2022/09/15 15:24:54 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0] CUDA available: True numpy_random_seed: 53054265 GPU 0,1,2,3: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/cache/share/cuda-11.1 NVCC: Cuda compilation tools, release 11.1, V11.1.74 GCC: gcc (GCC) 5.4.0 PyTorch: 1.9.0+cu111 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.1 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 - CuDNN 8.0.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.10.0+cu111 OpenCV: 4.5.4 MMEngine: 0.1.0 Runtime environment: cudnn_benchmark: True mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None Distributed launcher: slurm Distributed training: True GPU number: 4 ------------------------------------------------------------ 2022/09/15 15:24:55 - mmengine - INFO - Config: mj_rec_data_root = 'data/rec/Syn90k/' mj_rec_train = dict( type='OCRDataset', data_root='data/rec/Syn90k/', data_prefix=dict(img_path='mnt/ramdisk/max/90kDICT32px'), ann_file='train_labels.json', test_mode=False, pipeline=None) mj_sub_rec_train = dict( type='OCRDataset', data_root='data/rec/Syn90k/', data_prefix=dict(img_path='mnt/ramdisk/max/90kDICT32px'), ann_file='subset_train_labels.json', test_mode=False, pipeline=None) st_data_root = 'data/rec/SynthText/' st_rec_train = dict( type='OCRDataset', data_root='data/rec/SynthText/', data_prefix=dict(img_path='synthtext/SynthText_patch_horizontal'), ann_file='train_labels.json', test_mode=False, pipeline=None) st_an_rec_train = dict( type='OCRDataset', data_root='data/rec/SynthText/', data_prefix=dict(img_path='synthtext/SynthText_patch_horizontal'), ann_file='alphanumeric_train_labels.json', test_mode=False, pipeline=None) st_sub_rec_train = dict( type='OCRDataset', data_root='data/rec/SynthText/', data_prefix=dict(img_path='synthtext/SynthText_patch_horizontal'), ann_file='subset_train_labels.json', test_mode=False, pipeline=None) st_add_rec_data_root = 'data/rec/synthtext_add/' st_add_rec_train = dict( type='OCRDataset', data_root='data/rec/synthtext_add/', ann_file='train_labels.json', test_mode=False, pipeline=None) cute80_rec_data_root = 'data/rec/ct80/' cute80_rec_test = dict( type='OCRDataset', data_root='data/rec/ct80/', ann_file='test_labels.json', test_mode=True, pipeline=None) iiit5k_rec_data_root = 'data/rec/IIIT5K/' iiit5k_rec_train = dict( type='OCRDataset', data_root='data/rec/IIIT5K/', ann_file='train_labels.json', test_mode=False, pipeline=None) iiit5k_rec_test = dict( type='OCRDataset', data_root='data/rec/IIIT5K/', ann_file='test_labels.json', test_mode=True, pipeline=None) svt_rec_data_root = 'data/rec/svt/' svt_rec_test = dict( type='OCRDataset', data_root='data/rec/svt/', ann_file='test_labels.json', test_mode=True, pipeline=None) svtp_rec_data_root = 'data/rec/svtp/' svtp_rec_test = dict( type='OCRDataset', data_root='data/rec/svtp/', ann_file='test_labels.json', test_mode=True, pipeline=None) ic13_rec_data_root = 'data/rec/icdar_2013/' ic13_rec_train = dict( type='OCRDataset', data_root='data/rec/icdar_2013/', ann_file='train_labels.json', test_mode=False, pipeline=None) ic13_rec_test = dict( type='OCRDataset', data_root='data/rec/icdar_2013/', ann_file='test_labels.json', test_mode=True, pipeline=None) ic15_rec_data_root = 'data/rec/icdar_2015/' ic15_rec_train = dict( type='OCRDataset', data_root='data/rec/icdar_2015/', ann_file='train_labels.json', test_mode=False, pipeline=None) ic15_rec_test = dict( type='OCRDataset', data_root='data/rec/icdar_2015/', ann_file='test_labels.json', test_mode=True, pipeline=None) 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, out_dir='sproject:s3://1.0.0rc0_recog_retest'), 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 val_evaluator = dict( type='MultiDatasetsEvaluator', metrics=[dict(type='WordMetric', mode=['ignore_case_symbol'])], dataset_prefixes=['CUTE80', 'IIIT5K', 'SVT', 'SVTP', 'IC13', 'IC15']) test_evaluator = dict( type='MultiDatasetsEvaluator', metrics=[dict(type='WordMetric', mode=['ignore_case_symbol'])], dataset_prefixes=['CUTE80', 'IIIT5K', 'SVT', 'SVTP', 'IC13', 'IC15']) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='TextRecogLocalVisualizer', name='visualizer', vis_backends=[dict(type='LocalVisBackend')]) optim_wrapper = dict( type='OptimWrapper', optimizer=dict(type='Adam', lr=0.0004)) train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [ dict(type='LinearLR', end=100, by_epoch=False), dict(type='MultiStepLR', milestones=[11], end=12) ] file_client_args = dict(backend='disk') dictionary = dict( type='Dictionary', dict_file= 'configs/textrecog/master/../../../dicts/english_digits_symbols.txt', with_padding=True, with_unknown=True, same_start_end=True, with_start=True, with_end=True) model = dict( type='MASTER', backbone=dict( type='ResNet', in_channels=3, stem_channels=[64, 128], block_cfgs=dict( type='BasicBlock', plugins=dict( cfg=dict( type='GCAModule', ratio=0.0625, n_head=1, pooling_type='att', is_att_scale=False, fusion_type='channel_add'), position='after_conv2')), arch_layers=[1, 2, 5, 3], arch_channels=[256, 256, 512, 512], strides=[1, 1, 1, 1], plugins=[ dict( cfg=dict(type='Maxpool2d', kernel_size=2, stride=(2, 2)), stages=(True, True, False, False), position='before_stage'), dict( cfg=dict(type='Maxpool2d', kernel_size=(2, 1), stride=(2, 1)), stages=(False, False, True, False), position='before_stage'), dict( cfg=dict( type='ConvModule', kernel_size=3, stride=1, padding=1, norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU')), stages=(True, True, True, True), position='after_stage') ], init_cfg=[ dict(type='Kaiming', layer='Conv2d'), dict(type='Constant', val=1, layer='BatchNorm2d') ]), encoder=None, decoder=dict( type='MasterDecoder', d_model=512, n_head=8, attn_drop=0.0, ffn_drop=0.0, d_inner=2048, n_layers=3, feat_pe_drop=0.2, feat_size=240, postprocessor=dict(type='AttentionPostprocessor'), module_loss=dict( type='CEModuleLoss', reduction='mean', ignore_first_char=True), max_seq_len=30, dictionary=dict( type='Dictionary', dict_file= 'configs/textrecog/master/../../../dicts/english_digits_symbols.txt', with_padding=True, with_unknown=True, same_start_end=True, with_start=True, with_end=True)), data_preprocessor=dict( type='TextRecogDataPreprocessor', mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5])) train_pipeline = [ dict( type='LoadImageFromFile', file_client_args=dict(backend='disk'), ignore_empty=True, min_size=2), dict(type='LoadOCRAnnotations', with_text=True), dict( type='RescaleToHeight', height=48, min_width=48, max_width=160, width_divisor=16), dict(type='PadToWidth', width=160), dict( type='PackTextRecogInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio')) ] test_pipeline = [ dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')), dict( type='RescaleToHeight', height=48, min_width=48, max_width=160, width_divisor=16), dict(type='PadToWidth', width=160), dict(type='LoadOCRAnnotations', with_text=True), dict( type='PackTextRecogInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio')) ] train_list = [ dict( type='OCRDataset', data_root='data/rec/Syn90k/', data_prefix=dict(img_path='mnt/ramdisk/max/90kDICT32px'), ann_file='train_labels.json', test_mode=False, pipeline=None), dict( type='OCRDataset', data_root='data/rec/SynthText/', data_prefix=dict(img_path='synthtext/SynthText_patch_horizontal'), ann_file='train_labels.json', test_mode=False, pipeline=None), dict( type='OCRDataset', data_root='data/rec/synthtext_add/', ann_file='train_labels.json', test_mode=False, pipeline=None) ] test_list = [ dict( type='OCRDataset', data_root='data/rec/ct80/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/IIIT5K/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/svt/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/svtp/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/icdar_2013/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/icdar_2015/', ann_file='test_labels.json', test_mode=True, pipeline=None) ] train_dataset = dict( type='ConcatDataset', datasets=[ dict( type='OCRDataset', data_root='data/rec/Syn90k/', data_prefix=dict(img_path='mnt/ramdisk/max/90kDICT32px'), ann_file='train_labels.json', test_mode=False, pipeline=None), dict( type='OCRDataset', data_root='data/rec/SynthText/', data_prefix=dict(img_path='synthtext/SynthText_patch_horizontal'), ann_file='train_labels.json', test_mode=False, pipeline=None), dict( type='OCRDataset', data_root='data/rec/synthtext_add/', ann_file='train_labels.json', test_mode=False, pipeline=None) ], pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='disk'), ignore_empty=True, min_size=2), dict(type='LoadOCRAnnotations', with_text=True), dict( type='RescaleToHeight', height=48, min_width=48, max_width=160, width_divisor=16), dict(type='PadToWidth', width=160), dict( type='PackTextRecogInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio')) ]) test_dataset = dict( type='ConcatDataset', datasets=[ dict( type='OCRDataset', data_root='data/rec/ct80/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/IIIT5K/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/svt/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/svtp/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/icdar_2013/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/icdar_2015/', ann_file='test_labels.json', test_mode=True, pipeline=None) ], pipeline=[ dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')), dict( type='RescaleToHeight', height=48, min_width=48, max_width=160, width_divisor=16), dict(type='PadToWidth', width=160), dict(type='LoadOCRAnnotations', with_text=True), dict( type='PackTextRecogInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio')) ]) train_dataloader = dict( batch_size=512, num_workers=24, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='ConcatDataset', datasets=[ dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/Syn90k', data_prefix=dict(img_path='mnt/ramdisk/max/90kDICT32px'), ann_file='train_labels.json', test_mode=False, pipeline=None), dict( type='OCRDataset', data_root= 'openmmlab:s3://openmmlab/datasets/ocr/recog/SynthText', data_prefix=dict( img_path='synthtext/SynthText_patch_horizontal'), ann_file='train_labels.json', test_mode=False, pipeline=None), dict( type='OCRDataset', data_root= 'openmmlab:s3://openmmlab/datasets/ocr/recog/synthtext_add', ann_file='train_labels.json', test_mode=False, pipeline=None) ], pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='petrel'), ignore_empty=True, min_size=2), dict(type='LoadOCRAnnotations', with_text=True), dict( type='RescaleToHeight', height=48, min_width=48, max_width=160, width_divisor=16), dict(type='PadToWidth', width=160), dict( type='PackTextRecogInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio')) ])) test_dataloader = dict( batch_size=1, num_workers=4, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='ConcatDataset', datasets=[ dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/ct80', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/IIIT5K', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/svt', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/svtp', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root= 'openmmlab:s3://openmmlab/datasets/ocr/recog/icdar_2013', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root= 'openmmlab:s3://openmmlab/datasets/ocr/recog/icdar_2015', ann_file='test_labels.json', test_mode=True, pipeline=None) ], pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='petrel')), dict( type='RescaleToHeight', height=48, min_width=48, max_width=160, width_divisor=16), dict(type='PadToWidth', width=160), dict(type='LoadOCRAnnotations', with_text=True), dict( type='PackTextRecogInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio')) ])) val_dataloader = dict( batch_size=1, num_workers=4, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='ConcatDataset', datasets=[ dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/ct80', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/IIIT5K', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/svt', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/svtp', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root= 'openmmlab:s3://openmmlab/datasets/ocr/recog/icdar_2013', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root= 'openmmlab:s3://openmmlab/datasets/ocr/recog/icdar_2015', ann_file='test_labels.json', test_mode=True, pipeline=None) ], pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='petrel')), dict( type='RescaleToHeight', height=48, min_width=48, max_width=160, width_divisor=16), dict(type='PadToWidth', width=160), dict(type='LoadOCRAnnotations', with_text=True), dict( type='PackTextRecogInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio')) ])) launcher = 'slurm' work_dir = './work_dirs/master_resnet31_12e_st_mj_sa' Name of parameter - Initialization information backbone.stem_layers.0.conv.weight - torch.Size([64, 3, 3, 3]): Initialized by user-defined `init_weights` in ConvModule backbone.stem_layers.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MASTER backbone.stem_layers.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MASTER backbone.stem_layers.1.conv.weight - torch.Size([128, 64, 3, 3]): Initialized by user-defined `init_weights` in ConvModule backbone.stem_layers.1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MASTER backbone.stem_layers.1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MASTER backbone.conv_block_after_stage_1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in ConvModule backbone.conv_block_after_stage_1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.conv_block_after_stage_1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.layer1.0.conv1.weight - torch.Size([256, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.layer1.0.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.layer1.0.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.layer1.0.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.layer1.0.downsample.conv.weight - torch.Size([256, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.downsample.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.layer1.0.downsample.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.layer1.0.gca_module.conv_mask.weight - torch.Size([1, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.gca_module.conv_mask.bias - torch.Size([1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.gca_module.channel_add_conv.0.weight - torch.Size([16, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.gca_module.channel_add_conv.0.bias - torch.Size([16]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.gca_module.channel_add_conv.1.weight - torch.Size([16, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer1.0.gca_module.channel_add_conv.1.bias - torch.Size([16, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer1.0.gca_module.channel_add_conv.3.weight - torch.Size([256, 16, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.gca_module.channel_add_conv.3.bias - torch.Size([256]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.conv_block_after_stage_2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in ConvModule backbone.conv_block_after_stage_2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.conv_block_after_stage_2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.layer2.0.conv1.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.layer2.0.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.layer2.0.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.layer2.0.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.layer2.0.gca_module.conv_mask.weight - torch.Size([1, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.gca_module.conv_mask.bias - torch.Size([1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.gca_module.channel_add_conv.0.weight - torch.Size([16, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.gca_module.channel_add_conv.0.bias - torch.Size([16]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.gca_module.channel_add_conv.1.weight - torch.Size([16, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer2.0.gca_module.channel_add_conv.1.bias - torch.Size([16, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer2.0.gca_module.channel_add_conv.3.weight - torch.Size([256, 16, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.gca_module.channel_add_conv.3.bias - torch.Size([256]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.conv1.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.layer2.1.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.layer2.1.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.layer2.1.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MASTER backbone.layer2.1.gca_module.conv_mask.weight - torch.Size([1, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.gca_module.conv_mask.bias - torch.Size([1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.gca_module.channel_add_conv.0.weight - torch.Size([16, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.gca_module.channel_add_conv.0.bias - torch.Size([16]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.gca_module.channel_add_conv.1.weight - torch.Size([16, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer2.1.gca_module.channel_add_conv.1.bias - torch.Size([16, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer2.1.gca_module.channel_add_conv.3.weight - torch.Size([256, 16, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.gca_module.channel_add_conv.3.bias - torch.Size([256]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.conv_block_after_stage_3.conv.weight - torch.Size([512, 512, 3, 3]): Initialized by user-defined `init_weights` in ConvModule backbone.conv_block_after_stage_3.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.conv_block_after_stage_3.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.0.conv1.weight - torch.Size([512, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.0.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.0.bn2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.0.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.0.downsample.conv.weight - torch.Size([512, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.downsample.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.0.downsample.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.0.gca_module.conv_mask.weight - torch.Size([1, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.gca_module.conv_mask.bias - torch.Size([1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.gca_module.channel_add_conv.0.weight - torch.Size([32, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.gca_module.channel_add_conv.0.bias - torch.Size([32]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.gca_module.channel_add_conv.1.weight - torch.Size([32, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.0.gca_module.channel_add_conv.1.bias - torch.Size([32, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.0.gca_module.channel_add_conv.3.weight - torch.Size([512, 32, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.gca_module.channel_add_conv.3.bias - torch.Size([512]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.1.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.1.bn2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.1.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.1.gca_module.conv_mask.weight - torch.Size([1, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.gca_module.conv_mask.bias - torch.Size([1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.gca_module.channel_add_conv.0.weight - torch.Size([32, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.gca_module.channel_add_conv.0.bias - torch.Size([32]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.gca_module.channel_add_conv.1.weight - torch.Size([32, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.1.gca_module.channel_add_conv.1.bias - torch.Size([32, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.1.gca_module.channel_add_conv.3.weight - torch.Size([512, 32, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.gca_module.channel_add_conv.3.bias - torch.Size([512]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.2.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.2.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.2.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.2.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.2.bn2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.2.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.2.gca_module.conv_mask.weight - torch.Size([1, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.2.gca_module.conv_mask.bias - torch.Size([1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.2.gca_module.channel_add_conv.0.weight - torch.Size([32, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.2.gca_module.channel_add_conv.0.bias - torch.Size([32]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.2.gca_module.channel_add_conv.1.weight - torch.Size([32, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.2.gca_module.channel_add_conv.1.bias - torch.Size([32, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.2.gca_module.channel_add_conv.3.weight - torch.Size([512, 32, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.2.gca_module.channel_add_conv.3.bias - torch.Size([512]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.3.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.3.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.3.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.3.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.3.bn2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.3.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.3.gca_module.conv_mask.weight - torch.Size([1, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.3.gca_module.conv_mask.bias - torch.Size([1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.3.gca_module.channel_add_conv.0.weight - torch.Size([32, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.3.gca_module.channel_add_conv.0.bias - torch.Size([32]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.3.gca_module.channel_add_conv.1.weight - torch.Size([32, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.3.gca_module.channel_add_conv.1.bias - torch.Size([32, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.3.gca_module.channel_add_conv.3.weight - torch.Size([512, 32, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.3.gca_module.channel_add_conv.3.bias - torch.Size([512]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.4.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.4.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.4.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.4.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.4.bn2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.4.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.4.gca_module.conv_mask.weight - torch.Size([1, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.4.gca_module.conv_mask.bias - torch.Size([1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.4.gca_module.channel_add_conv.0.weight - torch.Size([32, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.4.gca_module.channel_add_conv.0.bias - torch.Size([32]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.4.gca_module.channel_add_conv.1.weight - torch.Size([32, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.4.gca_module.channel_add_conv.1.bias - torch.Size([32, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer3.4.gca_module.channel_add_conv.3.weight - torch.Size([512, 32, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.4.gca_module.channel_add_conv.3.bias - torch.Size([512]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.conv_block_after_stage_4.conv.weight - torch.Size([512, 512, 3, 3]): Initialized by user-defined `init_weights` in ConvModule backbone.conv_block_after_stage_4.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.conv_block_after_stage_4.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.0.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.0.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.0.bn2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.0.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.0.gca_module.conv_mask.weight - torch.Size([1, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.gca_module.conv_mask.bias - torch.Size([1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.gca_module.channel_add_conv.0.weight - torch.Size([32, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.gca_module.channel_add_conv.0.bias - torch.Size([32]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.gca_module.channel_add_conv.1.weight - torch.Size([32, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.0.gca_module.channel_add_conv.1.bias - torch.Size([32, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.0.gca_module.channel_add_conv.3.weight - torch.Size([512, 32, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.gca_module.channel_add_conv.3.bias - torch.Size([512]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.1.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.1.bn2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.1.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.1.gca_module.conv_mask.weight - torch.Size([1, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.gca_module.conv_mask.bias - torch.Size([1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.gca_module.channel_add_conv.0.weight - torch.Size([32, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.gca_module.channel_add_conv.0.bias - torch.Size([32]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.gca_module.channel_add_conv.1.weight - torch.Size([32, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.1.gca_module.channel_add_conv.1.bias - torch.Size([32, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.1.gca_module.channel_add_conv.3.weight - torch.Size([512, 32, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.gca_module.channel_add_conv.3.bias - torch.Size([512]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.2.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.2.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.2.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.2.bn2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.2.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.2.gca_module.conv_mask.weight - torch.Size([1, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.2.gca_module.conv_mask.bias - torch.Size([1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.2.gca_module.channel_add_conv.0.weight - torch.Size([32, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.2.gca_module.channel_add_conv.0.bias - torch.Size([32]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.2.gca_module.channel_add_conv.1.weight - torch.Size([32, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.2.gca_module.channel_add_conv.1.bias - torch.Size([32, 1, 1]): The value is the same before and after calling `init_weights` of MASTER backbone.layer4.2.gca_module.channel_add_conv.3.weight - torch.Size([512, 32, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.2.gca_module.channel_add_conv.3.bias - torch.Size([512]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 decoder.decoder_layers.0.attentions.0.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.0.attentions.0.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.0.attentions.0.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.0.attentions.0.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.0.attentions.1.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.0.attentions.1.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.0.attentions.1.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.0.attentions.1.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.0.ffns.0.layers.0.0.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.0.ffns.0.layers.0.0.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.0.ffns.0.layers.1.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.0.ffns.0.layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.0.norms.0.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.0.norms.0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.0.norms.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.0.norms.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.0.norms.2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.0.norms.2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.attentions.0.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.attentions.0.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.attentions.0.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.attentions.0.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.attentions.1.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.attentions.1.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.attentions.1.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.attentions.1.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.ffns.0.layers.0.0.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.ffns.0.layers.0.0.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.ffns.0.layers.1.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.ffns.0.layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.norms.0.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.norms.0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.norms.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.norms.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.norms.2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.1.norms.2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.attentions.0.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.attentions.0.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.attentions.0.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.attentions.0.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.attentions.1.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.attentions.1.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.attentions.1.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.attentions.1.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.ffns.0.layers.0.0.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.ffns.0.layers.0.0.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.ffns.0.layers.1.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.ffns.0.layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.norms.0.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.norms.0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.norms.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.norms.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.norms.2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.decoder_layers.2.norms.2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.cls.weight - torch.Size([93, 512]): The value is the same before and after calling `init_weights` of MASTER decoder.cls.bias - torch.Size([93]): The value is the same before and after calling `init_weights` of MASTER decoder.embedding.lut.weight - torch.Size([93, 512]): The value is the same before and after calling `init_weights` of MASTER decoder.norm.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER decoder.norm.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of MASTER 2022/09/15 15:28:03 - mmengine - INFO - Checkpoints will be saved to sproject:s3://1.0.0rc0_recog_retest/master_resnet31_12e_st_mj_sa by PetrelBackend. 2022/09/15 15:42:29 - mmengine - INFO - Epoch(train) [1][100/8498] lr: 4.0000e-04 eta: 10 days, 5:01:02 time: 1.7396 data_time: 0.3587 memory: 56984 loss_ce: 2.4949 loss: 2.4949 2022/09/15 15:44:47 - mmengine - INFO - Epoch(train) [1][200/8498] lr: 4.0000e-04 eta: 5 days, 21:58:34 time: 2.0698 data_time: 0.4186 memory: 36546 loss_ce: 2.2461 loss: 2.2461 2022/09/15 15:47:05 - mmengine - INFO - Epoch(train) [1][300/8498] lr: 4.0000e-04 eta: 4 days, 11:27:58 time: 1.3642 data_time: 0.0851 memory: 36546 loss_ce: 2.0131 loss: 2.0131 2022/09/15 15:49:18 - mmengine - INFO - Epoch(train) [1][400/8498] lr: 4.0000e-04 eta: 3 days, 17:56:36 time: 1.1046 data_time: 0.0827 memory: 36546 loss_ce: 1.2126 loss: 1.2126 2022/09/15 15:51:34 - mmengine - INFO - Epoch(train) [1][500/8498] lr: 4.0000e-04 eta: 3 days, 7:32:50 time: 0.9995 data_time: 0.0137 memory: 36546 loss_ce: 0.6186 loss: 0.6186 2022/09/15 15:53:52 - mmengine - INFO - Epoch(train) [1][600/8498] lr: 4.0000e-04 eta: 3 days, 0:42:03 time: 0.9347 data_time: 0.0069 memory: 36546 loss_ce: 0.5040 loss: 0.5040 2022/09/15 15:56:14 - mmengine - INFO - Epoch(train) [1][700/8498] lr: 4.0000e-04 eta: 2 days, 19:56:56 time: 1.6407 data_time: 0.2946 memory: 36546 loss_ce: 0.4169 loss: 0.4169 2022/09/15 15:58:29 - mmengine - INFO - Epoch(train) [1][800/8498] lr: 4.0000e-04 eta: 2 days, 16:09:37 time: 1.8177 data_time: 0.3003 memory: 36546 loss_ce: 0.3865 loss: 0.3865 2022/09/15 16:00:46 - mmengine - INFO - Epoch(train) [1][900/8498] lr: 4.0000e-04 eta: 2 days, 13:14:52 time: 1.4178 data_time: 0.1208 memory: 36546 loss_ce: 0.3449 loss: 0.3449 2022/09/15 16:03:00 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 16:03:00 - mmengine - INFO - Epoch(train) [1][1000/8498] lr: 4.0000e-04 eta: 2 days, 10:49:51 time: 1.0941 data_time: 0.1292 memory: 36546 loss_ce: 0.3158 loss: 0.3158 2022/09/15 16:05:15 - mmengine - INFO - Epoch(train) [1][1100/8498] lr: 4.0000e-04 eta: 2 days, 8:51:42 time: 0.9244 data_time: 0.0056 memory: 36546 loss_ce: 0.3132 loss: 0.3132 2022/09/15 16:07:30 - mmengine - INFO - Epoch(train) [1][1200/8498] lr: 4.0000e-04 eta: 2 days, 7:13:05 time: 0.9322 data_time: 0.0058 memory: 36546 loss_ce: 0.2819 loss: 0.2819 2022/09/15 16:09:55 - mmengine - INFO - Epoch(train) [1][1300/8498] lr: 4.0000e-04 eta: 2 days, 6:01:46 time: 1.7575 data_time: 0.3162 memory: 36546 loss_ce: 0.2771 loss: 0.2771 2022/09/15 16:12:11 - mmengine - INFO - Epoch(train) [1][1400/8498] lr: 4.0000e-04 eta: 2 days, 4:51:01 time: 1.8141 data_time: 0.3099 memory: 36546 loss_ce: 0.2477 loss: 0.2477 2022/09/15 16:14:27 - mmengine - INFO - Epoch(train) [1][1500/8498] lr: 4.0000e-04 eta: 2 days, 3:48:33 time: 1.3803 data_time: 0.1237 memory: 36546 loss_ce: 0.2523 loss: 0.2523 2022/09/15 16:16:43 - mmengine - INFO - Epoch(train) [1][1600/8498] lr: 4.0000e-04 eta: 2 days, 2:52:45 time: 1.1556 data_time: 0.1413 memory: 36546 loss_ce: 0.2327 loss: 0.2327 2022/09/15 16:18:57 - mmengine - INFO - Epoch(train) [1][1700/8498] lr: 4.0000e-04 eta: 2 days, 2:02:28 time: 0.9125 data_time: 0.0058 memory: 36546 loss_ce: 0.2216 loss: 0.2216 2022/09/15 16:21:11 - mmengine - INFO - Epoch(train) [1][1800/8498] lr: 4.0000e-04 eta: 2 days, 1:16:44 time: 0.8836 data_time: 0.0058 memory: 36546 loss_ce: 0.2318 loss: 0.2318 2022/09/15 16:23:32 - mmengine - INFO - Epoch(train) [1][1900/8498] lr: 4.0000e-04 eta: 2 days, 0:42:40 time: 1.7096 data_time: 0.3083 memory: 36546 loss_ce: 0.2163 loss: 0.2163 2022/09/15 16:25:48 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 16:25:48 - mmengine - INFO - Epoch(train) [1][2000/8498] lr: 4.0000e-04 eta: 2 days, 0:06:59 time: 1.8362 data_time: 0.3271 memory: 36546 loss_ce: 0.2272 loss: 0.2272 2022/09/15 16:28:06 - mmengine - INFO - Epoch(train) [1][2100/8498] lr: 4.0000e-04 eta: 1 day, 23:35:38 time: 1.3944 data_time: 0.1334 memory: 36546 loss_ce: 0.2268 loss: 0.2268 2022/09/15 16:30:20 - mmengine - INFO - Epoch(train) [1][2200/8498] lr: 4.0000e-04 eta: 1 day, 23:04:21 time: 1.0998 data_time: 0.1260 memory: 36546 loss_ce: 0.2190 loss: 0.2190 2022/09/15 16:32:35 - mmengine - INFO - Epoch(train) [1][2300/8498] lr: 4.0000e-04 eta: 1 day, 22:36:49 time: 0.9272 data_time: 0.0063 memory: 36546 loss_ce: 0.2035 loss: 0.2035 2022/09/15 16:34:51 - mmengine - INFO - Epoch(train) [1][2400/8498] lr: 4.0000e-04 eta: 1 day, 22:11:10 time: 0.9314 data_time: 0.0065 memory: 36546 loss_ce: 0.1899 loss: 0.1899 2022/09/15 16:37:13 - mmengine - INFO - Epoch(train) [1][2500/8498] lr: 4.0000e-04 eta: 1 day, 21:52:19 time: 1.7209 data_time: 0.2732 memory: 36546 loss_ce: 0.2193 loss: 0.2193 2022/09/15 16:39:29 - mmengine - INFO - Epoch(train) [1][2600/8498] lr: 4.0000e-04 eta: 1 day, 21:30:04 time: 1.8638 data_time: 0.3108 memory: 36546 loss_ce: 0.2165 loss: 0.2165 2022/09/15 16:41:45 - mmengine - INFO - Epoch(train) [1][2700/8498] lr: 4.0000e-04 eta: 1 day, 21:09:59 time: 1.4467 data_time: 0.1247 memory: 36546 loss_ce: 0.1960 loss: 0.1960 2022/09/15 16:44:00 - mmengine - INFO - Epoch(train) [1][2800/8498] lr: 4.0000e-04 eta: 1 day, 20:49:57 time: 1.1765 data_time: 0.1341 memory: 36546 loss_ce: 0.1799 loss: 0.1799 2022/09/15 16:46:16 - mmengine - INFO - Epoch(train) [1][2900/8498] lr: 4.0000e-04 eta: 1 day, 20:32:17 time: 0.9219 data_time: 0.0073 memory: 36546 loss_ce: 0.1973 loss: 0.1973 2022/09/15 16:48:33 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 16:48:33 - mmengine - INFO - Epoch(train) [1][3000/8498] lr: 4.0000e-04 eta: 1 day, 20:15:37 time: 0.9502 data_time: 0.0123 memory: 36546 loss_ce: 0.1882 loss: 0.1882 2022/09/15 16:50:57 - mmengine - INFO - Epoch(train) [1][3100/8498] lr: 4.0000e-04 eta: 1 day, 20:04:17 time: 1.7348 data_time: 0.2889 memory: 36546 loss_ce: 0.2468 loss: 0.2468 2022/09/15 16:53:14 - mmengine - INFO - Epoch(train) [1][3200/8498] lr: 4.0000e-04 eta: 1 day, 19:49:31 time: 1.8538 data_time: 0.3119 memory: 36546 loss_ce: 0.2090 loss: 0.2090 2022/09/15 16:55:32 - mmengine - INFO - Epoch(train) [1][3300/8498] lr: 4.0000e-04 eta: 1 day, 19:35:41 time: 1.4214 data_time: 0.1298 memory: 36546 loss_ce: 0.1930 loss: 0.1930 2022/09/15 16:57:48 - mmengine - INFO - Epoch(train) [1][3400/8498] lr: 4.0000e-04 eta: 1 day, 19:21:54 time: 1.1605 data_time: 0.1535 memory: 36546 loss_ce: 0.1970 loss: 0.1970 2022/09/15 17:00:01 - mmengine - INFO - Epoch(train) [1][3500/8498] lr: 4.0000e-04 eta: 1 day, 19:07:37 time: 0.9194 data_time: 0.0075 memory: 36546 loss_ce: 0.1748 loss: 0.1748 2022/09/15 17:02:16 - mmengine - INFO - Epoch(train) [1][3600/8498] lr: 4.0000e-04 eta: 1 day, 18:54:26 time: 0.9588 data_time: 0.0065 memory: 36546 loss_ce: 0.1675 loss: 0.1675 2022/09/15 17:04:38 - mmengine - INFO - Epoch(train) [1][3700/8498] lr: 4.0000e-04 eta: 1 day, 18:45:27 time: 1.7345 data_time: 0.2660 memory: 36546 loss_ce: 0.1661 loss: 0.1661 2022/09/15 17:06:51 - mmengine - INFO - Epoch(train) [1][3800/8498] lr: 4.0000e-04 eta: 1 day, 18:32:45 time: 1.8251 data_time: 0.3069 memory: 36546 loss_ce: 0.1624 loss: 0.1624 2022/09/15 17:09:07 - mmengine - INFO - Epoch(train) [1][3900/8498] lr: 4.0000e-04 eta: 1 day, 18:21:40 time: 1.4399 data_time: 0.1357 memory: 36546 loss_ce: 0.1590 loss: 0.1590 2022/09/15 17:11:21 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 17:11:21 - mmengine - INFO - Epoch(train) [1][4000/8498] lr: 4.0000e-04 eta: 1 day, 18:10:13 time: 1.1392 data_time: 0.1302 memory: 36546 loss_ce: 0.1578 loss: 0.1578 2022/09/15 17:13:34 - mmengine - INFO - Epoch(train) [1][4100/8498] lr: 4.0000e-04 eta: 1 day, 17:58:51 time: 0.9490 data_time: 0.0083 memory: 36546 loss_ce: 0.1567 loss: 0.1567 2022/09/15 17:15:48 - mmengine - INFO - Epoch(train) [1][4200/8498] lr: 4.0000e-04 eta: 1 day, 17:48:20 time: 0.9217 data_time: 0.0061 memory: 36546 loss_ce: 0.1619 loss: 0.1619 2022/09/15 17:18:09 - mmengine - INFO - Epoch(train) [1][4300/8498] lr: 4.0000e-04 eta: 1 day, 17:41:01 time: 1.6512 data_time: 0.2896 memory: 36546 loss_ce: 0.1638 loss: 0.1638 2022/09/15 17:20:25 - mmengine - INFO - Epoch(train) [1][4400/8498] lr: 4.0000e-04 eta: 1 day, 17:31:58 time: 1.8747 data_time: 0.2540 memory: 36546 loss_ce: 0.1615 loss: 0.1615 2022/09/15 17:22:41 - mmengine - INFO - Epoch(train) [1][4500/8498] lr: 4.0000e-04 eta: 1 day, 17:23:06 time: 1.4215 data_time: 0.1245 memory: 36546 loss_ce: 0.1532 loss: 0.1532 2022/09/15 17:24:55 - mmengine - INFO - Epoch(train) [1][4600/8498] lr: 4.0000e-04 eta: 1 day, 17:13:57 time: 1.1148 data_time: 0.1406 memory: 36546 loss_ce: 0.1473 loss: 0.1473 2022/09/15 17:27:09 - mmengine - INFO - Epoch(train) [1][4700/8498] lr: 4.0000e-04 eta: 1 day, 17:05:04 time: 0.9516 data_time: 0.0086 memory: 36546 loss_ce: 0.1513 loss: 0.1513 2022/09/15 17:29:23 - mmengine - INFO - Epoch(train) [1][4800/8498] lr: 4.0000e-04 eta: 1 day, 16:56:24 time: 0.9355 data_time: 0.0067 memory: 36546 loss_ce: 0.1459 loss: 0.1459 2022/09/15 17:31:45 - mmengine - INFO - Epoch(train) [1][4900/8498] lr: 4.0000e-04 eta: 1 day, 16:50:34 time: 1.6711 data_time: 0.2895 memory: 36546 loss_ce: 0.1497 loss: 0.1497 2022/09/15 17:34:01 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 17:34:01 - mmengine - INFO - Epoch(train) [1][5000/8498] lr: 4.0000e-04 eta: 1 day, 16:43:04 time: 1.7855 data_time: 0.2717 memory: 36546 loss_ce: 0.1683 loss: 0.1683 2022/09/15 17:36:17 - mmengine - INFO - Epoch(train) [1][5100/8498] lr: 4.0000e-04 eta: 1 day, 16:35:53 time: 1.4331 data_time: 0.1354 memory: 36546 loss_ce: 0.1528 loss: 0.1528 2022/09/15 17:38:30 - mmengine - INFO - Epoch(train) [1][5200/8498] lr: 4.0000e-04 eta: 1 day, 16:27:50 time: 1.1563 data_time: 0.1306 memory: 36546 loss_ce: 0.1524 loss: 0.1524 2022/09/15 17:40:46 - mmengine - INFO - Epoch(train) [1][5300/8498] lr: 4.0000e-04 eta: 1 day, 16:20:45 time: 0.9475 data_time: 0.0060 memory: 36546 loss_ce: 0.1336 loss: 0.1336 2022/09/15 17:42:59 - mmengine - INFO - Epoch(train) [1][5400/8498] lr: 4.0000e-04 eta: 1 day, 16:13:06 time: 0.9466 data_time: 0.0060 memory: 36546 loss_ce: 0.1396 loss: 0.1396 2022/09/15 17:45:21 - mmengine - INFO - Epoch(train) [1][5500/8498] lr: 4.0000e-04 eta: 1 day, 16:08:16 time: 1.7007 data_time: 0.3014 memory: 36546 loss_ce: 0.1369 loss: 0.1369 2022/09/15 17:47:39 - mmengine - INFO - Epoch(train) [1][5600/8498] lr: 4.0000e-04 eta: 1 day, 16:02:23 time: 1.7654 data_time: 0.2965 memory: 36546 loss_ce: 0.1446 loss: 0.1446 2022/09/15 17:49:56 - mmengine - INFO - Epoch(train) [1][5700/8498] lr: 4.0000e-04 eta: 1 day, 15:56:20 time: 1.4919 data_time: 0.1393 memory: 36546 loss_ce: 0.1464 loss: 0.1464 2022/09/15 17:52:12 - mmengine - INFO - Epoch(train) [1][5800/8498] lr: 4.0000e-04 eta: 1 day, 15:50:15 time: 1.1448 data_time: 0.1655 memory: 36546 loss_ce: 0.1597 loss: 0.1597 2022/09/15 17:54:26 - mmengine - INFO - Epoch(train) [1][5900/8498] lr: 4.0000e-04 eta: 1 day, 15:43:46 time: 0.9778 data_time: 0.0102 memory: 36546 loss_ce: 0.1532 loss: 0.1532 2022/09/15 17:56:43 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 17:56:43 - mmengine - INFO - Epoch(train) [1][6000/8498] lr: 4.0000e-04 eta: 1 day, 15:37:55 time: 0.9594 data_time: 0.0068 memory: 36546 loss_ce: 0.1339 loss: 0.1339 2022/09/15 17:59:05 - mmengine - INFO - Epoch(train) [1][6100/8498] lr: 4.0000e-04 eta: 1 day, 15:33:55 time: 1.6610 data_time: 0.3112 memory: 36546 loss_ce: 0.1486 loss: 0.1486 2022/09/15 18:01:23 - mmengine - INFO - Epoch(train) [1][6200/8498] lr: 4.0000e-04 eta: 1 day, 15:28:38 time: 1.7996 data_time: 0.3099 memory: 36546 loss_ce: 0.1340 loss: 0.1340 2022/09/15 18:03:40 - mmengine - INFO - Epoch(train) [1][6300/8498] lr: 4.0000e-04 eta: 1 day, 15:23:23 time: 1.4685 data_time: 0.1696 memory: 36546 loss_ce: 0.1318 loss: 0.1318 2022/09/15 18:05:55 - mmengine - INFO - Epoch(train) [1][6400/8498] lr: 4.0000e-04 eta: 1 day, 15:17:29 time: 1.1465 data_time: 0.1310 memory: 36546 loss_ce: 0.1430 loss: 0.1430 2022/09/15 18:08:09 - mmengine - INFO - Epoch(train) [1][6500/8498] lr: 4.0000e-04 eta: 1 day, 15:11:34 time: 0.9309 data_time: 0.0063 memory: 36546 loss_ce: 0.1279 loss: 0.1279 2022/09/15 18:10:24 - mmengine - INFO - Epoch(train) [1][6600/8498] lr: 4.0000e-04 eta: 1 day, 15:06:04 time: 0.9161 data_time: 0.0060 memory: 36546 loss_ce: 0.1496 loss: 0.1496 2022/09/15 18:12:45 - mmengine - INFO - Epoch(train) [1][6700/8498] lr: 4.0000e-04 eta: 1 day, 15:01:58 time: 1.7005 data_time: 0.2923 memory: 36546 loss_ce: 0.1389 loss: 0.1389 2022/09/15 18:15:02 - mmengine - INFO - Epoch(train) [1][6800/8498] lr: 4.0000e-04 eta: 1 day, 14:57:02 time: 1.8617 data_time: 0.3145 memory: 36546 loss_ce: 0.1515 loss: 0.1515 2022/09/15 18:17:17 - mmengine - INFO - Epoch(train) [1][6900/8498] lr: 4.0000e-04 eta: 1 day, 14:51:44 time: 1.4125 data_time: 0.1226 memory: 36546 loss_ce: 0.1399 loss: 0.1399 2022/09/15 18:19:32 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 18:19:32 - mmengine - INFO - Epoch(train) [1][7000/8498] lr: 4.0000e-04 eta: 1 day, 14:46:41 time: 1.1107 data_time: 0.1230 memory: 36546 loss_ce: 0.1278 loss: 0.1278 2022/09/15 18:21:46 - mmengine - INFO - Epoch(train) [1][7100/8498] lr: 4.0000e-04 eta: 1 day, 14:41:18 time: 0.9243 data_time: 0.0059 memory: 36546 loss_ce: 0.1335 loss: 0.1335 2022/09/15 18:24:00 - mmengine - INFO - Epoch(train) [1][7200/8498] lr: 4.0000e-04 eta: 1 day, 14:35:58 time: 0.9387 data_time: 0.0065 memory: 36546 loss_ce: 0.1317 loss: 0.1317 2022/09/15 18:26:21 - mmengine - INFO - Epoch(train) [1][7300/8498] lr: 4.0000e-04 eta: 1 day, 14:32:20 time: 1.6810 data_time: 0.3186 memory: 36546 loss_ce: 0.1264 loss: 0.1264 2022/09/15 18:28:36 - mmengine - INFO - Epoch(train) [1][7400/8498] lr: 4.0000e-04 eta: 1 day, 14:27:24 time: 1.8033 data_time: 0.2803 memory: 36546 loss_ce: 0.1387 loss: 0.1387 2022/09/15 18:30:51 - mmengine - INFO - Epoch(train) [1][7500/8498] lr: 4.0000e-04 eta: 1 day, 14:22:36 time: 1.4555 data_time: 0.0987 memory: 36546 loss_ce: 0.1267 loss: 0.1267 2022/09/15 18:33:05 - mmengine - INFO - Epoch(train) [1][7600/8498] lr: 4.0000e-04 eta: 1 day, 14:17:43 time: 1.1478 data_time: 0.1414 memory: 36546 loss_ce: 0.1347 loss: 0.1347 2022/09/15 18:35:18 - mmengine - INFO - Epoch(train) [1][7700/8498] lr: 4.0000e-04 eta: 1 day, 14:12:38 time: 0.9220 data_time: 0.0107 memory: 36546 loss_ce: 0.1334 loss: 0.1334 2022/09/15 18:37:32 - mmengine - INFO - Epoch(train) [1][7800/8498] lr: 4.0000e-04 eta: 1 day, 14:07:43 time: 0.9120 data_time: 0.0059 memory: 36546 loss_ce: 0.1376 loss: 0.1376 2022/09/15 18:39:54 - mmengine - INFO - Epoch(train) [1][7900/8498] lr: 4.0000e-04 eta: 1 day, 14:04:33 time: 1.6637 data_time: 0.2955 memory: 36546 loss_ce: 0.1167 loss: 0.1167 2022/09/15 18:42:09 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 18:42:09 - mmengine - INFO - Epoch(train) [1][8000/8498] lr: 4.0000e-04 eta: 1 day, 13:59:57 time: 1.7142 data_time: 0.2863 memory: 36546 loss_ce: 0.1242 loss: 0.1242 2022/09/15 18:44:24 - mmengine - INFO - Epoch(train) [1][8100/8498] lr: 4.0000e-04 eta: 1 day, 13:55:38 time: 1.4040 data_time: 0.1326 memory: 36546 loss_ce: 0.1443 loss: 0.1443 2022/09/15 18:46:36 - mmengine - INFO - Epoch(train) [1][8200/8498] lr: 4.0000e-04 eta: 1 day, 13:50:40 time: 1.1346 data_time: 0.1333 memory: 36546 loss_ce: 0.1308 loss: 0.1308 2022/09/15 18:48:51 - mmengine - INFO - Epoch(train) [1][8300/8498] lr: 4.0000e-04 eta: 1 day, 13:46:12 time: 0.9393 data_time: 0.0066 memory: 36546 loss_ce: 0.1285 loss: 0.1285 2022/09/15 18:51:07 - mmengine - INFO - Epoch(train) [1][8400/8498] lr: 4.0000e-04 eta: 1 day, 13:42:08 time: 0.9629 data_time: 0.0058 memory: 36546 loss_ce: 0.1261 loss: 0.1261 2022/09/15 18:53:25 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 18:53:25 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/09/15 18:56:59 - mmengine - INFO - Epoch(val) [1][100/1918] eta: 0:03:57 time: 0.1308 data_time: 0.0007 memory: 36546 2022/09/15 18:57:13 - mmengine - INFO - Epoch(val) [1][200/1918] eta: 0:03:43 time: 0.1299 data_time: 0.0007 memory: 1150 2022/09/15 18:57:27 - mmengine - INFO - Epoch(val) [1][300/1918] eta: 0:03:46 time: 0.1400 data_time: 0.0007 memory: 1150 2022/09/15 18:57:40 - mmengine - INFO - Epoch(val) [1][400/1918] eta: 0:03:23 time: 0.1343 data_time: 0.0007 memory: 1150 2022/09/15 18:57:54 - mmengine - INFO - Epoch(val) [1][500/1918] eta: 0:03:05 time: 0.1306 data_time: 0.0007 memory: 1150 2022/09/15 18:58:08 - mmengine - INFO - Epoch(val) [1][600/1918] eta: 0:02:50 time: 0.1296 data_time: 0.0007 memory: 1150 2022/09/15 18:58:21 - mmengine - INFO - Epoch(val) [1][700/1918] eta: 0:02:44 time: 0.1352 data_time: 0.0008 memory: 1150 2022/09/15 18:58:35 - mmengine - INFO - Epoch(val) [1][800/1918] eta: 0:02:27 time: 0.1318 data_time: 0.0007 memory: 1150 2022/09/15 18:58:49 - mmengine - INFO - Epoch(val) [1][900/1918] eta: 0:02:18 time: 0.1358 data_time: 0.0007 memory: 1150 2022/09/15 18:59:02 - mmengine - INFO - Epoch(val) [1][1000/1918] eta: 0:02:03 time: 0.1341 data_time: 0.0008 memory: 1150 2022/09/15 18:59:16 - mmengine - INFO - Epoch(val) [1][1100/1918] eta: 0:01:52 time: 0.1373 data_time: 0.0007 memory: 1150 2022/09/15 18:59:29 - mmengine - INFO - Epoch(val) [1][1200/1918] eta: 0:01:32 time: 0.1283 data_time: 0.0007 memory: 1150 2022/09/15 18:59:43 - mmengine - INFO - Epoch(val) [1][1300/1918] eta: 0:01:19 time: 0.1288 data_time: 0.0009 memory: 1150 2022/09/15 18:59:57 - mmengine - INFO - Epoch(val) [1][1400/1918] eta: 0:01:17 time: 0.1495 data_time: 0.0007 memory: 1150 2022/09/15 19:00:10 - mmengine - INFO - Epoch(val) [1][1500/1918] eta: 0:00:56 time: 0.1358 data_time: 0.0006 memory: 1150 2022/09/15 19:00:24 - mmengine - INFO - Epoch(val) [1][1600/1918] eta: 0:00:46 time: 0.1455 data_time: 0.0008 memory: 1150 2022/09/15 19:00:38 - mmengine - INFO - Epoch(val) [1][1700/1918] eta: 0:00:31 time: 0.1427 data_time: 0.0008 memory: 1150 2022/09/15 19:00:51 - mmengine - INFO - Epoch(val) [1][1800/1918] eta: 0:00:15 time: 0.1323 data_time: 0.0025 memory: 1150 2022/09/15 19:01:05 - mmengine - INFO - Epoch(val) [1][1900/1918] eta: 0:00:02 time: 0.1286 data_time: 0.0008 memory: 1150 2022/09/15 19:01:07 - mmengine - INFO - Epoch(val) [1][1918/1918] CUTE80/recog/word_acc_ignore_case_symbol: 0.8021 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9133 SVT/recog/word_acc_ignore_case_symbol: 0.8393 SVTP/recog/word_acc_ignore_case_symbol: 0.7116 IC13/recog/word_acc_ignore_case_symbol: 0.9182 IC15/recog/word_acc_ignore_case_symbol: 0.6769 2022/09/15 19:03:24 - mmengine - INFO - Epoch(train) [2][100/8498] lr: 4.0000e-04 eta: 1 day, 13:31:46 time: 1.3255 data_time: 0.1615 memory: 36546 loss_ce: 0.1217 loss: 0.1217 2022/09/15 19:05:30 - mmengine - INFO - Epoch(train) [2][200/8498] lr: 4.0000e-04 eta: 1 day, 13:25:56 time: 1.5565 data_time: 0.3910 memory: 36546 loss_ce: 0.1238 loss: 0.1238 2022/09/15 19:07:38 - mmengine - INFO - Epoch(train) [2][300/8498] lr: 4.0000e-04 eta: 1 day, 13:20:34 time: 1.3728 data_time: 0.2520 memory: 36546 loss_ce: 0.1208 loss: 0.1208 2022/09/15 19:09:44 - mmengine - INFO - Epoch(train) [2][400/8498] lr: 4.0000e-04 eta: 1 day, 13:15:03 time: 1.2507 data_time: 0.2049 memory: 36546 loss_ce: 0.1150 loss: 0.1150 2022/09/15 19:11:50 - mmengine - INFO - Epoch(train) [2][500/8498] lr: 4.0000e-04 eta: 1 day, 13:09:35 time: 0.8847 data_time: 0.0066 memory: 36546 loss_ce: 0.1237 loss: 0.1237 2022/09/15 19:11:53 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 19:13:56 - mmengine - INFO - Epoch(train) [2][600/8498] lr: 4.0000e-04 eta: 1 day, 13:04:04 time: 0.9614 data_time: 0.0061 memory: 36546 loss_ce: 0.1374 loss: 0.1374 2022/09/15 19:16:05 - mmengine - INFO - Epoch(train) [2][700/8498] lr: 4.0000e-04 eta: 1 day, 12:59:17 time: 1.3709 data_time: 0.1861 memory: 36546 loss_ce: 0.1047 loss: 0.1047 2022/09/15 19:18:13 - mmengine - INFO - Epoch(train) [2][800/8498] lr: 4.0000e-04 eta: 1 day, 12:54:11 time: 1.5745 data_time: 0.3969 memory: 36546 loss_ce: 0.1126 loss: 0.1126 2022/09/15 19:20:19 - mmengine - INFO - Epoch(train) [2][900/8498] lr: 4.0000e-04 eta: 1 day, 12:48:57 time: 1.3887 data_time: 0.2415 memory: 36546 loss_ce: 0.1135 loss: 0.1135 2022/09/15 19:22:23 - mmengine - INFO - Epoch(train) [2][1000/8498] lr: 4.0000e-04 eta: 1 day, 12:43:32 time: 1.2635 data_time: 0.2038 memory: 36546 loss_ce: 0.1203 loss: 0.1203 2022/09/15 19:24:28 - mmengine - INFO - Epoch(train) [2][1100/8498] lr: 4.0000e-04 eta: 1 day, 12:38:18 time: 0.9372 data_time: 0.0062 memory: 36546 loss_ce: 0.1157 loss: 0.1157 2022/09/15 19:26:33 - mmengine - INFO - Epoch(train) [2][1200/8498] lr: 4.0000e-04 eta: 1 day, 12:33:05 time: 0.9424 data_time: 0.0066 memory: 36546 loss_ce: 0.1160 loss: 0.1160 2022/09/15 19:28:42 - mmengine - INFO - Epoch(train) [2][1300/8498] lr: 4.0000e-04 eta: 1 day, 12:28:32 time: 1.3434 data_time: 0.1680 memory: 36546 loss_ce: 0.1108 loss: 0.1108 2022/09/15 19:30:47 - mmengine - INFO - Epoch(train) [2][1400/8498] lr: 4.0000e-04 eta: 1 day, 12:23:27 time: 1.5697 data_time: 0.3973 memory: 36546 loss_ce: 0.1122 loss: 0.1122 2022/09/15 19:32:51 - mmengine - INFO - Epoch(train) [2][1500/8498] lr: 4.0000e-04 eta: 1 day, 12:18:16 time: 1.3224 data_time: 0.2341 memory: 36546 loss_ce: 0.1312 loss: 0.1312 2022/09/15 19:32:53 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 19:34:54 - mmengine - INFO - Epoch(train) [2][1600/8498] lr: 4.0000e-04 eta: 1 day, 12:13:03 time: 1.1933 data_time: 0.1875 memory: 36546 loss_ce: 0.1134 loss: 0.1134 2022/09/15 19:36:57 - mmengine - INFO - Epoch(train) [2][1700/8498] lr: 4.0000e-04 eta: 1 day, 12:07:46 time: 0.8926 data_time: 0.0064 memory: 36546 loss_ce: 0.1213 loss: 0.1213 2022/09/15 19:39:01 - mmengine - INFO - Epoch(train) [2][1800/8498] lr: 4.0000e-04 eta: 1 day, 12:02:52 time: 0.9695 data_time: 0.0061 memory: 36546 loss_ce: 0.1131 loss: 0.1131 2022/09/15 19:41:10 - mmengine - INFO - Epoch(train) [2][1900/8498] lr: 4.0000e-04 eta: 1 day, 11:58:37 time: 1.3802 data_time: 0.1768 memory: 36546 loss_ce: 0.1046 loss: 0.1046 2022/09/15 19:43:16 - mmengine - INFO - Epoch(train) [2][2000/8498] lr: 4.0000e-04 eta: 1 day, 11:54:03 time: 1.5312 data_time: 0.3951 memory: 36546 loss_ce: 0.1271 loss: 0.1271 2022/09/15 19:45:20 - mmengine - INFO - Epoch(train) [2][2100/8498] lr: 4.0000e-04 eta: 1 day, 11:49:13 time: 1.3491 data_time: 0.2301 memory: 36546 loss_ce: 0.1187 loss: 0.1187 2022/09/15 19:47:25 - mmengine - INFO - Epoch(train) [2][2200/8498] lr: 4.0000e-04 eta: 1 day, 11:44:33 time: 1.2592 data_time: 0.1902 memory: 36546 loss_ce: 0.1088 loss: 0.1088 2022/09/15 19:49:28 - mmengine - INFO - Epoch(train) [2][2300/8498] lr: 4.0000e-04 eta: 1 day, 11:39:37 time: 0.8825 data_time: 0.0062 memory: 36546 loss_ce: 0.1137 loss: 0.1137 2022/09/15 19:51:31 - mmengine - INFO - Epoch(train) [2][2400/8498] lr: 4.0000e-04 eta: 1 day, 11:34:52 time: 0.9786 data_time: 0.0060 memory: 36546 loss_ce: 0.1845 loss: 0.1845 2022/09/15 19:53:37 - mmengine - INFO - Epoch(train) [2][2500/8498] lr: 4.0000e-04 eta: 1 day, 11:30:32 time: 1.3102 data_time: 0.1759 memory: 36546 loss_ce: 0.1505 loss: 0.1505 2022/09/15 19:53:40 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 19:55:45 - mmengine - INFO - Epoch(train) [2][2600/8498] lr: 4.0000e-04 eta: 1 day, 11:26:22 time: 1.6204 data_time: 0.4275 memory: 36546 loss_ce: 0.1261 loss: 0.1261 2022/09/15 19:57:49 - mmengine - INFO - Epoch(train) [2][2700/8498] lr: 4.0000e-04 eta: 1 day, 11:21:56 time: 1.3447 data_time: 0.2281 memory: 36546 loss_ce: 0.1251 loss: 0.1251 2022/09/15 19:59:53 - mmengine - INFO - Epoch(train) [2][2800/8498] lr: 4.0000e-04 eta: 1 day, 11:17:21 time: 1.1830 data_time: 0.1773 memory: 36546 loss_ce: 0.1197 loss: 0.1197 2022/09/15 20:01:56 - mmengine - INFO - Epoch(train) [2][2900/8498] lr: 4.0000e-04 eta: 1 day, 11:12:44 time: 0.8949 data_time: 0.0070 memory: 36546 loss_ce: 0.1162 loss: 0.1162 2022/09/15 20:03:59 - mmengine - INFO - Epoch(train) [2][3000/8498] lr: 4.0000e-04 eta: 1 day, 11:08:15 time: 0.9409 data_time: 0.0061 memory: 36546 loss_ce: 0.1206 loss: 0.1206 2022/09/15 20:06:07 - mmengine - INFO - Epoch(train) [2][3100/8498] lr: 4.0000e-04 eta: 1 day, 11:04:20 time: 1.3685 data_time: 0.1880 memory: 36546 loss_ce: 0.1251 loss: 0.1251 2022/09/15 20:08:13 - mmengine - INFO - Epoch(train) [2][3200/8498] lr: 4.0000e-04 eta: 1 day, 11:00:18 time: 1.5552 data_time: 0.3850 memory: 36546 loss_ce: 0.1105 loss: 0.1105 2022/09/15 20:10:19 - mmengine - INFO - Epoch(train) [2][3300/8498] lr: 4.0000e-04 eta: 1 day, 10:56:10 time: 1.3337 data_time: 0.2343 memory: 36546 loss_ce: 0.1090 loss: 0.1090 2022/09/15 20:12:22 - mmengine - INFO - Epoch(train) [2][3400/8498] lr: 4.0000e-04 eta: 1 day, 10:51:46 time: 1.2322 data_time: 0.1946 memory: 36546 loss_ce: 0.1050 loss: 0.1050 2022/09/15 20:14:25 - mmengine - INFO - Epoch(train) [2][3500/8498] lr: 4.0000e-04 eta: 1 day, 10:47:25 time: 0.8845 data_time: 0.0059 memory: 36546 loss_ce: 0.1109 loss: 0.1109 2022/09/15 20:14:27 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 20:16:28 - mmengine - INFO - Epoch(train) [2][3600/8498] lr: 4.0000e-04 eta: 1 day, 10:43:03 time: 0.9547 data_time: 0.0062 memory: 36546 loss_ce: 0.1128 loss: 0.1128 2022/09/15 20:18:34 - mmengine - INFO - Epoch(train) [2][3700/8498] lr: 4.0000e-04 eta: 1 day, 10:39:09 time: 1.3837 data_time: 0.1771 memory: 36546 loss_ce: 0.1118 loss: 0.1118 2022/09/15 20:20:40 - mmengine - INFO - Epoch(train) [2][3800/8498] lr: 4.0000e-04 eta: 1 day, 10:35:20 time: 1.5871 data_time: 0.4142 memory: 36546 loss_ce: 0.1041 loss: 0.1041 2022/09/15 20:22:45 - mmengine - INFO - Epoch(train) [2][3900/8498] lr: 4.0000e-04 eta: 1 day, 10:31:19 time: 1.3370 data_time: 0.2498 memory: 36546 loss_ce: 0.1081 loss: 0.1081 2022/09/15 20:24:49 - mmengine - INFO - Epoch(train) [2][4000/8498] lr: 4.0000e-04 eta: 1 day, 10:27:11 time: 1.1908 data_time: 0.1774 memory: 36546 loss_ce: 0.1039 loss: 0.1039 2022/09/15 20:26:52 - mmengine - INFO - Epoch(train) [2][4100/8498] lr: 4.0000e-04 eta: 1 day, 10:23:02 time: 0.8742 data_time: 0.0059 memory: 36546 loss_ce: 0.1092 loss: 0.1092 2022/09/15 20:28:55 - mmengine - INFO - Epoch(train) [2][4200/8498] lr: 4.0000e-04 eta: 1 day, 10:18:54 time: 0.9562 data_time: 0.0061 memory: 36546 loss_ce: 0.1111 loss: 0.1111 2022/09/15 20:31:01 - mmengine - INFO - Epoch(train) [2][4300/8498] lr: 4.0000e-04 eta: 1 day, 10:15:10 time: 1.3485 data_time: 0.1725 memory: 36546 loss_ce: 0.1075 loss: 0.1075 2022/09/15 20:33:06 - mmengine - INFO - Epoch(train) [2][4400/8498] lr: 4.0000e-04 eta: 1 day, 10:11:25 time: 1.5813 data_time: 0.3926 memory: 36546 loss_ce: 0.0969 loss: 0.0969 2022/09/15 20:35:12 - mmengine - INFO - Epoch(train) [2][4500/8498] lr: 4.0000e-04 eta: 1 day, 10:07:41 time: 1.3542 data_time: 0.2337 memory: 36546 loss_ce: 0.0996 loss: 0.0996 2022/09/15 20:35:14 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 20:37:16 - mmengine - INFO - Epoch(train) [2][4600/8498] lr: 4.0000e-04 eta: 1 day, 10:03:45 time: 1.2455 data_time: 0.1969 memory: 36546 loss_ce: 0.1144 loss: 0.1144 2022/09/15 20:39:20 - mmengine - INFO - Epoch(train) [2][4700/8498] lr: 4.0000e-04 eta: 1 day, 9:59:52 time: 0.8936 data_time: 0.0067 memory: 36546 loss_ce: 0.1142 loss: 0.1142 2022/09/15 20:41:26 - mmengine - INFO - Epoch(train) [2][4800/8498] lr: 4.0000e-04 eta: 1 day, 9:56:15 time: 0.9443 data_time: 0.0078 memory: 36546 loss_ce: 0.0979 loss: 0.0979 2022/09/15 20:43:33 - mmengine - INFO - Epoch(train) [2][4900/8498] lr: 4.0000e-04 eta: 1 day, 9:52:47 time: 1.3941 data_time: 0.1931 memory: 36546 loss_ce: 0.1051 loss: 0.1051 2022/09/15 20:45:38 - mmengine - INFO - Epoch(train) [2][5000/8498] lr: 4.0000e-04 eta: 1 day, 9:49:10 time: 1.6163 data_time: 0.4101 memory: 36546 loss_ce: 0.1055 loss: 0.1055 2022/09/15 20:47:44 - mmengine - INFO - Epoch(train) [2][5100/8498] lr: 4.0000e-04 eta: 1 day, 9:45:36 time: 1.3711 data_time: 0.2284 memory: 36546 loss_ce: 0.1022 loss: 0.1022 2022/09/15 20:49:48 - mmengine - INFO - Epoch(train) [2][5200/8498] lr: 4.0000e-04 eta: 1 day, 9:41:48 time: 1.1700 data_time: 0.1631 memory: 36546 loss_ce: 0.1126 loss: 0.1126 2022/09/15 20:51:50 - mmengine - INFO - Epoch(train) [2][5300/8498] lr: 4.0000e-04 eta: 1 day, 9:37:55 time: 0.8794 data_time: 0.0065 memory: 36546 loss_ce: 0.1167 loss: 0.1167 2022/09/15 20:53:54 - mmengine - INFO - Epoch(train) [2][5400/8498] lr: 4.0000e-04 eta: 1 day, 9:34:12 time: 0.9492 data_time: 0.0060 memory: 36546 loss_ce: 0.1159 loss: 0.1159 2022/09/15 20:56:03 - mmengine - INFO - Epoch(train) [2][5500/8498] lr: 4.0000e-04 eta: 1 day, 9:31:02 time: 1.3850 data_time: 0.1709 memory: 36546 loss_ce: 0.1085 loss: 0.1085 2022/09/15 20:56:05 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 20:58:08 - mmengine - INFO - Epoch(train) [2][5600/8498] lr: 4.0000e-04 eta: 1 day, 9:27:33 time: 1.5979 data_time: 0.4435 memory: 36546 loss_ce: 0.0998 loss: 0.0998 2022/09/15 21:00:14 - mmengine - INFO - Epoch(train) [2][5700/8498] lr: 4.0000e-04 eta: 1 day, 9:24:03 time: 1.3801 data_time: 0.2242 memory: 36546 loss_ce: 0.1042 loss: 0.1042 2022/09/15 21:02:17 - mmengine - INFO - Epoch(train) [2][5800/8498] lr: 4.0000e-04 eta: 1 day, 9:20:24 time: 1.2088 data_time: 0.1750 memory: 36546 loss_ce: 0.0985 loss: 0.0985 2022/09/15 21:04:20 - mmengine - INFO - Epoch(train) [2][5900/8498] lr: 4.0000e-04 eta: 1 day, 9:16:41 time: 0.8724 data_time: 0.0066 memory: 36546 loss_ce: 0.1117 loss: 0.1117 2022/09/15 21:06:23 - mmengine - INFO - Epoch(train) [2][6000/8498] lr: 4.0000e-04 eta: 1 day, 9:13:04 time: 0.9420 data_time: 0.0062 memory: 36546 loss_ce: 0.0949 loss: 0.0949 2022/09/15 21:08:30 - mmengine - INFO - Epoch(train) [2][6100/8498] lr: 4.0000e-04 eta: 1 day, 9:09:48 time: 1.3672 data_time: 0.1797 memory: 36546 loss_ce: 0.1094 loss: 0.1094 2022/09/15 21:10:37 - mmengine - INFO - Epoch(train) [2][6200/8498] lr: 4.0000e-04 eta: 1 day, 9:06:34 time: 1.6008 data_time: 0.4151 memory: 36546 loss_ce: 0.1021 loss: 0.1021 2022/09/15 21:12:42 - mmengine - INFO - Epoch(train) [2][6300/8498] lr: 4.0000e-04 eta: 1 day, 9:03:08 time: 1.3497 data_time: 0.2298 memory: 36546 loss_ce: 0.0921 loss: 0.0921 2022/09/15 21:14:46 - mmengine - INFO - Epoch(train) [2][6400/8498] lr: 4.0000e-04 eta: 1 day, 8:59:38 time: 1.2122 data_time: 0.1933 memory: 36546 loss_ce: 0.1071 loss: 0.1071 2022/09/15 21:16:48 - mmengine - INFO - Epoch(train) [2][6500/8498] lr: 4.0000e-04 eta: 1 day, 8:55:58 time: 0.8814 data_time: 0.0061 memory: 36546 loss_ce: 0.1092 loss: 0.1092 2022/09/15 21:16:50 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 21:18:52 - mmengine - INFO - Epoch(train) [2][6600/8498] lr: 4.0000e-04 eta: 1 day, 8:52:32 time: 0.9577 data_time: 0.0058 memory: 36546 loss_ce: 0.1111 loss: 0.1111 2022/09/15 21:21:01 - mmengine - INFO - Epoch(train) [2][6700/8498] lr: 4.0000e-04 eta: 1 day, 8:49:31 time: 1.3847 data_time: 0.1892 memory: 36546 loss_ce: 0.1082 loss: 0.1082 2022/09/15 21:23:06 - mmengine - INFO - Epoch(train) [2][6800/8498] lr: 4.0000e-04 eta: 1 day, 8:46:14 time: 1.6084 data_time: 0.4137 memory: 36546 loss_ce: 0.1022 loss: 0.1022 2022/09/15 21:25:10 - mmengine - INFO - Epoch(train) [2][6900/8498] lr: 4.0000e-04 eta: 1 day, 8:42:47 time: 1.3638 data_time: 0.2337 memory: 36546 loss_ce: 0.0980 loss: 0.0980 2022/09/15 21:27:12 - mmengine - INFO - Epoch(train) [2][7000/8498] lr: 4.0000e-04 eta: 1 day, 8:39:16 time: 1.2108 data_time: 0.1945 memory: 36546 loss_ce: 0.0970 loss: 0.0970 2022/09/15 21:29:14 - mmengine - INFO - Epoch(train) [2][7100/8498] lr: 4.0000e-04 eta: 1 day, 8:35:44 time: 0.8868 data_time: 0.0079 memory: 36546 loss_ce: 0.0956 loss: 0.0956 2022/09/15 21:31:19 - mmengine - INFO - Epoch(train) [2][7200/8498] lr: 4.0000e-04 eta: 1 day, 8:32:25 time: 0.9555 data_time: 0.0060 memory: 36546 loss_ce: 0.1054 loss: 0.1054 2022/09/15 21:33:27 - mmengine - INFO - Epoch(train) [2][7300/8498] lr: 4.0000e-04 eta: 1 day, 8:29:27 time: 1.3465 data_time: 0.1844 memory: 36546 loss_ce: 0.0974 loss: 0.0974 2022/09/15 21:35:34 - mmengine - INFO - Epoch(train) [2][7400/8498] lr: 4.0000e-04 eta: 1 day, 8:26:25 time: 1.6106 data_time: 0.4782 memory: 36546 loss_ce: 0.0993 loss: 0.0993 2022/09/15 21:37:40 - mmengine - INFO - Epoch(train) [2][7500/8498] lr: 4.0000e-04 eta: 1 day, 8:23:16 time: 1.3521 data_time: 0.2485 memory: 36546 loss_ce: 0.0940 loss: 0.0940 2022/09/15 21:37:42 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 21:39:44 - mmengine - INFO - Epoch(train) [2][7600/8498] lr: 4.0000e-04 eta: 1 day, 8:19:58 time: 1.1916 data_time: 0.1619 memory: 36546 loss_ce: 0.1053 loss: 0.1053 2022/09/15 21:41:48 - mmengine - INFO - Epoch(train) [2][7700/8498] lr: 4.0000e-04 eta: 1 day, 8:16:44 time: 0.8847 data_time: 0.0072 memory: 36546 loss_ce: 0.1077 loss: 0.1077 2022/09/15 21:43:53 - mmengine - INFO - Epoch(train) [2][7800/8498] lr: 4.0000e-04 eta: 1 day, 8:13:33 time: 0.9833 data_time: 0.0076 memory: 36546 loss_ce: 0.1022 loss: 0.1022 2022/09/15 21:46:01 - mmengine - INFO - Epoch(train) [2][7900/8498] lr: 4.0000e-04 eta: 1 day, 8:10:36 time: 1.3352 data_time: 0.1779 memory: 36546 loss_ce: 0.0841 loss: 0.0841 2022/09/15 21:48:05 - mmengine - INFO - Epoch(train) [2][8000/8498] lr: 4.0000e-04 eta: 1 day, 8:07:23 time: 1.5250 data_time: 0.4064 memory: 36546 loss_ce: 0.1014 loss: 0.1014 2022/09/15 21:50:08 - mmengine - INFO - Epoch(train) [2][8100/8498] lr: 4.0000e-04 eta: 1 day, 8:04:08 time: 1.3701 data_time: 0.2374 memory: 36546 loss_ce: 0.0894 loss: 0.0894 2022/09/15 21:52:11 - mmengine - INFO - Epoch(train) [2][8200/8498] lr: 4.0000e-04 eta: 1 day, 8:00:48 time: 1.2026 data_time: 0.1736 memory: 36546 loss_ce: 0.1017 loss: 0.1017 2022/09/15 21:54:14 - mmengine - INFO - Epoch(train) [2][8300/8498] lr: 4.0000e-04 eta: 1 day, 7:57:30 time: 0.9334 data_time: 0.0069 memory: 36546 loss_ce: 0.0910 loss: 0.0910 2022/09/15 21:56:17 - mmengine - INFO - Epoch(train) [2][8400/8498] lr: 4.0000e-04 eta: 1 day, 7:54:17 time: 0.9452 data_time: 0.0084 memory: 36546 loss_ce: 0.0984 loss: 0.0984 2022/09/15 21:58:12 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 21:58:12 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/09/15 21:58:52 - mmengine - INFO - Epoch(val) [2][100/1918] eta: 0:04:01 time: 0.1326 data_time: 0.0006 memory: 36546 2022/09/15 21:59:06 - mmengine - INFO - Epoch(val) [2][200/1918] eta: 0:03:50 time: 0.1340 data_time: 0.0007 memory: 1150 2022/09/15 21:59:19 - mmengine - INFO - Epoch(val) [2][300/1918] eta: 0:03:58 time: 0.1473 data_time: 0.0009 memory: 1150 2022/09/15 21:59:32 - mmengine - INFO - Epoch(val) [2][400/1918] eta: 0:03:16 time: 0.1296 data_time: 0.0005 memory: 1150 2022/09/15 21:59:45 - mmengine - INFO - Epoch(val) [2][500/1918] eta: 0:03:04 time: 0.1298 data_time: 0.0006 memory: 1150 2022/09/15 21:59:58 - mmengine - INFO - Epoch(val) [2][600/1918] eta: 0:02:50 time: 0.1290 data_time: 0.0005 memory: 1150 2022/09/15 22:00:11 - mmengine - INFO - Epoch(val) [2][700/1918] eta: 0:02:38 time: 0.1303 data_time: 0.0007 memory: 1150 2022/09/15 22:00:24 - mmengine - INFO - Epoch(val) [2][800/1918] eta: 0:02:25 time: 0.1297 data_time: 0.0006 memory: 1150 2022/09/15 22:00:37 - mmengine - INFO - Epoch(val) [2][900/1918] eta: 0:02:10 time: 0.1285 data_time: 0.0005 memory: 1150 2022/09/15 22:00:50 - mmengine - INFO - Epoch(val) [2][1000/1918] eta: 0:02:02 time: 0.1339 data_time: 0.0008 memory: 1150 2022/09/15 22:01:03 - mmengine - INFO - Epoch(val) [2][1100/1918] eta: 0:01:46 time: 0.1305 data_time: 0.0006 memory: 1150 2022/09/15 22:01:17 - mmengine - INFO - Epoch(val) [2][1200/1918] eta: 0:01:34 time: 0.1311 data_time: 0.0005 memory: 1150 2022/09/15 22:01:30 - mmengine - INFO - Epoch(val) [2][1300/1918] eta: 0:01:21 time: 0.1320 data_time: 0.0021 memory: 1150 2022/09/15 22:01:43 - mmengine - INFO - Epoch(val) [2][1400/1918] eta: 0:01:06 time: 0.1291 data_time: 0.0005 memory: 1150 2022/09/15 22:01:56 - mmengine - INFO - Epoch(val) [2][1500/1918] eta: 0:00:54 time: 0.1292 data_time: 0.0006 memory: 1150 2022/09/15 22:02:09 - mmengine - INFO - Epoch(val) [2][1600/1918] eta: 0:00:41 time: 0.1320 data_time: 0.0006 memory: 1150 2022/09/15 22:02:22 - mmengine - INFO - Epoch(val) [2][1700/1918] eta: 0:00:28 time: 0.1294 data_time: 0.0005 memory: 1150 2022/09/15 22:02:35 - mmengine - INFO - Epoch(val) [2][1800/1918] eta: 0:00:15 time: 0.1317 data_time: 0.0007 memory: 1150 2022/09/15 22:02:48 - mmengine - INFO - Epoch(val) [2][1900/1918] eta: 0:00:02 time: 0.1308 data_time: 0.0006 memory: 1150 2022/09/15 22:02:51 - mmengine - INFO - Epoch(val) [2][1918/1918] CUTE80/recog/word_acc_ignore_case_symbol: 0.8194 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9323 SVT/recog/word_acc_ignore_case_symbol: 0.8640 SVTP/recog/word_acc_ignore_case_symbol: 0.7473 IC13/recog/word_acc_ignore_case_symbol: 0.9241 IC15/recog/word_acc_ignore_case_symbol: 0.7020 2022/09/15 22:03:11 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 22:05:16 - mmengine - INFO - Epoch(train) [3][100/8498] lr: 4.0000e-04 eta: 1 day, 7:48:40 time: 1.7872 data_time: 0.3490 memory: 36546 loss_ce: 0.0995 loss: 0.0995 2022/09/15 22:07:27 - mmengine - INFO - Epoch(train) [3][200/8498] lr: 4.0000e-04 eta: 1 day, 7:46:04 time: 1.7786 data_time: 0.3531 memory: 36546 loss_ce: 0.0903 loss: 0.0903 2022/09/15 22:09:43 - mmengine - INFO - Epoch(train) [3][300/8498] lr: 4.0000e-04 eta: 1 day, 7:43:55 time: 1.3775 data_time: 0.0845 memory: 36546 loss_ce: 0.1040 loss: 0.1040 2022/09/15 22:11:55 - mmengine - INFO - Epoch(train) [3][400/8498] lr: 4.0000e-04 eta: 1 day, 7:41:28 time: 1.1430 data_time: 0.1112 memory: 36546 loss_ce: 0.1038 loss: 0.1038 2022/09/15 22:14:07 - mmengine - INFO - Epoch(train) [3][500/8498] lr: 4.0000e-04 eta: 1 day, 7:38:57 time: 1.0581 data_time: 0.1434 memory: 36546 loss_ce: 0.0932 loss: 0.0932 2022/09/15 22:16:18 - mmengine - INFO - Epoch(train) [3][600/8498] lr: 4.0000e-04 eta: 1 day, 7:36:25 time: 0.9080 data_time: 0.0057 memory: 36546 loss_ce: 0.0924 loss: 0.0924 2022/09/15 22:18:37 - mmengine - INFO - Epoch(train) [3][700/8498] lr: 4.0000e-04 eta: 1 day, 7:34:30 time: 1.6077 data_time: 0.2682 memory: 36546 loss_ce: 0.0879 loss: 0.0879 2022/09/15 22:20:49 - mmengine - INFO - Epoch(train) [3][800/8498] lr: 4.0000e-04 eta: 1 day, 7:31:59 time: 1.7192 data_time: 0.2955 memory: 36546 loss_ce: 0.0973 loss: 0.0973 2022/09/15 22:22:59 - mmengine - INFO - Epoch(train) [3][900/8498] lr: 4.0000e-04 eta: 1 day, 7:29:25 time: 1.3335 data_time: 0.1233 memory: 36546 loss_ce: 0.0980 loss: 0.0980 2022/09/15 22:25:09 - mmengine - INFO - Epoch(train) [3][1000/8498] lr: 4.0000e-04 eta: 1 day, 7:26:48 time: 1.1591 data_time: 0.1265 memory: 36546 loss_ce: 0.1010 loss: 0.1010 2022/09/15 22:25:13 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 22:27:19 - mmengine - INFO - Epoch(train) [3][1100/8498] lr: 4.0000e-04 eta: 1 day, 7:24:07 time: 1.0076 data_time: 0.1116 memory: 36546 loss_ce: 0.0981 loss: 0.0981 2022/09/15 22:29:27 - mmengine - INFO - Epoch(train) [3][1200/8498] lr: 4.0000e-04 eta: 1 day, 7:21:25 time: 0.8802 data_time: 0.0059 memory: 36546 loss_ce: 0.0887 loss: 0.0887 2022/09/15 22:31:46 - mmengine - INFO - Epoch(train) [3][1300/8498] lr: 4.0000e-04 eta: 1 day, 7:19:26 time: 1.6206 data_time: 0.2629 memory: 36546 loss_ce: 0.0887 loss: 0.0887 2022/09/15 22:33:56 - mmengine - INFO - Epoch(train) [3][1400/8498] lr: 4.0000e-04 eta: 1 day, 7:16:51 time: 1.7316 data_time: 0.2804 memory: 36546 loss_ce: 0.1036 loss: 0.1036 2022/09/15 22:36:07 - mmengine - INFO - Epoch(train) [3][1500/8498] lr: 4.0000e-04 eta: 1 day, 7:14:17 time: 1.4262 data_time: 0.1874 memory: 36546 loss_ce: 0.0945 loss: 0.0945 2022/09/15 22:38:16 - mmengine - INFO - Epoch(train) [3][1600/8498] lr: 4.0000e-04 eta: 1 day, 7:11:39 time: 1.1719 data_time: 0.1284 memory: 36546 loss_ce: 0.0869 loss: 0.0869 2022/09/15 22:40:24 - mmengine - INFO - Epoch(train) [3][1700/8498] lr: 4.0000e-04 eta: 1 day, 7:08:56 time: 1.0166 data_time: 0.1180 memory: 36546 loss_ce: 0.0848 loss: 0.0848 2022/09/15 22:42:35 - mmengine - INFO - Epoch(train) [3][1800/8498] lr: 4.0000e-04 eta: 1 day, 7:06:23 time: 0.9244 data_time: 0.0060 memory: 36546 loss_ce: 0.0871 loss: 0.0871 2022/09/15 22:44:50 - mmengine - INFO - Epoch(train) [3][1900/8498] lr: 4.0000e-04 eta: 1 day, 7:04:10 time: 1.5699 data_time: 0.2678 memory: 36546 loss_ce: 0.0879 loss: 0.0879 2022/09/15 22:47:00 - mmengine - INFO - Epoch(train) [3][2000/8498] lr: 4.0000e-04 eta: 1 day, 7:01:35 time: 1.7396 data_time: 0.2662 memory: 36546 loss_ce: 0.0840 loss: 0.0840 2022/09/15 22:47:05 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 22:49:11 - mmengine - INFO - Epoch(train) [3][2100/8498] lr: 4.0000e-04 eta: 1 day, 6:59:04 time: 1.3758 data_time: 0.1328 memory: 36546 loss_ce: 0.0914 loss: 0.0914 2022/09/15 22:51:20 - mmengine - INFO - Epoch(train) [3][2200/8498] lr: 4.0000e-04 eta: 1 day, 6:56:28 time: 1.2254 data_time: 0.1511 memory: 36546 loss_ce: 0.0867 loss: 0.0867 2022/09/15 22:53:38 - mmengine - INFO - Epoch(train) [3][2300/8498] lr: 4.0000e-04 eta: 1 day, 6:54:25 time: 1.0360 data_time: 0.1502 memory: 36546 loss_ce: 0.0920 loss: 0.0920 2022/09/15 22:55:53 - mmengine - INFO - Epoch(train) [3][2400/8498] lr: 4.0000e-04 eta: 1 day, 6:52:14 time: 0.8913 data_time: 0.0066 memory: 36546 loss_ce: 0.0974 loss: 0.0974 2022/09/15 22:58:17 - mmengine - INFO - Epoch(train) [3][2500/8498] lr: 4.0000e-04 eta: 1 day, 6:50:41 time: 1.7564 data_time: 0.3135 memory: 36546 loss_ce: 0.1054 loss: 0.1054 2022/09/15 23:00:36 - mmengine - INFO - Epoch(train) [3][2600/8498] lr: 4.0000e-04 eta: 1 day, 6:48:44 time: 1.7918 data_time: 0.3041 memory: 36546 loss_ce: 0.0872 loss: 0.0872 2022/09/15 23:02:55 - mmengine - INFO - Epoch(train) [3][2700/8498] lr: 4.0000e-04 eta: 1 day, 6:46:45 time: 1.4896 data_time: 0.1277 memory: 36546 loss_ce: 0.0982 loss: 0.0982 2022/09/15 23:05:12 - mmengine - INFO - Epoch(train) [3][2800/8498] lr: 4.0000e-04 eta: 1 day, 6:44:41 time: 1.2175 data_time: 0.1517 memory: 36546 loss_ce: 0.0854 loss: 0.0854 2022/09/15 23:07:28 - mmengine - INFO - Epoch(train) [3][2900/8498] lr: 4.0000e-04 eta: 1 day, 6:42:33 time: 1.0324 data_time: 0.1417 memory: 36546 loss_ce: 0.0866 loss: 0.0866 2022/09/15 23:09:47 - mmengine - INFO - Epoch(train) [3][3000/8498] lr: 4.0000e-04 eta: 1 day, 6:40:35 time: 0.9377 data_time: 0.0066 memory: 36546 loss_ce: 0.0881 loss: 0.0881 2022/09/15 23:09:59 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 23:12:12 - mmengine - INFO - Epoch(train) [3][3100/8498] lr: 4.0000e-04 eta: 1 day, 6:39:03 time: 1.7461 data_time: 0.3032 memory: 36546 loss_ce: 0.0854 loss: 0.0854 2022/09/15 23:14:30 - mmengine - INFO - Epoch(train) [3][3200/8498] lr: 4.0000e-04 eta: 1 day, 6:37:02 time: 1.9130 data_time: 0.3422 memory: 36546 loss_ce: 0.0814 loss: 0.0814 2022/09/15 23:16:49 - mmengine - INFO - Epoch(train) [3][3300/8498] lr: 4.0000e-04 eta: 1 day, 6:35:04 time: 1.5459 data_time: 0.1633 memory: 36546 loss_ce: 0.0780 loss: 0.0780 2022/09/15 23:19:06 - mmengine - INFO - Epoch(train) [3][3400/8498] lr: 4.0000e-04 eta: 1 day, 6:32:57 time: 1.1962 data_time: 0.1243 memory: 36546 loss_ce: 0.0892 loss: 0.0892 2022/09/15 23:21:23 - mmengine - INFO - Epoch(train) [3][3500/8498] lr: 4.0000e-04 eta: 1 day, 6:30:49 time: 1.0167 data_time: 0.1202 memory: 36546 loss_ce: 0.0987 loss: 0.0987 2022/09/15 23:23:38 - mmengine - INFO - Epoch(train) [3][3600/8498] lr: 4.0000e-04 eta: 1 day, 6:28:36 time: 0.8855 data_time: 0.0061 memory: 36546 loss_ce: 0.0890 loss: 0.0890 2022/09/15 23:26:03 - mmengine - INFO - Epoch(train) [3][3700/8498] lr: 4.0000e-04 eta: 1 day, 6:27:04 time: 1.5880 data_time: 0.2966 memory: 36546 loss_ce: 0.0867 loss: 0.0867 2022/09/15 23:28:22 - mmengine - INFO - Epoch(train) [3][3800/8498] lr: 4.0000e-04 eta: 1 day, 6:25:02 time: 1.9721 data_time: 0.3215 memory: 36546 loss_ce: 0.0758 loss: 0.0758 2022/09/15 23:30:41 - mmengine - INFO - Epoch(train) [3][3900/8498] lr: 4.0000e-04 eta: 1 day, 6:23:04 time: 1.4619 data_time: 0.1534 memory: 36546 loss_ce: 0.0759 loss: 0.0759 2022/09/15 23:32:58 - mmengine - INFO - Epoch(train) [3][4000/8498] lr: 4.0000e-04 eta: 1 day, 6:20:58 time: 1.2158 data_time: 0.1623 memory: 36546 loss_ce: 0.0919 loss: 0.0919 2022/09/15 23:33:02 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 23:35:16 - mmengine - INFO - Epoch(train) [3][4100/8498] lr: 4.0000e-04 eta: 1 day, 6:18:53 time: 1.0691 data_time: 0.1511 memory: 36546 loss_ce: 0.0835 loss: 0.0835 2022/09/15 23:37:32 - mmengine - INFO - Epoch(train) [3][4200/8498] lr: 4.0000e-04 eta: 1 day, 6:16:44 time: 0.9370 data_time: 0.0067 memory: 36546 loss_ce: 0.0951 loss: 0.0951 2022/09/15 23:39:59 - mmengine - INFO - Epoch(train) [3][4300/8498] lr: 4.0000e-04 eta: 1 day, 6:15:12 time: 1.7213 data_time: 0.3206 memory: 36546 loss_ce: 0.0917 loss: 0.0917 2022/09/15 23:42:17 - mmengine - INFO - Epoch(train) [3][4400/8498] lr: 4.0000e-04 eta: 1 day, 6:13:12 time: 1.8693 data_time: 0.2820 memory: 36546 loss_ce: 0.0860 loss: 0.0860 2022/09/15 23:44:37 - mmengine - INFO - Epoch(train) [3][4500/8498] lr: 4.0000e-04 eta: 1 day, 6:11:13 time: 1.5117 data_time: 0.1395 memory: 36546 loss_ce: 0.0801 loss: 0.0801 2022/09/15 23:46:54 - mmengine - INFO - Epoch(train) [3][4600/8498] lr: 4.0000e-04 eta: 1 day, 6:09:07 time: 1.2080 data_time: 0.1533 memory: 36546 loss_ce: 0.0809 loss: 0.0809 2022/09/15 23:49:12 - mmengine - INFO - Epoch(train) [3][4700/8498] lr: 4.0000e-04 eta: 1 day, 6:07:01 time: 1.0357 data_time: 0.1390 memory: 36546 loss_ce: 0.0847 loss: 0.0847 2022/09/15 23:51:30 - mmengine - INFO - Epoch(train) [3][4800/8498] lr: 4.0000e-04 eta: 1 day, 6:04:57 time: 0.8906 data_time: 0.0080 memory: 36546 loss_ce: 0.0917 loss: 0.0917 2022/09/15 23:53:55 - mmengine - INFO - Epoch(train) [3][4900/8498] lr: 4.0000e-04 eta: 1 day, 6:03:20 time: 1.7058 data_time: 0.3049 memory: 36546 loss_ce: 0.0815 loss: 0.0815 2022/09/15 23:56:15 - mmengine - INFO - Epoch(train) [3][5000/8498] lr: 4.0000e-04 eta: 1 day, 6:01:21 time: 1.9161 data_time: 0.3321 memory: 36546 loss_ce: 0.0802 loss: 0.0802 2022/09/15 23:56:20 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/15 23:58:33 - mmengine - INFO - Epoch(train) [3][5100/8498] lr: 4.0000e-04 eta: 1 day, 5:59:17 time: 1.5295 data_time: 0.1470 memory: 36546 loss_ce: 0.0892 loss: 0.0892 2022/09/16 00:00:55 - mmengine - INFO - Epoch(train) [3][5200/8498] lr: 4.0000e-04 eta: 1 day, 5:57:26 time: 1.2193 data_time: 0.1610 memory: 36546 loss_ce: 0.0860 loss: 0.0860 2022/09/16 00:03:09 - mmengine - INFO - Epoch(train) [3][5300/8498] lr: 4.0000e-04 eta: 1 day, 5:55:05 time: 1.0107 data_time: 0.1267 memory: 36546 loss_ce: 0.1115 loss: 0.1115 2022/09/16 00:05:25 - mmengine - INFO - Epoch(train) [3][5400/8498] lr: 4.0000e-04 eta: 1 day, 5:52:55 time: 0.9367 data_time: 0.0071 memory: 36546 loss_ce: 0.0883 loss: 0.0883 2022/09/16 00:07:50 - mmengine - INFO - Epoch(train) [3][5500/8498] lr: 4.0000e-04 eta: 1 day, 5:51:15 time: 1.7155 data_time: 0.3325 memory: 36546 loss_ce: 0.0842 loss: 0.0842 2022/09/16 00:10:08 - mmengine - INFO - Epoch(train) [3][5600/8498] lr: 4.0000e-04 eta: 1 day, 5:49:07 time: 1.8576 data_time: 0.3185 memory: 36546 loss_ce: 0.0903 loss: 0.0903 2022/09/16 00:12:26 - mmengine - INFO - Epoch(train) [3][5700/8498] lr: 4.0000e-04 eta: 1 day, 5:47:04 time: 1.4482 data_time: 0.1303 memory: 36546 loss_ce: 0.0782 loss: 0.0782 2022/09/16 00:14:44 - mmengine - INFO - Epoch(train) [3][5800/8498] lr: 4.0000e-04 eta: 1 day, 5:44:58 time: 1.2266 data_time: 0.1508 memory: 36546 loss_ce: 0.0853 loss: 0.0853 2022/09/16 00:17:01 - mmengine - INFO - Epoch(train) [3][5900/8498] lr: 4.0000e-04 eta: 1 day, 5:42:48 time: 1.0477 data_time: 0.1530 memory: 36546 loss_ce: 0.0859 loss: 0.0859 2022/09/16 00:19:19 - mmengine - INFO - Epoch(train) [3][6000/8498] lr: 4.0000e-04 eta: 1 day, 5:40:42 time: 0.8948 data_time: 0.0063 memory: 36546 loss_ce: 0.0964 loss: 0.0964 2022/09/16 00:19:31 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 00:21:45 - mmengine - INFO - Epoch(train) [3][6100/8498] lr: 4.0000e-04 eta: 1 day, 5:39:04 time: 1.7226 data_time: 0.3171 memory: 36546 loss_ce: 0.0928 loss: 0.0928 2022/09/16 00:24:05 - mmengine - INFO - Epoch(train) [3][6200/8498] lr: 4.0000e-04 eta: 1 day, 5:37:05 time: 1.8992 data_time: 0.3315 memory: 36546 loss_ce: 0.0797 loss: 0.0797 2022/09/16 00:26:24 - mmengine - INFO - Epoch(train) [3][6300/8498] lr: 4.0000e-04 eta: 1 day, 5:35:01 time: 1.5013 data_time: 0.1410 memory: 36546 loss_ce: 0.0861 loss: 0.0861 2022/09/16 00:28:42 - mmengine - INFO - Epoch(train) [3][6400/8498] lr: 4.0000e-04 eta: 1 day, 5:32:53 time: 1.2532 data_time: 0.1538 memory: 36546 loss_ce: 0.0842 loss: 0.0842 2022/09/16 00:30:59 - mmengine - INFO - Epoch(train) [3][6500/8498] lr: 4.0000e-04 eta: 1 day, 5:30:45 time: 1.0467 data_time: 0.1496 memory: 36546 loss_ce: 0.0870 loss: 0.0870 2022/09/16 00:33:17 - mmengine - INFO - Epoch(train) [3][6600/8498] lr: 4.0000e-04 eta: 1 day, 5:28:38 time: 0.9343 data_time: 0.0066 memory: 36546 loss_ce: 0.0764 loss: 0.0764 2022/09/16 00:35:43 - mmengine - INFO - Epoch(train) [3][6700/8498] lr: 4.0000e-04 eta: 1 day, 5:26:58 time: 1.7228 data_time: 0.3113 memory: 36546 loss_ce: 0.0814 loss: 0.0814 2022/09/16 00:38:02 - mmengine - INFO - Epoch(train) [3][6800/8498] lr: 4.0000e-04 eta: 1 day, 5:24:52 time: 1.8315 data_time: 0.2873 memory: 36546 loss_ce: 0.0854 loss: 0.0854 2022/09/16 00:40:18 - mmengine - INFO - Epoch(train) [3][6900/8498] lr: 4.0000e-04 eta: 1 day, 5:22:39 time: 1.4916 data_time: 0.1570 memory: 36546 loss_ce: 0.0840 loss: 0.0840 2022/09/16 00:42:34 - mmengine - INFO - Epoch(train) [3][7000/8498] lr: 4.0000e-04 eta: 1 day, 5:20:27 time: 1.1687 data_time: 0.1413 memory: 36546 loss_ce: 0.0795 loss: 0.0795 2022/09/16 00:42:38 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 00:44:53 - mmengine - INFO - Epoch(train) [3][7100/8498] lr: 4.0000e-04 eta: 1 day, 5:18:21 time: 1.0373 data_time: 0.1426 memory: 36546 loss_ce: 0.0837 loss: 0.0837 2022/09/16 00:47:11 - mmengine - INFO - Epoch(train) [3][7200/8498] lr: 4.0000e-04 eta: 1 day, 5:16:14 time: 0.9032 data_time: 0.0062 memory: 36546 loss_ce: 0.0853 loss: 0.0853 2022/09/16 00:49:35 - mmengine - INFO - Epoch(train) [3][7300/8498] lr: 4.0000e-04 eta: 1 day, 5:14:27 time: 1.7403 data_time: 0.3369 memory: 36546 loss_ce: 0.0828 loss: 0.0828 2022/09/16 00:51:56 - mmengine - INFO - Epoch(train) [3][7400/8498] lr: 4.0000e-04 eta: 1 day, 5:12:29 time: 1.8696 data_time: 0.3305 memory: 36546 loss_ce: 0.0806 loss: 0.0806 2022/09/16 00:54:16 - mmengine - INFO - Epoch(train) [3][7500/8498] lr: 4.0000e-04 eta: 1 day, 5:10:26 time: 1.5157 data_time: 0.1491 memory: 36546 loss_ce: 0.0760 loss: 0.0760 2022/09/16 00:56:33 - mmengine - INFO - Epoch(train) [3][7600/8498] lr: 4.0000e-04 eta: 1 day, 5:08:14 time: 1.2351 data_time: 0.1347 memory: 36546 loss_ce: 0.0707 loss: 0.0707 2022/09/16 00:58:49 - mmengine - INFO - Epoch(train) [3][7700/8498] lr: 4.0000e-04 eta: 1 day, 5:06:02 time: 1.0564 data_time: 0.1618 memory: 36546 loss_ce: 0.0810 loss: 0.0810 2022/09/16 01:01:05 - mmengine - INFO - Epoch(train) [3][7800/8498] lr: 4.0000e-04 eta: 1 day, 5:03:47 time: 0.9429 data_time: 0.0070 memory: 36546 loss_ce: 0.0906 loss: 0.0906 2022/09/16 01:03:28 - mmengine - INFO - Epoch(train) [3][7900/8498] lr: 4.0000e-04 eta: 1 day, 5:01:55 time: 1.6950 data_time: 0.2988 memory: 36546 loss_ce: 0.0904 loss: 0.0904 2022/09/16 01:05:47 - mmengine - INFO - Epoch(train) [3][8000/8498] lr: 4.0000e-04 eta: 1 day, 4:59:49 time: 1.8947 data_time: 0.2897 memory: 36546 loss_ce: 0.0838 loss: 0.0838 2022/09/16 01:05:52 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 01:08:07 - mmengine - INFO - Epoch(train) [3][8100/8498] lr: 4.0000e-04 eta: 1 day, 4:57:48 time: 1.5761 data_time: 0.1738 memory: 36546 loss_ce: 0.0875 loss: 0.0875 2022/09/16 01:10:25 - mmengine - INFO - Epoch(train) [3][8200/8498] lr: 4.0000e-04 eta: 1 day, 4:55:39 time: 1.1944 data_time: 0.1468 memory: 36546 loss_ce: 0.0868 loss: 0.0868 2022/09/16 01:12:44 - mmengine - INFO - Epoch(train) [3][8300/8498] lr: 4.0000e-04 eta: 1 day, 4:53:35 time: 1.0834 data_time: 0.1806 memory: 36546 loss_ce: 0.0839 loss: 0.0839 2022/09/16 01:14:59 - mmengine - INFO - Epoch(train) [3][8400/8498] lr: 4.0000e-04 eta: 1 day, 4:51:14 time: 0.8950 data_time: 0.0071 memory: 36546 loss_ce: 0.0839 loss: 0.0839 2022/09/16 01:17:08 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 01:17:08 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/09/16 01:17:48 - mmengine - INFO - Epoch(val) [3][100/1918] eta: 0:04:19 time: 0.1427 data_time: 0.0009 memory: 36546 2022/09/16 01:18:02 - mmengine - INFO - Epoch(val) [3][200/1918] eta: 0:03:56 time: 0.1374 data_time: 0.0009 memory: 1150 2022/09/16 01:18:16 - mmengine - INFO - Epoch(val) [3][300/1918] eta: 0:03:41 time: 0.1366 data_time: 0.0008 memory: 1150 2022/09/16 01:18:30 - mmengine - INFO - Epoch(val) [3][400/1918] eta: 0:03:27 time: 0.1370 data_time: 0.0008 memory: 1150 2022/09/16 01:18:44 - mmengine - INFO - Epoch(val) [3][500/1918] eta: 0:03:12 time: 0.1357 data_time: 0.0008 memory: 1150 2022/09/16 01:18:58 - mmengine - INFO - Epoch(val) [3][600/1918] eta: 0:03:00 time: 0.1369 data_time: 0.0011 memory: 1150 2022/09/16 01:19:12 - mmengine - INFO - Epoch(val) [3][700/1918] eta: 0:02:42 time: 0.1337 data_time: 0.0008 memory: 1150 2022/09/16 01:19:26 - mmengine - INFO - Epoch(val) [3][800/1918] eta: 0:02:27 time: 0.1320 data_time: 0.0008 memory: 1150 2022/09/16 01:19:40 - mmengine - INFO - Epoch(val) [3][900/1918] eta: 0:02:18 time: 0.1360 data_time: 0.0008 memory: 1150 2022/09/16 01:19:54 - mmengine - INFO - Epoch(val) [3][1000/1918] eta: 0:02:04 time: 0.1357 data_time: 0.0008 memory: 1150 2022/09/16 01:20:07 - mmengine - INFO - Epoch(val) [3][1100/1918] eta: 0:01:54 time: 0.1395 data_time: 0.0008 memory: 1150 2022/09/16 01:20:22 - mmengine - INFO - Epoch(val) [3][1200/1918] eta: 0:01:39 time: 0.1391 data_time: 0.0008 memory: 1150 2022/09/16 01:20:35 - mmengine - INFO - Epoch(val) [3][1300/1918] eta: 0:01:22 time: 0.1341 data_time: 0.0008 memory: 1150 2022/09/16 01:20:49 - mmengine - INFO - Epoch(val) [3][1400/1918] eta: 0:01:11 time: 0.1388 data_time: 0.0009 memory: 1150 2022/09/16 01:21:03 - mmengine - INFO - Epoch(val) [3][1500/1918] eta: 0:00:57 time: 0.1380 data_time: 0.0009 memory: 1150 2022/09/16 01:21:17 - mmengine - INFO - Epoch(val) [3][1600/1918] eta: 0:00:42 time: 0.1334 data_time: 0.0008 memory: 1150 2022/09/16 01:21:31 - mmengine - INFO - Epoch(val) [3][1700/1918] eta: 0:00:33 time: 0.1516 data_time: 0.0009 memory: 1150 2022/09/16 01:21:45 - mmengine - INFO - Epoch(val) [3][1800/1918] eta: 0:00:15 time: 0.1327 data_time: 0.0008 memory: 1150 2022/09/16 01:21:59 - mmengine - INFO - Epoch(val) [3][1900/1918] eta: 0:00:02 time: 0.1572 data_time: 0.0022 memory: 1150 2022/09/16 01:22:01 - mmengine - INFO - Epoch(val) [3][1918/1918] CUTE80/recog/word_acc_ignore_case_symbol: 0.8368 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9350 SVT/recog/word_acc_ignore_case_symbol: 0.8532 SVTP/recog/word_acc_ignore_case_symbol: 0.7597 IC13/recog/word_acc_ignore_case_symbol: 0.9340 IC15/recog/word_acc_ignore_case_symbol: 0.7097 2022/09/16 01:24:24 - mmengine - INFO - Epoch(train) [4][100/8498] lr: 4.0000e-04 eta: 1 day, 4:46:33 time: 1.5825 data_time: 0.3404 memory: 36546 loss_ce: 0.0804 loss: 0.0804 2022/09/16 01:26:33 - mmengine - INFO - Epoch(train) [4][200/8498] lr: 4.0000e-04 eta: 1 day, 4:43:58 time: 1.7644 data_time: 0.4557 memory: 36546 loss_ce: 0.0797 loss: 0.0797 2022/09/16 01:28:39 - mmengine - INFO - Epoch(train) [4][300/8498] lr: 4.0000e-04 eta: 1 day, 4:41:14 time: 1.2398 data_time: 0.1398 memory: 36546 loss_ce: 0.0759 loss: 0.0759 2022/09/16 01:30:46 - mmengine - INFO - Epoch(train) [4][400/8498] lr: 4.0000e-04 eta: 1 day, 4:38:33 time: 1.1053 data_time: 0.1374 memory: 36546 loss_ce: 0.0744 loss: 0.0744 2022/09/16 01:32:54 - mmengine - INFO - Epoch(train) [4][500/8498] lr: 4.0000e-04 eta: 1 day, 4:35:54 time: 0.9598 data_time: 0.0372 memory: 36546 loss_ce: 0.0769 loss: 0.0769 2022/09/16 01:33:04 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 01:35:02 - mmengine - INFO - Epoch(train) [4][600/8498] lr: 4.0000e-04 eta: 1 day, 4:33:18 time: 0.9791 data_time: 0.0366 memory: 36546 loss_ce: 0.0728 loss: 0.0728 2022/09/16 01:37:15 - mmengine - INFO - Epoch(train) [4][700/8498] lr: 4.0000e-04 eta: 1 day, 4:30:55 time: 1.4852 data_time: 0.2712 memory: 36546 loss_ce: 0.0718 loss: 0.0718 2022/09/16 01:39:25 - mmengine - INFO - Epoch(train) [4][800/8498] lr: 4.0000e-04 eta: 1 day, 4:28:25 time: 1.7682 data_time: 0.3842 memory: 36546 loss_ce: 0.0749 loss: 0.0749 2022/09/16 01:41:33 - mmengine - INFO - Epoch(train) [4][900/8498] lr: 4.0000e-04 eta: 1 day, 4:25:47 time: 1.2466 data_time: 0.1046 memory: 36546 loss_ce: 0.0954 loss: 0.0954 2022/09/16 01:43:40 - mmengine - INFO - Epoch(train) [4][1000/8498] lr: 4.0000e-04 eta: 1 day, 4:23:08 time: 1.1195 data_time: 0.1455 memory: 36546 loss_ce: 0.0764 loss: 0.0764 2022/09/16 01:45:48 - mmengine - INFO - Epoch(train) [4][1100/8498] lr: 4.0000e-04 eta: 1 day, 4:20:31 time: 1.0302 data_time: 0.0383 memory: 36546 loss_ce: 0.0705 loss: 0.0705 2022/09/16 01:47:55 - mmengine - INFO - Epoch(train) [4][1200/8498] lr: 4.0000e-04 eta: 1 day, 4:17:52 time: 1.0308 data_time: 0.0481 memory: 36546 loss_ce: 0.0802 loss: 0.0802 2022/09/16 01:50:04 - mmengine - INFO - Epoch(train) [4][1300/8498] lr: 4.0000e-04 eta: 1 day, 4:15:19 time: 1.4560 data_time: 0.2524 memory: 36546 loss_ce: 0.0851 loss: 0.0851 2022/09/16 01:52:13 - mmengine - INFO - Epoch(train) [4][1400/8498] lr: 4.0000e-04 eta: 1 day, 4:12:47 time: 1.6985 data_time: 0.4266 memory: 36546 loss_ce: 0.0828 loss: 0.0828 2022/09/16 01:54:19 - mmengine - INFO - Epoch(train) [4][1500/8498] lr: 4.0000e-04 eta: 1 day, 4:10:06 time: 1.1960 data_time: 0.1164 memory: 36546 loss_ce: 0.0822 loss: 0.0822 2022/09/16 01:54:26 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 01:56:27 - mmengine - INFO - Epoch(train) [4][1600/8498] lr: 4.0000e-04 eta: 1 day, 4:07:30 time: 1.0719 data_time: 0.1298 memory: 36546 loss_ce: 0.0762 loss: 0.0762 2022/09/16 01:58:34 - mmengine - INFO - Epoch(train) [4][1700/8498] lr: 4.0000e-04 eta: 1 day, 4:04:52 time: 0.9487 data_time: 0.0365 memory: 36546 loss_ce: 0.0749 loss: 0.0749 2022/09/16 02:00:43 - mmengine - INFO - Epoch(train) [4][1800/8498] lr: 4.0000e-04 eta: 1 day, 4:02:20 time: 1.0034 data_time: 0.0414 memory: 36546 loss_ce: 0.0703 loss: 0.0703 2022/09/16 02:02:56 - mmengine - INFO - Epoch(train) [4][1900/8498] lr: 4.0000e-04 eta: 1 day, 3:59:59 time: 1.5702 data_time: 0.3044 memory: 36546 loss_ce: 0.0835 loss: 0.0835 2022/09/16 02:05:05 - mmengine - INFO - Epoch(train) [4][2000/8498] lr: 4.0000e-04 eta: 1 day, 3:57:26 time: 1.6773 data_time: 0.3897 memory: 36546 loss_ce: 0.0780 loss: 0.0780 2022/09/16 02:07:12 - mmengine - INFO - Epoch(train) [4][2100/8498] lr: 4.0000e-04 eta: 1 day, 3:54:50 time: 1.2493 data_time: 0.1043 memory: 36546 loss_ce: 0.0782 loss: 0.0782 2022/09/16 02:09:20 - mmengine - INFO - Epoch(train) [4][2200/8498] lr: 4.0000e-04 eta: 1 day, 3:52:15 time: 1.1189 data_time: 0.1605 memory: 36546 loss_ce: 0.0807 loss: 0.0807 2022/09/16 02:11:27 - mmengine - INFO - Epoch(train) [4][2300/8498] lr: 4.0000e-04 eta: 1 day, 3:49:38 time: 0.9983 data_time: 0.0471 memory: 36546 loss_ce: 0.0745 loss: 0.0745 2022/09/16 02:13:34 - mmengine - INFO - Epoch(train) [4][2400/8498] lr: 4.0000e-04 eta: 1 day, 3:47:02 time: 1.0787 data_time: 0.0392 memory: 36546 loss_ce: 0.0816 loss: 0.0816 2022/09/16 02:15:49 - mmengine - INFO - Epoch(train) [4][2500/8498] lr: 4.0000e-04 eta: 1 day, 3:44:47 time: 1.6138 data_time: 0.2757 memory: 36546 loss_ce: 0.0765 loss: 0.0765 2022/09/16 02:15:56 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 02:17:58 - mmengine - INFO - Epoch(train) [4][2600/8498] lr: 4.0000e-04 eta: 1 day, 3:42:17 time: 1.7091 data_time: 0.4074 memory: 36546 loss_ce: 0.0734 loss: 0.0734 2022/09/16 02:20:06 - mmengine - INFO - Epoch(train) [4][2700/8498] lr: 4.0000e-04 eta: 1 day, 3:39:43 time: 1.2309 data_time: 0.1086 memory: 36546 loss_ce: 0.0725 loss: 0.0725 2022/09/16 02:22:13 - mmengine - INFO - Epoch(train) [4][2800/8498] lr: 4.0000e-04 eta: 1 day, 3:37:08 time: 1.0659 data_time: 0.1306 memory: 36546 loss_ce: 0.0815 loss: 0.0815 2022/09/16 02:24:22 - mmengine - INFO - Epoch(train) [4][2900/8498] lr: 4.0000e-04 eta: 1 day, 3:34:38 time: 1.0162 data_time: 0.0378 memory: 36546 loss_ce: 0.0743 loss: 0.0743 2022/09/16 02:26:27 - mmengine - INFO - Epoch(train) [4][3000/8498] lr: 4.0000e-04 eta: 1 day, 3:31:56 time: 1.0333 data_time: 0.0383 memory: 36546 loss_ce: 0.0802 loss: 0.0802 2022/09/16 02:28:40 - mmengine - INFO - Epoch(train) [4][3100/8498] lr: 4.0000e-04 eta: 1 day, 3:29:37 time: 1.6144 data_time: 0.2760 memory: 36546 loss_ce: 0.0802 loss: 0.0802 2022/09/16 02:30:48 - mmengine - INFO - Epoch(train) [4][3200/8498] lr: 4.0000e-04 eta: 1 day, 3:27:04 time: 1.6813 data_time: 0.3675 memory: 36546 loss_ce: 0.0756 loss: 0.0756 2022/09/16 02:32:54 - mmengine - INFO - Epoch(train) [4][3300/8498] lr: 4.0000e-04 eta: 1 day, 3:24:28 time: 1.2664 data_time: 0.1306 memory: 36546 loss_ce: 0.0686 loss: 0.0686 2022/09/16 02:35:01 - mmengine - INFO - Epoch(train) [4][3400/8498] lr: 4.0000e-04 eta: 1 day, 3:21:51 time: 1.1092 data_time: 0.1369 memory: 36546 loss_ce: 0.0767 loss: 0.0767 2022/09/16 02:37:08 - mmengine - INFO - Epoch(train) [4][3500/8498] lr: 4.0000e-04 eta: 1 day, 3:19:18 time: 1.0130 data_time: 0.0348 memory: 36546 loss_ce: 0.0807 loss: 0.0807 2022/09/16 02:37:19 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 02:39:16 - mmengine - INFO - Epoch(train) [4][3600/8498] lr: 4.0000e-04 eta: 1 day, 3:16:45 time: 0.9975 data_time: 0.0355 memory: 36546 loss_ce: 0.0745 loss: 0.0745 2022/09/16 02:41:27 - mmengine - INFO - Epoch(train) [4][3700/8498] lr: 4.0000e-04 eta: 1 day, 3:14:23 time: 1.5307 data_time: 0.2650 memory: 36546 loss_ce: 0.0747 loss: 0.0747 2022/09/16 02:43:36 - mmengine - INFO - Epoch(train) [4][3800/8498] lr: 4.0000e-04 eta: 1 day, 3:11:53 time: 1.7276 data_time: 0.3856 memory: 36546 loss_ce: 0.0790 loss: 0.0790 2022/09/16 02:45:42 - mmengine - INFO - Epoch(train) [4][3900/8498] lr: 4.0000e-04 eta: 1 day, 3:09:17 time: 1.2331 data_time: 0.1360 memory: 36546 loss_ce: 0.0708 loss: 0.0708 2022/09/16 02:47:49 - mmengine - INFO - Epoch(train) [4][4000/8498] lr: 4.0000e-04 eta: 1 day, 3:06:44 time: 1.0473 data_time: 0.1250 memory: 36546 loss_ce: 0.0744 loss: 0.0744 2022/09/16 02:49:57 - mmengine - INFO - Epoch(train) [4][4100/8498] lr: 4.0000e-04 eta: 1 day, 3:04:11 time: 1.0065 data_time: 0.0353 memory: 36546 loss_ce: 0.0772 loss: 0.0772 2022/09/16 02:52:04 - mmengine - INFO - Epoch(train) [4][4200/8498] lr: 4.0000e-04 eta: 1 day, 3:01:39 time: 1.0536 data_time: 0.0356 memory: 36546 loss_ce: 0.0802 loss: 0.0802 2022/09/16 02:54:15 - mmengine - INFO - Epoch(train) [4][4300/8498] lr: 4.0000e-04 eta: 1 day, 2:59:16 time: 1.5319 data_time: 0.2704 memory: 36546 loss_ce: 0.0733 loss: 0.0733 2022/09/16 02:56:23 - mmengine - INFO - Epoch(train) [4][4400/8498] lr: 4.0000e-04 eta: 1 day, 2:56:45 time: 1.7068 data_time: 0.3920 memory: 36546 loss_ce: 0.0791 loss: 0.0791 2022/09/16 02:58:31 - mmengine - INFO - Epoch(train) [4][4500/8498] lr: 4.0000e-04 eta: 1 day, 2:54:14 time: 1.2635 data_time: 0.1414 memory: 36546 loss_ce: 0.0771 loss: 0.0771 2022/09/16 02:58:37 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 03:00:38 - mmengine - INFO - Epoch(train) [4][4600/8498] lr: 4.0000e-04 eta: 1 day, 2:51:41 time: 1.0354 data_time: 0.1253 memory: 36546 loss_ce: 0.0685 loss: 0.0685 2022/09/16 03:02:42 - mmengine - INFO - Epoch(train) [4][4700/8498] lr: 4.0000e-04 eta: 1 day, 2:49:03 time: 0.9979 data_time: 0.0383 memory: 36546 loss_ce: 0.0744 loss: 0.0744 2022/09/16 03:04:49 - mmengine - INFO - Epoch(train) [4][4800/8498] lr: 4.0000e-04 eta: 1 day, 2:46:31 time: 1.0464 data_time: 0.0355 memory: 36546 loss_ce: 0.0713 loss: 0.0713 2022/09/16 03:06:59 - mmengine - INFO - Epoch(train) [4][4900/8498] lr: 4.0000e-04 eta: 1 day, 2:44:06 time: 1.5418 data_time: 0.2745 memory: 36546 loss_ce: 0.0719 loss: 0.0719 2022/09/16 03:09:09 - mmengine - INFO - Epoch(train) [4][5000/8498] lr: 4.0000e-04 eta: 1 day, 2:41:39 time: 1.7094 data_time: 0.4056 memory: 36546 loss_ce: 0.0799 loss: 0.0799 2022/09/16 03:11:15 - mmengine - INFO - Epoch(train) [4][5100/8498] lr: 4.0000e-04 eta: 1 day, 2:39:05 time: 1.2001 data_time: 0.1077 memory: 36546 loss_ce: 0.0735 loss: 0.0735 2022/09/16 03:13:22 - mmengine - INFO - Epoch(train) [4][5200/8498] lr: 4.0000e-04 eta: 1 day, 2:36:34 time: 1.0876 data_time: 0.1256 memory: 36546 loss_ce: 0.0779 loss: 0.0779 2022/09/16 03:15:29 - mmengine - INFO - Epoch(train) [4][5300/8498] lr: 4.0000e-04 eta: 1 day, 2:34:04 time: 0.9653 data_time: 0.0357 memory: 36546 loss_ce: 0.0789 loss: 0.0789 2022/09/16 03:17:37 - mmengine - INFO - Epoch(train) [4][5400/8498] lr: 4.0000e-04 eta: 1 day, 2:31:33 time: 1.0583 data_time: 0.0361 memory: 36546 loss_ce: 0.0702 loss: 0.0702 2022/09/16 03:19:51 - mmengine - INFO - Epoch(train) [4][5500/8498] lr: 4.0000e-04 eta: 1 day, 2:29:18 time: 1.5656 data_time: 0.2684 memory: 36546 loss_ce: 0.0763 loss: 0.0763 2022/09/16 03:19:58 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 03:21:59 - mmengine - INFO - Epoch(train) [4][5600/8498] lr: 4.0000e-04 eta: 1 day, 2:26:49 time: 1.6470 data_time: 0.3888 memory: 36546 loss_ce: 0.0746 loss: 0.0746 2022/09/16 03:24:05 - mmengine - INFO - Epoch(train) [4][5700/8498] lr: 4.0000e-04 eta: 1 day, 2:24:17 time: 1.1951 data_time: 0.1275 memory: 36546 loss_ce: 0.0696 loss: 0.0696 2022/09/16 03:26:12 - mmengine - INFO - Epoch(train) [4][5800/8498] lr: 4.0000e-04 eta: 1 day, 2:21:45 time: 1.0928 data_time: 0.1203 memory: 36546 loss_ce: 0.0740 loss: 0.0740 2022/09/16 03:28:19 - mmengine - INFO - Epoch(train) [4][5900/8498] lr: 4.0000e-04 eta: 1 day, 2:19:14 time: 0.9820 data_time: 0.0502 memory: 36546 loss_ce: 0.0691 loss: 0.0691 2022/09/16 03:30:26 - mmengine - INFO - Epoch(train) [4][6000/8498] lr: 4.0000e-04 eta: 1 day, 2:16:45 time: 1.0197 data_time: 0.0538 memory: 36546 loss_ce: 0.0759 loss: 0.0759 2022/09/16 03:32:39 - mmengine - INFO - Epoch(train) [4][6100/8498] lr: 4.0000e-04 eta: 1 day, 2:14:28 time: 1.5253 data_time: 0.2680 memory: 36546 loss_ce: 0.0751 loss: 0.0751 2022/09/16 03:34:48 - mmengine - INFO - Epoch(train) [4][6200/8498] lr: 4.0000e-04 eta: 1 day, 2:12:01 time: 1.7376 data_time: 0.3877 memory: 36546 loss_ce: 0.0866 loss: 0.0866 2022/09/16 03:36:54 - mmengine - INFO - Epoch(train) [4][6300/8498] lr: 4.0000e-04 eta: 1 day, 2:09:30 time: 1.2331 data_time: 0.1096 memory: 36546 loss_ce: 0.0693 loss: 0.0693 2022/09/16 03:39:01 - mmengine - INFO - Epoch(train) [4][6400/8498] lr: 4.0000e-04 eta: 1 day, 2:06:59 time: 1.0853 data_time: 0.1409 memory: 36546 loss_ce: 0.0669 loss: 0.0669 2022/09/16 03:41:07 - mmengine - INFO - Epoch(train) [4][6500/8498] lr: 4.0000e-04 eta: 1 day, 2:04:28 time: 0.9962 data_time: 0.0370 memory: 36546 loss_ce: 0.0786 loss: 0.0786 2022/09/16 03:41:17 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 03:43:17 - mmengine - INFO - Epoch(train) [4][6600/8498] lr: 4.0000e-04 eta: 1 day, 2:02:04 time: 1.0477 data_time: 0.0345 memory: 36546 loss_ce: 0.0834 loss: 0.0834 2022/09/16 03:45:28 - mmengine - INFO - Epoch(train) [4][6700/8498] lr: 4.0000e-04 eta: 1 day, 1:59:44 time: 1.5353 data_time: 0.2676 memory: 36546 loss_ce: 0.0806 loss: 0.0806 2022/09/16 03:47:38 - mmengine - INFO - Epoch(train) [4][6800/8498] lr: 4.0000e-04 eta: 1 day, 1:57:20 time: 1.7125 data_time: 0.4054 memory: 36546 loss_ce: 0.0710 loss: 0.0710 2022/09/16 03:49:44 - mmengine - INFO - Epoch(train) [4][6900/8498] lr: 4.0000e-04 eta: 1 day, 1:54:48 time: 1.1970 data_time: 0.1223 memory: 36546 loss_ce: 0.0812 loss: 0.0812 2022/09/16 03:51:49 - mmengine - INFO - Epoch(train) [4][7000/8498] lr: 4.0000e-04 eta: 1 day, 1:52:15 time: 1.0532 data_time: 0.1255 memory: 36546 loss_ce: 0.0740 loss: 0.0740 2022/09/16 03:53:58 - mmengine - INFO - Epoch(train) [4][7100/8498] lr: 4.0000e-04 eta: 1 day, 1:49:50 time: 0.9872 data_time: 0.0466 memory: 36546 loss_ce: 0.0735 loss: 0.0735 2022/09/16 03:56:04 - mmengine - INFO - Epoch(train) [4][7200/8498] lr: 4.0000e-04 eta: 1 day, 1:47:20 time: 1.0717 data_time: 0.0378 memory: 36546 loss_ce: 0.0763 loss: 0.0763 2022/09/16 03:58:17 - mmengine - INFO - Epoch(train) [4][7300/8498] lr: 4.0000e-04 eta: 1 day, 1:45:03 time: 1.5973 data_time: 0.3030 memory: 36546 loss_ce: 0.0707 loss: 0.0707 2022/09/16 04:00:25 - mmengine - INFO - Epoch(train) [4][7400/8498] lr: 4.0000e-04 eta: 1 day, 1:42:36 time: 1.6437 data_time: 0.3843 memory: 36546 loss_ce: 0.0752 loss: 0.0752 2022/09/16 04:02:31 - mmengine - INFO - Epoch(train) [4][7500/8498] lr: 4.0000e-04 eta: 1 day, 1:40:06 time: 1.1857 data_time: 0.0969 memory: 36546 loss_ce: 0.0828 loss: 0.0828 2022/09/16 04:02:37 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 04:04:38 - mmengine - INFO - Epoch(train) [4][7600/8498] lr: 4.0000e-04 eta: 1 day, 1:37:38 time: 1.1031 data_time: 0.1480 memory: 36546 loss_ce: 0.0706 loss: 0.0706 2022/09/16 04:06:44 - mmengine - INFO - Epoch(train) [4][7700/8498] lr: 4.0000e-04 eta: 1 day, 1:35:07 time: 0.9819 data_time: 0.0355 memory: 36546 loss_ce: 0.0793 loss: 0.0793 2022/09/16 04:08:51 - mmengine - INFO - Epoch(train) [4][7800/8498] lr: 4.0000e-04 eta: 1 day, 1:32:40 time: 1.0808 data_time: 0.0350 memory: 36546 loss_ce: 0.0811 loss: 0.0811 2022/09/16 04:11:02 - mmengine - INFO - Epoch(train) [4][7900/8498] lr: 4.0000e-04 eta: 1 day, 1:30:20 time: 1.5300 data_time: 0.2786 memory: 36546 loss_ce: 0.0749 loss: 0.0749 2022/09/16 04:13:08 - mmengine - INFO - Epoch(train) [4][8000/8498] lr: 4.0000e-04 eta: 1 day, 1:27:49 time: 1.6312 data_time: 0.3624 memory: 36546 loss_ce: 0.0772 loss: 0.0772 2022/09/16 04:15:16 - mmengine - INFO - Epoch(train) [4][8100/8498] lr: 4.0000e-04 eta: 1 day, 1:25:23 time: 1.2492 data_time: 0.1152 memory: 36546 loss_ce: 0.0694 loss: 0.0694 2022/09/16 04:17:22 - mmengine - INFO - Epoch(train) [4][8200/8498] lr: 4.0000e-04 eta: 1 day, 1:22:54 time: 1.0695 data_time: 0.1227 memory: 36546 loss_ce: 0.0695 loss: 0.0695 2022/09/16 04:19:29 - mmengine - INFO - Epoch(train) [4][8300/8498] lr: 4.0000e-04 eta: 1 day, 1:20:27 time: 0.9959 data_time: 0.0624 memory: 36546 loss_ce: 0.0705 loss: 0.0705 2022/09/16 04:21:37 - mmengine - INFO - Epoch(train) [4][8400/8498] lr: 4.0000e-04 eta: 1 day, 1:18:00 time: 1.0363 data_time: 0.0374 memory: 36546 loss_ce: 0.0731 loss: 0.0731 2022/09/16 04:23:36 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 04:23:36 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/09/16 04:24:15 - mmengine - INFO - Epoch(val) [4][100/1918] eta: 0:04:46 time: 0.1574 data_time: 0.0019 memory: 36546 2022/09/16 04:24:29 - mmengine - INFO - Epoch(val) [4][200/1918] eta: 0:04:35 time: 0.1604 data_time: 0.0009 memory: 1150 2022/09/16 04:24:43 - mmengine - INFO - Epoch(val) [4][300/1918] eta: 0:03:41 time: 0.1371 data_time: 0.0009 memory: 1150 2022/09/16 04:24:57 - mmengine - INFO - Epoch(val) [4][400/1918] eta: 0:03:26 time: 0.1361 data_time: 0.0008 memory: 1150 2022/09/16 04:25:11 - mmengine - INFO - Epoch(val) [4][500/1918] eta: 0:03:18 time: 0.1397 data_time: 0.0008 memory: 1150 2022/09/16 04:25:25 - mmengine - INFO - Epoch(val) [4][600/1918] eta: 0:03:24 time: 0.1551 data_time: 0.0009 memory: 1150 2022/09/16 04:25:39 - mmengine - INFO - Epoch(val) [4][700/1918] eta: 0:02:45 time: 0.1360 data_time: 0.0008 memory: 1150 2022/09/16 04:25:53 - mmengine - INFO - Epoch(val) [4][800/1918] eta: 0:02:33 time: 0.1375 data_time: 0.0008 memory: 1150 2022/09/16 04:26:07 - mmengine - INFO - Epoch(val) [4][900/1918] eta: 0:02:19 time: 0.1373 data_time: 0.0008 memory: 1150 2022/09/16 04:26:21 - mmengine - INFO - Epoch(val) [4][1000/1918] eta: 0:02:05 time: 0.1368 data_time: 0.0008 memory: 1150 2022/09/16 04:26:35 - mmengine - INFO - Epoch(val) [4][1100/1918] eta: 0:02:10 time: 0.1594 data_time: 0.0009 memory: 1150 2022/09/16 04:26:49 - mmengine - INFO - Epoch(val) [4][1200/1918] eta: 0:01:41 time: 0.1417 data_time: 0.0009 memory: 1150 2022/09/16 04:27:03 - mmengine - INFO - Epoch(val) [4][1300/1918] eta: 0:01:24 time: 0.1360 data_time: 0.0008 memory: 1150 2022/09/16 04:27:17 - mmengine - INFO - Epoch(val) [4][1400/1918] eta: 0:01:11 time: 0.1372 data_time: 0.0009 memory: 1150 2022/09/16 04:27:31 - mmengine - INFO - Epoch(val) [4][1500/1918] eta: 0:00:56 time: 0.1348 data_time: 0.0008 memory: 1150 2022/09/16 04:27:45 - mmengine - INFO - Epoch(val) [4][1600/1918] eta: 0:00:48 time: 0.1523 data_time: 0.0009 memory: 1150 2022/09/16 04:27:59 - mmengine - INFO - Epoch(val) [4][1700/1918] eta: 0:00:29 time: 0.1339 data_time: 0.0008 memory: 1150 2022/09/16 04:28:13 - mmengine - INFO - Epoch(val) [4][1800/1918] eta: 0:00:15 time: 0.1337 data_time: 0.0008 memory: 1150 2022/09/16 04:28:27 - mmengine - INFO - Epoch(val) [4][1900/1918] eta: 0:00:02 time: 0.1332 data_time: 0.0009 memory: 1150 2022/09/16 04:28:30 - mmengine - INFO - Epoch(val) [4][1918/1918] CUTE80/recog/word_acc_ignore_case_symbol: 0.8785 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9387 SVT/recog/word_acc_ignore_case_symbol: 0.8794 SVTP/recog/word_acc_ignore_case_symbol: 0.7721 IC13/recog/word_acc_ignore_case_symbol: 0.9369 IC15/recog/word_acc_ignore_case_symbol: 0.7347 2022/09/16 04:28:53 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 04:30:58 - mmengine - INFO - Epoch(train) [5][100/8498] lr: 4.0000e-04 eta: 1 day, 1:13:27 time: 1.6041 data_time: 0.3120 memory: 36546 loss_ce: 0.0633 loss: 0.0633 2022/09/16 04:33:15 - mmengine - INFO - Epoch(train) [5][200/8498] lr: 4.0000e-04 eta: 1 day, 1:11:18 time: 1.7019 data_time: 0.2761 memory: 36546 loss_ce: 0.0711 loss: 0.0711 2022/09/16 04:35:29 - mmengine - INFO - Epoch(train) [5][300/8498] lr: 4.0000e-04 eta: 1 day, 1:09:06 time: 1.4130 data_time: 0.0068 memory: 36546 loss_ce: 0.0728 loss: 0.0728 2022/09/16 04:37:47 - mmengine - INFO - Epoch(train) [5][400/8498] lr: 4.0000e-04 eta: 1 day, 1:06:59 time: 1.1674 data_time: 0.0067 memory: 36546 loss_ce: 0.0800 loss: 0.0800 2022/09/16 04:40:03 - mmengine - INFO - Epoch(train) [5][500/8498] lr: 4.0000e-04 eta: 1 day, 1:04:51 time: 1.1249 data_time: 0.0901 memory: 36546 loss_ce: 0.0621 loss: 0.0621 2022/09/16 04:42:19 - mmengine - INFO - Epoch(train) [5][600/8498] lr: 4.0000e-04 eta: 1 day, 1:02:41 time: 1.1338 data_time: 0.1328 memory: 36546 loss_ce: 0.0681 loss: 0.0681 2022/09/16 04:44:42 - mmengine - INFO - Epoch(train) [5][700/8498] lr: 4.0000e-04 eta: 1 day, 1:00:44 time: 1.5081 data_time: 0.3617 memory: 36546 loss_ce: 0.0663 loss: 0.0663 2022/09/16 04:46:59 - mmengine - INFO - Epoch(train) [5][800/8498] lr: 4.0000e-04 eta: 1 day, 0:58:38 time: 1.7869 data_time: 0.3337 memory: 36546 loss_ce: 0.0693 loss: 0.0693 2022/09/16 04:49:15 - mmengine - INFO - Epoch(train) [5][900/8498] lr: 4.0000e-04 eta: 1 day, 0:56:27 time: 1.4402 data_time: 0.0068 memory: 36546 loss_ce: 0.0716 loss: 0.0716 2022/09/16 04:51:33 - mmengine - INFO - Epoch(train) [5][1000/8498] lr: 4.0000e-04 eta: 1 day, 0:54:21 time: 1.2048 data_time: 0.0080 memory: 36546 loss_ce: 0.0712 loss: 0.0712 2022/09/16 04:51:42 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 04:53:54 - mmengine - INFO - Epoch(train) [5][1100/8498] lr: 4.0000e-04 eta: 1 day, 0:52:22 time: 1.1736 data_time: 0.1451 memory: 36546 loss_ce: 0.0636 loss: 0.0636 2022/09/16 04:56:10 - mmengine - INFO - Epoch(train) [5][1200/8498] lr: 4.0000e-04 eta: 1 day, 0:50:12 time: 1.1142 data_time: 0.1265 memory: 36546 loss_ce: 0.0654 loss: 0.0654 2022/09/16 04:58:32 - mmengine - INFO - Epoch(train) [5][1300/8498] lr: 4.0000e-04 eta: 1 day, 0:48:14 time: 1.6139 data_time: 0.3818 memory: 36546 loss_ce: 0.0695 loss: 0.0695 2022/09/16 05:00:52 - mmengine - INFO - Epoch(train) [5][1400/8498] lr: 4.0000e-04 eta: 1 day, 0:46:10 time: 1.7591 data_time: 0.2951 memory: 36546 loss_ce: 0.0690 loss: 0.0690 2022/09/16 05:03:09 - mmengine - INFO - Epoch(train) [5][1500/8498] lr: 4.0000e-04 eta: 1 day, 0:44:02 time: 1.4807 data_time: 0.0068 memory: 36546 loss_ce: 0.0749 loss: 0.0749 2022/09/16 05:05:27 - mmengine - INFO - Epoch(train) [5][1600/8498] lr: 4.0000e-04 eta: 1 day, 0:41:55 time: 1.2055 data_time: 0.0071 memory: 36546 loss_ce: 0.0692 loss: 0.0692 2022/09/16 05:07:45 - mmengine - INFO - Epoch(train) [5][1700/8498] lr: 4.0000e-04 eta: 1 day, 0:39:49 time: 1.0994 data_time: 0.1509 memory: 36546 loss_ce: 0.0599 loss: 0.0599 2022/09/16 05:10:00 - mmengine - INFO - Epoch(train) [5][1800/8498] lr: 4.0000e-04 eta: 1 day, 0:37:38 time: 1.1323 data_time: 0.1424 memory: 36546 loss_ce: 0.0682 loss: 0.0682 2022/09/16 05:12:24 - mmengine - INFO - Epoch(train) [5][1900/8498] lr: 4.0000e-04 eta: 1 day, 0:35:41 time: 1.5044 data_time: 0.3599 memory: 36546 loss_ce: 0.0780 loss: 0.0780 2022/09/16 05:14:42 - mmengine - INFO - Epoch(train) [5][2000/8498] lr: 4.0000e-04 eta: 1 day, 0:33:36 time: 1.7013 data_time: 0.2977 memory: 36546 loss_ce: 0.0690 loss: 0.0690 2022/09/16 05:14:51 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 05:16:59 - mmengine - INFO - Epoch(train) [5][2100/8498] lr: 4.0000e-04 eta: 1 day, 0:31:28 time: 1.4562 data_time: 0.0068 memory: 36546 loss_ce: 0.0817 loss: 0.0817 2022/09/16 05:19:17 - mmengine - INFO - Epoch(train) [5][2200/8498] lr: 4.0000e-04 eta: 1 day, 0:29:20 time: 1.1685 data_time: 0.0067 memory: 36546 loss_ce: 0.0714 loss: 0.0714 2022/09/16 05:21:36 - mmengine - INFO - Epoch(train) [5][2300/8498] lr: 4.0000e-04 eta: 1 day, 0:27:16 time: 1.1209 data_time: 0.1198 memory: 36546 loss_ce: 0.0737 loss: 0.0737 2022/09/16 05:23:50 - mmengine - INFO - Epoch(train) [5][2400/8498] lr: 4.0000e-04 eta: 1 day, 0:25:01 time: 1.0687 data_time: 0.1208 memory: 36546 loss_ce: 0.0659 loss: 0.0659 2022/09/16 05:26:14 - mmengine - INFO - Epoch(train) [5][2500/8498] lr: 4.0000e-04 eta: 1 day, 0:23:05 time: 1.5806 data_time: 0.3499 memory: 36546 loss_ce: 0.0687 loss: 0.0687 2022/09/16 05:28:33 - mmengine - INFO - Epoch(train) [5][2600/8498] lr: 4.0000e-04 eta: 1 day, 0:20:59 time: 1.7860 data_time: 0.3456 memory: 36546 loss_ce: 0.0752 loss: 0.0752 2022/09/16 05:30:50 - mmengine - INFO - Epoch(train) [5][2700/8498] lr: 4.0000e-04 eta: 1 day, 0:18:51 time: 1.4805 data_time: 0.0073 memory: 36546 loss_ce: 0.0691 loss: 0.0691 2022/09/16 05:33:07 - mmengine - INFO - Epoch(train) [5][2800/8498] lr: 4.0000e-04 eta: 1 day, 0:16:42 time: 1.1385 data_time: 0.0065 memory: 36546 loss_ce: 0.0618 loss: 0.0618 2022/09/16 05:35:25 - mmengine - INFO - Epoch(train) [5][2900/8498] lr: 4.0000e-04 eta: 1 day, 0:14:35 time: 1.0884 data_time: 0.0940 memory: 36546 loss_ce: 0.0678 loss: 0.0678 2022/09/16 05:37:43 - mmengine - INFO - Epoch(train) [5][3000/8498] lr: 4.0000e-04 eta: 1 day, 0:12:27 time: 1.1439 data_time: 0.1065 memory: 36546 loss_ce: 0.0707 loss: 0.0707 2022/09/16 05:37:59 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 05:40:05 - mmengine - INFO - Epoch(train) [5][3100/8498] lr: 4.0000e-04 eta: 1 day, 0:10:27 time: 1.5528 data_time: 0.3841 memory: 36546 loss_ce: 0.0614 loss: 0.0614 2022/09/16 05:42:22 - mmengine - INFO - Epoch(train) [5][3200/8498] lr: 4.0000e-04 eta: 1 day, 0:08:18 time: 1.7756 data_time: 0.3598 memory: 36546 loss_ce: 0.0654 loss: 0.0654 2022/09/16 05:44:39 - mmengine - INFO - Epoch(train) [5][3300/8498] lr: 4.0000e-04 eta: 1 day, 0:06:08 time: 1.4589 data_time: 0.0134 memory: 36546 loss_ce: 0.0693 loss: 0.0693 2022/09/16 05:46:55 - mmengine - INFO - Epoch(train) [5][3400/8498] lr: 4.0000e-04 eta: 1 day, 0:03:58 time: 1.2159 data_time: 0.0073 memory: 36546 loss_ce: 0.0660 loss: 0.0660 2022/09/16 05:49:14 - mmengine - INFO - Epoch(train) [5][3500/8498] lr: 4.0000e-04 eta: 1 day, 0:01:51 time: 1.1141 data_time: 0.1244 memory: 36546 loss_ce: 0.0709 loss: 0.0709 2022/09/16 05:51:30 - mmengine - INFO - Epoch(train) [5][3600/8498] lr: 4.0000e-04 eta: 23:59:41 time: 1.1233 data_time: 0.1444 memory: 36546 loss_ce: 0.0648 loss: 0.0648 2022/09/16 05:53:53 - mmengine - INFO - Epoch(train) [5][3700/8498] lr: 4.0000e-04 eta: 23:57:41 time: 1.5775 data_time: 0.3408 memory: 36546 loss_ce: 0.0673 loss: 0.0673 2022/09/16 05:56:10 - mmengine - INFO - Epoch(train) [5][3800/8498] lr: 4.0000e-04 eta: 23:55:33 time: 1.7423 data_time: 0.3497 memory: 36546 loss_ce: 0.0691 loss: 0.0691 2022/09/16 05:58:27 - mmengine - INFO - Epoch(train) [5][3900/8498] lr: 4.0000e-04 eta: 23:53:22 time: 1.4374 data_time: 0.0072 memory: 36546 loss_ce: 0.0581 loss: 0.0581 2022/09/16 06:00:44 - mmengine - INFO - Epoch(train) [5][4000/8498] lr: 4.0000e-04 eta: 23:51:13 time: 1.1515 data_time: 0.0145 memory: 36546 loss_ce: 0.0580 loss: 0.0580 2022/09/16 06:00:52 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 06:03:01 - mmengine - INFO - Epoch(train) [5][4100/8498] lr: 4.0000e-04 eta: 23:49:04 time: 1.0994 data_time: 0.1281 memory: 36546 loss_ce: 0.0686 loss: 0.0686 2022/09/16 06:05:17 - mmengine - INFO - Epoch(train) [5][4200/8498] lr: 4.0000e-04 eta: 23:46:53 time: 1.1111 data_time: 0.1313 memory: 36546 loss_ce: 0.0700 loss: 0.0700 2022/09/16 06:07:39 - mmengine - INFO - Epoch(train) [5][4300/8498] lr: 4.0000e-04 eta: 23:44:51 time: 1.5085 data_time: 0.3358 memory: 36546 loss_ce: 0.0672 loss: 0.0672 2022/09/16 06:09:59 - mmengine - INFO - Epoch(train) [5][4400/8498] lr: 4.0000e-04 eta: 23:42:46 time: 1.7652 data_time: 0.3774 memory: 36546 loss_ce: 0.0734 loss: 0.0734 2022/09/16 06:12:17 - mmengine - INFO - Epoch(train) [5][4500/8498] lr: 4.0000e-04 eta: 23:40:38 time: 1.4111 data_time: 0.0086 memory: 36546 loss_ce: 0.0704 loss: 0.0704 2022/09/16 06:14:34 - mmengine - INFO - Epoch(train) [5][4600/8498] lr: 4.0000e-04 eta: 23:38:28 time: 1.2006 data_time: 0.0075 memory: 36546 loss_ce: 0.0739 loss: 0.0739 2022/09/16 06:16:52 - mmengine - INFO - Epoch(train) [5][4700/8498] lr: 4.0000e-04 eta: 23:36:20 time: 1.1087 data_time: 0.1200 memory: 36546 loss_ce: 0.0680 loss: 0.0680 2022/09/16 06:19:09 - mmengine - INFO - Epoch(train) [5][4800/8498] lr: 4.0000e-04 eta: 23:34:10 time: 1.0840 data_time: 0.1286 memory: 36546 loss_ce: 0.0659 loss: 0.0659 2022/09/16 06:21:32 - mmengine - INFO - Epoch(train) [5][4900/8498] lr: 4.0000e-04 eta: 23:32:09 time: 1.5641 data_time: 0.3343 memory: 36546 loss_ce: 0.0739 loss: 0.0739 2022/09/16 06:23:50 - mmengine - INFO - Epoch(train) [5][5000/8498] lr: 4.0000e-04 eta: 23:30:02 time: 1.7566 data_time: 0.3431 memory: 36546 loss_ce: 0.0647 loss: 0.0647 2022/09/16 06:23:59 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 06:26:06 - mmengine - INFO - Epoch(train) [5][5100/8498] lr: 4.0000e-04 eta: 23:27:50 time: 1.4472 data_time: 0.0068 memory: 36546 loss_ce: 0.0754 loss: 0.0754 2022/09/16 06:28:23 - mmengine - INFO - Epoch(train) [5][5200/8498] lr: 4.0000e-04 eta: 23:25:40 time: 1.1779 data_time: 0.0070 memory: 36546 loss_ce: 0.0687 loss: 0.0687 2022/09/16 06:30:40 - mmengine - INFO - Epoch(train) [5][5300/8498] lr: 4.0000e-04 eta: 23:23:29 time: 1.0951 data_time: 0.1376 memory: 36546 loss_ce: 0.0707 loss: 0.0707 2022/09/16 06:32:55 - mmengine - INFO - Epoch(train) [5][5400/8498] lr: 4.0000e-04 eta: 23:21:17 time: 1.0910 data_time: 0.1218 memory: 36546 loss_ce: 0.0671 loss: 0.0671 2022/09/16 06:35:17 - mmengine - INFO - Epoch(train) [5][5500/8498] lr: 4.0000e-04 eta: 23:19:14 time: 1.4995 data_time: 0.2794 memory: 36546 loss_ce: 0.0666 loss: 0.0666 2022/09/16 06:37:33 - mmengine - INFO - Epoch(train) [5][5600/8498] lr: 4.0000e-04 eta: 23:17:02 time: 1.7331 data_time: 0.3584 memory: 36546 loss_ce: 0.0782 loss: 0.0782 2022/09/16 06:39:51 - mmengine - INFO - Epoch(train) [5][5700/8498] lr: 4.0000e-04 eta: 23:14:54 time: 1.4829 data_time: 0.0078 memory: 36546 loss_ce: 0.0575 loss: 0.0575 2022/09/16 06:42:08 - mmengine - INFO - Epoch(train) [5][5800/8498] lr: 4.0000e-04 eta: 23:12:44 time: 1.1825 data_time: 0.0070 memory: 36546 loss_ce: 0.0632 loss: 0.0632 2022/09/16 06:44:25 - mmengine - INFO - Epoch(train) [5][5900/8498] lr: 4.0000e-04 eta: 23:10:33 time: 1.0818 data_time: 0.1266 memory: 36546 loss_ce: 0.0737 loss: 0.0737 2022/09/16 06:46:41 - mmengine - INFO - Epoch(train) [5][6000/8498] lr: 4.0000e-04 eta: 23:08:22 time: 1.0981 data_time: 0.1317 memory: 36546 loss_ce: 0.0787 loss: 0.0787 2022/09/16 06:46:57 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 06:49:03 - mmengine - INFO - Epoch(train) [5][6100/8498] lr: 4.0000e-04 eta: 23:06:19 time: 1.5157 data_time: 0.3386 memory: 36546 loss_ce: 0.0743 loss: 0.0743 2022/09/16 06:51:20 - mmengine - INFO - Epoch(train) [5][6200/8498] lr: 4.0000e-04 eta: 23:04:09 time: 1.7611 data_time: 0.3221 memory: 36546 loss_ce: 0.0729 loss: 0.0729 2022/09/16 06:53:37 - mmengine - INFO - Epoch(train) [5][6300/8498] lr: 4.0000e-04 eta: 23:01:57 time: 1.4075 data_time: 0.0160 memory: 36546 loss_ce: 0.0607 loss: 0.0607 2022/09/16 06:55:52 - mmengine - INFO - Epoch(train) [5][6400/8498] lr: 4.0000e-04 eta: 22:59:44 time: 1.1843 data_time: 0.0077 memory: 36546 loss_ce: 0.0638 loss: 0.0638 2022/09/16 06:58:09 - mmengine - INFO - Epoch(train) [5][6500/8498] lr: 4.0000e-04 eta: 22:57:33 time: 1.1150 data_time: 0.1505 memory: 36546 loss_ce: 0.0667 loss: 0.0667 2022/09/16 07:00:25 - mmengine - INFO - Epoch(train) [5][6600/8498] lr: 4.0000e-04 eta: 22:55:22 time: 1.1266 data_time: 0.1273 memory: 36546 loss_ce: 0.0688 loss: 0.0688 2022/09/16 07:02:47 - mmengine - INFO - Epoch(train) [5][6700/8498] lr: 4.0000e-04 eta: 22:53:18 time: 1.5118 data_time: 0.3279 memory: 36546 loss_ce: 0.0766 loss: 0.0766 2022/09/16 07:05:06 - mmengine - INFO - Epoch(train) [5][6800/8498] lr: 4.0000e-04 eta: 22:51:10 time: 1.7708 data_time: 0.3494 memory: 36546 loss_ce: 0.0621 loss: 0.0621 2022/09/16 07:07:22 - mmengine - INFO - Epoch(train) [5][6900/8498] lr: 4.0000e-04 eta: 22:48:59 time: 1.5009 data_time: 0.0067 memory: 36546 loss_ce: 0.0705 loss: 0.0705 2022/09/16 07:09:38 - mmengine - INFO - Epoch(train) [5][7000/8498] lr: 4.0000e-04 eta: 22:46:45 time: 1.1515 data_time: 0.0061 memory: 36546 loss_ce: 0.0634 loss: 0.0634 2022/09/16 07:09:46 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 07:11:50 - mmengine - INFO - Epoch(train) [5][7100/8498] lr: 4.0000e-04 eta: 22:44:28 time: 1.1174 data_time: 0.1277 memory: 36546 loss_ce: 0.0715 loss: 0.0715 2022/09/16 07:14:04 - mmengine - INFO - Epoch(train) [5][7200/8498] lr: 4.0000e-04 eta: 22:42:13 time: 1.0835 data_time: 0.1257 memory: 36546 loss_ce: 0.0697 loss: 0.0697 2022/09/16 07:16:27 - mmengine - INFO - Epoch(train) [5][7300/8498] lr: 4.0000e-04 eta: 22:40:11 time: 1.5779 data_time: 0.3398 memory: 36546 loss_ce: 0.0673 loss: 0.0673 2022/09/16 07:18:44 - mmengine - INFO - Epoch(train) [5][7400/8498] lr: 4.0000e-04 eta: 22:38:00 time: 1.6770 data_time: 0.3460 memory: 36546 loss_ce: 0.0709 loss: 0.0709 2022/09/16 07:21:01 - mmengine - INFO - Epoch(train) [5][7500/8498] lr: 4.0000e-04 eta: 22:35:49 time: 1.4621 data_time: 0.0075 memory: 36546 loss_ce: 0.0602 loss: 0.0602 2022/09/16 07:23:17 - mmengine - INFO - Epoch(train) [5][7600/8498] lr: 4.0000e-04 eta: 22:33:36 time: 1.1147 data_time: 0.0063 memory: 36546 loss_ce: 0.0656 loss: 0.0656 2022/09/16 07:25:33 - mmengine - INFO - Epoch(train) [5][7700/8498] lr: 4.0000e-04 eta: 22:31:25 time: 1.1304 data_time: 0.1484 memory: 36546 loss_ce: 0.0639 loss: 0.0639 2022/09/16 07:27:51 - mmengine - INFO - Epoch(train) [5][7800/8498] lr: 4.0000e-04 eta: 22:29:15 time: 1.1039 data_time: 0.1172 memory: 36546 loss_ce: 0.0780 loss: 0.0780 2022/09/16 07:30:14 - mmengine - INFO - Epoch(train) [5][7900/8498] lr: 4.0000e-04 eta: 22:27:12 time: 1.5137 data_time: 0.3459 memory: 36546 loss_ce: 0.0635 loss: 0.0635 2022/09/16 07:32:32 - mmengine - INFO - Epoch(train) [5][8000/8498] lr: 4.0000e-04 eta: 22:25:03 time: 1.7595 data_time: 0.3626 memory: 36546 loss_ce: 0.0658 loss: 0.0658 2022/09/16 07:32:41 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 07:34:48 - mmengine - INFO - Epoch(train) [5][8100/8498] lr: 4.0000e-04 eta: 22:22:50 time: 1.4224 data_time: 0.0068 memory: 36546 loss_ce: 0.0617 loss: 0.0617 2022/09/16 07:37:04 - mmengine - INFO - Epoch(train) [5][8200/8498] lr: 4.0000e-04 eta: 22:20:37 time: 1.2015 data_time: 0.0070 memory: 36546 loss_ce: 0.0624 loss: 0.0624 2022/09/16 07:39:20 - mmengine - INFO - Epoch(train) [5][8300/8498] lr: 4.0000e-04 eta: 22:18:25 time: 1.0885 data_time: 0.1251 memory: 36546 loss_ce: 0.0587 loss: 0.0587 2022/09/16 07:41:37 - mmengine - INFO - Epoch(train) [5][8400/8498] lr: 4.0000e-04 eta: 22:16:14 time: 1.1005 data_time: 0.1451 memory: 36546 loss_ce: 0.0617 loss: 0.0617 2022/09/16 07:43:45 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 07:43:45 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/09/16 07:44:29 - mmengine - INFO - Epoch(val) [5][100/1918] eta: 0:04:08 time: 0.1365 data_time: 0.0008 memory: 36546 2022/09/16 07:44:43 - mmengine - INFO - Epoch(val) [5][200/1918] eta: 0:04:06 time: 0.1434 data_time: 0.0022 memory: 1150 2022/09/16 07:44:57 - mmengine - INFO - Epoch(val) [5][300/1918] eta: 0:03:32 time: 0.1314 data_time: 0.0008 memory: 1150 2022/09/16 07:45:11 - mmengine - INFO - Epoch(val) [5][400/1918] eta: 0:03:20 time: 0.1319 data_time: 0.0007 memory: 1150 2022/09/16 07:45:24 - mmengine - INFO - Epoch(val) [5][500/1918] eta: 0:03:02 time: 0.1285 data_time: 0.0006 memory: 1150 2022/09/16 07:45:37 - mmengine - INFO - Epoch(val) [5][600/1918] eta: 0:02:47 time: 0.1271 data_time: 0.0007 memory: 1150 2022/09/16 07:45:50 - mmengine - INFO - Epoch(val) [5][700/1918] eta: 0:02:38 time: 0.1301 data_time: 0.0019 memory: 1150 2022/09/16 07:46:03 - mmengine - INFO - Epoch(val) [5][800/1918] eta: 0:02:23 time: 0.1280 data_time: 0.0006 memory: 1150 2022/09/16 07:46:16 - mmengine - INFO - Epoch(val) [5][900/1918] eta: 0:02:18 time: 0.1361 data_time: 0.0008 memory: 1150 2022/09/16 07:46:30 - mmengine - INFO - Epoch(val) [5][1000/1918] eta: 0:02:07 time: 0.1391 data_time: 0.0008 memory: 1150 2022/09/16 07:46:44 - mmengine - INFO - Epoch(val) [5][1100/1918] eta: 0:01:52 time: 0.1380 data_time: 0.0008 memory: 1150 2022/09/16 07:46:58 - mmengine - INFO - Epoch(val) [5][1200/1918] eta: 0:01:35 time: 0.1325 data_time: 0.0008 memory: 1150 2022/09/16 07:47:12 - mmengine - INFO - Epoch(val) [5][1300/1918] eta: 0:01:36 time: 0.1564 data_time: 0.0008 memory: 1150 2022/09/16 07:47:26 - mmengine - INFO - Epoch(val) [5][1400/1918] eta: 0:01:13 time: 0.1426 data_time: 0.0008 memory: 1150 2022/09/16 07:47:40 - mmengine - INFO - Epoch(val) [5][1500/1918] eta: 0:00:57 time: 0.1386 data_time: 0.0010 memory: 1150 2022/09/16 07:47:55 - mmengine - INFO - Epoch(val) [5][1600/1918] eta: 0:00:42 time: 0.1340 data_time: 0.0008 memory: 1150 2022/09/16 07:48:08 - mmengine - INFO - Epoch(val) [5][1700/1918] eta: 0:00:29 time: 0.1351 data_time: 0.0008 memory: 1150 2022/09/16 07:48:22 - mmengine - INFO - Epoch(val) [5][1800/1918] eta: 0:00:15 time: 0.1313 data_time: 0.0008 memory: 1150 2022/09/16 07:48:36 - mmengine - INFO - Epoch(val) [5][1900/1918] eta: 0:00:02 time: 0.1364 data_time: 0.0011 memory: 1150 2022/09/16 07:48:39 - mmengine - INFO - Epoch(val) [5][1918/1918] CUTE80/recog/word_acc_ignore_case_symbol: 0.8750 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9453 SVT/recog/word_acc_ignore_case_symbol: 0.8686 SVTP/recog/word_acc_ignore_case_symbol: 0.7721 IC13/recog/word_acc_ignore_case_symbol: 0.9271 IC15/recog/word_acc_ignore_case_symbol: 0.7260 2022/09/16 07:51:05 - mmengine - INFO - Epoch(train) [6][100/8498] lr: 4.0000e-04 eta: 22:11:50 time: 1.6263 data_time: 0.3799 memory: 36546 loss_ce: 0.0645 loss: 0.0645 2022/09/16 07:53:22 - mmengine - INFO - Epoch(train) [6][200/8498] lr: 4.0000e-04 eta: 22:09:39 time: 1.7740 data_time: 0.3671 memory: 36546 loss_ce: 0.0630 loss: 0.0630 2022/09/16 07:55:39 - mmengine - INFO - Epoch(train) [6][300/8498] lr: 4.0000e-04 eta: 22:07:27 time: 1.3769 data_time: 0.1531 memory: 36546 loss_ce: 0.0764 loss: 0.0764 2022/09/16 07:57:55 - mmengine - INFO - Epoch(train) [6][400/8498] lr: 4.0000e-04 eta: 22:05:14 time: 1.2263 data_time: 0.0064 memory: 36546 loss_ce: 0.0633 loss: 0.0633 2022/09/16 08:00:12 - mmengine - INFO - Epoch(train) [6][500/8498] lr: 4.0000e-04 eta: 22:03:03 time: 1.0854 data_time: 0.1073 memory: 36546 loss_ce: 0.0709 loss: 0.0709 2022/09/16 08:00:29 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 08:02:30 - mmengine - INFO - Epoch(train) [6][600/8498] lr: 4.0000e-04 eta: 22:00:54 time: 1.1386 data_time: 0.1299 memory: 36546 loss_ce: 0.0527 loss: 0.0527 2022/09/16 08:04:54 - mmengine - INFO - Epoch(train) [6][700/8498] lr: 4.0000e-04 eta: 21:58:51 time: 1.6820 data_time: 0.4063 memory: 36546 loss_ce: 0.0588 loss: 0.0588 2022/09/16 08:07:13 - mmengine - INFO - Epoch(train) [6][800/8498] lr: 4.0000e-04 eta: 21:56:42 time: 1.8648 data_time: 0.3167 memory: 36546 loss_ce: 0.0593 loss: 0.0593 2022/09/16 08:09:32 - mmengine - INFO - Epoch(train) [6][900/8498] lr: 4.0000e-04 eta: 21:54:34 time: 1.3963 data_time: 0.1689 memory: 36546 loss_ce: 0.0604 loss: 0.0604 2022/09/16 08:11:48 - mmengine - INFO - Epoch(train) [6][1000/8498] lr: 4.0000e-04 eta: 21:52:22 time: 1.2154 data_time: 0.0069 memory: 36546 loss_ce: 0.0661 loss: 0.0661 2022/09/16 08:14:07 - mmengine - INFO - Epoch(train) [6][1100/8498] lr: 4.0000e-04 eta: 21:50:13 time: 1.0395 data_time: 0.1273 memory: 36546 loss_ce: 0.0583 loss: 0.0583 2022/09/16 08:16:25 - mmengine - INFO - Epoch(train) [6][1200/8498] lr: 4.0000e-04 eta: 21:48:02 time: 1.1319 data_time: 0.1388 memory: 36546 loss_ce: 0.0676 loss: 0.0676 2022/09/16 08:18:48 - mmengine - INFO - Epoch(train) [6][1300/8498] lr: 4.0000e-04 eta: 21:45:59 time: 1.6662 data_time: 0.3948 memory: 36546 loss_ce: 0.0639 loss: 0.0639 2022/09/16 08:21:05 - mmengine - INFO - Epoch(train) [6][1400/8498] lr: 4.0000e-04 eta: 21:43:48 time: 1.6497 data_time: 0.2865 memory: 36546 loss_ce: 0.0586 loss: 0.0586 2022/09/16 08:23:20 - mmengine - INFO - Epoch(train) [6][1500/8498] lr: 4.0000e-04 eta: 21:41:33 time: 1.4199 data_time: 0.1528 memory: 36546 loss_ce: 0.0644 loss: 0.0644 2022/09/16 08:23:31 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 08:25:38 - mmengine - INFO - Epoch(train) [6][1600/8498] lr: 4.0000e-04 eta: 21:39:22 time: 1.2066 data_time: 0.0067 memory: 36546 loss_ce: 0.0687 loss: 0.0687 2022/09/16 08:27:57 - mmengine - INFO - Epoch(train) [6][1700/8498] lr: 4.0000e-04 eta: 21:37:13 time: 1.0735 data_time: 0.1423 memory: 36546 loss_ce: 0.0605 loss: 0.0605 2022/09/16 08:30:13 - mmengine - INFO - Epoch(train) [6][1800/8498] lr: 4.0000e-04 eta: 21:35:01 time: 1.0918 data_time: 0.1267 memory: 36546 loss_ce: 0.0718 loss: 0.0718 2022/09/16 08:32:37 - mmengine - INFO - Epoch(train) [6][1900/8498] lr: 4.0000e-04 eta: 21:32:58 time: 1.7089 data_time: 0.4221 memory: 36546 loss_ce: 0.0591 loss: 0.0591 2022/09/16 08:34:54 - mmengine - INFO - Epoch(train) [6][2000/8498] lr: 4.0000e-04 eta: 21:30:47 time: 1.8048 data_time: 0.3276 memory: 36546 loss_ce: 0.0544 loss: 0.0544 2022/09/16 08:37:12 - mmengine - INFO - Epoch(train) [6][2100/8498] lr: 4.0000e-04 eta: 21:28:36 time: 1.4601 data_time: 0.1557 memory: 36546 loss_ce: 0.0597 loss: 0.0597 2022/09/16 08:39:29 - mmengine - INFO - Epoch(train) [6][2200/8498] lr: 4.0000e-04 eta: 21:26:24 time: 1.2282 data_time: 0.0067 memory: 36546 loss_ce: 0.0689 loss: 0.0689 2022/09/16 08:41:48 - mmengine - INFO - Epoch(train) [6][2300/8498] lr: 4.0000e-04 eta: 21:24:15 time: 1.0897 data_time: 0.0965 memory: 36546 loss_ce: 0.0618 loss: 0.0618 2022/09/16 08:44:05 - mmengine - INFO - Epoch(train) [6][2400/8498] lr: 4.0000e-04 eta: 21:22:02 time: 1.0788 data_time: 0.1222 memory: 36546 loss_ce: 0.0735 loss: 0.0735 2022/09/16 08:46:27 - mmengine - INFO - Epoch(train) [6][2500/8498] lr: 4.0000e-04 eta: 21:19:57 time: 1.5958 data_time: 0.3874 memory: 36546 loss_ce: 0.0619 loss: 0.0619 2022/09/16 08:46:40 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 08:48:45 - mmengine - INFO - Epoch(train) [6][2600/8498] lr: 4.0000e-04 eta: 21:17:46 time: 1.7192 data_time: 0.3640 memory: 36546 loss_ce: 0.0634 loss: 0.0634 2022/09/16 08:51:01 - mmengine - INFO - Epoch(train) [6][2700/8498] lr: 4.0000e-04 eta: 21:15:33 time: 1.3286 data_time: 0.1274 memory: 36546 loss_ce: 0.0631 loss: 0.0631 2022/09/16 08:53:18 - mmengine - INFO - Epoch(train) [6][2800/8498] lr: 4.0000e-04 eta: 21:13:21 time: 1.2468 data_time: 0.0073 memory: 36546 loss_ce: 0.0618 loss: 0.0618 2022/09/16 08:55:37 - mmengine - INFO - Epoch(train) [6][2900/8498] lr: 4.0000e-04 eta: 21:11:12 time: 1.0354 data_time: 0.1253 memory: 36546 loss_ce: 0.0664 loss: 0.0664 2022/09/16 08:57:48 - mmengine - INFO - Epoch(train) [6][3000/8498] lr: 4.0000e-04 eta: 21:08:52 time: 1.1079 data_time: 0.1317 memory: 36546 loss_ce: 0.0609 loss: 0.0609 2022/09/16 09:00:09 - mmengine - INFO - Epoch(train) [6][3100/8498] lr: 4.0000e-04 eta: 21:06:45 time: 1.5748 data_time: 0.3656 memory: 36546 loss_ce: 0.0599 loss: 0.0599 2022/09/16 09:02:29 - mmengine - INFO - Epoch(train) [6][3200/8498] lr: 4.0000e-04 eta: 21:04:36 time: 1.7533 data_time: 0.3668 memory: 36546 loss_ce: 0.0603 loss: 0.0603 2022/09/16 09:04:44 - mmengine - INFO - Epoch(train) [6][3300/8498] lr: 4.0000e-04 eta: 21:02:22 time: 1.3771 data_time: 0.1486 memory: 36546 loss_ce: 0.0646 loss: 0.0646 2022/09/16 09:06:58 - mmengine - INFO - Epoch(train) [6][3400/8498] lr: 4.0000e-04 eta: 21:00:06 time: 1.2332 data_time: 0.0062 memory: 36546 loss_ce: 0.0591 loss: 0.0591 2022/09/16 09:09:17 - mmengine - INFO - Epoch(train) [6][3500/8498] lr: 4.0000e-04 eta: 20:57:56 time: 1.0370 data_time: 0.1477 memory: 36546 loss_ce: 0.0658 loss: 0.0658 2022/09/16 09:09:32 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 09:11:33 - mmengine - INFO - Epoch(train) [6][3600/8498] lr: 4.0000e-04 eta: 20:55:43 time: 1.1037 data_time: 0.1223 memory: 36546 loss_ce: 0.0615 loss: 0.0615 2022/09/16 09:13:53 - mmengine - INFO - Epoch(train) [6][3700/8498] lr: 4.0000e-04 eta: 20:53:34 time: 1.6214 data_time: 0.4163 memory: 36546 loss_ce: 0.0615 loss: 0.0615 2022/09/16 09:16:12 - mmengine - INFO - Epoch(train) [6][3800/8498] lr: 4.0000e-04 eta: 20:51:25 time: 1.7775 data_time: 0.3533 memory: 36546 loss_ce: 0.0618 loss: 0.0618 2022/09/16 09:18:30 - mmengine - INFO - Epoch(train) [6][3900/8498] lr: 4.0000e-04 eta: 20:49:13 time: 1.4331 data_time: 0.1418 memory: 36546 loss_ce: 0.0562 loss: 0.0562 2022/09/16 09:20:45 - mmengine - INFO - Epoch(train) [6][4000/8498] lr: 4.0000e-04 eta: 20:46:59 time: 1.3275 data_time: 0.0070 memory: 36546 loss_ce: 0.0676 loss: 0.0676 2022/09/16 09:23:02 - mmengine - INFO - Epoch(train) [6][4100/8498] lr: 4.0000e-04 eta: 20:44:47 time: 1.0783 data_time: 0.1145 memory: 36546 loss_ce: 0.0627 loss: 0.0627 2022/09/16 09:25:18 - mmengine - INFO - Epoch(train) [6][4200/8498] lr: 4.0000e-04 eta: 20:42:33 time: 1.0939 data_time: 0.1338 memory: 36546 loss_ce: 0.0640 loss: 0.0640 2022/09/16 09:27:40 - mmengine - INFO - Epoch(train) [6][4300/8498] lr: 4.0000e-04 eta: 20:40:27 time: 1.6601 data_time: 0.4173 memory: 36546 loss_ce: 0.0606 loss: 0.0606 2022/09/16 09:29:57 - mmengine - INFO - Epoch(train) [6][4400/8498] lr: 4.0000e-04 eta: 20:38:14 time: 1.7359 data_time: 0.3339 memory: 36546 loss_ce: 0.0612 loss: 0.0612 2022/09/16 09:32:11 - mmengine - INFO - Epoch(train) [6][4500/8498] lr: 4.0000e-04 eta: 20:35:59 time: 1.2988 data_time: 0.1266 memory: 36546 loss_ce: 0.0574 loss: 0.0574 2022/09/16 09:32:22 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 09:34:26 - mmengine - INFO - Epoch(train) [6][4600/8498] lr: 4.0000e-04 eta: 20:33:44 time: 1.2596 data_time: 0.0075 memory: 36546 loss_ce: 0.0611 loss: 0.0611 2022/09/16 09:36:41 - mmengine - INFO - Epoch(train) [6][4700/8498] lr: 4.0000e-04 eta: 20:31:29 time: 1.0120 data_time: 0.1253 memory: 36546 loss_ce: 0.0633 loss: 0.0633 2022/09/16 09:38:58 - mmengine - INFO - Epoch(train) [6][4800/8498] lr: 4.0000e-04 eta: 20:29:17 time: 1.1385 data_time: 0.1544 memory: 36546 loss_ce: 0.0588 loss: 0.0588 2022/09/16 09:41:19 - mmengine - INFO - Epoch(train) [6][4900/8498] lr: 4.0000e-04 eta: 20:27:09 time: 1.6192 data_time: 0.3664 memory: 36546 loss_ce: 0.0596 loss: 0.0596 2022/09/16 09:43:37 - mmengine - INFO - Epoch(train) [6][5000/8498] lr: 4.0000e-04 eta: 20:24:57 time: 1.7901 data_time: 0.3459 memory: 36546 loss_ce: 0.0650 loss: 0.0650 2022/09/16 09:45:55 - mmengine - INFO - Epoch(train) [6][5100/8498] lr: 4.0000e-04 eta: 20:22:45 time: 1.3668 data_time: 0.1576 memory: 36546 loss_ce: 0.0532 loss: 0.0532 2022/09/16 09:48:09 - mmengine - INFO - Epoch(train) [6][5200/8498] lr: 4.0000e-04 eta: 20:20:30 time: 1.2513 data_time: 0.0073 memory: 36546 loss_ce: 0.0628 loss: 0.0628 2022/09/16 09:50:27 - mmengine - INFO - Epoch(train) [6][5300/8498] lr: 4.0000e-04 eta: 20:18:18 time: 1.0800 data_time: 0.1324 memory: 36546 loss_ce: 0.0607 loss: 0.0607 2022/09/16 09:52:44 - mmengine - INFO - Epoch(train) [6][5400/8498] lr: 4.0000e-04 eta: 20:16:05 time: 1.1117 data_time: 0.1402 memory: 36546 loss_ce: 0.0597 loss: 0.0597 2022/09/16 09:55:06 - mmengine - INFO - Epoch(train) [6][5500/8498] lr: 4.0000e-04 eta: 20:13:58 time: 1.6338 data_time: 0.4398 memory: 36546 loss_ce: 0.0615 loss: 0.0615 2022/09/16 09:55:18 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 09:57:22 - mmengine - INFO - Epoch(train) [6][5600/8498] lr: 4.0000e-04 eta: 20:11:46 time: 1.7678 data_time: 0.3275 memory: 36546 loss_ce: 0.0588 loss: 0.0588 2022/09/16 09:59:38 - mmengine - INFO - Epoch(train) [6][5700/8498] lr: 4.0000e-04 eta: 20:09:31 time: 1.4001 data_time: 0.1551 memory: 36546 loss_ce: 0.0472 loss: 0.0472 2022/09/16 10:01:54 - mmengine - INFO - Epoch(train) [6][5800/8498] lr: 4.0000e-04 eta: 20:07:18 time: 1.1952 data_time: 0.0065 memory: 36546 loss_ce: 0.0638 loss: 0.0638 2022/09/16 10:04:12 - mmengine - INFO - Epoch(train) [6][5900/8498] lr: 4.0000e-04 eta: 20:05:06 time: 1.0366 data_time: 0.0998 memory: 36546 loss_ce: 0.0871 loss: 0.0871 2022/09/16 10:06:28 - mmengine - INFO - Epoch(train) [6][6000/8498] lr: 4.0000e-04 eta: 20:02:52 time: 1.1388 data_time: 0.1723 memory: 36546 loss_ce: 0.0711 loss: 0.0711 2022/09/16 10:08:45 - mmengine - INFO - Epoch(train) [6][6100/8498] lr: 4.0000e-04 eta: 20:00:40 time: 1.4825 data_time: 0.3361 memory: 36546 loss_ce: 0.0672 loss: 0.0672 2022/09/16 10:11:01 - mmengine - INFO - Epoch(train) [6][6200/8498] lr: 4.0000e-04 eta: 19:58:26 time: 1.7148 data_time: 0.3278 memory: 36546 loss_ce: 0.0668 loss: 0.0668 2022/09/16 10:13:19 - mmengine - INFO - Epoch(train) [6][6300/8498] lr: 4.0000e-04 eta: 19:56:14 time: 1.4511 data_time: 0.1773 memory: 36546 loss_ce: 0.0611 loss: 0.0611 2022/09/16 10:15:34 - mmengine - INFO - Epoch(train) [6][6400/8498] lr: 4.0000e-04 eta: 19:54:00 time: 1.1814 data_time: 0.0074 memory: 36546 loss_ce: 0.0702 loss: 0.0702 2022/09/16 10:17:52 - mmengine - INFO - Epoch(train) [6][6500/8498] lr: 4.0000e-04 eta: 19:51:48 time: 1.0443 data_time: 0.1453 memory: 36546 loss_ce: 0.0624 loss: 0.0624 2022/09/16 10:18:08 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 10:20:08 - mmengine - INFO - Epoch(train) [6][6600/8498] lr: 4.0000e-04 eta: 19:49:34 time: 1.1069 data_time: 0.1487 memory: 36546 loss_ce: 0.0654 loss: 0.0654 2022/09/16 10:22:29 - mmengine - INFO - Epoch(train) [6][6700/8498] lr: 4.0000e-04 eta: 19:47:25 time: 1.6250 data_time: 0.4153 memory: 36546 loss_ce: 0.0548 loss: 0.0548 2022/09/16 10:24:48 - mmengine - INFO - Epoch(train) [6][6800/8498] lr: 4.0000e-04 eta: 19:45:14 time: 1.7512 data_time: 0.3619 memory: 36546 loss_ce: 0.0630 loss: 0.0630 2022/09/16 10:27:04 - mmengine - INFO - Epoch(train) [6][6900/8498] lr: 4.0000e-04 eta: 19:43:00 time: 1.4131 data_time: 0.1528 memory: 36546 loss_ce: 0.0659 loss: 0.0659 2022/09/16 10:29:19 - mmengine - INFO - Epoch(train) [6][7000/8498] lr: 4.0000e-04 eta: 19:40:46 time: 1.2293 data_time: 0.0071 memory: 36546 loss_ce: 0.0645 loss: 0.0645 2022/09/16 10:31:37 - mmengine - INFO - Epoch(train) [6][7100/8498] lr: 4.0000e-04 eta: 19:38:33 time: 1.0439 data_time: 0.1524 memory: 36546 loss_ce: 0.0614 loss: 0.0614 2022/09/16 10:33:52 - mmengine - INFO - Epoch(train) [6][7200/8498] lr: 4.0000e-04 eta: 19:36:18 time: 1.0900 data_time: 0.1360 memory: 36546 loss_ce: 0.0552 loss: 0.0552 2022/09/16 10:36:13 - mmengine - INFO - Epoch(train) [6][7300/8498] lr: 4.0000e-04 eta: 19:34:10 time: 1.6088 data_time: 0.4160 memory: 36546 loss_ce: 0.0591 loss: 0.0591 2022/09/16 10:38:30 - mmengine - INFO - Epoch(train) [6][7400/8498] lr: 4.0000e-04 eta: 19:31:57 time: 1.7337 data_time: 0.3186 memory: 36546 loss_ce: 0.0612 loss: 0.0612 2022/09/16 10:40:46 - mmengine - INFO - Epoch(train) [6][7500/8498] lr: 4.0000e-04 eta: 19:29:43 time: 1.3901 data_time: 0.1632 memory: 36546 loss_ce: 0.0655 loss: 0.0655 2022/09/16 10:40:57 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 10:43:00 - mmengine - INFO - Epoch(train) [6][7600/8498] lr: 4.0000e-04 eta: 19:27:27 time: 1.2715 data_time: 0.0068 memory: 36546 loss_ce: 0.0549 loss: 0.0549 2022/09/16 10:45:13 - mmengine - INFO - Epoch(train) [6][7700/8498] lr: 4.0000e-04 eta: 19:25:10 time: 1.0295 data_time: 0.0808 memory: 36546 loss_ce: 0.0568 loss: 0.0568 2022/09/16 10:47:26 - mmengine - INFO - Epoch(train) [6][7800/8498] lr: 4.0000e-04 eta: 19:22:53 time: 1.1009 data_time: 0.1275 memory: 36546 loss_ce: 0.0557 loss: 0.0557 2022/09/16 10:49:45 - mmengine - INFO - Epoch(train) [6][7900/8498] lr: 4.0000e-04 eta: 19:20:42 time: 1.6126 data_time: 0.4251 memory: 36546 loss_ce: 0.0504 loss: 0.0504 2022/09/16 10:52:02 - mmengine - INFO - Epoch(train) [6][8000/8498] lr: 4.0000e-04 eta: 19:18:28 time: 1.7176 data_time: 0.3427 memory: 36546 loss_ce: 0.0602 loss: 0.0602 2022/09/16 10:54:17 - mmengine - INFO - Epoch(train) [6][8100/8498] lr: 4.0000e-04 eta: 19:16:14 time: 1.3700 data_time: 0.1411 memory: 36546 loss_ce: 0.0660 loss: 0.0660 2022/09/16 10:56:32 - mmengine - INFO - Epoch(train) [6][8200/8498] lr: 4.0000e-04 eta: 19:13:59 time: 1.2453 data_time: 0.0079 memory: 36546 loss_ce: 0.0639 loss: 0.0639 2022/09/16 10:58:47 - mmengine - INFO - Epoch(train) [6][8300/8498] lr: 4.0000e-04 eta: 19:11:44 time: 1.0089 data_time: 0.1207 memory: 36546 loss_ce: 0.0564 loss: 0.0564 2022/09/16 11:01:01 - mmengine - INFO - Epoch(train) [6][8400/8498] lr: 4.0000e-04 eta: 19:09:28 time: 1.0936 data_time: 0.1272 memory: 36546 loss_ce: 0.0559 loss: 0.0559 2022/09/16 11:03:08 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 11:03:08 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/09/16 11:03:50 - mmengine - INFO - Epoch(val) [6][100/1918] eta: 0:04:23 time: 0.1451 data_time: 0.0008 memory: 36546 2022/09/16 11:04:04 - mmengine - INFO - Epoch(val) [6][200/1918] eta: 0:03:56 time: 0.1374 data_time: 0.0010 memory: 1150 2022/09/16 11:04:18 - mmengine - INFO - Epoch(val) [6][300/1918] eta: 0:03:45 time: 0.1395 data_time: 0.0009 memory: 1150 2022/09/16 11:04:33 - mmengine - INFO - Epoch(val) [6][400/1918] eta: 0:03:54 time: 0.1543 data_time: 0.0008 memory: 1150 2022/09/16 11:04:46 - mmengine - INFO - Epoch(val) [6][500/1918] eta: 0:03:18 time: 0.1397 data_time: 0.0008 memory: 1150 2022/09/16 11:05:01 - mmengine - INFO - Epoch(val) [6][600/1918] eta: 0:03:04 time: 0.1399 data_time: 0.0009 memory: 1150 2022/09/16 11:05:14 - mmengine - INFO - Epoch(val) [6][700/1918] eta: 0:02:58 time: 0.1468 data_time: 0.0009 memory: 1150 2022/09/16 11:05:28 - mmengine - INFO - Epoch(val) [6][800/1918] eta: 0:02:27 time: 0.1317 data_time: 0.0008 memory: 1150 2022/09/16 11:05:42 - mmengine - INFO - Epoch(val) [6][900/1918] eta: 0:02:16 time: 0.1342 data_time: 0.0008 memory: 1150 2022/09/16 11:05:56 - mmengine - INFO - Epoch(val) [6][1000/1918] eta: 0:02:01 time: 0.1321 data_time: 0.0007 memory: 1150 2022/09/16 11:06:09 - mmengine - INFO - Epoch(val) [6][1100/1918] eta: 0:01:50 time: 0.1346 data_time: 0.0007 memory: 1150 2022/09/16 11:06:23 - mmengine - INFO - Epoch(val) [6][1200/1918] eta: 0:01:39 time: 0.1383 data_time: 0.0008 memory: 1150 2022/09/16 11:06:37 - mmengine - INFO - Epoch(val) [6][1300/1918] eta: 0:01:21 time: 0.1319 data_time: 0.0008 memory: 1150 2022/09/16 11:06:51 - mmengine - INFO - Epoch(val) [6][1400/1918] eta: 0:01:13 time: 0.1413 data_time: 0.0008 memory: 1150 2022/09/16 11:07:05 - mmengine - INFO - Epoch(val) [6][1500/1918] eta: 0:01:07 time: 0.1610 data_time: 0.0009 memory: 1150 2022/09/16 11:07:19 - mmengine - INFO - Epoch(val) [6][1600/1918] eta: 0:00:43 time: 0.1363 data_time: 0.0008 memory: 1150 2022/09/16 11:07:34 - mmengine - INFO - Epoch(val) [6][1700/1918] eta: 0:00:37 time: 0.1702 data_time: 0.0009 memory: 1150 2022/09/16 11:07:47 - mmengine - INFO - Epoch(val) [6][1800/1918] eta: 0:00:16 time: 0.1362 data_time: 0.0020 memory: 1150 2022/09/16 11:08:01 - mmengine - INFO - Epoch(val) [6][1900/1918] eta: 0:00:02 time: 0.1341 data_time: 0.0008 memory: 1150 2022/09/16 11:08:04 - mmengine - INFO - Epoch(val) [6][1918/1918] CUTE80/recog/word_acc_ignore_case_symbol: 0.8681 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9440 SVT/recog/word_acc_ignore_case_symbol: 0.8841 SVTP/recog/word_acc_ignore_case_symbol: 0.8000 IC13/recog/word_acc_ignore_case_symbol: 0.9419 IC15/recog/word_acc_ignore_case_symbol: 0.7419 2022/09/16 11:08:36 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 11:10:37 - mmengine - INFO - Epoch(train) [7][100/8498] lr: 4.0000e-04 eta: 19:05:06 time: 1.6786 data_time: 0.4498 memory: 36546 loss_ce: 0.0551 loss: 0.0551 2022/09/16 11:12:55 - mmengine - INFO - Epoch(train) [7][200/8498] lr: 4.0000e-04 eta: 19:02:54 time: 1.9664 data_time: 0.4497 memory: 36546 loss_ce: 0.0544 loss: 0.0544 2022/09/16 11:15:10 - mmengine - INFO - Epoch(train) [7][300/8498] lr: 4.0000e-04 eta: 19:00:39 time: 1.6119 data_time: 0.1568 memory: 36546 loss_ce: 0.0647 loss: 0.0647 2022/09/16 11:17:28 - mmengine - INFO - Epoch(train) [7][400/8498] lr: 4.0000e-04 eta: 18:58:26 time: 0.9468 data_time: 0.0222 memory: 36546 loss_ce: 0.0522 loss: 0.0522 2022/09/16 11:19:43 - mmengine - INFO - Epoch(train) [7][500/8498] lr: 4.0000e-04 eta: 18:56:11 time: 0.9122 data_time: 0.0207 memory: 36546 loss_ce: 0.0588 loss: 0.0588 2022/09/16 11:21:55 - mmengine - INFO - Epoch(train) [7][600/8498] lr: 4.0000e-04 eta: 18:53:54 time: 0.9282 data_time: 0.0306 memory: 36546 loss_ce: 0.0690 loss: 0.0690 2022/09/16 11:24:20 - mmengine - INFO - Epoch(train) [7][700/8498] lr: 4.0000e-04 eta: 18:51:48 time: 1.6919 data_time: 0.4462 memory: 36546 loss_ce: 0.0623 loss: 0.0623 2022/09/16 11:26:40 - mmengine - INFO - Epoch(train) [7][800/8498] lr: 4.0000e-04 eta: 18:49:38 time: 1.9876 data_time: 0.4447 memory: 36546 loss_ce: 0.0644 loss: 0.0644 2022/09/16 11:28:59 - mmengine - INFO - Epoch(train) [7][900/8498] lr: 4.0000e-04 eta: 18:47:26 time: 1.6205 data_time: 0.1528 memory: 36546 loss_ce: 0.0604 loss: 0.0604 2022/09/16 11:31:15 - mmengine - INFO - Epoch(train) [7][1000/8498] lr: 4.0000e-04 eta: 18:45:13 time: 0.9449 data_time: 0.0216 memory: 36546 loss_ce: 0.0651 loss: 0.0651 2022/09/16 11:31:34 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 11:33:33 - mmengine - INFO - Epoch(train) [7][1100/8498] lr: 4.0000e-04 eta: 18:43:00 time: 0.9709 data_time: 0.0236 memory: 36546 loss_ce: 0.0577 loss: 0.0577 2022/09/16 11:35:45 - mmengine - INFO - Epoch(train) [7][1200/8498] lr: 4.0000e-04 eta: 18:40:42 time: 0.9571 data_time: 0.0225 memory: 36546 loss_ce: 0.0563 loss: 0.0563 2022/09/16 11:38:08 - mmengine - INFO - Epoch(train) [7][1300/8498] lr: 4.0000e-04 eta: 18:38:34 time: 1.6799 data_time: 0.4390 memory: 36546 loss_ce: 0.0512 loss: 0.0512 2022/09/16 11:40:25 - mmengine - INFO - Epoch(train) [7][1400/8498] lr: 4.0000e-04 eta: 18:36:22 time: 1.8453 data_time: 0.4279 memory: 36546 loss_ce: 0.0543 loss: 0.0543 2022/09/16 11:42:43 - mmengine - INFO - Epoch(train) [7][1500/8498] lr: 4.0000e-04 eta: 18:34:09 time: 1.5973 data_time: 0.1643 memory: 36546 loss_ce: 0.0632 loss: 0.0632 2022/09/16 11:45:02 - mmengine - INFO - Epoch(train) [7][1600/8498] lr: 4.0000e-04 eta: 18:31:58 time: 0.9789 data_time: 0.0377 memory: 36546 loss_ce: 0.0593 loss: 0.0593 2022/09/16 11:47:18 - mmengine - INFO - Epoch(train) [7][1700/8498] lr: 4.0000e-04 eta: 18:29:44 time: 0.9351 data_time: 0.0463 memory: 36546 loss_ce: 0.0613 loss: 0.0613 2022/09/16 11:49:35 - mmengine - INFO - Epoch(train) [7][1800/8498] lr: 4.0000e-04 eta: 18:27:30 time: 0.9514 data_time: 0.0334 memory: 36546 loss_ce: 0.0558 loss: 0.0558 2022/09/16 11:52:01 - mmengine - INFO - Epoch(train) [7][1900/8498] lr: 4.0000e-04 eta: 18:25:25 time: 1.5990 data_time: 0.4019 memory: 36546 loss_ce: 0.0587 loss: 0.0587 2022/09/16 11:54:20 - mmengine - INFO - Epoch(train) [7][2000/8498] lr: 4.0000e-04 eta: 18:23:13 time: 1.9753 data_time: 0.4262 memory: 36546 loss_ce: 0.0556 loss: 0.0556 2022/09/16 11:54:31 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 11:56:36 - mmengine - INFO - Epoch(train) [7][2100/8498] lr: 4.0000e-04 eta: 18:20:59 time: 1.6570 data_time: 0.1661 memory: 36546 loss_ce: 0.0556 loss: 0.0556 2022/09/16 11:58:49 - mmengine - INFO - Epoch(train) [7][2200/8498] lr: 4.0000e-04 eta: 18:18:42 time: 1.0117 data_time: 0.0363 memory: 36546 loss_ce: 0.0527 loss: 0.0527 2022/09/16 12:01:10 - mmengine - INFO - Epoch(train) [7][2300/8498] lr: 4.0000e-04 eta: 18:16:32 time: 0.9854 data_time: 0.0358 memory: 36546 loss_ce: 0.0610 loss: 0.0610 2022/09/16 12:03:27 - mmengine - INFO - Epoch(train) [7][2400/8498] lr: 4.0000e-04 eta: 18:14:19 time: 0.9344 data_time: 0.0208 memory: 36546 loss_ce: 0.0523 loss: 0.0523 2022/09/16 12:05:51 - mmengine - INFO - Epoch(train) [7][2500/8498] lr: 4.0000e-04 eta: 18:12:12 time: 1.6149 data_time: 0.4101 memory: 36546 loss_ce: 0.0567 loss: 0.0567 2022/09/16 12:08:10 - mmengine - INFO - Epoch(train) [7][2600/8498] lr: 4.0000e-04 eta: 18:10:00 time: 1.8476 data_time: 0.3265 memory: 36546 loss_ce: 0.0528 loss: 0.0528 2022/09/16 12:10:31 - mmengine - INFO - Epoch(train) [7][2700/8498] lr: 4.0000e-04 eta: 18:07:50 time: 1.7508 data_time: 0.1656 memory: 36546 loss_ce: 0.0517 loss: 0.0517 2022/09/16 12:12:51 - mmengine - INFO - Epoch(train) [7][2800/8498] lr: 4.0000e-04 eta: 18:05:40 time: 1.1046 data_time: 0.1466 memory: 36546 loss_ce: 0.0529 loss: 0.0529 2022/09/16 12:15:09 - mmengine - INFO - Epoch(train) [7][2900/8498] lr: 4.0000e-04 eta: 18:03:27 time: 1.0423 data_time: 0.1331 memory: 36546 loss_ce: 0.0551 loss: 0.0551 2022/09/16 12:17:26 - mmengine - INFO - Epoch(train) [7][3000/8498] lr: 4.0000e-04 eta: 18:01:13 time: 0.9197 data_time: 0.0359 memory: 36546 loss_ce: 0.0575 loss: 0.0575 2022/09/16 12:17:47 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 12:19:48 - mmengine - INFO - Epoch(train) [7][3100/8498] lr: 4.0000e-04 eta: 17:59:04 time: 1.4505 data_time: 0.3171 memory: 36546 loss_ce: 0.0624 loss: 0.0624 2022/09/16 12:22:08 - mmengine - INFO - Epoch(train) [7][3200/8498] lr: 4.0000e-04 eta: 17:56:52 time: 1.8529 data_time: 0.3172 memory: 36546 loss_ce: 0.0546 loss: 0.0546 2022/09/16 12:24:23 - mmengine - INFO - Epoch(train) [7][3300/8498] lr: 4.0000e-04 eta: 17:54:37 time: 1.6523 data_time: 0.1539 memory: 36546 loss_ce: 0.0558 loss: 0.0558 2022/09/16 12:26:40 - mmengine - INFO - Epoch(train) [7][3400/8498] lr: 4.0000e-04 eta: 17:52:23 time: 1.0769 data_time: 0.1532 memory: 36546 loss_ce: 0.0553 loss: 0.0553 2022/09/16 12:28:56 - mmengine - INFO - Epoch(train) [7][3500/8498] lr: 4.0000e-04 eta: 17:50:09 time: 1.0648 data_time: 0.1460 memory: 36546 loss_ce: 0.0673 loss: 0.0673 2022/09/16 12:31:11 - mmengine - INFO - Epoch(train) [7][3600/8498] lr: 4.0000e-04 eta: 17:47:54 time: 0.8950 data_time: 0.0235 memory: 36546 loss_ce: 0.0526 loss: 0.0526 2022/09/16 12:33:35 - mmengine - INFO - Epoch(train) [7][3700/8498] lr: 4.0000e-04 eta: 17:45:46 time: 1.4705 data_time: 0.3497 memory: 36546 loss_ce: 0.0547 loss: 0.0547 2022/09/16 12:35:51 - mmengine - INFO - Epoch(train) [7][3800/8498] lr: 4.0000e-04 eta: 17:43:32 time: 1.8255 data_time: 0.3169 memory: 36546 loss_ce: 0.0556 loss: 0.0556 2022/09/16 12:38:10 - mmengine - INFO - Epoch(train) [7][3900/8498] lr: 4.0000e-04 eta: 17:41:19 time: 1.6937 data_time: 0.1676 memory: 36546 loss_ce: 0.0565 loss: 0.0565 2022/09/16 12:40:28 - mmengine - INFO - Epoch(train) [7][4000/8498] lr: 4.0000e-04 eta: 17:39:07 time: 1.0907 data_time: 0.1464 memory: 36546 loss_ce: 0.0638 loss: 0.0638 2022/09/16 12:40:45 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 12:42:47 - mmengine - INFO - Epoch(train) [7][4100/8498] lr: 4.0000e-04 eta: 17:36:54 time: 1.0907 data_time: 0.1465 memory: 36546 loss_ce: 0.0610 loss: 0.0610 2022/09/16 12:45:06 - mmengine - INFO - Epoch(train) [7][4200/8498] lr: 4.0000e-04 eta: 17:34:42 time: 0.9396 data_time: 0.0267 memory: 36546 loss_ce: 0.0598 loss: 0.0598 2022/09/16 12:47:28 - mmengine - INFO - Epoch(train) [7][4300/8498] lr: 4.0000e-04 eta: 17:32:33 time: 1.4017 data_time: 0.3241 memory: 36546 loss_ce: 0.0804 loss: 0.0804 2022/09/16 12:49:50 - mmengine - INFO - Epoch(train) [7][4400/8498] lr: 4.0000e-04 eta: 17:30:24 time: 1.8546 data_time: 0.3083 memory: 36546 loss_ce: 0.0598 loss: 0.0598 2022/09/16 12:52:07 - mmengine - INFO - Epoch(train) [7][4500/8498] lr: 4.0000e-04 eta: 17:28:09 time: 1.7167 data_time: 0.1742 memory: 36546 loss_ce: 0.0617 loss: 0.0617 2022/09/16 12:54:25 - mmengine - INFO - Epoch(train) [7][4600/8498] lr: 4.0000e-04 eta: 17:25:56 time: 1.0799 data_time: 0.1421 memory: 36546 loss_ce: 0.0577 loss: 0.0577 2022/09/16 12:56:41 - mmengine - INFO - Epoch(train) [7][4700/8498] lr: 4.0000e-04 eta: 17:23:41 time: 1.1142 data_time: 0.1402 memory: 36546 loss_ce: 0.0572 loss: 0.0572 2022/09/16 12:58:58 - mmengine - INFO - Epoch(train) [7][4800/8498] lr: 4.0000e-04 eta: 17:21:28 time: 0.9708 data_time: 0.0228 memory: 36546 loss_ce: 0.0502 loss: 0.0502 2022/09/16 13:01:19 - mmengine - INFO - Epoch(train) [7][4900/8498] lr: 4.0000e-04 eta: 17:19:17 time: 1.3986 data_time: 0.3028 memory: 36546 loss_ce: 0.0596 loss: 0.0596 2022/09/16 13:03:38 - mmengine - INFO - Epoch(train) [7][5000/8498] lr: 4.0000e-04 eta: 17:17:05 time: 1.8349 data_time: 0.3130 memory: 36546 loss_ce: 0.0653 loss: 0.0653 2022/09/16 13:03:51 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 13:05:56 - mmengine - INFO - Epoch(train) [7][5100/8498] lr: 4.0000e-04 eta: 17:14:52 time: 1.7485 data_time: 0.1471 memory: 36546 loss_ce: 0.0571 loss: 0.0571 2022/09/16 13:08:14 - mmengine - INFO - Epoch(train) [7][5200/8498] lr: 4.0000e-04 eta: 17:12:39 time: 1.1687 data_time: 0.1725 memory: 36546 loss_ce: 0.0508 loss: 0.0508 2022/09/16 13:10:30 - mmengine - INFO - Epoch(train) [7][5300/8498] lr: 4.0000e-04 eta: 17:10:24 time: 1.2447 data_time: 0.1588 memory: 36546 loss_ce: 0.0530 loss: 0.0530 2022/09/16 13:12:51 - mmengine - INFO - Epoch(train) [7][5400/8498] lr: 4.0000e-04 eta: 17:08:13 time: 1.0410 data_time: 0.0233 memory: 36546 loss_ce: 0.0580 loss: 0.0580 2022/09/16 13:15:18 - mmengine - INFO - Epoch(train) [7][5500/8498] lr: 4.0000e-04 eta: 17:06:07 time: 1.4728 data_time: 0.2838 memory: 36546 loss_ce: 0.0619 loss: 0.0619 2022/09/16 13:17:44 - mmengine - INFO - Epoch(train) [7][5600/8498] lr: 4.0000e-04 eta: 17:04:00 time: 1.7130 data_time: 0.3060 memory: 36546 loss_ce: 0.0598 loss: 0.0598 2022/09/16 13:20:09 - mmengine - INFO - Epoch(train) [7][5700/8498] lr: 4.0000e-04 eta: 17:01:53 time: 1.6127 data_time: 0.1413 memory: 36546 loss_ce: 0.0545 loss: 0.0545 2022/09/16 13:22:36 - mmengine - INFO - Epoch(train) [7][5800/8498] lr: 4.0000e-04 eta: 16:59:46 time: 1.2943 data_time: 0.1914 memory: 36546 loss_ce: 0.0574 loss: 0.0574 2022/09/16 13:25:00 - mmengine - INFO - Epoch(train) [7][5900/8498] lr: 4.0000e-04 eta: 16:57:38 time: 1.5814 data_time: 0.1892 memory: 36546 loss_ce: 0.0618 loss: 0.0618 2022/09/16 13:27:22 - mmengine - INFO - Epoch(train) [7][6000/8498] lr: 4.0000e-04 eta: 16:55:27 time: 1.2447 data_time: 0.0326 memory: 36546 loss_ce: 0.0567 loss: 0.0567 2022/09/16 13:27:41 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 13:29:48 - mmengine - INFO - Epoch(train) [7][6100/8498] lr: 4.0000e-04 eta: 16:53:20 time: 1.3847 data_time: 0.2815 memory: 36546 loss_ce: 0.0610 loss: 0.0610 2022/09/16 13:32:13 - mmengine - INFO - Epoch(train) [7][6200/8498] lr: 4.0000e-04 eta: 16:51:12 time: 1.5625 data_time: 0.3066 memory: 36546 loss_ce: 0.0584 loss: 0.0584 2022/09/16 13:34:36 - mmengine - INFO - Epoch(train) [7][6300/8498] lr: 4.0000e-04 eta: 16:49:03 time: 1.5900 data_time: 0.1287 memory: 36546 loss_ce: 0.0559 loss: 0.0559 2022/09/16 13:37:01 - mmengine - INFO - Epoch(train) [7][6400/8498] lr: 4.0000e-04 eta: 16:46:55 time: 1.2900 data_time: 0.1959 memory: 36546 loss_ce: 0.0640 loss: 0.0640 2022/09/16 13:39:27 - mmengine - INFO - Epoch(train) [7][6500/8498] lr: 4.0000e-04 eta: 16:44:47 time: 1.4609 data_time: 0.1659 memory: 36546 loss_ce: 0.0543 loss: 0.0543 2022/09/16 13:41:49 - mmengine - INFO - Epoch(train) [7][6600/8498] lr: 4.0000e-04 eta: 16:42:37 time: 1.1833 data_time: 0.0219 memory: 36546 loss_ce: 0.0599 loss: 0.0599 2022/09/16 13:44:17 - mmengine - INFO - Epoch(train) [7][6700/8498] lr: 4.0000e-04 eta: 16:40:31 time: 1.4071 data_time: 0.2939 memory: 36546 loss_ce: 0.0660 loss: 0.0660 2022/09/16 13:46:42 - mmengine - INFO - Epoch(train) [7][6800/8498] lr: 4.0000e-04 eta: 16:38:22 time: 1.5615 data_time: 0.2783 memory: 36546 loss_ce: 0.0542 loss: 0.0542 2022/09/16 13:49:03 - mmengine - INFO - Epoch(train) [7][6900/8498] lr: 4.0000e-04 eta: 16:36:11 time: 1.6796 data_time: 0.1625 memory: 36546 loss_ce: 0.0506 loss: 0.0506 2022/09/16 13:51:28 - mmengine - INFO - Epoch(train) [7][7000/8498] lr: 4.0000e-04 eta: 16:34:03 time: 1.2733 data_time: 0.2070 memory: 36546 loss_ce: 0.0588 loss: 0.0588 2022/09/16 13:51:46 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 13:53:54 - mmengine - INFO - Epoch(train) [7][7100/8498] lr: 4.0000e-04 eta: 16:31:55 time: 1.5215 data_time: 0.1701 memory: 36546 loss_ce: 0.0604 loss: 0.0604 2022/09/16 13:56:19 - mmengine - INFO - Epoch(train) [7][7200/8498] lr: 4.0000e-04 eta: 16:29:46 time: 1.1887 data_time: 0.0225 memory: 36546 loss_ce: 0.0786 loss: 0.0786 2022/09/16 13:58:45 - mmengine - INFO - Epoch(train) [7][7300/8498] lr: 4.0000e-04 eta: 16:27:39 time: 1.4543 data_time: 0.2846 memory: 36546 loss_ce: 0.0609 loss: 0.0609 2022/09/16 14:01:12 - mmengine - INFO - Epoch(train) [7][7400/8498] lr: 4.0000e-04 eta: 16:25:31 time: 1.5195 data_time: 0.3090 memory: 36546 loss_ce: 0.0543 loss: 0.0543 2022/09/16 14:03:37 - mmengine - INFO - Epoch(train) [7][7500/8498] lr: 4.0000e-04 eta: 16:23:22 time: 1.6166 data_time: 0.1405 memory: 36546 loss_ce: 0.0621 loss: 0.0621 2022/09/16 14:06:01 - mmengine - INFO - Epoch(train) [7][7600/8498] lr: 4.0000e-04 eta: 16:21:13 time: 1.2485 data_time: 0.1636 memory: 36546 loss_ce: 0.0510 loss: 0.0510 2022/09/16 14:08:28 - mmengine - INFO - Epoch(train) [7][7700/8498] lr: 4.0000e-04 eta: 16:19:05 time: 1.5331 data_time: 0.1842 memory: 36546 loss_ce: 0.0570 loss: 0.0570 2022/09/16 14:10:50 - mmengine - INFO - Epoch(train) [7][7800/8498] lr: 4.0000e-04 eta: 16:16:55 time: 1.2045 data_time: 0.0223 memory: 36546 loss_ce: 0.0616 loss: 0.0616 2022/09/16 14:13:17 - mmengine - INFO - Epoch(train) [7][7900/8498] lr: 4.0000e-04 eta: 16:14:47 time: 1.4202 data_time: 0.2988 memory: 36546 loss_ce: 0.0628 loss: 0.0628 2022/09/16 14:15:44 - mmengine - INFO - Epoch(train) [7][8000/8498] lr: 4.0000e-04 eta: 16:12:39 time: 1.5312 data_time: 0.3351 memory: 36546 loss_ce: 0.0593 loss: 0.0593 2022/09/16 14:16:01 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 14:18:10 - mmengine - INFO - Epoch(train) [7][8100/8498] lr: 4.0000e-04 eta: 16:10:31 time: 1.5648 data_time: 0.1347 memory: 36546 loss_ce: 0.0558 loss: 0.0558 2022/09/16 14:20:36 - mmengine - INFO - Epoch(train) [7][8200/8498] lr: 4.0000e-04 eta: 16:08:23 time: 1.3729 data_time: 0.2200 memory: 36546 loss_ce: 0.0556 loss: 0.0556 2022/09/16 14:23:00 - mmengine - INFO - Epoch(train) [7][8300/8498] lr: 4.0000e-04 eta: 16:06:13 time: 1.4181 data_time: 0.1554 memory: 36546 loss_ce: 0.0526 loss: 0.0526 2022/09/16 14:25:20 - mmengine - INFO - Epoch(train) [7][8400/8498] lr: 4.0000e-04 eta: 16:04:00 time: 1.1972 data_time: 0.0299 memory: 36546 loss_ce: 0.0556 loss: 0.0556 2022/09/16 14:27:35 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 14:27:35 - mmengine - INFO - Saving checkpoint at 7 epochs 2022/09/16 14:28:06 - mmengine - INFO - Epoch(val) [7][100/1918] eta: 0:04:08 time: 0.1369 data_time: 0.0008 memory: 36546 2022/09/16 14:28:20 - mmengine - INFO - Epoch(val) [7][200/1918] eta: 0:03:56 time: 0.1379 data_time: 0.0009 memory: 1150 2022/09/16 14:28:34 - mmengine - INFO - Epoch(val) [7][300/1918] eta: 0:03:40 time: 0.1365 data_time: 0.0008 memory: 1150 2022/09/16 14:28:48 - mmengine - INFO - Epoch(val) [7][400/1918] eta: 0:03:31 time: 0.1396 data_time: 0.0008 memory: 1150 2022/09/16 14:29:02 - mmengine - INFO - Epoch(val) [7][500/1918] eta: 0:03:17 time: 0.1394 data_time: 0.0008 memory: 1150 2022/09/16 14:29:16 - mmengine - INFO - Epoch(val) [7][600/1918] eta: 0:02:53 time: 0.1316 data_time: 0.0008 memory: 1150 2022/09/16 14:29:30 - mmengine - INFO - Epoch(val) [7][700/1918] eta: 0:02:52 time: 0.1420 data_time: 0.0008 memory: 1150 2022/09/16 14:29:44 - mmengine - INFO - Epoch(val) [7][800/1918] eta: 0:02:50 time: 0.1522 data_time: 0.0036 memory: 1150 2022/09/16 14:29:58 - mmengine - INFO - Epoch(val) [7][900/1918] eta: 0:02:14 time: 0.1321 data_time: 0.0008 memory: 1150 2022/09/16 14:30:11 - mmengine - INFO - Epoch(val) [7][1000/1918] eta: 0:02:06 time: 0.1380 data_time: 0.0008 memory: 1150 2022/09/16 14:30:25 - mmengine - INFO - Epoch(val) [7][1100/1918] eta: 0:01:51 time: 0.1359 data_time: 0.0008 memory: 1150 2022/09/16 14:30:39 - mmengine - INFO - Epoch(val) [7][1200/1918] eta: 0:01:34 time: 0.1322 data_time: 0.0008 memory: 1150 2022/09/16 14:30:52 - mmengine - INFO - Epoch(val) [7][1300/1918] eta: 0:01:26 time: 0.1392 data_time: 0.0008 memory: 1150 2022/09/16 14:31:05 - mmengine - INFO - Epoch(val) [7][1400/1918] eta: 0:01:10 time: 0.1359 data_time: 0.0007 memory: 1150 2022/09/16 14:31:19 - mmengine - INFO - Epoch(val) [7][1500/1918] eta: 0:00:59 time: 0.1418 data_time: 0.0008 memory: 1150 2022/09/16 14:31:33 - mmengine - INFO - Epoch(val) [7][1600/1918] eta: 0:00:45 time: 0.1421 data_time: 0.0008 memory: 1150 2022/09/16 14:31:47 - mmengine - INFO - Epoch(val) [7][1700/1918] eta: 0:00:29 time: 0.1376 data_time: 0.0008 memory: 1150 2022/09/16 14:32:01 - mmengine - INFO - Epoch(val) [7][1800/1918] eta: 0:00:15 time: 0.1339 data_time: 0.0008 memory: 1150 2022/09/16 14:32:15 - mmengine - INFO - Epoch(val) [7][1900/1918] eta: 0:00:02 time: 0.1478 data_time: 0.0046 memory: 1150 2022/09/16 14:32:17 - mmengine - INFO - Epoch(val) [7][1918/1918] CUTE80/recog/word_acc_ignore_case_symbol: 0.8646 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9497 SVT/recog/word_acc_ignore_case_symbol: 0.8764 SVTP/recog/word_acc_ignore_case_symbol: 0.7984 IC13/recog/word_acc_ignore_case_symbol: 0.9389 IC15/recog/word_acc_ignore_case_symbol: 0.7448 2022/09/16 14:34:55 - mmengine - INFO - Epoch(train) [8][100/8498] lr: 4.0000e-04 eta: 15:59:42 time: 1.9212 data_time: 0.3331 memory: 36546 loss_ce: 0.0574 loss: 0.0574 2022/09/16 14:37:20 - mmengine - INFO - Epoch(train) [8][200/8498] lr: 4.0000e-04 eta: 15:57:33 time: 2.0575 data_time: 0.3605 memory: 36546 loss_ce: 0.0504 loss: 0.0504 2022/09/16 14:39:43 - mmengine - INFO - Epoch(train) [8][300/8498] lr: 4.0000e-04 eta: 15:55:22 time: 1.4061 data_time: 0.0097 memory: 36546 loss_ce: 0.0553 loss: 0.0553 2022/09/16 14:42:07 - mmengine - INFO - Epoch(train) [8][400/8498] lr: 4.0000e-04 eta: 15:53:12 time: 1.0653 data_time: 0.1360 memory: 36546 loss_ce: 0.0569 loss: 0.0569 2022/09/16 14:44:30 - mmengine - INFO - Epoch(train) [8][500/8498] lr: 4.0000e-04 eta: 15:51:01 time: 1.0036 data_time: 0.1418 memory: 36546 loss_ce: 0.0552 loss: 0.0552 2022/09/16 14:44:55 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 14:46:55 - mmengine - INFO - Epoch(train) [8][600/8498] lr: 4.0000e-04 eta: 15:48:52 time: 1.0396 data_time: 0.1240 memory: 36546 loss_ce: 0.0529 loss: 0.0529 2022/09/16 14:49:27 - mmengine - INFO - Epoch(train) [8][700/8498] lr: 4.0000e-04 eta: 15:46:47 time: 1.8510 data_time: 0.3135 memory: 36546 loss_ce: 0.0501 loss: 0.0501 2022/09/16 14:51:53 - mmengine - INFO - Epoch(train) [8][800/8498] lr: 4.0000e-04 eta: 15:44:38 time: 2.0616 data_time: 0.3403 memory: 36546 loss_ce: 0.0515 loss: 0.0515 2022/09/16 14:54:19 - mmengine - INFO - Epoch(train) [8][900/8498] lr: 4.0000e-04 eta: 15:42:29 time: 1.4595 data_time: 0.0068 memory: 36546 loss_ce: 0.0547 loss: 0.0547 2022/09/16 14:56:46 - mmengine - INFO - Epoch(train) [8][1000/8498] lr: 4.0000e-04 eta: 15:40:20 time: 1.0969 data_time: 0.1492 memory: 36546 loss_ce: 0.0714 loss: 0.0714 2022/09/16 14:59:09 - mmengine - INFO - Epoch(train) [8][1100/8498] lr: 4.0000e-04 eta: 15:38:09 time: 0.9777 data_time: 0.1282 memory: 36546 loss_ce: 0.0535 loss: 0.0535 2022/09/16 15:01:31 - mmengine - INFO - Epoch(train) [8][1200/8498] lr: 4.0000e-04 eta: 15:35:57 time: 1.0989 data_time: 0.1358 memory: 36546 loss_ce: 0.0511 loss: 0.0511 2022/09/16 15:04:04 - mmengine - INFO - Epoch(train) [8][1300/8498] lr: 4.0000e-04 eta: 15:33:53 time: 1.8944 data_time: 0.3399 memory: 36546 loss_ce: 0.0561 loss: 0.0561 2022/09/16 15:06:29 - mmengine - INFO - Epoch(train) [8][1400/8498] lr: 4.0000e-04 eta: 15:31:43 time: 2.0768 data_time: 0.3408 memory: 36546 loss_ce: 0.0489 loss: 0.0489 2022/09/16 15:08:54 - mmengine - INFO - Epoch(train) [8][1500/8498] lr: 4.0000e-04 eta: 15:29:32 time: 1.3645 data_time: 0.0076 memory: 36546 loss_ce: 0.0564 loss: 0.0564 2022/09/16 15:09:15 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 15:11:19 - mmengine - INFO - Epoch(train) [8][1600/8498] lr: 4.0000e-04 eta: 15:27:23 time: 1.0682 data_time: 0.1261 memory: 36546 loss_ce: 0.0480 loss: 0.0480 2022/09/16 15:13:43 - mmengine - INFO - Epoch(train) [8][1700/8498] lr: 4.0000e-04 eta: 15:25:12 time: 1.0019 data_time: 0.1550 memory: 36546 loss_ce: 0.0529 loss: 0.0529 2022/09/16 15:16:06 - mmengine - INFO - Epoch(train) [8][1800/8498] lr: 4.0000e-04 eta: 15:23:00 time: 1.0489 data_time: 0.1194 memory: 36546 loss_ce: 0.0511 loss: 0.0511 2022/09/16 15:18:41 - mmengine - INFO - Epoch(train) [8][1900/8498] lr: 4.0000e-04 eta: 15:20:56 time: 1.9629 data_time: 0.3016 memory: 36546 loss_ce: 0.0505 loss: 0.0505 2022/09/16 15:21:07 - mmengine - INFO - Epoch(train) [8][2000/8498] lr: 4.0000e-04 eta: 15:18:47 time: 2.0184 data_time: 0.3554 memory: 36546 loss_ce: 0.0544 loss: 0.0544 2022/09/16 15:23:32 - mmengine - INFO - Epoch(train) [8][2100/8498] lr: 4.0000e-04 eta: 15:16:37 time: 1.4713 data_time: 0.0068 memory: 36546 loss_ce: 0.0463 loss: 0.0463 2022/09/16 15:26:00 - mmengine - INFO - Epoch(train) [8][2200/8498] lr: 4.0000e-04 eta: 15:14:28 time: 1.1004 data_time: 0.1660 memory: 36546 loss_ce: 0.0482 loss: 0.0482 2022/09/16 15:28:24 - mmengine - INFO - Epoch(train) [8][2300/8498] lr: 4.0000e-04 eta: 15:12:17 time: 1.0043 data_time: 0.1421 memory: 36546 loss_ce: 0.0529 loss: 0.0529 2022/09/16 15:30:50 - mmengine - INFO - Epoch(train) [8][2400/8498] lr: 4.0000e-04 eta: 15:10:07 time: 1.0615 data_time: 0.1324 memory: 36546 loss_ce: 0.0559 loss: 0.0559 2022/09/16 15:33:25 - mmengine - INFO - Epoch(train) [8][2500/8498] lr: 4.0000e-04 eta: 15:08:03 time: 1.8885 data_time: 0.2978 memory: 36546 loss_ce: 0.0535 loss: 0.0535 2022/09/16 15:33:41 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 15:35:51 - mmengine - INFO - Epoch(train) [8][2600/8498] lr: 4.0000e-04 eta: 15:05:53 time: 1.9394 data_time: 0.3045 memory: 36546 loss_ce: 0.0525 loss: 0.0525 2022/09/16 15:38:14 - mmengine - INFO - Epoch(train) [8][2700/8498] lr: 4.0000e-04 eta: 15:03:41 time: 1.4522 data_time: 0.0066 memory: 36546 loss_ce: 0.0548 loss: 0.0548 2022/09/16 15:40:38 - mmengine - INFO - Epoch(train) [8][2800/8498] lr: 4.0000e-04 eta: 15:01:30 time: 1.0661 data_time: 0.1300 memory: 36546 loss_ce: 0.0513 loss: 0.0513 2022/09/16 15:43:01 - mmengine - INFO - Epoch(train) [8][2900/8498] lr: 4.0000e-04 eta: 14:59:17 time: 1.0022 data_time: 0.1390 memory: 36546 loss_ce: 0.0561 loss: 0.0561 2022/09/16 15:45:19 - mmengine - INFO - Epoch(train) [8][3000/8498] lr: 4.0000e-04 eta: 14:57:02 time: 1.2594 data_time: 0.3108 memory: 36546 loss_ce: 0.0507 loss: 0.0507 2022/09/16 15:47:40 - mmengine - INFO - Epoch(train) [8][3100/8498] lr: 4.0000e-04 eta: 14:54:49 time: 1.4893 data_time: 0.1494 memory: 36546 loss_ce: 0.0544 loss: 0.0544 2022/09/16 15:50:00 - mmengine - INFO - Epoch(train) [8][3200/8498] lr: 4.0000e-04 eta: 14:52:35 time: 1.6220 data_time: 0.1645 memory: 36546 loss_ce: 0.0514 loss: 0.0514 2022/09/16 15:52:18 - mmengine - INFO - Epoch(train) [8][3300/8498] lr: 4.0000e-04 eta: 14:50:20 time: 1.3120 data_time: 0.0887 memory: 36546 loss_ce: 0.0542 loss: 0.0542 2022/09/16 15:54:38 - mmengine - INFO - Epoch(train) [8][3400/8498] lr: 4.0000e-04 eta: 14:48:06 time: 1.1337 data_time: 0.1910 memory: 36546 loss_ce: 0.0609 loss: 0.0609 2022/09/16 15:56:56 - mmengine - INFO - Epoch(train) [8][3500/8498] lr: 4.0000e-04 eta: 14:45:50 time: 1.2506 data_time: 0.2804 memory: 36546 loss_ce: 0.0502 loss: 0.0502 2022/09/16 15:57:17 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 15:59:21 - mmengine - INFO - Epoch(train) [8][3600/8498] lr: 4.0000e-04 eta: 14:43:39 time: 1.5565 data_time: 0.3455 memory: 36546 loss_ce: 0.0549 loss: 0.0549 2022/09/16 16:01:45 - mmengine - INFO - Epoch(train) [8][3700/8498] lr: 4.0000e-04 eta: 14:41:28 time: 1.5197 data_time: 0.1864 memory: 36546 loss_ce: 0.0465 loss: 0.0465 2022/09/16 16:04:08 - mmengine - INFO - Epoch(train) [8][3800/8498] lr: 4.0000e-04 eta: 14:39:15 time: 1.6622 data_time: 0.2108 memory: 36546 loss_ce: 0.0550 loss: 0.0550 2022/09/16 16:06:25 - mmengine - INFO - Epoch(train) [8][3900/8498] lr: 4.0000e-04 eta: 14:37:00 time: 1.2019 data_time: 0.0221 memory: 36546 loss_ce: 0.0605 loss: 0.0605 2022/09/16 16:08:46 - mmengine - INFO - Epoch(train) [8][4000/8498] lr: 4.0000e-04 eta: 14:34:46 time: 1.2635 data_time: 0.2526 memory: 36546 loss_ce: 0.0545 loss: 0.0545 2022/09/16 16:11:05 - mmengine - INFO - Epoch(train) [8][4100/8498] lr: 4.0000e-04 eta: 14:32:31 time: 1.2518 data_time: 0.2695 memory: 36546 loss_ce: 0.0513 loss: 0.0513 2022/09/16 16:13:19 - mmengine - INFO - Epoch(train) [8][4200/8498] lr: 4.0000e-04 eta: 14:30:13 time: 1.3834 data_time: 0.2548 memory: 36546 loss_ce: 0.0553 loss: 0.0553 2022/09/16 16:15:42 - mmengine - INFO - Epoch(train) [8][4300/8498] lr: 4.0000e-04 eta: 14:28:01 time: 1.5203 data_time: 0.2005 memory: 36546 loss_ce: 0.0514 loss: 0.0514 2022/09/16 16:18:03 - mmengine - INFO - Epoch(train) [8][4400/8498] lr: 4.0000e-04 eta: 14:25:47 time: 1.5811 data_time: 0.1827 memory: 36546 loss_ce: 0.0497 loss: 0.0497 2022/09/16 16:20:22 - mmengine - INFO - Epoch(train) [8][4500/8498] lr: 4.0000e-04 eta: 14:23:32 time: 1.2268 data_time: 0.0495 memory: 36546 loss_ce: 0.0549 loss: 0.0549 2022/09/16 16:20:44 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 16:22:44 - mmengine - INFO - Epoch(train) [8][4600/8498] lr: 4.0000e-04 eta: 14:21:19 time: 1.2078 data_time: 0.2528 memory: 36546 loss_ce: 0.0566 loss: 0.0566 2022/09/16 16:25:03 - mmengine - INFO - Epoch(train) [8][4700/8498] lr: 4.0000e-04 eta: 14:19:04 time: 1.3046 data_time: 0.2791 memory: 36546 loss_ce: 0.0533 loss: 0.0533 2022/09/16 16:27:21 - mmengine - INFO - Epoch(train) [8][4800/8498] lr: 4.0000e-04 eta: 14:16:49 time: 1.3303 data_time: 0.2526 memory: 36546 loss_ce: 0.0566 loss: 0.0566 2022/09/16 16:29:44 - mmengine - INFO - Epoch(train) [8][4900/8498] lr: 4.0000e-04 eta: 14:14:36 time: 1.5431 data_time: 0.2065 memory: 36546 loss_ce: 0.0557 loss: 0.0557 2022/09/16 16:32:06 - mmengine - INFO - Epoch(train) [8][5000/8498] lr: 4.0000e-04 eta: 14:12:23 time: 1.5620 data_time: 0.1977 memory: 36546 loss_ce: 0.0537 loss: 0.0537 2022/09/16 16:34:24 - mmengine - INFO - Epoch(train) [8][5100/8498] lr: 4.0000e-04 eta: 14:10:08 time: 1.2141 data_time: 0.0236 memory: 36546 loss_ce: 0.0545 loss: 0.0545 2022/09/16 16:36:42 - mmengine - INFO - Epoch(train) [8][5200/8498] lr: 4.0000e-04 eta: 14:07:52 time: 1.1777 data_time: 0.1929 memory: 36546 loss_ce: 0.0611 loss: 0.0611 2022/09/16 16:39:03 - mmengine - INFO - Epoch(train) [8][5300/8498] lr: 4.0000e-04 eta: 14:05:39 time: 1.2985 data_time: 0.3093 memory: 36546 loss_ce: 0.0609 loss: 0.0609 2022/09/16 16:41:24 - mmengine - INFO - Epoch(train) [8][5400/8498] lr: 4.0000e-04 eta: 14:03:25 time: 1.4185 data_time: 0.2964 memory: 36546 loss_ce: 0.0561 loss: 0.0561 2022/09/16 16:43:44 - mmengine - INFO - Epoch(train) [8][5500/8498] lr: 4.0000e-04 eta: 14:01:10 time: 1.4971 data_time: 0.1968 memory: 36546 loss_ce: 0.0509 loss: 0.0509 2022/09/16 16:44:02 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 16:46:05 - mmengine - INFO - Epoch(train) [8][5600/8498] lr: 4.0000e-04 eta: 13:58:56 time: 1.6608 data_time: 0.1990 memory: 36546 loss_ce: 0.0551 loss: 0.0551 2022/09/16 16:48:20 - mmengine - INFO - Epoch(train) [8][5700/8498] lr: 4.0000e-04 eta: 13:56:39 time: 1.1712 data_time: 0.0384 memory: 36546 loss_ce: 0.0538 loss: 0.0538 2022/09/16 16:50:39 - mmengine - INFO - Epoch(train) [8][5800/8498] lr: 4.0000e-04 eta: 13:54:24 time: 1.2130 data_time: 0.1970 memory: 36546 loss_ce: 0.0534 loss: 0.0534 2022/09/16 16:52:58 - mmengine - INFO - Epoch(train) [8][5900/8498] lr: 4.0000e-04 eta: 13:52:09 time: 1.3085 data_time: 0.2484 memory: 36546 loss_ce: 0.0545 loss: 0.0545 2022/09/16 16:55:19 - mmengine - INFO - Epoch(train) [8][6000/8498] lr: 4.0000e-04 eta: 13:49:55 time: 1.4339 data_time: 0.3596 memory: 36546 loss_ce: 0.0536 loss: 0.0536 2022/09/16 16:57:40 - mmengine - INFO - Epoch(train) [8][6100/8498] lr: 4.0000e-04 eta: 13:47:41 time: 1.4353 data_time: 0.1731 memory: 36546 loss_ce: 0.0584 loss: 0.0584 2022/09/16 17:00:00 - mmengine - INFO - Epoch(train) [8][6200/8498] lr: 4.0000e-04 eta: 13:45:27 time: 1.6792 data_time: 0.2148 memory: 36546 loss_ce: 0.0467 loss: 0.0467 2022/09/16 17:02:18 - mmengine - INFO - Epoch(train) [8][6300/8498] lr: 4.0000e-04 eta: 13:43:11 time: 1.1731 data_time: 0.0348 memory: 36546 loss_ce: 0.0584 loss: 0.0584 2022/09/16 17:04:39 - mmengine - INFO - Epoch(train) [8][6400/8498] lr: 4.0000e-04 eta: 13:40:57 time: 1.2768 data_time: 0.1986 memory: 36546 loss_ce: 0.0551 loss: 0.0551 2022/09/16 17:06:58 - mmengine - INFO - Epoch(train) [8][6500/8498] lr: 4.0000e-04 eta: 13:38:41 time: 1.2641 data_time: 0.2806 memory: 36546 loss_ce: 0.0486 loss: 0.0486 2022/09/16 17:07:18 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 17:09:17 - mmengine - INFO - Epoch(train) [8][6600/8498] lr: 4.0000e-04 eta: 13:36:27 time: 1.3028 data_time: 0.2583 memory: 36546 loss_ce: 0.0500 loss: 0.0500 2022/09/16 17:11:41 - mmengine - INFO - Epoch(train) [8][6700/8498] lr: 4.0000e-04 eta: 13:34:14 time: 1.5469 data_time: 0.1666 memory: 36546 loss_ce: 0.0574 loss: 0.0574 2022/09/16 17:14:01 - mmengine - INFO - Epoch(train) [8][6800/8498] lr: 4.0000e-04 eta: 13:32:00 time: 1.6694 data_time: 0.1997 memory: 36546 loss_ce: 0.0557 loss: 0.0557 2022/09/16 17:16:20 - mmengine - INFO - Epoch(train) [8][6900/8498] lr: 4.0000e-04 eta: 13:29:44 time: 1.2437 data_time: 0.0210 memory: 36546 loss_ce: 0.0569 loss: 0.0569 2022/09/16 17:18:42 - mmengine - INFO - Epoch(train) [8][7000/8498] lr: 4.0000e-04 eta: 13:27:31 time: 1.1840 data_time: 0.2374 memory: 36546 loss_ce: 0.0552 loss: 0.0552 2022/09/16 17:21:02 - mmengine - INFO - Epoch(train) [8][7100/8498] lr: 4.0000e-04 eta: 13:25:16 time: 1.2851 data_time: 0.2474 memory: 36546 loss_ce: 0.0552 loss: 0.0552 2022/09/16 17:23:21 - mmengine - INFO - Epoch(train) [8][7200/8498] lr: 4.0000e-04 eta: 13:23:01 time: 1.3813 data_time: 0.2865 memory: 36546 loss_ce: 0.0633 loss: 0.0633 2022/09/16 17:25:41 - mmengine - INFO - Epoch(train) [8][7300/8498] lr: 4.0000e-04 eta: 13:20:46 time: 1.5878 data_time: 0.2304 memory: 36546 loss_ce: 0.0544 loss: 0.0544 2022/09/16 17:28:03 - mmengine - INFO - Epoch(train) [8][7400/8498] lr: 4.0000e-04 eta: 13:18:32 time: 1.6657 data_time: 0.2619 memory: 36546 loss_ce: 0.0633 loss: 0.0633 2022/09/16 17:30:21 - mmengine - INFO - Epoch(train) [8][7500/8498] lr: 4.0000e-04 eta: 13:16:17 time: 1.1938 data_time: 0.0222 memory: 36546 loss_ce: 0.0542 loss: 0.0542 2022/09/16 17:30:43 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 17:32:43 - mmengine - INFO - Epoch(train) [8][7600/8498] lr: 4.0000e-04 eta: 13:14:03 time: 1.2114 data_time: 0.2190 memory: 36546 loss_ce: 0.0486 loss: 0.0486 2022/09/16 17:35:03 - mmengine - INFO - Epoch(train) [8][7700/8498] lr: 4.0000e-04 eta: 13:11:48 time: 1.2791 data_time: 0.2513 memory: 36546 loss_ce: 0.0533 loss: 0.0533 2022/09/16 17:37:24 - mmengine - INFO - Epoch(train) [8][7800/8498] lr: 4.0000e-04 eta: 13:09:34 time: 1.4397 data_time: 0.3092 memory: 36546 loss_ce: 0.0512 loss: 0.0512 2022/09/16 17:39:46 - mmengine - INFO - Epoch(train) [8][7900/8498] lr: 4.0000e-04 eta: 13:07:20 time: 1.5324 data_time: 0.1849 memory: 36546 loss_ce: 0.0565 loss: 0.0565 2022/09/16 17:42:08 - mmengine - INFO - Epoch(train) [8][8000/8498] lr: 4.0000e-04 eta: 13:05:06 time: 1.6305 data_time: 0.2272 memory: 36546 loss_ce: 0.0550 loss: 0.0550 2022/09/16 17:44:28 - mmengine - INFO - Epoch(train) [8][8100/8498] lr: 4.0000e-04 eta: 13:02:51 time: 1.2653 data_time: 0.0577 memory: 36546 loss_ce: 0.0502 loss: 0.0502 2022/09/16 17:46:48 - mmengine - INFO - Epoch(train) [8][8200/8498] lr: 4.0000e-04 eta: 13:00:37 time: 1.2409 data_time: 0.2198 memory: 36546 loss_ce: 0.0574 loss: 0.0574 2022/09/16 17:49:10 - mmengine - INFO - Epoch(train) [8][8300/8498] lr: 4.0000e-04 eta: 12:58:22 time: 1.2819 data_time: 0.2859 memory: 36546 loss_ce: 0.0591 loss: 0.0591 2022/09/16 17:51:30 - mmengine - INFO - Epoch(train) [8][8400/8498] lr: 4.0000e-04 eta: 12:56:08 time: 1.2855 data_time: 0.2515 memory: 36546 loss_ce: 0.0500 loss: 0.0500 2022/09/16 17:53:40 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 17:53:40 - mmengine - INFO - Saving checkpoint at 8 epochs 2022/09/16 17:55:12 - mmengine - INFO - Epoch(val) [8][100/1918] eta: 0:04:07 time: 0.1364 data_time: 0.0007 memory: 36546 2022/09/16 17:55:26 - mmengine - INFO - Epoch(val) [8][200/1918] eta: 0:04:00 time: 0.1401 data_time: 0.0008 memory: 1150 2022/09/16 17:55:40 - mmengine - INFO - Epoch(val) [8][300/1918] eta: 0:03:39 time: 0.1355 data_time: 0.0008 memory: 1150 2022/09/16 17:55:55 - mmengine - INFO - Epoch(val) [8][400/1918] eta: 0:03:27 time: 0.1364 data_time: 0.0008 memory: 1150 2022/09/16 17:56:09 - mmengine - INFO - Epoch(val) [8][500/1918] eta: 0:03:05 time: 0.1307 data_time: 0.0008 memory: 1150 2022/09/16 17:56:23 - mmengine - INFO - Epoch(val) [8][600/1918] eta: 0:03:08 time: 0.1432 data_time: 0.0008 memory: 1150 2022/09/16 17:56:36 - mmengine - INFO - Epoch(val) [8][700/1918] eta: 0:02:53 time: 0.1426 data_time: 0.0009 memory: 1150 2022/09/16 17:56:50 - mmengine - INFO - Epoch(val) [8][800/1918] eta: 0:02:32 time: 0.1368 data_time: 0.0009 memory: 1150 2022/09/16 17:57:05 - mmengine - INFO - Epoch(val) [8][900/1918] eta: 0:02:39 time: 0.1570 data_time: 0.0010 memory: 1150 2022/09/16 17:57:19 - mmengine - INFO - Epoch(val) [8][1000/1918] eta: 0:02:14 time: 0.1464 data_time: 0.0008 memory: 1150 2022/09/16 17:57:32 - mmengine - INFO - Epoch(val) [8][1100/1918] eta: 0:01:54 time: 0.1398 data_time: 0.0009 memory: 1150 2022/09/16 17:57:46 - mmengine - INFO - Epoch(val) [8][1200/1918] eta: 0:01:37 time: 0.1364 data_time: 0.0008 memory: 1150 2022/09/16 17:58:00 - mmengine - INFO - Epoch(val) [8][1300/1918] eta: 0:01:25 time: 0.1389 data_time: 0.0008 memory: 1150 2022/09/16 17:58:14 - mmengine - INFO - Epoch(val) [8][1400/1918] eta: 0:01:12 time: 0.1404 data_time: 0.0009 memory: 1150 2022/09/16 17:58:28 - mmengine - INFO - Epoch(val) [8][1500/1918] eta: 0:01:02 time: 0.1486 data_time: 0.0012 memory: 1150 2022/09/16 17:58:42 - mmengine - INFO - Epoch(val) [8][1600/1918] eta: 0:00:42 time: 0.1332 data_time: 0.0008 memory: 1150 2022/09/16 17:58:55 - mmengine - INFO - Epoch(val) [8][1700/1918] eta: 0:00:30 time: 0.1376 data_time: 0.0019 memory: 1150 2022/09/16 17:59:09 - mmengine - INFO - Epoch(val) [8][1800/1918] eta: 0:00:16 time: 0.1397 data_time: 0.0013 memory: 1150 2022/09/16 17:59:23 - mmengine - INFO - Epoch(val) [8][1900/1918] eta: 0:00:02 time: 0.1329 data_time: 0.0010 memory: 1150 2022/09/16 17:59:26 - mmengine - INFO - Epoch(val) [8][1918/1918] CUTE80/recog/word_acc_ignore_case_symbol: 0.8090 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9077 SVT/recog/word_acc_ignore_case_symbol: 0.8501 SVTP/recog/word_acc_ignore_case_symbol: 0.8000 IC13/recog/word_acc_ignore_case_symbol: 0.9291 IC15/recog/word_acc_ignore_case_symbol: 0.7159 2022/09/16 18:00:13 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 18:02:12 - mmengine - INFO - Epoch(train) [9][100/8498] lr: 4.0000e-04 eta: 12:51:46 time: 1.6806 data_time: 0.2345 memory: 36546 loss_ce: 0.0480 loss: 0.0480 2022/09/16 18:04:43 - mmengine - INFO - Epoch(train) [9][200/8498] lr: 4.0000e-04 eta: 12:49:37 time: 1.8479 data_time: 0.2618 memory: 36546 loss_ce: 0.0507 loss: 0.0507 2022/09/16 18:07:11 - mmengine - INFO - Epoch(train) [9][300/8498] lr: 4.0000e-04 eta: 12:47:25 time: 1.5815 data_time: 0.3414 memory: 36546 loss_ce: 0.0432 loss: 0.0432 2022/09/16 18:09:42 - mmengine - INFO - Epoch(train) [9][400/8498] lr: 4.0000e-04 eta: 12:45:16 time: 1.4131 data_time: 0.1860 memory: 36546 loss_ce: 0.0529 loss: 0.0529 2022/09/16 18:12:12 - mmengine - INFO - Epoch(train) [9][500/8498] lr: 4.0000e-04 eta: 12:43:06 time: 1.3902 data_time: 0.1583 memory: 36546 loss_ce: 0.0570 loss: 0.0570 2022/09/16 18:14:42 - mmengine - INFO - Epoch(train) [9][600/8498] lr: 4.0000e-04 eta: 12:40:55 time: 1.3318 data_time: 0.0606 memory: 36546 loss_ce: 0.0573 loss: 0.0573 2022/09/16 18:17:21 - mmengine - INFO - Epoch(train) [9][700/8498] lr: 4.0000e-04 eta: 12:38:49 time: 1.9443 data_time: 0.2877 memory: 36546 loss_ce: 0.0520 loss: 0.0520 2022/09/16 18:19:56 - mmengine - INFO - Epoch(train) [9][800/8498] lr: 4.0000e-04 eta: 12:36:42 time: 1.6229 data_time: 0.0066 memory: 36546 loss_ce: 0.0527 loss: 0.0527 2022/09/16 18:22:29 - mmengine - INFO - Epoch(train) [9][900/8498] lr: 4.0000e-04 eta: 12:34:32 time: 1.4359 data_time: 0.0384 memory: 36546 loss_ce: 0.0544 loss: 0.0544 2022/09/16 18:25:08 - mmengine - INFO - Epoch(train) [9][1000/8498] lr: 4.0000e-04 eta: 12:32:26 time: 1.6514 data_time: 0.4203 memory: 36546 loss_ce: 0.0499 loss: 0.0499 2022/09/16 18:25:31 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 18:27:39 - mmengine - INFO - Epoch(train) [9][1100/8498] lr: 4.0000e-04 eta: 12:30:16 time: 1.6306 data_time: 0.4104 memory: 36546 loss_ce: 0.0498 loss: 0.0498 2022/09/16 18:30:16 - mmengine - INFO - Epoch(train) [9][1200/8498] lr: 4.0000e-04 eta: 12:28:09 time: 1.7073 data_time: 0.4961 memory: 36546 loss_ce: 0.0542 loss: 0.0542 2022/09/16 18:33:06 - mmengine - INFO - Epoch(train) [9][1300/8498] lr: 4.0000e-04 eta: 12:26:08 time: 1.8801 data_time: 0.3536 memory: 36546 loss_ce: 0.0474 loss: 0.0474 2022/09/16 18:35:42 - mmengine - INFO - Epoch(train) [9][1400/8498] lr: 4.0000e-04 eta: 12:23:59 time: 1.6107 data_time: 0.0063 memory: 36546 loss_ce: 0.0452 loss: 0.0452 2022/09/16 18:38:15 - mmengine - INFO - Epoch(train) [9][1500/8498] lr: 4.0000e-04 eta: 12:21:50 time: 1.5934 data_time: 0.1438 memory: 36546 loss_ce: 0.0519 loss: 0.0519 2022/09/16 18:42:27 - mmengine - INFO - Epoch(train) [9][1600/8498] lr: 4.0000e-04 eta: 12:20:26 time: 1.3608 data_time: 0.1539 memory: 36546 loss_ce: 0.0498 loss: 0.0498 2022/09/16 18:45:06 - mmengine - INFO - Epoch(train) [9][1700/8498] lr: 4.0000e-04 eta: 12:18:19 time: 1.7457 data_time: 0.1487 memory: 36546 loss_ce: 0.0508 loss: 0.0508 2022/09/16 18:47:46 - mmengine - INFO - Epoch(train) [9][1800/8498] lr: 4.0000e-04 eta: 12:16:13 time: 1.9285 data_time: 0.3447 memory: 36546 loss_ce: 0.0496 loss: 0.0496 2022/09/16 18:50:23 - mmengine - INFO - Epoch(train) [9][1900/8498] lr: 4.0000e-04 eta: 12:14:05 time: 1.5492 data_time: 0.2109 memory: 36546 loss_ce: 0.0521 loss: 0.0521 2022/09/16 18:52:56 - mmengine - INFO - Epoch(train) [9][2000/8498] lr: 4.0000e-04 eta: 12:11:55 time: 1.4257 data_time: 0.0358 memory: 36546 loss_ce: 0.0498 loss: 0.0498 2022/09/16 18:53:23 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 18:55:33 - mmengine - INFO - Epoch(train) [9][2100/8498] lr: 4.0000e-04 eta: 12:09:46 time: 1.2805 data_time: 0.1927 memory: 36546 loss_ce: 0.0516 loss: 0.0516 2022/09/16 18:58:14 - mmengine - INFO - Epoch(train) [9][2200/8498] lr: 4.0000e-04 eta: 12:07:40 time: 1.6667 data_time: 0.3705 memory: 36546 loss_ce: 0.0464 loss: 0.0464 2022/09/16 19:00:54 - mmengine - INFO - Epoch(train) [9][2300/8498] lr: 4.0000e-04 eta: 12:05:33 time: 1.6756 data_time: 0.2769 memory: 36546 loss_ce: 0.0589 loss: 0.0589 2022/09/16 19:03:45 - mmengine - INFO - Epoch(train) [9][2400/8498] lr: 4.0000e-04 eta: 12:03:30 time: 3.0124 data_time: 1.5979 memory: 36546 loss_ce: 0.0566 loss: 0.0566 2022/09/16 19:06:22 - mmengine - INFO - Epoch(train) [9][2500/8498] lr: 4.0000e-04 eta: 12:01:22 time: 1.5522 data_time: 0.1745 memory: 36546 loss_ce: 0.0529 loss: 0.0529 2022/09/16 19:08:56 - mmengine - INFO - Epoch(train) [9][2600/8498] lr: 4.0000e-04 eta: 11:59:12 time: 1.5493 data_time: 0.0067 memory: 36546 loss_ce: 0.0577 loss: 0.0577 2022/09/16 19:11:31 - mmengine - INFO - Epoch(train) [9][2700/8498] lr: 4.0000e-04 eta: 11:57:02 time: 1.4152 data_time: 0.0066 memory: 36546 loss_ce: 0.0460 loss: 0.0460 2022/09/16 19:14:09 - mmengine - INFO - Epoch(train) [9][2800/8498] lr: 4.0000e-04 eta: 11:54:53 time: 1.5815 data_time: 0.3079 memory: 36546 loss_ce: 0.0557 loss: 0.0557 2022/09/16 19:16:49 - mmengine - INFO - Epoch(train) [9][2900/8498] lr: 4.0000e-04 eta: 11:52:46 time: 1.7342 data_time: 0.2992 memory: 36546 loss_ce: 0.0520 loss: 0.0520 2022/09/16 19:19:27 - mmengine - INFO - Epoch(train) [9][3000/8498] lr: 4.0000e-04 eta: 11:50:37 time: 1.8618 data_time: 0.4349 memory: 36546 loss_ce: 0.0448 loss: 0.0448 2022/09/16 19:19:53 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 19:22:06 - mmengine - INFO - Epoch(train) [9][3100/8498] lr: 4.0000e-04 eta: 11:48:29 time: 1.5558 data_time: 0.1700 memory: 36546 loss_ce: 0.0530 loss: 0.0530 2022/09/16 19:24:43 - mmengine - INFO - Epoch(train) [9][3200/8498] lr: 4.0000e-04 eta: 11:46:19 time: 1.5245 data_time: 0.0068 memory: 36546 loss_ce: 0.0480 loss: 0.0480 2022/09/16 19:27:20 - mmengine - INFO - Epoch(train) [9][3300/8498] lr: 4.0000e-04 eta: 11:44:10 time: 1.5144 data_time: 0.0075 memory: 36546 loss_ce: 0.0546 loss: 0.0546 2022/09/16 19:30:02 - mmengine - INFO - Epoch(train) [9][3400/8498] lr: 4.0000e-04 eta: 11:42:03 time: 1.5116 data_time: 0.2631 memory: 36546 loss_ce: 0.0436 loss: 0.0436 2022/09/16 19:32:40 - mmengine - INFO - Epoch(train) [9][3500/8498] lr: 4.0000e-04 eta: 11:39:54 time: 1.6315 data_time: 0.2546 memory: 36546 loss_ce: 0.0475 loss: 0.0475 2022/09/16 19:35:20 - mmengine - INFO - Epoch(train) [9][3600/8498] lr: 4.0000e-04 eta: 11:37:46 time: 1.9671 data_time: 0.4370 memory: 36546 loss_ce: 0.0496 loss: 0.0496 2022/09/16 19:38:01 - mmengine - INFO - Epoch(train) [9][3700/8498] lr: 4.0000e-04 eta: 11:35:38 time: 1.6604 data_time: 0.1421 memory: 36546 loss_ce: 0.0494 loss: 0.0494 2022/09/16 19:40:46 - mmengine - INFO - Epoch(train) [9][3800/8498] lr: 4.0000e-04 eta: 11:33:31 time: 2.0161 data_time: 0.0085 memory: 36546 loss_ce: 0.0530 loss: 0.0530 2022/09/16 19:43:20 - mmengine - INFO - Epoch(train) [9][3900/8498] lr: 4.0000e-04 eta: 11:31:20 time: 1.6197 data_time: 0.0166 memory: 36546 loss_ce: 0.0475 loss: 0.0475 2022/09/16 19:45:57 - mmengine - INFO - Epoch(train) [9][4000/8498] lr: 4.0000e-04 eta: 11:29:11 time: 1.6112 data_time: 0.4137 memory: 36546 loss_ce: 0.0492 loss: 0.0492 2022/09/16 19:46:21 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 19:48:26 - mmengine - INFO - Epoch(train) [9][4100/8498] lr: 4.0000e-04 eta: 11:26:57 time: 1.4078 data_time: 0.0670 memory: 36546 loss_ce: 0.0505 loss: 0.0505 2022/09/16 19:51:20 - mmengine - INFO - Epoch(train) [9][4200/8498] lr: 4.0000e-04 eta: 11:24:54 time: 2.9970 data_time: 1.4724 memory: 36546 loss_ce: 0.0451 loss: 0.0451 2022/09/16 19:53:56 - mmengine - INFO - Epoch(train) [9][4300/8498] lr: 4.0000e-04 eta: 11:22:44 time: 1.3487 data_time: 0.0532 memory: 36546 loss_ce: 0.0451 loss: 0.0451 2022/09/16 19:56:56 - mmengine - INFO - Epoch(train) [9][4400/8498] lr: 4.0000e-04 eta: 11:20:43 time: 1.5219 data_time: 0.0073 memory: 36546 loss_ce: 0.0538 loss: 0.0538 2022/09/16 19:59:53 - mmengine - INFO - Epoch(train) [9][4500/8498] lr: 4.0000e-04 eta: 11:18:41 time: 1.3830 data_time: 0.0084 memory: 36546 loss_ce: 0.0484 loss: 0.0484 2022/09/16 20:02:34 - mmengine - INFO - Epoch(train) [9][4600/8498] lr: 4.0000e-04 eta: 11:16:32 time: 1.5435 data_time: 0.3879 memory: 36546 loss_ce: 0.0512 loss: 0.0512 2022/09/16 20:05:11 - mmengine - INFO - Epoch(train) [9][4700/8498] lr: 4.0000e-04 eta: 11:14:22 time: 1.6911 data_time: 0.3630 memory: 36546 loss_ce: 0.0506 loss: 0.0506 2022/09/16 20:07:46 - mmengine - INFO - Epoch(train) [9][4800/8498] lr: 4.0000e-04 eta: 11:12:10 time: 1.7729 data_time: 0.3130 memory: 36546 loss_ce: 0.0487 loss: 0.0487 2022/09/16 20:10:15 - mmengine - INFO - Epoch(train) [9][4900/8498] lr: 4.0000e-04 eta: 11:09:56 time: 1.5848 data_time: 0.1232 memory: 36546 loss_ce: 0.0471 loss: 0.0471 2022/09/16 20:12:43 - mmengine - INFO - Epoch(train) [9][5000/8498] lr: 4.0000e-04 eta: 11:07:42 time: 1.6206 data_time: 0.0763 memory: 36546 loss_ce: 0.0496 loss: 0.0496 2022/09/16 20:13:06 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 20:15:09 - mmengine - INFO - Epoch(train) [9][5100/8498] lr: 4.0000e-04 eta: 11:05:27 time: 1.4678 data_time: 0.0990 memory: 36546 loss_ce: 0.0481 loss: 0.0481 2022/09/16 20:17:41 - mmengine - INFO - Epoch(train) [9][5200/8498] lr: 4.0000e-04 eta: 11:03:14 time: 1.4977 data_time: 0.3094 memory: 36546 loss_ce: 0.0578 loss: 0.0578 2022/09/16 20:20:07 - mmengine - INFO - Epoch(train) [9][5300/8498] lr: 4.0000e-04 eta: 11:00:59 time: 1.3985 data_time: 0.2617 memory: 36546 loss_ce: 0.0524 loss: 0.0524 2022/09/16 20:22:36 - mmengine - INFO - Epoch(train) [9][5400/8498] lr: 4.0000e-04 eta: 10:58:45 time: 1.6252 data_time: 0.3478 memory: 36546 loss_ce: 0.0523 loss: 0.0523 2022/09/16 20:25:02 - mmengine - INFO - Epoch(train) [9][5500/8498] lr: 4.0000e-04 eta: 10:56:29 time: 1.6203 data_time: 0.1374 memory: 36546 loss_ce: 0.0437 loss: 0.0437 2022/09/16 20:27:29 - mmengine - INFO - Epoch(train) [9][5600/8498] lr: 4.0000e-04 eta: 10:54:15 time: 1.6164 data_time: 0.0628 memory: 36546 loss_ce: 0.0451 loss: 0.0451 2022/09/16 20:29:57 - mmengine - INFO - Epoch(train) [9][5700/8498] lr: 4.0000e-04 eta: 10:52:00 time: 1.4613 data_time: 0.0783 memory: 36546 loss_ce: 0.0466 loss: 0.0466 2022/09/16 20:32:28 - mmengine - INFO - Epoch(train) [9][5800/8498] lr: 4.0000e-04 eta: 10:49:47 time: 1.4979 data_time: 0.3158 memory: 36546 loss_ce: 0.0503 loss: 0.0503 2022/09/16 20:34:55 - mmengine - INFO - Epoch(train) [9][5900/8498] lr: 4.0000e-04 eta: 10:47:32 time: 1.4500 data_time: 0.2749 memory: 36546 loss_ce: 0.0453 loss: 0.0453 2022/09/16 20:37:22 - mmengine - INFO - Epoch(train) [9][6000/8498] lr: 4.0000e-04 eta: 10:45:17 time: 1.6681 data_time: 0.3412 memory: 36546 loss_ce: 0.0540 loss: 0.0540 2022/09/16 20:37:48 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 20:39:54 - mmengine - INFO - Epoch(train) [9][6100/8498] lr: 4.0000e-04 eta: 10:43:04 time: 1.6585 data_time: 0.1360 memory: 36546 loss_ce: 0.0435 loss: 0.0435 2022/09/16 20:42:20 - mmengine - INFO - Epoch(train) [9][6200/8498] lr: 4.0000e-04 eta: 10:40:48 time: 1.5282 data_time: 0.0963 memory: 36546 loss_ce: 0.0544 loss: 0.0544 2022/09/16 20:44:44 - mmengine - INFO - Epoch(train) [9][6300/8498] lr: 4.0000e-04 eta: 10:38:32 time: 1.3445 data_time: 0.0782 memory: 36546 loss_ce: 0.0504 loss: 0.0504 2022/09/16 20:47:16 - mmengine - INFO - Epoch(train) [9][6400/8498] lr: 4.0000e-04 eta: 10:36:18 time: 1.5105 data_time: 0.3071 memory: 36546 loss_ce: 0.0453 loss: 0.0453 2022/09/16 20:49:44 - mmengine - INFO - Epoch(train) [9][6500/8498] lr: 4.0000e-04 eta: 10:34:04 time: 1.7439 data_time: 0.3201 memory: 36546 loss_ce: 0.0481 loss: 0.0481 2022/09/16 20:52:07 - mmengine - INFO - Epoch(train) [9][6600/8498] lr: 4.0000e-04 eta: 10:31:47 time: 1.6585 data_time: 0.3667 memory: 36546 loss_ce: 0.0470 loss: 0.0470 2022/09/16 20:54:33 - mmengine - INFO - Epoch(train) [9][6700/8498] lr: 4.0000e-04 eta: 10:29:31 time: 1.3867 data_time: 0.1421 memory: 36546 loss_ce: 0.0517 loss: 0.0517 2022/09/16 20:56:58 - mmengine - INFO - Epoch(train) [9][6800/8498] lr: 4.0000e-04 eta: 10:27:15 time: 1.3434 data_time: 0.0229 memory: 36546 loss_ce: 0.0486 loss: 0.0486 2022/09/16 20:59:18 - mmengine - INFO - Epoch(train) [9][6900/8498] lr: 4.0000e-04 eta: 10:24:58 time: 1.2346 data_time: 0.0200 memory: 36546 loss_ce: 0.0490 loss: 0.0490 2022/09/16 21:01:46 - mmengine - INFO - Epoch(train) [9][7000/8498] lr: 4.0000e-04 eta: 10:22:43 time: 1.4757 data_time: 0.2839 memory: 36546 loss_ce: 0.0518 loss: 0.0518 2022/09/16 21:02:08 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 21:04:34 - mmengine - INFO - Epoch(train) [9][7100/8498] lr: 4.0000e-04 eta: 10:20:35 time: 1.5142 data_time: 0.1273 memory: 36546 loss_ce: 0.0488 loss: 0.0488 2022/09/16 21:07:26 - mmengine - INFO - Epoch(train) [9][7200/8498] lr: 4.0000e-04 eta: 10:18:28 time: 1.6270 data_time: 0.3757 memory: 36546 loss_ce: 0.0492 loss: 0.0492 2022/09/16 21:09:59 - mmengine - INFO - Epoch(train) [9][7300/8498] lr: 4.0000e-04 eta: 10:16:15 time: 1.9073 data_time: 0.2924 memory: 36546 loss_ce: 0.0468 loss: 0.0468 2022/09/16 21:12:29 - mmengine - INFO - Epoch(train) [9][7400/8498] lr: 4.0000e-04 eta: 10:14:00 time: 1.7758 data_time: 0.1972 memory: 36546 loss_ce: 0.0524 loss: 0.0524 2022/09/16 21:15:02 - mmengine - INFO - Epoch(train) [9][7500/8498] lr: 4.0000e-04 eta: 10:11:47 time: 1.3526 data_time: 0.0063 memory: 36546 loss_ce: 0.0544 loss: 0.0544 2022/09/16 21:17:37 - mmengine - INFO - Epoch(train) [9][7600/8498] lr: 4.0000e-04 eta: 10:09:34 time: 1.6582 data_time: 0.1652 memory: 36546 loss_ce: 0.0530 loss: 0.0530 2022/09/16 21:20:02 - mmengine - INFO - Epoch(train) [9][7700/8498] lr: 4.0000e-04 eta: 10:07:18 time: 1.6833 data_time: 0.0958 memory: 36546 loss_ce: 0.0474 loss: 0.0474 2022/09/16 21:23:09 - mmengine - INFO - Epoch(train) [9][7800/8498] lr: 4.0000e-04 eta: 10:05:16 time: 2.3520 data_time: 0.3782 memory: 36546 loss_ce: 0.0517 loss: 0.0517 2022/09/16 21:25:32 - mmengine - INFO - Epoch(train) [9][7900/8498] lr: 4.0000e-04 eta: 10:02:59 time: 1.9599 data_time: 0.4683 memory: 36546 loss_ce: 0.0536 loss: 0.0536 2022/09/16 21:27:59 - mmengine - INFO - Epoch(train) [9][8000/8498] lr: 4.0000e-04 eta: 10:00:43 time: 1.9567 data_time: 0.2864 memory: 36546 loss_ce: 0.0554 loss: 0.0554 2022/09/16 21:28:24 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 21:30:23 - mmengine - INFO - Epoch(train) [9][8100/8498] lr: 4.0000e-04 eta: 9:58:26 time: 1.2197 data_time: 0.0062 memory: 36546 loss_ce: 0.0553 loss: 0.0553 2022/09/16 21:32:47 - mmengine - INFO - Epoch(train) [9][8200/8498] lr: 4.0000e-04 eta: 9:56:09 time: 1.3904 data_time: 0.0519 memory: 36546 loss_ce: 0.0511 loss: 0.0511 2022/09/16 21:35:05 - mmengine - INFO - Epoch(train) [9][8300/8498] lr: 4.0000e-04 eta: 9:53:50 time: 1.3114 data_time: 0.1065 memory: 36546 loss_ce: 0.0495 loss: 0.0495 2022/09/16 21:37:54 - mmengine - INFO - Epoch(train) [9][8400/8498] lr: 4.0000e-04 eta: 9:51:42 time: 1.5592 data_time: 0.4055 memory: 36546 loss_ce: 0.0484 loss: 0.0484 2022/09/16 21:41:26 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 21:41:26 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/09/16 21:41:57 - mmengine - INFO - Epoch(val) [9][100/1918] eta: 0:04:16 time: 0.1412 data_time: 0.0008 memory: 36546 2022/09/16 21:42:11 - mmengine - INFO - Epoch(val) [9][200/1918] eta: 0:03:45 time: 0.1310 data_time: 0.0008 memory: 1150 2022/09/16 21:42:25 - mmengine - INFO - Epoch(val) [9][300/1918] eta: 0:03:51 time: 0.1428 data_time: 0.0010 memory: 1150 2022/09/16 21:42:39 - mmengine - INFO - Epoch(val) [9][400/1918] eta: 0:03:25 time: 0.1353 data_time: 0.0007 memory: 1150 2022/09/16 21:42:53 - mmengine - INFO - Epoch(val) [9][500/1918] eta: 0:03:07 time: 0.1325 data_time: 0.0008 memory: 1150 2022/09/16 21:43:07 - mmengine - INFO - Epoch(val) [9][600/1918] eta: 0:03:00 time: 0.1371 data_time: 0.0008 memory: 1150 2022/09/16 21:43:21 - mmengine - INFO - Epoch(val) [9][700/1918] eta: 0:02:47 time: 0.1377 data_time: 0.0008 memory: 1150 2022/09/16 21:43:35 - mmengine - INFO - Epoch(val) [9][800/1918] eta: 0:02:56 time: 0.1577 data_time: 0.0017 memory: 1150 2022/09/16 21:43:49 - mmengine - INFO - Epoch(val) [9][900/1918] eta: 0:02:36 time: 0.1537 data_time: 0.0010 memory: 1150 2022/09/16 21:44:03 - mmengine - INFO - Epoch(val) [9][1000/1918] eta: 0:02:04 time: 0.1359 data_time: 0.0008 memory: 1150 2022/09/16 21:44:16 - mmengine - INFO - Epoch(val) [9][1100/1918] eta: 0:01:50 time: 0.1355 data_time: 0.0009 memory: 1150 2022/09/16 21:44:30 - mmengine - INFO - Epoch(val) [9][1200/1918] eta: 0:01:39 time: 0.1380 data_time: 0.0009 memory: 1150 2022/09/16 21:44:44 - mmengine - INFO - Epoch(val) [9][1300/1918] eta: 0:01:22 time: 0.1338 data_time: 0.0008 memory: 1150 2022/09/16 21:44:58 - mmengine - INFO - Epoch(val) [9][1400/1918] eta: 0:01:11 time: 0.1372 data_time: 0.0009 memory: 1150 2022/09/16 21:45:12 - mmengine - INFO - Epoch(val) [9][1500/1918] eta: 0:00:57 time: 0.1364 data_time: 0.0010 memory: 1150 2022/09/16 21:45:26 - mmengine - INFO - Epoch(val) [9][1600/1918] eta: 0:00:44 time: 0.1398 data_time: 0.0020 memory: 1150 2022/09/16 21:45:39 - mmengine - INFO - Epoch(val) [9][1700/1918] eta: 0:00:29 time: 0.1348 data_time: 0.0009 memory: 1150 2022/09/16 21:45:53 - mmengine - INFO - Epoch(val) [9][1800/1918] eta: 0:00:16 time: 0.1394 data_time: 0.0039 memory: 1150 2022/09/16 21:46:07 - mmengine - INFO - Epoch(val) [9][1900/1918] eta: 0:00:02 time: 0.1320 data_time: 0.0008 memory: 1150 2022/09/16 21:47:00 - mmengine - INFO - Epoch(val) [9][1918/1918] CUTE80/recog/word_acc_ignore_case_symbol: 0.8611 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9457 SVT/recog/word_acc_ignore_case_symbol: 0.8717 SVTP/recog/word_acc_ignore_case_symbol: 0.8047 IC13/recog/word_acc_ignore_case_symbol: 0.9409 IC15/recog/word_acc_ignore_case_symbol: 0.7482 2022/09/16 21:49:59 - mmengine - INFO - Epoch(train) [10][100/8498] lr: 4.0000e-04 eta: 9:47:43 time: 1.2600 data_time: 0.2276 memory: 36546 loss_ce: 0.0499 loss: 0.0499 2022/09/16 21:52:36 - mmengine - INFO - Epoch(train) [10][200/8498] lr: 4.0000e-04 eta: 9:45:30 time: 1.4236 data_time: 0.1795 memory: 36546 loss_ce: 0.0420 loss: 0.0420 2022/09/16 21:55:17 - mmengine - INFO - Epoch(train) [10][300/8498] lr: 4.0000e-04 eta: 9:43:18 time: 1.5350 data_time: 0.2207 memory: 36546 loss_ce: 0.0500 loss: 0.0500 2022/09/16 21:58:01 - mmengine - INFO - Epoch(train) [10][400/8498] lr: 4.0000e-04 eta: 9:41:08 time: 1.7856 data_time: 0.2150 memory: 36546 loss_ce: 0.0443 loss: 0.0443 2022/09/16 22:00:36 - mmengine - INFO - Epoch(train) [10][500/8498] lr: 4.0000e-04 eta: 9:38:54 time: 1.8646 data_time: 0.2551 memory: 36546 loss_ce: 0.0406 loss: 0.0406 2022/09/16 22:01:08 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 22:03:17 - mmengine - INFO - Epoch(train) [10][600/8498] lr: 4.0000e-04 eta: 9:36:42 time: 1.2897 data_time: 0.2459 memory: 36546 loss_ce: 0.0445 loss: 0.0445 2022/09/16 22:05:56 - mmengine - INFO - Epoch(train) [10][700/8498] lr: 4.0000e-04 eta: 9:34:30 time: 1.5058 data_time: 0.2269 memory: 36546 loss_ce: 0.0449 loss: 0.0449 2022/09/16 22:08:38 - mmengine - INFO - Epoch(train) [10][800/8498] lr: 4.0000e-04 eta: 9:32:18 time: 1.2963 data_time: 0.0073 memory: 36546 loss_ce: 0.0463 loss: 0.0463 2022/09/16 22:11:14 - mmengine - INFO - Epoch(train) [10][900/8498] lr: 4.0000e-04 eta: 9:30:04 time: 1.5309 data_time: 0.2053 memory: 36546 loss_ce: 0.0456 loss: 0.0456 2022/09/16 22:15:46 - mmengine - INFO - Epoch(train) [10][1000/8498] lr: 4.0000e-04 eta: 9:28:27 time: 4.4848 data_time: 0.2339 memory: 36546 loss_ce: 0.0521 loss: 0.0521 2022/09/16 22:18:37 - mmengine - INFO - Epoch(train) [10][1100/8498] lr: 4.0000e-04 eta: 9:26:18 time: 1.5493 data_time: 0.1544 memory: 36546 loss_ce: 0.0458 loss: 0.0458 2022/09/16 22:21:18 - mmengine - INFO - Epoch(train) [10][1200/8498] lr: 4.0000e-04 eta: 9:24:05 time: 1.4496 data_time: 0.3606 memory: 36546 loss_ce: 0.0521 loss: 0.0521 2022/09/16 22:24:01 - mmengine - INFO - Epoch(train) [10][1300/8498] lr: 4.0000e-04 eta: 9:21:53 time: 1.7721 data_time: 0.3297 memory: 36546 loss_ce: 0.0447 loss: 0.0447 2022/09/16 22:26:39 - mmengine - INFO - Epoch(train) [10][1400/8498] lr: 4.0000e-04 eta: 9:19:40 time: 1.2895 data_time: 0.0082 memory: 36546 loss_ce: 0.0480 loss: 0.0480 2022/09/16 22:29:18 - mmengine - INFO - Epoch(train) [10][1500/8498] lr: 4.0000e-04 eta: 9:17:26 time: 1.4204 data_time: 0.0761 memory: 36546 loss_ce: 0.0416 loss: 0.0416 2022/09/16 22:29:51 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 22:32:04 - mmengine - INFO - Epoch(train) [10][1600/8498] lr: 4.0000e-04 eta: 9:15:15 time: 1.6157 data_time: 0.0945 memory: 36546 loss_ce: 0.0431 loss: 0.0431 2022/09/16 22:34:46 - mmengine - INFO - Epoch(train) [10][1700/8498] lr: 4.0000e-04 eta: 9:13:02 time: 1.6822 data_time: 0.1543 memory: 36546 loss_ce: 0.0453 loss: 0.0453 2022/09/16 22:37:31 - mmengine - INFO - Epoch(train) [10][1800/8498] lr: 4.0000e-04 eta: 9:10:51 time: 1.5507 data_time: 0.4175 memory: 36546 loss_ce: 0.0533 loss: 0.0533 2022/09/16 22:40:14 - mmengine - INFO - Epoch(train) [10][1900/8498] lr: 4.0000e-04 eta: 9:08:38 time: 1.8329 data_time: 0.3898 memory: 36546 loss_ce: 0.0479 loss: 0.0479 2022/09/16 22:42:54 - mmengine - INFO - Epoch(train) [10][2000/8498] lr: 4.0000e-04 eta: 9:06:25 time: 1.3117 data_time: 0.0081 memory: 36546 loss_ce: 0.0495 loss: 0.0495 2022/09/16 22:45:33 - mmengine - INFO - Epoch(train) [10][2100/8498] lr: 4.0000e-04 eta: 9:04:11 time: 1.3831 data_time: 0.0636 memory: 36546 loss_ce: 0.0475 loss: 0.0475 2022/09/16 22:48:14 - mmengine - INFO - Epoch(train) [10][2200/8498] lr: 4.0000e-04 eta: 9:01:58 time: 1.5848 data_time: 0.0793 memory: 36546 loss_ce: 0.0462 loss: 0.0462 2022/09/16 22:50:55 - mmengine - INFO - Epoch(train) [10][2300/8498] lr: 4.0000e-04 eta: 8:59:45 time: 1.7288 data_time: 0.1515 memory: 36546 loss_ce: 0.0506 loss: 0.0506 2022/09/16 22:53:39 - mmengine - INFO - Epoch(train) [10][2400/8498] lr: 4.0000e-04 eta: 8:57:32 time: 1.4803 data_time: 0.3670 memory: 36546 loss_ce: 0.0542 loss: 0.0542 2022/09/16 22:56:23 - mmengine - INFO - Epoch(train) [10][2500/8498] lr: 4.0000e-04 eta: 8:55:19 time: 1.8015 data_time: 0.3453 memory: 36546 loss_ce: 0.0504 loss: 0.0504 2022/09/16 22:56:52 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 22:59:02 - mmengine - INFO - Epoch(train) [10][2600/8498] lr: 4.0000e-04 eta: 8:53:05 time: 1.2921 data_time: 0.0063 memory: 36546 loss_ce: 0.0504 loss: 0.0504 2022/09/16 23:01:43 - mmengine - INFO - Epoch(train) [10][2700/8498] lr: 4.0000e-04 eta: 8:50:52 time: 1.4921 data_time: 0.0625 memory: 36546 loss_ce: 0.0489 loss: 0.0489 2022/09/16 23:04:27 - mmengine - INFO - Epoch(train) [10][2800/8498] lr: 4.0000e-04 eta: 8:48:39 time: 1.5022 data_time: 0.0602 memory: 36546 loss_ce: 0.0486 loss: 0.0486 2022/09/16 23:07:07 - mmengine - INFO - Epoch(train) [10][2900/8498] lr: 4.0000e-04 eta: 8:46:25 time: 1.7460 data_time: 0.1303 memory: 36546 loss_ce: 0.0488 loss: 0.0488 2022/09/16 23:09:51 - mmengine - INFO - Epoch(train) [10][3000/8498] lr: 4.0000e-04 eta: 8:44:12 time: 1.4611 data_time: 0.3894 memory: 36546 loss_ce: 0.0480 loss: 0.0480 2022/09/16 23:12:35 - mmengine - INFO - Epoch(train) [10][3100/8498] lr: 4.0000e-04 eta: 8:41:59 time: 1.7591 data_time: 0.3182 memory: 36546 loss_ce: 0.0479 loss: 0.0479 2022/09/16 23:15:14 - mmengine - INFO - Epoch(train) [10][3200/8498] lr: 4.0000e-04 eta: 8:39:44 time: 1.2845 data_time: 0.0143 memory: 36546 loss_ce: 0.0431 loss: 0.0431 2022/09/16 23:17:54 - mmengine - INFO - Epoch(train) [10][3300/8498] lr: 4.0000e-04 eta: 8:37:30 time: 1.4512 data_time: 0.0982 memory: 36546 loss_ce: 0.0463 loss: 0.0463 2022/09/16 23:20:38 - mmengine - INFO - Epoch(train) [10][3400/8498] lr: 4.0000e-04 eta: 8:35:17 time: 1.5259 data_time: 0.0594 memory: 36546 loss_ce: 0.0477 loss: 0.0477 2022/09/16 23:23:14 - mmengine - INFO - Epoch(train) [10][3500/8498] lr: 4.0000e-04 eta: 8:33:01 time: 1.6618 data_time: 0.1574 memory: 36546 loss_ce: 0.0539 loss: 0.0539 2022/09/16 23:23:46 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 23:26:00 - mmengine - INFO - Epoch(train) [10][3600/8498] lr: 4.0000e-04 eta: 8:30:49 time: 1.6363 data_time: 0.4253 memory: 36546 loss_ce: 0.0475 loss: 0.0475 2022/09/16 23:28:41 - mmengine - INFO - Epoch(train) [10][3700/8498] lr: 4.0000e-04 eta: 8:28:34 time: 1.8062 data_time: 0.3520 memory: 36546 loss_ce: 0.0582 loss: 0.0582 2022/09/16 23:31:22 - mmengine - INFO - Epoch(train) [10][3800/8498] lr: 4.0000e-04 eta: 8:26:20 time: 1.3782 data_time: 0.0068 memory: 36546 loss_ce: 0.0548 loss: 0.0548 2022/09/16 23:34:00 - mmengine - INFO - Epoch(train) [10][3900/8498] lr: 4.0000e-04 eta: 8:24:05 time: 1.3855 data_time: 0.0759 memory: 36546 loss_ce: 0.0515 loss: 0.0515 2022/09/16 23:36:44 - mmengine - INFO - Epoch(train) [10][4000/8498] lr: 4.0000e-04 eta: 8:21:51 time: 1.5452 data_time: 0.1103 memory: 36546 loss_ce: 0.0523 loss: 0.0523 2022/09/16 23:39:24 - mmengine - INFO - Epoch(train) [10][4100/8498] lr: 4.0000e-04 eta: 8:19:36 time: 1.6058 data_time: 0.1502 memory: 36546 loss_ce: 0.0500 loss: 0.0500 2022/09/16 23:42:05 - mmengine - INFO - Epoch(train) [10][4200/8498] lr: 4.0000e-04 eta: 8:17:22 time: 1.5032 data_time: 0.4361 memory: 36546 loss_ce: 0.0498 loss: 0.0498 2022/09/16 23:44:47 - mmengine - INFO - Epoch(train) [10][4300/8498] lr: 4.0000e-04 eta: 8:15:07 time: 1.8319 data_time: 0.3201 memory: 36546 loss_ce: 0.0484 loss: 0.0484 2022/09/16 23:47:29 - mmengine - INFO - Epoch(train) [10][4400/8498] lr: 4.0000e-04 eta: 8:12:53 time: 1.3482 data_time: 0.0072 memory: 36546 loss_ce: 0.0456 loss: 0.0456 2022/09/16 23:50:10 - mmengine - INFO - Epoch(train) [10][4500/8498] lr: 4.0000e-04 eta: 8:10:38 time: 1.4264 data_time: 0.1082 memory: 36546 loss_ce: 0.0477 loss: 0.0477 2022/09/16 23:50:42 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/16 23:52:53 - mmengine - INFO - Epoch(train) [10][4600/8498] lr: 4.0000e-04 eta: 8:08:24 time: 1.6432 data_time: 0.1281 memory: 36546 loss_ce: 0.0541 loss: 0.0541 2022/09/16 23:55:34 - mmengine - INFO - Epoch(train) [10][4700/8498] lr: 4.0000e-04 eta: 8:06:09 time: 1.6517 data_time: 0.1596 memory: 36546 loss_ce: 0.0481 loss: 0.0481 2022/09/16 23:58:17 - mmengine - INFO - Epoch(train) [10][4800/8498] lr: 4.0000e-04 eta: 8:03:54 time: 1.4425 data_time: 0.2813 memory: 36546 loss_ce: 0.0459 loss: 0.0459 2022/09/17 00:01:01 - mmengine - INFO - Epoch(train) [10][4900/8498] lr: 4.0000e-04 eta: 8:01:40 time: 1.8192 data_time: 0.3644 memory: 36546 loss_ce: 0.0509 loss: 0.0509 2022/09/17 00:03:41 - mmengine - INFO - Epoch(train) [10][5000/8498] lr: 4.0000e-04 eta: 7:59:24 time: 1.2831 data_time: 0.0065 memory: 36546 loss_ce: 0.0478 loss: 0.0478 2022/09/17 00:06:22 - mmengine - INFO - Epoch(train) [10][5100/8498] lr: 4.0000e-04 eta: 7:57:09 time: 1.4473 data_time: 0.1251 memory: 36546 loss_ce: 0.0451 loss: 0.0451 2022/09/17 00:09:07 - mmengine - INFO - Epoch(train) [10][5200/8498] lr: 4.0000e-04 eta: 7:54:55 time: 1.5874 data_time: 0.1199 memory: 36546 loss_ce: 0.0547 loss: 0.0547 2022/09/17 00:11:49 - mmengine - INFO - Epoch(train) [10][5300/8498] lr: 4.0000e-04 eta: 7:52:40 time: 1.6343 data_time: 0.1531 memory: 36546 loss_ce: 0.0478 loss: 0.0478 2022/09/17 00:14:33 - mmengine - INFO - Epoch(train) [10][5400/8498] lr: 4.0000e-04 eta: 7:50:25 time: 1.5357 data_time: 0.3435 memory: 36546 loss_ce: 0.0477 loss: 0.0477 2022/09/17 00:17:15 - mmengine - INFO - Epoch(train) [10][5500/8498] lr: 4.0000e-04 eta: 7:48:10 time: 1.8458 data_time: 0.3516 memory: 36546 loss_ce: 0.0479 loss: 0.0479 2022/09/17 00:17:45 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 00:19:56 - mmengine - INFO - Epoch(train) [10][5600/8498] lr: 4.0000e-04 eta: 7:45:54 time: 1.2894 data_time: 0.0083 memory: 36546 loss_ce: 0.0449 loss: 0.0449 2022/09/17 00:22:36 - mmengine - INFO - Epoch(train) [10][5700/8498] lr: 4.0000e-04 eta: 7:43:38 time: 1.4719 data_time: 0.1122 memory: 36546 loss_ce: 0.0453 loss: 0.0453 2022/09/17 00:25:19 - mmengine - INFO - Epoch(train) [10][5800/8498] lr: 4.0000e-04 eta: 7:41:23 time: 1.5532 data_time: 0.1173 memory: 36546 loss_ce: 0.0486 loss: 0.0486 2022/09/17 00:27:58 - mmengine - INFO - Epoch(train) [10][5900/8498] lr: 4.0000e-04 eta: 7:39:07 time: 1.6613 data_time: 0.1667 memory: 36546 loss_ce: 0.0462 loss: 0.0462 2022/09/17 00:30:42 - mmengine - INFO - Epoch(train) [10][6000/8498] lr: 4.0000e-04 eta: 7:36:52 time: 1.5655 data_time: 0.3641 memory: 36546 loss_ce: 0.0524 loss: 0.0524 2022/09/17 00:33:24 - mmengine - INFO - Epoch(train) [10][6100/8498] lr: 4.0000e-04 eta: 7:34:37 time: 1.8887 data_time: 0.3428 memory: 36546 loss_ce: 0.0507 loss: 0.0507 2022/09/17 00:36:00 - mmengine - INFO - Epoch(train) [10][6200/8498] lr: 4.0000e-04 eta: 7:32:19 time: 1.2799 data_time: 0.0065 memory: 36546 loss_ce: 0.0502 loss: 0.0502 2022/09/17 00:38:40 - mmengine - INFO - Epoch(train) [10][6300/8498] lr: 4.0000e-04 eta: 7:30:03 time: 1.4552 data_time: 0.1228 memory: 36546 loss_ce: 0.0434 loss: 0.0434 2022/09/17 00:41:49 - mmengine - INFO - Epoch(train) [10][6400/8498] lr: 4.0000e-04 eta: 7:27:54 time: 1.1944 data_time: 0.0072 memory: 36546 loss_ce: 0.0446 loss: 0.0446 2022/09/17 00:44:16 - mmengine - INFO - Epoch(train) [10][6500/8498] lr: 4.0000e-04 eta: 7:25:34 time: 1.3028 data_time: 0.1389 memory: 36546 loss_ce: 0.0441 loss: 0.0441 2022/09/17 00:44:46 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 00:46:48 - mmengine - INFO - Epoch(train) [10][6600/8498] lr: 4.0000e-04 eta: 7:23:16 time: 1.5822 data_time: 0.2836 memory: 36546 loss_ce: 0.0459 loss: 0.0459 2022/09/17 00:49:21 - mmengine - INFO - Epoch(train) [10][6700/8498] lr: 4.0000e-04 eta: 7:20:58 time: 1.5962 data_time: 0.0068 memory: 36546 loss_ce: 0.0455 loss: 0.0455 2022/09/17 00:51:53 - mmengine - INFO - Epoch(train) [10][6800/8498] lr: 4.0000e-04 eta: 7:18:40 time: 1.4055 data_time: 0.0063 memory: 36546 loss_ce: 0.0467 loss: 0.0467 2022/09/17 00:54:26 - mmengine - INFO - Epoch(train) [10][6900/8498] lr: 4.0000e-04 eta: 7:16:22 time: 1.9868 data_time: 0.5548 memory: 36546 loss_ce: 0.0471 loss: 0.0471 2022/09/17 00:58:22 - mmengine - INFO - Epoch(train) [10][7000/8498] lr: 4.0000e-04 eta: 7:14:22 time: 1.6397 data_time: 0.3440 memory: 36546 loss_ce: 0.0483 loss: 0.0483 2022/09/17 01:00:54 - mmengine - INFO - Epoch(train) [10][7100/8498] lr: 4.0000e-04 eta: 7:12:04 time: 1.7173 data_time: 0.2969 memory: 36546 loss_ce: 0.0465 loss: 0.0465 2022/09/17 01:03:26 - mmengine - INFO - Epoch(train) [10][7200/8498] lr: 4.0000e-04 eta: 7:09:45 time: 1.5231 data_time: 0.0217 memory: 36546 loss_ce: 0.0431 loss: 0.0431 2022/09/17 01:06:00 - mmengine - INFO - Epoch(train) [10][7300/8498] lr: 4.0000e-04 eta: 7:07:27 time: 1.5448 data_time: 0.0772 memory: 36546 loss_ce: 0.0484 loss: 0.0484 2022/09/17 01:08:37 - mmengine - INFO - Epoch(train) [10][7400/8498] lr: 4.0000e-04 eta: 7:05:09 time: 1.5142 data_time: 0.1404 memory: 36546 loss_ce: 0.0410 loss: 0.0410 2022/09/17 01:11:02 - mmengine - INFO - Epoch(train) [10][7500/8498] lr: 4.0000e-04 eta: 7:02:49 time: 1.4359 data_time: 0.3098 memory: 36546 loss_ce: 0.0493 loss: 0.0493 2022/09/17 01:11:30 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 01:13:33 - mmengine - INFO - Epoch(train) [10][7600/8498] lr: 4.0000e-04 eta: 7:00:30 time: 1.4981 data_time: 0.3417 memory: 36546 loss_ce: 0.0397 loss: 0.0397 2022/09/17 01:16:02 - mmengine - INFO - Epoch(train) [10][7700/8498] lr: 4.0000e-04 eta: 6:58:11 time: 1.5862 data_time: 0.2444 memory: 36546 loss_ce: 0.0499 loss: 0.0499 2022/09/17 01:18:34 - mmengine - INFO - Epoch(train) [10][7800/8498] lr: 4.0000e-04 eta: 6:55:52 time: 1.5504 data_time: 0.1345 memory: 36546 loss_ce: 0.0508 loss: 0.0508 2022/09/17 01:21:04 - mmengine - INFO - Epoch(train) [10][7900/8498] lr: 4.0000e-04 eta: 6:53:33 time: 1.4996 data_time: 0.1253 memory: 36546 loss_ce: 0.0533 loss: 0.0533 2022/09/17 01:23:33 - mmengine - INFO - Epoch(train) [10][8000/8498] lr: 4.0000e-04 eta: 6:51:14 time: 1.5169 data_time: 0.1651 memory: 36546 loss_ce: 0.0504 loss: 0.0504 2022/09/17 01:26:03 - mmengine - INFO - Epoch(train) [10][8100/8498] lr: 4.0000e-04 eta: 6:48:55 time: 1.4407 data_time: 0.3245 memory: 36546 loss_ce: 0.0450 loss: 0.0450 2022/09/17 01:28:34 - mmengine - INFO - Epoch(train) [10][8200/8498] lr: 4.0000e-04 eta: 6:46:36 time: 1.4560 data_time: 0.2974 memory: 36546 loss_ce: 0.0484 loss: 0.0484 2022/09/17 01:31:06 - mmengine - INFO - Epoch(train) [10][8300/8498] lr: 4.0000e-04 eta: 6:44:17 time: 1.5718 data_time: 0.2447 memory: 36546 loss_ce: 0.0444 loss: 0.0444 2022/09/17 01:33:37 - mmengine - INFO - Epoch(train) [10][8400/8498] lr: 4.0000e-04 eta: 6:41:58 time: 1.5281 data_time: 0.1313 memory: 36546 loss_ce: 0.0499 loss: 0.0499 2022/09/17 01:35:51 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 01:35:51 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/09/17 01:36:22 - mmengine - INFO - Epoch(val) [10][100/1918] eta: 0:04:02 time: 0.1332 data_time: 0.0007 memory: 36546 2022/09/17 01:36:36 - mmengine - INFO - Epoch(val) [10][200/1918] eta: 0:04:06 time: 0.1437 data_time: 0.0009 memory: 1150 2022/09/17 01:36:49 - mmengine - INFO - Epoch(val) [10][300/1918] eta: 0:03:37 time: 0.1345 data_time: 0.0008 memory: 1150 2022/09/17 01:37:04 - mmengine - INFO - Epoch(val) [10][400/1918] eta: 0:03:35 time: 0.1418 data_time: 0.0009 memory: 1150 2022/09/17 01:37:18 - mmengine - INFO - Epoch(val) [10][500/1918] eta: 0:04:04 time: 0.1723 data_time: 0.0013 memory: 1150 2022/09/17 01:37:32 - mmengine - INFO - Epoch(val) [10][600/1918] eta: 0:03:03 time: 0.1389 data_time: 0.0008 memory: 1150 2022/09/17 01:37:46 - mmengine - INFO - Epoch(val) [10][700/1918] eta: 0:02:47 time: 0.1377 data_time: 0.0009 memory: 1150 2022/09/17 01:38:00 - mmengine - INFO - Epoch(val) [10][800/1918] eta: 0:02:39 time: 0.1426 data_time: 0.0009 memory: 1150 2022/09/17 01:38:14 - mmengine - INFO - Epoch(val) [10][900/1918] eta: 0:02:20 time: 0.1380 data_time: 0.0008 memory: 1150 2022/09/17 01:38:28 - mmengine - INFO - Epoch(val) [10][1000/1918] eta: 0:02:12 time: 0.1439 data_time: 0.0062 memory: 1150 2022/09/17 01:38:43 - mmengine - INFO - Epoch(val) [10][1100/1918] eta: 0:01:59 time: 0.1462 data_time: 0.0008 memory: 1150 2022/09/17 01:38:57 - mmengine - INFO - Epoch(val) [10][1200/1918] eta: 0:01:40 time: 0.1398 data_time: 0.0010 memory: 1150 2022/09/17 01:39:11 - mmengine - INFO - Epoch(val) [10][1300/1918] eta: 0:01:22 time: 0.1329 data_time: 0.0008 memory: 1150 2022/09/17 01:39:24 - mmengine - INFO - Epoch(val) [10][1400/1918] eta: 0:01:11 time: 0.1378 data_time: 0.0008 memory: 1150 2022/09/17 01:39:39 - mmengine - INFO - Epoch(val) [10][1500/1918] eta: 0:00:59 time: 0.1413 data_time: 0.0011 memory: 1150 2022/09/17 01:39:52 - mmengine - INFO - Epoch(val) [10][1600/1918] eta: 0:00:43 time: 0.1374 data_time: 0.0009 memory: 1150 2022/09/17 01:40:06 - mmengine - INFO - Epoch(val) [10][1700/1918] eta: 0:00:29 time: 0.1357 data_time: 0.0009 memory: 1150 2022/09/17 01:40:20 - mmengine - INFO - Epoch(val) [10][1800/1918] eta: 0:00:19 time: 0.1666 data_time: 0.0017 memory: 1150 2022/09/17 01:40:34 - mmengine - INFO - Epoch(val) [10][1900/1918] eta: 0:00:02 time: 0.1329 data_time: 0.0008 memory: 1150 2022/09/17 01:40:37 - mmengine - INFO - Epoch(val) [10][1918/1918] CUTE80/recog/word_acc_ignore_case_symbol: 0.8819 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9480 SVT/recog/word_acc_ignore_case_symbol: 0.8934 SVTP/recog/word_acc_ignore_case_symbol: 0.8124 IC13/recog/word_acc_ignore_case_symbol: 0.9399 IC15/recog/word_acc_ignore_case_symbol: 0.7583 2022/09/17 01:41:16 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 01:43:05 - mmengine - INFO - Epoch(train) [11][100/8498] lr: 4.0000e-04 eta: 6:37:17 time: 1.5162 data_time: 0.2814 memory: 36546 loss_ce: 0.0453 loss: 0.0453 2022/09/17 01:45:14 - mmengine - INFO - Epoch(train) [11][200/8498] lr: 4.0000e-04 eta: 6:34:54 time: 1.6294 data_time: 0.4284 memory: 36546 loss_ce: 0.0443 loss: 0.0443 2022/09/17 01:47:19 - mmengine - INFO - Epoch(train) [11][300/8498] lr: 4.0000e-04 eta: 6:32:29 time: 1.1858 data_time: 0.1243 memory: 36546 loss_ce: 0.0477 loss: 0.0477 2022/09/17 01:49:27 - mmengine - INFO - Epoch(train) [11][400/8498] lr: 4.0000e-04 eta: 6:30:06 time: 1.0453 data_time: 0.0210 memory: 36546 loss_ce: 0.0390 loss: 0.0390 2022/09/17 01:51:37 - mmengine - INFO - Epoch(train) [11][500/8498] lr: 4.0000e-04 eta: 6:27:43 time: 1.0155 data_time: 0.0815 memory: 36546 loss_ce: 0.0513 loss: 0.0513 2022/09/17 01:53:46 - mmengine - INFO - Epoch(train) [11][600/8498] lr: 4.0000e-04 eta: 6:25:19 time: 1.0130 data_time: 0.0217 memory: 36546 loss_ce: 0.0470 loss: 0.0470 2022/09/17 01:56:00 - mmengine - INFO - Epoch(train) [11][700/8498] lr: 4.0000e-04 eta: 6:22:57 time: 1.5757 data_time: 0.3005 memory: 36546 loss_ce: 0.0457 loss: 0.0457 2022/09/17 01:58:08 - mmengine - INFO - Epoch(train) [11][800/8498] lr: 4.0000e-04 eta: 6:20:34 time: 1.6279 data_time: 0.4373 memory: 36546 loss_ce: 0.0406 loss: 0.0406 2022/09/17 02:00:20 - mmengine - INFO - Epoch(train) [11][900/8498] lr: 4.0000e-04 eta: 6:18:11 time: 1.5942 data_time: 0.1180 memory: 36546 loss_ce: 0.0406 loss: 0.0406 2022/09/17 02:02:29 - mmengine - INFO - Epoch(train) [11][1000/8498] lr: 4.0000e-04 eta: 6:15:48 time: 1.0282 data_time: 0.0248 memory: 36546 loss_ce: 0.0445 loss: 0.0445 2022/09/17 02:02:57 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 02:04:38 - mmengine - INFO - Epoch(train) [11][1100/8498] lr: 4.0000e-04 eta: 6:13:25 time: 0.9828 data_time: 0.0368 memory: 36546 loss_ce: 0.0508 loss: 0.0508 2022/09/17 02:06:45 - mmengine - INFO - Epoch(train) [11][1200/8498] lr: 4.0000e-04 eta: 6:11:01 time: 1.0376 data_time: 0.0218 memory: 36546 loss_ce: 0.0434 loss: 0.0434 2022/09/17 02:08:59 - mmengine - INFO - Epoch(train) [11][1300/8498] lr: 4.0000e-04 eta: 6:08:39 time: 1.5211 data_time: 0.2968 memory: 36546 loss_ce: 0.0487 loss: 0.0487 2022/09/17 02:11:10 - mmengine - INFO - Epoch(train) [11][1400/8498] lr: 4.0000e-04 eta: 6:06:16 time: 1.6992 data_time: 0.4304 memory: 36546 loss_ce: 0.0494 loss: 0.0494 2022/09/17 02:13:19 - mmengine - INFO - Epoch(train) [11][1500/8498] lr: 4.0000e-04 eta: 6:03:53 time: 1.2882 data_time: 0.1480 memory: 36546 loss_ce: 0.0441 loss: 0.0441 2022/09/17 02:15:28 - mmengine - INFO - Epoch(train) [11][1600/8498] lr: 4.0000e-04 eta: 6:01:30 time: 1.0477 data_time: 0.0216 memory: 36546 loss_ce: 0.0485 loss: 0.0485 2022/09/17 02:17:37 - mmengine - INFO - Epoch(train) [11][1700/8498] lr: 4.0000e-04 eta: 5:59:07 time: 1.0307 data_time: 0.0365 memory: 36546 loss_ce: 0.0445 loss: 0.0445 2022/09/17 02:19:44 - mmengine - INFO - Epoch(train) [11][1800/8498] lr: 4.0000e-04 eta: 5:56:44 time: 0.9734 data_time: 0.0218 memory: 36546 loss_ce: 0.0497 loss: 0.0497 2022/09/17 02:21:59 - mmengine - INFO - Epoch(train) [11][1900/8498] lr: 4.0000e-04 eta: 5:54:22 time: 1.5257 data_time: 0.3115 memory: 36546 loss_ce: 0.0533 loss: 0.0533 2022/09/17 02:24:06 - mmengine - INFO - Epoch(train) [11][2000/8498] lr: 4.0000e-04 eta: 5:51:59 time: 1.6710 data_time: 0.5229 memory: 36546 loss_ce: 0.0406 loss: 0.0406 2022/09/17 02:24:32 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 02:26:15 - mmengine - INFO - Epoch(train) [11][2100/8498] lr: 4.0000e-04 eta: 5:49:36 time: 1.3422 data_time: 0.1234 memory: 36546 loss_ce: 0.0485 loss: 0.0485 2022/09/17 02:28:24 - mmengine - INFO - Epoch(train) [11][2200/8498] lr: 4.0000e-04 eta: 5:47:13 time: 1.0054 data_time: 0.0218 memory: 36546 loss_ce: 0.0414 loss: 0.0414 2022/09/17 02:30:32 - mmengine - INFO - Epoch(train) [11][2300/8498] lr: 4.0000e-04 eta: 5:44:50 time: 1.0222 data_time: 0.0766 memory: 36546 loss_ce: 0.0451 loss: 0.0451 2022/09/17 02:32:41 - mmengine - INFO - Epoch(train) [11][2400/8498] lr: 4.0000e-04 eta: 5:42:27 time: 0.9854 data_time: 0.0207 memory: 36546 loss_ce: 0.0429 loss: 0.0429 2022/09/17 02:34:54 - mmengine - INFO - Epoch(train) [11][2500/8498] lr: 4.0000e-04 eta: 5:40:05 time: 1.5403 data_time: 0.3172 memory: 36546 loss_ce: 0.0439 loss: 0.0439 2022/09/17 02:37:05 - mmengine - INFO - Epoch(train) [11][2600/8498] lr: 4.0000e-04 eta: 5:37:43 time: 1.7201 data_time: 0.4337 memory: 36546 loss_ce: 0.0521 loss: 0.0521 2022/09/17 02:39:16 - mmengine - INFO - Epoch(train) [11][2700/8498] lr: 4.0000e-04 eta: 5:35:20 time: 1.3201 data_time: 0.1410 memory: 36546 loss_ce: 0.0452 loss: 0.0452 2022/09/17 02:41:23 - mmengine - INFO - Epoch(train) [11][2800/8498] lr: 4.0000e-04 eta: 5:32:57 time: 1.0441 data_time: 0.0225 memory: 36546 loss_ce: 0.0499 loss: 0.0499 2022/09/17 02:43:33 - mmengine - INFO - Epoch(train) [11][2900/8498] lr: 4.0000e-04 eta: 5:30:35 time: 1.0211 data_time: 0.0463 memory: 36546 loss_ce: 0.0400 loss: 0.0400 2022/09/17 02:45:42 - mmengine - INFO - Epoch(train) [11][3000/8498] lr: 4.0000e-04 eta: 5:28:12 time: 1.0102 data_time: 0.0208 memory: 36546 loss_ce: 0.0483 loss: 0.0483 2022/09/17 02:46:09 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 02:47:54 - mmengine - INFO - Epoch(train) [11][3100/8498] lr: 4.0000e-04 eta: 5:25:50 time: 1.5064 data_time: 0.2816 memory: 36546 loss_ce: 0.0449 loss: 0.0449 2022/09/17 02:50:05 - mmengine - INFO - Epoch(train) [11][3200/8498] lr: 4.0000e-04 eta: 5:23:28 time: 1.6693 data_time: 0.3469 memory: 36546 loss_ce: 0.0406 loss: 0.0406 2022/09/17 02:52:15 - mmengine - INFO - Epoch(train) [11][3300/8498] lr: 4.0000e-04 eta: 5:21:06 time: 1.3561 data_time: 0.1423 memory: 36546 loss_ce: 0.0459 loss: 0.0459 2022/09/17 02:54:22 - mmengine - INFO - Epoch(train) [11][3400/8498] lr: 4.0000e-04 eta: 5:18:43 time: 1.0029 data_time: 0.0230 memory: 36546 loss_ce: 0.0482 loss: 0.0482 2022/09/17 02:56:32 - mmengine - INFO - Epoch(train) [11][3500/8498] lr: 4.0000e-04 eta: 5:16:21 time: 1.0300 data_time: 0.0357 memory: 36546 loss_ce: 0.0387 loss: 0.0387 2022/09/17 02:58:38 - mmengine - INFO - Epoch(train) [11][3600/8498] lr: 4.0000e-04 eta: 5:13:58 time: 0.9743 data_time: 0.0318 memory: 36546 loss_ce: 0.0416 loss: 0.0416 2022/09/17 03:00:51 - mmengine - INFO - Epoch(train) [11][3700/8498] lr: 4.0000e-04 eta: 5:11:36 time: 1.5521 data_time: 0.3423 memory: 36546 loss_ce: 0.0415 loss: 0.0415 2022/09/17 03:03:03 - mmengine - INFO - Epoch(train) [11][3800/8498] lr: 4.0000e-04 eta: 5:09:14 time: 1.6765 data_time: 0.4521 memory: 36546 loss_ce: 0.0470 loss: 0.0470 2022/09/17 03:05:12 - mmengine - INFO - Epoch(train) [11][3900/8498] lr: 4.0000e-04 eta: 5:06:52 time: 1.3566 data_time: 0.1310 memory: 36546 loss_ce: 0.0424 loss: 0.0424 2022/09/17 03:07:24 - mmengine - INFO - Epoch(train) [11][4000/8498] lr: 4.0000e-04 eta: 5:04:30 time: 1.0617 data_time: 0.0230 memory: 36546 loss_ce: 0.0415 loss: 0.0415 2022/09/17 03:07:51 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 03:09:34 - mmengine - INFO - Epoch(train) [11][4100/8498] lr: 4.0000e-04 eta: 5:02:08 time: 1.0161 data_time: 0.0390 memory: 36546 loss_ce: 0.0424 loss: 0.0424 2022/09/17 03:11:43 - mmengine - INFO - Epoch(train) [11][4200/8498] lr: 4.0000e-04 eta: 4:59:46 time: 0.9719 data_time: 0.0234 memory: 36546 loss_ce: 0.0403 loss: 0.0403 2022/09/17 03:13:58 - mmengine - INFO - Epoch(train) [11][4300/8498] lr: 4.0000e-04 eta: 4:57:24 time: 1.5278 data_time: 0.3197 memory: 36546 loss_ce: 0.0395 loss: 0.0395 2022/09/17 03:16:07 - mmengine - INFO - Epoch(train) [11][4400/8498] lr: 4.0000e-04 eta: 4:55:02 time: 1.6198 data_time: 0.4040 memory: 36546 loss_ce: 0.0463 loss: 0.0463 2022/09/17 03:18:16 - mmengine - INFO - Epoch(train) [11][4500/8498] lr: 4.0000e-04 eta: 4:52:40 time: 1.3033 data_time: 0.1070 memory: 36546 loss_ce: 0.0484 loss: 0.0484 2022/09/17 03:20:26 - mmengine - INFO - Epoch(train) [11][4600/8498] lr: 4.0000e-04 eta: 4:50:18 time: 1.0387 data_time: 0.0547 memory: 36546 loss_ce: 0.0447 loss: 0.0447 2022/09/17 03:22:34 - mmengine - INFO - Epoch(train) [11][4700/8498] lr: 4.0000e-04 eta: 4:47:56 time: 1.0053 data_time: 0.0353 memory: 36546 loss_ce: 0.0444 loss: 0.0444 2022/09/17 03:24:44 - mmengine - INFO - Epoch(train) [11][4800/8498] lr: 4.0000e-04 eta: 4:45:34 time: 0.9996 data_time: 0.0394 memory: 36546 loss_ce: 0.0464 loss: 0.0464 2022/09/17 03:26:58 - mmengine - INFO - Epoch(train) [11][4900/8498] lr: 4.0000e-04 eta: 4:43:12 time: 1.5173 data_time: 0.2997 memory: 36546 loss_ce: 0.0434 loss: 0.0434 2022/09/17 03:29:07 - mmengine - INFO - Epoch(train) [11][5000/8498] lr: 4.0000e-04 eta: 4:40:50 time: 1.6517 data_time: 0.4047 memory: 36546 loss_ce: 0.0419 loss: 0.0419 2022/09/17 03:29:33 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 03:31:17 - mmengine - INFO - Epoch(train) [11][5100/8498] lr: 4.0000e-04 eta: 4:38:28 time: 1.2990 data_time: 0.1452 memory: 36546 loss_ce: 0.0421 loss: 0.0421 2022/09/17 03:33:25 - mmengine - INFO - Epoch(train) [11][5200/8498] lr: 4.0000e-04 eta: 4:36:06 time: 1.0324 data_time: 0.0213 memory: 36546 loss_ce: 0.0364 loss: 0.0364 2022/09/17 03:35:32 - mmengine - INFO - Epoch(train) [11][5300/8498] lr: 4.0000e-04 eta: 4:33:44 time: 0.9810 data_time: 0.0365 memory: 36546 loss_ce: 0.0458 loss: 0.0458 2022/09/17 03:37:39 - mmengine - INFO - Epoch(train) [11][5400/8498] lr: 4.0000e-04 eta: 4:31:22 time: 0.9819 data_time: 0.0224 memory: 36546 loss_ce: 0.0402 loss: 0.0402 2022/09/17 03:39:54 - mmengine - INFO - Epoch(train) [11][5500/8498] lr: 4.0000e-04 eta: 4:29:01 time: 1.5898 data_time: 0.2532 memory: 36546 loss_ce: 0.0405 loss: 0.0405 2022/09/17 03:42:04 - mmengine - INFO - Epoch(train) [11][5600/8498] lr: 4.0000e-04 eta: 4:26:39 time: 1.6562 data_time: 0.4348 memory: 36546 loss_ce: 0.0429 loss: 0.0429 2022/09/17 03:44:14 - mmengine - INFO - Epoch(train) [11][5700/8498] lr: 4.0000e-04 eta: 4:24:17 time: 1.3425 data_time: 0.1485 memory: 36546 loss_ce: 0.0470 loss: 0.0470 2022/09/17 03:46:21 - mmengine - INFO - Epoch(train) [11][5800/8498] lr: 4.0000e-04 eta: 4:21:56 time: 1.0368 data_time: 0.0215 memory: 36546 loss_ce: 0.0416 loss: 0.0416 2022/09/17 03:48:31 - mmengine - INFO - Epoch(train) [11][5900/8498] lr: 4.0000e-04 eta: 4:19:34 time: 1.0012 data_time: 0.0402 memory: 36546 loss_ce: 0.0418 loss: 0.0418 2022/09/17 03:50:40 - mmengine - INFO - Epoch(train) [11][6000/8498] lr: 4.0000e-04 eta: 4:17:12 time: 1.0146 data_time: 0.0253 memory: 36546 loss_ce: 0.0411 loss: 0.0411 2022/09/17 03:51:08 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 03:52:52 - mmengine - INFO - Epoch(train) [11][6100/8498] lr: 4.0000e-04 eta: 4:14:51 time: 1.4825 data_time: 0.3180 memory: 36546 loss_ce: 0.0465 loss: 0.0465 2022/09/17 03:55:05 - mmengine - INFO - Epoch(train) [11][6200/8498] lr: 4.0000e-04 eta: 4:12:30 time: 1.6209 data_time: 0.4511 memory: 36546 loss_ce: 0.0470 loss: 0.0470 2022/09/17 03:57:14 - mmengine - INFO - Epoch(train) [11][6300/8498] lr: 4.0000e-04 eta: 4:10:08 time: 1.3200 data_time: 0.1085 memory: 36546 loss_ce: 0.0413 loss: 0.0413 2022/09/17 03:59:23 - mmengine - INFO - Epoch(train) [11][6400/8498] lr: 4.0000e-04 eta: 4:07:46 time: 1.0401 data_time: 0.0226 memory: 36546 loss_ce: 0.0417 loss: 0.0417 2022/09/17 04:01:31 - mmengine - INFO - Epoch(train) [11][6500/8498] lr: 4.0000e-04 eta: 4:05:24 time: 1.0291 data_time: 0.0377 memory: 36546 loss_ce: 0.0428 loss: 0.0428 2022/09/17 04:03:40 - mmengine - INFO - Epoch(train) [11][6600/8498] lr: 4.0000e-04 eta: 4:03:03 time: 1.0382 data_time: 0.0216 memory: 36546 loss_ce: 0.0489 loss: 0.0489 2022/09/17 04:05:54 - mmengine - INFO - Epoch(train) [11][6700/8498] lr: 4.0000e-04 eta: 4:00:42 time: 1.6021 data_time: 0.3072 memory: 36546 loss_ce: 0.0448 loss: 0.0448 2022/09/17 04:08:05 - mmengine - INFO - Epoch(train) [11][6800/8498] lr: 4.0000e-04 eta: 3:58:21 time: 1.6175 data_time: 0.3876 memory: 36546 loss_ce: 0.0451 loss: 0.0451 2022/09/17 04:10:13 - mmengine - INFO - Epoch(train) [11][6900/8498] lr: 4.0000e-04 eta: 3:55:59 time: 1.2711 data_time: 0.1228 memory: 36546 loss_ce: 0.0439 loss: 0.0439 2022/09/17 04:12:21 - mmengine - INFO - Epoch(train) [11][7000/8498] lr: 4.0000e-04 eta: 3:53:37 time: 1.0306 data_time: 0.0229 memory: 36546 loss_ce: 0.0430 loss: 0.0430 2022/09/17 04:12:49 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 04:14:32 - mmengine - INFO - Epoch(train) [11][7100/8498] lr: 4.0000e-04 eta: 3:51:16 time: 1.0106 data_time: 0.0377 memory: 36546 loss_ce: 0.0419 loss: 0.0419 2022/09/17 04:16:41 - mmengine - INFO - Epoch(train) [11][7200/8498] lr: 4.0000e-04 eta: 3:48:55 time: 1.0007 data_time: 0.0211 memory: 36546 loss_ce: 0.0466 loss: 0.0466 2022/09/17 04:18:55 - mmengine - INFO - Epoch(train) [11][7300/8498] lr: 4.0000e-04 eta: 3:46:34 time: 1.5315 data_time: 0.3266 memory: 36546 loss_ce: 0.0481 loss: 0.0481 2022/09/17 04:21:05 - mmengine - INFO - Epoch(train) [11][7400/8498] lr: 4.0000e-04 eta: 3:44:13 time: 1.6152 data_time: 0.4075 memory: 36546 loss_ce: 0.0419 loss: 0.0419 2022/09/17 04:23:16 - mmengine - INFO - Epoch(train) [11][7500/8498] lr: 4.0000e-04 eta: 3:41:51 time: 1.3500 data_time: 0.1130 memory: 36546 loss_ce: 0.0369 loss: 0.0369 2022/09/17 04:25:25 - mmengine - INFO - Epoch(train) [11][7600/8498] lr: 4.0000e-04 eta: 3:39:30 time: 1.0013 data_time: 0.0214 memory: 36546 loss_ce: 0.0454 loss: 0.0454 2022/09/17 04:27:36 - mmengine - INFO - Epoch(train) [11][7700/8498] lr: 4.0000e-04 eta: 3:37:09 time: 1.0372 data_time: 0.0370 memory: 36546 loss_ce: 0.0467 loss: 0.0467 2022/09/17 04:29:46 - mmengine - INFO - Epoch(train) [11][7800/8498] lr: 4.0000e-04 eta: 3:34:48 time: 1.0156 data_time: 0.0218 memory: 36546 loss_ce: 0.0473 loss: 0.0473 2022/09/17 04:32:00 - mmengine - INFO - Epoch(train) [11][7900/8498] lr: 4.0000e-04 eta: 3:32:27 time: 1.6335 data_time: 0.3101 memory: 36546 loss_ce: 0.0405 loss: 0.0405 2022/09/17 04:34:09 - mmengine - INFO - Epoch(train) [11][8000/8498] lr: 4.0000e-04 eta: 3:30:06 time: 1.6550 data_time: 0.4250 memory: 36546 loss_ce: 0.0403 loss: 0.0403 2022/09/17 04:34:35 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 04:36:19 - mmengine - INFO - Epoch(train) [11][8100/8498] lr: 4.0000e-04 eta: 3:27:45 time: 1.3156 data_time: 0.1427 memory: 36546 loss_ce: 0.0433 loss: 0.0433 2022/09/17 04:38:28 - mmengine - INFO - Epoch(train) [11][8200/8498] lr: 4.0000e-04 eta: 3:25:24 time: 0.9966 data_time: 0.0224 memory: 36546 loss_ce: 0.0402 loss: 0.0402 2022/09/17 04:40:37 - mmengine - INFO - Epoch(train) [11][8300/8498] lr: 4.0000e-04 eta: 3:23:02 time: 1.0305 data_time: 0.0551 memory: 36546 loss_ce: 0.0372 loss: 0.0372 2022/09/17 04:42:45 - mmengine - INFO - Epoch(train) [11][8400/8498] lr: 4.0000e-04 eta: 3:20:41 time: 0.9783 data_time: 0.0218 memory: 36546 loss_ce: 0.0429 loss: 0.0429 2022/09/17 04:44:46 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 04:44:47 - mmengine - INFO - Saving checkpoint at 11 epochs 2022/09/17 04:45:18 - mmengine - INFO - Epoch(val) [11][100/1918] eta: 0:04:00 time: 0.1324 data_time: 0.0008 memory: 36546 2022/09/17 04:45:32 - mmengine - INFO - Epoch(val) [11][200/1918] eta: 0:04:01 time: 0.1405 data_time: 0.0009 memory: 1150 2022/09/17 04:45:46 - mmengine - INFO - Epoch(val) [11][300/1918] eta: 0:03:36 time: 0.1336 data_time: 0.0028 memory: 1150 2022/09/17 04:45:59 - mmengine - INFO - Epoch(val) [11][400/1918] eta: 0:03:12 time: 0.1270 data_time: 0.0007 memory: 1150 2022/09/17 04:46:12 - mmengine - INFO - Epoch(val) [11][500/1918] eta: 0:03:00 time: 0.1275 data_time: 0.0006 memory: 1150 2022/09/17 04:46:25 - mmengine - INFO - Epoch(val) [11][600/1918] eta: 0:02:51 time: 0.1300 data_time: 0.0007 memory: 1150 2022/09/17 04:46:38 - mmengine - INFO - Epoch(val) [11][700/1918] eta: 0:02:35 time: 0.1279 data_time: 0.0007 memory: 1150 2022/09/17 04:46:51 - mmengine - INFO - Epoch(val) [11][800/1918] eta: 0:02:28 time: 0.1325 data_time: 0.0007 memory: 1150 2022/09/17 04:47:04 - mmengine - INFO - Epoch(val) [11][900/1918] eta: 0:02:15 time: 0.1335 data_time: 0.0007 memory: 1150 2022/09/17 04:47:17 - mmengine - INFO - Epoch(val) [11][1000/1918] eta: 0:02:00 time: 0.1313 data_time: 0.0019 memory: 1150 2022/09/17 04:47:30 - mmengine - INFO - Epoch(val) [11][1100/1918] eta: 0:01:43 time: 0.1268 data_time: 0.0007 memory: 1150 2022/09/17 04:47:43 - mmengine - INFO - Epoch(val) [11][1200/1918] eta: 0:01:30 time: 0.1262 data_time: 0.0006 memory: 1150 2022/09/17 04:47:56 - mmengine - INFO - Epoch(val) [11][1300/1918] eta: 0:01:21 time: 0.1314 data_time: 0.0007 memory: 1150 2022/09/17 04:48:09 - mmengine - INFO - Epoch(val) [11][1400/1918] eta: 0:01:06 time: 0.1292 data_time: 0.0007 memory: 1150 2022/09/17 04:48:22 - mmengine - INFO - Epoch(val) [11][1500/1918] eta: 0:00:53 time: 0.1279 data_time: 0.0006 memory: 1150 2022/09/17 04:48:35 - mmengine - INFO - Epoch(val) [11][1600/1918] eta: 0:00:40 time: 0.1275 data_time: 0.0006 memory: 1150 2022/09/17 04:48:48 - mmengine - INFO - Epoch(val) [11][1700/1918] eta: 0:00:29 time: 0.1333 data_time: 0.0006 memory: 1150 2022/09/17 04:49:00 - mmengine - INFO - Epoch(val) [11][1800/1918] eta: 0:00:15 time: 0.1339 data_time: 0.0006 memory: 1150 2022/09/17 04:49:14 - mmengine - INFO - Epoch(val) [11][1900/1918] eta: 0:00:02 time: 0.1318 data_time: 0.0005 memory: 1150 2022/09/17 04:49:17 - mmengine - INFO - Epoch(val) [11][1918/1918] CUTE80/recog/word_acc_ignore_case_symbol: 0.8785 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9493 SVT/recog/word_acc_ignore_case_symbol: 0.8887 SVTP/recog/word_acc_ignore_case_symbol: 0.8202 IC13/recog/word_acc_ignore_case_symbol: 0.9399 IC15/recog/word_acc_ignore_case_symbol: 0.7496 2022/09/17 04:51:29 - mmengine - INFO - Epoch(train) [12][100/8498] lr: 4.0000e-05 eta: 3:16:01 time: 1.3545 data_time: 0.2223 memory: 36546 loss_ce: 0.0347 loss: 0.0347 2022/09/17 04:53:34 - mmengine - INFO - Epoch(train) [12][200/8498] lr: 4.0000e-05 eta: 3:13:40 time: 1.4325 data_time: 0.2181 memory: 36546 loss_ce: 0.0354 loss: 0.0354 2022/09/17 04:55:40 - mmengine - INFO - Epoch(train) [12][300/8498] lr: 4.0000e-05 eta: 3:11:18 time: 1.3611 data_time: 0.2090 memory: 36546 loss_ce: 0.0339 loss: 0.0339 2022/09/17 04:57:43 - mmengine - INFO - Epoch(train) [12][400/8498] lr: 4.0000e-05 eta: 3:08:57 time: 1.1477 data_time: 0.2145 memory: 36546 loss_ce: 0.0345 loss: 0.0345 2022/09/17 04:59:44 - mmengine - INFO - Epoch(train) [12][500/8498] lr: 4.0000e-05 eta: 3:06:35 time: 1.0090 data_time: 0.0878 memory: 36546 loss_ce: 0.0315 loss: 0.0315 2022/09/17 05:00:12 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 05:01:45 - mmengine - INFO - Epoch(train) [12][600/8498] lr: 4.0000e-05 eta: 3:04:14 time: 0.8771 data_time: 0.0062 memory: 36546 loss_ce: 0.0357 loss: 0.0357 2022/09/17 05:03:51 - mmengine - INFO - Epoch(train) [12][700/8498] lr: 4.0000e-05 eta: 3:01:53 time: 1.3516 data_time: 0.2162 memory: 36546 loss_ce: 0.0326 loss: 0.0326 2022/09/17 05:05:54 - mmengine - INFO - Epoch(train) [12][800/8498] lr: 4.0000e-05 eta: 2:59:31 time: 1.4850 data_time: 0.2330 memory: 36546 loss_ce: 0.0344 loss: 0.0344 2022/09/17 05:07:56 - mmengine - INFO - Epoch(train) [12][900/8498] lr: 4.0000e-05 eta: 2:57:10 time: 1.3687 data_time: 0.1975 memory: 36546 loss_ce: 0.0365 loss: 0.0365 2022/09/17 05:09:58 - mmengine - INFO - Epoch(train) [12][1000/8498] lr: 4.0000e-05 eta: 2:54:49 time: 1.1669 data_time: 0.2437 memory: 36546 loss_ce: 0.0310 loss: 0.0310 2022/09/17 05:12:00 - mmengine - INFO - Epoch(train) [12][1100/8498] lr: 4.0000e-05 eta: 2:52:27 time: 1.0384 data_time: 0.0857 memory: 36546 loss_ce: 0.0359 loss: 0.0359 2022/09/17 05:14:01 - mmengine - INFO - Epoch(train) [12][1200/8498] lr: 4.0000e-05 eta: 2:50:06 time: 0.9203 data_time: 0.0062 memory: 36546 loss_ce: 0.0313 loss: 0.0313 2022/09/17 05:16:07 - mmengine - INFO - Epoch(train) [12][1300/8498] lr: 4.0000e-05 eta: 2:47:45 time: 1.2485 data_time: 0.2130 memory: 36546 loss_ce: 0.0364 loss: 0.0364 2022/09/17 05:18:11 - mmengine - INFO - Epoch(train) [12][1400/8498] lr: 4.0000e-05 eta: 2:45:24 time: 1.4903 data_time: 0.2245 memory: 36546 loss_ce: 0.0398 loss: 0.0398 2022/09/17 05:20:13 - mmengine - INFO - Epoch(train) [12][1500/8498] lr: 4.0000e-05 eta: 2:43:03 time: 1.3566 data_time: 0.1922 memory: 36546 loss_ce: 0.0336 loss: 0.0336 2022/09/17 05:20:40 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 05:22:14 - mmengine - INFO - Epoch(train) [12][1600/8498] lr: 4.0000e-05 eta: 2:40:42 time: 1.1579 data_time: 0.2085 memory: 36546 loss_ce: 0.0342 loss: 0.0342 2022/09/17 05:24:16 - mmengine - INFO - Epoch(train) [12][1700/8498] lr: 4.0000e-05 eta: 2:38:21 time: 0.9505 data_time: 0.0563 memory: 36546 loss_ce: 0.0317 loss: 0.0317 2022/09/17 05:26:17 - mmengine - INFO - Epoch(train) [12][1800/8498] lr: 4.0000e-05 eta: 2:36:00 time: 0.9279 data_time: 0.0064 memory: 36546 loss_ce: 0.0349 loss: 0.0349 2022/09/17 05:28:22 - mmengine - INFO - Epoch(train) [12][1900/8498] lr: 4.0000e-05 eta: 2:33:39 time: 1.2610 data_time: 0.1769 memory: 36546 loss_ce: 0.0352 loss: 0.0352 2022/09/17 05:30:24 - mmengine - INFO - Epoch(train) [12][2000/8498] lr: 4.0000e-05 eta: 2:31:18 time: 1.3964 data_time: 0.1885 memory: 36546 loss_ce: 0.0305 loss: 0.0305 2022/09/17 05:32:27 - mmengine - INFO - Epoch(train) [12][2100/8498] lr: 4.0000e-05 eta: 2:28:57 time: 1.4294 data_time: 0.2852 memory: 36546 loss_ce: 0.0338 loss: 0.0338 2022/09/17 05:34:28 - mmengine - INFO - Epoch(train) [12][2200/8498] lr: 4.0000e-05 eta: 2:26:36 time: 1.1093 data_time: 0.2029 memory: 36546 loss_ce: 0.0348 loss: 0.0348 2022/09/17 05:36:28 - mmengine - INFO - Epoch(train) [12][2300/8498] lr: 4.0000e-05 eta: 2:24:15 time: 0.9577 data_time: 0.0845 memory: 36546 loss_ce: 0.0339 loss: 0.0339 2022/09/17 05:38:29 - mmengine - INFO - Epoch(train) [12][2400/8498] lr: 4.0000e-05 eta: 2:21:54 time: 0.8772 data_time: 0.0064 memory: 36546 loss_ce: 0.0360 loss: 0.0360 2022/09/17 05:40:32 - mmengine - INFO - Epoch(train) [12][2500/8498] lr: 4.0000e-05 eta: 2:19:34 time: 1.3040 data_time: 0.2179 memory: 36546 loss_ce: 0.0386 loss: 0.0386 2022/09/17 05:40:59 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 05:42:36 - mmengine - INFO - Epoch(train) [12][2600/8498] lr: 4.0000e-05 eta: 2:17:13 time: 1.5325 data_time: 0.1839 memory: 36546 loss_ce: 0.0325 loss: 0.0325 2022/09/17 05:44:37 - mmengine - INFO - Epoch(train) [12][2700/8498] lr: 4.0000e-05 eta: 2:14:52 time: 1.3401 data_time: 0.1795 memory: 36546 loss_ce: 0.0317 loss: 0.0317 2022/09/17 05:46:37 - mmengine - INFO - Epoch(train) [12][2800/8498] lr: 4.0000e-05 eta: 2:12:32 time: 1.1669 data_time: 0.2168 memory: 36546 loss_ce: 0.0358 loss: 0.0358 2022/09/17 05:48:38 - mmengine - INFO - Epoch(train) [12][2900/8498] lr: 4.0000e-05 eta: 2:10:11 time: 0.9864 data_time: 0.0749 memory: 36546 loss_ce: 0.0344 loss: 0.0344 2022/09/17 05:50:38 - mmengine - INFO - Epoch(train) [12][3000/8498] lr: 4.0000e-05 eta: 2:07:50 time: 0.9465 data_time: 0.0060 memory: 36546 loss_ce: 0.0349 loss: 0.0349 2022/09/17 05:52:42 - mmengine - INFO - Epoch(train) [12][3100/8498] lr: 4.0000e-05 eta: 2:05:30 time: 1.2399 data_time: 0.2195 memory: 36546 loss_ce: 0.0335 loss: 0.0335 2022/09/17 05:54:43 - mmengine - INFO - Epoch(train) [12][3200/8498] lr: 4.0000e-05 eta: 2:03:09 time: 1.4715 data_time: 0.2158 memory: 36546 loss_ce: 0.0379 loss: 0.0379 2022/09/17 05:56:46 - mmengine - INFO - Epoch(train) [12][3300/8498] lr: 4.0000e-05 eta: 2:00:49 time: 1.3619 data_time: 0.1774 memory: 36546 loss_ce: 0.0328 loss: 0.0328 2022/09/17 05:58:49 - mmengine - INFO - Epoch(train) [12][3400/8498] lr: 4.0000e-05 eta: 1:58:29 time: 1.1888 data_time: 0.2530 memory: 36546 loss_ce: 0.0333 loss: 0.0333 2022/09/17 06:00:53 - mmengine - INFO - Epoch(train) [12][3500/8498] lr: 4.0000e-05 eta: 1:56:08 time: 0.9859 data_time: 0.0568 memory: 36546 loss_ce: 0.0370 loss: 0.0370 2022/09/17 06:01:20 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 06:02:54 - mmengine - INFO - Epoch(train) [12][3600/8498] lr: 4.0000e-05 eta: 1:53:48 time: 0.8755 data_time: 0.0067 memory: 36546 loss_ce: 0.0379 loss: 0.0379 2022/09/17 06:05:00 - mmengine - INFO - Epoch(train) [12][3700/8498] lr: 4.0000e-05 eta: 1:51:28 time: 1.2959 data_time: 0.1970 memory: 36546 loss_ce: 0.0357 loss: 0.0357 2022/09/17 06:07:02 - mmengine - INFO - Epoch(train) [12][3800/8498] lr: 4.0000e-05 eta: 1:49:08 time: 1.4444 data_time: 0.2092 memory: 36546 loss_ce: 0.0330 loss: 0.0330 2022/09/17 06:09:05 - mmengine - INFO - Epoch(train) [12][3900/8498] lr: 4.0000e-05 eta: 1:46:48 time: 1.3987 data_time: 0.2758 memory: 36546 loss_ce: 0.0324 loss: 0.0324 2022/09/17 06:11:06 - mmengine - INFO - Epoch(train) [12][4000/8498] lr: 4.0000e-05 eta: 1:44:27 time: 1.1379 data_time: 0.2139 memory: 36546 loss_ce: 0.0364 loss: 0.0364 2022/09/17 06:13:08 - mmengine - INFO - Epoch(train) [12][4100/8498] lr: 4.0000e-05 eta: 1:42:07 time: 1.0028 data_time: 0.0959 memory: 36546 loss_ce: 0.0298 loss: 0.0298 2022/09/17 06:15:08 - mmengine - INFO - Epoch(train) [12][4200/8498] lr: 4.0000e-05 eta: 1:39:47 time: 0.9106 data_time: 0.0071 memory: 36546 loss_ce: 0.0387 loss: 0.0387 2022/09/17 06:17:13 - mmengine - INFO - Epoch(train) [12][4300/8498] lr: 4.0000e-05 eta: 1:37:27 time: 1.2361 data_time: 0.2218 memory: 36546 loss_ce: 0.0311 loss: 0.0311 2022/09/17 06:19:15 - mmengine - INFO - Epoch(train) [12][4400/8498] lr: 4.0000e-05 eta: 1:35:07 time: 1.4707 data_time: 0.1866 memory: 36546 loss_ce: 0.0347 loss: 0.0347 2022/09/17 06:21:18 - mmengine - INFO - Epoch(train) [12][4500/8498] lr: 4.0000e-05 eta: 1:32:47 time: 1.3510 data_time: 0.1983 memory: 36546 loss_ce: 0.0276 loss: 0.0276 2022/09/17 06:21:45 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 06:23:20 - mmengine - INFO - Epoch(train) [12][4600/8498] lr: 4.0000e-05 eta: 1:30:27 time: 1.1251 data_time: 0.2352 memory: 36546 loss_ce: 0.0338 loss: 0.0338 2022/09/17 06:25:20 - mmengine - INFO - Epoch(train) [12][4700/8498] lr: 4.0000e-05 eta: 1:28:07 time: 0.9891 data_time: 0.0728 memory: 36546 loss_ce: 0.0337 loss: 0.0337 2022/09/17 06:27:22 - mmengine - INFO - Epoch(train) [12][4800/8498] lr: 4.0000e-05 eta: 1:25:47 time: 0.8803 data_time: 0.0063 memory: 36546 loss_ce: 0.0340 loss: 0.0340 2022/09/17 06:29:26 - mmengine - INFO - Epoch(train) [12][4900/8498] lr: 4.0000e-05 eta: 1:23:28 time: 1.3052 data_time: 0.2282 memory: 36546 loss_ce: 0.0348 loss: 0.0348 2022/09/17 06:31:32 - mmengine - INFO - Epoch(train) [12][5000/8498] lr: 4.0000e-05 eta: 1:21:08 time: 1.5353 data_time: 0.2615 memory: 36546 loss_ce: 0.0295 loss: 0.0295 2022/09/17 06:33:34 - mmengine - INFO - Epoch(train) [12][5100/8498] lr: 4.0000e-05 eta: 1:18:48 time: 1.3327 data_time: 0.1758 memory: 36546 loss_ce: 0.0333 loss: 0.0333 2022/09/17 06:35:36 - mmengine - INFO - Epoch(train) [12][5200/8498] lr: 4.0000e-05 eta: 1:16:28 time: 1.1996 data_time: 0.2460 memory: 36546 loss_ce: 0.0300 loss: 0.0300 2022/09/17 06:37:36 - mmengine - INFO - Epoch(train) [12][5300/8498] lr: 4.0000e-05 eta: 1:14:09 time: 0.9670 data_time: 0.0694 memory: 36546 loss_ce: 0.0317 loss: 0.0317 2022/09/17 06:39:35 - mmengine - INFO - Epoch(train) [12][5400/8498] lr: 4.0000e-05 eta: 1:11:49 time: 0.9086 data_time: 0.0061 memory: 36546 loss_ce: 0.0390 loss: 0.0390 2022/09/17 06:41:41 - mmengine - INFO - Epoch(train) [12][5500/8498] lr: 4.0000e-05 eta: 1:09:29 time: 1.2840 data_time: 0.1897 memory: 36546 loss_ce: 0.0331 loss: 0.0331 2022/09/17 06:42:07 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 06:43:41 - mmengine - INFO - Epoch(train) [12][5600/8498] lr: 4.0000e-05 eta: 1:07:10 time: 1.3983 data_time: 0.1880 memory: 36546 loss_ce: 0.0345 loss: 0.0345 2022/09/17 06:45:43 - mmengine - INFO - Epoch(train) [12][5700/8498] lr: 4.0000e-05 eta: 1:04:50 time: 1.3541 data_time: 0.2121 memory: 36546 loss_ce: 0.0312 loss: 0.0312 2022/09/17 06:47:44 - mmengine - INFO - Epoch(train) [12][5800/8498] lr: 4.0000e-05 eta: 1:02:31 time: 1.1419 data_time: 0.2193 memory: 36546 loss_ce: 0.0291 loss: 0.0291 2022/09/17 06:49:44 - mmengine - INFO - Epoch(train) [12][5900/8498] lr: 4.0000e-05 eta: 1:00:11 time: 0.9946 data_time: 0.0739 memory: 36546 loss_ce: 0.0344 loss: 0.0344 2022/09/17 06:51:46 - mmengine - INFO - Epoch(train) [12][6000/8498] lr: 4.0000e-05 eta: 0:57:52 time: 0.9215 data_time: 0.0067 memory: 36546 loss_ce: 0.0292 loss: 0.0292 2022/09/17 06:53:51 - mmengine - INFO - Epoch(train) [12][6100/8498] lr: 4.0000e-05 eta: 0:55:32 time: 1.2998 data_time: 0.2051 memory: 36546 loss_ce: 0.0329 loss: 0.0329 2022/09/17 06:55:54 - mmengine - INFO - Epoch(train) [12][6200/8498] lr: 4.0000e-05 eta: 0:53:13 time: 1.5146 data_time: 0.1812 memory: 36546 loss_ce: 0.0359 loss: 0.0359 2022/09/17 06:57:58 - mmengine - INFO - Epoch(train) [12][6300/8498] lr: 4.0000e-05 eta: 0:50:54 time: 1.3213 data_time: 0.1865 memory: 36546 loss_ce: 0.0321 loss: 0.0321 2022/09/17 06:59:59 - mmengine - INFO - Epoch(train) [12][6400/8498] lr: 4.0000e-05 eta: 0:48:34 time: 1.1461 data_time: 0.2046 memory: 36546 loss_ce: 0.0311 loss: 0.0311 2022/09/17 07:02:02 - mmengine - INFO - Epoch(train) [12][6500/8498] lr: 4.0000e-05 eta: 0:46:15 time: 1.0103 data_time: 0.0838 memory: 36546 loss_ce: 0.0299 loss: 0.0299 2022/09/17 07:02:29 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 07:04:03 - mmengine - INFO - Epoch(train) [12][6600/8498] lr: 4.0000e-05 eta: 0:43:56 time: 0.9367 data_time: 0.0062 memory: 36546 loss_ce: 0.0286 loss: 0.0286 2022/09/17 07:06:07 - mmengine - INFO - Epoch(train) [12][6700/8498] lr: 4.0000e-05 eta: 0:41:37 time: 1.2164 data_time: 0.1857 memory: 36546 loss_ce: 0.0355 loss: 0.0355 2022/09/17 07:08:09 - mmengine - INFO - Epoch(train) [12][6800/8498] lr: 4.0000e-05 eta: 0:39:18 time: 1.4974 data_time: 0.2165 memory: 36546 loss_ce: 0.0344 loss: 0.0344 2022/09/17 07:10:13 - mmengine - INFO - Epoch(train) [12][6900/8498] lr: 4.0000e-05 eta: 0:36:58 time: 1.3274 data_time: 0.1788 memory: 36546 loss_ce: 0.0304 loss: 0.0304 2022/09/17 07:12:16 - mmengine - INFO - Epoch(train) [12][7000/8498] lr: 4.0000e-05 eta: 0:34:39 time: 1.1585 data_time: 0.2137 memory: 36546 loss_ce: 0.0315 loss: 0.0315 2022/09/17 07:14:17 - mmengine - INFO - Epoch(train) [12][7100/8498] lr: 4.0000e-05 eta: 0:32:20 time: 1.0029 data_time: 0.0536 memory: 36546 loss_ce: 0.0290 loss: 0.0290 2022/09/17 07:16:19 - mmengine - INFO - Epoch(train) [12][7200/8498] lr: 4.0000e-05 eta: 0:30:01 time: 0.8907 data_time: 0.0087 memory: 36546 loss_ce: 0.0309 loss: 0.0309 2022/09/17 07:18:25 - mmengine - INFO - Epoch(train) [12][7300/8498] lr: 4.0000e-05 eta: 0:27:42 time: 1.2499 data_time: 0.1935 memory: 36546 loss_ce: 0.0279 loss: 0.0279 2022/09/17 07:20:28 - mmengine - INFO - Epoch(train) [12][7400/8498] lr: 4.0000e-05 eta: 0:25:23 time: 1.4709 data_time: 0.1927 memory: 36546 loss_ce: 0.0280 loss: 0.0280 2022/09/17 07:22:31 - mmengine - INFO - Epoch(train) [12][7500/8498] lr: 4.0000e-05 eta: 0:23:04 time: 1.3590 data_time: 0.2183 memory: 36546 loss_ce: 0.0332 loss: 0.0332 2022/09/17 07:22:58 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 07:24:32 - mmengine - INFO - Epoch(train) [12][7600/8498] lr: 4.0000e-05 eta: 0:20:45 time: 1.1533 data_time: 0.2104 memory: 36546 loss_ce: 0.0316 loss: 0.0316 2022/09/17 07:26:34 - mmengine - INFO - Epoch(train) [12][7700/8498] lr: 4.0000e-05 eta: 0:18:27 time: 0.9625 data_time: 0.0815 memory: 36546 loss_ce: 0.0323 loss: 0.0323 2022/09/17 07:28:35 - mmengine - INFO - Epoch(train) [12][7800/8498] lr: 4.0000e-05 eta: 0:16:08 time: 0.9302 data_time: 0.0062 memory: 36546 loss_ce: 0.0310 loss: 0.0310 2022/09/17 07:30:41 - mmengine - INFO - Epoch(train) [12][7900/8498] lr: 4.0000e-05 eta: 0:13:49 time: 1.2935 data_time: 0.1938 memory: 36546 loss_ce: 0.0359 loss: 0.0359 2022/09/17 07:32:43 - mmengine - INFO - Epoch(train) [12][8000/8498] lr: 4.0000e-05 eta: 0:11:30 time: 1.4777 data_time: 0.1757 memory: 36546 loss_ce: 0.0303 loss: 0.0303 2022/09/17 07:34:46 - mmengine - INFO - Epoch(train) [12][8100/8498] lr: 4.0000e-05 eta: 0:09:11 time: 1.3221 data_time: 0.1789 memory: 36546 loss_ce: 0.0338 loss: 0.0338 2022/09/17 07:36:48 - mmengine - INFO - Epoch(train) [12][8200/8498] lr: 4.0000e-05 eta: 0:06:53 time: 1.1150 data_time: 0.2078 memory: 36546 loss_ce: 0.0319 loss: 0.0319 2022/09/17 07:38:51 - mmengine - INFO - Epoch(train) [12][8300/8498] lr: 4.0000e-05 eta: 0:04:34 time: 1.0212 data_time: 0.0882 memory: 36546 loss_ce: 0.0330 loss: 0.0330 2022/09/17 07:40:52 - mmengine - INFO - Epoch(train) [12][8400/8498] lr: 4.0000e-05 eta: 0:02:15 time: 0.9268 data_time: 0.0067 memory: 36546 loss_ce: 0.0367 loss: 0.0367 2022/09/17 07:42:47 - mmengine - INFO - Exp name: master_resnet31_12e_st_mj_sa_20220915_152443 2022/09/17 07:42:47 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/09/17 07:43:17 - mmengine - INFO - Epoch(val) [12][100/1918] eta: 0:03:54 time: 0.1287 data_time: 0.0006 memory: 36546 2022/09/17 07:43:31 - mmengine - INFO - Epoch(val) [12][200/1918] eta: 0:03:46 time: 0.1321 data_time: 0.0006 memory: 1150 2022/09/17 07:43:44 - mmengine - INFO - Epoch(val) [12][300/1918] eta: 0:03:35 time: 0.1330 data_time: 0.0006 memory: 1150 2022/09/17 07:43:57 - mmengine - INFO - Epoch(val) [12][400/1918] eta: 0:03:20 time: 0.1323 data_time: 0.0006 memory: 1150 2022/09/17 07:44:11 - mmengine - INFO - Epoch(val) [12][500/1918] eta: 0:03:03 time: 0.1293 data_time: 0.0005 memory: 1150 2022/09/17 07:44:24 - mmengine - INFO - Epoch(val) [12][600/1918] eta: 0:02:49 time: 0.1288 data_time: 0.0006 memory: 1150 2022/09/17 07:44:37 - mmengine - INFO - Epoch(val) [12][700/1918] eta: 0:02:39 time: 0.1307 data_time: 0.0006 memory: 1150 2022/09/17 07:44:50 - mmengine - INFO - Epoch(val) [12][800/1918] eta: 0:02:29 time: 0.1334 data_time: 0.0007 memory: 1150 2022/09/17 07:45:04 - mmengine - INFO - Epoch(val) [12][900/1918] eta: 0:02:18 time: 0.1357 data_time: 0.0023 memory: 1150 2022/09/17 07:45:17 - mmengine - INFO - Epoch(val) [12][1000/1918] eta: 0:02:00 time: 0.1315 data_time: 0.0006 memory: 1150 2022/09/17 07:45:30 - mmengine - INFO - Epoch(val) [12][1100/1918] eta: 0:01:47 time: 0.1318 data_time: 0.0006 memory: 1150 2022/09/17 07:45:43 - mmengine - INFO - Epoch(val) [12][1200/1918] eta: 0:01:35 time: 0.1336 data_time: 0.0006 memory: 1150 2022/09/17 07:45:56 - mmengine - INFO - Epoch(val) [12][1300/1918] eta: 0:01:20 time: 0.1304 data_time: 0.0006 memory: 1150 2022/09/17 07:46:10 - mmengine - INFO - Epoch(val) [12][1400/1918] eta: 0:01:08 time: 0.1315 data_time: 0.0006 memory: 1150 2022/09/17 07:46:23 - mmengine - INFO - Epoch(val) [12][1500/1918] eta: 0:00:56 time: 0.1361 data_time: 0.0006 memory: 1150 2022/09/17 07:46:36 - mmengine - INFO - Epoch(val) [12][1600/1918] eta: 0:00:42 time: 0.1324 data_time: 0.0007 memory: 1150 2022/09/17 07:46:49 - mmengine - INFO - Epoch(val) [12][1700/1918] eta: 0:00:28 time: 0.1321 data_time: 0.0006 memory: 1150 2022/09/17 07:47:02 - mmengine - INFO - Epoch(val) [12][1800/1918] eta: 0:00:15 time: 0.1305 data_time: 0.0005 memory: 1150 2022/09/17 07:47:15 - mmengine - INFO - Epoch(val) [12][1900/1918] eta: 0:00:02 time: 0.1283 data_time: 0.0005 memory: 1150 2022/09/17 07:47:18 - mmengine - INFO - Epoch(val) [12][1918/1918] CUTE80/recog/word_acc_ignore_case_symbol: 0.8854 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9490 SVT/recog/word_acc_ignore_case_symbol: 0.8964 SVTP/recog/word_acc_ignore_case_symbol: 0.8465 IC13/recog/word_acc_ignore_case_symbol: 0.9517 IC15/recog/word_acc_ignore_case_symbol: 0.7631