0000e-03 eta: 0:06:55 time: 0.0259 data_time: 0.0029 memory: 624 loss_node: 0.2047 loss_edge: 0.0000 acc_node: 91.8919 acc_edge: 100.0000 loss: 0.2047 2022/08/31 19:35:10 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:35:10 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/08/31 19:35:11 - mmengine - INFO - Epoch(val) [10][100/472] eta: 0:00:01 time: 0.0041 data_time: 0.0009 memory: 299 2022/08/31 19:35:11 - mmengine - INFO - Epoch(val) [10][200/472] eta: 0:00:01 time: 0.0049 data_time: 0.0008 memory: 138 2022/08/31 19:35:12 - mmengine - INFO - Epoch(val) [10][300/472] eta: 0:00:00 time: 0.0053 data_time: 0.0009 memory: 329 2022/08/31 19:35:12 - mmengine - INFO - Epoch(val) [10][400/472] eta: 0:00:00 time: 0.0044 data_time: 0.0009 memory: 76 2022/08/31 19:35:13 - mmengine - INFO - Epoch(val) [10][472/472] kie/macro_f1: 0.8258 2022/08/31 19:35:15 - mmengine - INFO - Epoch(train) [11][100/317] lr: 1.0000e-03 eta: 0:06:51 time: 0.0311 data_time: 0.0029 memory: 980 loss_node: 0.2501 loss_edge: 0.0000 acc_node: 96.3917 acc_edge: 100.0000 loss: 0.2502 2022/08/31 19:35:18 - mmengine - INFO - Epoch(train) [11][200/317] lr: 1.0000e-03 eta: 0:06:50 time: 0.0266 data_time: 0.0030 memory: 872 loss_node: 0.2315 loss_edge: 0.0000 acc_node: 96.4029 acc_edge: 100.0000 loss: 0.2315 2022/08/31 19:35:21 - mmengine - INFO - Epoch(train) [11][300/317] lr: 1.0000e-03 eta: 0:06:48 time: 0.0292 data_time: 0.0028 memory: 536 loss_node: 0.2071 loss_edge: 0.0000 acc_node: 94.1176 acc_edge: 100.0000 loss: 0.2072 2022/08/31 19:35:21 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:35:21 - mmengine - INFO - Saving checkpoint at 11 epochs 2022/08/31 19:35:22 - mmengine - INFO - Epoch(val) [11][100/472] eta: 0:00:01 time: 0.0036 data_time: 0.0007 memory: 268 2022/08/31 19:35:22 - mmengine - INFO - Epoch(val) [11][200/472] eta: 0:00:01 time: 0.0046 data_time: 0.0009 memory: 138 2022/08/31 19:35:23 - mmengine - INFO - Epoch(val) [11][300/472] eta: 0:00:00 time: 0.0047 data_time: 0.0009 memory: 329 2022/08/31 19:35:23 - mmengine - INFO - Epoch(val) [11][400/472] eta: 0:00:00 time: 0.0044 data_time: 0.0008 memory: 76 2022/08/31 19:35:24 - mmengine - INFO - Epoch(val) [11][472/472] kie/macro_f1: 0.8341 2022/08/31 19:35:26 - mmengine - INFO - Epoch(train) [12][100/317] lr: 1.0000e-03 eta: 0:06:44 time: 0.0227 data_time: 0.0028 memory: 498 loss_node: 0.2254 loss_edge: 0.0000 acc_node: 96.4286 acc_edge: 100.0000 loss: 0.2254 2022/08/31 19:35:29 - mmengine - INFO - Epoch(train) [12][200/317] lr: 1.0000e-03 eta: 0:06:42 time: 0.0289 data_time: 0.0030 memory: 684 loss_node: 0.2336 loss_edge: 0.0000 acc_node: 91.6230 acc_edge: 100.0000 loss: 0.2337 2022/08/31 19:35:32 - mmengine - INFO - Epoch(train) [12][300/317] lr: 1.0000e-03 eta: 0:06:40 time: 0.0274 data_time: 0.0030 memory: 898 loss_node: 0.1815 loss_edge: 0.0000 acc_node: 90.8163 acc_edge: 100.0000 loss: 0.1815 2022/08/31 19:35:32 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:35:32 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/08/31 19:35:33 - mmengine - INFO - Epoch(val) [12][100/472] eta: 0:00:01 time: 0.0043 data_time: 0.0009 memory: 215 2022/08/31 19:35:33 - mmengine - INFO - Epoch(val) [12][200/472] eta: 0:00:01 time: 0.0049 data_time: 0.0008 memory: 138 2022/08/31 19:35:34 - mmengine - INFO - Epoch(val) [12][300/472] eta: 0:00:00 time: 0.0050 data_time: 0.0009 memory: 329 2022/08/31 19:35:34 - mmengine - INFO - Epoch(val) [12][400/472] eta: 0:00:00 time: 0.0045 data_time: 0.0009 memory: 76 2022/08/31 19:35:35 - mmengine - INFO - Epoch(val) [12][472/472] kie/macro_f1: 0.8340 2022/08/31 19:35:38 - mmengine - INFO - Epoch(train) [13][100/317] lr: 1.0000e-03 eta: 0:06:37 time: 0.0272 data_time: 0.0029 memory: 943 loss_node: 0.1677 loss_edge: 0.0000 acc_node: 96.9697 acc_edge: 100.0000 loss: 0.1677 2022/08/31 19:35:40 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:35:40 - mmengine - INFO - Epoch(train) [13][200/317] lr: 1.0000e-03 eta: 0:06:35 time: 0.0256 data_time: 0.0029 memory: 551 loss_node: 0.2276 loss_edge: 0.0000 acc_node: 95.3216 acc_edge: 100.0000 loss: 0.2276 2022/08/31 19:35:43 - mmengine - INFO - Epoch(train) [13][300/317] lr: 1.0000e-03 eta: 0:06:33 time: 0.0271 data_time: 0.0031 memory: 891 loss_node: 0.2047 loss_edge: 0.0000 acc_node: 90.6977 acc_edge: 100.0000 loss: 0.2048 2022/08/31 19:35:44 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:35:44 - mmengine - INFO - Saving checkpoint at 13 epochs 2022/08/31 19:35:44 - mmengine - INFO - Epoch(val) [13][100/472] eta: 0:00:01 time: 0.0038 data_time: 0.0008 memory: 392 2022/08/31 19:35:45 - mmengine - INFO - Epoch(val) [13][200/472] eta: 0:00:01 time: 0.0048 data_time: 0.0009 memory: 138 2022/08/31 19:35:45 - mmengine - INFO - Epoch(val) [13][300/472] eta: 0:00:00 time: 0.0052 data_time: 0.0010 memory: 329 2022/08/31 19:35:46 - mmengine - INFO - Epoch(val) [13][400/472] eta: 0:00:00 time: 0.0041 data_time: 0.0008 memory: 76 2022/08/31 19:35:46 - mmengine - INFO - Epoch(val) [13][472/472] kie/macro_f1: 0.8232 2022/08/31 19:35:49 - mmengine - INFO - Epoch(train) [14][100/317] lr: 1.0000e-03 eta: 0:06:29 time: 0.0259 data_time: 0.0031 memory: 509 loss_node: 0.1522 loss_edge: 0.0000 acc_node: 97.6190 acc_edge: 100.0000 loss: 0.1523 2022/08/31 19:35:51 - mmengine - INFO - Epoch(train) [14][200/317] lr: 1.0000e-03 eta: 0:06:28 time: 0.0291 data_time: 0.0031 memory: 1152 loss_node: 0.1623 loss_edge: 0.0000 acc_node: 93.2990 acc_edge: 100.0000 loss: 0.1624 2022/08/31 19:35:54 - mmengine - INFO - Epoch(train) [14][300/317] lr: 1.0000e-03 eta: 0:06:25 time: 0.0279 data_time: 0.0027 memory: 621 loss_node: 0.1517 loss_edge: 0.0000 acc_node: 89.1775 acc_edge: 100.0000 loss: 0.1517 2022/08/31 19:35:55 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:35:55 - mmengine - INFO - Saving checkpoint at 14 epochs 2022/08/31 19:35:55 - mmengine - INFO - Epoch(val) [14][100/472] eta: 0:00:01 time: 0.0037 data_time: 0.0007 memory: 359 2022/08/31 19:35:56 - mmengine - INFO - Epoch(val) [14][200/472] eta: 0:00:01 time: 0.0043 data_time: 0.0007 memory: 138 2022/08/31 19:35:56 - mmengine - INFO - Epoch(val) [14][300/472] eta: 0:00:00 time: 0.0046 data_time: 0.0008 memory: 329 2022/08/31 19:35:57 - mmengine - INFO - Epoch(val) [14][400/472] eta: 0:00:00 time: 0.0043 data_time: 0.0009 memory: 76 2022/08/31 19:35:57 - mmengine - INFO - Epoch(val) [14][472/472] kie/macro_f1: 0.8396 2022/08/31 19:35:59 - mmengine - INFO - Epoch(train) [15][100/317] lr: 1.0000e-03 eta: 0:06:22 time: 0.0272 data_time: 0.0031 memory: 915 loss_node: 0.1560 loss_edge: 0.0000 acc_node: 94.7977 acc_edge: 100.0000 loss: 0.1560 2022/08/31 19:36:02 - mmengine - INFO - Epoch(train) [15][200/317] lr: 1.0000e-03 eta: 0:06:19 time: 0.0254 data_time: 0.0027 memory: 596 loss_node: 0.1558 loss_edge: 0.0000 acc_node: 93.5897 acc_edge: 100.0000 loss: 0.1558 2022/08/31 19:36:05 - mmengine - INFO - Epoch(train) [15][300/317] lr: 1.0000e-03 eta: 0:06:16 time: 0.0331 data_time: 0.0028 memory: 626 loss_node: 0.1579 loss_edge: 0.0000 acc_node: 96.4758 acc_edge: 100.0000 loss: 0.1580 2022/08/31 19:36:05 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:36:05 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/08/31 19:36:06 - mmengine - INFO - Epoch(val) [15][100/472] eta: 0:00:01 time: 0.0036 data_time: 0.0007 memory: 415 2022/08/31 19:36:06 - mmengine - INFO - Epoch(val) [15][200/472] eta: 0:00:01 time: 0.0046 data_time: 0.0009 memory: 138 2022/08/31 19:36:07 - mmengine - INFO - Epoch(val) [15][300/472] eta: 0:00:00 time: 0.0044 data_time: 0.0008 memory: 329 2022/08/31 19:36:07 - mmengine - INFO - Epoch(val) [15][400/472] eta: 0:00:00 time: 0.0039 data_time: 0.0007 memory: 76 2022/08/31 19:36:07 - mmengine - INFO - Epoch(val) [15][472/472] kie/macro_f1: 0.8300 2022/08/31 19:36:10 - mmengine - INFO - Epoch(train) [16][100/317] lr: 1.0000e-03 eta: 0:06:12 time: 0.0228 data_time: 0.0030 memory: 477 loss_node: 0.1506 loss_edge: 0.0000 acc_node: 89.7638 acc_edge: 100.0000 loss: 0.1506 2022/08/31 19:36:12 - mmengine - INFO - Epoch(train) [16][200/317] lr: 1.0000e-03 eta: 0:06:09 time: 0.0269 data_time: 0.0028 memory: 669 loss_node: 0.1424 loss_edge: 0.0000 acc_node: 91.7647 acc_edge: 100.0000 loss: 0.1425 2022/08/31 19:36:14 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:36:15 - mmengine - INFO - Epoch(train) [16][300/317] lr: 1.0000e-03 eta: 0:06:07 time: 0.0246 data_time: 0.0028 memory: 824 loss_node: 0.1815 loss_edge: 0.0000 acc_node: 91.9075 acc_edge: 100.0000 loss: 0.1815 2022/08/31 19:36:16 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:36:16 - mmengine - INFO - Saving checkpoint at 16 epochs 2022/08/31 19:36:16 - mmengine - INFO - Epoch(val) [16][100/472] eta: 0:00:01 time: 0.0037 data_time: 0.0007 memory: 1036 2022/08/31 19:36:17 - mmengine - INFO - Epoch(val) [16][200/472] eta: 0:00:01 time: 0.0046 data_time: 0.0009 memory: 138 2022/08/31 19:36:17 - mmengine - INFO - Epoch(val) [16][300/472] eta: 0:00:00 time: 0.0044 data_time: 0.0008 memory: 329 2022/08/31 19:36:18 - mmengine - INFO - Epoch(val) [16][400/472] eta: 0:00:00 time: 0.0041 data_time: 0.0008 memory: 76 2022/08/31 19:36:18 - mmengine - INFO - Epoch(val) [16][472/472] kie/macro_f1: 0.8472 2022/08/31 19:36:21 - mmengine - INFO - Epoch(train) [17][100/317] lr: 1.0000e-03 eta: 0:06:03 time: 0.0265 data_time: 0.0028 memory: 550 loss_node: 0.1436 loss_edge: 0.0000 acc_node: 94.8276 acc_edge: 100.0000 loss: 0.1436 2022/08/31 19:36:23 - mmengine - INFO - Epoch(train) [17][200/317] lr: 1.0000e-03 eta: 0:06:01 time: 0.0278 data_time: 0.0031 memory: 815 loss_node: 0.1607 loss_edge: 0.0000 acc_node: 98.1982 acc_edge: 100.0000 loss: 0.1607 2022/08/31 19:36:26 - mmengine - INFO - Epoch(train) [17][300/317] lr: 1.0000e-03 eta: 0:06:00 time: 0.0342 data_time: 0.0028 memory: 942 loss_node: 0.1540 loss_edge: 0.0000 acc_node: 94.0828 acc_edge: 100.0000 loss: 0.1540 2022/08/31 19:36:27 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:36:27 - mmengine - INFO - Saving checkpoint at 17 epochs 2022/08/31 19:36:27 - mmengine - INFO - Epoch(val) [17][100/472] eta: 0:00:01 time: 0.0045 data_time: 0.0010 memory: 487 2022/08/31 19:36:28 - mmengine - INFO - Epoch(val) [17][200/472] eta: 0:00:01 time: 0.0051 data_time: 0.0010 memory: 138 2022/08/31 19:36:28 - mmengine - INFO - Epoch(val) [17][300/472] eta: 0:00:00 time: 0.0048 data_time: 0.0008 memory: 329 2022/08/31 19:36:29 - mmengine - INFO - Epoch(val) [17][400/472] eta: 0:00:00 time: 0.0047 data_time: 0.0009 memory: 76 2022/08/31 19:36:29 - mmengine - INFO - Epoch(val) [17][472/472] kie/macro_f1: 0.8670 2022/08/31 19:36:32 - mmengine - INFO - Epoch(train) [18][100/317] lr: 1.0000e-03 eta: 0:05:56 time: 0.0267 data_time: 0.0030 memory: 355 loss_node: 0.1458 loss_edge: 0.0000 acc_node: 97.0149 acc_edge: 100.0000 loss: 0.1458 2022/08/31 19:36:35 - mmengine - INFO - Epoch(train) [18][200/317] lr: 1.0000e-03 eta: 0:05:54 time: 0.0257 data_time: 0.0027 memory: 913 loss_node: 0.1684 loss_edge: 0.0000 acc_node: 97.5000 acc_edge: 100.0000 loss: 0.1684 2022/08/31 19:36:37 - mmengine - INFO - Epoch(train) [18][300/317] lr: 1.0000e-03 eta: 0:05:51 time: 0.0252 data_time: 0.0027 memory: 501 loss_node: 0.1219 loss_edge: 0.0000 acc_node: 94.3820 acc_edge: 100.0000 loss: 0.1220 2022/08/31 19:36:38 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:36:38 - mmengine - INFO - Saving checkpoint at 18 epochs 2022/08/31 19:36:39 - mmengine - INFO - Epoch(val) [18][100/472] eta: 0:00:01 time: 0.0041 data_time: 0.0009 memory: 257 2022/08/31 19:36:39 - mmengine - INFO - Epoch(val) [18][200/472] eta: 0:00:01 time: 0.0047 data_time: 0.0008 memory: 138 2022/08/31 19:36:40 - mmengine - INFO - Epoch(val) [18][300/472] eta: 0:00:00 time: 0.0046 data_time: 0.0009 memory: 329 2022/08/31 19:36:40 - mmengine - INFO - Epoch(val) [18][400/472] eta: 0:00:00 time: 0.0041 data_time: 0.0008 memory: 76 2022/08/31 19:36:40 - mmengine - INFO - Epoch(val) [18][472/472] kie/macro_f1: 0.8328 2022/08/31 19:36:43 - mmengine - INFO - Epoch(train) [19][100/317] lr: 1.0000e-03 eta: 0:05:48 time: 0.0238 data_time: 0.0029 memory: 863 loss_node: 0.1108 loss_edge: 0.0000 acc_node: 97.5806 acc_edge: 100.0000 loss: 0.1108 2022/08/31 19:36:46 - mmengine - INFO - Epoch(train) [19][200/317] lr: 1.0000e-03 eta: 0:05:46 time: 0.0266 data_time: 0.0030 memory: 903 loss_node: 0.1932 loss_edge: 0.0000 acc_node: 92.3077 acc_edge: 100.0000 loss: 0.1933 2022/08/31 19:36:48 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:36:48 - mmengine - INFO - Epoch(train) [19][300/317] lr: 1.0000e-03 eta: 0:05:43 time: 0.0237 data_time: 0.0027 memory: 481 loss_node: 0.1197 loss_edge: 0.0000 acc_node: 96.3190 acc_edge: 100.0000 loss: 0.1197 2022/08/31 19:36:49 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:36:49 - mmengine - INFO - Saving checkpoint at 19 epochs 2022/08/31 19:36:49 - mmengine - INFO - Epoch(val) [19][100/472] eta: 0:00:01 time: 0.0042 data_time: 0.0011 memory: 381 2022/08/31 19:36:50 - mmengine - INFO - Epoch(val) [19][200/472] eta: 0:00:01 time: 0.0045 data_time: 0.0009 memory: 138 2022/08/31 19:36:50 - mmengine - INFO - Epoch(val) [19][300/472] eta: 0:00:00 time: 0.0046 data_time: 0.0008 memory: 329 2022/08/31 19:36:51 - mmengine - INFO - Epoch(val) [19][400/472] eta: 0:00:00 time: 0.0041 data_time: 0.0008 memory: 76 2022/08/31 19:36:51 - mmengine - INFO - Epoch(val) [19][472/472] kie/macro_f1: 0.8542 2022/08/31 19:36:54 - mmengine - INFO - Epoch(train) [20][100/317] lr: 1.0000e-03 eta: 0:05:39 time: 0.0250 data_time: 0.0027 memory: 951 loss_node: 0.0886 loss_edge: 0.0000 acc_node: 96.4286 acc_edge: 100.0000 loss: 0.0886 2022/08/31 19:36:56 - mmengine - INFO - Epoch(train) [20][200/317] lr: 1.0000e-03 eta: 0:05:37 time: 0.0260 data_time: 0.0027 memory: 697 loss_node: 0.1350 loss_edge: 0.0000 acc_node: 97.0588 acc_edge: 100.0000 loss: 0.1350 2022/08/31 19:36:59 - mmengine - INFO - Epoch(train) [20][300/317] lr: 1.0000e-03 eta: 0:05:34 time: 0.0241 data_time: 0.0027 memory: 490 loss_node: 0.1094 loss_edge: 0.0000 acc_node: 90.2439 acc_edge: 100.0000 loss: 0.1094 2022/08/31 19:36:59 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:36:59 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/08/31 19:37:00 - mmengine - INFO - Epoch(val) [20][100/472] eta: 0:00:01 time: 0.0036 data_time: 0.0008 memory: 352 2022/08/31 19:37:00 - mmengine - INFO - Epoch(val) [20][200/472] eta: 0:00:01 time: 0.0043 data_time: 0.0007 memory: 138 2022/08/31 19:37:01 - mmengine - INFO - Epoch(val) [20][300/472] eta: 0:00:00 time: 0.0044 data_time: 0.0008 memory: 329 2022/08/31 19:37:01 - mmengine - INFO - Epoch(val) [20][400/472] eta: 0:00:00 time: 0.0048 data_time: 0.0010 memory: 76 2022/08/31 19:37:01 - mmengine - INFO - Epoch(val) [20][472/472] kie/macro_f1: 0.8560 2022/08/31 19:37:04 - mmengine - INFO - Epoch(train) [21][100/317] lr: 1.0000e-03 eta: 0:05:30 time: 0.0270 data_time: 0.0028 memory: 541 loss_node: 0.1483 loss_edge: 0.0000 acc_node: 98.7500 acc_edge: 100.0000 loss: 0.1483 2022/08/31 19:37:07 - mmengine - INFO - Epoch(train) [21][200/317] lr: 1.0000e-03 eta: 0:05:28 time: 0.0226 data_time: 0.0031 memory: 980 loss_node: 0.0877 loss_edge: 0.0000 acc_node: 94.4751 acc_edge: 100.0000 loss: 0.0877 2022/08/31 19:37:09 - mmengine - INFO - Epoch(train) [21][300/317] lr: 1.0000e-03 eta: 0:05:25 time: 0.0270 data_time: 0.0028 memory: 656 loss_node: 0.1467 loss_edge: 0.0000 acc_node: 95.2586 acc_edge: 100.0000 loss: 0.1467 2022/08/31 19:37:10 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:37:10 - mmengine - INFO - Saving checkpoint at 21 epochs 2022/08/31 19:37:10 - mmengine - INFO - Epoch(val) [21][100/472] eta: 0:00:01 time: 0.0037 data_time: 0.0008 memory: 281 2022/08/31 19:37:11 - mmengine - INFO - Epoch(val) [21][200/472] eta: 0:00:01 time: 0.0044 data_time: 0.0008 memory: 138 2022/08/31 19:37:11 - mmengine - INFO - Epoch(val) [21][300/472] eta: 0:00:01 time: 0.0096 data_time: 0.0047 memory: 329 2022/08/31 19:37:12 - mmengine - INFO - Epoch(val) [21][400/472] eta: 0:00:00 time: 0.0048 data_time: 0.0009 memory: 76 2022/08/31 19:37:12 - mmengine - INFO - Epoch(val) [21][472/472] kie/macro_f1: 0.8644 2022/08/31 19:37:15 - mmengine - INFO - Epoch(train) [22][100/317] lr: 1.0000e-03 eta: 0:05:22 time: 0.0290 data_time: 0.0029 memory: 780 loss_node: 0.0812 loss_edge: 0.0000 acc_node: 99.2308 acc_edge: 100.0000 loss: 0.0813 2022/08/31 19:37:18 - mmengine - INFO - Epoch(train) [22][200/317] lr: 1.0000e-03 eta: 0:05:20 time: 0.0251 data_time: 0.0029 memory: 931 loss_node: 0.1332 loss_edge: 0.0000 acc_node: 97.0297 acc_edge: 100.0000 loss: 0.1333 2022/08/31 19:37:21 - mmengine - INFO - Epoch(train) [22][300/317] lr: 1.0000e-03 eta: 0:05:17 time: 0.0307 data_time: 0.0027 memory: 963 loss_node: 0.1651 loss_edge: 0.0000 acc_node: 94.6154 acc_edge: 100.0000 loss: 0.1651 2022/08/31 19:37:21 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:37:21 - mmengine - INFO - Saving checkpoint at 22 epochs 2022/08/31 19:37:22 - mmengine - INFO - Epoch(val) [22][100/472] eta: 0:00:01 time: 0.0036 data_time: 0.0007 memory: 395 2022/08/31 19:37:22 - mmengine - INFO - Epoch(val) [22][200/472] eta: 0:00:01 time: 0.0046 data_time: 0.0008 memory: 138 2022/08/31 19:37:23 - mmengine - INFO - Epoch(val) [22][300/472] eta: 0:00:00 time: 0.0048 data_time: 0.0008 memory: 329 2022/08/31 19:37:23 - mmengine - INFO - Epoch(val) [22][400/472] eta: 0:00:00 time: 0.0040 data_time: 0.0007 memory: 76 2022/08/31 19:37:23 - mmengine - INFO - Epoch(val) [22][472/472] kie/macro_f1: 0.8491 2022/08/31 19:37:24 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:37:26 - mmengine - INFO - Epoch(train) [23][100/317] lr: 1.0000e-03 eta: 0:05:14 time: 0.0305 data_time: 0.0030 memory: 878 loss_node: 0.0890 loss_edge: 0.0000 acc_node: 96.7532 acc_edge: 100.0000 loss: 0.0890 2022/08/31 19:37:29 - mmengine - INFO - Epoch(train) [23][200/317] lr: 1.0000e-03 eta: 0:05:12 time: 0.0289 data_time: 0.0028 memory: 705 loss_node: 0.0783 loss_edge: 0.0000 acc_node: 97.0414 acc_edge: 100.0000 loss: 0.0783 2022/08/31 19:37:32 - mmengine - INFO - Epoch(train) [23][300/317] lr: 1.0000e-03 eta: 0:05:09 time: 0.0300 data_time: 0.0028 memory: 842 loss_node: 0.1252 loss_edge: 0.0000 acc_node: 94.1520 acc_edge: 100.0000 loss: 0.1252 2022/08/31 19:37:32 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:37:32 - mmengine - INFO - Saving checkpoint at 23 epochs 2022/08/31 19:37:33 - mmengine - INFO - Epoch(val) [23][100/472] eta: 0:00:01 time: 0.0036 data_time: 0.0007 memory: 384 2022/08/31 19:37:33 - mmengine - INFO - Epoch(val) [23][200/472] eta: 0:00:01 time: 0.0043 data_time: 0.0007 memory: 138 2022/08/31 19:37:33 - mmengine - INFO - Epoch(val) [23][300/472] eta: 0:00:00 time: 0.0048 data_time: 0.0008 memory: 329 2022/08/31 19:37:34 - mmengine - INFO - Epoch(val) [23][400/472] eta: 0:00:00 time: 0.0041 data_time: 0.0007 memory: 76 2022/08/31 19:37:34 - mmengine - INFO - Epoch(val) [23][472/472] kie/macro_f1: 0.8634 2022/08/31 19:37:37 - mmengine - INFO - Epoch(train) [24][100/317] lr: 1.0000e-03 eta: 0:05:06 time: 0.0245 data_time: 0.0030 memory: 928 loss_node: 0.0949 loss_edge: 0.0000 acc_node: 97.4026 acc_edge: 100.0000 loss: 0.0949 2022/08/31 19:37:40 - mmengine - INFO - Epoch(train) [24][200/317] lr: 1.0000e-03 eta: 0:05:03 time: 0.0282 data_time: 0.0031 memory: 558 loss_node: 0.0979 loss_edge: 0.0000 acc_node: 98.2456 acc_edge: 100.0000 loss: 0.0979 2022/08/31 19:37:42 - mmengine - INFO - Epoch(train) [24][300/317] lr: 1.0000e-03 eta: 0:05:01 time: 0.0272 data_time: 0.0028 memory: 472 loss_node: 0.1369 loss_edge: 0.0000 acc_node: 96.4029 acc_edge: 100.0000 loss: 0.1369 2022/08/31 19:37:43 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:37:43 - mmengine - INFO - Saving checkpoint at 24 epochs 2022/08/31 19:37:43 - mmengine - INFO - Epoch(val) [24][100/472] eta: 0:00:01 time: 0.0036 data_time: 0.0007 memory: 333 2022/08/31 19:37:44 - mmengine - INFO - Epoch(val) [24][200/472] eta: 0:00:01 time: 0.0052 data_time: 0.0011 memory: 138 2022/08/31 19:37:44 - mmengine - INFO - Epoch(val) [24][300/472] eta: 0:00:00 time: 0.0054 data_time: 0.0010 memory: 329 2022/08/31 19:37:45 - mmengine - INFO - Epoch(val) [24][400/472] eta: 0:00:00 time: 0.0043 data_time: 0.0008 memory: 76 2022/08/31 19:37:45 - mmengine - INFO - Epoch(val) [24][472/472] kie/macro_f1: 0.8388 2022/08/31 19:37:48 - mmengine - INFO - Epoch(train) [25][100/317] lr: 1.0000e-03 eta: 0:04:58 time: 0.0298 data_time: 0.0028 memory: 910 loss_node: 0.1217 loss_edge: 0.0000 acc_node: 97.8022 acc_edge: 100.0000 loss: 0.1217 2022/08/31 19:37:51 - mmengine - INFO - Epoch(train) [25][200/317] lr: 1.0000e-03 eta: 0:04:56 time: 0.0296 data_time: 0.0031 memory: 829 loss_node: 0.0825 loss_edge: 0.0000 acc_node: 97.2028 acc_edge: 100.0000 loss: 0.0825 2022/08/31 19:37:54 - mmengine - INFO - Epoch(train) [25][300/317] lr: 1.0000e-03 eta: 0:04:53 time: 0.0265 data_time: 0.0029 memory: 673 loss_node: 0.1694 loss_edge: 0.0000 acc_node: 92.2581 acc_edge: 100.0000 loss: 0.1694 2022/08/31 19:37:54 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:37:54 - mmengine - INFO - Saving checkpoint at 25 epochs 2022/08/31 19:37:55 - mmengine - INFO - Epoch(val) [25][100/472] eta: 0:00:01 time: 0.0041 data_time: 0.0009 memory: 211 2022/08/31 19:37:55 - mmengine - INFO - Epoch(val) [25][200/472] eta: 0:00:01 time: 0.0047 data_time: 0.0008 memory: 138 2022/08/31 19:37:56 - mmengine - INFO - Epoch(val) [25][300/472] eta: 0:00:00 time: 0.0047 data_time: 0.0009 memory: 329 2022/08/31 19:37:56 - mmengine - INFO - Epoch(val) [25][400/472] eta: 0:00:00 time: 0.0042 data_time: 0.0009 memory: 76 2022/08/31 19:37:56 - mmengine - INFO - Epoch(val) [25][472/472] kie/macro_f1: 0.8520 2022/08/31 19:37:58 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:37:59 - mmengine - INFO - Epoch(train) [26][100/317] lr: 1.0000e-03 eta: 0:04:50 time: 0.0283 data_time: 0.0028 memory: 868 loss_node: 0.1352 loss_edge: 0.0000 acc_node: 92.4658 acc_edge: 100.0000 loss: 0.1352 2022/08/31 19:38:02 - mmengine - INFO - Epoch(train) [26][200/317] lr: 1.0000e-03 eta: 0:04:47 time: 0.0264 data_time: 0.0033 memory: 997 loss_node: 0.1069 loss_edge: 0.0000 acc_node: 94.3038 acc_edge: 100.0000 loss: 0.1069 2022/08/31 19:38:05 - mmengine - INFO - Epoch(train) [26][300/317] lr: 1.0000e-03 eta: 0:04:45 time: 0.0234 data_time: 0.0028 memory: 742 loss_node: 0.0815 loss_edge: 0.0000 acc_node: 93.4641 acc_edge: 100.0000 loss: 0.0815 2022/08/31 19:38:05 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:38:05 - mmengine - INFO - Saving checkpoint at 26 epochs 2022/08/31 19:38:06 - mmengine - INFO - Epoch(val) [26][100/472] eta: 0:00:01 time: 0.0043 data_time: 0.0009 memory: 284 2022/08/31 19:38:06 - mmengine - INFO - Epoch(val) [26][200/472] eta: 0:00:01 time: 0.0045 data_time: 0.0008 memory: 138 2022/08/31 19:38:07 - mmengine - INFO - Epoch(val) [26][300/472] eta: 0:00:00 time: 0.0047 data_time: 0.0008 memory: 329 2022/08/31 19:38:07 - mmengine - INFO - Epoch(val) [26][400/472] eta: 0:00:00 time: 0.0048 data_time: 0.0010 memory: 76 2022/08/31 19:38:07 - mmengine - INFO - Epoch(val) [26][472/472] kie/macro_f1: 0.8545 2022/08/31 19:38:10 - mmengine - INFO - Epoch(train) [27][100/317] lr: 1.0000e-03 eta: 0:04:42 time: 0.0308 data_time: 0.0028 memory: 558 loss_node: 0.0605 loss_edge: 0.0000 acc_node: 98.8235 acc_edge: 100.0000 loss: 0.0605 2022/08/31 19:38:13 - mmengine - INFO - Epoch(train) [27][200/317] lr: 1.0000e-03 eta: 0:04:39 time: 0.0223 data_time: 0.0029 memory: 920 loss_node: 0.0725 loss_edge: 0.0000 acc_node: 96.7033 acc_edge: 100.0000 loss: 0.0725 2022/08/31 19:38:16 - mmengine - INFO - Epoch(train) [27][300/317] lr: 1.0000e-03 eta: 0:04:37 time: 0.0300 data_time: 0.0030 memory: 877 loss_node: 0.0841 loss_edge: 0.0000 acc_node: 96.5035 acc_edge: 100.0000 loss: 0.0841 2022/08/31 19:38:16 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:38:16 - mmengine - INFO - Saving checkpoint at 27 epochs 2022/08/31 19:38:17 - mmengine - INFO - Epoch(val) [27][100/472] eta: 0:00:01 time: 0.0041 data_time: 0.0008 memory: 301 2022/08/31 19:38:18 - mmengine - INFO - Epoch(val) [27][200/472] eta: 0:00:01 time: 0.0047 data_time: 0.0008 memory: 138 2022/08/31 19:38:18 - mmengine - INFO - Epoch(val) [27][300/472] eta: 0:00:00 time: 0.0053 data_time: 0.0010 memory: 329 2022/08/31 19:38:18 - mmengine - INFO - Epoch(val) [27][400/472] eta: 0:00:00 time: 0.0047 data_time: 0.0010 memory: 76 2022/08/31 19:38:19 - mmengine - INFO - Epoch(val) [27][472/472] kie/macro_f1: 0.8598 2022/08/31 19:38:22 - mmengine - INFO - Epoch(train) [28][100/317] lr: 1.0000e-03 eta: 0:04:34 time: 0.0286 data_time: 0.0030 memory: 964 loss_node: 0.0697 loss_edge: 0.0000 acc_node: 93.7173 acc_edge: 100.0000 loss: 0.0697 2022/08/31 19:38:25 - mmengine - INFO - Epoch(train) [28][200/317] lr: 1.0000e-03 eta: 0:04:31 time: 0.0340 data_time: 0.0031 memory: 615 loss_node: 0.1383 loss_edge: 0.0000 acc_node: 93.8889 acc_edge: 100.0000 loss: 0.1383 2022/08/31 19:38:27 - mmengine - INFO - Epoch(train) [28][300/317] lr: 1.0000e-03 eta: 0:04:29 time: 0.0283 data_time: 0.0029 memory: 586 loss_node: 0.0735 loss_edge: 0.0000 acc_node: 97.7778 acc_edge: 100.0000 loss: 0.0735 2022/08/31 19:38:28 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:38:28 - mmengine - INFO - Saving checkpoint at 28 epochs 2022/08/31 19:38:29 - mmengine - INFO - Epoch(val) [28][100/472] eta: 0:00:01 time: 0.0040 data_time: 0.0009 memory: 242 2022/08/31 19:38:29 - mmengine - INFO - Epoch(val) [28][200/472] eta: 0:00:01 time: 0.0047 data_time: 0.0009 memory: 138 2022/08/31 19:38:29 - mmengine - INFO - Epoch(val) [28][300/472] eta: 0:00:01 time: 0.0060 data_time: 0.0011 memory: 329 2022/08/31 19:38:30 - mmengine - INFO - Epoch(val) [28][400/472] eta: 0:00:00 time: 0.0048 data_time: 0.0010 memory: 76 2022/08/31 19:38:30 - mmengine - INFO - Epoch(val) [28][472/472] kie/macro_f1: 0.8588 2022/08/31 19:38:33 - mmengine - INFO - Epoch(train) [29][100/317] lr: 1.0000e-03 eta: 0:04:26 time: 0.0253 data_time: 0.0029 memory: 481 loss_node: 0.0872 loss_edge: 0.0000 acc_node: 97.4026 acc_edge: 100.0000 loss: 0.0872 2022/08/31 19:38:34 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:38:36 - mmengine - INFO - Epoch(train) [29][200/317] lr: 1.0000e-03 eta: 0:04:23 time: 0.0258 data_time: 0.0028 memory: 561 loss_node: 0.1001 loss_edge: 0.0000 acc_node: 93.2692 acc_edge: 100.0000 loss: 0.1001 2022/08/31 19:38:38 - mmengine - INFO - Epoch(train) [29][300/317] lr: 1.0000e-03 eta: 0:04:20 time: 0.0248 data_time: 0.0029 memory: 924 loss_node: 0.1524 loss_edge: 0.0000 acc_node: 93.2643 acc_edge: 100.0000 loss: 0.1524 2022/08/31 19:38:39 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:38:39 - mmengine - INFO - Saving checkpoint at 29 epochs 2022/08/31 19:38:39 - mmengine - INFO - Epoch(val) [29][100/472] eta: 0:00:01 time: 0.0038 data_time: 0.0008 memory: 600 2022/08/31 19:38:40 - mmengine - INFO - Epoch(val) [29][200/472] eta: 0:00:01 time: 0.0046 data_time: 0.0009 memory: 138 2022/08/31 19:38:40 - mmengine - INFO - Epoch(val) [29][300/472] eta: 0:00:00 time: 0.0047 data_time: 0.0008 memory: 329 2022/08/31 19:38:41 - mmengine - INFO - Epoch(val) [29][400/472] eta: 0:00:00 time: 0.0041 data_time: 0.0008 memory: 76 2022/08/31 19:38:41 - mmengine - INFO - Epoch(val) [29][472/472] kie/macro_f1: 0.8600 2022/08/31 19:38:44 - mmengine - INFO - Epoch(train) [30][100/317] lr: 1.0000e-03 eta: 0:04:17 time: 0.0272 data_time: 0.0029 memory: 617 loss_node: 0.0808 loss_edge: 0.0000 acc_node: 98.7500 acc_edge: 100.0000 loss: 0.0809 2022/08/31 19:38:47 - mmengine - INFO - Epoch(train) [30][200/317] lr: 1.0000e-03 eta: 0:04:15 time: 0.0253 data_time: 0.0028 memory: 993 loss_node: 0.0773 loss_edge: 0.0000 acc_node: 97.9381 acc_edge: 100.0000 loss: 0.0773 2022/08/31 19:38:49 - mmengine - INFO - Epoch(train) [30][300/317] lr: 1.0000e-03 eta: 0:04:12 time: 0.0362 data_time: 0.0033 memory: 710 loss_node: 0.1029 loss_edge: 0.0000 acc_node: 91.1392 acc_edge: 100.0000 loss: 0.1029 2022/08/31 19:38:50 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:38:50 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/08/31 19:38:51 - mmengine - INFO - Epoch(val) [30][100/472] eta: 0:00:01 time: 0.0046 data_time: 0.0010 memory: 311 2022/08/31 19:38:51 - mmengine - INFO - Epoch(val) [30][200/472] eta: 0:00:01 time: 0.0051 data_time: 0.0010 memory: 138 2022/08/31 19:38:52 - mmengine - INFO - Epoch(val) [30][300/472] eta: 0:00:00 time: 0.0055 data_time: 0.0010 memory: 329 2022/08/31 19:38:52 - mmengine - INFO - Epoch(val) [30][400/472] eta: 0:00:00 time: 0.0044 data_time: 0.0008 memory: 76 2022/08/31 19:38:52 - mmengine - INFO - Epoch(val) [30][472/472] kie/macro_f1: 0.8550 2022/08/31 19:38:55 - mmengine - INFO - Epoch(train) [31][100/317] lr: 1.0000e-03 eta: 0:04:09 time: 0.0316 data_time: 0.0030 memory: 814 loss_node: 0.0888 loss_edge: 0.0000 acc_node: 95.4248 acc_edge: 100.0000 loss: 0.0888 2022/08/31 19:38:58 - mmengine - INFO - Epoch(train) [31][200/317] lr: 1.0000e-03 eta: 0:04:06 time: 0.0312 data_time: 0.0028 memory: 957 loss_node: 0.0813 loss_edge: 0.0000 acc_node: 98.8439 acc_edge: 100.0000 loss: 0.0813 2022/08/31 19:39:01 - mmengine - INFO - Epoch(train) [31][300/317] lr: 1.0000e-03 eta: 0:04:04 time: 0.0274 data_time: 0.0029 memory: 625 loss_node: 0.0776 loss_edge: 0.0000 acc_node: 99.4083 acc_edge: 100.0000 loss: 0.0777 2022/08/31 19:39:01 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:39:01 - mmengine - INFO - Saving checkpoint at 31 epochs 2022/08/31 19:39:02 - mmengine - INFO - Epoch(val) [31][100/472] eta: 0:00:01 time: 0.0037 data_time: 0.0008 memory: 275 2022/08/31 19:39:02 - mmengine - INFO - Epoch(val) [31][200/472] eta: 0:00:01 time: 0.0046 data_time: 0.0009 memory: 138 2022/08/31 19:39:03 - mmengine - INFO - Epoch(val) [31][300/472] eta: 0:00:00 time: 0.0048 data_time: 0.0008 memory: 329 2022/08/31 19:39:03 - mmengine - INFO - Epoch(val) [31][400/472] eta: 0:00:00 time: 0.0046 data_time: 0.0009 memory: 76 2022/08/31 19:39:03 - mmengine - INFO - Epoch(val) [31][472/472] kie/macro_f1: 0.8553 2022/08/31 19:39:06 - mmengine - INFO - Epoch(train) [32][100/317] lr: 1.0000e-03 eta: 0:04:01 time: 0.0255 data_time: 0.0029 memory: 837 loss_node: 0.0672 loss_edge: 0.0000 acc_node: 98.1308 acc_edge: 100.0000 loss: 0.0672 2022/08/31 19:39:08 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:39:09 - mmengine - INFO - Epoch(train) [32][200/317] lr: 1.0000e-03 eta: 0:03:58 time: 0.0296 data_time: 0.0031 memory: 913 loss_node: 0.0638 loss_edge: 0.0000 acc_node: 98.4772 acc_edge: 100.0000 loss: 0.0638 2022/08/31 19:39:12 - mmengine - INFO - Epoch(train) [32][300/317] lr: 1.0000e-03 eta: 0:03:55 time: 0.0224 data_time: 0.0026 memory: 462 loss_node: 0.0634 loss_edge: 0.0000 acc_node: 97.0000 acc_edge: 100.0000 loss: 0.0634 2022/08/31 19:39:12 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:39:12 - mmengine - INFO - Saving checkpoint at 32 epochs 2022/08/31 19:39:13 - mmengine - INFO - Epoch(val) [32][100/472] eta: 0:00:01 time: 0.0039 data_time: 0.0008 memory: 227 2022/08/31 19:39:13 - mmengine - INFO - Epoch(val) [32][200/472] eta: 0:00:01 time: 0.0045 data_time: 0.0008 memory: 138 2022/08/31 19:39:14 - mmengine - INFO - Epoch(val) [32][300/472] eta: 0:00:00 time: 0.0056 data_time: 0.0010 memory: 329 2022/08/31 19:39:14 - mmengine - INFO - Epoch(val) [32][400/472] eta: 0:00:00 time: 0.0041 data_time: 0.0009 memory: 76 2022/08/31 19:39:14 - mmengine - INFO - Epoch(val) [32][472/472] kie/macro_f1: 0.8539 2022/08/31 19:39:17 - mmengine - INFO - Epoch(train) [33][100/317] lr: 1.0000e-03 eta: 0:03:52 time: 0.0259 data_time: 0.0028 memory: 966 loss_node: 0.0566 loss_edge: 0.0000 acc_node: 96.1290 acc_edge: 100.0000 loss: 0.0566 2022/08/31 19:39:20 - mmengine - INFO - Epoch(train) [33][200/317] lr: 1.0000e-03 eta: 0:03:50 time: 0.0212 data_time: 0.0028 memory: 829 loss_node: 0.0688 loss_edge: 0.0000 acc_node: 99.2308 acc_edge: 100.0000 loss: 0.0688 2022/08/31 19:39:23 - mmengine - INFO - Epoch(train) [33][300/317] lr: 1.0000e-03 eta: 0:03:47 time: 0.0243 data_time: 0.0028 memory: 551 loss_node: 0.1313 loss_edge: 0.0000 acc_node: 95.9276 acc_edge: 100.0000 loss: 0.1313 2022/08/31 19:39:23 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:39:23 - mmengine - INFO - Saving checkpoint at 33 epochs 2022/08/31 19:39:24 - mmengine - INFO - Epoch(val) [33][100/472] eta: 0:00:01 time: 0.0039 data_time: 0.0008 memory: 254 2022/08/31 19:39:24 - mmengine - INFO - Epoch(val) [33][200/472] eta: 0:00:01 time: 0.0045 data_time: 0.0008 memory: 138 2022/08/31 19:39:25 - mmengine - INFO - Epoch(val) [33][300/472] eta: 0:00:00 time: 0.0055 data_time: 0.0010 memory: 329 2022/08/31 19:39:25 - mmengine - INFO - Epoch(val) [33][400/472] eta: 0:00:00 time: 0.0047 data_time: 0.0010 memory: 76 2022/08/31 19:39:25 - mmengine - INFO - Epoch(val) [33][472/472] kie/macro_f1: 0.8578 2022/08/31 19:39:28 - mmengine - INFO - Epoch(train) [34][100/317] lr: 1.0000e-03 eta: 0:03:44 time: 0.0251 data_time: 0.0028 memory: 933 loss_node: 0.0765 loss_edge: 0.0000 acc_node: 97.8142 acc_edge: 100.0000 loss: 0.0765 2022/08/31 19:39:31 - mmengine - INFO - Epoch(train) [34][200/317] lr: 1.0000e-03 eta: 0:03:41 time: 0.0279 data_time: 0.0029 memory: 683 loss_node: 0.0773 loss_edge: 0.0000 acc_node: 97.4684 acc_edge: 100.0000 loss: 0.0774 2022/08/31 19:39:34 - mmengine - INFO - Epoch(train) [34][300/317] lr: 1.0000e-03 eta: 0:03:39 time: 0.0228 data_time: 0.0027 memory: 627 loss_node: 0.0675 loss_edge: 0.0000 acc_node: 95.8904 acc_edge: 100.0000 loss: 0.0675 2022/08/31 19:39:34 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:39:34 - mmengine - INFO - Saving checkpoint at 34 epochs 2022/08/31 19:39:35 - mmengine - INFO - Epoch(val) [34][100/472] eta: 0:00:01 time: 0.0046 data_time: 0.0010 memory: 317 2022/08/31 19:39:35 - mmengine - INFO - Epoch(val) [34][200/472] eta: 0:00:01 time: 0.0045 data_time: 0.0008 memory: 138 2022/08/31 19:39:36 - mmengine - INFO - Epoch(val) [34][300/472] eta: 0:00:00 time: 0.0054 data_time: 0.0011 memory: 329 2022/08/31 19:39:36 - mmengine - INFO - Epoch(val) [34][400/472] eta: 0:00:00 time: 0.0042 data_time: 0.0008 memory: 76 2022/08/31 19:39:37 - mmengine - INFO - Epoch(val) [34][472/472] kie/macro_f1: 0.8548 2022/08/31 19:39:39 - mmengine - INFO - Epoch(train) [35][100/317] lr: 1.0000e-03 eta: 0:03:35 time: 0.0283 data_time: 0.0029 memory: 923 loss_node: 0.0375 loss_edge: 0.0000 acc_node: 97.9452 acc_edge: 100.0000 loss: 0.0376 2022/08/31 19:39:42 - mmengine - INFO - Epoch(train) [35][200/317] lr: 1.0000e-03 eta: 0:03:33 time: 0.0319 data_time: 0.0029 memory: 859 loss_node: 0.0787 loss_edge: 0.0000 acc_node: 98.6111 acc_edge: 100.0000 loss: 0.0788 2022/08/31 19:39:43 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:39:45 - mmengine - INFO - Epoch(train) [35][300/317] lr: 1.0000e-03 eta: 0:03:30 time: 0.0291 data_time: 0.0031 memory: 483 loss_node: 0.1069 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.1070 2022/08/31 19:39:45 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:39:45 - mmengine - INFO - Saving checkpoint at 35 epochs 2022/08/31 19:39:46 - mmengine - INFO - Epoch(val) [35][100/472] eta: 0:00:01 time: 0.0043 data_time: 0.0010 memory: 328 2022/08/31 19:39:46 - mmengine - INFO - Epoch(val) [35][200/472] eta: 0:00:01 time: 0.0050 data_time: 0.0008 memory: 138 2022/08/31 19:39:47 - mmengine - INFO - Epoch(val) [35][300/472] eta: 0:00:00 time: 0.0051 data_time: 0.0010 memory: 329 2022/08/31 19:39:47 - mmengine - INFO - Epoch(val) [35][400/472] eta: 0:00:00 time: 0.0048 data_time: 0.0010 memory: 76 2022/08/31 19:39:48 - mmengine - INFO - Epoch(val) [35][472/472] kie/macro_f1: 0.8533 2022/08/31 19:39:50 - mmengine - INFO - Epoch(train) [36][100/317] lr: 1.0000e-03 eta: 0:03:27 time: 0.0304 data_time: 0.0029 memory: 622 loss_node: 0.0465 loss_edge: 0.0000 acc_node: 99.1304 acc_edge: 100.0000 loss: 0.0465 2022/08/31 19:39:53 - mmengine - INFO - Epoch(train) [36][200/317] lr: 1.0000e-03 eta: 0:03:24 time: 0.0265 data_time: 0.0028 memory: 1143 loss_node: 0.0903 loss_edge: 0.0000 acc_node: 93.5961 acc_edge: 100.0000 loss: 0.0903 2022/08/31 19:39:56 - mmengine - INFO - Epoch(train) [36][300/317] lr: 1.0000e-03 eta: 0:03:22 time: 0.0270 data_time: 0.0029 memory: 673 loss_node: 0.0649 loss_edge: 0.0000 acc_node: 95.8904 acc_edge: 100.0000 loss: 0.0649 2022/08/31 19:39:56 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:39:56 - mmengine - INFO - Saving checkpoint at 36 epochs 2022/08/31 19:39:57 - mmengine - INFO - Epoch(val) [36][100/472] eta: 0:00:01 time: 0.0045 data_time: 0.0010 memory: 414 2022/08/31 19:39:57 - mmengine - INFO - Epoch(val) [36][200/472] eta: 0:00:01 time: 0.0054 data_time: 0.0010 memory: 138 2022/08/31 19:39:58 - mmengine - INFO - Epoch(val) [36][300/472] eta: 0:00:01 time: 0.0062 data_time: 0.0011 memory: 329 2022/08/31 19:39:58 - mmengine - INFO - Epoch(val) [36][400/472] eta: 0:00:00 time: 0.0049 data_time: 0.0010 memory: 76 2022/08/31 19:39:59 - mmengine - INFO - Epoch(val) [36][472/472] kie/macro_f1: 0.8523 2022/08/31 19:40:02 - mmengine - INFO - Epoch(train) [37][100/317] lr: 1.0000e-03 eta: 0:03:19 time: 0.0245 data_time: 0.0027 memory: 955 loss_node: 0.0566 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0566 2022/08/31 19:40:04 - mmengine - INFO - Epoch(train) [37][200/317] lr: 1.0000e-03 eta: 0:03:16 time: 0.0258 data_time: 0.0027 memory: 852 loss_node: 0.0603 loss_edge: 0.0000 acc_node: 94.7368 acc_edge: 100.0000 loss: 0.0603 2022/08/31 19:40:07 - mmengine - INFO - Epoch(train) [37][300/317] lr: 1.0000e-03 eta: 0:03:13 time: 0.0282 data_time: 0.0030 memory: 524 loss_node: 0.0613 loss_edge: 0.0000 acc_node: 97.1831 acc_edge: 100.0000 loss: 0.0613 2022/08/31 19:40:07 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:40:07 - mmengine - INFO - Saving checkpoint at 37 epochs 2022/08/31 19:40:08 - mmengine - INFO - Epoch(val) [37][100/472] eta: 0:00:01 time: 0.0036 data_time: 0.0007 memory: 265 2022/08/31 19:40:08 - mmengine - INFO - Epoch(val) [37][200/472] eta: 0:00:01 time: 0.0049 data_time: 0.0010 memory: 138 2022/08/31 19:40:09 - mmengine - INFO - Epoch(val) [37][300/472] eta: 0:00:00 time: 0.0044 data_time: 0.0008 memory: 329 2022/08/31 19:40:09 - mmengine - INFO - Epoch(val) [37][400/472] eta: 0:00:00 time: 0.0042 data_time: 0.0008 memory: 76 2022/08/31 19:40:10 - mmengine - INFO - Epoch(val) [37][472/472] kie/macro_f1: 0.8589 2022/08/31 19:40:12 - mmengine - INFO - Epoch(train) [38][100/317] lr: 1.0000e-03 eta: 0:03:10 time: 0.0274 data_time: 0.0028 memory: 409 loss_node: 0.0947 loss_edge: 0.0000 acc_node: 95.3216 acc_edge: 100.0000 loss: 0.0947 2022/08/31 19:40:15 - mmengine - INFO - Epoch(train) [38][200/317] lr: 1.0000e-03 eta: 0:03:07 time: 0.0254 data_time: 0.0027 memory: 692 loss_node: 0.0402 loss_edge: 0.0000 acc_node: 98.7654 acc_edge: 100.0000 loss: 0.0402 2022/08/31 19:40:17 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:40:18 - mmengine - INFO - Epoch(train) [38][300/317] lr: 1.0000e-03 eta: 0:03:05 time: 0.0255 data_time: 0.0028 memory: 893 loss_node: 0.0608 loss_edge: 0.0000 acc_node: 96.4072 acc_edge: 100.0000 loss: 0.0608 2022/08/31 19:40:18 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:40:18 - mmengine - INFO - Saving checkpoint at 38 epochs 2022/08/31 19:40:19 - mmengine - INFO - Epoch(val) [38][100/472] eta: 0:00:01 time: 0.0039 data_time: 0.0008 memory: 485 2022/08/31 19:40:19 - mmengine - INFO - Epoch(val) [38][200/472] eta: 0:00:01 time: 0.0043 data_time: 0.0007 memory: 138 2022/08/31 19:40:20 - mmengine - INFO - Epoch(val) [38][300/472] eta: 0:00:00 time: 0.0052 data_time: 0.0009 memory: 329 2022/08/31 19:40:20 - mmengine - INFO - Epoch(val) [38][400/472] eta: 0:00:00 time: 0.0042 data_time: 0.0008 memory: 76 2022/08/31 19:40:21 - mmengine - INFO - Epoch(val) [38][472/472] kie/macro_f1: 0.8569 2022/08/31 19:40:23 - mmengine - INFO - Epoch(train) [39][100/317] lr: 1.0000e-03 eta: 0:03:02 time: 0.0295 data_time: 0.0030 memory: 919 loss_node: 0.0377 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0377 2022/08/31 19:40:26 - mmengine - INFO - Epoch(train) [39][200/317] lr: 1.0000e-03 eta: 0:02:59 time: 0.0308 data_time: 0.0027 memory: 820 loss_node: 0.0792 loss_edge: 0.0000 acc_node: 97.4684 acc_edge: 100.0000 loss: 0.0792 2022/08/31 19:40:29 - mmengine - INFO - Epoch(train) [39][300/317] lr: 1.0000e-03 eta: 0:02:57 time: 0.0259 data_time: 0.0028 memory: 384 loss_node: 0.0519 loss_edge: 0.0000 acc_node: 98.5714 acc_edge: 100.0000 loss: 0.0519 2022/08/31 19:40:29 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:40:29 - mmengine - INFO - Saving checkpoint at 39 epochs 2022/08/31 19:40:30 - mmengine - INFO - Epoch(val) [39][100/472] eta: 0:00:01 time: 0.0046 data_time: 0.0010 memory: 261 2022/08/31 19:40:30 - mmengine - INFO - Epoch(val) [39][200/472] eta: 0:00:01 time: 0.0044 data_time: 0.0008 memory: 138 2022/08/31 19:40:31 - mmengine - INFO - Epoch(val) [39][300/472] eta: 0:00:00 time: 0.0043 data_time: 0.0007 memory: 329 2022/08/31 19:40:31 - mmengine - INFO - Epoch(val) [39][400/472] eta: 0:00:00 time: 0.0048 data_time: 0.0011 memory: 76 2022/08/31 19:40:32 - mmengine - INFO - Epoch(val) [39][472/472] kie/macro_f1: 0.8564 2022/08/31 19:40:34 - mmengine - INFO - Epoch(train) [40][100/317] lr: 1.0000e-03 eta: 0:02:53 time: 0.0231 data_time: 0.0028 memory: 638 loss_node: 0.0434 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0434 2022/08/31 19:40:37 - mmengine - INFO - Epoch(train) [40][200/317] lr: 1.0000e-03 eta: 0:02:51 time: 0.0374 data_time: 0.0031 memory: 946 loss_node: 0.0661 loss_edge: 0.0000 acc_node: 97.2414 acc_edge: 100.0000 loss: 0.0661 2022/08/31 19:40:40 - mmengine - INFO - Epoch(train) [40][300/317] lr: 1.0000e-03 eta: 0:02:48 time: 0.0293 data_time: 0.0029 memory: 840 loss_node: 0.0634 loss_edge: 0.0000 acc_node: 98.4375 acc_edge: 100.0000 loss: 0.0634 2022/08/31 19:40:40 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:40:40 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/08/31 19:40:41 - mmengine - INFO - Epoch(val) [40][100/472] eta: 0:00:01 time: 0.0041 data_time: 0.0009 memory: 595 2022/08/31 19:40:41 - mmengine - INFO - Epoch(val) [40][200/472] eta: 0:00:01 time: 0.0045 data_time: 0.0008 memory: 138 2022/08/31 19:40:42 - mmengine - INFO - Epoch(val) [40][300/472] eta: 0:00:00 time: 0.0047 data_time: 0.0008 memory: 329 2022/08/31 19:40:42 - mmengine - INFO - Epoch(val) [40][400/472] eta: 0:00:00 time: 0.0042 data_time: 0.0009 memory: 76 2022/08/31 19:40:43 - mmengine - INFO - Epoch(val) [40][472/472] kie/macro_f1: 0.8583 2022/08/31 19:40:45 - mmengine - INFO - Epoch(train) [41][100/317] lr: 1.0000e-04 eta: 0:02:45 time: 0.0356 data_time: 0.0031 memory: 921 loss_node: 0.0302 loss_edge: 0.0000 acc_node: 98.8679 acc_edge: 100.0000 loss: 0.0303 2022/08/31 19:40:48 - mmengine - INFO - Epoch(train) [41][200/317] lr: 1.0000e-04 eta: 0:02:42 time: 0.0269 data_time: 0.0029 memory: 840 loss_node: 0.0346 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0346 2022/08/31 19:40:51 - mmengine - INFO - Epoch(train) [41][300/317] lr: 1.0000e-04 eta: 0:02:40 time: 0.0274 data_time: 0.0030 memory: 795 loss_node: 0.0259 loss_edge: 0.0000 acc_node: 97.5904 acc_edge: 100.0000 loss: 0.0259 2022/08/31 19:40:51 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:40:51 - mmengine - INFO - Saving checkpoint at 41 epochs 2022/08/31 19:40:52 - mmengine - INFO - Epoch(val) [41][100/472] eta: 0:00:01 time: 0.0047 data_time: 0.0010 memory: 531 2022/08/31 19:40:52 - mmengine - INFO - Epoch(val) [41][200/472] eta: 0:00:01 time: 0.0047 data_time: 0.0009 memory: 138 2022/08/31 19:40:53 - mmengine - INFO - Epoch(val) [41][300/472] eta: 0:00:00 time: 0.0051 data_time: 0.0011 memory: 329 2022/08/31 19:40:53 - mmengine - INFO - Epoch(val) [41][400/472] eta: 0:00:00 time: 0.0041 data_time: 0.0008 memory: 76 2022/08/31 19:40:54 - mmengine - INFO - Epoch(val) [41][472/472] kie/macro_f1: 0.8726 2022/08/31 19:40:54 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:40:56 - mmengine - INFO - Epoch(train) [42][100/317] lr: 1.0000e-04 eta: 0:02:37 time: 0.0252 data_time: 0.0030 memory: 708 loss_node: 0.0246 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0246 2022/08/31 19:40:59 - mmengine - INFO - Epoch(train) [42][200/317] lr: 1.0000e-04 eta: 0:02:34 time: 0.0250 data_time: 0.0027 memory: 898 loss_node: 0.0244 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0244 2022/08/31 19:41:02 - mmengine - INFO - Epoch(train) [42][300/317] lr: 1.0000e-04 eta: 0:02:31 time: 0.0247 data_time: 0.0028 memory: 873 loss_node: 0.0281 loss_edge: 0.0000 acc_node: 98.8571 acc_edge: 100.0000 loss: 0.0281 2022/08/31 19:41:02 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:41:02 - mmengine - INFO - Saving checkpoint at 42 epochs 2022/08/31 19:41:03 - mmengine - INFO - Epoch(val) [42][100/472] eta: 0:00:01 time: 0.0037 data_time: 0.0007 memory: 337 2022/08/31 19:41:03 - mmengine - INFO - Epoch(val) [42][200/472] eta: 0:00:01 time: 0.0044 data_time: 0.0007 memory: 138 2022/08/31 19:41:04 - mmengine - INFO - Epoch(val) [42][300/472] eta: 0:00:00 time: 0.0049 data_time: 0.0009 memory: 329 2022/08/31 19:41:04 - mmengine - INFO - Epoch(val) [42][400/472] eta: 0:00:00 time: 0.0041 data_time: 0.0008 memory: 76 2022/08/31 19:41:05 - mmengine - INFO - Epoch(val) [42][472/472] kie/macro_f1: 0.8727 2022/08/31 19:41:07 - mmengine - INFO - Epoch(train) [43][100/317] lr: 1.0000e-04 eta: 0:02:28 time: 0.0290 data_time: 0.0030 memory: 606 loss_node: 0.0231 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0231 2022/08/31 19:41:10 - mmengine - INFO - Epoch(train) [43][200/317] lr: 1.0000e-04 eta: 0:02:26 time: 0.0264 data_time: 0.0030 memory: 390 loss_node: 0.0237 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0237 2022/08/31 19:41:13 - mmengine - INFO - Epoch(train) [43][300/317] lr: 1.0000e-04 eta: 0:02:23 time: 0.0366 data_time: 0.0032 memory: 873 loss_node: 0.0224 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0224 2022/08/31 19:41:13 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:41:13 - mmengine - INFO - Saving checkpoint at 43 epochs 2022/08/31 19:41:14 - mmengine - INFO - Epoch(val) [43][100/472] eta: 0:00:01 time: 0.0038 data_time: 0.0007 memory: 345 2022/08/31 19:41:14 - mmengine - INFO - Epoch(val) [43][200/472] eta: 0:00:01 time: 0.0045 data_time: 0.0008 memory: 138 2022/08/31 19:41:15 - mmengine - INFO - Epoch(val) [43][300/472] eta: 0:00:00 time: 0.0053 data_time: 0.0010 memory: 329 2022/08/31 19:41:15 - mmengine - INFO - Epoch(val) [43][400/472] eta: 0:00:00 time: 0.0042 data_time: 0.0008 memory: 76 2022/08/31 19:41:15 - mmengine - INFO - Epoch(val) [43][472/472] kie/macro_f1: 0.8708 2022/08/31 19:41:18 - mmengine - INFO - Epoch(train) [44][100/317] lr: 1.0000e-04 eta: 0:02:20 time: 0.0289 data_time: 0.0027 memory: 578 loss_node: 0.0156 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0156 2022/08/31 19:41:21 - mmengine - INFO - Epoch(train) [44][200/317] lr: 1.0000e-04 eta: 0:02:17 time: 0.0274 data_time: 0.0029 memory: 951 loss_node: 0.0130 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0130 2022/08/31 19:41:24 - mmengine - INFO - Epoch(train) [44][300/317] lr: 1.0000e-04 eta: 0:02:15 time: 0.0247 data_time: 0.0027 memory: 626 loss_node: 0.0147 loss_edge: 0.0000 acc_node: 99.4186 acc_edge: 100.0000 loss: 0.0147 2022/08/31 19:41:24 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:41:24 - mmengine - INFO - Saving checkpoint at 44 epochs 2022/08/31 19:41:25 - mmengine - INFO - Epoch(val) [44][100/472] eta: 0:00:01 time: 0.0036 data_time: 0.0007 memory: 203 2022/08/31 19:41:25 - mmengine - INFO - Epoch(val) [44][200/472] eta: 0:00:01 time: 0.0044 data_time: 0.0007 memory: 138 2022/08/31 19:41:26 - mmengine - INFO - Epoch(val) [44][300/472] eta: 0:00:00 time: 0.0045 data_time: 0.0009 memory: 329 2022/08/31 19:41:26 - mmengine - INFO - Epoch(val) [44][400/472] eta: 0:00:00 time: 0.0040 data_time: 0.0008 memory: 76 2022/08/31 19:41:26 - mmengine - INFO - Epoch(val) [44][472/472] kie/macro_f1: 0.8705 2022/08/31 19:41:28 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:41:29 - mmengine - INFO - Epoch(train) [45][100/317] lr: 1.0000e-04 eta: 0:02:11 time: 0.0266 data_time: 0.0028 memory: 1086 loss_node: 0.0103 loss_edge: 0.0000 acc_node: 99.5283 acc_edge: 100.0000 loss: 0.0103 2022/08/31 19:41:32 - mmengine - INFO - Epoch(train) [45][200/317] lr: 1.0000e-04 eta: 0:02:09 time: 0.0258 data_time: 0.0029 memory: 647 loss_node: 0.0091 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0091 2022/08/31 19:41:34 - mmengine - INFO - Epoch(train) [45][300/317] lr: 1.0000e-04 eta: 0:02:06 time: 0.0257 data_time: 0.0028 memory: 507 loss_node: 0.0207 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0208 2022/08/31 19:41:35 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:41:35 - mmengine - INFO - Saving checkpoint at 45 epochs 2022/08/31 19:41:36 - mmengine - INFO - Epoch(val) [45][100/472] eta: 0:00:01 time: 0.0044 data_time: 0.0009 memory: 723 2022/08/31 19:41:36 - mmengine - INFO - Epoch(val) [45][200/472] eta: 0:00:01 time: 0.0048 data_time: 0.0009 memory: 138 2022/08/31 19:41:37 - mmengine - INFO - Epoch(val) [45][300/472] eta: 0:00:00 time: 0.0047 data_time: 0.0009 memory: 329 2022/08/31 19:41:37 - mmengine - INFO - Epoch(val) [45][400/472] eta: 0:00:00 time: 0.0041 data_time: 0.0008 memory: 76 2022/08/31 19:41:38 - mmengine - INFO - Epoch(val) [45][472/472] kie/macro_f1: 0.8671 2022/08/31 19:41:40 - mmengine - INFO - Epoch(train) [46][100/317] lr: 1.0000e-04 eta: 0:02:03 time: 0.0292 data_time: 0.0028 memory: 863 loss_node: 0.0125 loss_edge: 0.0000 acc_node: 98.7805 acc_edge: 100.0000 loss: 0.0125 2022/08/31 19:41:43 - mmengine - INFO - Epoch(train) [46][200/317] lr: 1.0000e-04 eta: 0:02:00 time: 0.0243 data_time: 0.0030 memory: 409 loss_node: 0.0107 loss_edge: 0.0000 acc_node: 98.1013 acc_edge: 100.0000 loss: 0.0107 2022/08/31 19:41:46 - mmengine - INFO - Epoch(train) [46][300/317] lr: 1.0000e-04 eta: 0:01:58 time: 0.0248 data_time: 0.0028 memory: 911 loss_node: 0.0220 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0220 2022/08/31 19:41:46 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:41:46 - mmengine - INFO - Saving checkpoint at 46 epochs 2022/08/31 19:41:47 - mmengine - INFO - Epoch(val) [46][100/472] eta: 0:00:01 time: 0.0039 data_time: 0.0007 memory: 389 2022/08/31 19:41:47 - mmengine - INFO - Epoch(val) [46][200/472] eta: 0:00:01 time: 0.0044 data_time: 0.0008 memory: 138 2022/08/31 19:41:48 - mmengine - INFO - Epoch(val) [46][300/472] eta: 0:00:00 time: 0.0054 data_time: 0.0010 memory: 329 2022/08/31 19:41:48 - mmengine - INFO - Epoch(val) [46][400/472] eta: 0:00:00 time: 0.0041 data_time: 0.0008 memory: 76 2022/08/31 19:41:49 - mmengine - INFO - Epoch(val) [46][472/472] kie/macro_f1: 0.8705 2022/08/31 19:41:51 - mmengine - INFO - Epoch(train) [47][100/317] lr: 1.0000e-04 eta: 0:01:55 time: 0.0254 data_time: 0.0029 memory: 575 loss_node: 0.0160 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0160 2022/08/31 19:41:54 - mmengine - INFO - Epoch(train) [47][200/317] lr: 1.0000e-04 eta: 0:01:52 time: 0.0301 data_time: 0.0031 memory: 820 loss_node: 0.0094 loss_edge: 0.0000 acc_node: 99.5192 acc_edge: 100.0000 loss: 0.0094 2022/08/31 19:41:57 - mmengine - INFO - Epoch(train) [47][300/317] lr: 1.0000e-04 eta: 0:01:49 time: 0.0245 data_time: 0.0027 memory: 909 loss_node: 0.0139 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0139 2022/08/31 19:41:57 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:41:57 - mmengine - INFO - Saving checkpoint at 47 epochs 2022/08/31 19:41:58 - mmengine - INFO - Epoch(val) [47][100/472] eta: 0:00:01 time: 0.0043 data_time: 0.0009 memory: 688 2022/08/31 19:41:59 - mmengine - INFO - Epoch(val) [47][200/472] eta: 0:00:01 time: 0.0050 data_time: 0.0009 memory: 138 2022/08/31 19:41:59 - mmengine - INFO - Epoch(val) [47][300/472] eta: 0:00:00 time: 0.0049 data_time: 0.0009 memory: 329 2022/08/31 19:41:59 - mmengine - INFO - Epoch(val) [47][400/472] eta: 0:00:00 time: 0.0042 data_time: 0.0008 memory: 76 2022/08/31 19:42:00 - mmengine - INFO - Epoch(val) [47][472/472] kie/macro_f1: 0.8694 2022/08/31 19:42:02 - mmengine - INFO - Epoch(train) [48][100/317] lr: 1.0000e-04 eta: 0:01:46 time: 0.0251 data_time: 0.0028 memory: 999 loss_node: 0.0102 loss_edge: 0.0000 acc_node: 98.3740 acc_edge: 100.0000 loss: 0.0102 2022/08/31 19:42:02 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:42:05 - mmengine - INFO - Epoch(train) [48][200/317] lr: 1.0000e-04 eta: 0:01:44 time: 0.0281 data_time: 0.0027 memory: 552 loss_node: 0.0107 loss_edge: 0.0000 acc_node: 98.6395 acc_edge: 100.0000 loss: 0.0107 2022/08/31 19:42:08 - mmengine - INFO - Epoch(train) [48][300/317] lr: 1.0000e-04 eta: 0:01:41 time: 0.0353 data_time: 0.0032 memory: 949 loss_node: 0.0181 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0181 2022/08/31 19:42:08 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:42:08 - mmengine - INFO - Saving checkpoint at 48 epochs 2022/08/31 19:42:09 - mmengine - INFO - Epoch(val) [48][100/472] eta: 0:00:01 time: 0.0039 data_time: 0.0009 memory: 666 2022/08/31 19:42:09 - mmengine - INFO - Epoch(val) [48][200/472] eta: 0:00:01 time: 0.0045 data_time: 0.0009 memory: 138 2022/08/31 19:42:10 - mmengine - INFO - Epoch(val) [48][300/472] eta: 0:00:00 time: 0.0046 data_time: 0.0009 memory: 329 2022/08/31 19:42:10 - mmengine - INFO - Epoch(val) [48][400/472] eta: 0:00:00 time: 0.0040 data_time: 0.0008 memory: 76 2022/08/31 19:42:11 - mmengine - INFO - Epoch(val) [48][472/472] kie/macro_f1: 0.8687 2022/08/31 19:42:13 - mmengine - INFO - Epoch(train) [49][100/317] lr: 1.0000e-04 eta: 0:01:38 time: 0.0268 data_time: 0.0028 memory: 827 loss_node: 0.0198 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0198 2022/08/31 19:42:16 - mmengine - INFO - Epoch(train) [49][200/317] lr: 1.0000e-04 eta: 0:01:35 time: 0.0238 data_time: 0.0028 memory: 668 loss_node: 0.0106 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0106 2022/08/31 19:42:19 - mmengine - INFO - Epoch(train) [49][300/317] lr: 1.0000e-04 eta: 0:01:32 time: 0.0338 data_time: 0.0044 memory: 951 loss_node: 0.0064 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0064 2022/08/31 19:42:19 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:42:19 - mmengine - INFO - Saving checkpoint at 49 epochs 2022/08/31 19:42:20 - mmengine - INFO - Epoch(val) [49][100/472] eta: 0:00:01 time: 0.0041 data_time: 0.0009 memory: 456 2022/08/31 19:42:20 - mmengine - INFO - Epoch(val) [49][200/472] eta: 0:00:01 time: 0.0056 data_time: 0.0011 memory: 138 2022/08/31 19:42:21 - mmengine - INFO - Epoch(val) [49][300/472] eta: 0:00:00 time: 0.0049 data_time: 0.0009 memory: 329 2022/08/31 19:42:21 - mmengine - INFO - Epoch(val) [49][400/472] eta: 0:00:00 time: 0.0044 data_time: 0.0008 memory: 76 2022/08/31 19:42:22 - mmengine - INFO - Epoch(val) [49][472/472] kie/macro_f1: 0.8684 2022/08/31 19:42:25 - mmengine - INFO - Epoch(train) [50][100/317] lr: 1.0000e-04 eta: 0:01:29 time: 0.0243 data_time: 0.0028 memory: 665 loss_node: 0.0099 loss_edge: 0.0000 acc_node: 99.4681 acc_edge: 100.0000 loss: 0.0099 2022/08/31 19:42:28 - mmengine - INFO - Epoch(train) [50][200/317] lr: 1.0000e-04 eta: 0:01:27 time: 0.0313 data_time: 0.0028 memory: 891 loss_node: 0.0166 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0166 2022/08/31 19:42:30 - mmengine - INFO - Epoch(train) [50][300/317] lr: 1.0000e-04 eta: 0:01:24 time: 0.0249 data_time: 0.0029 memory: 339 loss_node: 0.0144 loss_edge: 0.0000 acc_node: 99.3151 acc_edge: 100.0000 loss: 0.0144 2022/08/31 19:42:31 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:42:31 - mmengine - INFO - Saving checkpoint at 50 epochs 2022/08/31 19:42:31 - mmengine - INFO - Epoch(val) [50][100/472] eta: 0:00:01 time: 0.0037 data_time: 0.0008 memory: 380 2022/08/31 19:42:32 - mmengine - INFO - Epoch(val) [50][200/472] eta: 0:00:01 time: 0.0044 data_time: 0.0008 memory: 138 2022/08/31 19:42:32 - mmengine - INFO - Epoch(val) [50][300/472] eta: 0:00:00 time: 0.0046 data_time: 0.0008 memory: 329 2022/08/31 19:42:33 - mmengine - INFO - Epoch(val) [50][400/472] eta: 0:00:00 time: 0.0042 data_time: 0.0009 memory: 76 2022/08/31 19:42:33 - mmengine - INFO - Epoch(val) [50][472/472] kie/macro_f1: 0.8669 2022/08/31 19:42:36 - mmengine - INFO - Epoch(train) [51][100/317] lr: 1.0000e-05 eta: 0:01:21 time: 0.0249 data_time: 0.0028 memory: 934 loss_node: 0.0098 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0099 2022/08/31 19:42:37 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:42:39 - mmengine - INFO - Epoch(train) [51][200/317] lr: 1.0000e-05 eta: 0:01:18 time: 0.0299 data_time: 0.0030 memory: 714 loss_node: 0.0098 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0098 2022/08/31 19:42:42 - mmengine - INFO - Epoch(train) [51][300/317] lr: 1.0000e-05 eta: 0:01:16 time: 0.0284 data_time: 0.0032 memory: 848 loss_node: 0.0085 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0085 2022/08/31 19:42:42 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:42:42 - mmengine - INFO - Saving checkpoint at 51 epochs 2022/08/31 19:42:43 - mmengine - INFO - Epoch(val) [51][100/472] eta: 0:00:01 time: 0.0038 data_time: 0.0008 memory: 239 2022/08/31 19:42:43 - mmengine - INFO - Epoch(val) [51][200/472] eta: 0:00:01 time: 0.0047 data_time: 0.0009 memory: 138 2022/08/31 19:42:44 - mmengine - INFO - Epoch(val) [51][300/472] eta: 0:00:00 time: 0.0046 data_time: 0.0009 memory: 329 2022/08/31 19:42:44 - mmengine - INFO - Epoch(val) [51][400/472] eta: 0:00:00 time: 0.0040 data_time: 0.0007 memory: 76 2022/08/31 19:42:44 - mmengine - INFO - Epoch(val) [51][472/472] kie/macro_f1: 0.8681 2022/08/31 19:42:47 - mmengine - INFO - Epoch(train) [52][100/317] lr: 1.0000e-05 eta: 0:01:13 time: 0.0299 data_time: 0.0030 memory: 950 loss_node: 0.0083 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0083 2022/08/31 19:42:50 - mmengine - INFO - Epoch(train) [52][200/317] lr: 1.0000e-05 eta: 0:01:10 time: 0.0254 data_time: 0.0030 memory: 580 loss_node: 0.0080 loss_edge: 0.0000 acc_node: 99.3590 acc_edge: 100.0000 loss: 0.0080 2022/08/31 19:42:53 - mmengine - INFO - Epoch(train) [52][300/317] lr: 1.0000e-05 eta: 0:01:07 time: 0.0309 data_time: 0.0029 memory: 654 loss_node: 0.0179 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0179 2022/08/31 19:42:53 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:42:53 - mmengine - INFO - Saving checkpoint at 52 epochs 2022/08/31 19:42:54 - mmengine - INFO - Epoch(val) [52][100/472] eta: 0:00:01 time: 0.0039 data_time: 0.0008 memory: 226 2022/08/31 19:42:54 - mmengine - INFO - Epoch(val) [52][200/472] eta: 0:00:01 time: 0.0045 data_time: 0.0008 memory: 138 2022/08/31 19:42:55 - mmengine - INFO - Epoch(val) [52][300/472] eta: 0:00:00 time: 0.0046 data_time: 0.0009 memory: 329 2022/08/31 19:42:55 - mmengine - INFO - Epoch(val) [52][400/472] eta: 0:00:00 time: 0.0042 data_time: 0.0009 memory: 76 2022/08/31 19:42:55 - mmengine - INFO - Epoch(val) [52][472/472] kie/macro_f1: 0.8685 2022/08/31 19:42:58 - mmengine - INFO - Epoch(train) [53][100/317] lr: 1.0000e-05 eta: 0:01:04 time: 0.0247 data_time: 0.0028 memory: 826 loss_node: 0.0049 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0049 2022/08/31 19:43:01 - mmengine - INFO - Epoch(train) [53][200/317] lr: 1.0000e-05 eta: 0:01:02 time: 0.0277 data_time: 0.0032 memory: 649 loss_node: 0.0110 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0110 2022/08/31 19:43:03 - mmengine - INFO - Epoch(train) [53][300/317] lr: 1.0000e-05 eta: 0:00:59 time: 0.0334 data_time: 0.0028 memory: 1001 loss_node: 0.0059 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0059 2022/08/31 19:43:04 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:43:04 - mmengine - INFO - Saving checkpoint at 53 epochs 2022/08/31 19:43:05 - mmengine - INFO - Epoch(val) [53][100/472] eta: 0:00:01 time: 0.0038 data_time: 0.0009 memory: 478 2022/08/31 19:43:05 - mmengine - INFO - Epoch(val) [53][200/472] eta: 0:00:01 time: 0.0045 data_time: 0.0008 memory: 138 2022/08/31 19:43:05 - mmengine - INFO - Epoch(val) [53][300/472] eta: 0:00:00 time: 0.0054 data_time: 0.0010 memory: 329 2022/08/31 19:43:06 - mmengine - INFO - Epoch(val) [53][400/472] eta: 0:00:00 time: 0.0039 data_time: 0.0007 memory: 76 2022/08/31 19:43:06 - mmengine - INFO - Epoch(val) [53][472/472] kie/macro_f1: 0.8678 2022/08/31 19:43:09 - mmengine - INFO - Epoch(train) [54][100/317] lr: 1.0000e-05 eta: 0:00:56 time: 0.0283 data_time: 0.0031 memory: 950 loss_node: 0.0054 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0054 2022/08/31 19:43:12 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:43:12 - mmengine - INFO - Epoch(train) [54][200/317] lr: 1.0000e-05 eta: 0:00:53 time: 0.0231 data_time: 0.0029 memory: 841 loss_node: 0.0095 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0095 2022/08/31 19:43:15 - mmengine - INFO - Epoch(train) [54][300/317] lr: 1.0000e-05 eta: 0:00:51 time: 0.0247 data_time: 0.0030 memory: 415 loss_node: 0.0108 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0108 2022/08/31 19:43:15 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:43:15 - mmengine - INFO - Saving checkpoint at 54 epochs 2022/08/31 19:43:16 - mmengine - INFO - Epoch(val) [54][100/472] eta: 0:00:01 time: 0.0036 data_time: 0.0007 memory: 655 2022/08/31 19:43:17 - mmengine - INFO - Epoch(val) [54][200/472] eta: 0:00:01 time: 0.0047 data_time: 0.0009 memory: 138 2022/08/31 19:43:17 - mmengine - INFO - Epoch(val) [54][300/472] eta: 0:00:00 time: 0.0051 data_time: 0.0010 memory: 329 2022/08/31 19:43:18 - mmengine - INFO - Epoch(val) [54][400/472] eta: 0:00:00 time: 0.0051 data_time: 0.0011 memory: 76 2022/08/31 19:43:18 - mmengine - INFO - Epoch(val) [54][472/472] kie/macro_f1: 0.8686 2022/08/31 19:43:21 - mmengine - INFO - Epoch(train) [55][100/317] lr: 1.0000e-05 eta: 0:00:47 time: 0.0308 data_time: 0.0035 memory: 534 loss_node: 0.0082 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0083 2022/08/31 19:43:23 - mmengine - INFO - Epoch(train) [55][200/317] lr: 1.0000e-05 eta: 0:00:45 time: 0.0260 data_time: 0.0029 memory: 919 loss_node: 0.0091 loss_edge: 0.0000 acc_node: 99.6324 acc_edge: 100.0000 loss: 0.0091 2022/08/31 19:43:26 - mmengine - INFO - Epoch(train) [55][300/317] lr: 1.0000e-05 eta: 0:00:42 time: 0.0274 data_time: 0.0028 memory: 833 loss_node: 0.0063 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0063 2022/08/31 19:43:26 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:43:26 - mmengine - INFO - Saving checkpoint at 55 epochs 2022/08/31 19:43:27 - mmengine - INFO - Epoch(val) [55][100/472] eta: 0:00:01 time: 0.0036 data_time: 0.0007 memory: 211 2022/08/31 19:43:28 - mmengine - INFO - Epoch(val) [55][200/472] eta: 0:00:01 time: 0.0054 data_time: 0.0011 memory: 138 2022/08/31 19:43:28 - mmengine - INFO - Epoch(val) [55][300/472] eta: 0:00:00 time: 0.0045 data_time: 0.0008 memory: 329 2022/08/31 19:43:29 - mmengine - INFO - Epoch(val) [55][400/472] eta: 0:00:00 time: 0.0045 data_time: 0.0010 memory: 76 2022/08/31 19:43:29 - mmengine - INFO - Epoch(val) [55][472/472] kie/macro_f1: 0.8689 2022/08/31 19:43:32 - mmengine - INFO - Epoch(train) [56][100/317] lr: 1.0000e-05 eta: 0:00:39 time: 0.0425 data_time: 0.0031 memory: 896 loss_node: 0.0049 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0049 2022/08/31 19:43:34 - mmengine - INFO - Epoch(train) [56][200/317] lr: 1.0000e-05 eta: 0:00:36 time: 0.0293 data_time: 0.0033 memory: 853 loss_node: 0.0077 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0077 2022/08/31 19:43:38 - mmengine - INFO - Epoch(train) [56][300/317] lr: 1.0000e-05 eta: 0:00:34 time: 0.0397 data_time: 0.0031 memory: 560 loss_node: 0.0135 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0135 2022/08/31 19:43:39 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:43:39 - mmengine - INFO - Saving checkpoint at 56 epochs 2022/08/31 19:43:39 - mmengine - INFO - Epoch(val) [56][100/472] eta: 0:00:01 time: 0.0041 data_time: 0.0009 memory: 551 2022/08/31 19:43:40 - mmengine - INFO - Epoch(val) [56][200/472] eta: 0:00:01 time: 0.0046 data_time: 0.0008 memory: 138 2022/08/31 19:43:40 - mmengine - INFO - Epoch(val) [56][300/472] eta: 0:00:00 time: 0.0055 data_time: 0.0011 memory: 329 2022/08/31 19:43:41 - mmengine - INFO - Epoch(val) [56][400/472] eta: 0:00:00 time: 0.0044 data_time: 0.0009 memory: 76 2022/08/31 19:43:41 - mmengine - INFO - Epoch(val) [56][472/472] kie/macro_f1: 0.8687 2022/08/31 19:43:44 - mmengine - INFO - Epoch(train) [57][100/317] lr: 1.0000e-05 eta: 0:00:31 time: 0.0286 data_time: 0.0030 memory: 778 loss_node: 0.0075 loss_edge: 0.0000 acc_node: 99.3333 acc_edge: 100.0000 loss: 0.0075 2022/08/31 19:43:47 - mmengine - INFO - Epoch(train) [57][200/317] lr: 1.0000e-05 eta: 0:00:28 time: 0.0301 data_time: 0.0034 memory: 379 loss_node: 0.0174 loss_edge: 0.0000 acc_node: 99.3548 acc_edge: 100.0000 loss: 0.0174 2022/08/31 19:43:48 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:43:50 - mmengine - INFO - Epoch(train) [57][300/317] lr: 1.0000e-05 eta: 0:00:25 time: 0.0257 data_time: 0.0028 memory: 922 loss_node: 0.0110 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0110 2022/08/31 19:43:50 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:43:50 - mmengine - INFO - Saving checkpoint at 57 epochs 2022/08/31 19:43:51 - mmengine - INFO - Epoch(val) [57][100/472] eta: 0:00:01 time: 0.0044 data_time: 0.0010 memory: 298 2022/08/31 19:43:51 - mmengine - INFO - Epoch(val) [57][200/472] eta: 0:00:01 time: 0.0053 data_time: 0.0009 memory: 138 2022/08/31 19:43:52 - mmengine - INFO - Epoch(val) [57][300/472] eta: 0:00:00 time: 0.0057 data_time: 0.0012 memory: 329 2022/08/31 19:43:52 - mmengine - INFO - Epoch(val) [57][400/472] eta: 0:00:00 time: 0.0053 data_time: 0.0012 memory: 76 2022/08/31 19:43:53 - mmengine - INFO - Epoch(val) [57][472/472] kie/macro_f1: 0.8693 2022/08/31 19:43:55 - mmengine - INFO - Epoch(train) [58][100/317] lr: 1.0000e-05 eta: 0:00:22 time: 0.0288 data_time: 0.0030 memory: 690 loss_node: 0.0035 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0035 2022/08/31 19:43:58 - mmengine - INFO - Epoch(train) [58][200/317] lr: 1.0000e-05 eta: 0:00:20 time: 0.0292 data_time: 0.0029 memory: 907 loss_node: 0.0108 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0108 2022/08/31 19:44:01 - mmengine - INFO - Epoch(train) [58][300/317] lr: 1.0000e-05 eta: 0:00:17 time: 0.0394 data_time: 0.0031 memory: 887 loss_node: 0.0038 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0038 2022/08/31 19:44:02 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:44:02 - mmengine - INFO - Saving checkpoint at 58 epochs 2022/08/31 19:44:02 - mmengine - INFO - Epoch(val) [58][100/472] eta: 0:00:01 time: 0.0044 data_time: 0.0009 memory: 439 2022/08/31 19:44:03 - mmengine - INFO - Epoch(val) [58][200/472] eta: 0:00:01 time: 0.0051 data_time: 0.0010 memory: 138 2022/08/31 19:44:03 - mmengine - INFO - Epoch(val) [58][300/472] eta: 0:00:01 time: 0.0062 data_time: 0.0012 memory: 329 2022/08/31 19:44:04 - mmengine - INFO - Epoch(val) [58][400/472] eta: 0:00:00 time: 0.0046 data_time: 0.0009 memory: 76 2022/08/31 19:44:04 - mmengine - INFO - Epoch(val) [58][472/472] kie/macro_f1: 0.8694 2022/08/31 19:44:07 - mmengine - INFO - Epoch(train) [59][100/317] lr: 1.0000e-05 eta: 0:00:14 time: 0.0353 data_time: 0.0030 memory: 838 loss_node: 0.0113 loss_edge: 0.0000 acc_node: 98.8095 acc_edge: 100.0000 loss: 0.0113 2022/08/31 19:44:10 - mmengine - INFO - Epoch(train) [59][200/317] lr: 1.0000e-05 eta: 0:00:11 time: 0.0289 data_time: 0.0031 memory: 470 loss_node: 0.0064 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0064 2022/08/31 19:44:13 - mmengine - INFO - Epoch(train) [59][300/317] lr: 1.0000e-05 eta: 0:00:08 time: 0.0279 data_time: 0.0029 memory: 1131 loss_node: 0.0062 loss_edge: 0.0000 acc_node: 99.0909 acc_edge: 100.0000 loss: 0.0062 2022/08/31 19:44:14 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:44:14 - mmengine - INFO - Saving checkpoint at 59 epochs 2022/08/31 19:44:15 - mmengine - INFO - Epoch(val) [59][100/472] eta: 0:00:02 time: 0.0055 data_time: 0.0011 memory: 307 2022/08/31 19:44:15 - mmengine - INFO - Epoch(val) [59][200/472] eta: 0:00:01 time: 0.0056 data_time: 0.0010 memory: 138 2022/08/31 19:44:16 - mmengine - INFO - Epoch(val) [59][300/472] eta: 0:00:00 time: 0.0058 data_time: 0.0011 memory: 329 2022/08/31 19:44:16 - mmengine - INFO - Epoch(val) [59][400/472] eta: 0:00:00 time: 0.0049 data_time: 0.0011 memory: 76 2022/08/31 19:44:17 - mmengine - INFO - Epoch(val) [59][472/472] kie/macro_f1: 0.8676 2022/08/31 19:44:20 - mmengine - INFO - Epoch(train) [60][100/317] lr: 1.0000e-05 eta: 0:00:05 time: 0.0252 data_time: 0.0031 memory: 498 loss_node: 0.0039 loss_edge: 0.0000 acc_node: 100.0000 acc_edge: 100.0000 loss: 0.0039 2022/08/31 19:44:23 - mmengine - INFO - Epoch(train) [60][200/317] lr: 1.0000e-05 eta: 0:00:03 time: 0.0280 data_time: 0.0030 memory: 804 loss_node: 0.0064 loss_edge: 0.0000 acc_node: 99.5475 acc_edge: 100.0000 loss: 0.0064 2022/08/31 19:44:25 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:44:26 - mmengine - INFO - Epoch(train) [60][300/317] lr: 1.0000e-05 eta: 0:00:00 time: 0.0297 data_time: 0.0038 memory: 942 loss_node: 0.0083 loss_edge: 0.0000 acc_node: 99.4350 acc_edge: 100.0000 loss: 0.0083 2022/08/31 19:44:26 - mmengine - INFO - Exp name: sdmgr_novisual_60e_wildreceipt_20220831_193317 2022/08/31 19:44:26 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/08/31 19:44:27 - mmengine - INFO - Epoch(val) [60][100/472] eta: 0:00:01 time: 0.0048 data_time: 0.0010 memory: 627 2022/08/31 19:44:28 - mmengine - INFO - Epoch(val) [60][200/472] eta: 0:00:01 time: 0.0057 data_time: 0.0010 memory: 138 2022/08/31 19:44:28 - mmengine - INFO - Epoch(val) [60][300/472] eta: 0:00:01 time: 0.0110 data_time: 0.0039 memory: 329 2022/08/31 19:44:29 - mmengine - INFO - Epoch(val) [60][400/472] eta: 0:00:00 time: 0.0052 data_time: 0.0011 memory: 76 2022/08/31 19:44:29 - mmengine - INFO - Epoch(val) [60][472/472] kie/macro_f1: 0.8677