2022/10/05 01:26:29 - 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: 1122486353 GPU 0,1,2,3,4,5,6,7: 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: 8 ------------------------------------------------------------ 2022/10/05 01:26:31 - 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) 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=0.0001)) train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=20, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [ dict( type='LinearLR', end=2, start_factor=0.001, convert_to_iter_based=True), dict(type='MultiStepLR', milestones=[16, 18], end=20) ] dictionary = dict( type='Dictionary', dict_file= 'configs/textrecog/abinet/../../../dicts/lower_english_digits.txt', with_start=True, with_end=True, same_start_end=True, with_padding=False, with_unknown=False) model = dict( type='ABINet', backbone=dict(type='ResNetABI'), encoder=dict( type='ABIEncoder', n_layers=3, n_head=8, d_model=512, d_inner=2048, dropout=0.1, max_len=256), decoder=dict( type='ABIFuser', vision_decoder=dict( type='ABIVisionDecoder', in_channels=512, num_channels=64, attn_height=8, attn_width=32, attn_mode='nearest', init_cfg=dict(type='Xavier', layer='Conv2d')), module_loss=dict(type='ABIModuleLoss', letter_case='lower'), postprocessor=dict(type='AttentionPostprocessor'), dictionary=dict( type='Dictionary', dict_file= 'configs/textrecog/abinet/../../../dicts/lower_english_digits.txt', with_start=True, with_end=True, same_start_end=True, with_padding=False, with_unknown=False), max_seq_len=26, d_model=512, num_iters=3, language_decoder=dict( type='ABILanguageDecoder', d_model=512, n_head=8, d_inner=2048, n_layers=4, dropout=0.1, detach_tokens=True, use_self_attn=False)), data_preprocessor=dict( type='TextRecogDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375])) file_client_args = dict(backend='disk') 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='Resize', scale=(128, 32)), dict( type='RandomApply', prob=0.5, transforms=[ dict( type='RandomChoice', transforms=[ dict(type='RandomRotate', max_angle=15), dict( type='TorchVisionWrapper', op='RandomAffine', degrees=15, translate=(0.3, 0.3), scale=(0.5, 2.0), shear=(-45, 45)), dict( type='TorchVisionWrapper', op='RandomPerspective', distortion_scale=0.5, p=1) ]) ]), dict( type='RandomApply', prob=0.25, transforms=[ dict(type='PyramidRescale'), dict( type='mmdet.Albu', transforms=[ dict(type='GaussNoise', var_limit=(20, 20), p=0.5), dict(type='MotionBlur', blur_limit=6, p=0.5) ]) ]), dict( type='RandomApply', prob=0.25, transforms=[ dict( type='TorchVisionWrapper', op='ColorJitter', brightness=0.5, saturation=0.5, contrast=0.5, hue=0.1) ]), 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='Resize', scale=(128, 32)), 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='alphanumeric_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='alphanumeric_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='Resize', scale=(128, 32)), dict( type='RandomApply', prob=0.5, transforms=[ dict( type='RandomChoice', transforms=[ dict(type='RandomRotate', max_angle=15), dict( type='TorchVisionWrapper', op='RandomAffine', degrees=15, translate=(0.3, 0.3), scale=(0.5, 2.0), shear=(-45, 45)), dict( type='TorchVisionWrapper', op='RandomPerspective', distortion_scale=0.5, p=1) ]) ]), dict( type='RandomApply', prob=0.25, transforms=[ dict(type='PyramidRescale'), dict( type='mmdet.Albu', transforms=[ dict(type='GaussNoise', var_limit=(20, 20), p=0.5), dict(type='MotionBlur', blur_limit=6, p=0.5) ]) ]), dict( type='RandomApply', prob=0.25, transforms=[ dict( type='TorchVisionWrapper', op='ColorJitter', brightness=0.5, saturation=0.5, contrast=0.5, hue=0.1) ]), 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='Resize', scale=(128, 32)), dict(type='LoadOCRAnnotations', with_text=True), dict( type='PackTextRecogInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio')) ]) train_dataloader = dict( batch_size=192, num_workers=32, 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='alphanumeric_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='Resize', scale=(128, 32)), dict( type='RandomApply', prob=0.5, transforms=[ dict( type='RandomChoice', transforms=[ dict(type='RandomRotate', max_angle=15), dict( type='TorchVisionWrapper', op='RandomAffine', degrees=15, translate=(0.3, 0.3), scale=(0.5, 2.0), shear=(-45, 45)), dict( type='TorchVisionWrapper', op='RandomPerspective', distortion_scale=0.5, p=1) ]) ]), dict( type='RandomApply', prob=0.25, transforms=[ dict(type='PyramidRescale'), dict( type='mmdet.Albu', transforms=[ dict(type='GaussNoise', var_limit=(20, 20), p=0.5), dict(type='MotionBlur', blur_limit=6, p=0.5) ]) ]), dict( type='RandomApply', prob=0.25, transforms=[ dict( type='TorchVisionWrapper', op='ColorJitter', brightness=0.5, saturation=0.5, contrast=0.5, hue=0.1) ]), 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='Resize', scale=(128, 32)), 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='Resize', scale=(128, 32)), 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/abinet_20e_st-an_mj' Name of parameter - Initialization information backbone.conv1.weight - torch.Size([32, 3, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.conv1.bias - torch.Size([32]): XavierInit: gain=1, distribution=normal, bias=0 backbone.bn1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of ABINet backbone.bn1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of ABINet backbone.layer1.0.conv1.weight - torch.Size([32, 32, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer1.0.conv2.weight - torch.Size([32, 32, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer1.0.bn1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of ABINet backbone.layer1.0.bn1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of ABINet backbone.layer1.0.bn2.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of ABINet backbone.layer1.0.bn2.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of ABINet backbone.layer1.0.downsample.0.weight - torch.Size([32, 32, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer1.0.downsample.1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of ABINet backbone.layer1.0.downsample.1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of ABINet backbone.layer1.1.conv1.weight - torch.Size([32, 32, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer1.1.conv2.weight - torch.Size([32, 32, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer1.1.bn1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of ABINet backbone.layer1.1.bn1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of ABINet backbone.layer1.1.bn2.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of ABINet backbone.layer1.1.bn2.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of ABINet backbone.layer1.2.conv1.weight - torch.Size([32, 32, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer1.2.conv2.weight - torch.Size([32, 32, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer1.2.bn1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of ABINet backbone.layer1.2.bn1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of ABINet backbone.layer1.2.bn2.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of ABINet backbone.layer1.2.bn2.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.0.conv1.weight - torch.Size([64, 32, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer2.0.conv2.weight - torch.Size([64, 64, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer2.0.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.0.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.0.bn2.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.0.bn2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.0.downsample.0.weight - torch.Size([64, 32, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer2.0.downsample.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.0.downsample.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.1.conv1.weight - torch.Size([64, 64, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer2.1.conv2.weight - torch.Size([64, 64, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer2.1.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.1.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.1.bn2.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.1.bn2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.2.conv1.weight - torch.Size([64, 64, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer2.2.conv2.weight - torch.Size([64, 64, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer2.2.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.2.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.2.bn2.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.2.bn2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.3.conv1.weight - torch.Size([64, 64, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer2.3.conv2.weight - torch.Size([64, 64, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer2.3.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.3.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.3.bn2.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer2.3.bn2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.0.conv1.weight - torch.Size([128, 64, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer3.0.conv2.weight - torch.Size([128, 128, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer3.0.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.0.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.0.bn2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.0.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.0.downsample.0.weight - torch.Size([128, 64, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer3.0.downsample.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.0.downsample.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.1.conv1.weight - torch.Size([128, 128, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer3.1.conv2.weight - torch.Size([128, 128, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer3.1.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.1.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.1.bn2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.1.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.2.conv1.weight - torch.Size([128, 128, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer3.2.conv2.weight - torch.Size([128, 128, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer3.2.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.2.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.2.bn2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.2.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.3.conv1.weight - torch.Size([128, 128, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer3.3.conv2.weight - torch.Size([128, 128, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer3.3.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.3.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.3.bn2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.3.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.4.conv1.weight - torch.Size([128, 128, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer3.4.conv2.weight - torch.Size([128, 128, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer3.4.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.4.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.4.bn2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.4.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.5.conv1.weight - torch.Size([128, 128, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer3.5.conv2.weight - torch.Size([128, 128, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer3.5.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.5.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.5.bn2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer3.5.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.0.conv1.weight - torch.Size([256, 128, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer4.0.conv2.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer4.0.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.0.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.0.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.0.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.0.downsample.0.weight - torch.Size([256, 128, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer4.0.downsample.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.0.downsample.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.1.conv1.weight - torch.Size([256, 256, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer4.1.conv2.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer4.1.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.1.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.1.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.1.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.2.conv1.weight - torch.Size([256, 256, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer4.2.conv2.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer4.2.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.2.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.2.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.2.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.3.conv1.weight - torch.Size([256, 256, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer4.3.conv2.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer4.3.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.3.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.3.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.3.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.4.conv1.weight - torch.Size([256, 256, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer4.4.conv2.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer4.4.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.4.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.4.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.4.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.5.conv1.weight - torch.Size([256, 256, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer4.5.conv2.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer4.5.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.5.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.5.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer4.5.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ABINet backbone.layer5.0.conv1.weight - torch.Size([512, 256, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer5.0.conv2.weight - torch.Size([512, 512, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer5.0.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet backbone.layer5.0.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet backbone.layer5.0.bn2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet backbone.layer5.0.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet backbone.layer5.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer5.0.downsample.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet backbone.layer5.0.downsample.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet backbone.layer5.1.conv1.weight - torch.Size([512, 512, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer5.1.conv2.weight - torch.Size([512, 512, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer5.1.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet backbone.layer5.1.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet backbone.layer5.1.bn2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet backbone.layer5.1.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet backbone.layer5.2.conv1.weight - torch.Size([512, 512, 1, 1]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer5.2.conv2.weight - torch.Size([512, 512, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 backbone.layer5.2.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet backbone.layer5.2.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet backbone.layer5.2.bn2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet backbone.layer5.2.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.0.attentions.0.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.0.attentions.0.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.0.attentions.0.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.0.attentions.0.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.0.ffns.0.layers.0.0.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.0.ffns.0.layers.0.0.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.0.ffns.0.layers.1.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.0.ffns.0.layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.0.norms.0.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.0.norms.0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.0.norms.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.0.norms.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.1.attentions.0.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.1.attentions.0.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.1.attentions.0.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.1.attentions.0.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.1.ffns.0.layers.0.0.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.1.ffns.0.layers.0.0.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.1.ffns.0.layers.1.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.1.ffns.0.layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.1.norms.0.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.1.norms.0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.1.norms.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.1.norms.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.2.attentions.0.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.2.attentions.0.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.2.attentions.0.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.2.attentions.0.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.2.ffns.0.layers.0.0.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.2.ffns.0.layers.0.0.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.2.ffns.0.layers.1.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.2.ffns.0.layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.2.norms.0.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.2.norms.0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.2.norms.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet encoder.transformer.2.norms.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.w_att.weight - torch.Size([512, 1024]): The value is the same before and after calling `init_weights` of ABINet decoder.w_att.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.cls.weight - torch.Size([37, 512]): The value is the same before and after calling `init_weights` of ABINet decoder.cls.bias - torch.Size([37]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.k_encoder.0.conv.weight - torch.Size([64, 512, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 decoder.vision_decoder.k_encoder.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.k_encoder.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.k_encoder.1.conv.weight - torch.Size([64, 64, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 decoder.vision_decoder.k_encoder.1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.k_encoder.1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.k_encoder.2.conv.weight - torch.Size([64, 64, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 decoder.vision_decoder.k_encoder.2.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.k_encoder.2.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.k_encoder.3.conv.weight - torch.Size([64, 64, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 decoder.vision_decoder.k_encoder.3.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.k_encoder.3.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.k_decoder.0.1.conv.weight - torch.Size([64, 64, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 decoder.vision_decoder.k_decoder.0.1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.k_decoder.0.1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.k_decoder.1.1.conv.weight - torch.Size([64, 64, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 decoder.vision_decoder.k_decoder.1.1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.k_decoder.1.1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.k_decoder.2.1.conv.weight - torch.Size([64, 64, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 decoder.vision_decoder.k_decoder.2.1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.k_decoder.2.1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.k_decoder.3.1.conv.weight - torch.Size([512, 64, 3, 3]): XavierInit: gain=1, distribution=normal, bias=0 decoder.vision_decoder.k_decoder.3.1.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.k_decoder.3.1.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.project.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.project.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.cls.weight - torch.Size([37, 512]): The value is the same before and after calling `init_weights` of ABINet decoder.vision_decoder.cls.bias - torch.Size([37]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.proj.weight - torch.Size([512, 37]): The value is the same before and after calling `init_weights` of ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_decoder.decoder_layers.0.ffns.0.layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.0.norms.0.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.0.norms.0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.0.norms.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.0.norms.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_decoder.decoder_layers.1.ffns.0.layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.1.norms.0.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.1.norms.0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.1.norms.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.1.norms.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_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 ABINet decoder.language_decoder.decoder_layers.2.ffns.0.layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.2.norms.0.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.2.norms.0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.2.norms.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.2.norms.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.3.attentions.0.attn.in_proj_weight - torch.Size([1536, 512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.3.attentions.0.attn.in_proj_bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.3.attentions.0.attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.3.attentions.0.attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.3.ffns.0.layers.0.0.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.3.ffns.0.layers.0.0.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.3.ffns.0.layers.1.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.3.ffns.0.layers.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.3.norms.0.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.3.norms.0.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.3.norms.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.decoder_layers.3.norms.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.cls.weight - torch.Size([37, 512]): The value is the same before and after calling `init_weights` of ABINet decoder.language_decoder.cls.bias - torch.Size([37]): The value is the same before and after calling `init_weights` of ABINet 2022/10/05 01:30:43 - mmengine - INFO - Checkpoints will be saved to sproject:s3://1.0.0rc0_recog_retest/abinet_20e_st-an_mj by PetrelBackend. 2022/10/05 02:01:43 - mmengine - INFO - Epoch(train) [1][100/10520] lr: 5.7008e-07 eta: 45 days, 6:03:16 time: 0.9530 data_time: 0.1059 memory: 18366 loss_visual: 2.5746 loss_lang: 3.1317 loss_fusion: 3.3317 loss: 9.0380 2022/10/05 02:02:53 - mmengine - INFO - Epoch(train) [1][200/10520] lr: 1.0449e-06 eta: 23 days, 11:11:47 time: 1.2174 data_time: 0.1737 memory: 17203 loss_visual: 1.6659 loss_lang: 1.8992 loss_fusion: 2.1783 loss: 5.7435 2022/10/05 02:04:04 - mmengine - INFO - Epoch(train) [1][300/10520] lr: 1.5197e-06 eta: 16 days, 5:09:43 time: 1.1969 data_time: 0.1162 memory: 17203 loss_visual: 1.4045 loss_lang: 1.3714 loss_fusion: 1.4840 loss: 4.2599 2022/10/05 02:05:14 - mmengine - INFO - Epoch(train) [1][400/10520] lr: 1.9946e-06 eta: 12 days, 13:57:28 time: 0.6728 data_time: 0.0211 memory: 17203 loss_visual: 1.2126 loss_lang: 1.1882 loss_fusion: 1.2795 loss: 3.6803 2022/10/05 02:06:22 - mmengine - INFO - Epoch(train) [1][500/10520] lr: 2.4694e-06 eta: 10 days, 9:26:01 time: 0.3692 data_time: 0.0343 memory: 17203 loss_visual: 1.0568 loss_lang: 1.0574 loss_fusion: 1.1213 loss: 3.2356 2022/10/05 02:07:30 - mmengine - INFO - Epoch(train) [1][600/10520] lr: 2.9442e-06 eta: 8 days, 22:21:29 time: 0.3687 data_time: 0.0353 memory: 17203 loss_visual: 0.9940 loss_lang: 0.9980 loss_fusion: 1.0313 loss: 3.0234 2022/10/05 02:08:37 - mmengine - INFO - Epoch(train) [1][700/10520] lr: 3.4191e-06 eta: 7 days, 21:12:30 time: 0.4276 data_time: 0.0032 memory: 17203 loss_visual: 0.9648 loss_lang: 0.9769 loss_fusion: 0.9869 loss: 2.9285 2022/10/05 02:09:44 - mmengine - INFO - Epoch(train) [1][800/10520] lr: 3.8939e-06 eta: 7 days, 2:21:10 time: 0.5364 data_time: 0.0029 memory: 17203 loss_visual: 0.9320 loss_lang: 0.9462 loss_fusion: 0.9427 loss: 2.8209 2022/10/05 02:10:53 - mmengine - INFO - Epoch(train) [1][900/10520] lr: 4.3687e-06 eta: 6 days, 11:50:12 time: 0.8765 data_time: 0.0908 memory: 17203 loss_visual: 0.9015 loss_lang: 0.9091 loss_fusion: 0.9071 loss: 2.7176 2022/10/05 02:12:02 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 02:12:02 - mmengine - INFO - Epoch(train) [1][1000/10520] lr: 4.8436e-06 eta: 6 days, 0:09:37 time: 1.0767 data_time: 0.1856 memory: 17203 loss_visual: 0.8835 loss_lang: 0.8888 loss_fusion: 0.8862 loss: 2.6585 2022/10/05 02:13:09 - mmengine - INFO - Epoch(train) [1][1100/10520] lr: 5.3184e-06 eta: 5 days, 14:32:17 time: 0.9698 data_time: 0.1173 memory: 17203 loss_visual: 0.8938 loss_lang: 0.8984 loss_fusion: 0.8940 loss: 2.6862 2022/10/05 02:14:15 - mmengine - INFO - Epoch(train) [1][1200/10520] lr: 5.7932e-06 eta: 5 days, 6:28:04 time: 0.6832 data_time: 0.0222 memory: 17203 loss_visual: 0.8682 loss_lang: 0.8731 loss_fusion: 0.8672 loss: 2.6084 2022/10/05 02:15:20 - mmengine - INFO - Epoch(train) [1][1300/10520] lr: 6.2681e-06 eta: 4 days, 23:35:51 time: 0.3662 data_time: 0.0317 memory: 17203 loss_visual: 0.8656 loss_lang: 0.8673 loss_fusion: 0.8627 loss: 2.5956 2022/10/05 02:16:26 - mmengine - INFO - Epoch(train) [1][1400/10520] lr: 6.7429e-06 eta: 4 days, 17:43:29 time: 0.3655 data_time: 0.0307 memory: 17203 loss_visual: 0.8667 loss_lang: 0.8711 loss_fusion: 0.8637 loss: 2.6015 2022/10/05 02:17:32 - mmengine - INFO - Epoch(train) [1][1500/10520] lr: 7.2177e-06 eta: 4 days, 12:39:25 time: 0.4120 data_time: 0.0031 memory: 17203 loss_visual: 0.8579 loss_lang: 0.8608 loss_fusion: 0.8535 loss: 2.5722 2022/10/05 02:21:53 - mmengine - INFO - Epoch(train) [1][1600/10520] lr: 7.6926e-06 eta: 4 days, 15:16:53 time: 0.6004 data_time: 0.0033 memory: 17203 loss_visual: 0.8495 loss_lang: 0.8486 loss_fusion: 0.8415 loss: 2.5396 2022/10/05 02:23:10 - mmengine - INFO - Epoch(train) [1][1700/10520] lr: 8.1674e-06 eta: 4 days, 11:18:40 time: 0.9007 data_time: 0.1218 memory: 17203 loss_visual: 0.8428 loss_lang: 0.8425 loss_fusion: 0.8329 loss: 2.5182 2022/10/05 02:24:19 - mmengine - INFO - Epoch(train) [1][1800/10520] lr: 8.6422e-06 eta: 4 days, 7:31:19 time: 1.1597 data_time: 0.1761 memory: 17203 loss_visual: 0.8232 loss_lang: 0.8224 loss_fusion: 0.8133 loss: 2.4589 2022/10/05 02:25:27 - mmengine - INFO - Epoch(train) [1][1900/10520] lr: 9.1171e-06 eta: 4 days, 4:06:10 time: 1.0702 data_time: 0.1206 memory: 17203 loss_visual: 0.8423 loss_lang: 0.8426 loss_fusion: 0.8320 loss: 2.5169 2022/10/05 02:26:34 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 02:26:34 - mmengine - INFO - Epoch(train) [1][2000/10520] lr: 9.5919e-06 eta: 4 days, 0:59:28 time: 0.6800 data_time: 0.0216 memory: 17203 loss_visual: 0.8356 loss_lang: 0.8371 loss_fusion: 0.8250 loss: 2.4976 2022/10/05 02:31:23 - mmengine - INFO - Epoch(train) [1][2100/10520] lr: 1.0067e-05 eta: 4 days, 4:16:02 time: 2.6194 data_time: 0.0348 memory: 17203 loss_visual: 0.8344 loss_lang: 0.8311 loss_fusion: 0.8214 loss: 2.4869 2022/10/05 02:32:29 - mmengine - INFO - Epoch(train) [1][2200/10520] lr: 1.0542e-05 eta: 4 days, 1:24:25 time: 0.3797 data_time: 0.0306 memory: 17203 loss_visual: 0.8221 loss_lang: 0.8199 loss_fusion: 0.8104 loss: 2.4524 2022/10/05 02:33:35 - mmengine - INFO - Epoch(train) [1][2300/10520] lr: 1.1016e-05 eta: 3 days, 22:47:09 time: 0.4164 data_time: 0.0030 memory: 17203 loss_visual: 0.8340 loss_lang: 0.8339 loss_fusion: 0.8226 loss: 2.4905 2022/10/05 02:34:41 - mmengine - INFO - Epoch(train) [1][2400/10520] lr: 1.1491e-05 eta: 3 days, 20:23:03 time: 0.5655 data_time: 0.0033 memory: 17203 loss_visual: 0.8288 loss_lang: 0.8322 loss_fusion: 0.8188 loss: 2.4799 2022/10/05 02:35:49 - mmengine - INFO - Epoch(train) [1][2500/10520] lr: 1.1966e-05 eta: 3 days, 18:13:27 time: 0.8712 data_time: 0.1238 memory: 17203 loss_visual: 0.8211 loss_lang: 0.8221 loss_fusion: 0.8116 loss: 2.4548 2022/10/05 02:36:58 - mmengine - INFO - Epoch(train) [1][2600/10520] lr: 1.2441e-05 eta: 3 days, 16:14:22 time: 1.1292 data_time: 0.1813 memory: 17203 loss_visual: 0.8257 loss_lang: 0.8298 loss_fusion: 0.8165 loss: 2.4720 2022/10/05 02:38:04 - mmengine - INFO - Epoch(train) [1][2700/10520] lr: 1.2916e-05 eta: 3 days, 14:20:31 time: 1.0173 data_time: 0.1016 memory: 17203 loss_visual: 0.7995 loss_lang: 0.8030 loss_fusion: 0.7918 loss: 2.3944 2022/10/05 02:39:11 - mmengine - INFO - Epoch(train) [1][2800/10520] lr: 1.3391e-05 eta: 3 days, 12:36:04 time: 0.7147 data_time: 0.0217 memory: 17203 loss_visual: 0.8262 loss_lang: 0.8297 loss_fusion: 0.8184 loss: 2.4743 2022/10/05 02:40:18 - mmengine - INFO - Epoch(train) [1][2900/10520] lr: 1.3865e-05 eta: 3 days, 10:58:03 time: 0.3648 data_time: 0.0306 memory: 17203 loss_visual: 0.8060 loss_lang: 0.8117 loss_fusion: 0.7996 loss: 2.4173 2022/10/05 02:41:24 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 02:41:24 - mmengine - INFO - Epoch(train) [1][3000/10520] lr: 1.4340e-05 eta: 3 days, 9:25:41 time: 0.4175 data_time: 0.0348 memory: 17203 loss_visual: 0.8046 loss_lang: 0.8111 loss_fusion: 0.7976 loss: 2.4133 2022/10/05 02:42:32 - mmengine - INFO - Epoch(train) [1][3100/10520] lr: 1.4815e-05 eta: 3 days, 8:01:47 time: 0.4000 data_time: 0.0041 memory: 17203 loss_visual: 0.8001 loss_lang: 0.8072 loss_fusion: 0.7936 loss: 2.4009 2022/10/05 02:43:39 - mmengine - INFO - Epoch(train) [1][3200/10520] lr: 1.5290e-05 eta: 3 days, 6:42:31 time: 0.5821 data_time: 0.0037 memory: 17203 loss_visual: 0.8188 loss_lang: 0.8274 loss_fusion: 0.8138 loss: 2.4599 2022/10/05 02:44:48 - mmengine - INFO - Epoch(train) [1][3300/10520] lr: 1.5765e-05 eta: 3 days, 5:28:56 time: 0.8544 data_time: 0.0987 memory: 17203 loss_visual: 0.7973 loss_lang: 0.8070 loss_fusion: 0.7930 loss: 2.3972 2022/10/05 02:45:58 - mmengine - INFO - Epoch(train) [1][3400/10520] lr: 1.6240e-05 eta: 3 days, 4:20:18 time: 1.1409 data_time: 0.2353 memory: 17203 loss_visual: 0.8120 loss_lang: 0.8214 loss_fusion: 0.8069 loss: 2.4404 2022/10/05 02:47:05 - mmengine - INFO - Epoch(train) [1][3500/10520] lr: 1.6714e-05 eta: 3 days, 3:13:52 time: 0.9688 data_time: 0.1239 memory: 17203 loss_visual: 0.7971 loss_lang: 0.8056 loss_fusion: 0.7912 loss: 2.3939 2022/10/05 02:48:10 - mmengine - INFO - Epoch(train) [1][3600/10520] lr: 1.7189e-05 eta: 3 days, 2:09:01 time: 0.7029 data_time: 0.0226 memory: 17203 loss_visual: 0.7857 loss_lang: 0.7942 loss_fusion: 0.7799 loss: 2.3598 2022/10/05 02:49:16 - mmengine - INFO - Epoch(train) [1][3700/10520] lr: 1.7664e-05 eta: 3 days, 1:07:34 time: 0.3815 data_time: 0.0295 memory: 17203 loss_visual: 0.7852 loss_lang: 0.7956 loss_fusion: 0.7799 loss: 2.3607 2022/10/05 02:50:24 - mmengine - INFO - Epoch(train) [1][3800/10520] lr: 1.8139e-05 eta: 3 days, 0:11:45 time: 0.4310 data_time: 0.0663 memory: 17203 loss_visual: 0.7797 loss_lang: 0.7925 loss_fusion: 0.7765 loss: 2.3487 2022/10/05 02:51:29 - mmengine - INFO - Epoch(train) [1][3900/10520] lr: 1.8614e-05 eta: 2 days, 23:16:14 time: 0.4097 data_time: 0.0029 memory: 17203 loss_visual: 0.7881 loss_lang: 0.8012 loss_fusion: 0.7855 loss: 2.3747 2022/10/05 02:52:36 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 02:52:36 - mmengine - INFO - Epoch(train) [1][4000/10520] lr: 1.9089e-05 eta: 2 days, 22:24:23 time: 0.5245 data_time: 0.0056 memory: 17203 loss_visual: 0.7721 loss_lang: 0.7869 loss_fusion: 0.7698 loss: 2.3288 2022/10/05 02:53:45 - mmengine - INFO - Epoch(train) [1][4100/10520] lr: 1.9563e-05 eta: 2 days, 21:37:31 time: 0.8840 data_time: 0.0878 memory: 17203 loss_visual: 0.7877 loss_lang: 0.8057 loss_fusion: 0.7861 loss: 2.3796 2022/10/05 02:54:53 - mmengine - INFO - Epoch(train) [1][4200/10520] lr: 2.0038e-05 eta: 2 days, 20:52:11 time: 1.1730 data_time: 0.1740 memory: 17203 loss_visual: 0.7603 loss_lang: 0.7809 loss_fusion: 0.7587 loss: 2.2999 2022/10/05 02:56:00 - mmengine - INFO - Epoch(train) [1][4300/10520] lr: 2.0513e-05 eta: 2 days, 20:07:22 time: 1.0592 data_time: 0.1031 memory: 17203 loss_visual: 0.7569 loss_lang: 0.7850 loss_fusion: 0.7565 loss: 2.2984 2022/10/05 02:57:05 - mmengine - INFO - Epoch(train) [1][4400/10520] lr: 2.0988e-05 eta: 2 days, 19:22:57 time: 0.6540 data_time: 0.0229 memory: 17203 loss_visual: 0.7545 loss_lang: 0.7928 loss_fusion: 0.7551 loss: 2.3024 2022/10/05 02:58:11 - mmengine - INFO - Epoch(train) [1][4500/10520] lr: 2.1463e-05 eta: 2 days, 18:41:57 time: 0.3790 data_time: 0.0293 memory: 17203 loss_visual: 0.7316 loss_lang: 0.7756 loss_fusion: 0.7324 loss: 2.2396 2022/10/05 02:59:17 - mmengine - INFO - Epoch(train) [1][4600/10520] lr: 2.1938e-05 eta: 2 days, 18:02:09 time: 0.3764 data_time: 0.0288 memory: 17203 loss_visual: 0.7205 loss_lang: 0.7738 loss_fusion: 0.7216 loss: 2.2158 2022/10/05 03:00:23 - mmengine - INFO - Epoch(train) [1][4700/10520] lr: 2.2412e-05 eta: 2 days, 17:24:20 time: 0.4407 data_time: 0.0029 memory: 17203 loss_visual: 0.7141 loss_lang: 0.7750 loss_fusion: 0.7148 loss: 2.2038 2022/10/05 03:01:31 - mmengine - INFO - Epoch(train) [1][4800/10520] lr: 2.2887e-05 eta: 2 days, 16:49:04 time: 0.5536 data_time: 0.0030 memory: 17203 loss_visual: 0.7054 loss_lang: 0.7816 loss_fusion: 0.7068 loss: 2.1939 2022/10/05 03:02:40 - mmengine - INFO - Epoch(train) [1][4900/10520] lr: 2.3362e-05 eta: 2 days, 16:16:13 time: 0.8741 data_time: 0.0941 memory: 17203 loss_visual: 0.7000 loss_lang: 0.7912 loss_fusion: 0.7020 loss: 2.1932 2022/10/05 03:03:50 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 03:03:50 - mmengine - INFO - Epoch(train) [1][5000/10520] lr: 2.3837e-05 eta: 2 days, 15:44:49 time: 1.1227 data_time: 0.1811 memory: 17203 loss_visual: 0.6646 loss_lang: 0.7738 loss_fusion: 0.6680 loss: 2.1064 2022/10/05 03:04:57 - mmengine - INFO - Epoch(train) [1][5100/10520] lr: 2.4312e-05 eta: 2 days, 15:13:00 time: 0.9851 data_time: 0.1165 memory: 17203 loss_visual: 0.6492 loss_lang: 0.7808 loss_fusion: 0.6514 loss: 2.0814 2022/10/05 03:06:02 - mmengine - INFO - Epoch(train) [1][5200/10520] lr: 2.4787e-05 eta: 2 days, 14:41:03 time: 0.6920 data_time: 0.0396 memory: 17203 loss_visual: 0.6055 loss_lang: 0.7591 loss_fusion: 0.6074 loss: 1.9719 2022/10/05 03:07:07 - mmengine - INFO - Epoch(train) [1][5300/10520] lr: 2.5261e-05 eta: 2 days, 14:10:24 time: 0.3663 data_time: 0.0300 memory: 17203 loss_visual: 0.5839 loss_lang: 0.7621 loss_fusion: 0.5854 loss: 1.9315 2022/10/05 03:08:14 - mmengine - INFO - Epoch(train) [1][5400/10520] lr: 2.5736e-05 eta: 2 days, 13:41:31 time: 0.3759 data_time: 0.0323 memory: 17203 loss_visual: 0.5860 loss_lang: 0.7762 loss_fusion: 0.5865 loss: 1.9487 2022/10/05 03:09:21 - mmengine - INFO - Epoch(train) [1][5500/10520] lr: 2.6211e-05 eta: 2 days, 13:13:58 time: 0.4171 data_time: 0.0030 memory: 17203 loss_visual: 0.5586 loss_lang: 0.7896 loss_fusion: 0.5598 loss: 1.9080 2022/10/05 03:10:28 - mmengine - INFO - Epoch(train) [1][5600/10520] lr: 2.6686e-05 eta: 2 days, 12:47:38 time: 0.5311 data_time: 0.0031 memory: 17203 loss_visual: 0.5142 loss_lang: 0.7543 loss_fusion: 0.5141 loss: 1.7827 2022/10/05 03:11:37 - mmengine - INFO - Epoch(train) [1][5700/10520] lr: 2.7161e-05 eta: 2 days, 12:23:16 time: 0.8904 data_time: 0.0881 memory: 17203 loss_visual: 0.5065 loss_lang: 0.7730 loss_fusion: 0.5065 loss: 1.7859 2022/10/05 03:12:45 - mmengine - INFO - Epoch(train) [1][5800/10520] lr: 2.7636e-05 eta: 2 days, 11:59:02 time: 1.1571 data_time: 0.1703 memory: 17203 loss_visual: 0.4568 loss_lang: 0.7266 loss_fusion: 0.4556 loss: 1.6390 2022/10/05 03:13:51 - mmengine - INFO - Epoch(train) [1][5900/10520] lr: 2.8110e-05 eta: 2 days, 11:34:31 time: 1.0313 data_time: 0.1084 memory: 17203 loss_visual: 0.4628 loss_lang: 0.7445 loss_fusion: 0.4608 loss: 1.6681 2022/10/05 03:14:57 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 03:14:57 - mmengine - INFO - Epoch(train) [1][6000/10520] lr: 2.8585e-05 eta: 2 days, 11:10:35 time: 0.6847 data_time: 0.0214 memory: 17203 loss_visual: 0.4286 loss_lang: 0.7248 loss_fusion: 0.4257 loss: 1.5791 2022/10/05 03:16:02 - mmengine - INFO - Epoch(train) [1][6100/10520] lr: 2.9060e-05 eta: 2 days, 10:47:09 time: 0.3801 data_time: 0.0349 memory: 17203 loss_visual: 0.4265 loss_lang: 0.7182 loss_fusion: 0.4246 loss: 1.5693 2022/10/05 03:17:07 - mmengine - INFO - Epoch(train) [1][6200/10520] lr: 2.9535e-05 eta: 2 days, 10:24:17 time: 0.3695 data_time: 0.0283 memory: 17203 loss_visual: 0.4145 loss_lang: 0.7090 loss_fusion: 0.4115 loss: 1.5350 2022/10/05 03:18:13 - mmengine - INFO - Epoch(train) [1][6300/10520] lr: 3.0010e-05 eta: 2 days, 10:02:23 time: 0.4155 data_time: 0.0027 memory: 17203 loss_visual: 0.4115 loss_lang: 0.7079 loss_fusion: 0.4080 loss: 1.5273 2022/10/05 03:19:19 - mmengine - INFO - Epoch(train) [1][6400/10520] lr: 3.0485e-05 eta: 2 days, 9:41:33 time: 0.5118 data_time: 0.0029 memory: 17203 loss_visual: 0.3889 loss_lang: 0.6817 loss_fusion: 0.3841 loss: 1.4547 2022/10/05 03:20:28 - mmengine - INFO - Epoch(train) [1][6500/10520] lr: 3.0959e-05 eta: 2 days, 9:22:38 time: 0.8560 data_time: 0.1206 memory: 17203 loss_visual: 0.3782 loss_lang: 0.6857 loss_fusion: 0.3740 loss: 1.4379 2022/10/05 03:21:38 - mmengine - INFO - Epoch(train) [1][6600/10520] lr: 3.1434e-05 eta: 2 days, 9:04:28 time: 1.1032 data_time: 0.1677 memory: 17203 loss_visual: 0.3805 loss_lang: 0.6854 loss_fusion: 0.3765 loss: 1.4424 2022/10/05 03:22:45 - mmengine - INFO - Epoch(train) [1][6700/10520] lr: 3.1909e-05 eta: 2 days, 8:46:01 time: 1.0665 data_time: 0.1214 memory: 17203 loss_visual: 0.3520 loss_lang: 0.6566 loss_fusion: 0.3466 loss: 1.3552 2022/10/05 03:23:50 - mmengine - INFO - Epoch(train) [1][6800/10520] lr: 3.2384e-05 eta: 2 days, 8:26:45 time: 0.6920 data_time: 0.0216 memory: 17203 loss_visual: 0.3473 loss_lang: 0.6393 loss_fusion: 0.3419 loss: 1.3285 2022/10/05 03:24:56 - mmengine - INFO - Epoch(train) [1][6900/10520] lr: 3.2859e-05 eta: 2 days, 8:08:31 time: 0.3652 data_time: 0.0321 memory: 17203 loss_visual: 0.3425 loss_lang: 0.6484 loss_fusion: 0.3362 loss: 1.3270 2022/10/05 03:26:02 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 03:26:02 - mmengine - INFO - Epoch(train) [1][7000/10520] lr: 3.3334e-05 eta: 2 days, 7:50:37 time: 0.3609 data_time: 0.0282 memory: 17203 loss_visual: 0.3384 loss_lang: 0.6421 loss_fusion: 0.3312 loss: 1.3117 2022/10/05 03:27:08 - mmengine - INFO - Epoch(train) [1][7100/10520] lr: 3.3808e-05 eta: 2 days, 7:32:59 time: 0.4229 data_time: 0.0030 memory: 17203 loss_visual: 0.3392 loss_lang: 0.6301 loss_fusion: 0.3340 loss: 1.3033 2022/10/05 03:28:13 - mmengine - INFO - Epoch(train) [1][7200/10520] lr: 3.4283e-05 eta: 2 days, 7:15:59 time: 0.5289 data_time: 0.0029 memory: 17203 loss_visual: 0.3111 loss_lang: 0.6267 loss_fusion: 0.3054 loss: 1.2431 2022/10/05 03:29:21 - mmengine - INFO - Epoch(train) [1][7300/10520] lr: 3.4758e-05 eta: 2 days, 7:00:33 time: 0.8593 data_time: 0.1056 memory: 17203 loss_visual: 0.3367 loss_lang: 0.6359 loss_fusion: 0.3301 loss: 1.3027 2022/10/05 03:30:29 - mmengine - INFO - Epoch(train) [1][7400/10520] lr: 3.5233e-05 eta: 2 days, 6:45:14 time: 1.1398 data_time: 0.1752 memory: 17203 loss_visual: 0.3082 loss_lang: 0.6098 loss_fusion: 0.3011 loss: 1.2190 2022/10/05 03:31:35 - mmengine - INFO - Epoch(train) [1][7500/10520] lr: 3.5708e-05 eta: 2 days, 6:29:40 time: 1.0126 data_time: 0.1339 memory: 17203 loss_visual: 0.3225 loss_lang: 0.6188 loss_fusion: 0.3161 loss: 1.2573 2022/10/05 03:32:40 - mmengine - INFO - Epoch(train) [1][7600/10520] lr: 3.6183e-05 eta: 2 days, 6:14:06 time: 0.7370 data_time: 0.0402 memory: 17203 loss_visual: 0.3111 loss_lang: 0.6029 loss_fusion: 0.3050 loss: 1.2190 2022/10/05 03:33:45 - mmengine - INFO - Epoch(train) [1][7700/10520] lr: 3.6657e-05 eta: 2 days, 5:58:41 time: 0.3930 data_time: 0.0272 memory: 17203 loss_visual: 0.2997 loss_lang: 0.5959 loss_fusion: 0.2927 loss: 1.1884 2022/10/05 03:34:49 - mmengine - INFO - Epoch(train) [1][7800/10520] lr: 3.7132e-05 eta: 2 days, 5:43:21 time: 0.3703 data_time: 0.0350 memory: 17203 loss_visual: 0.3113 loss_lang: 0.6069 loss_fusion: 0.3034 loss: 1.2216 2022/10/05 03:35:56 - mmengine - INFO - Epoch(train) [1][7900/10520] lr: 3.7607e-05 eta: 2 days, 5:29:14 time: 0.4204 data_time: 0.0029 memory: 17203 loss_visual: 0.2874 loss_lang: 0.5765 loss_fusion: 0.2799 loss: 1.1439 2022/10/05 03:37:03 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 03:37:03 - mmengine - INFO - Epoch(train) [1][8000/10520] lr: 3.8082e-05 eta: 2 days, 5:16:06 time: 0.5741 data_time: 0.0035 memory: 17203 loss_visual: 0.2866 loss_lang: 0.5758 loss_fusion: 0.2790 loss: 1.1414 2022/10/05 03:38:11 - mmengine - INFO - Epoch(train) [1][8100/10520] lr: 3.8557e-05 eta: 2 days, 5:03:28 time: 0.8404 data_time: 0.1041 memory: 17203 loss_visual: 0.2732 loss_lang: 0.5700 loss_fusion: 0.2653 loss: 1.1086 2022/10/05 03:39:20 - mmengine - INFO - Epoch(train) [1][8200/10520] lr: 3.9032e-05 eta: 2 days, 4:51:06 time: 1.0991 data_time: 0.1671 memory: 17203 loss_visual: 0.2751 loss_lang: 0.5724 loss_fusion: 0.2663 loss: 1.1139 2022/10/05 03:40:26 - mmengine - INFO - Epoch(train) [1][8300/10520] lr: 3.9506e-05 eta: 2 days, 4:38:10 time: 0.9648 data_time: 0.1184 memory: 17203 loss_visual: 0.2636 loss_lang: 0.5470 loss_fusion: 0.2541 loss: 1.0647 2022/10/05 03:41:31 - mmengine - INFO - Epoch(train) [1][8400/10520] lr: 3.9981e-05 eta: 2 days, 4:25:06 time: 0.7104 data_time: 0.0213 memory: 17203 loss_visual: 0.2563 loss_lang: 0.5416 loss_fusion: 0.2475 loss: 1.0455 2022/10/05 03:42:36 - mmengine - INFO - Epoch(train) [1][8500/10520] lr: 4.0456e-05 eta: 2 days, 4:12:14 time: 0.3759 data_time: 0.0314 memory: 17203 loss_visual: 0.2758 loss_lang: 0.5570 loss_fusion: 0.2658 loss: 1.0986 2022/10/05 03:43:39 - mmengine - INFO - Epoch(train) [1][8600/10520] lr: 4.0931e-05 eta: 2 days, 3:59:06 time: 0.3709 data_time: 0.0324 memory: 17203 loss_visual: 0.2626 loss_lang: 0.5529 loss_fusion: 0.2546 loss: 1.0701 2022/10/05 03:44:44 - mmengine - INFO - Epoch(train) [1][8700/10520] lr: 4.1406e-05 eta: 2 days, 3:46:51 time: 0.4056 data_time: 0.0031 memory: 17203 loss_visual: 0.2603 loss_lang: 0.5494 loss_fusion: 0.2514 loss: 1.0611 2022/10/05 03:45:50 - mmengine - INFO - Epoch(train) [1][8800/10520] lr: 4.1881e-05 eta: 2 days, 3:35:11 time: 0.5666 data_time: 0.0033 memory: 17203 loss_visual: 0.2774 loss_lang: 0.5717 loss_fusion: 0.2688 loss: 1.1179 2022/10/05 03:46:58 - mmengine - INFO - Epoch(train) [1][8900/10520] lr: 4.2355e-05 eta: 2 days, 3:24:41 time: 0.9154 data_time: 0.0847 memory: 17203 loss_visual: 0.2637 loss_lang: 0.5483 loss_fusion: 0.2554 loss: 1.0674 2022/10/05 03:48:05 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 03:48:05 - mmengine - INFO - Epoch(train) [1][9000/10520] lr: 4.2830e-05 eta: 2 days, 3:13:53 time: 1.1653 data_time: 0.1788 memory: 17203 loss_visual: 0.2526 loss_lang: 0.5371 loss_fusion: 0.2439 loss: 1.0336 2022/10/05 03:49:10 - mmengine - INFO - Epoch(train) [1][9100/10520] lr: 4.3305e-05 eta: 2 days, 3:02:27 time: 0.9261 data_time: 0.1171 memory: 17203 loss_visual: 0.2453 loss_lang: 0.5283 loss_fusion: 0.2351 loss: 1.0086 2022/10/05 03:50:15 - mmengine - INFO - Epoch(train) [1][9200/10520] lr: 4.3780e-05 eta: 2 days, 2:51:15 time: 0.5906 data_time: 0.0185 memory: 17203 loss_visual: 0.2336 loss_lang: 0.5150 loss_fusion: 0.2233 loss: 0.9719 2022/10/05 03:51:20 - mmengine - INFO - Epoch(train) [1][9300/10520] lr: 4.4255e-05 eta: 2 days, 2:40:18 time: 0.3814 data_time: 0.0311 memory: 17203 loss_visual: 0.2453 loss_lang: 0.5302 loss_fusion: 0.2354 loss: 1.0110 2022/10/05 03:52:26 - mmengine - INFO - Epoch(train) [1][9400/10520] lr: 4.4730e-05 eta: 2 days, 2:30:00 time: 0.3666 data_time: 0.0340 memory: 17203 loss_visual: 0.2368 loss_lang: 0.5039 loss_fusion: 0.2258 loss: 0.9665 2022/10/05 03:53:32 - mmengine - INFO - Epoch(train) [1][9500/10520] lr: 4.5204e-05 eta: 2 days, 2:19:48 time: 0.3970 data_time: 0.0031 memory: 17203 loss_visual: 0.2512 loss_lang: 0.5292 loss_fusion: 0.2400 loss: 1.0205 2022/10/05 03:54:38 - mmengine - INFO - Epoch(train) [1][9600/10520] lr: 4.5679e-05 eta: 2 days, 2:10:12 time: 0.5933 data_time: 0.0032 memory: 17203 loss_visual: 0.2363 loss_lang: 0.5103 loss_fusion: 0.2254 loss: 0.9720 2022/10/05 03:55:47 - mmengine - INFO - Epoch(train) [1][9700/10520] lr: 4.6154e-05 eta: 2 days, 2:01:22 time: 0.9182 data_time: 0.1056 memory: 17203 loss_visual: 0.2303 loss_lang: 0.5097 loss_fusion: 0.2200 loss: 0.9601 2022/10/05 03:56:55 - mmengine - INFO - Epoch(train) [1][9800/10520] lr: 4.6629e-05 eta: 2 days, 1:52:15 time: 1.1077 data_time: 0.1649 memory: 17203 loss_visual: 0.2374 loss_lang: 0.5076 loss_fusion: 0.2265 loss: 0.9714 2022/10/05 03:58:01 - mmengine - INFO - Epoch(train) [1][9900/10520] lr: 4.7104e-05 eta: 2 days, 1:42:56 time: 0.9083 data_time: 0.1229 memory: 17203 loss_visual: 0.2270 loss_lang: 0.5097 loss_fusion: 0.2154 loss: 0.9521 2022/10/05 03:59:06 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 03:59:06 - mmengine - INFO - Epoch(train) [1][10000/10520] lr: 4.7578e-05 eta: 2 days, 1:33:15 time: 0.6320 data_time: 0.0213 memory: 17203 loss_visual: 0.2185 loss_lang: 0.4962 loss_fusion: 0.2089 loss: 0.9236 2022/10/05 04:00:10 - mmengine - INFO - Epoch(train) [1][10100/10520] lr: 4.8053e-05 eta: 2 days, 1:23:42 time: 0.3934 data_time: 0.0354 memory: 17203 loss_visual: 0.2180 loss_lang: 0.4809 loss_fusion: 0.2065 loss: 0.9054 2022/10/05 04:01:14 - mmengine - INFO - Epoch(train) [1][10200/10520] lr: 4.8528e-05 eta: 2 days, 1:14:11 time: 0.3619 data_time: 0.0273 memory: 17203 loss_visual: 0.2214 loss_lang: 0.4925 loss_fusion: 0.2106 loss: 0.9245 2022/10/05 04:02:20 - mmengine - INFO - Epoch(train) [1][10300/10520] lr: 4.9003e-05 eta: 2 days, 1:05:08 time: 0.4007 data_time: 0.0028 memory: 17203 loss_visual: 0.2202 loss_lang: 0.4900 loss_fusion: 0.2102 loss: 0.9203 2022/10/05 04:03:26 - mmengine - INFO - Epoch(train) [1][10400/10520] lr: 4.9478e-05 eta: 2 days, 0:56:42 time: 0.5885 data_time: 0.0034 memory: 17203 loss_visual: 0.2164 loss_lang: 0.4842 loss_fusion: 0.2067 loss: 0.9073 2022/10/05 04:04:31 - mmengine - INFO - Epoch(train) [1][10500/10520] lr: 4.9953e-05 eta: 2 days, 0:47:49 time: 0.6346 data_time: 0.0727 memory: 17203 loss_visual: 0.2081 loss_lang: 0.4677 loss_fusion: 0.1987 loss: 0.8745 2022/10/05 04:04:41 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 04:04:41 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/10/05 04:09:33 - mmengine - INFO - Epoch(val) [1][100/959] eta: 0:00:40 time: 0.0469 data_time: 0.0041 memory: 17203 2022/10/05 04:09:38 - mmengine - INFO - Epoch(val) [1][200/959] eta: 0:00:38 time: 0.0505 data_time: 0.0018 memory: 734 2022/10/05 04:09:43 - mmengine - INFO - Epoch(val) [1][300/959] eta: 0:00:34 time: 0.0529 data_time: 0.0009 memory: 734 2022/10/05 04:09:48 - mmengine - INFO - Epoch(val) [1][400/959] eta: 0:00:26 time: 0.0466 data_time: 0.0009 memory: 734 2022/10/05 04:09:53 - mmengine - INFO - Epoch(val) [1][500/959] eta: 0:00:22 time: 0.0501 data_time: 0.0028 memory: 734 2022/10/05 04:09:58 - mmengine - INFO - Epoch(val) [1][600/959] eta: 0:00:16 time: 0.0449 data_time: 0.0011 memory: 734 2022/10/05 04:10:03 - mmengine - INFO - Epoch(val) [1][700/959] eta: 0:00:11 time: 0.0428 data_time: 0.0010 memory: 734 2022/10/05 04:10:08 - mmengine - INFO - Epoch(val) [1][800/959] eta: 0:00:07 time: 0.0486 data_time: 0.0016 memory: 734 2022/10/05 04:10:13 - mmengine - INFO - Epoch(val) [1][900/959] eta: 0:00:02 time: 0.0506 data_time: 0.0022 memory: 734 2022/10/05 04:10:17 - mmengine - INFO - Epoch(val) [1][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.5208 IIIT5K/recog/word_acc_ignore_case_symbol: 0.7513 SVT/recog/word_acc_ignore_case_symbol: 0.6986 SVTP/recog/word_acc_ignore_case_symbol: 0.5380 IC13/recog/word_acc_ignore_case_symbol: 0.7734 IC15/recog/word_acc_ignore_case_symbol: 0.5065 2022/10/05 04:11:18 - mmengine - INFO - Epoch(train) [2][100/10520] lr: 5.0522e-05 eta: 2 days, 0:35:11 time: 0.6978 data_time: 0.0989 memory: 17203 loss_visual: 0.2205 loss_lang: 0.4772 loss_fusion: 0.2088 loss: 0.9065 2022/10/05 04:12:13 - mmengine - INFO - Epoch(train) [2][200/10520] lr: 5.0997e-05 eta: 2 days, 0:23:30 time: 0.8293 data_time: 0.1307 memory: 17203 loss_visual: 0.2138 loss_lang: 0.4704 loss_fusion: 0.2030 loss: 0.8872 2022/10/05 04:13:08 - mmengine - INFO - Epoch(train) [2][300/10520] lr: 5.1472e-05 eta: 2 days, 0:12:15 time: 0.5855 data_time: 0.1771 memory: 17203 loss_visual: 0.2103 loss_lang: 0.4747 loss_fusion: 0.1984 loss: 0.8834 2022/10/05 04:14:03 - mmengine - INFO - Epoch(train) [2][400/10520] lr: 5.1947e-05 eta: 2 days, 0:00:57 time: 0.5196 data_time: 0.0609 memory: 17203 loss_visual: 0.2097 loss_lang: 0.4680 loss_fusion: 0.1982 loss: 0.8759 2022/10/05 04:14:45 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 04:14:58 - mmengine - INFO - Epoch(train) [2][500/10520] lr: 5.2422e-05 eta: 1 day, 23:50:07 time: 0.4662 data_time: 0.0031 memory: 17203 loss_visual: 0.2155 loss_lang: 0.4675 loss_fusion: 0.2040 loss: 0.8870 2022/10/05 04:15:53 - mmengine - INFO - Epoch(train) [2][600/10520] lr: 5.2897e-05 eta: 1 day, 23:39:13 time: 0.4437 data_time: 0.0033 memory: 17203 loss_visual: 0.2182 loss_lang: 0.4744 loss_fusion: 0.2060 loss: 0.8986 2022/10/05 04:16:47 - mmengine - INFO - Epoch(train) [2][700/10520] lr: 5.3371e-05 eta: 1 day, 23:28:22 time: 0.3598 data_time: 0.0032 memory: 17203 loss_visual: 0.2024 loss_lang: 0.4633 loss_fusion: 0.1906 loss: 0.8563 2022/10/05 04:17:42 - mmengine - INFO - Epoch(train) [2][800/10520] lr: 5.3846e-05 eta: 1 day, 23:17:58 time: 0.4015 data_time: 0.0034 memory: 17203 loss_visual: 0.2027 loss_lang: 0.4633 loss_fusion: 0.1903 loss: 0.8563 2022/10/05 04:18:41 - mmengine - INFO - Epoch(train) [2][900/10520] lr: 5.4321e-05 eta: 1 day, 23:08:36 time: 0.7465 data_time: 0.1010 memory: 17203 loss_visual: 0.1819 loss_lang: 0.4574 loss_fusion: 0.1702 loss: 0.8095 2022/10/05 04:19:35 - mmengine - INFO - Epoch(train) [2][1000/10520] lr: 5.4796e-05 eta: 1 day, 22:58:19 time: 0.8382 data_time: 0.1546 memory: 17203 loss_visual: 0.2037 loss_lang: 0.4525 loss_fusion: 0.1919 loss: 0.8481 2022/10/05 04:20:32 - mmengine - INFO - Epoch(train) [2][1100/10520] lr: 5.5271e-05 eta: 1 day, 22:48:46 time: 0.5883 data_time: 0.1828 memory: 17203 loss_visual: 0.1945 loss_lang: 0.4534 loss_fusion: 0.1817 loss: 0.8296 2022/10/05 04:21:28 - mmengine - INFO - Epoch(train) [2][1200/10520] lr: 5.5746e-05 eta: 1 day, 22:39:16 time: 0.4930 data_time: 0.0630 memory: 17203 loss_visual: 0.1902 loss_lang: 0.4394 loss_fusion: 0.1779 loss: 0.8076 2022/10/05 04:22:23 - mmengine - INFO - Epoch(train) [2][1300/10520] lr: 5.6220e-05 eta: 1 day, 22:29:40 time: 0.4253 data_time: 0.0032 memory: 17203 loss_visual: 0.2047 loss_lang: 0.4597 loss_fusion: 0.1930 loss: 0.8575 2022/10/05 04:23:18 - mmengine - INFO - Epoch(train) [2][1400/10520] lr: 5.6695e-05 eta: 1 day, 22:20:04 time: 0.4336 data_time: 0.0029 memory: 17203 loss_visual: 0.1923 loss_lang: 0.4418 loss_fusion: 0.1800 loss: 0.8141 2022/10/05 04:24:04 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 04:24:12 - mmengine - INFO - Epoch(train) [2][1500/10520] lr: 5.7170e-05 eta: 1 day, 22:10:27 time: 0.3513 data_time: 0.0034 memory: 17203 loss_visual: 0.1882 loss_lang: 0.4350 loss_fusion: 0.1754 loss: 0.7986 2022/10/05 04:25:07 - mmengine - INFO - Epoch(train) [2][1600/10520] lr: 5.7645e-05 eta: 1 day, 22:01:10 time: 0.3431 data_time: 0.0033 memory: 17203 loss_visual: 0.1845 loss_lang: 0.4420 loss_fusion: 0.1725 loss: 0.7990 2022/10/05 04:26:05 - mmengine - INFO - Epoch(train) [2][1700/10520] lr: 5.8120e-05 eta: 1 day, 21:53:00 time: 0.7538 data_time: 0.1215 memory: 17203 loss_visual: 0.1922 loss_lang: 0.4375 loss_fusion: 0.1792 loss: 0.8089 2022/10/05 04:27:01 - mmengine - INFO - Epoch(train) [2][1800/10520] lr: 5.8595e-05 eta: 1 day, 21:44:14 time: 0.8604 data_time: 0.1374 memory: 17203 loss_visual: 0.1869 loss_lang: 0.4360 loss_fusion: 0.1735 loss: 0.7964 2022/10/05 04:27:55 - mmengine - INFO - Epoch(train) [2][1900/10520] lr: 5.9069e-05 eta: 1 day, 21:35:10 time: 0.5938 data_time: 0.1613 memory: 17203 loss_visual: 0.1822 loss_lang: 0.4316 loss_fusion: 0.1705 loss: 0.7842 2022/10/05 04:28:51 - mmengine - INFO - Epoch(train) [2][2000/10520] lr: 5.9544e-05 eta: 1 day, 21:26:38 time: 0.5339 data_time: 0.0813 memory: 17203 loss_visual: 0.1956 loss_lang: 0.4498 loss_fusion: 0.1849 loss: 0.8303 2022/10/05 04:29:47 - mmengine - INFO - Epoch(train) [2][2100/10520] lr: 6.0019e-05 eta: 1 day, 21:18:18 time: 0.4204 data_time: 0.0030 memory: 17203 loss_visual: 0.2021 loss_lang: 0.4458 loss_fusion: 0.1892 loss: 0.8371 2022/10/05 04:30:42 - mmengine - INFO - Epoch(train) [2][2200/10520] lr: 6.0494e-05 eta: 1 day, 21:09:48 time: 0.4294 data_time: 0.0032 memory: 17203 loss_visual: 0.1940 loss_lang: 0.4398 loss_fusion: 0.1801 loss: 0.8139 2022/10/05 04:31:37 - mmengine - INFO - Epoch(train) [2][2300/10520] lr: 6.0969e-05 eta: 1 day, 21:01:20 time: 0.3453 data_time: 0.0033 memory: 17203 loss_visual: 0.1868 loss_lang: 0.4325 loss_fusion: 0.1739 loss: 0.7932 2022/10/05 04:32:31 - mmengine - INFO - Epoch(train) [2][2400/10520] lr: 6.1444e-05 eta: 1 day, 20:52:55 time: 0.3483 data_time: 0.0060 memory: 17203 loss_visual: 0.1805 loss_lang: 0.4183 loss_fusion: 0.1679 loss: 0.7668 2022/10/05 04:33:19 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 04:33:30 - mmengine - INFO - Epoch(train) [2][2500/10520] lr: 6.1918e-05 eta: 1 day, 20:45:42 time: 0.7186 data_time: 0.0977 memory: 17203 loss_visual: 0.1886 loss_lang: 0.4204 loss_fusion: 0.1761 loss: 0.7851 2022/10/05 04:34:26 - mmengine - INFO - Epoch(train) [2][2600/10520] lr: 6.2393e-05 eta: 1 day, 20:37:53 time: 0.8737 data_time: 0.1224 memory: 17203 loss_visual: 0.1794 loss_lang: 0.4207 loss_fusion: 0.1669 loss: 0.7671 2022/10/05 04:35:20 - mmengine - INFO - Epoch(train) [2][2700/10520] lr: 6.2868e-05 eta: 1 day, 20:29:53 time: 0.6128 data_time: 0.1907 memory: 17203 loss_visual: 0.1840 loss_lang: 0.4182 loss_fusion: 0.1710 loss: 0.7732 2022/10/05 04:36:15 - mmengine - INFO - Epoch(train) [2][2800/10520] lr: 6.3343e-05 eta: 1 day, 20:21:57 time: 0.4927 data_time: 0.0626 memory: 17203 loss_visual: 0.1822 loss_lang: 0.4138 loss_fusion: 0.1693 loss: 0.7653 2022/10/05 04:37:11 - mmengine - INFO - Epoch(train) [2][2900/10520] lr: 6.3818e-05 eta: 1 day, 20:14:22 time: 0.4258 data_time: 0.0031 memory: 17203 loss_visual: 0.1778 loss_lang: 0.4042 loss_fusion: 0.1647 loss: 0.7466 2022/10/05 04:38:05 - mmengine - INFO - Epoch(train) [2][3000/10520] lr: 6.4293e-05 eta: 1 day, 20:06:39 time: 0.4208 data_time: 0.0034 memory: 17203 loss_visual: 0.1785 loss_lang: 0.4149 loss_fusion: 0.1651 loss: 0.7585 2022/10/05 04:39:00 - mmengine - INFO - Epoch(train) [2][3100/10520] lr: 6.4767e-05 eta: 1 day, 19:59:06 time: 0.3780 data_time: 0.0037 memory: 17203 loss_visual: 0.1692 loss_lang: 0.3978 loss_fusion: 0.1546 loss: 0.7216 2022/10/05 04:39:54 - mmengine - INFO - Epoch(train) [2][3200/10520] lr: 6.5242e-05 eta: 1 day, 19:51:30 time: 0.3460 data_time: 0.0033 memory: 17203 loss_visual: 0.1784 loss_lang: 0.4179 loss_fusion: 0.1654 loss: 0.7617 2022/10/05 04:40:53 - mmengine - INFO - Epoch(train) [2][3300/10520] lr: 6.5717e-05 eta: 1 day, 19:45:03 time: 0.7370 data_time: 0.1030 memory: 17203 loss_visual: 0.1868 loss_lang: 0.4110 loss_fusion: 0.1728 loss: 0.7706 2022/10/05 04:41:49 - mmengine - INFO - Epoch(train) [2][3400/10520] lr: 6.6192e-05 eta: 1 day, 19:38:01 time: 0.8424 data_time: 0.1528 memory: 17203 loss_visual: 0.1740 loss_lang: 0.4055 loss_fusion: 0.1603 loss: 0.7398 2022/10/05 04:42:31 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 04:42:44 - mmengine - INFO - Epoch(train) [2][3500/10520] lr: 6.6667e-05 eta: 1 day, 19:30:53 time: 0.6016 data_time: 0.1674 memory: 17203 loss_visual: 0.1681 loss_lang: 0.3991 loss_fusion: 0.1553 loss: 0.7225 2022/10/05 04:43:39 - mmengine - INFO - Epoch(train) [2][3600/10520] lr: 6.7142e-05 eta: 1 day, 19:23:55 time: 0.5286 data_time: 0.0897 memory: 17203 loss_visual: 0.1730 loss_lang: 0.3945 loss_fusion: 0.1592 loss: 0.7268 2022/10/05 04:44:35 - mmengine - INFO - Epoch(train) [2][3700/10520] lr: 6.7616e-05 eta: 1 day, 19:17:04 time: 0.4779 data_time: 0.0050 memory: 17203 loss_visual: 0.1816 loss_lang: 0.4043 loss_fusion: 0.1683 loss: 0.7543 2022/10/05 04:45:30 - mmengine - INFO - Epoch(train) [2][3800/10520] lr: 6.8091e-05 eta: 1 day, 19:10:05 time: 0.4242 data_time: 0.0034 memory: 17203 loss_visual: 0.1563 loss_lang: 0.3963 loss_fusion: 0.1413 loss: 0.6939 2022/10/05 04:46:25 - mmengine - INFO - Epoch(train) [2][3900/10520] lr: 6.8566e-05 eta: 1 day, 19:03:13 time: 0.3546 data_time: 0.0034 memory: 17203 loss_visual: 0.1670 loss_lang: 0.3897 loss_fusion: 0.1537 loss: 0.7104 2022/10/05 04:47:19 - mmengine - INFO - Epoch(train) [2][4000/10520] lr: 6.9041e-05 eta: 1 day, 18:56:21 time: 0.3439 data_time: 0.0029 memory: 17203 loss_visual: 0.1725 loss_lang: 0.3968 loss_fusion: 0.1605 loss: 0.7298 2022/10/05 04:48:18 - mmengine - INFO - Epoch(train) [2][4100/10520] lr: 6.9516e-05 eta: 1 day, 18:50:33 time: 0.7635 data_time: 0.1185 memory: 17203 loss_visual: 0.1770 loss_lang: 0.3984 loss_fusion: 0.1639 loss: 0.7392 2022/10/05 04:49:14 - mmengine - INFO - Epoch(train) [2][4200/10520] lr: 6.9991e-05 eta: 1 day, 18:44:09 time: 0.9135 data_time: 0.1405 memory: 17203 loss_visual: 0.1639 loss_lang: 0.3796 loss_fusion: 0.1488 loss: 0.6923 2022/10/05 04:50:08 - mmengine - INFO - Epoch(train) [2][4300/10520] lr: 7.0465e-05 eta: 1 day, 18:37:37 time: 0.6054 data_time: 0.1758 memory: 17203 loss_visual: 0.1612 loss_lang: 0.3744 loss_fusion: 0.1474 loss: 0.6830 2022/10/05 04:51:03 - mmengine - INFO - Epoch(train) [2][4400/10520] lr: 7.0940e-05 eta: 1 day, 18:31:03 time: 0.4924 data_time: 0.0633 memory: 17203 loss_visual: 0.1702 loss_lang: 0.3859 loss_fusion: 0.1577 loss: 0.7138 2022/10/05 04:51:44 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 04:51:58 - mmengine - INFO - Epoch(train) [2][4500/10520] lr: 7.1415e-05 eta: 1 day, 18:24:38 time: 0.4328 data_time: 0.0029 memory: 17203 loss_visual: 0.1604 loss_lang: 0.3858 loss_fusion: 0.1474 loss: 0.6936 2022/10/05 04:52:53 - mmengine - INFO - Epoch(train) [2][4600/10520] lr: 7.1890e-05 eta: 1 day, 18:18:24 time: 0.4444 data_time: 0.0030 memory: 17203 loss_visual: 0.1756 loss_lang: 0.3948 loss_fusion: 0.1621 loss: 0.7325 2022/10/05 04:53:48 - mmengine - INFO - Epoch(train) [2][4700/10520] lr: 7.2365e-05 eta: 1 day, 18:12:10 time: 0.3442 data_time: 0.0037 memory: 17203 loss_visual: 0.1632 loss_lang: 0.3759 loss_fusion: 0.1489 loss: 0.6880 2022/10/05 04:54:42 - mmengine - INFO - Epoch(train) [2][4800/10520] lr: 7.2840e-05 eta: 1 day, 18:05:51 time: 0.3443 data_time: 0.0031 memory: 17203 loss_visual: 0.1702 loss_lang: 0.3759 loss_fusion: 0.1564 loss: 0.7025 2022/10/05 04:55:41 - mmengine - INFO - Epoch(train) [2][4900/10520] lr: 7.3314e-05 eta: 1 day, 18:00:42 time: 0.7329 data_time: 0.1137 memory: 17203 loss_visual: 0.1676 loss_lang: 0.3756 loss_fusion: 0.1536 loss: 0.6968 2022/10/05 04:56:36 - mmengine - INFO - Epoch(train) [2][5000/10520] lr: 7.3789e-05 eta: 1 day, 17:54:43 time: 0.8567 data_time: 0.1372 memory: 17203 loss_visual: 0.1651 loss_lang: 0.3822 loss_fusion: 0.1521 loss: 0.6994 2022/10/05 04:57:32 - mmengine - INFO - Epoch(train) [2][5100/10520] lr: 7.4264e-05 eta: 1 day, 17:48:51 time: 0.5914 data_time: 0.1776 memory: 17203 loss_visual: 0.1722 loss_lang: 0.3881 loss_fusion: 0.1590 loss: 0.7194 2022/10/05 04:58:26 - mmengine - INFO - Epoch(train) [2][5200/10520] lr: 7.4739e-05 eta: 1 day, 17:42:46 time: 0.4781 data_time: 0.0627 memory: 17203 loss_visual: 0.1647 loss_lang: 0.3738 loss_fusion: 0.1493 loss: 0.6878 2022/10/05 04:59:21 - mmengine - INFO - Epoch(train) [2][5300/10520] lr: 7.5214e-05 eta: 1 day, 17:37:03 time: 0.4266 data_time: 0.0032 memory: 17203 loss_visual: 0.1412 loss_lang: 0.3525 loss_fusion: 0.1280 loss: 0.6217 2022/10/05 05:00:16 - mmengine - INFO - Epoch(train) [2][5400/10520] lr: 7.5689e-05 eta: 1 day, 17:31:07 time: 0.4547 data_time: 0.0079 memory: 17203 loss_visual: 0.1473 loss_lang: 0.3565 loss_fusion: 0.1335 loss: 0.6373 2022/10/05 05:01:01 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 05:01:10 - mmengine - INFO - Epoch(train) [2][5500/10520] lr: 7.6163e-05 eta: 1 day, 17:25:15 time: 0.3464 data_time: 0.0032 memory: 17203 loss_visual: 0.1509 loss_lang: 0.3543 loss_fusion: 0.1382 loss: 0.6435 2022/10/05 05:02:04 - mmengine - INFO - Epoch(train) [2][5600/10520] lr: 7.6638e-05 eta: 1 day, 17:19:29 time: 0.3533 data_time: 0.0034 memory: 17203 loss_visual: 0.1506 loss_lang: 0.3575 loss_fusion: 0.1368 loss: 0.6449 2022/10/05 05:03:02 - mmengine - INFO - Epoch(train) [2][5700/10520] lr: 7.7113e-05 eta: 1 day, 17:14:28 time: 0.7258 data_time: 0.1019 memory: 17203 loss_visual: 0.1542 loss_lang: 0.3627 loss_fusion: 0.1401 loss: 0.6570 2022/10/05 05:03:58 - mmengine - INFO - Epoch(train) [2][5800/10520] lr: 7.7588e-05 eta: 1 day, 17:09:13 time: 0.8630 data_time: 0.1412 memory: 17203 loss_visual: 0.1676 loss_lang: 0.3653 loss_fusion: 0.1528 loss: 0.6857 2022/10/05 05:04:53 - mmengine - INFO - Epoch(train) [2][5900/10520] lr: 7.8063e-05 eta: 1 day, 17:03:42 time: 0.6246 data_time: 0.1762 memory: 17203 loss_visual: 0.1569 loss_lang: 0.3560 loss_fusion: 0.1407 loss: 0.6536 2022/10/05 05:05:48 - mmengine - INFO - Epoch(train) [2][6000/10520] lr: 7.8538e-05 eta: 1 day, 16:58:09 time: 0.5086 data_time: 0.0810 memory: 17203 loss_visual: 0.1636 loss_lang: 0.3715 loss_fusion: 0.1508 loss: 0.6859 2022/10/05 05:06:42 - mmengine - INFO - Epoch(train) [2][6100/10520] lr: 7.9012e-05 eta: 1 day, 16:52:44 time: 0.4199 data_time: 0.0030 memory: 17203 loss_visual: 0.1478 loss_lang: 0.3543 loss_fusion: 0.1341 loss: 0.6362 2022/10/05 05:07:37 - mmengine - INFO - Epoch(train) [2][6200/10520] lr: 7.9487e-05 eta: 1 day, 16:47:24 time: 0.4539 data_time: 0.0035 memory: 17203 loss_visual: 0.1699 loss_lang: 0.3645 loss_fusion: 0.1562 loss: 0.6905 2022/10/05 05:08:32 - mmengine - INFO - Epoch(train) [2][6300/10520] lr: 7.9962e-05 eta: 1 day, 16:42:05 time: 0.3812 data_time: 0.0033 memory: 17203 loss_visual: 0.1416 loss_lang: 0.3430 loss_fusion: 0.1271 loss: 0.6116 2022/10/05 05:09:26 - mmengine - INFO - Epoch(train) [2][6400/10520] lr: 8.0437e-05 eta: 1 day, 16:36:43 time: 0.3451 data_time: 0.0031 memory: 17203 loss_visual: 0.1430 loss_lang: 0.3430 loss_fusion: 0.1293 loss: 0.6153 2022/10/05 05:10:13 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 05:10:25 - mmengine - INFO - Epoch(train) [2][6500/10520] lr: 8.0912e-05 eta: 1 day, 16:32:14 time: 0.7381 data_time: 0.1304 memory: 17203 loss_visual: 0.1589 loss_lang: 0.3565 loss_fusion: 0.1434 loss: 0.6588 2022/10/05 05:11:30 - mmengine - INFO - Epoch(train) [2][6600/10520] lr: 8.1387e-05 eta: 1 day, 16:29:01 time: 1.7117 data_time: 0.1427 memory: 17203 loss_visual: 0.1448 loss_lang: 0.3401 loss_fusion: 0.1306 loss: 0.6156 2022/10/05 05:12:33 - mmengine - INFO - Epoch(train) [2][6700/10520] lr: 8.1861e-05 eta: 1 day, 16:25:28 time: 0.6275 data_time: 0.1869 memory: 17203 loss_visual: 0.1484 loss_lang: 0.3508 loss_fusion: 0.1339 loss: 0.6331 2022/10/05 05:13:39 - mmengine - INFO - Epoch(train) [2][6800/10520] lr: 8.2336e-05 eta: 1 day, 16:22:30 time: 0.5547 data_time: 0.0855 memory: 17203 loss_visual: 0.1451 loss_lang: 0.3416 loss_fusion: 0.1304 loss: 0.6172 2022/10/05 05:14:38 - mmengine - INFO - Epoch(train) [2][6900/10520] lr: 8.2811e-05 eta: 1 day, 16:18:15 time: 0.4615 data_time: 0.0030 memory: 17203 loss_visual: 0.1623 loss_lang: 0.3507 loss_fusion: 0.1471 loss: 0.6600 2022/10/05 05:15:36 - mmengine - INFO - Epoch(train) [2][7000/10520] lr: 8.3286e-05 eta: 1 day, 16:13:47 time: 0.4322 data_time: 0.0031 memory: 17203 loss_visual: 0.1527 loss_lang: 0.3424 loss_fusion: 0.1384 loss: 0.6335 2022/10/05 05:16:30 - mmengine - INFO - Epoch(train) [2][7100/10520] lr: 8.3761e-05 eta: 1 day, 16:08:46 time: 0.3449 data_time: 0.0031 memory: 17203 loss_visual: 0.1410 loss_lang: 0.3274 loss_fusion: 0.1272 loss: 0.5955 2022/10/05 05:17:24 - mmengine - INFO - Epoch(train) [2][7200/10520] lr: 8.4236e-05 eta: 1 day, 16:03:48 time: 0.3636 data_time: 0.0031 memory: 17203 loss_visual: 0.1396 loss_lang: 0.3228 loss_fusion: 0.1255 loss: 0.5879 2022/10/05 05:18:22 - mmengine - INFO - Epoch(train) [2][7300/10520] lr: 8.4710e-05 eta: 1 day, 15:59:25 time: 0.7423 data_time: 0.1232 memory: 17203 loss_visual: 0.1525 loss_lang: 0.3516 loss_fusion: 0.1387 loss: 0.6428 2022/10/05 05:19:18 - mmengine - INFO - Epoch(train) [2][7400/10520] lr: 8.5185e-05 eta: 1 day, 15:54:54 time: 0.8424 data_time: 0.1545 memory: 17203 loss_visual: 0.1449 loss_lang: 0.3288 loss_fusion: 0.1314 loss: 0.6052 2022/10/05 05:20:02 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 05:20:14 - mmengine - INFO - Epoch(train) [2][7500/10520] lr: 8.5660e-05 eta: 1 day, 15:50:16 time: 0.5981 data_time: 0.1760 memory: 17203 loss_visual: 0.1462 loss_lang: 0.3272 loss_fusion: 0.1317 loss: 0.6051 2022/10/05 05:21:09 - mmengine - INFO - Epoch(train) [2][7600/10520] lr: 8.6135e-05 eta: 1 day, 15:45:29 time: 0.4813 data_time: 0.0697 memory: 17203 loss_visual: 0.1434 loss_lang: 0.3313 loss_fusion: 0.1304 loss: 0.6050 2022/10/05 05:22:04 - mmengine - INFO - Epoch(train) [2][7700/10520] lr: 8.6610e-05 eta: 1 day, 15:40:55 time: 0.4779 data_time: 0.0028 memory: 17203 loss_visual: 0.1475 loss_lang: 0.3310 loss_fusion: 0.1327 loss: 0.6112 2022/10/05 05:22:59 - mmengine - INFO - Epoch(train) [2][7800/10520] lr: 8.7085e-05 eta: 1 day, 15:36:20 time: 0.4476 data_time: 0.0029 memory: 17203 loss_visual: 0.1433 loss_lang: 0.3222 loss_fusion: 0.1285 loss: 0.5940 2022/10/05 05:23:53 - mmengine - INFO - Epoch(train) [2][7900/10520] lr: 8.7559e-05 eta: 1 day, 15:31:36 time: 0.3468 data_time: 0.0031 memory: 17203 loss_visual: 0.1551 loss_lang: 0.3326 loss_fusion: 0.1393 loss: 0.6270 2022/10/05 05:24:48 - mmengine - INFO - Epoch(train) [2][8000/10520] lr: 8.8034e-05 eta: 1 day, 15:27:04 time: 0.3525 data_time: 0.0053 memory: 17203 loss_visual: 0.1427 loss_lang: 0.3209 loss_fusion: 0.1295 loss: 0.5931 2022/10/05 05:25:47 - mmengine - INFO - Epoch(train) [2][8100/10520] lr: 8.8509e-05 eta: 1 day, 15:23:07 time: 0.7289 data_time: 0.1012 memory: 17203 loss_visual: 0.1469 loss_lang: 0.3186 loss_fusion: 0.1332 loss: 0.5987 2022/10/05 05:26:42 - mmengine - INFO - Epoch(train) [2][8200/10520] lr: 8.8984e-05 eta: 1 day, 15:18:45 time: 0.8755 data_time: 0.1478 memory: 17203 loss_visual: 0.1534 loss_lang: 0.3274 loss_fusion: 0.1391 loss: 0.6199 2022/10/05 05:27:37 - mmengine - INFO - Epoch(train) [2][8300/10520] lr: 8.9459e-05 eta: 1 day, 15:14:20 time: 0.6235 data_time: 0.2022 memory: 17203 loss_visual: 0.1478 loss_lang: 0.3266 loss_fusion: 0.1340 loss: 0.6085 2022/10/05 05:28:32 - mmengine - INFO - Epoch(train) [2][8400/10520] lr: 8.9934e-05 eta: 1 day, 15:09:58 time: 0.4735 data_time: 0.0632 memory: 17203 loss_visual: 0.1326 loss_lang: 0.3095 loss_fusion: 0.1191 loss: 0.5612 2022/10/05 05:29:14 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 05:29:27 - mmengine - INFO - Epoch(train) [2][8500/10520] lr: 9.0408e-05 eta: 1 day, 15:05:35 time: 0.4315 data_time: 0.0033 memory: 17203 loss_visual: 0.1481 loss_lang: 0.3187 loss_fusion: 0.1321 loss: 0.5989 2022/10/05 05:30:22 - mmengine - INFO - Epoch(train) [2][8600/10520] lr: 9.0883e-05 eta: 1 day, 15:01:12 time: 0.4405 data_time: 0.0031 memory: 17203 loss_visual: 0.1413 loss_lang: 0.3235 loss_fusion: 0.1287 loss: 0.5934 2022/10/05 05:31:16 - mmengine - INFO - Epoch(train) [2][8700/10520] lr: 9.1358e-05 eta: 1 day, 14:56:44 time: 0.3475 data_time: 0.0033 memory: 17203 loss_visual: 0.1421 loss_lang: 0.3134 loss_fusion: 0.1273 loss: 0.5828 2022/10/05 05:32:10 - mmengine - INFO - Epoch(train) [2][8800/10520] lr: 9.1833e-05 eta: 1 day, 14:52:27 time: 0.3442 data_time: 0.0027 memory: 17203 loss_visual: 0.1345 loss_lang: 0.3040 loss_fusion: 0.1198 loss: 0.5583 2022/10/05 05:33:09 - mmengine - INFO - Epoch(train) [2][8900/10520] lr: 9.2308e-05 eta: 1 day, 14:48:52 time: 0.7481 data_time: 0.1256 memory: 17203 loss_visual: 0.1423 loss_lang: 0.3137 loss_fusion: 0.1268 loss: 0.5828 2022/10/05 05:34:06 - mmengine - INFO - Epoch(train) [2][9000/10520] lr: 9.2783e-05 eta: 1 day, 14:44:55 time: 0.8571 data_time: 0.1260 memory: 17203 loss_visual: 0.1391 loss_lang: 0.3085 loss_fusion: 0.1247 loss: 0.5723 2022/10/05 05:35:01 - mmengine - INFO - Epoch(train) [2][9100/10520] lr: 9.3257e-05 eta: 1 day, 14:40:53 time: 0.6417 data_time: 0.1881 memory: 17203 loss_visual: 0.1496 loss_lang: 0.3147 loss_fusion: 0.1341 loss: 0.5983 2022/10/05 05:35:56 - mmengine - INFO - Epoch(train) [2][9200/10520] lr: 9.3732e-05 eta: 1 day, 14:36:43 time: 0.4884 data_time: 0.0605 memory: 17203 loss_visual: 0.1413 loss_lang: 0.3081 loss_fusion: 0.1280 loss: 0.5774 2022/10/05 05:36:52 - mmengine - INFO - Epoch(train) [2][9300/10520] lr: 9.4207e-05 eta: 1 day, 14:32:45 time: 0.4281 data_time: 0.0032 memory: 17203 loss_visual: 0.1375 loss_lang: 0.3057 loss_fusion: 0.1234 loss: 0.5666 2022/10/05 05:37:46 - mmengine - INFO - Epoch(train) [2][9400/10520] lr: 9.4682e-05 eta: 1 day, 14:28:39 time: 0.4657 data_time: 0.0031 memory: 17203 loss_visual: 0.1388 loss_lang: 0.3020 loss_fusion: 0.1250 loss: 0.5657 2022/10/05 05:38:33 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 05:38:42 - mmengine - INFO - Epoch(train) [2][9500/10520] lr: 9.5157e-05 eta: 1 day, 14:24:38 time: 0.3658 data_time: 0.0031 memory: 17203 loss_visual: 0.1336 loss_lang: 0.3053 loss_fusion: 0.1201 loss: 0.5590 2022/10/05 05:39:37 - mmengine - INFO - Epoch(train) [2][9600/10520] lr: 9.5632e-05 eta: 1 day, 14:20:46 time: 0.3524 data_time: 0.0029 memory: 17203 loss_visual: 0.1258 loss_lang: 0.2902 loss_fusion: 0.1115 loss: 0.5275 2022/10/05 05:40:36 - mmengine - INFO - Epoch(train) [2][9700/10520] lr: 9.6106e-05 eta: 1 day, 14:17:21 time: 0.7704 data_time: 0.0973 memory: 17203 loss_visual: 0.1352 loss_lang: 0.2884 loss_fusion: 0.1194 loss: 0.5430 2022/10/05 05:41:32 - mmengine - INFO - Epoch(train) [2][9800/10520] lr: 9.6581e-05 eta: 1 day, 14:13:37 time: 0.8575 data_time: 0.1468 memory: 17203 loss_visual: 0.1331 loss_lang: 0.2969 loss_fusion: 0.1200 loss: 0.5500 2022/10/05 05:42:28 - mmengine - INFO - Epoch(train) [2][9900/10520] lr: 9.7056e-05 eta: 1 day, 14:09:50 time: 0.5990 data_time: 0.1694 memory: 17203 loss_visual: 0.1336 loss_lang: 0.3025 loss_fusion: 0.1193 loss: 0.5554 2022/10/05 05:43:24 - mmengine - INFO - Epoch(train) [2][10000/10520] lr: 9.7531e-05 eta: 1 day, 14:06:06 time: 0.4952 data_time: 0.0786 memory: 17203 loss_visual: 0.1428 loss_lang: 0.3045 loss_fusion: 0.1297 loss: 0.5770 2022/10/05 05:44:20 - mmengine - INFO - Epoch(train) [2][10100/10520] lr: 9.8006e-05 eta: 1 day, 14:02:28 time: 0.4441 data_time: 0.0033 memory: 17203 loss_visual: 0.1366 loss_lang: 0.2975 loss_fusion: 0.1222 loss: 0.5564 2022/10/05 05:45:16 - mmengine - INFO - Epoch(train) [2][10200/10520] lr: 9.8481e-05 eta: 1 day, 13:58:48 time: 0.4297 data_time: 0.0033 memory: 17203 loss_visual: 0.1430 loss_lang: 0.3071 loss_fusion: 0.1290 loss: 0.5790 2022/10/05 05:46:10 - mmengine - INFO - Epoch(train) [2][10300/10520] lr: 9.8955e-05 eta: 1 day, 13:54:52 time: 0.3471 data_time: 0.0033 memory: 17203 loss_visual: 0.1319 loss_lang: 0.2916 loss_fusion: 0.1166 loss: 0.5401 2022/10/05 05:47:04 - mmengine - INFO - Epoch(train) [2][10400/10520] lr: 9.9430e-05 eta: 1 day, 13:50:55 time: 0.3414 data_time: 0.0032 memory: 17203 loss_visual: 0.1227 loss_lang: 0.2816 loss_fusion: 0.1085 loss: 0.5129 2022/10/05 05:47:50 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 05:48:00 - mmengine - INFO - Epoch(train) [2][10500/10520] lr: 9.9905e-05 eta: 1 day, 13:47:19 time: 0.5851 data_time: 0.0648 memory: 17203 loss_visual: 0.1205 loss_lang: 0.2896 loss_fusion: 0.1080 loss: 0.5181 2022/10/05 05:48:08 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 05:48:08 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/10/05 05:48:22 - mmengine - INFO - Epoch(val) [2][100/959] eta: 0:00:39 time: 0.0459 data_time: 0.0015 memory: 17203 2022/10/05 05:48:27 - mmengine - INFO - Epoch(val) [2][200/959] eta: 0:00:31 time: 0.0410 data_time: 0.0012 memory: 734 2022/10/05 05:48:32 - mmengine - INFO - Epoch(val) [2][300/959] eta: 0:00:32 time: 0.0487 data_time: 0.0022 memory: 734 2022/10/05 05:48:37 - mmengine - INFO - Epoch(val) [2][400/959] eta: 0:00:30 time: 0.0543 data_time: 0.0048 memory: 734 2022/10/05 05:48:42 - mmengine - INFO - Epoch(val) [2][500/959] eta: 0:00:22 time: 0.0496 data_time: 0.0016 memory: 734 2022/10/05 05:48:47 - mmengine - INFO - Epoch(val) [2][600/959] eta: 0:00:17 time: 0.0490 data_time: 0.0011 memory: 734 2022/10/05 05:48:51 - mmengine - INFO - Epoch(val) [2][700/959] eta: 0:00:11 time: 0.0443 data_time: 0.0011 memory: 734 2022/10/05 05:48:56 - mmengine - INFO - Epoch(val) [2][800/959] eta: 0:00:05 time: 0.0342 data_time: 0.0010 memory: 734 2022/10/05 05:48:58 - mmengine - INFO - Epoch(val) [2][900/959] eta: 0:00:01 time: 0.0220 data_time: 0.0006 memory: 734 2022/10/05 05:49:00 - mmengine - INFO - Epoch(val) [2][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.7292 IIIT5K/recog/word_acc_ignore_case_symbol: 0.8780 SVT/recog/word_acc_ignore_case_symbol: 0.8423 SVTP/recog/word_acc_ignore_case_symbol: 0.7426 IC13/recog/word_acc_ignore_case_symbol: 0.8660 IC15/recog/word_acc_ignore_case_symbol: 0.6726 2022/10/05 05:50:05 - mmengine - INFO - Epoch(train) [3][100/10520] lr: 1.0000e-04 eta: 1 day, 13:43:51 time: 0.7896 data_time: 0.1684 memory: 17203 loss_visual: 0.1295 loss_lang: 0.2891 loss_fusion: 0.1165 loss: 0.5351 2022/10/05 05:51:03 - mmengine - INFO - Epoch(train) [3][200/10520] lr: 1.0000e-04 eta: 1 day, 13:40:36 time: 0.9781 data_time: 0.1643 memory: 17203 loss_visual: 0.1351 loss_lang: 0.2929 loss_fusion: 0.1204 loss: 0.5484 2022/10/05 05:52:00 - mmengine - INFO - Epoch(train) [3][300/10520] lr: 1.0000e-04 eta: 1 day, 13:37:10 time: 0.7361 data_time: 0.0133 memory: 17203 loss_visual: 0.1308 loss_lang: 0.2829 loss_fusion: 0.1153 loss: 0.5290 2022/10/05 05:52:55 - mmengine - INFO - Epoch(train) [3][400/10520] lr: 1.0000e-04 eta: 1 day, 13:33:38 time: 0.4628 data_time: 0.0032 memory: 17203 loss_visual: 0.1377 loss_lang: 0.2919 loss_fusion: 0.1225 loss: 0.5520 2022/10/05 05:53:51 - mmengine - INFO - Epoch(train) [3][500/10520] lr: 1.0000e-04 eta: 1 day, 13:30:10 time: 0.4681 data_time: 0.0206 memory: 17203 loss_visual: 0.1302 loss_lang: 0.2836 loss_fusion: 0.1163 loss: 0.5300 2022/10/05 05:54:48 - mmengine - INFO - Epoch(train) [3][600/10520] lr: 1.0000e-04 eta: 1 day, 13:26:53 time: 0.3771 data_time: 0.0193 memory: 17203 loss_visual: 0.1325 loss_lang: 0.2833 loss_fusion: 0.1174 loss: 0.5332 2022/10/05 05:55:44 - mmengine - INFO - Epoch(train) [3][700/10520] lr: 1.0000e-04 eta: 1 day, 13:23:27 time: 0.3564 data_time: 0.0287 memory: 17203 loss_visual: 0.1385 loss_lang: 0.2815 loss_fusion: 0.1253 loss: 0.5453 2022/10/05 05:56:41 - mmengine - INFO - Epoch(train) [3][800/10520] lr: 1.0000e-04 eta: 1 day, 13:20:12 time: 0.3793 data_time: 0.0125 memory: 17203 loss_visual: 0.1233 loss_lang: 0.2768 loss_fusion: 0.1086 loss: 0.5087 2022/10/05 05:57:42 - mmengine - INFO - Epoch(train) [3][900/10520] lr: 1.0000e-04 eta: 1 day, 13:17:32 time: 0.7686 data_time: 0.1770 memory: 17203 loss_visual: 0.1307 loss_lang: 0.2770 loss_fusion: 0.1150 loss: 0.5227 2022/10/05 05:58:14 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 05:58:41 - mmengine - INFO - Epoch(train) [3][1000/10520] lr: 1.0000e-04 eta: 1 day, 13:14:37 time: 0.9502 data_time: 0.1705 memory: 17203 loss_visual: 0.1243 loss_lang: 0.2802 loss_fusion: 0.1106 loss: 0.5151 2022/10/05 05:59:38 - mmengine - INFO - Epoch(train) [3][1100/10520] lr: 1.0000e-04 eta: 1 day, 13:11:22 time: 0.7149 data_time: 0.0376 memory: 17203 loss_visual: 0.1223 loss_lang: 0.2695 loss_fusion: 0.1093 loss: 0.5011 2022/10/05 06:00:37 - mmengine - INFO - Epoch(train) [3][1200/10520] lr: 1.0000e-04 eta: 1 day, 13:08:30 time: 0.4618 data_time: 0.0033 memory: 17203 loss_visual: 0.1317 loss_lang: 0.2831 loss_fusion: 0.1185 loss: 0.5333 2022/10/05 06:01:35 - mmengine - INFO - Epoch(train) [3][1300/10520] lr: 1.0000e-04 eta: 1 day, 13:05:24 time: 0.4691 data_time: 0.0198 memory: 17203 loss_visual: 0.1281 loss_lang: 0.2749 loss_fusion: 0.1123 loss: 0.5153 2022/10/05 06:02:32 - mmengine - INFO - Epoch(train) [3][1400/10520] lr: 1.0000e-04 eta: 1 day, 13:02:16 time: 0.3653 data_time: 0.0211 memory: 17203 loss_visual: 0.1263 loss_lang: 0.2733 loss_fusion: 0.1115 loss: 0.5111 2022/10/05 06:03:30 - mmengine - INFO - Epoch(train) [3][1500/10520] lr: 1.0000e-04 eta: 1 day, 12:59:22 time: 0.3740 data_time: 0.0301 memory: 17203 loss_visual: 0.1269 loss_lang: 0.2713 loss_fusion: 0.1131 loss: 0.5113 2022/10/05 06:04:45 - mmengine - INFO - Epoch(train) [3][1600/10520] lr: 1.0000e-04 eta: 1 day, 12:58:46 time: 0.6927 data_time: 0.0136 memory: 17203 loss_visual: 0.1262 loss_lang: 0.2707 loss_fusion: 0.1130 loss: 0.5099 2022/10/05 06:05:52 - mmengine - INFO - Epoch(train) [3][1700/10520] lr: 1.0000e-04 eta: 1 day, 12:56:58 time: 0.7854 data_time: 0.1723 memory: 17203 loss_visual: 0.1322 loss_lang: 0.2777 loss_fusion: 0.1194 loss: 0.5293 2022/10/05 06:07:04 - mmengine - INFO - Epoch(train) [3][1800/10520] lr: 1.0000e-04 eta: 1 day, 12:55:58 time: 1.2472 data_time: 0.1655 memory: 17203 loss_visual: 0.1248 loss_lang: 0.2715 loss_fusion: 0.1115 loss: 0.5078 2022/10/05 06:08:02 - mmengine - INFO - Epoch(train) [3][1900/10520] lr: 1.0000e-04 eta: 1 day, 12:53:04 time: 0.7208 data_time: 0.0210 memory: 17203 loss_visual: 0.1263 loss_lang: 0.2697 loss_fusion: 0.1121 loss: 0.5081 2022/10/05 06:08:37 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 06:08:59 - mmengine - INFO - Epoch(train) [3][2000/10520] lr: 1.0000e-04 eta: 1 day, 12:49:58 time: 0.4621 data_time: 0.0034 memory: 17203 loss_visual: 0.1185 loss_lang: 0.2656 loss_fusion: 0.1046 loss: 0.4886 2022/10/05 06:09:55 - mmengine - INFO - Epoch(train) [3][2100/10520] lr: 1.0000e-04 eta: 1 day, 12:46:52 time: 0.4362 data_time: 0.0207 memory: 17203 loss_visual: 0.1316 loss_lang: 0.2750 loss_fusion: 0.1173 loss: 0.5239 2022/10/05 06:10:52 - mmengine - INFO - Epoch(train) [3][2200/10520] lr: 1.0000e-04 eta: 1 day, 12:43:51 time: 0.4107 data_time: 0.0418 memory: 17203 loss_visual: 0.1178 loss_lang: 0.2596 loss_fusion: 0.1020 loss: 0.4794 2022/10/05 06:11:49 - mmengine - INFO - Epoch(train) [3][2300/10520] lr: 1.0000e-04 eta: 1 day, 12:40:48 time: 0.3783 data_time: 0.0453 memory: 17203 loss_visual: 0.1225 loss_lang: 0.2576 loss_fusion: 0.1102 loss: 0.4903 2022/10/05 06:12:46 - mmengine - INFO - Epoch(train) [3][2400/10520] lr: 1.0000e-04 eta: 1 day, 12:37:45 time: 0.3591 data_time: 0.0104 memory: 17203 loss_visual: 0.1079 loss_lang: 0.2476 loss_fusion: 0.0939 loss: 0.4493 2022/10/05 06:13:47 - mmengine - INFO - Epoch(train) [3][2500/10520] lr: 1.0000e-04 eta: 1 day, 12:35:21 time: 0.8327 data_time: 0.1738 memory: 17203 loss_visual: 0.1278 loss_lang: 0.2668 loss_fusion: 0.1144 loss: 0.5090 2022/10/05 06:14:45 - mmengine - INFO - Epoch(train) [3][2600/10520] lr: 1.0000e-04 eta: 1 day, 12:32:31 time: 0.9797 data_time: 0.1561 memory: 17203 loss_visual: 0.1221 loss_lang: 0.2646 loss_fusion: 0.1082 loss: 0.4949 2022/10/05 06:15:41 - mmengine - INFO - Epoch(train) [3][2700/10520] lr: 1.0000e-04 eta: 1 day, 12:29:30 time: 0.6831 data_time: 0.0264 memory: 17203 loss_visual: 0.1183 loss_lang: 0.2585 loss_fusion: 0.1062 loss: 0.4830 2022/10/05 06:16:39 - mmengine - INFO - Epoch(train) [3][2800/10520] lr: 1.0000e-04 eta: 1 day, 12:26:38 time: 0.4539 data_time: 0.0033 memory: 17203 loss_visual: 0.1302 loss_lang: 0.2646 loss_fusion: 0.1174 loss: 0.5122 2022/10/05 06:17:36 - mmengine - INFO - Epoch(train) [3][2900/10520] lr: 1.0000e-04 eta: 1 day, 12:23:44 time: 0.4700 data_time: 0.0206 memory: 17203 loss_visual: 0.1046 loss_lang: 0.2470 loss_fusion: 0.0920 loss: 0.4436 2022/10/05 06:18:10 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 06:18:31 - mmengine - INFO - Epoch(train) [3][3000/10520] lr: 1.0000e-04 eta: 1 day, 12:20:42 time: 0.3782 data_time: 0.0203 memory: 17203 loss_visual: 0.1305 loss_lang: 0.2607 loss_fusion: 0.1160 loss: 0.5071 2022/10/05 06:19:28 - mmengine - INFO - Epoch(train) [3][3100/10520] lr: 1.0000e-04 eta: 1 day, 12:17:47 time: 0.3616 data_time: 0.0315 memory: 17203 loss_visual: 0.1232 loss_lang: 0.2617 loss_fusion: 0.1087 loss: 0.4936 2022/10/05 06:20:25 - mmengine - INFO - Epoch(train) [3][3200/10520] lr: 1.0000e-04 eta: 1 day, 12:14:53 time: 0.3639 data_time: 0.0138 memory: 17203 loss_visual: 0.1178 loss_lang: 0.2503 loss_fusion: 0.1027 loss: 0.4708 2022/10/05 06:21:26 - mmengine - INFO - Epoch(train) [3][3300/10520] lr: 1.0000e-04 eta: 1 day, 12:12:33 time: 0.8213 data_time: 0.1642 memory: 17203 loss_visual: 0.1263 loss_lang: 0.2542 loss_fusion: 0.1089 loss: 0.4895 2022/10/05 06:22:24 - mmengine - INFO - Epoch(train) [3][3400/10520] lr: 1.0000e-04 eta: 1 day, 12:09:53 time: 0.9734 data_time: 0.1613 memory: 17203 loss_visual: 0.1333 loss_lang: 0.2683 loss_fusion: 0.1192 loss: 0.5209 2022/10/05 06:23:21 - mmengine - INFO - Epoch(train) [3][3500/10520] lr: 1.0000e-04 eta: 1 day, 12:07:03 time: 0.7001 data_time: 0.0241 memory: 17203 loss_visual: 0.1283 loss_lang: 0.2637 loss_fusion: 0.1152 loss: 0.5072 2022/10/05 06:24:18 - mmengine - INFO - Epoch(train) [3][3600/10520] lr: 1.0000e-04 eta: 1 day, 12:04:18 time: 0.4482 data_time: 0.0035 memory: 17203 loss_visual: 0.1207 loss_lang: 0.2608 loss_fusion: 0.1081 loss: 0.4895 2022/10/05 06:25:15 - mmengine - INFO - Epoch(train) [3][3700/10520] lr: 1.0000e-04 eta: 1 day, 12:01:32 time: 0.4262 data_time: 0.0204 memory: 17203 loss_visual: 0.1107 loss_lang: 0.2472 loss_fusion: 0.0976 loss: 0.4555 2022/10/05 06:26:11 - mmengine - INFO - Epoch(train) [3][3800/10520] lr: 1.0000e-04 eta: 1 day, 11:58:40 time: 0.3738 data_time: 0.0198 memory: 17203 loss_visual: 0.1177 loss_lang: 0.2526 loss_fusion: 0.1050 loss: 0.4753 2022/10/05 06:27:07 - mmengine - INFO - Epoch(train) [3][3900/10520] lr: 1.0000e-04 eta: 1 day, 11:55:48 time: 0.3618 data_time: 0.0269 memory: 17203 loss_visual: 0.1177 loss_lang: 0.2449 loss_fusion: 0.1026 loss: 0.4653 2022/10/05 06:27:42 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 06:28:03 - mmengine - INFO - Epoch(train) [3][4000/10520] lr: 1.0000e-04 eta: 1 day, 11:52:52 time: 0.3569 data_time: 0.0112 memory: 17203 loss_visual: 0.1201 loss_lang: 0.2471 loss_fusion: 0.1044 loss: 0.4716 2022/10/05 06:29:03 - mmengine - INFO - Epoch(train) [3][4100/10520] lr: 1.0000e-04 eta: 1 day, 11:50:35 time: 0.8024 data_time: 0.1773 memory: 17203 loss_visual: 0.1265 loss_lang: 0.2597 loss_fusion: 0.1110 loss: 0.4973 2022/10/05 06:30:02 - mmengine - INFO - Epoch(train) [3][4200/10520] lr: 1.0000e-04 eta: 1 day, 11:48:01 time: 0.9499 data_time: 0.1689 memory: 17203 loss_visual: 0.1268 loss_lang: 0.2564 loss_fusion: 0.1117 loss: 0.4949 2022/10/05 06:30:58 - mmengine - INFO - Epoch(train) [3][4300/10520] lr: 1.0000e-04 eta: 1 day, 11:45:15 time: 0.7457 data_time: 0.0290 memory: 17203 loss_visual: 0.1134 loss_lang: 0.2445 loss_fusion: 0.1004 loss: 0.4583 2022/10/05 06:31:54 - mmengine - INFO - Epoch(train) [3][4400/10520] lr: 1.0000e-04 eta: 1 day, 11:42:28 time: 0.4781 data_time: 0.0031 memory: 17203 loss_visual: 0.1104 loss_lang: 0.2377 loss_fusion: 0.0978 loss: 0.4458 2022/10/05 06:32:52 - mmengine - INFO - Epoch(train) [3][4500/10520] lr: 1.0000e-04 eta: 1 day, 11:39:53 time: 0.4457 data_time: 0.0447 memory: 17203 loss_visual: 0.1231 loss_lang: 0.2448 loss_fusion: 0.1108 loss: 0.4787 2022/10/05 06:33:49 - mmengine - INFO - Epoch(train) [3][4600/10520] lr: 1.0000e-04 eta: 1 day, 11:37:14 time: 0.3653 data_time: 0.0183 memory: 17203 loss_visual: 0.1145 loss_lang: 0.2462 loss_fusion: 0.1004 loss: 0.4612 2022/10/05 06:34:46 - mmengine - INFO - Epoch(train) [3][4700/10520] lr: 1.0000e-04 eta: 1 day, 11:34:37 time: 0.4195 data_time: 0.0313 memory: 17203 loss_visual: 0.1137 loss_lang: 0.2437 loss_fusion: 0.0996 loss: 0.4570 2022/10/05 06:35:42 - mmengine - INFO - Epoch(train) [3][4800/10520] lr: 1.0000e-04 eta: 1 day, 11:31:53 time: 0.3550 data_time: 0.0103 memory: 17203 loss_visual: 0.1143 loss_lang: 0.2458 loss_fusion: 0.1024 loss: 0.4625 2022/10/05 06:37:01 - mmengine - INFO - Epoch(train) [3][4900/10520] lr: 1.0000e-04 eta: 1 day, 11:31:49 time: 0.7728 data_time: 0.1975 memory: 17203 loss_visual: 0.1177 loss_lang: 0.2397 loss_fusion: 0.1038 loss: 0.4613 2022/10/05 06:37:34 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 06:38:07 - mmengine - INFO - Epoch(train) [3][5000/10520] lr: 1.0000e-04 eta: 1 day, 11:30:15 time: 0.9804 data_time: 0.1662 memory: 17203 loss_visual: 0.1249 loss_lang: 0.2512 loss_fusion: 0.1116 loss: 0.4877 2022/10/05 06:39:07 - mmengine - INFO - Epoch(train) [3][5100/10520] lr: 1.0000e-04 eta: 1 day, 11:28:04 time: 0.8389 data_time: 0.0268 memory: 17203 loss_visual: 0.1118 loss_lang: 0.2344 loss_fusion: 0.0969 loss: 0.4431 2022/10/05 06:40:10 - mmengine - INFO - Epoch(train) [3][5200/10520] lr: 1.0000e-04 eta: 1 day, 11:26:09 time: 0.4453 data_time: 0.0033 memory: 17203 loss_visual: 0.1092 loss_lang: 0.2251 loss_fusion: 0.0960 loss: 0.4303 2022/10/05 06:41:06 - mmengine - INFO - Epoch(train) [3][5300/10520] lr: 1.0000e-04 eta: 1 day, 11:23:27 time: 0.4334 data_time: 0.0202 memory: 17203 loss_visual: 0.1086 loss_lang: 0.2306 loss_fusion: 0.0942 loss: 0.4334 2022/10/05 06:42:02 - mmengine - INFO - Epoch(train) [3][5400/10520] lr: 1.0000e-04 eta: 1 day, 11:20:48 time: 0.3873 data_time: 0.0204 memory: 17203 loss_visual: 0.1278 loss_lang: 0.2437 loss_fusion: 0.1150 loss: 0.4866 2022/10/05 06:42:59 - mmengine - INFO - Epoch(train) [3][5500/10520] lr: 1.0000e-04 eta: 1 day, 11:18:12 time: 0.3614 data_time: 0.0286 memory: 17203 loss_visual: 0.1079 loss_lang: 0.2361 loss_fusion: 0.0956 loss: 0.4396 2022/10/05 06:43:56 - mmengine - INFO - Epoch(train) [3][5600/10520] lr: 1.0000e-04 eta: 1 day, 11:15:37 time: 0.3611 data_time: 0.0147 memory: 17203 loss_visual: 0.1060 loss_lang: 0.2251 loss_fusion: 0.0909 loss: 0.4220 2022/10/05 06:44:56 - mmengine - INFO - Epoch(train) [3][5700/10520] lr: 1.0000e-04 eta: 1 day, 11:13:28 time: 0.8311 data_time: 0.1697 memory: 17203 loss_visual: 0.1144 loss_lang: 0.2390 loss_fusion: 0.1009 loss: 0.4543 2022/10/05 06:45:54 - mmengine - INFO - Epoch(train) [3][5800/10520] lr: 1.0000e-04 eta: 1 day, 11:11:04 time: 0.9161 data_time: 0.1564 memory: 17203 loss_visual: 0.1134 loss_lang: 0.2383 loss_fusion: 0.1008 loss: 0.4525 2022/10/05 06:46:51 - mmengine - INFO - Epoch(train) [3][5900/10520] lr: 1.0000e-04 eta: 1 day, 11:08:34 time: 0.7274 data_time: 0.0278 memory: 17203 loss_visual: 0.1208 loss_lang: 0.2396 loss_fusion: 0.1066 loss: 0.4669 2022/10/05 06:47:27 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 06:47:48 - mmengine - INFO - Epoch(train) [3][6000/10520] lr: 1.0000e-04 eta: 1 day, 11:06:01 time: 0.4438 data_time: 0.0032 memory: 17203 loss_visual: 0.1144 loss_lang: 0.2315 loss_fusion: 0.0985 loss: 0.4445 2022/10/05 06:48:46 - mmengine - INFO - Epoch(train) [3][6100/10520] lr: 1.0000e-04 eta: 1 day, 11:03:37 time: 0.4204 data_time: 0.0190 memory: 17203 loss_visual: 0.1202 loss_lang: 0.2438 loss_fusion: 0.1079 loss: 0.4720 2022/10/05 06:49:43 - mmengine - INFO - Epoch(train) [3][6200/10520] lr: 1.0000e-04 eta: 1 day, 11:01:09 time: 0.4008 data_time: 0.0213 memory: 17203 loss_visual: 0.1197 loss_lang: 0.2384 loss_fusion: 0.1061 loss: 0.4641 2022/10/05 06:50:39 - mmengine - INFO - Epoch(train) [3][6300/10520] lr: 1.0000e-04 eta: 1 day, 10:58:35 time: 0.3637 data_time: 0.0299 memory: 17203 loss_visual: 0.1033 loss_lang: 0.2235 loss_fusion: 0.0914 loss: 0.4182 2022/10/05 06:51:36 - mmengine - INFO - Epoch(train) [3][6400/10520] lr: 1.0000e-04 eta: 1 day, 10:56:08 time: 0.3678 data_time: 0.0148 memory: 17203 loss_visual: 0.1102 loss_lang: 0.2300 loss_fusion: 0.0972 loss: 0.4374 2022/10/05 06:52:37 - mmengine - INFO - Epoch(train) [3][6500/10520] lr: 1.0000e-04 eta: 1 day, 10:54:11 time: 0.8509 data_time: 0.1903 memory: 17203 loss_visual: 0.1037 loss_lang: 0.2211 loss_fusion: 0.0912 loss: 0.4160 2022/10/05 06:53:35 - mmengine - INFO - Epoch(train) [3][6600/10520] lr: 1.0000e-04 eta: 1 day, 10:51:51 time: 0.9482 data_time: 0.1570 memory: 17203 loss_visual: 0.1169 loss_lang: 0.2330 loss_fusion: 0.1035 loss: 0.4534 2022/10/05 06:54:32 - mmengine - INFO - Epoch(train) [3][6700/10520] lr: 1.0000e-04 eta: 1 day, 10:49:22 time: 0.7373 data_time: 0.0242 memory: 17203 loss_visual: 0.1159 loss_lang: 0.2341 loss_fusion: 0.1035 loss: 0.4535 2022/10/05 06:55:28 - mmengine - INFO - Epoch(train) [3][6800/10520] lr: 1.0000e-04 eta: 1 day, 10:46:56 time: 0.4455 data_time: 0.0037 memory: 17203 loss_visual: 0.1118 loss_lang: 0.2314 loss_fusion: 0.0996 loss: 0.4428 2022/10/05 06:56:26 - mmengine - INFO - Epoch(train) [3][6900/10520] lr: 1.0000e-04 eta: 1 day, 10:44:33 time: 0.4200 data_time: 0.0229 memory: 17203 loss_visual: 0.1081 loss_lang: 0.2244 loss_fusion: 0.0952 loss: 0.4277 2022/10/05 06:57:00 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 06:57:22 - mmengine - INFO - Epoch(train) [3][7000/10520] lr: 1.0000e-04 eta: 1 day, 10:42:04 time: 0.3676 data_time: 0.0215 memory: 17203 loss_visual: 0.1117 loss_lang: 0.2302 loss_fusion: 0.0990 loss: 0.4409 2022/10/05 06:58:18 - mmengine - INFO - Epoch(train) [3][7100/10520] lr: 1.0000e-04 eta: 1 day, 10:39:37 time: 0.3685 data_time: 0.0292 memory: 17203 loss_visual: 0.1088 loss_lang: 0.2233 loss_fusion: 0.0953 loss: 0.4273 2022/10/05 06:59:15 - mmengine - INFO - Epoch(train) [3][7200/10520] lr: 1.0000e-04 eta: 1 day, 10:37:16 time: 0.3730 data_time: 0.0109 memory: 17203 loss_visual: 0.1124 loss_lang: 0.2315 loss_fusion: 0.0989 loss: 0.4428 2022/10/05 07:00:17 - mmengine - INFO - Epoch(train) [3][7300/10520] lr: 1.0000e-04 eta: 1 day, 10:35:22 time: 0.7871 data_time: 0.1758 memory: 17203 loss_visual: 0.1151 loss_lang: 0.2336 loss_fusion: 0.1031 loss: 0.4518 2022/10/05 07:01:16 - mmengine - INFO - Epoch(train) [3][7400/10520] lr: 1.0000e-04 eta: 1 day, 10:33:12 time: 0.9993 data_time: 0.1625 memory: 17203 loss_visual: 0.1128 loss_lang: 0.2244 loss_fusion: 0.0981 loss: 0.4353 2022/10/05 07:02:13 - mmengine - INFO - Epoch(train) [3][7500/10520] lr: 1.0000e-04 eta: 1 day, 10:30:51 time: 0.7375 data_time: 0.0286 memory: 17203 loss_visual: 0.1047 loss_lang: 0.2175 loss_fusion: 0.0906 loss: 0.4128 2022/10/05 07:03:11 - mmengine - INFO - Epoch(train) [3][7600/10520] lr: 1.0000e-04 eta: 1 day, 10:28:38 time: 0.5046 data_time: 0.0036 memory: 17203 loss_visual: 0.1148 loss_lang: 0.2281 loss_fusion: 0.1028 loss: 0.4457 2022/10/05 07:04:08 - mmengine - INFO - Epoch(train) [3][7700/10520] lr: 1.0000e-04 eta: 1 day, 10:26:20 time: 0.4407 data_time: 0.0217 memory: 17203 loss_visual: 0.1040 loss_lang: 0.2233 loss_fusion: 0.0906 loss: 0.4179 2022/10/05 07:05:05 - mmengine - INFO - Epoch(train) [3][7800/10520] lr: 1.0000e-04 eta: 1 day, 10:23:58 time: 0.3603 data_time: 0.0193 memory: 17203 loss_visual: 0.1018 loss_lang: 0.2183 loss_fusion: 0.0907 loss: 0.4108 2022/10/05 07:06:02 - mmengine - INFO - Epoch(train) [3][7900/10520] lr: 1.0000e-04 eta: 1 day, 10:21:40 time: 0.3616 data_time: 0.0313 memory: 17203 loss_visual: 0.1074 loss_lang: 0.2203 loss_fusion: 0.0943 loss: 0.4220 2022/10/05 07:06:37 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 07:06:58 - mmengine - INFO - Epoch(train) [3][8000/10520] lr: 1.0000e-04 eta: 1 day, 10:19:18 time: 0.3740 data_time: 0.0131 memory: 17203 loss_visual: 0.1123 loss_lang: 0.2219 loss_fusion: 0.0979 loss: 0.4322 2022/10/05 07:07:58 - mmengine - INFO - Epoch(train) [3][8100/10520] lr: 1.0000e-04 eta: 1 day, 10:17:19 time: 0.7797 data_time: 0.1839 memory: 17203 loss_visual: 0.0996 loss_lang: 0.2101 loss_fusion: 0.0867 loss: 0.3964 2022/10/05 07:08:56 - mmengine - INFO - Epoch(train) [3][8200/10520] lr: 1.0000e-04 eta: 1 day, 10:15:11 time: 1.0046 data_time: 0.1569 memory: 17203 loss_visual: 0.1008 loss_lang: 0.2092 loss_fusion: 0.0889 loss: 0.3989 2022/10/05 07:09:53 - mmengine - INFO - Epoch(train) [3][8300/10520] lr: 1.0000e-04 eta: 1 day, 10:12:52 time: 0.7214 data_time: 0.0255 memory: 17203 loss_visual: 0.1172 loss_lang: 0.2298 loss_fusion: 0.1036 loss: 0.4507 2022/10/05 07:10:50 - mmengine - INFO - Epoch(train) [3][8400/10520] lr: 1.0000e-04 eta: 1 day, 10:10:40 time: 0.4926 data_time: 0.0031 memory: 17203 loss_visual: 0.1118 loss_lang: 0.2181 loss_fusion: 0.0972 loss: 0.4271 2022/10/05 07:11:47 - mmengine - INFO - Epoch(train) [3][8500/10520] lr: 1.0000e-04 eta: 1 day, 10:08:19 time: 0.4277 data_time: 0.0220 memory: 17203 loss_visual: 0.1060 loss_lang: 0.2094 loss_fusion: 0.0925 loss: 0.4078 2022/10/05 07:12:44 - mmengine - INFO - Epoch(train) [3][8600/10520] lr: 1.0000e-04 eta: 1 day, 10:06:04 time: 0.4046 data_time: 0.0223 memory: 17203 loss_visual: 0.1015 loss_lang: 0.2157 loss_fusion: 0.0900 loss: 0.4072 2022/10/05 07:13:41 - mmengine - INFO - Epoch(train) [3][8700/10520] lr: 1.0000e-04 eta: 1 day, 10:03:49 time: 0.3605 data_time: 0.0298 memory: 17203 loss_visual: 0.1054 loss_lang: 0.2157 loss_fusion: 0.0915 loss: 0.4126 2022/10/05 07:14:39 - mmengine - INFO - Epoch(train) [3][8800/10520] lr: 1.0000e-04 eta: 1 day, 10:01:43 time: 0.4026 data_time: 0.0201 memory: 17203 loss_visual: 0.1090 loss_lang: 0.2206 loss_fusion: 0.0962 loss: 0.4258 2022/10/05 07:15:40 - mmengine - INFO - Epoch(train) [3][8900/10520] lr: 1.0000e-04 eta: 1 day, 9:59:55 time: 0.7947 data_time: 0.1712 memory: 17203 loss_visual: 0.1046 loss_lang: 0.2142 loss_fusion: 0.0926 loss: 0.4114 2022/10/05 07:16:11 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 07:16:38 - mmengine - INFO - Epoch(train) [3][9000/10520] lr: 1.0000e-04 eta: 1 day, 9:57:46 time: 0.9803 data_time: 0.1596 memory: 17203 loss_visual: 0.1125 loss_lang: 0.2232 loss_fusion: 0.1007 loss: 0.4364 2022/10/05 07:17:34 - mmengine - INFO - Epoch(train) [3][9100/10520] lr: 1.0000e-04 eta: 1 day, 9:55:32 time: 0.7290 data_time: 0.0380 memory: 17203 loss_visual: 0.1071 loss_lang: 0.2149 loss_fusion: 0.0952 loss: 0.4172 2022/10/05 07:18:32 - mmengine - INFO - Epoch(train) [3][9200/10520] lr: 1.0000e-04 eta: 1 day, 9:53:24 time: 0.4698 data_time: 0.0033 memory: 17203 loss_visual: 0.1073 loss_lang: 0.2171 loss_fusion: 0.0938 loss: 0.4182 2022/10/05 07:19:29 - mmengine - INFO - Epoch(train) [3][9300/10520] lr: 1.0000e-04 eta: 1 day, 9:51:12 time: 0.4390 data_time: 0.0190 memory: 17203 loss_visual: 0.1083 loss_lang: 0.2169 loss_fusion: 0.0954 loss: 0.4205 2022/10/05 07:20:26 - mmengine - INFO - Epoch(train) [3][9400/10520] lr: 1.0000e-04 eta: 1 day, 9:49:01 time: 0.4062 data_time: 0.0194 memory: 17203 loss_visual: 0.1055 loss_lang: 0.2103 loss_fusion: 0.0920 loss: 0.4078 2022/10/05 07:21:22 - mmengine - INFO - Epoch(train) [3][9500/10520] lr: 1.0000e-04 eta: 1 day, 9:46:45 time: 0.3933 data_time: 0.0296 memory: 17203 loss_visual: 0.1034 loss_lang: 0.2152 loss_fusion: 0.0904 loss: 0.4090 2022/10/05 07:22:19 - mmengine - INFO - Epoch(train) [3][9600/10520] lr: 1.0000e-04 eta: 1 day, 9:44:36 time: 0.3611 data_time: 0.0125 memory: 17203 loss_visual: 0.1070 loss_lang: 0.2120 loss_fusion: 0.0932 loss: 0.4122 2022/10/05 07:23:20 - mmengine - INFO - Epoch(train) [3][9700/10520] lr: 1.0000e-04 eta: 1 day, 9:42:49 time: 0.7941 data_time: 0.1684 memory: 17203 loss_visual: 0.0997 loss_lang: 0.2079 loss_fusion: 0.0857 loss: 0.3933 2022/10/05 07:24:18 - mmengine - INFO - Epoch(train) [3][9800/10520] lr: 1.0000e-04 eta: 1 day, 9:40:48 time: 0.9632 data_time: 0.1616 memory: 17203 loss_visual: 0.0930 loss_lang: 0.1998 loss_fusion: 0.0809 loss: 0.3736 2022/10/05 07:25:14 - mmengine - INFO - Epoch(train) [3][9900/10520] lr: 1.0000e-04 eta: 1 day, 9:38:32 time: 0.6709 data_time: 0.0251 memory: 17203 loss_visual: 0.1005 loss_lang: 0.2066 loss_fusion: 0.0880 loss: 0.3952 2022/10/05 07:25:50 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 07:26:11 - mmengine - INFO - Epoch(train) [3][10000/10520] lr: 1.0000e-04 eta: 1 day, 9:36:26 time: 0.4462 data_time: 0.0031 memory: 17203 loss_visual: 0.1002 loss_lang: 0.2075 loss_fusion: 0.0882 loss: 0.3959 2022/10/05 07:27:08 - mmengine - INFO - Epoch(train) [3][10100/10520] lr: 1.0000e-04 eta: 1 day, 9:34:16 time: 0.4553 data_time: 0.0260 memory: 17203 loss_visual: 0.1171 loss_lang: 0.2233 loss_fusion: 0.1015 loss: 0.4419 2022/10/05 07:28:04 - mmengine - INFO - Epoch(train) [3][10200/10520] lr: 1.0000e-04 eta: 1 day, 9:32:05 time: 0.3683 data_time: 0.0212 memory: 17203 loss_visual: 0.1047 loss_lang: 0.2135 loss_fusion: 0.0924 loss: 0.4105 2022/10/05 07:29:01 - mmengine - INFO - Epoch(train) [3][10300/10520] lr: 1.0000e-04 eta: 1 day, 9:29:54 time: 0.3664 data_time: 0.0315 memory: 17203 loss_visual: 0.1059 loss_lang: 0.2048 loss_fusion: 0.0908 loss: 0.4015 2022/10/05 07:29:58 - mmengine - INFO - Epoch(train) [3][10400/10520] lr: 1.0000e-04 eta: 1 day, 9:27:48 time: 0.3591 data_time: 0.0113 memory: 17203 loss_visual: 0.1016 loss_lang: 0.2071 loss_fusion: 0.0887 loss: 0.3974 2022/10/05 07:30:58 - mmengine - INFO - Epoch(train) [3][10500/10520] lr: 1.0000e-04 eta: 1 day, 9:25:59 time: 0.6072 data_time: 0.1069 memory: 17203 loss_visual: 0.1094 loss_lang: 0.2083 loss_fusion: 0.0954 loss: 0.4131 2022/10/05 07:31:06 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 07:31:06 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/10/05 07:31:20 - mmengine - INFO - Epoch(val) [3][100/959] eta: 0:00:42 time: 0.0497 data_time: 0.0016 memory: 17203 2022/10/05 07:31:25 - mmengine - INFO - Epoch(val) [3][200/959] eta: 0:00:35 time: 0.0472 data_time: 0.0011 memory: 734 2022/10/05 07:31:30 - mmengine - INFO - Epoch(val) [3][300/959] eta: 0:00:33 time: 0.0505 data_time: 0.0027 memory: 734 2022/10/05 07:31:35 - mmengine - INFO - Epoch(val) [3][400/959] eta: 0:00:27 time: 0.0484 data_time: 0.0010 memory: 734 2022/10/05 07:31:39 - mmengine - INFO - Epoch(val) [3][500/959] eta: 0:00:22 time: 0.0483 data_time: 0.0020 memory: 734 2022/10/05 07:31:45 - mmengine - INFO - Epoch(val) [3][600/959] eta: 0:00:18 time: 0.0517 data_time: 0.0021 memory: 734 2022/10/05 07:31:50 - mmengine - INFO - Epoch(val) [3][700/959] eta: 0:00:13 time: 0.0507 data_time: 0.0035 memory: 734 2022/10/05 07:31:54 - mmengine - INFO - Epoch(val) [3][800/959] eta: 0:00:03 time: 0.0246 data_time: 0.0006 memory: 734 2022/10/05 07:31:56 - mmengine - INFO - Epoch(val) [3][900/959] eta: 0:00:01 time: 0.0212 data_time: 0.0005 memory: 734 2022/10/05 07:31:59 - mmengine - INFO - Epoch(val) [3][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.7917 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9230 SVT/recog/word_acc_ignore_case_symbol: 0.8918 SVTP/recog/word_acc_ignore_case_symbol: 0.8093 IC13/recog/word_acc_ignore_case_symbol: 0.9025 IC15/recog/word_acc_ignore_case_symbol: 0.7381 2022/10/05 07:33:00 - mmengine - INFO - Epoch(train) [4][100/10520] lr: 1.0000e-04 eta: 1 day, 9:23:35 time: 0.6698 data_time: 0.1713 memory: 17203 loss_visual: 0.1001 loss_lang: 0.2018 loss_fusion: 0.0875 loss: 0.3894 2022/10/05 07:33:56 - mmengine - INFO - Epoch(train) [4][200/10520] lr: 1.0000e-04 eta: 1 day, 9:21:27 time: 0.7636 data_time: 0.1660 memory: 17203 loss_visual: 0.1020 loss_lang: 0.2076 loss_fusion: 0.0905 loss: 0.4001 2022/10/05 07:34:52 - mmengine - INFO - Epoch(train) [4][300/10520] lr: 1.0000e-04 eta: 1 day, 9:19:16 time: 0.6915 data_time: 0.0174 memory: 17203 loss_visual: 0.1098 loss_lang: 0.2154 loss_fusion: 0.0975 loss: 0.4228 2022/10/05 07:35:47 - mmengine - INFO - Epoch(train) [4][400/10520] lr: 1.0000e-04 eta: 1 day, 9:16:57 time: 0.5965 data_time: 0.0136 memory: 17203 loss_visual: 0.0974 loss_lang: 0.2038 loss_fusion: 0.0848 loss: 0.3859 2022/10/05 07:36:07 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 07:36:40 - mmengine - INFO - Epoch(train) [4][500/10520] lr: 1.0000e-04 eta: 1 day, 9:14:34 time: 0.4418 data_time: 0.0214 memory: 17203 loss_visual: 0.1114 loss_lang: 0.2171 loss_fusion: 0.0987 loss: 0.4271 2022/10/05 07:37:35 - mmengine - INFO - Epoch(train) [4][600/10520] lr: 1.0000e-04 eta: 1 day, 9:12:18 time: 0.3539 data_time: 0.0029 memory: 17203 loss_visual: 0.1102 loss_lang: 0.2125 loss_fusion: 0.0967 loss: 0.4193 2022/10/05 07:38:29 - mmengine - INFO - Epoch(train) [4][700/10520] lr: 1.0000e-04 eta: 1 day, 9:09:58 time: 0.3959 data_time: 0.0032 memory: 17203 loss_visual: 0.1147 loss_lang: 0.2102 loss_fusion: 0.1023 loss: 0.4272 2022/10/05 07:39:23 - mmengine - INFO - Epoch(train) [4][800/10520] lr: 1.0000e-04 eta: 1 day, 9:07:41 time: 0.3586 data_time: 0.0104 memory: 17203 loss_visual: 0.0979 loss_lang: 0.1976 loss_fusion: 0.0859 loss: 0.3814 2022/10/05 07:40:20 - mmengine - INFO - Epoch(train) [4][900/10520] lr: 1.0000e-04 eta: 1 day, 9:05:39 time: 0.6304 data_time: 0.1618 memory: 17203 loss_visual: 0.0923 loss_lang: 0.1928 loss_fusion: 0.0789 loss: 0.3641 2022/10/05 07:41:16 - mmengine - INFO - Epoch(train) [4][1000/10520] lr: 1.0000e-04 eta: 1 day, 9:03:31 time: 0.7741 data_time: 0.1669 memory: 17203 loss_visual: 0.1094 loss_lang: 0.2112 loss_fusion: 0.0969 loss: 0.4174 2022/10/05 07:42:12 - mmengine - INFO - Epoch(train) [4][1100/10520] lr: 1.0000e-04 eta: 1 day, 9:01:24 time: 0.6214 data_time: 0.0198 memory: 17203 loss_visual: 0.1117 loss_lang: 0.2157 loss_fusion: 0.1000 loss: 0.4274 2022/10/05 07:43:07 - mmengine - INFO - Epoch(train) [4][1200/10520] lr: 1.0000e-04 eta: 1 day, 8:59:14 time: 0.5927 data_time: 0.0336 memory: 17203 loss_visual: 0.0943 loss_lang: 0.1897 loss_fusion: 0.0818 loss: 0.3658 2022/10/05 07:44:01 - mmengine - INFO - Epoch(train) [4][1300/10520] lr: 1.0000e-04 eta: 1 day, 8:56:57 time: 0.4626 data_time: 0.0196 memory: 17203 loss_visual: 0.1148 loss_lang: 0.2148 loss_fusion: 0.1017 loss: 0.4314 2022/10/05 07:44:54 - mmengine - INFO - Epoch(train) [4][1400/10520] lr: 1.0000e-04 eta: 1 day, 8:54:39 time: 0.3517 data_time: 0.0031 memory: 17203 loss_visual: 0.1116 loss_lang: 0.2084 loss_fusion: 0.0989 loss: 0.4189 2022/10/05 07:45:15 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 07:45:49 - mmengine - INFO - Epoch(train) [4][1500/10520] lr: 1.0000e-04 eta: 1 day, 8:52:28 time: 0.3685 data_time: 0.0032 memory: 17203 loss_visual: 0.1077 loss_lang: 0.2048 loss_fusion: 0.0943 loss: 0.4068 2022/10/05 07:46:45 - mmengine - INFO - Epoch(train) [4][1600/10520] lr: 1.0000e-04 eta: 1 day, 8:50:22 time: 0.3577 data_time: 0.0100 memory: 17203 loss_visual: 0.1010 loss_lang: 0.1942 loss_fusion: 0.0882 loss: 0.3834 2022/10/05 07:47:43 - mmengine - INFO - Epoch(train) [4][1700/10520] lr: 1.0000e-04 eta: 1 day, 8:48:31 time: 0.6512 data_time: 0.1671 memory: 17203 loss_visual: 0.1052 loss_lang: 0.2018 loss_fusion: 0.0921 loss: 0.3991 2022/10/05 07:48:39 - mmengine - INFO - Epoch(train) [4][1800/10520] lr: 1.0000e-04 eta: 1 day, 8:46:26 time: 0.7536 data_time: 0.1633 memory: 17203 loss_visual: 0.0961 loss_lang: 0.1883 loss_fusion: 0.0838 loss: 0.3682 2022/10/05 07:49:35 - mmengine - INFO - Epoch(train) [4][1900/10520] lr: 1.0000e-04 eta: 1 day, 8:44:22 time: 0.6133 data_time: 0.0214 memory: 17203 loss_visual: 0.1013 loss_lang: 0.2074 loss_fusion: 0.0884 loss: 0.3970 2022/10/05 07:50:30 - mmengine - INFO - Epoch(train) [4][2000/10520] lr: 1.0000e-04 eta: 1 day, 8:42:15 time: 0.6164 data_time: 0.0185 memory: 17203 loss_visual: 0.0998 loss_lang: 0.1921 loss_fusion: 0.0877 loss: 0.3796 2022/10/05 07:51:24 - mmengine - INFO - Epoch(train) [4][2100/10520] lr: 1.0000e-04 eta: 1 day, 8:40:05 time: 0.4780 data_time: 0.0205 memory: 17203 loss_visual: 0.1125 loss_lang: 0.2082 loss_fusion: 0.0985 loss: 0.4192 2022/10/05 07:52:19 - mmengine - INFO - Epoch(train) [4][2200/10520] lr: 1.0000e-04 eta: 1 day, 8:37:58 time: 0.3520 data_time: 0.0032 memory: 17203 loss_visual: 0.1036 loss_lang: 0.2074 loss_fusion: 0.0911 loss: 0.4021 2022/10/05 07:53:14 - mmengine - INFO - Epoch(train) [4][2300/10520] lr: 1.0000e-04 eta: 1 day, 8:35:47 time: 0.3667 data_time: 0.0056 memory: 17203 loss_visual: 0.1003 loss_lang: 0.1998 loss_fusion: 0.0886 loss: 0.3888 2022/10/05 07:54:09 - mmengine - INFO - Epoch(train) [4][2400/10520] lr: 1.0000e-04 eta: 1 day, 8:33:41 time: 0.3585 data_time: 0.0117 memory: 17203 loss_visual: 0.1051 loss_lang: 0.1997 loss_fusion: 0.0923 loss: 0.3970 2022/10/05 07:54:33 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 07:55:06 - mmengine - INFO - Epoch(train) [4][2500/10520] lr: 1.0000e-04 eta: 1 day, 8:31:47 time: 0.6864 data_time: 0.1708 memory: 17203 loss_visual: 0.1034 loss_lang: 0.1940 loss_fusion: 0.0917 loss: 0.3891 2022/10/05 07:56:01 - mmengine - INFO - Epoch(train) [4][2600/10520] lr: 1.0000e-04 eta: 1 day, 8:29:43 time: 0.7521 data_time: 0.1534 memory: 17203 loss_visual: 0.1049 loss_lang: 0.2038 loss_fusion: 0.0928 loss: 0.4015 2022/10/05 07:56:57 - mmengine - INFO - Epoch(train) [4][2700/10520] lr: 1.0000e-04 eta: 1 day, 8:27:44 time: 0.6889 data_time: 0.0253 memory: 17203 loss_visual: 0.0975 loss_lang: 0.1944 loss_fusion: 0.0855 loss: 0.3774 2022/10/05 07:57:52 - mmengine - INFO - Epoch(train) [4][2800/10520] lr: 1.0000e-04 eta: 1 day, 8:25:39 time: 0.6289 data_time: 0.0239 memory: 17203 loss_visual: 0.0998 loss_lang: 0.2000 loss_fusion: 0.0881 loss: 0.3879 2022/10/05 07:58:47 - mmengine - INFO - Epoch(train) [4][2900/10520] lr: 1.0000e-04 eta: 1 day, 8:23:33 time: 0.4431 data_time: 0.0256 memory: 17203 loss_visual: 0.0978 loss_lang: 0.1955 loss_fusion: 0.0864 loss: 0.3797 2022/10/05 07:59:42 - mmengine - INFO - Epoch(train) [4][3000/10520] lr: 1.0000e-04 eta: 1 day, 8:21:32 time: 0.3598 data_time: 0.0034 memory: 17203 loss_visual: 0.1052 loss_lang: 0.2008 loss_fusion: 0.0924 loss: 0.3984 2022/10/05 08:00:37 - mmengine - INFO - Epoch(train) [4][3100/10520] lr: 1.0000e-04 eta: 1 day, 8:19:28 time: 0.3935 data_time: 0.0033 memory: 17203 loss_visual: 0.0955 loss_lang: 0.1961 loss_fusion: 0.0837 loss: 0.3753 2022/10/05 08:01:32 - mmengine - INFO - Epoch(train) [4][3200/10520] lr: 1.0000e-04 eta: 1 day, 8:17:24 time: 0.3600 data_time: 0.0131 memory: 17203 loss_visual: 0.0951 loss_lang: 0.1872 loss_fusion: 0.0824 loss: 0.3647 2022/10/05 08:02:30 - mmengine - INFO - Epoch(train) [4][3300/10520] lr: 1.0000e-04 eta: 1 day, 8:15:38 time: 0.6660 data_time: 0.1781 memory: 17203 loss_visual: 0.0993 loss_lang: 0.1964 loss_fusion: 0.0878 loss: 0.3836 2022/10/05 08:03:28 - mmengine - INFO - Epoch(train) [4][3400/10520] lr: 1.0000e-04 eta: 1 day, 8:13:47 time: 0.7625 data_time: 0.1393 memory: 17203 loss_visual: 0.0979 loss_lang: 0.1925 loss_fusion: 0.0845 loss: 0.3749 2022/10/05 08:03:50 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 08:04:24 - mmengine - INFO - Epoch(train) [4][3500/10520] lr: 1.0000e-04 eta: 1 day, 8:11:52 time: 0.6705 data_time: 0.0246 memory: 17203 loss_visual: 0.0964 loss_lang: 0.1910 loss_fusion: 0.0842 loss: 0.3716 2022/10/05 08:05:18 - mmengine - INFO - Epoch(train) [4][3600/10520] lr: 1.0000e-04 eta: 1 day, 8:09:45 time: 0.5992 data_time: 0.0264 memory: 17203 loss_visual: 0.1078 loss_lang: 0.2000 loss_fusion: 0.0953 loss: 0.4030 2022/10/05 08:06:12 - mmengine - INFO - Epoch(train) [4][3700/10520] lr: 1.0000e-04 eta: 1 day, 8:07:43 time: 0.5088 data_time: 0.0451 memory: 17203 loss_visual: 0.1050 loss_lang: 0.1959 loss_fusion: 0.0916 loss: 0.3924 2022/10/05 08:07:07 - mmengine - INFO - Epoch(train) [4][3800/10520] lr: 1.0000e-04 eta: 1 day, 8:05:40 time: 0.3502 data_time: 0.0030 memory: 17203 loss_visual: 0.0940 loss_lang: 0.1896 loss_fusion: 0.0820 loss: 0.3656 2022/10/05 08:08:02 - mmengine - INFO - Epoch(train) [4][3900/10520] lr: 1.0000e-04 eta: 1 day, 8:03:39 time: 0.3647 data_time: 0.0033 memory: 17203 loss_visual: 0.1062 loss_lang: 0.1974 loss_fusion: 0.0927 loss: 0.3962 2022/10/05 08:08:57 - mmengine - INFO - Epoch(train) [4][4000/10520] lr: 1.0000e-04 eta: 1 day, 8:01:38 time: 0.3598 data_time: 0.0122 memory: 17203 loss_visual: 0.0999 loss_lang: 0.1972 loss_fusion: 0.0871 loss: 0.3842 2022/10/05 08:09:54 - mmengine - INFO - Epoch(train) [4][4100/10520] lr: 1.0000e-04 eta: 1 day, 7:59:50 time: 0.6646 data_time: 0.1767 memory: 17203 loss_visual: 0.1020 loss_lang: 0.1965 loss_fusion: 0.0892 loss: 0.3877 2022/10/05 08:10:49 - mmengine - INFO - Epoch(train) [4][4200/10520] lr: 1.0000e-04 eta: 1 day, 7:57:52 time: 0.7674 data_time: 0.1621 memory: 17203 loss_visual: 0.1043 loss_lang: 0.1985 loss_fusion: 0.0916 loss: 0.3945 2022/10/05 08:11:45 - mmengine - INFO - Epoch(train) [4][4300/10520] lr: 1.0000e-04 eta: 1 day, 7:55:55 time: 0.6483 data_time: 0.0417 memory: 17203 loss_visual: 0.1056 loss_lang: 0.1988 loss_fusion: 0.0931 loss: 0.3974 2022/10/05 08:12:41 - mmengine - INFO - Epoch(train) [4][4400/10520] lr: 1.0000e-04 eta: 1 day, 7:54:01 time: 0.5983 data_time: 0.0256 memory: 17203 loss_visual: 0.1019 loss_lang: 0.1951 loss_fusion: 0.0906 loss: 0.3875 2022/10/05 08:13:02 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 08:13:36 - mmengine - INFO - Epoch(train) [4][4500/10520] lr: 1.0000e-04 eta: 1 day, 7:52:05 time: 0.4860 data_time: 0.0237 memory: 17203 loss_visual: 0.1053 loss_lang: 0.1964 loss_fusion: 0.0914 loss: 0.3931 2022/10/05 08:14:31 - mmengine - INFO - Epoch(train) [4][4600/10520] lr: 1.0000e-04 eta: 1 day, 7:50:09 time: 0.3724 data_time: 0.0033 memory: 17203 loss_visual: 0.0933 loss_lang: 0.1901 loss_fusion: 0.0812 loss: 0.3646 2022/10/05 08:15:26 - mmengine - INFO - Epoch(train) [4][4700/10520] lr: 1.0000e-04 eta: 1 day, 7:48:11 time: 0.3643 data_time: 0.0033 memory: 17203 loss_visual: 0.0920 loss_lang: 0.1827 loss_fusion: 0.0802 loss: 0.3549 2022/10/05 08:16:21 - mmengine - INFO - Epoch(train) [4][4800/10520] lr: 1.0000e-04 eta: 1 day, 7:46:13 time: 0.3794 data_time: 0.0114 memory: 17203 loss_visual: 0.0881 loss_lang: 0.1812 loss_fusion: 0.0777 loss: 0.3471 2022/10/05 08:17:19 - mmengine - INFO - Epoch(train) [4][4900/10520] lr: 1.0000e-04 eta: 1 day, 7:44:29 time: 0.6557 data_time: 0.1803 memory: 17203 loss_visual: 0.1002 loss_lang: 0.1921 loss_fusion: 0.0884 loss: 0.3807 2022/10/05 08:18:16 - mmengine - INFO - Epoch(train) [4][5000/10520] lr: 1.0000e-04 eta: 1 day, 7:42:40 time: 0.7844 data_time: 0.1566 memory: 17203 loss_visual: 0.0924 loss_lang: 0.1934 loss_fusion: 0.0795 loss: 0.3653 2022/10/05 08:19:12 - mmengine - INFO - Epoch(train) [4][5100/10520] lr: 1.0000e-04 eta: 1 day, 7:40:49 time: 0.6972 data_time: 0.0240 memory: 17203 loss_visual: 0.0912 loss_lang: 0.1812 loss_fusion: 0.0792 loss: 0.3516 2022/10/05 08:20:06 - mmengine - INFO - Epoch(train) [4][5200/10520] lr: 1.0000e-04 eta: 1 day, 7:38:51 time: 0.5819 data_time: 0.0230 memory: 17203 loss_visual: 0.0971 loss_lang: 0.1876 loss_fusion: 0.0841 loss: 0.3688 2022/10/05 08:21:00 - mmengine - INFO - Epoch(train) [4][5300/10520] lr: 1.0000e-04 eta: 1 day, 7:36:51 time: 0.4382 data_time: 0.0369 memory: 17203 loss_visual: 0.0982 loss_lang: 0.1899 loss_fusion: 0.0868 loss: 0.3749 2022/10/05 08:21:55 - mmengine - INFO - Epoch(train) [4][5400/10520] lr: 1.0000e-04 eta: 1 day, 7:34:53 time: 0.3597 data_time: 0.0064 memory: 17203 loss_visual: 0.1058 loss_lang: 0.1987 loss_fusion: 0.0927 loss: 0.3972 2022/10/05 08:22:16 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 08:22:50 - mmengine - INFO - Epoch(train) [4][5500/10520] lr: 1.0000e-04 eta: 1 day, 7:32:57 time: 0.3655 data_time: 0.0033 memory: 17203 loss_visual: 0.0971 loss_lang: 0.1889 loss_fusion: 0.0849 loss: 0.3709 2022/10/05 08:23:44 - mmengine - INFO - Epoch(train) [4][5600/10520] lr: 1.0000e-04 eta: 1 day, 7:31:01 time: 0.3729 data_time: 0.0113 memory: 17203 loss_visual: 0.0960 loss_lang: 0.1857 loss_fusion: 0.0842 loss: 0.3659 2022/10/05 08:24:41 - mmengine - INFO - Epoch(train) [4][5700/10520] lr: 1.0000e-04 eta: 1 day, 7:29:14 time: 0.6569 data_time: 0.1791 memory: 17203 loss_visual: 0.0990 loss_lang: 0.1866 loss_fusion: 0.0873 loss: 0.3730 2022/10/05 08:25:36 - mmengine - INFO - Epoch(train) [4][5800/10520] lr: 1.0000e-04 eta: 1 day, 7:27:23 time: 0.7824 data_time: 0.1859 memory: 17203 loss_visual: 0.0981 loss_lang: 0.1915 loss_fusion: 0.0850 loss: 0.3746 2022/10/05 08:26:32 - mmengine - INFO - Epoch(train) [4][5900/10520] lr: 1.0000e-04 eta: 1 day, 7:25:34 time: 0.6723 data_time: 0.0240 memory: 17203 loss_visual: 0.0911 loss_lang: 0.1858 loss_fusion: 0.0793 loss: 0.3562 2022/10/05 08:27:26 - mmengine - INFO - Epoch(train) [4][6000/10520] lr: 1.0000e-04 eta: 1 day, 7:23:36 time: 0.6179 data_time: 0.0263 memory: 17203 loss_visual: 0.0968 loss_lang: 0.1850 loss_fusion: 0.0845 loss: 0.3663 2022/10/05 08:28:20 - mmengine - INFO - Epoch(train) [4][6100/10520] lr: 1.0000e-04 eta: 1 day, 7:21:39 time: 0.4577 data_time: 0.0243 memory: 17203 loss_visual: 0.1007 loss_lang: 0.1907 loss_fusion: 0.0878 loss: 0.3792 2022/10/05 08:29:16 - mmengine - INFO - Epoch(train) [4][6200/10520] lr: 1.0000e-04 eta: 1 day, 7:19:46 time: 0.3518 data_time: 0.0031 memory: 17203 loss_visual: 0.1026 loss_lang: 0.1940 loss_fusion: 0.0899 loss: 0.3864 2022/10/05 08:30:10 - mmengine - INFO - Epoch(train) [4][6300/10520] lr: 1.0000e-04 eta: 1 day, 7:17:52 time: 0.3684 data_time: 0.0031 memory: 17203 loss_visual: 0.0890 loss_lang: 0.1781 loss_fusion: 0.0773 loss: 0.3443 2022/10/05 08:31:04 - mmengine - INFO - Epoch(train) [4][6400/10520] lr: 1.0000e-04 eta: 1 day, 7:15:56 time: 0.3602 data_time: 0.0118 memory: 17203 loss_visual: 0.0905 loss_lang: 0.1792 loss_fusion: 0.0800 loss: 0.3497 2022/10/05 08:31:29 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 08:32:02 - mmengine - INFO - Epoch(train) [4][6500/10520] lr: 1.0000e-04 eta: 1 day, 7:14:17 time: 0.6482 data_time: 0.1926 memory: 17203 loss_visual: 0.0955 loss_lang: 0.1837 loss_fusion: 0.0828 loss: 0.3620 2022/10/05 08:32:58 - mmengine - INFO - Epoch(train) [4][6600/10520] lr: 1.0000e-04 eta: 1 day, 7:12:29 time: 0.7274 data_time: 0.1615 memory: 17203 loss_visual: 0.0965 loss_lang: 0.1842 loss_fusion: 0.0849 loss: 0.3655 2022/10/05 08:33:54 - mmengine - INFO - Epoch(train) [4][6700/10520] lr: 1.0000e-04 eta: 1 day, 7:10:41 time: 0.6284 data_time: 0.0273 memory: 17203 loss_visual: 0.1040 loss_lang: 0.1933 loss_fusion: 0.0925 loss: 0.3898 2022/10/05 08:34:50 - mmengine - INFO - Epoch(train) [4][6800/10520] lr: 1.0000e-04 eta: 1 day, 7:08:55 time: 0.6394 data_time: 0.0250 memory: 17203 loss_visual: 0.1043 loss_lang: 0.1988 loss_fusion: 0.0908 loss: 0.3939 2022/10/05 08:35:44 - mmengine - INFO - Epoch(train) [4][6900/10520] lr: 1.0000e-04 eta: 1 day, 7:07:03 time: 0.4427 data_time: 0.0247 memory: 17203 loss_visual: 0.1047 loss_lang: 0.1951 loss_fusion: 0.0921 loss: 0.3919 2022/10/05 08:36:39 - mmengine - INFO - Epoch(train) [4][7000/10520] lr: 1.0000e-04 eta: 1 day, 7:05:10 time: 0.3656 data_time: 0.0034 memory: 17203 loss_visual: 0.0916 loss_lang: 0.1819 loss_fusion: 0.0800 loss: 0.3535 2022/10/05 08:37:34 - mmengine - INFO - Epoch(train) [4][7100/10520] lr: 1.0000e-04 eta: 1 day, 7:03:19 time: 0.3678 data_time: 0.0030 memory: 17203 loss_visual: 0.0955 loss_lang: 0.1869 loss_fusion: 0.0832 loss: 0.3656 2022/10/05 08:38:28 - mmengine - INFO - Epoch(train) [4][7200/10520] lr: 1.0000e-04 eta: 1 day, 7:01:26 time: 0.3601 data_time: 0.0110 memory: 17203 loss_visual: 0.0977 loss_lang: 0.1879 loss_fusion: 0.0866 loss: 0.3721 2022/10/05 08:39:25 - mmengine - INFO - Epoch(train) [4][7300/10520] lr: 1.0000e-04 eta: 1 day, 6:59:47 time: 0.6565 data_time: 0.1663 memory: 17203 loss_visual: 0.0851 loss_lang: 0.1741 loss_fusion: 0.0735 loss: 0.3326 2022/10/05 08:40:21 - mmengine - INFO - Epoch(train) [4][7400/10520] lr: 1.0000e-04 eta: 1 day, 6:57:58 time: 0.7253 data_time: 0.1559 memory: 17203 loss_visual: 0.0953 loss_lang: 0.1849 loss_fusion: 0.0822 loss: 0.3624 2022/10/05 08:40:43 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 08:41:17 - mmengine - INFO - Epoch(train) [4][7500/10520] lr: 1.0000e-04 eta: 1 day, 6:56:16 time: 0.7177 data_time: 0.0242 memory: 17203 loss_visual: 0.1016 loss_lang: 0.1865 loss_fusion: 0.0893 loss: 0.3774 2022/10/05 08:42:12 - mmengine - INFO - Epoch(train) [4][7600/10520] lr: 1.0000e-04 eta: 1 day, 6:54:25 time: 0.6112 data_time: 0.0249 memory: 17203 loss_visual: 0.0910 loss_lang: 0.1778 loss_fusion: 0.0801 loss: 0.3489 2022/10/05 08:43:06 - mmengine - INFO - Epoch(train) [4][7700/10520] lr: 1.0000e-04 eta: 1 day, 6:52:35 time: 0.4390 data_time: 0.0220 memory: 17203 loss_visual: 0.1066 loss_lang: 0.1912 loss_fusion: 0.0932 loss: 0.3909 2022/10/05 08:44:01 - mmengine - INFO - Epoch(train) [4][7800/10520] lr: 1.0000e-04 eta: 1 day, 6:50:46 time: 0.3521 data_time: 0.0031 memory: 17203 loss_visual: 0.1041 loss_lang: 0.1930 loss_fusion: 0.0933 loss: 0.3904 2022/10/05 08:44:56 - mmengine - INFO - Epoch(train) [4][7900/10520] lr: 1.0000e-04 eta: 1 day, 6:48:58 time: 0.3665 data_time: 0.0032 memory: 17203 loss_visual: 0.0988 loss_lang: 0.1885 loss_fusion: 0.0868 loss: 0.3741 2022/10/05 08:45:51 - mmengine - INFO - Epoch(train) [4][8000/10520] lr: 1.0000e-04 eta: 1 day, 6:47:08 time: 0.3538 data_time: 0.0118 memory: 17203 loss_visual: 0.0924 loss_lang: 0.1806 loss_fusion: 0.0815 loss: 0.3545 2022/10/05 08:46:48 - mmengine - INFO - Epoch(train) [4][8100/10520] lr: 1.0000e-04 eta: 1 day, 6:45:32 time: 0.6748 data_time: 0.1638 memory: 17203 loss_visual: 0.1006 loss_lang: 0.1901 loss_fusion: 0.0888 loss: 0.3795 2022/10/05 08:47:44 - mmengine - INFO - Epoch(train) [4][8200/10520] lr: 1.0000e-04 eta: 1 day, 6:43:48 time: 0.7333 data_time: 0.1646 memory: 17203 loss_visual: 0.0932 loss_lang: 0.1769 loss_fusion: 0.0819 loss: 0.3519 2022/10/05 08:48:40 - mmengine - INFO - Epoch(train) [4][8300/10520] lr: 1.0000e-04 eta: 1 day, 6:42:06 time: 0.6617 data_time: 0.0495 memory: 17203 loss_visual: 0.1001 loss_lang: 0.1892 loss_fusion: 0.0890 loss: 0.3783 2022/10/05 08:49:35 - mmengine - INFO - Epoch(train) [4][8400/10520] lr: 1.0000e-04 eta: 1 day, 6:40:20 time: 0.6279 data_time: 0.0256 memory: 17203 loss_visual: 0.1005 loss_lang: 0.1847 loss_fusion: 0.0883 loss: 0.3734 2022/10/05 08:49:56 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 08:50:29 - mmengine - INFO - Epoch(train) [4][8500/10520] lr: 1.0000e-04 eta: 1 day, 6:38:29 time: 0.4471 data_time: 0.0265 memory: 17203 loss_visual: 0.0946 loss_lang: 0.1814 loss_fusion: 0.0828 loss: 0.3589 2022/10/05 08:51:24 - mmengine - INFO - Epoch(train) [4][8600/10520] lr: 1.0000e-04 eta: 1 day, 6:36:43 time: 0.3738 data_time: 0.0030 memory: 17203 loss_visual: 0.1032 loss_lang: 0.1887 loss_fusion: 0.0923 loss: 0.3841 2022/10/05 08:52:19 - mmengine - INFO - Epoch(train) [4][8700/10520] lr: 1.0000e-04 eta: 1 day, 6:34:56 time: 0.4198 data_time: 0.0031 memory: 17203 loss_visual: 0.0894 loss_lang: 0.1726 loss_fusion: 0.0787 loss: 0.3408 2022/10/05 08:53:14 - mmengine - INFO - Epoch(train) [4][8800/10520] lr: 1.0000e-04 eta: 1 day, 6:33:10 time: 0.3565 data_time: 0.0119 memory: 17203 loss_visual: 0.1000 loss_lang: 0.1825 loss_fusion: 0.0896 loss: 0.3721 2022/10/05 08:54:12 - mmengine - INFO - Epoch(train) [4][8900/10520] lr: 1.0000e-04 eta: 1 day, 6:31:36 time: 0.6497 data_time: 0.1497 memory: 17203 loss_visual: 0.0922 loss_lang: 0.1766 loss_fusion: 0.0804 loss: 0.3492 2022/10/05 08:55:07 - mmengine - INFO - Epoch(train) [4][9000/10520] lr: 1.0000e-04 eta: 1 day, 6:29:52 time: 0.7613 data_time: 0.1655 memory: 17203 loss_visual: 0.0925 loss_lang: 0.1774 loss_fusion: 0.0787 loss: 0.3485 2022/10/05 08:56:03 - mmengine - INFO - Epoch(train) [4][9100/10520] lr: 1.0000e-04 eta: 1 day, 6:28:10 time: 0.6370 data_time: 0.0228 memory: 17203 loss_visual: 0.0871 loss_lang: 0.1754 loss_fusion: 0.0753 loss: 0.3378 2022/10/05 08:56:58 - mmengine - INFO - Epoch(train) [4][9200/10520] lr: 1.0000e-04 eta: 1 day, 6:26:26 time: 0.5636 data_time: 0.0248 memory: 17203 loss_visual: 0.0904 loss_lang: 0.1735 loss_fusion: 0.0776 loss: 0.3415 2022/10/05 08:57:52 - mmengine - INFO - Epoch(train) [4][9300/10520] lr: 1.0000e-04 eta: 1 day, 6:24:39 time: 0.4363 data_time: 0.0400 memory: 17203 loss_visual: 0.0898 loss_lang: 0.1730 loss_fusion: 0.0760 loss: 0.3388 2022/10/05 08:58:46 - mmengine - INFO - Epoch(train) [4][9400/10520] lr: 1.0000e-04 eta: 1 day, 6:22:49 time: 0.3498 data_time: 0.0033 memory: 17203 loss_visual: 0.0887 loss_lang: 0.1718 loss_fusion: 0.0773 loss: 0.3378 2022/10/05 08:59:07 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 08:59:41 - mmengine - INFO - Epoch(train) [4][9500/10520] lr: 1.0000e-04 eta: 1 day, 6:21:08 time: 0.3645 data_time: 0.0031 memory: 17203 loss_visual: 0.0940 loss_lang: 0.1742 loss_fusion: 0.0811 loss: 0.3493 2022/10/05 09:00:36 - mmengine - INFO - Epoch(train) [4][9600/10520] lr: 1.0000e-04 eta: 1 day, 6:19:21 time: 0.3549 data_time: 0.0104 memory: 17203 loss_visual: 0.0871 loss_lang: 0.1752 loss_fusion: 0.0747 loss: 0.3370 2022/10/05 09:01:34 - mmengine - INFO - Epoch(train) [4][9700/10520] lr: 1.0000e-04 eta: 1 day, 6:17:51 time: 0.6543 data_time: 0.1760 memory: 17203 loss_visual: 0.0964 loss_lang: 0.1816 loss_fusion: 0.0845 loss: 0.3624 2022/10/05 09:02:30 - mmengine - INFO - Epoch(train) [4][9800/10520] lr: 1.0000e-04 eta: 1 day, 6:16:13 time: 0.7513 data_time: 0.1616 memory: 17203 loss_visual: 0.0921 loss_lang: 0.1773 loss_fusion: 0.0802 loss: 0.3497 2022/10/05 09:03:27 - mmengine - INFO - Epoch(train) [4][9900/10520] lr: 1.0000e-04 eta: 1 day, 6:14:36 time: 0.7092 data_time: 0.0250 memory: 17203 loss_visual: 0.0883 loss_lang: 0.1800 loss_fusion: 0.0763 loss: 0.3445 2022/10/05 09:04:22 - mmengine - INFO - Epoch(train) [4][10000/10520] lr: 1.0000e-04 eta: 1 day, 6:12:54 time: 0.6681 data_time: 0.0307 memory: 17203 loss_visual: 0.0974 loss_lang: 0.1841 loss_fusion: 0.0854 loss: 0.3669 2022/10/05 09:05:17 - mmengine - INFO - Epoch(train) [4][10100/10520] lr: 1.0000e-04 eta: 1 day, 6:11:12 time: 0.4719 data_time: 0.0238 memory: 17203 loss_visual: 0.0809 loss_lang: 0.1682 loss_fusion: 0.0703 loss: 0.3194 2022/10/05 09:06:12 - mmengine - INFO - Epoch(train) [4][10200/10520] lr: 1.0000e-04 eta: 1 day, 6:09:30 time: 0.3545 data_time: 0.0034 memory: 17203 loss_visual: 0.0999 loss_lang: 0.1873 loss_fusion: 0.0874 loss: 0.3746 2022/10/05 09:07:07 - mmengine - INFO - Epoch(train) [4][10300/10520] lr: 1.0000e-04 eta: 1 day, 6:07:47 time: 0.3684 data_time: 0.0031 memory: 17203 loss_visual: 0.0828 loss_lang: 0.1675 loss_fusion: 0.0704 loss: 0.3207 2022/10/05 09:08:02 - mmengine - INFO - Epoch(train) [4][10400/10520] lr: 1.0000e-04 eta: 1 day, 6:06:05 time: 0.3538 data_time: 0.0106 memory: 17203 loss_visual: 0.0950 loss_lang: 0.1789 loss_fusion: 0.0824 loss: 0.3564 2022/10/05 09:08:27 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 09:08:58 - mmengine - INFO - Epoch(train) [4][10500/10520] lr: 1.0000e-04 eta: 1 day, 6:04:29 time: 0.5302 data_time: 0.1064 memory: 17203 loss_visual: 0.0925 loss_lang: 0.1727 loss_fusion: 0.0807 loss: 0.3459 2022/10/05 09:09:06 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 09:09:06 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/10/05 09:09:22 - mmengine - INFO - Epoch(val) [4][100/959] eta: 0:00:39 time: 0.0463 data_time: 0.0017 memory: 17203 2022/10/05 09:09:26 - mmengine - INFO - Epoch(val) [4][200/959] eta: 0:00:35 time: 0.0463 data_time: 0.0013 memory: 734 2022/10/05 09:09:31 - mmengine - INFO - Epoch(val) [4][300/959] eta: 0:00:35 time: 0.0536 data_time: 0.0023 memory: 734 2022/10/05 09:09:36 - mmengine - INFO - Epoch(val) [4][400/959] eta: 0:00:27 time: 0.0498 data_time: 0.0021 memory: 734 2022/10/05 09:09:41 - mmengine - INFO - Epoch(val) [4][500/959] eta: 0:00:22 time: 0.0480 data_time: 0.0010 memory: 734 2022/10/05 09:09:46 - mmengine - INFO - Epoch(val) [4][600/959] eta: 0:00:18 time: 0.0515 data_time: 0.0048 memory: 734 2022/10/05 09:09:52 - mmengine - INFO - Epoch(val) [4][700/959] eta: 0:00:13 time: 0.0505 data_time: 0.0017 memory: 734 2022/10/05 09:09:55 - mmengine - INFO - Epoch(val) [4][800/959] eta: 0:00:03 time: 0.0215 data_time: 0.0006 memory: 734 2022/10/05 09:09:57 - mmengine - INFO - Epoch(val) [4][900/959] eta: 0:00:01 time: 0.0237 data_time: 0.0006 memory: 734 2022/10/05 09:09:59 - mmengine - INFO - Epoch(val) [4][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.8160 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9297 SVT/recog/word_acc_ignore_case_symbol: 0.9088 SVTP/recog/word_acc_ignore_case_symbol: 0.8295 IC13/recog/word_acc_ignore_case_symbol: 0.9034 IC15/recog/word_acc_ignore_case_symbol: 0.7468 2022/10/05 09:11:05 - mmengine - INFO - Epoch(train) [5][100/10520] lr: 1.0000e-04 eta: 1 day, 6:02:56 time: 0.8105 data_time: 0.1590 memory: 17203 loss_visual: 0.0888 loss_lang: 0.1721 loss_fusion: 0.0765 loss: 0.3374 2022/10/05 09:12:00 - mmengine - INFO - Epoch(train) [5][200/10520] lr: 1.0000e-04 eta: 1 day, 6:01:17 time: 0.9391 data_time: 0.1549 memory: 17203 loss_visual: 0.0873 loss_lang: 0.1709 loss_fusion: 0.0753 loss: 0.3335 2022/10/05 09:12:56 - mmengine - INFO - Epoch(train) [5][300/10520] lr: 1.0000e-04 eta: 1 day, 5:59:37 time: 0.6561 data_time: 0.0221 memory: 17203 loss_visual: 0.1025 loss_lang: 0.1858 loss_fusion: 0.0909 loss: 0.3792 2022/10/05 09:13:51 - mmengine - INFO - Epoch(train) [5][400/10520] lr: 1.0000e-04 eta: 1 day, 5:57:56 time: 0.4845 data_time: 0.0277 memory: 17203 loss_visual: 0.0942 loss_lang: 0.1782 loss_fusion: 0.0830 loss: 0.3554 2022/10/05 09:14:46 - mmengine - INFO - Epoch(train) [5][500/10520] lr: 1.0000e-04 eta: 1 day, 5:56:16 time: 0.4010 data_time: 0.0105 memory: 17203 loss_visual: 0.0879 loss_lang: 0.1707 loss_fusion: 0.0759 loss: 0.3344 2022/10/05 09:15:41 - mmengine - INFO - Epoch(train) [5][600/10520] lr: 1.0000e-04 eta: 1 day, 5:54:37 time: 0.3597 data_time: 0.0029 memory: 17203 loss_visual: 0.0902 loss_lang: 0.1762 loss_fusion: 0.0787 loss: 0.3452 2022/10/05 09:16:37 - mmengine - INFO - Epoch(train) [5][700/10520] lr: 1.0000e-04 eta: 1 day, 5:53:01 time: 0.3578 data_time: 0.0136 memory: 17203 loss_visual: 0.0953 loss_lang: 0.1812 loss_fusion: 0.0829 loss: 0.3595 2022/10/05 09:17:31 - mmengine - INFO - Epoch(train) [5][800/10520] lr: 1.0000e-04 eta: 1 day, 5:51:16 time: 0.3734 data_time: 0.0331 memory: 17203 loss_visual: 0.0911 loss_lang: 0.1712 loss_fusion: 0.0792 loss: 0.3415 2022/10/05 09:18:29 - mmengine - INFO - Epoch(train) [5][900/10520] lr: 1.0000e-04 eta: 1 day, 5:49:49 time: 0.7784 data_time: 0.1655 memory: 17203 loss_visual: 0.0891 loss_lang: 0.1729 loss_fusion: 0.0768 loss: 0.3387 2022/10/05 09:18:38 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 09:19:25 - mmengine - INFO - Epoch(train) [5][1000/10520] lr: 1.0000e-04 eta: 1 day, 5:48:13 time: 0.8899 data_time: 0.1505 memory: 17203 loss_visual: 0.0983 loss_lang: 0.1829 loss_fusion: 0.0868 loss: 0.3681 2022/10/05 09:20:21 - mmengine - INFO - Epoch(train) [5][1100/10520] lr: 1.0000e-04 eta: 1 day, 5:46:38 time: 0.6637 data_time: 0.0191 memory: 17203 loss_visual: 0.0849 loss_lang: 0.1661 loss_fusion: 0.0718 loss: 0.3227 2022/10/05 09:21:15 - mmengine - INFO - Epoch(train) [5][1200/10520] lr: 1.0000e-04 eta: 1 day, 5:44:55 time: 0.4562 data_time: 0.0528 memory: 17203 loss_visual: 0.0909 loss_lang: 0.1758 loss_fusion: 0.0804 loss: 0.3472 2022/10/05 09:22:10 - mmengine - INFO - Epoch(train) [5][1300/10520] lr: 1.0000e-04 eta: 1 day, 5:43:15 time: 0.4585 data_time: 0.0290 memory: 17203 loss_visual: 0.0927 loss_lang: 0.1738 loss_fusion: 0.0801 loss: 0.3466 2022/10/05 09:23:05 - mmengine - INFO - Epoch(train) [5][1400/10520] lr: 1.0000e-04 eta: 1 day, 5:41:34 time: 0.3616 data_time: 0.0032 memory: 17203 loss_visual: 0.0900 loss_lang: 0.1705 loss_fusion: 0.0786 loss: 0.3391 2022/10/05 09:24:00 - mmengine - INFO - Epoch(train) [5][1500/10520] lr: 1.0000e-04 eta: 1 day, 5:39:59 time: 0.3905 data_time: 0.0120 memory: 17203 loss_visual: 0.0887 loss_lang: 0.1780 loss_fusion: 0.0776 loss: 0.3443 2022/10/05 09:24:55 - mmengine - INFO - Epoch(train) [5][1600/10520] lr: 1.0000e-04 eta: 1 day, 5:38:17 time: 0.3521 data_time: 0.0120 memory: 17203 loss_visual: 0.0882 loss_lang: 0.1696 loss_fusion: 0.0783 loss: 0.3361 2022/10/05 09:25:55 - mmengine - INFO - Epoch(train) [5][1700/10520] lr: 1.0000e-04 eta: 1 day, 5:36:58 time: 0.8340 data_time: 0.1604 memory: 17203 loss_visual: 0.0865 loss_lang: 0.1691 loss_fusion: 0.0749 loss: 0.3306 2022/10/05 09:26:51 - mmengine - INFO - Epoch(train) [5][1800/10520] lr: 1.0000e-04 eta: 1 day, 5:35:23 time: 0.9268 data_time: 0.1476 memory: 17203 loss_visual: 0.0887 loss_lang: 0.1720 loss_fusion: 0.0776 loss: 0.3383 2022/10/05 09:27:47 - mmengine - INFO - Epoch(train) [5][1900/10520] lr: 1.0000e-04 eta: 1 day, 5:33:49 time: 0.6611 data_time: 0.0247 memory: 17203 loss_visual: 0.0921 loss_lang: 0.1771 loss_fusion: 0.0796 loss: 0.3488 2022/10/05 09:27:54 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 09:28:42 - mmengine - INFO - Epoch(train) [5][2000/10520] lr: 1.0000e-04 eta: 1 day, 5:32:13 time: 0.4801 data_time: 0.0499 memory: 17203 loss_visual: 0.0931 loss_lang: 0.1725 loss_fusion: 0.0807 loss: 0.3462 2022/10/05 09:29:37 - mmengine - INFO - Epoch(train) [5][2100/10520] lr: 1.0000e-04 eta: 1 day, 5:30:35 time: 0.3870 data_time: 0.0111 memory: 17203 loss_visual: 0.0930 loss_lang: 0.1733 loss_fusion: 0.0815 loss: 0.3478 2022/10/05 09:30:32 - mmengine - INFO - Epoch(train) [5][2200/10520] lr: 1.0000e-04 eta: 1 day, 5:28:58 time: 0.3955 data_time: 0.0042 memory: 17203 loss_visual: 0.0896 loss_lang: 0.1687 loss_fusion: 0.0772 loss: 0.3355 2022/10/05 09:31:27 - mmengine - INFO - Epoch(train) [5][2300/10520] lr: 1.0000e-04 eta: 1 day, 5:27:21 time: 0.3690 data_time: 0.0115 memory: 17203 loss_visual: 0.0870 loss_lang: 0.1706 loss_fusion: 0.0769 loss: 0.3345 2022/10/05 09:32:23 - mmengine - INFO - Epoch(train) [5][2400/10520] lr: 1.0000e-04 eta: 1 day, 5:25:46 time: 0.3755 data_time: 0.0352 memory: 17203 loss_visual: 0.0833 loss_lang: 0.1693 loss_fusion: 0.0720 loss: 0.3246 2022/10/05 09:33:22 - mmengine - INFO - Epoch(train) [5][2500/10520] lr: 1.0000e-04 eta: 1 day, 5:24:26 time: 0.8252 data_time: 0.1748 memory: 17203 loss_visual: 0.0875 loss_lang: 0.1645 loss_fusion: 0.0740 loss: 0.3259 2022/10/05 09:34:18 - mmengine - INFO - Epoch(train) [5][2600/10520] lr: 1.0000e-04 eta: 1 day, 5:22:53 time: 0.8991 data_time: 0.1521 memory: 17203 loss_visual: 0.0908 loss_lang: 0.1730 loss_fusion: 0.0801 loss: 0.3438 2022/10/05 09:35:14 - mmengine - INFO - Epoch(train) [5][2700/10520] lr: 1.0000e-04 eta: 1 day, 5:21:19 time: 0.6472 data_time: 0.0262 memory: 17203 loss_visual: 0.0855 loss_lang: 0.1649 loss_fusion: 0.0741 loss: 0.3244 2022/10/05 09:36:09 - mmengine - INFO - Epoch(train) [5][2800/10520] lr: 1.0000e-04 eta: 1 day, 5:19:43 time: 0.4538 data_time: 0.0331 memory: 17203 loss_visual: 0.0843 loss_lang: 0.1620 loss_fusion: 0.0725 loss: 0.3187 2022/10/05 09:37:04 - mmengine - INFO - Epoch(train) [5][2900/10520] lr: 1.0000e-04 eta: 1 day, 5:18:07 time: 0.3810 data_time: 0.0112 memory: 17203 loss_visual: 0.0901 loss_lang: 0.1779 loss_fusion: 0.0773 loss: 0.3454 2022/10/05 09:37:17 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 09:38:00 - mmengine - INFO - Epoch(train) [5][3000/10520] lr: 1.0000e-04 eta: 1 day, 5:16:33 time: 0.3596 data_time: 0.0037 memory: 17203 loss_visual: 0.0948 loss_lang: 0.1743 loss_fusion: 0.0840 loss: 0.3531 2022/10/05 09:38:55 - mmengine - INFO - Epoch(train) [5][3100/10520] lr: 1.0000e-04 eta: 1 day, 5:15:00 time: 0.3845 data_time: 0.0112 memory: 17203 loss_visual: 0.0861 loss_lang: 0.1641 loss_fusion: 0.0748 loss: 0.3249 2022/10/05 09:39:50 - mmengine - INFO - Epoch(train) [5][3200/10520] lr: 1.0000e-04 eta: 1 day, 5:13:23 time: 0.3814 data_time: 0.0112 memory: 17203 loss_visual: 0.0898 loss_lang: 0.1718 loss_fusion: 0.0788 loss: 0.3405 2022/10/05 09:40:49 - mmengine - INFO - Epoch(train) [5][3300/10520] lr: 1.0000e-04 eta: 1 day, 5:12:03 time: 0.8003 data_time: 0.1544 memory: 17203 loss_visual: 0.0922 loss_lang: 0.1698 loss_fusion: 0.0814 loss: 0.3433 2022/10/05 09:41:45 - mmengine - INFO - Epoch(train) [5][3400/10520] lr: 1.0000e-04 eta: 1 day, 5:10:32 time: 0.9363 data_time: 0.1435 memory: 17203 loss_visual: 0.0944 loss_lang: 0.1779 loss_fusion: 0.0838 loss: 0.3561 2022/10/05 09:42:42 - mmengine - INFO - Epoch(train) [5][3500/10520] lr: 1.0000e-04 eta: 1 day, 5:09:02 time: 0.7017 data_time: 0.0266 memory: 17203 loss_visual: 0.0764 loss_lang: 0.1592 loss_fusion: 0.0662 loss: 0.3018 2022/10/05 09:43:37 - mmengine - INFO - Epoch(train) [5][3600/10520] lr: 1.0000e-04 eta: 1 day, 5:07:28 time: 0.4396 data_time: 0.0332 memory: 17203 loss_visual: 0.0806 loss_lang: 0.1635 loss_fusion: 0.0690 loss: 0.3131 2022/10/05 09:44:33 - mmengine - INFO - Epoch(train) [5][3700/10520] lr: 1.0000e-04 eta: 1 day, 5:05:58 time: 0.4164 data_time: 0.0138 memory: 17203 loss_visual: 0.0874 loss_lang: 0.1687 loss_fusion: 0.0766 loss: 0.3328 2022/10/05 09:45:29 - mmengine - INFO - Epoch(train) [5][3800/10520] lr: 1.0000e-04 eta: 1 day, 5:04:26 time: 0.3708 data_time: 0.0033 memory: 17203 loss_visual: 0.0891 loss_lang: 0.1801 loss_fusion: 0.0794 loss: 0.3487 2022/10/05 09:46:25 - mmengine - INFO - Epoch(train) [5][3900/10520] lr: 1.0000e-04 eta: 1 day, 5:02:54 time: 0.3557 data_time: 0.0124 memory: 17203 loss_visual: 0.0857 loss_lang: 0.1664 loss_fusion: 0.0758 loss: 0.3279 2022/10/05 09:46:38 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 09:47:20 - mmengine - INFO - Epoch(train) [5][4000/10520] lr: 1.0000e-04 eta: 1 day, 5:01:21 time: 0.3750 data_time: 0.0117 memory: 17203 loss_visual: 0.0960 loss_lang: 0.1708 loss_fusion: 0.0842 loss: 0.3510 2022/10/05 09:48:20 - mmengine - INFO - Epoch(train) [5][4100/10520] lr: 1.0000e-04 eta: 1 day, 5:00:03 time: 0.7422 data_time: 0.1492 memory: 17203 loss_visual: 0.0925 loss_lang: 0.1666 loss_fusion: 0.0809 loss: 0.3399 2022/10/05 09:49:16 - mmengine - INFO - Epoch(train) [5][4200/10520] lr: 1.0000e-04 eta: 1 day, 4:58:35 time: 0.9359 data_time: 0.1474 memory: 17203 loss_visual: 0.0854 loss_lang: 0.1692 loss_fusion: 0.0744 loss: 0.3290 2022/10/05 09:50:13 - mmengine - INFO - Epoch(train) [5][4300/10520] lr: 1.0000e-04 eta: 1 day, 4:57:08 time: 0.6857 data_time: 0.0260 memory: 17203 loss_visual: 0.0863 loss_lang: 0.1604 loss_fusion: 0.0756 loss: 0.3223 2022/10/05 09:51:08 - mmengine - INFO - Epoch(train) [5][4400/10520] lr: 1.0000e-04 eta: 1 day, 4:55:33 time: 0.4393 data_time: 0.0353 memory: 17203 loss_visual: 0.1004 loss_lang: 0.1778 loss_fusion: 0.0886 loss: 0.3669 2022/10/05 09:52:03 - mmengine - INFO - Epoch(train) [5][4500/10520] lr: 1.0000e-04 eta: 1 day, 4:53:58 time: 0.3846 data_time: 0.0111 memory: 17203 loss_visual: 0.0840 loss_lang: 0.1624 loss_fusion: 0.0734 loss: 0.3199 2022/10/05 09:52:58 - mmengine - INFO - Epoch(train) [5][4600/10520] lr: 1.0000e-04 eta: 1 day, 4:52:25 time: 0.3610 data_time: 0.0031 memory: 17203 loss_visual: 0.0878 loss_lang: 0.1634 loss_fusion: 0.0770 loss: 0.3282 2022/10/05 09:53:53 - mmengine - INFO - Epoch(train) [5][4700/10520] lr: 1.0000e-04 eta: 1 day, 4:50:54 time: 0.3536 data_time: 0.0121 memory: 17203 loss_visual: 0.0926 loss_lang: 0.1732 loss_fusion: 0.0815 loss: 0.3472 2022/10/05 09:54:48 - mmengine - INFO - Epoch(train) [5][4800/10520] lr: 1.0000e-04 eta: 1 day, 4:49:20 time: 0.3583 data_time: 0.0102 memory: 17203 loss_visual: 0.0885 loss_lang: 0.1683 loss_fusion: 0.0754 loss: 0.3323 2022/10/05 09:55:47 - mmengine - INFO - Epoch(train) [5][4900/10520] lr: 1.0000e-04 eta: 1 day, 4:48:02 time: 0.8044 data_time: 0.1571 memory: 17203 loss_visual: 0.0900 loss_lang: 0.1650 loss_fusion: 0.0788 loss: 0.3339 2022/10/05 09:55:56 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 09:56:43 - mmengine - INFO - Epoch(train) [5][5000/10520] lr: 1.0000e-04 eta: 1 day, 4:46:31 time: 0.8960 data_time: 0.1519 memory: 17203 loss_visual: 0.1015 loss_lang: 0.1810 loss_fusion: 0.0905 loss: 0.3730 2022/10/05 09:57:38 - mmengine - INFO - Epoch(train) [5][5100/10520] lr: 1.0000e-04 eta: 1 day, 4:44:59 time: 0.6230 data_time: 0.0250 memory: 17203 loss_visual: 0.0832 loss_lang: 0.1633 loss_fusion: 0.0726 loss: 0.3191 2022/10/05 09:58:33 - mmengine - INFO - Epoch(train) [5][5200/10520] lr: 1.0000e-04 eta: 1 day, 4:43:26 time: 0.4654 data_time: 0.0322 memory: 17203 loss_visual: 0.0873 loss_lang: 0.1692 loss_fusion: 0.0762 loss: 0.3326 2022/10/05 09:59:28 - mmengine - INFO - Epoch(train) [5][5300/10520] lr: 1.0000e-04 eta: 1 day, 4:41:54 time: 0.3862 data_time: 0.0102 memory: 17203 loss_visual: 0.0819 loss_lang: 0.1589 loss_fusion: 0.0710 loss: 0.3117 2022/10/05 10:00:22 - mmengine - INFO - Epoch(train) [5][5400/10520] lr: 1.0000e-04 eta: 1 day, 4:40:19 time: 0.3593 data_time: 0.0034 memory: 17203 loss_visual: 0.0826 loss_lang: 0.1632 loss_fusion: 0.0725 loss: 0.3183 2022/10/05 10:01:17 - mmengine - INFO - Epoch(train) [5][5500/10520] lr: 1.0000e-04 eta: 1 day, 4:38:46 time: 0.3680 data_time: 0.0114 memory: 17203 loss_visual: 0.0830 loss_lang: 0.1640 loss_fusion: 0.0730 loss: 0.3199 2022/10/05 10:02:12 - mmengine - INFO - Epoch(train) [5][5600/10520] lr: 1.0000e-04 eta: 1 day, 4:37:15 time: 0.3537 data_time: 0.0110 memory: 17203 loss_visual: 0.0978 loss_lang: 0.1762 loss_fusion: 0.0848 loss: 0.3589 2022/10/05 10:03:11 - mmengine - INFO - Epoch(train) [5][5700/10520] lr: 1.0000e-04 eta: 1 day, 4:35:58 time: 0.8022 data_time: 0.1549 memory: 17203 loss_visual: 0.0944 loss_lang: 0.1738 loss_fusion: 0.0831 loss: 0.3512 2022/10/05 10:04:07 - mmengine - INFO - Epoch(train) [5][5800/10520] lr: 1.0000e-04 eta: 1 day, 4:34:29 time: 0.9239 data_time: 0.1700 memory: 17203 loss_visual: 0.0881 loss_lang: 0.1684 loss_fusion: 0.0762 loss: 0.3327 2022/10/05 10:05:03 - mmengine - INFO - Epoch(train) [5][5900/10520] lr: 1.0000e-04 eta: 1 day, 4:33:00 time: 0.6680 data_time: 0.0238 memory: 17203 loss_visual: 0.0834 loss_lang: 0.1667 loss_fusion: 0.0740 loss: 0.3240 2022/10/05 10:05:10 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 10:05:58 - mmengine - INFO - Epoch(train) [5][6000/10520] lr: 1.0000e-04 eta: 1 day, 4:31:30 time: 0.4956 data_time: 0.0313 memory: 17203 loss_visual: 0.0820 loss_lang: 0.1581 loss_fusion: 0.0705 loss: 0.3107 2022/10/05 10:06:53 - mmengine - INFO - Epoch(train) [5][6100/10520] lr: 1.0000e-04 eta: 1 day, 4:29:59 time: 0.3919 data_time: 0.0169 memory: 17203 loss_visual: 0.0971 loss_lang: 0.1781 loss_fusion: 0.0847 loss: 0.3599 2022/10/05 10:07:49 - mmengine - INFO - Epoch(train) [5][6200/10520] lr: 1.0000e-04 eta: 1 day, 4:28:30 time: 0.3600 data_time: 0.0031 memory: 17203 loss_visual: 0.0879 loss_lang: 0.1633 loss_fusion: 0.0771 loss: 0.3283 2022/10/05 10:08:45 - mmengine - INFO - Epoch(train) [5][6300/10520] lr: 1.0000e-04 eta: 1 day, 4:27:02 time: 0.3779 data_time: 0.0126 memory: 17203 loss_visual: 0.0846 loss_lang: 0.1611 loss_fusion: 0.0743 loss: 0.3201 2022/10/05 10:09:40 - mmengine - INFO - Epoch(train) [5][6400/10520] lr: 1.0000e-04 eta: 1 day, 4:25:32 time: 0.3595 data_time: 0.0117 memory: 17203 loss_visual: 0.0812 loss_lang: 0.1637 loss_fusion: 0.0709 loss: 0.3159 2022/10/05 10:10:39 - mmengine - INFO - Epoch(train) [5][6500/10520] lr: 1.0000e-04 eta: 1 day, 4:24:14 time: 0.7934 data_time: 0.1681 memory: 17203 loss_visual: 0.0878 loss_lang: 0.1707 loss_fusion: 0.0753 loss: 0.3338 2022/10/05 10:14:22 - mmengine - INFO - Epoch(train) [5][6600/10520] lr: 1.0000e-04 eta: 1 day, 4:32:02 time: 0.9214 data_time: 0.1551 memory: 17203 loss_visual: 0.0892 loss_lang: 0.1674 loss_fusion: 0.0773 loss: 0.3338 2022/10/05 10:15:42 - mmengine - INFO - Epoch(train) [5][6700/10520] lr: 1.0000e-04 eta: 1 day, 4:31:54 time: 0.6450 data_time: 0.0243 memory: 17203 loss_visual: 0.0849 loss_lang: 0.1681 loss_fusion: 0.0749 loss: 0.3279 2022/10/05 10:16:37 - mmengine - INFO - Epoch(train) [5][6800/10520] lr: 1.0000e-04 eta: 1 day, 4:30:22 time: 0.4676 data_time: 0.0332 memory: 17203 loss_visual: 0.0725 loss_lang: 0.1481 loss_fusion: 0.0605 loss: 0.2811 2022/10/05 10:17:32 - mmengine - INFO - Epoch(train) [5][6900/10520] lr: 1.0000e-04 eta: 1 day, 4:28:49 time: 0.4095 data_time: 0.0115 memory: 17203 loss_visual: 0.0927 loss_lang: 0.1694 loss_fusion: 0.0820 loss: 0.3441 2022/10/05 10:17:44 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 10:18:26 - mmengine - INFO - Epoch(train) [5][7000/10520] lr: 1.0000e-04 eta: 1 day, 4:27:17 time: 0.3904 data_time: 0.0211 memory: 17203 loss_visual: 0.0871 loss_lang: 0.1636 loss_fusion: 0.0765 loss: 0.3272 2022/10/05 10:19:22 - mmengine - INFO - Epoch(train) [5][7100/10520] lr: 1.0000e-04 eta: 1 day, 4:25:47 time: 0.3557 data_time: 0.0124 memory: 17203 loss_visual: 0.0852 loss_lang: 0.1609 loss_fusion: 0.0741 loss: 0.3201 2022/10/05 10:20:17 - mmengine - INFO - Epoch(train) [5][7200/10520] lr: 1.0000e-04 eta: 1 day, 4:24:17 time: 0.3682 data_time: 0.0127 memory: 17203 loss_visual: 0.0828 loss_lang: 0.1604 loss_fusion: 0.0723 loss: 0.3155 2022/10/05 10:21:16 - mmengine - INFO - Epoch(train) [5][7300/10520] lr: 1.0000e-04 eta: 1 day, 4:22:59 time: 0.7806 data_time: 0.1634 memory: 17203 loss_visual: 0.0822 loss_lang: 0.1588 loss_fusion: 0.0733 loss: 0.3143 2022/10/05 10:22:12 - mmengine - INFO - Epoch(train) [5][7400/10520] lr: 1.0000e-04 eta: 1 day, 4:21:32 time: 0.8946 data_time: 0.1357 memory: 17203 loss_visual: 0.0901 loss_lang: 0.1703 loss_fusion: 0.0798 loss: 0.3402 2022/10/05 10:23:08 - mmengine - INFO - Epoch(train) [5][7500/10520] lr: 1.0000e-04 eta: 1 day, 4:20:02 time: 0.7127 data_time: 0.0265 memory: 17203 loss_visual: 0.0919 loss_lang: 0.1690 loss_fusion: 0.0807 loss: 0.3416 2022/10/05 10:24:03 - mmengine - INFO - Epoch(train) [5][7600/10520] lr: 1.0000e-04 eta: 1 day, 4:18:32 time: 0.4355 data_time: 0.0329 memory: 17203 loss_visual: 0.0878 loss_lang: 0.1648 loss_fusion: 0.0757 loss: 0.3284 2022/10/05 10:24:58 - mmengine - INFO - Epoch(train) [5][7700/10520] lr: 1.0000e-04 eta: 1 day, 4:17:00 time: 0.3874 data_time: 0.0129 memory: 17203 loss_visual: 0.0825 loss_lang: 0.1617 loss_fusion: 0.0710 loss: 0.3152 2022/10/05 10:25:53 - mmengine - INFO - Epoch(train) [5][7800/10520] lr: 1.0000e-04 eta: 1 day, 4:15:32 time: 0.3771 data_time: 0.0030 memory: 17203 loss_visual: 0.0913 loss_lang: 0.1725 loss_fusion: 0.0809 loss: 0.3448 2022/10/05 10:26:48 - mmengine - INFO - Epoch(train) [5][7900/10520] lr: 1.0000e-04 eta: 1 day, 4:14:01 time: 0.3715 data_time: 0.0320 memory: 17203 loss_visual: 0.0846 loss_lang: 0.1580 loss_fusion: 0.0748 loss: 0.3174 2022/10/05 10:27:01 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 10:27:43 - mmengine - INFO - Epoch(train) [5][8000/10520] lr: 1.0000e-04 eta: 1 day, 4:12:29 time: 0.3570 data_time: 0.0135 memory: 17203 loss_visual: 0.0869 loss_lang: 0.1596 loss_fusion: 0.0756 loss: 0.3221 2022/10/05 10:28:41 - mmengine - INFO - Epoch(train) [5][8100/10520] lr: 1.0000e-04 eta: 1 day, 4:11:12 time: 0.7993 data_time: 0.1618 memory: 17203 loss_visual: 0.0805 loss_lang: 0.1594 loss_fusion: 0.0703 loss: 0.3101 2022/10/05 10:29:37 - mmengine - INFO - Epoch(train) [5][8200/10520] lr: 1.0000e-04 eta: 1 day, 4:09:44 time: 0.9028 data_time: 0.1557 memory: 17203 loss_visual: 0.0806 loss_lang: 0.1527 loss_fusion: 0.0691 loss: 0.3024 2022/10/05 10:30:34 - mmengine - INFO - Epoch(train) [5][8300/10520] lr: 1.0000e-04 eta: 1 day, 4:08:21 time: 0.6624 data_time: 0.0264 memory: 17203 loss_visual: 0.0742 loss_lang: 0.1531 loss_fusion: 0.0646 loss: 0.2919 2022/10/05 10:31:28 - mmengine - INFO - Epoch(train) [5][8400/10520] lr: 1.0000e-04 eta: 1 day, 4:06:49 time: 0.4708 data_time: 0.0337 memory: 17203 loss_visual: 0.0928 loss_lang: 0.1748 loss_fusion: 0.0817 loss: 0.3492 2022/10/05 10:32:23 - mmengine - INFO - Epoch(train) [5][8500/10520] lr: 1.0000e-04 eta: 1 day, 4:05:18 time: 0.3867 data_time: 0.0101 memory: 17203 loss_visual: 0.1001 loss_lang: 0.1790 loss_fusion: 0.0887 loss: 0.3678 2022/10/05 10:33:17 - mmengine - INFO - Epoch(train) [5][8600/10520] lr: 1.0000e-04 eta: 1 day, 4:03:47 time: 0.3850 data_time: 0.0030 memory: 17203 loss_visual: 0.0823 loss_lang: 0.1610 loss_fusion: 0.0723 loss: 0.3156 2022/10/05 10:34:12 - mmengine - INFO - Epoch(train) [5][8700/10520] lr: 1.0000e-04 eta: 1 day, 4:02:17 time: 0.3756 data_time: 0.0099 memory: 17203 loss_visual: 0.0851 loss_lang: 0.1623 loss_fusion: 0.0747 loss: 0.3221 2022/10/05 10:35:07 - mmengine - INFO - Epoch(train) [5][8800/10520] lr: 1.0000e-04 eta: 1 day, 4:00:48 time: 0.3605 data_time: 0.0103 memory: 17203 loss_visual: 0.0876 loss_lang: 0.1636 loss_fusion: 0.0761 loss: 0.3274 2022/10/05 10:36:05 - mmengine - INFO - Epoch(train) [5][8900/10520] lr: 1.0000e-04 eta: 1 day, 3:59:28 time: 0.7859 data_time: 0.1733 memory: 17203 loss_visual: 0.0944 loss_lang: 0.1658 loss_fusion: 0.0830 loss: 0.3433 2022/10/05 10:36:14 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 10:37:00 - mmengine - INFO - Epoch(train) [5][9000/10520] lr: 1.0000e-04 eta: 1 day, 3:58:00 time: 0.8683 data_time: 0.1359 memory: 17203 loss_visual: 0.0853 loss_lang: 0.1603 loss_fusion: 0.0732 loss: 0.3188 2022/10/05 10:37:56 - mmengine - INFO - Epoch(train) [5][9100/10520] lr: 1.0000e-04 eta: 1 day, 3:56:34 time: 0.7012 data_time: 0.0237 memory: 17203 loss_visual: 0.0847 loss_lang: 0.1615 loss_fusion: 0.0721 loss: 0.3183 2022/10/05 10:38:51 - mmengine - INFO - Epoch(train) [5][9200/10520] lr: 1.0000e-04 eta: 1 day, 3:55:04 time: 0.4375 data_time: 0.0324 memory: 17203 loss_visual: 0.0820 loss_lang: 0.1605 loss_fusion: 0.0717 loss: 0.3142 2022/10/05 10:39:46 - mmengine - INFO - Epoch(train) [5][9300/10520] lr: 1.0000e-04 eta: 1 day, 3:53:37 time: 0.3872 data_time: 0.0112 memory: 17203 loss_visual: 0.0873 loss_lang: 0.1657 loss_fusion: 0.0766 loss: 0.3296 2022/10/05 10:40:41 - mmengine - INFO - Epoch(train) [5][9400/10520] lr: 1.0000e-04 eta: 1 day, 3:52:07 time: 0.3595 data_time: 0.0029 memory: 17203 loss_visual: 0.0797 loss_lang: 0.1532 loss_fusion: 0.0695 loss: 0.3024 2022/10/05 10:41:35 - mmengine - INFO - Epoch(train) [5][9500/10520] lr: 1.0000e-04 eta: 1 day, 3:50:35 time: 0.3773 data_time: 0.0353 memory: 17203 loss_visual: 0.0884 loss_lang: 0.1610 loss_fusion: 0.0762 loss: 0.3256 2022/10/05 10:42:29 - mmengine - INFO - Epoch(train) [5][9600/10520] lr: 1.0000e-04 eta: 1 day, 3:49:04 time: 0.3655 data_time: 0.0172 memory: 17203 loss_visual: 0.0740 loss_lang: 0.1511 loss_fusion: 0.0648 loss: 0.2899 2022/10/05 10:43:29 - mmengine - INFO - Epoch(train) [5][9700/10520] lr: 1.0000e-04 eta: 1 day, 3:47:53 time: 0.8380 data_time: 0.1587 memory: 17203 loss_visual: 0.0853 loss_lang: 0.1619 loss_fusion: 0.0732 loss: 0.3204 2022/10/05 10:45:55 - mmengine - INFO - Epoch(train) [5][9800/10520] lr: 1.0000e-04 eta: 1 day, 3:51:02 time: 4.0287 data_time: 0.5125 memory: 17203 loss_visual: 0.0868 loss_lang: 0.1630 loss_fusion: 0.0756 loss: 0.3254 2022/10/05 10:48:24 - mmengine - INFO - Epoch(train) [5][9900/10520] lr: 1.0000e-04 eta: 1 day, 3:54:21 time: 0.6600 data_time: 0.0280 memory: 17203 loss_visual: 0.0796 loss_lang: 0.1516 loss_fusion: 0.0689 loss: 0.3001 2022/10/05 10:48:31 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 10:49:19 - mmengine - INFO - Epoch(train) [5][10000/10520] lr: 1.0000e-04 eta: 1 day, 3:52:53 time: 0.4789 data_time: 0.0361 memory: 17203 loss_visual: 0.0827 loss_lang: 0.1604 loss_fusion: 0.0720 loss: 0.3151 2022/10/05 10:50:15 - mmengine - INFO - Epoch(train) [5][10100/10520] lr: 1.0000e-04 eta: 1 day, 3:51:26 time: 0.4351 data_time: 0.0100 memory: 17203 loss_visual: 0.0792 loss_lang: 0.1467 loss_fusion: 0.0667 loss: 0.2927 2022/10/05 10:51:10 - mmengine - INFO - Epoch(train) [5][10200/10520] lr: 1.0000e-04 eta: 1 day, 3:49:56 time: 0.3687 data_time: 0.0031 memory: 17203 loss_visual: 0.0803 loss_lang: 0.1551 loss_fusion: 0.0683 loss: 0.3037 2022/10/05 10:52:05 - mmengine - INFO - Epoch(train) [5][10300/10520] lr: 1.0000e-04 eta: 1 day, 3:48:28 time: 0.3541 data_time: 0.0114 memory: 17203 loss_visual: 0.0806 loss_lang: 0.1575 loss_fusion: 0.0693 loss: 0.3073 2022/10/05 10:53:00 - mmengine - INFO - Epoch(train) [5][10400/10520] lr: 1.0000e-04 eta: 1 day, 3:47:01 time: 0.3860 data_time: 0.0408 memory: 17203 loss_visual: 0.0880 loss_lang: 0.1651 loss_fusion: 0.0772 loss: 0.3302 2022/10/05 10:53:56 - mmengine - INFO - Epoch(train) [5][10500/10520] lr: 1.0000e-04 eta: 1 day, 3:45:34 time: 0.5901 data_time: 0.0969 memory: 17203 loss_visual: 0.0814 loss_lang: 0.1550 loss_fusion: 0.0700 loss: 0.3065 2022/10/05 10:54:04 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 10:54:04 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/10/05 10:54:18 - mmengine - INFO - Epoch(val) [5][100/959] eta: 0:00:47 time: 0.0551 data_time: 0.0020 memory: 17203 2022/10/05 10:54:23 - mmengine - INFO - Epoch(val) [5][200/959] eta: 0:00:38 time: 0.0502 data_time: 0.0030 memory: 734 2022/10/05 10:54:28 - mmengine - INFO - Epoch(val) [5][300/959] eta: 0:00:31 time: 0.0474 data_time: 0.0022 memory: 734 2022/10/05 10:54:33 - mmengine - INFO - Epoch(val) [5][400/959] eta: 0:00:26 time: 0.0481 data_time: 0.0010 memory: 734 2022/10/05 10:54:38 - mmengine - INFO - Epoch(val) [5][500/959] eta: 0:00:24 time: 0.0540 data_time: 0.0018 memory: 734 2022/10/05 10:54:42 - mmengine - INFO - Epoch(val) [5][600/959] eta: 0:00:17 time: 0.0494 data_time: 0.0013 memory: 734 2022/10/05 10:54:48 - mmengine - INFO - Epoch(val) [5][700/959] eta: 0:00:14 time: 0.0560 data_time: 0.0066 memory: 734 2022/10/05 10:54:52 - mmengine - INFO - Epoch(val) [5][800/959] eta: 0:00:04 time: 0.0277 data_time: 0.0009 memory: 734 2022/10/05 10:54:54 - mmengine - INFO - Epoch(val) [5][900/959] eta: 0:00:01 time: 0.0213 data_time: 0.0006 memory: 734 2022/10/05 10:54:56 - mmengine - INFO - Epoch(val) [5][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.8090 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9323 SVT/recog/word_acc_ignore_case_symbol: 0.9289 SVTP/recog/word_acc_ignore_case_symbol: 0.8419 IC13/recog/word_acc_ignore_case_symbol: 0.9261 IC15/recog/word_acc_ignore_case_symbol: 0.7723 2022/10/05 10:55:59 - mmengine - INFO - Epoch(train) [6][100/10520] lr: 1.0000e-04 eta: 1 day, 3:44:03 time: 0.7645 data_time: 0.1792 memory: 17203 loss_visual: 0.0829 loss_lang: 0.1573 loss_fusion: 0.0732 loss: 0.3134 2022/10/05 10:56:54 - mmengine - INFO - Epoch(train) [6][200/10520] lr: 1.0000e-04 eta: 1 day, 3:42:36 time: 0.8833 data_time: 0.1698 memory: 17203 loss_visual: 0.0850 loss_lang: 0.1621 loss_fusion: 0.0738 loss: 0.3210 2022/10/05 10:58:08 - mmengine - INFO - Epoch(train) [6][300/10520] lr: 1.0000e-04 eta: 1 day, 3:42:05 time: 0.7613 data_time: 0.1552 memory: 17203 loss_visual: 0.0938 loss_lang: 0.1717 loss_fusion: 0.0824 loss: 0.3478 2022/10/05 10:59:13 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 10:59:13 - mmengine - INFO - Epoch(train) [6][400/10520] lr: 1.0000e-04 eta: 1 day, 3:41:06 time: 0.5347 data_time: 0.0286 memory: 17203 loss_visual: 0.0816 loss_lang: 0.1590 loss_fusion: 0.0715 loss: 0.3121 2022/10/05 11:00:09 - mmengine - INFO - Epoch(train) [6][500/10520] lr: 1.0000e-04 eta: 1 day, 3:39:40 time: 0.4941 data_time: 0.0333 memory: 17203 loss_visual: 0.0863 loss_lang: 0.1648 loss_fusion: 0.0753 loss: 0.3263 2022/10/05 11:01:04 - mmengine - INFO - Epoch(train) [6][600/10520] lr: 1.0000e-04 eta: 1 day, 3:38:12 time: 0.4129 data_time: 0.0118 memory: 17203 loss_visual: 0.0886 loss_lang: 0.1664 loss_fusion: 0.0785 loss: 0.3334 2022/10/05 11:01:57 - mmengine - INFO - Epoch(train) [6][700/10520] lr: 1.0000e-04 eta: 1 day, 3:36:41 time: 0.3505 data_time: 0.0106 memory: 17203 loss_visual: 0.0894 loss_lang: 0.1610 loss_fusion: 0.0785 loss: 0.3289 2022/10/05 11:02:51 - mmengine - INFO - Epoch(train) [6][800/10520] lr: 1.0000e-04 eta: 1 day, 3:35:11 time: 0.3481 data_time: 0.0031 memory: 17203 loss_visual: 0.0943 loss_lang: 0.1666 loss_fusion: 0.0822 loss: 0.3431 2022/10/05 11:03:50 - mmengine - INFO - Epoch(train) [6][900/10520] lr: 1.0000e-04 eta: 1 day, 3:33:52 time: 0.7827 data_time: 0.1779 memory: 17203 loss_visual: 0.0822 loss_lang: 0.1584 loss_fusion: 0.0704 loss: 0.3110 2022/10/05 11:04:46 - mmengine - INFO - Epoch(train) [6][1000/10520] lr: 1.0000e-04 eta: 1 day, 3:32:28 time: 0.9215 data_time: 0.1935 memory: 17203 loss_visual: 0.0796 loss_lang: 0.1476 loss_fusion: 0.0676 loss: 0.2948 2022/10/05 11:05:42 - mmengine - INFO - Epoch(train) [6][1100/10520] lr: 1.0000e-04 eta: 1 day, 3:31:04 time: 0.8073 data_time: 0.1543 memory: 17203 loss_visual: 0.0872 loss_lang: 0.1687 loss_fusion: 0.0770 loss: 0.3329 2022/10/05 11:06:41 - mmengine - INFO - Epoch(train) [6][1200/10520] lr: 1.0000e-04 eta: 1 day, 3:29:48 time: 0.5372 data_time: 0.0176 memory: 17203 loss_visual: 0.0839 loss_lang: 0.1627 loss_fusion: 0.0745 loss: 0.3211 2022/10/05 11:11:11 - mmengine - INFO - Epoch(train) [6][1300/10520] lr: 1.0000e-04 eta: 1 day, 3:38:47 time: 0.4388 data_time: 0.0172 memory: 17203 loss_visual: 0.0786 loss_lang: 0.1543 loss_fusion: 0.0683 loss: 0.3013 2022/10/05 11:12:07 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 11:12:07 - mmengine - INFO - Epoch(train) [6][1400/10520] lr: 1.0000e-04 eta: 1 day, 3:37:20 time: 0.3908 data_time: 0.0110 memory: 17203 loss_visual: 0.0857 loss_lang: 0.1609 loss_fusion: 0.0763 loss: 0.3228 2022/10/05 11:13:01 - mmengine - INFO - Epoch(train) [6][1500/10520] lr: 1.0000e-04 eta: 1 day, 3:35:49 time: 0.3533 data_time: 0.0130 memory: 17203 loss_visual: 0.0812 loss_lang: 0.1576 loss_fusion: 0.0705 loss: 0.3092 2022/10/05 11:13:56 - mmengine - INFO - Epoch(train) [6][1600/10520] lr: 1.0000e-04 eta: 1 day, 3:34:20 time: 0.3458 data_time: 0.0032 memory: 17203 loss_visual: 0.0873 loss_lang: 0.1647 loss_fusion: 0.0775 loss: 0.3296 2022/10/05 11:14:54 - mmengine - INFO - Epoch(train) [6][1700/10520] lr: 1.0000e-04 eta: 1 day, 3:33:02 time: 0.7852 data_time: 0.1602 memory: 17203 loss_visual: 0.0831 loss_lang: 0.1600 loss_fusion: 0.0724 loss: 0.3155 2022/10/05 11:15:50 - mmengine - INFO - Epoch(train) [6][1800/10520] lr: 1.0000e-04 eta: 1 day, 3:31:35 time: 0.8745 data_time: 0.1681 memory: 17203 loss_visual: 0.0804 loss_lang: 0.1569 loss_fusion: 0.0696 loss: 0.3069 2022/10/05 11:16:45 - mmengine - INFO - Epoch(train) [6][1900/10520] lr: 1.0000e-04 eta: 1 day, 3:30:09 time: 0.8301 data_time: 0.1735 memory: 17203 loss_visual: 0.0783 loss_lang: 0.1536 loss_fusion: 0.0677 loss: 0.2996 2022/10/05 11:17:39 - mmengine - INFO - Epoch(train) [6][2000/10520] lr: 1.0000e-04 eta: 1 day, 3:28:38 time: 0.5163 data_time: 0.0340 memory: 17203 loss_visual: 0.0739 loss_lang: 0.1510 loss_fusion: 0.0646 loss: 0.2895 2022/10/05 11:18:35 - mmengine - INFO - Epoch(train) [6][2100/10520] lr: 1.0000e-04 eta: 1 day, 3:27:12 time: 0.4301 data_time: 0.0167 memory: 17203 loss_visual: 0.0836 loss_lang: 0.1559 loss_fusion: 0.0725 loss: 0.3120 2022/10/05 11:19:30 - mmengine - INFO - Epoch(train) [6][2200/10520] lr: 1.0000e-04 eta: 1 day, 3:25:45 time: 0.3834 data_time: 0.0125 memory: 17203 loss_visual: 0.0786 loss_lang: 0.1573 loss_fusion: 0.0700 loss: 0.3060 2022/10/05 11:20:24 - mmengine - INFO - Epoch(train) [6][2300/10520] lr: 1.0000e-04 eta: 1 day, 3:24:16 time: 0.3546 data_time: 0.0127 memory: 17203 loss_visual: 0.0744 loss_lang: 0.1519 loss_fusion: 0.0654 loss: 0.2917 2022/10/05 11:21:19 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 11:21:19 - mmengine - INFO - Epoch(train) [6][2400/10520] lr: 1.0000e-04 eta: 1 day, 3:22:48 time: 0.3423 data_time: 0.0032 memory: 17203 loss_visual: 0.0806 loss_lang: 0.1518 loss_fusion: 0.0690 loss: 0.3013 2022/10/05 11:22:19 - mmengine - INFO - Epoch(train) [6][2500/10520] lr: 1.0000e-04 eta: 1 day, 3:21:34 time: 0.7896 data_time: 0.1614 memory: 17203 loss_visual: 0.0955 loss_lang: 0.1712 loss_fusion: 0.0843 loss: 0.3510 2022/10/05 11:23:15 - mmengine - INFO - Epoch(train) [6][2600/10520] lr: 1.0000e-04 eta: 1 day, 3:20:11 time: 0.9482 data_time: 0.1762 memory: 17203 loss_visual: 0.0797 loss_lang: 0.1566 loss_fusion: 0.0690 loss: 0.3052 2022/10/05 11:24:11 - mmengine - INFO - Epoch(train) [6][2700/10520] lr: 1.0000e-04 eta: 1 day, 3:18:47 time: 0.7932 data_time: 0.1863 memory: 17203 loss_visual: 0.0831 loss_lang: 0.1598 loss_fusion: 0.0725 loss: 0.3154 2022/10/05 11:25:07 - mmengine - INFO - Epoch(train) [6][2800/10520] lr: 1.0000e-04 eta: 1 day, 3:17:22 time: 0.4914 data_time: 0.0171 memory: 17203 loss_visual: 0.0904 loss_lang: 0.1613 loss_fusion: 0.0791 loss: 0.3308 2022/10/05 11:26:02 - mmengine - INFO - Epoch(train) [6][2900/10520] lr: 1.0000e-04 eta: 1 day, 3:15:55 time: 0.4390 data_time: 0.0190 memory: 17203 loss_visual: 0.0844 loss_lang: 0.1574 loss_fusion: 0.0752 loss: 0.3170 2022/10/05 11:26:56 - mmengine - INFO - Epoch(train) [6][3000/10520] lr: 1.0000e-04 eta: 1 day, 3:14:26 time: 0.4129 data_time: 0.0133 memory: 17203 loss_visual: 0.0767 loss_lang: 0.1544 loss_fusion: 0.0647 loss: 0.2958 2022/10/05 11:27:52 - mmengine - INFO - Epoch(train) [6][3100/10520] lr: 1.0000e-04 eta: 1 day, 3:13:01 time: 0.4121 data_time: 0.0102 memory: 17203 loss_visual: 0.0741 loss_lang: 0.1536 loss_fusion: 0.0640 loss: 0.2918 2022/10/05 11:28:46 - mmengine - INFO - Epoch(train) [6][3200/10520] lr: 1.0000e-04 eta: 1 day, 3:11:33 time: 0.3440 data_time: 0.0035 memory: 17203 loss_visual: 0.0867 loss_lang: 0.1613 loss_fusion: 0.0749 loss: 0.3229 2022/10/05 11:29:46 - mmengine - INFO - Epoch(train) [6][3300/10520] lr: 1.0000e-04 eta: 1 day, 3:10:20 time: 0.8025 data_time: 0.1731 memory: 17203 loss_visual: 0.0859 loss_lang: 0.1616 loss_fusion: 0.0742 loss: 0.3217 2022/10/05 11:30:42 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 11:30:42 - mmengine - INFO - Epoch(train) [6][3400/10520] lr: 1.0000e-04 eta: 1 day, 3:08:57 time: 0.9341 data_time: 0.1695 memory: 17203 loss_visual: 0.0829 loss_lang: 0.1598 loss_fusion: 0.0728 loss: 0.3155 2022/10/05 11:31:37 - mmengine - INFO - Epoch(train) [6][3500/10520] lr: 1.0000e-04 eta: 1 day, 3:07:31 time: 0.7499 data_time: 0.1350 memory: 17203 loss_visual: 0.0862 loss_lang: 0.1649 loss_fusion: 0.0753 loss: 0.3263 2022/10/05 11:32:32 - mmengine - INFO - Epoch(train) [6][3600/10520] lr: 1.0000e-04 eta: 1 day, 3:06:06 time: 0.5113 data_time: 0.0158 memory: 17203 loss_visual: 0.0738 loss_lang: 0.1458 loss_fusion: 0.0625 loss: 0.2822 2022/10/05 11:33:27 - mmengine - INFO - Epoch(train) [6][3700/10520] lr: 1.0000e-04 eta: 1 day, 3:04:39 time: 0.4624 data_time: 0.0180 memory: 17203 loss_visual: 0.0849 loss_lang: 0.1656 loss_fusion: 0.0752 loss: 0.3257 2022/10/05 11:34:23 - mmengine - INFO - Epoch(train) [6][3800/10520] lr: 1.0000e-04 eta: 1 day, 3:03:16 time: 0.4328 data_time: 0.0205 memory: 17203 loss_visual: 0.0818 loss_lang: 0.1584 loss_fusion: 0.0722 loss: 0.3125 2022/10/05 11:35:18 - mmengine - INFO - Epoch(train) [6][3900/10520] lr: 1.0000e-04 eta: 1 day, 3:01:51 time: 0.3501 data_time: 0.0107 memory: 17203 loss_visual: 0.0793 loss_lang: 0.1556 loss_fusion: 0.0696 loss: 0.3044 2022/10/05 11:36:13 - mmengine - INFO - Epoch(train) [6][4000/10520] lr: 1.0000e-04 eta: 1 day, 3:00:25 time: 0.3679 data_time: 0.0032 memory: 17203 loss_visual: 0.0735 loss_lang: 0.1493 loss_fusion: 0.0640 loss: 0.2867 2022/10/05 11:37:13 - mmengine - INFO - Epoch(train) [6][4100/10520] lr: 1.0000e-04 eta: 1 day, 2:59:11 time: 0.8322 data_time: 0.2144 memory: 17203 loss_visual: 0.0765 loss_lang: 0.1555 loss_fusion: 0.0675 loss: 0.2995 2022/10/05 11:38:07 - mmengine - INFO - Epoch(train) [6][4200/10520] lr: 1.0000e-04 eta: 1 day, 2:57:44 time: 0.8701 data_time: 0.1678 memory: 17203 loss_visual: 0.0844 loss_lang: 0.1555 loss_fusion: 0.0735 loss: 0.3134 2022/10/05 11:39:01 - mmengine - INFO - Epoch(train) [6][4300/10520] lr: 1.0000e-04 eta: 1 day, 2:56:17 time: 0.7378 data_time: 0.1444 memory: 17203 loss_visual: 0.0933 loss_lang: 0.1696 loss_fusion: 0.0823 loss: 0.3452 2022/10/05 11:39:57 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 11:39:57 - mmengine - INFO - Epoch(train) [6][4400/10520] lr: 1.0000e-04 eta: 1 day, 2:54:54 time: 0.5523 data_time: 0.0346 memory: 17203 loss_visual: 0.0771 loss_lang: 0.1501 loss_fusion: 0.0663 loss: 0.2935 2022/10/05 11:40:52 - mmengine - INFO - Epoch(train) [6][4500/10520] lr: 1.0000e-04 eta: 1 day, 2:53:27 time: 0.4365 data_time: 0.0166 memory: 17203 loss_visual: 0.0851 loss_lang: 0.1542 loss_fusion: 0.0734 loss: 0.3127 2022/10/05 11:41:47 - mmengine - INFO - Epoch(train) [6][4600/10520] lr: 1.0000e-04 eta: 1 day, 2:52:03 time: 0.4246 data_time: 0.0111 memory: 17203 loss_visual: 0.0788 loss_lang: 0.1495 loss_fusion: 0.0686 loss: 0.2969 2022/10/05 11:42:41 - mmengine - INFO - Epoch(train) [6][4700/10520] lr: 1.0000e-04 eta: 1 day, 2:50:37 time: 0.3530 data_time: 0.0125 memory: 17203 loss_visual: 0.0812 loss_lang: 0.1527 loss_fusion: 0.0690 loss: 0.3029 2022/10/05 11:43:36 - mmengine - INFO - Epoch(train) [6][4800/10520] lr: 1.0000e-04 eta: 1 day, 2:49:12 time: 0.3692 data_time: 0.0031 memory: 17203 loss_visual: 0.0804 loss_lang: 0.1519 loss_fusion: 0.0698 loss: 0.3021 2022/10/05 11:44:35 - mmengine - INFO - Epoch(train) [6][4900/10520] lr: 1.0000e-04 eta: 1 day, 2:47:57 time: 0.7875 data_time: 0.1805 memory: 17203 loss_visual: 0.0751 loss_lang: 0.1476 loss_fusion: 0.0651 loss: 0.2878 2022/10/05 11:45:30 - mmengine - INFO - Epoch(train) [6][5000/10520] lr: 1.0000e-04 eta: 1 day, 2:46:33 time: 0.8457 data_time: 0.1810 memory: 17203 loss_visual: 0.0815 loss_lang: 0.1570 loss_fusion: 0.0712 loss: 0.3097 2022/10/05 11:46:26 - mmengine - INFO - Epoch(train) [6][5100/10520] lr: 1.0000e-04 eta: 1 day, 2:45:12 time: 0.7437 data_time: 0.1728 memory: 17203 loss_visual: 0.0826 loss_lang: 0.1561 loss_fusion: 0.0721 loss: 0.3109 2022/10/05 11:47:21 - mmengine - INFO - Epoch(train) [6][5200/10520] lr: 1.0000e-04 eta: 1 day, 2:43:46 time: 0.4650 data_time: 0.0167 memory: 17203 loss_visual: 0.0775 loss_lang: 0.1535 loss_fusion: 0.0684 loss: 0.2994 2022/10/05 11:48:15 - mmengine - INFO - Epoch(train) [6][5300/10520] lr: 1.0000e-04 eta: 1 day, 2:42:20 time: 0.4865 data_time: 0.0155 memory: 17203 loss_visual: 0.0847 loss_lang: 0.1592 loss_fusion: 0.0740 loss: 0.3178 2022/10/05 11:49:09 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 11:49:09 - mmengine - INFO - Epoch(train) [6][5400/10520] lr: 1.0000e-04 eta: 1 day, 2:40:53 time: 0.3860 data_time: 0.0120 memory: 17203 loss_visual: 0.0899 loss_lang: 0.1601 loss_fusion: 0.0776 loss: 0.3275 2022/10/05 11:50:04 - mmengine - INFO - Epoch(train) [6][5500/10520] lr: 1.0000e-04 eta: 1 day, 2:39:28 time: 0.3526 data_time: 0.0138 memory: 17203 loss_visual: 0.0790 loss_lang: 0.1549 loss_fusion: 0.0699 loss: 0.3038 2022/10/05 11:50:58 - mmengine - INFO - Epoch(train) [6][5600/10520] lr: 1.0000e-04 eta: 1 day, 2:38:01 time: 0.3470 data_time: 0.0031 memory: 17203 loss_visual: 0.0824 loss_lang: 0.1512 loss_fusion: 0.0726 loss: 0.3062 2022/10/05 11:51:57 - mmengine - INFO - Epoch(train) [6][5700/10520] lr: 1.0000e-04 eta: 1 day, 2:36:47 time: 0.7635 data_time: 0.1721 memory: 17203 loss_visual: 0.0763 loss_lang: 0.1514 loss_fusion: 0.0668 loss: 0.2945 2022/10/05 11:52:52 - mmengine - INFO - Epoch(train) [6][5800/10520] lr: 1.0000e-04 eta: 1 day, 2:35:25 time: 0.8986 data_time: 0.1755 memory: 17203 loss_visual: 0.0889 loss_lang: 0.1636 loss_fusion: 0.0781 loss: 0.3306 2022/10/05 11:53:48 - mmengine - INFO - Epoch(train) [6][5900/10520] lr: 1.0000e-04 eta: 1 day, 2:34:02 time: 0.7965 data_time: 0.1491 memory: 17203 loss_visual: 0.0804 loss_lang: 0.1525 loss_fusion: 0.0703 loss: 0.3031 2022/10/05 11:54:43 - mmengine - INFO - Epoch(train) [6][6000/10520] lr: 1.0000e-04 eta: 1 day, 2:32:40 time: 0.4849 data_time: 0.0147 memory: 17203 loss_visual: 0.0776 loss_lang: 0.1521 loss_fusion: 0.0679 loss: 0.2976 2022/10/05 11:55:38 - mmengine - INFO - Epoch(train) [6][6100/10520] lr: 1.0000e-04 eta: 1 day, 2:31:15 time: 0.4321 data_time: 0.0157 memory: 17203 loss_visual: 0.0801 loss_lang: 0.1489 loss_fusion: 0.0691 loss: 0.2980 2022/10/05 11:56:32 - mmengine - INFO - Epoch(train) [6][6200/10520] lr: 1.0000e-04 eta: 1 day, 2:29:50 time: 0.4128 data_time: 0.0113 memory: 17203 loss_visual: 0.0904 loss_lang: 0.1643 loss_fusion: 0.0799 loss: 0.3346 2022/10/05 11:57:27 - mmengine - INFO - Epoch(train) [6][6300/10520] lr: 1.0000e-04 eta: 1 day, 2:28:27 time: 0.3795 data_time: 0.0158 memory: 17203 loss_visual: 0.0757 loss_lang: 0.1447 loss_fusion: 0.0657 loss: 0.2861 2022/10/05 11:58:22 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 11:58:22 - mmengine - INFO - Epoch(train) [6][6400/10520] lr: 1.0000e-04 eta: 1 day, 2:27:04 time: 0.3430 data_time: 0.0031 memory: 17203 loss_visual: 0.0775 loss_lang: 0.1531 loss_fusion: 0.0678 loss: 0.2984 2022/10/05 11:59:20 - mmengine - INFO - Epoch(train) [6][6500/10520] lr: 1.0000e-04 eta: 1 day, 2:25:49 time: 0.7447 data_time: 0.1735 memory: 17203 loss_visual: 0.0742 loss_lang: 0.1462 loss_fusion: 0.0645 loss: 0.2849 2022/10/05 12:00:19 - mmengine - INFO - Epoch(train) [6][6600/10520] lr: 1.0000e-04 eta: 1 day, 2:24:34 time: 0.8885 data_time: 0.1706 memory: 17203 loss_visual: 0.0743 loss_lang: 0.1428 loss_fusion: 0.0634 loss: 0.2805 2022/10/05 12:01:15 - mmengine - INFO - Epoch(train) [6][6700/10520] lr: 1.0000e-04 eta: 1 day, 2:23:13 time: 0.7645 data_time: 0.1478 memory: 17203 loss_visual: 0.0813 loss_lang: 0.1579 loss_fusion: 0.0720 loss: 0.3112 2022/10/05 12:02:10 - mmengine - INFO - Epoch(train) [6][6800/10520] lr: 1.0000e-04 eta: 1 day, 2:21:51 time: 0.4998 data_time: 0.0157 memory: 17203 loss_visual: 0.0887 loss_lang: 0.1603 loss_fusion: 0.0800 loss: 0.3291 2022/10/05 12:03:04 - mmengine - INFO - Epoch(train) [6][6900/10520] lr: 1.0000e-04 eta: 1 day, 2:20:26 time: 0.5005 data_time: 0.0170 memory: 17203 loss_visual: 0.0719 loss_lang: 0.1467 loss_fusion: 0.0618 loss: 0.2804 2022/10/05 12:03:59 - mmengine - INFO - Epoch(train) [6][7000/10520] lr: 1.0000e-04 eta: 1 day, 2:19:03 time: 0.3869 data_time: 0.0114 memory: 17203 loss_visual: 0.0781 loss_lang: 0.1487 loss_fusion: 0.0681 loss: 0.2949 2022/10/05 12:04:54 - mmengine - INFO - Epoch(train) [6][7100/10520] lr: 1.0000e-04 eta: 1 day, 2:17:40 time: 0.3539 data_time: 0.0123 memory: 17203 loss_visual: 0.0802 loss_lang: 0.1542 loss_fusion: 0.0700 loss: 0.3043 2022/10/05 12:05:48 - mmengine - INFO - Epoch(train) [6][7200/10520] lr: 1.0000e-04 eta: 1 day, 2:16:16 time: 0.3522 data_time: 0.0046 memory: 17203 loss_visual: 0.0746 loss_lang: 0.1497 loss_fusion: 0.0635 loss: 0.2879 2022/10/05 12:06:47 - mmengine - INFO - Epoch(train) [6][7300/10520] lr: 1.0000e-04 eta: 1 day, 2:15:03 time: 0.7540 data_time: 0.1424 memory: 17203 loss_visual: 0.0843 loss_lang: 0.1566 loss_fusion: 0.0746 loss: 0.3154 2022/10/05 12:07:42 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 12:07:42 - mmengine - INFO - Epoch(train) [6][7400/10520] lr: 1.0000e-04 eta: 1 day, 2:13:41 time: 0.8870 data_time: 0.1574 memory: 17203 loss_visual: 0.0819 loss_lang: 0.1565 loss_fusion: 0.0705 loss: 0.3089 2022/10/05 12:08:38 - mmengine - INFO - Epoch(train) [6][7500/10520] lr: 1.0000e-04 eta: 1 day, 2:12:21 time: 0.7716 data_time: 0.1511 memory: 17203 loss_visual: 0.0861 loss_lang: 0.1527 loss_fusion: 0.0754 loss: 0.3142 2022/10/05 12:09:33 - mmengine - INFO - Epoch(train) [6][7600/10520] lr: 1.0000e-04 eta: 1 day, 2:10:59 time: 0.4969 data_time: 0.0165 memory: 17203 loss_visual: 0.0874 loss_lang: 0.1590 loss_fusion: 0.0775 loss: 0.3239 2022/10/05 12:10:27 - mmengine - INFO - Epoch(train) [6][7700/10520] lr: 1.0000e-04 eta: 1 day, 2:09:36 time: 0.4830 data_time: 0.0361 memory: 17203 loss_visual: 0.0901 loss_lang: 0.1631 loss_fusion: 0.0792 loss: 0.3324 2022/10/05 12:11:22 - mmengine - INFO - Epoch(train) [6][7800/10520] lr: 1.0000e-04 eta: 1 day, 2:08:12 time: 0.3932 data_time: 0.0118 memory: 17203 loss_visual: 0.0811 loss_lang: 0.1570 loss_fusion: 0.0709 loss: 0.3089 2022/10/05 12:12:16 - mmengine - INFO - Epoch(train) [6][7900/10520] lr: 1.0000e-04 eta: 1 day, 2:06:49 time: 0.3503 data_time: 0.0118 memory: 17203 loss_visual: 0.0799 loss_lang: 0.1538 loss_fusion: 0.0695 loss: 0.3032 2022/10/05 12:13:11 - mmengine - INFO - Epoch(train) [6][8000/10520] lr: 1.0000e-04 eta: 1 day, 2:05:26 time: 0.3422 data_time: 0.0028 memory: 17203 loss_visual: 0.0721 loss_lang: 0.1460 loss_fusion: 0.0627 loss: 0.2808 2022/10/05 12:14:09 - mmengine - INFO - Epoch(train) [6][8100/10520] lr: 1.0000e-04 eta: 1 day, 2:04:12 time: 0.7673 data_time: 0.1714 memory: 17203 loss_visual: 0.0849 loss_lang: 0.1576 loss_fusion: 0.0747 loss: 0.3171 2022/10/05 12:15:04 - mmengine - INFO - Epoch(train) [6][8200/10520] lr: 1.0000e-04 eta: 1 day, 2:02:50 time: 0.8560 data_time: 0.1651 memory: 17203 loss_visual: 0.0774 loss_lang: 0.1537 loss_fusion: 0.0670 loss: 0.2981 2022/10/05 12:15:59 - mmengine - INFO - Epoch(train) [6][8300/10520] lr: 1.0000e-04 eta: 1 day, 2:01:29 time: 0.7665 data_time: 0.1419 memory: 17203 loss_visual: 0.0762 loss_lang: 0.1470 loss_fusion: 0.0664 loss: 0.2895 2022/10/05 12:16:53 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 12:16:53 - mmengine - INFO - Epoch(train) [6][8400/10520] lr: 1.0000e-04 eta: 1 day, 2:00:05 time: 0.4952 data_time: 0.0158 memory: 17203 loss_visual: 0.0775 loss_lang: 0.1503 loss_fusion: 0.0671 loss: 0.2949 2022/10/05 12:17:48 - mmengine - INFO - Epoch(train) [6][8500/10520] lr: 1.0000e-04 eta: 1 day, 1:58:43 time: 0.4709 data_time: 0.0186 memory: 17203 loss_visual: 0.0731 loss_lang: 0.1479 loss_fusion: 0.0648 loss: 0.2858 2022/10/05 12:18:42 - mmengine - INFO - Epoch(train) [6][8600/10520] lr: 1.0000e-04 eta: 1 day, 1:57:21 time: 0.4428 data_time: 0.0133 memory: 17203 loss_visual: 0.0744 loss_lang: 0.1456 loss_fusion: 0.0649 loss: 0.2849 2022/10/05 12:19:38 - mmengine - INFO - Epoch(train) [6][8700/10520] lr: 1.0000e-04 eta: 1 day, 1:56:01 time: 0.3741 data_time: 0.0121 memory: 17203 loss_visual: 0.0864 loss_lang: 0.1550 loss_fusion: 0.0758 loss: 0.3173 2022/10/05 12:20:32 - mmengine - INFO - Epoch(train) [6][8800/10520] lr: 1.0000e-04 eta: 1 day, 1:54:38 time: 0.3830 data_time: 0.0028 memory: 17203 loss_visual: 0.0817 loss_lang: 0.1538 loss_fusion: 0.0699 loss: 0.3054 2022/10/05 12:21:31 - mmengine - INFO - Epoch(train) [6][8900/10520] lr: 1.0000e-04 eta: 1 day, 1:53:26 time: 0.7203 data_time: 0.1660 memory: 17203 loss_visual: 0.0835 loss_lang: 0.1545 loss_fusion: 0.0731 loss: 0.3111 2022/10/05 12:25:45 - mmengine - INFO - Epoch(train) [6][9000/10520] lr: 1.0000e-04 eta: 1 day, 2:00:06 time: 0.8909 data_time: 0.1808 memory: 17203 loss_visual: 0.0825 loss_lang: 0.1567 loss_fusion: 0.0723 loss: 0.3116 2022/10/05 12:26:52 - mmengine - INFO - Epoch(train) [6][9100/10520] lr: 1.0000e-04 eta: 1 day, 1:59:14 time: 0.7400 data_time: 0.1520 memory: 17203 loss_visual: 0.0787 loss_lang: 0.1543 loss_fusion: 0.0694 loss: 0.3024 2022/10/05 12:27:48 - mmengine - INFO - Epoch(train) [6][9200/10520] lr: 1.0000e-04 eta: 1 day, 1:57:54 time: 0.5375 data_time: 0.0152 memory: 17203 loss_visual: 0.0771 loss_lang: 0.1476 loss_fusion: 0.0664 loss: 0.2911 2022/10/05 12:28:43 - mmengine - INFO - Epoch(train) [6][9300/10520] lr: 1.0000e-04 eta: 1 day, 1:56:33 time: 0.4642 data_time: 0.0158 memory: 17203 loss_visual: 0.0855 loss_lang: 0.1553 loss_fusion: 0.0740 loss: 0.3148 2022/10/05 12:29:39 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 12:29:39 - mmengine - INFO - Epoch(train) [6][9400/10520] lr: 1.0000e-04 eta: 1 day, 1:55:12 time: 0.4298 data_time: 0.0118 memory: 17203 loss_visual: 0.0802 loss_lang: 0.1520 loss_fusion: 0.0694 loss: 0.3016 2022/10/05 12:30:34 - mmengine - INFO - Epoch(train) [6][9500/10520] lr: 1.0000e-04 eta: 1 day, 1:53:51 time: 0.3812 data_time: 0.0122 memory: 17203 loss_visual: 0.0864 loss_lang: 0.1596 loss_fusion: 0.0749 loss: 0.3210 2022/10/05 12:31:30 - mmengine - INFO - Epoch(train) [6][9600/10520] lr: 1.0000e-04 eta: 1 day, 1:52:31 time: 0.3457 data_time: 0.0030 memory: 17203 loss_visual: 0.0775 loss_lang: 0.1474 loss_fusion: 0.0687 loss: 0.2937 2022/10/05 12:32:29 - mmengine - INFO - Epoch(train) [6][9700/10520] lr: 1.0000e-04 eta: 1 day, 1:51:20 time: 0.7844 data_time: 0.1741 memory: 17203 loss_visual: 0.0806 loss_lang: 0.1561 loss_fusion: 0.0707 loss: 0.3074 2022/10/05 12:33:26 - mmengine - INFO - Epoch(train) [6][9800/10520] lr: 1.0000e-04 eta: 1 day, 1:50:02 time: 0.9322 data_time: 0.1766 memory: 17203 loss_visual: 0.0786 loss_lang: 0.1495 loss_fusion: 0.0691 loss: 0.2972 2022/10/05 12:34:22 - mmengine - INFO - Epoch(train) [6][9900/10520] lr: 1.0000e-04 eta: 1 day, 1:48:45 time: 0.7802 data_time: 0.1611 memory: 17203 loss_visual: 0.0726 loss_lang: 0.1425 loss_fusion: 0.0628 loss: 0.2779 2022/10/05 12:35:18 - mmengine - INFO - Epoch(train) [6][10000/10520] lr: 1.0000e-04 eta: 1 day, 1:47:24 time: 0.5170 data_time: 0.0159 memory: 17203 loss_visual: 0.0807 loss_lang: 0.1485 loss_fusion: 0.0706 loss: 0.2997 2022/10/05 12:36:13 - mmengine - INFO - Epoch(train) [6][10100/10520] lr: 1.0000e-04 eta: 1 day, 1:46:04 time: 0.4851 data_time: 0.0380 memory: 17203 loss_visual: 0.0722 loss_lang: 0.1399 loss_fusion: 0.0632 loss: 0.2753 2022/10/05 12:37:09 - mmengine - INFO - Epoch(train) [6][10200/10520] lr: 1.0000e-04 eta: 1 day, 1:44:45 time: 0.4194 data_time: 0.0420 memory: 17203 loss_visual: 0.0878 loss_lang: 0.1596 loss_fusion: 0.0782 loss: 0.3256 2022/10/05 12:38:04 - mmengine - INFO - Epoch(train) [6][10300/10520] lr: 1.0000e-04 eta: 1 day, 1:43:24 time: 0.3512 data_time: 0.0120 memory: 17203 loss_visual: 0.0824 loss_lang: 0.1535 loss_fusion: 0.0711 loss: 0.3069 2022/10/05 12:38:58 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 12:38:58 - mmengine - INFO - Epoch(train) [6][10400/10520] lr: 1.0000e-04 eta: 1 day, 1:42:01 time: 0.3524 data_time: 0.0036 memory: 17203 loss_visual: 0.0810 loss_lang: 0.1505 loss_fusion: 0.0712 loss: 0.3027 2022/10/05 12:39:55 - mmengine - INFO - Epoch(train) [6][10500/10520] lr: 1.0000e-04 eta: 1 day, 1:40:44 time: 0.5593 data_time: 0.1023 memory: 17203 loss_visual: 0.0771 loss_lang: 0.1460 loss_fusion: 0.0658 loss: 0.2889 2022/10/05 12:40:03 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 12:40:03 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/10/05 12:40:17 - mmengine - INFO - Epoch(val) [6][100/959] eta: 0:00:35 time: 0.0414 data_time: 0.0013 memory: 17203 2022/10/05 12:40:22 - mmengine - INFO - Epoch(val) [6][200/959] eta: 0:00:42 time: 0.0557 data_time: 0.0017 memory: 734 2022/10/05 12:40:27 - mmengine - INFO - Epoch(val) [6][300/959] eta: 0:00:32 time: 0.0497 data_time: 0.0008 memory: 734 2022/10/05 12:40:32 - mmengine - INFO - Epoch(val) [6][400/959] eta: 0:00:26 time: 0.0473 data_time: 0.0015 memory: 734 2022/10/05 12:40:37 - mmengine - INFO - Epoch(val) [6][500/959] eta: 0:00:24 time: 0.0537 data_time: 0.0010 memory: 734 2022/10/05 12:40:42 - mmengine - INFO - Epoch(val) [6][600/959] eta: 0:00:16 time: 0.0472 data_time: 0.0016 memory: 734 2022/10/05 12:40:47 - mmengine - INFO - Epoch(val) [6][700/959] eta: 0:00:11 time: 0.0444 data_time: 0.0012 memory: 734 2022/10/05 12:40:51 - mmengine - INFO - Epoch(val) [6][800/959] eta: 0:00:04 time: 0.0262 data_time: 0.0010 memory: 734 2022/10/05 12:40:53 - mmengine - INFO - Epoch(val) [6][900/959] eta: 0:00:01 time: 0.0215 data_time: 0.0005 memory: 734 2022/10/05 12:40:55 - mmengine - INFO - Epoch(val) [6][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.8437 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9420 SVT/recog/word_acc_ignore_case_symbol: 0.9212 SVTP/recog/word_acc_ignore_case_symbol: 0.8419 IC13/recog/word_acc_ignore_case_symbol: 0.9320 IC15/recog/word_acc_ignore_case_symbol: 0.7665 2022/10/05 12:41:59 - mmengine - INFO - Epoch(train) [7][100/10520] lr: 1.0000e-04 eta: 1 day, 1:39:21 time: 0.8738 data_time: 0.1449 memory: 17203 loss_visual: 0.0868 loss_lang: 0.1568 loss_fusion: 0.0753 loss: 0.3189 2022/10/05 12:42:53 - mmengine - INFO - Epoch(train) [7][200/10520] lr: 1.0000e-04 eta: 1 day, 1:37:58 time: 0.8679 data_time: 0.1858 memory: 17203 loss_visual: 0.0810 loss_lang: 0.1502 loss_fusion: 0.0690 loss: 0.3001 2022/10/05 12:43:48 - mmengine - INFO - Epoch(train) [7][300/10520] lr: 1.0000e-04 eta: 1 day, 1:36:35 time: 0.6986 data_time: 0.1144 memory: 17203 loss_visual: 0.0738 loss_lang: 0.1440 loss_fusion: 0.0633 loss: 0.2812 2022/10/05 12:44:42 - mmengine - INFO - Epoch(train) [7][400/10520] lr: 1.0000e-04 eta: 1 day, 1:35:13 time: 0.4899 data_time: 0.0309 memory: 17203 loss_visual: 0.0816 loss_lang: 0.1533 loss_fusion: 0.0718 loss: 0.3066 2022/10/05 12:45:36 - mmengine - INFO - Epoch(train) [7][500/10520] lr: 1.0000e-04 eta: 1 day, 1:33:51 time: 0.3674 data_time: 0.0308 memory: 17203 loss_visual: 0.0855 loss_lang: 0.1629 loss_fusion: 0.0775 loss: 0.3259 2022/10/05 12:46:29 - mmengine - INFO - Epoch(train) [7][600/10520] lr: 1.0000e-04 eta: 1 day, 1:32:25 time: 0.3864 data_time: 0.0525 memory: 17203 loss_visual: 0.0831 loss_lang: 0.1553 loss_fusion: 0.0723 loss: 0.3107 2022/10/05 12:47:22 - mmengine - INFO - Epoch(train) [7][700/10520] lr: 1.0000e-04 eta: 1 day, 1:31:02 time: 0.3522 data_time: 0.0032 memory: 17203 loss_visual: 0.0795 loss_lang: 0.1506 loss_fusion: 0.0709 loss: 0.3011 2022/10/05 12:48:16 - mmengine - INFO - Epoch(train) [7][800/10520] lr: 1.0000e-04 eta: 1 day, 1:29:37 time: 0.3773 data_time: 0.0033 memory: 17203 loss_visual: 0.0816 loss_lang: 0.1520 loss_fusion: 0.0706 loss: 0.3042 2022/10/05 12:49:02 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 12:49:13 - mmengine - INFO - Epoch(train) [7][900/10520] lr: 1.0000e-04 eta: 1 day, 1:28:23 time: 0.8120 data_time: 0.1408 memory: 17203 loss_visual: 0.0799 loss_lang: 0.1520 loss_fusion: 0.0703 loss: 0.3022 2022/10/05 12:50:07 - mmengine - INFO - Epoch(train) [7][1000/10520] lr: 1.0000e-04 eta: 1 day, 1:27:01 time: 0.9022 data_time: 0.1560 memory: 17203 loss_visual: 0.0738 loss_lang: 0.1444 loss_fusion: 0.0659 loss: 0.2841 2022/10/05 12:51:01 - mmengine - INFO - Epoch(train) [7][1100/10520] lr: 1.0000e-04 eta: 1 day, 1:25:37 time: 0.6726 data_time: 0.1196 memory: 17203 loss_visual: 0.0781 loss_lang: 0.1501 loss_fusion: 0.0692 loss: 0.2974 2022/10/05 12:51:54 - mmengine - INFO - Epoch(train) [7][1200/10520] lr: 1.0000e-04 eta: 1 day, 1:24:13 time: 0.4205 data_time: 0.0284 memory: 17203 loss_visual: 0.0804 loss_lang: 0.1508 loss_fusion: 0.0708 loss: 0.3020 2022/10/05 12:52:48 - mmengine - INFO - Epoch(train) [7][1300/10520] lr: 1.0000e-04 eta: 1 day, 1:22:51 time: 0.3648 data_time: 0.0285 memory: 17203 loss_visual: 0.0667 loss_lang: 0.1371 loss_fusion: 0.0572 loss: 0.2610 2022/10/05 12:53:41 - mmengine - INFO - Epoch(train) [7][1400/10520] lr: 1.0000e-04 eta: 1 day, 1:21:27 time: 0.3690 data_time: 0.0288 memory: 17203 loss_visual: 0.0753 loss_lang: 0.1433 loss_fusion: 0.0652 loss: 0.2838 2022/10/05 12:54:34 - mmengine - INFO - Epoch(train) [7][1500/10520] lr: 1.0000e-04 eta: 1 day, 1:20:03 time: 0.3628 data_time: 0.0032 memory: 17203 loss_visual: 0.0891 loss_lang: 0.1641 loss_fusion: 0.0800 loss: 0.3331 2022/10/05 12:55:27 - mmengine - INFO - Epoch(train) [7][1600/10520] lr: 1.0000e-04 eta: 1 day, 1:18:39 time: 0.3722 data_time: 0.0035 memory: 17203 loss_visual: 0.0796 loss_lang: 0.1487 loss_fusion: 0.0680 loss: 0.2963 2022/10/05 12:56:25 - mmengine - INFO - Epoch(train) [7][1700/10520] lr: 1.0000e-04 eta: 1 day, 1:17:26 time: 0.8513 data_time: 0.1110 memory: 17203 loss_visual: 0.0853 loss_lang: 0.1545 loss_fusion: 0.0733 loss: 0.3132 2022/10/05 12:57:20 - mmengine - INFO - Epoch(train) [7][1800/10520] lr: 1.0000e-04 eta: 1 day, 1:16:07 time: 0.9292 data_time: 0.1644 memory: 17203 loss_visual: 0.0814 loss_lang: 0.1474 loss_fusion: 0.0708 loss: 0.2997 2022/10/05 12:58:01 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 12:58:14 - mmengine - INFO - Epoch(train) [7][1900/10520] lr: 1.0000e-04 eta: 1 day, 1:14:44 time: 0.6746 data_time: 0.1186 memory: 17203 loss_visual: 0.0903 loss_lang: 0.1591 loss_fusion: 0.0792 loss: 0.3286 2022/10/05 12:59:07 - mmengine - INFO - Epoch(train) [7][2000/10520] lr: 1.0000e-04 eta: 1 day, 1:13:21 time: 0.4382 data_time: 0.0289 memory: 17203 loss_visual: 0.0769 loss_lang: 0.1458 loss_fusion: 0.0659 loss: 0.2886 2022/10/05 12:59:59 - mmengine - INFO - Epoch(train) [7][2100/10520] lr: 1.0000e-04 eta: 1 day, 1:11:55 time: 0.3784 data_time: 0.0286 memory: 17203 loss_visual: 0.0787 loss_lang: 0.1511 loss_fusion: 0.0691 loss: 0.2989 2022/10/05 13:00:52 - mmengine - INFO - Epoch(train) [7][2200/10520] lr: 1.0000e-04 eta: 1 day, 1:10:31 time: 0.3660 data_time: 0.0307 memory: 17203 loss_visual: 0.0833 loss_lang: 0.1505 loss_fusion: 0.0730 loss: 0.3068 2022/10/05 13:01:45 - mmengine - INFO - Epoch(train) [7][2300/10520] lr: 1.0000e-04 eta: 1 day, 1:09:08 time: 0.3508 data_time: 0.0032 memory: 17203 loss_visual: 0.0803 loss_lang: 0.1497 loss_fusion: 0.0702 loss: 0.3002 2022/10/05 13:02:38 - mmengine - INFO - Epoch(train) [7][2400/10520] lr: 1.0000e-04 eta: 1 day, 1:07:45 time: 0.3619 data_time: 0.0029 memory: 17203 loss_visual: 0.0762 loss_lang: 0.1477 loss_fusion: 0.0669 loss: 0.2907 2022/10/05 13:03:37 - mmengine - INFO - Epoch(train) [7][2500/10520] lr: 1.0000e-04 eta: 1 day, 1:06:34 time: 0.8693 data_time: 0.1419 memory: 17203 loss_visual: 0.0784 loss_lang: 0.1505 loss_fusion: 0.0681 loss: 0.2970 2022/10/05 13:04:30 - mmengine - INFO - Epoch(train) [7][2600/10520] lr: 1.0000e-04 eta: 1 day, 1:05:12 time: 0.8494 data_time: 0.1510 memory: 17203 loss_visual: 0.0809 loss_lang: 0.1467 loss_fusion: 0.0695 loss: 0.2971 2022/10/05 13:05:24 - mmengine - INFO - Epoch(train) [7][2700/10520] lr: 1.0000e-04 eta: 1 day, 1:03:50 time: 0.6677 data_time: 0.1094 memory: 17203 loss_visual: 0.0793 loss_lang: 0.1477 loss_fusion: 0.0693 loss: 0.2963 2022/10/05 13:06:17 - mmengine - INFO - Epoch(train) [7][2800/10520] lr: 1.0000e-04 eta: 1 day, 1:02:28 time: 0.4474 data_time: 0.0294 memory: 17203 loss_visual: 0.0787 loss_lang: 0.1546 loss_fusion: 0.0695 loss: 0.3028 2022/10/05 13:06:57 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 13:07:10 - mmengine - INFO - Epoch(train) [7][2900/10520] lr: 1.0000e-04 eta: 1 day, 1:01:04 time: 0.3728 data_time: 0.0299 memory: 17203 loss_visual: 0.0819 loss_lang: 0.1491 loss_fusion: 0.0711 loss: 0.3021 2022/10/05 13:08:03 - mmengine - INFO - Epoch(train) [7][3000/10520] lr: 1.0000e-04 eta: 1 day, 0:59:41 time: 0.3777 data_time: 0.0430 memory: 17203 loss_visual: 0.0794 loss_lang: 0.1535 loss_fusion: 0.0686 loss: 0.3016 2022/10/05 13:08:56 - mmengine - INFO - Epoch(train) [7][3100/10520] lr: 1.0000e-04 eta: 1 day, 0:58:20 time: 0.3685 data_time: 0.0033 memory: 17203 loss_visual: 0.0850 loss_lang: 0.1549 loss_fusion: 0.0746 loss: 0.3145 2022/10/05 13:09:50 - mmengine - INFO - Epoch(train) [7][3200/10520] lr: 1.0000e-04 eta: 1 day, 0:56:57 time: 0.3945 data_time: 0.0033 memory: 17203 loss_visual: 0.0825 loss_lang: 0.1501 loss_fusion: 0.0744 loss: 0.3071 2022/10/05 13:10:47 - mmengine - INFO - Epoch(train) [7][3300/10520] lr: 1.0000e-04 eta: 1 day, 0:55:44 time: 0.8205 data_time: 0.1332 memory: 17203 loss_visual: 0.0862 loss_lang: 0.1589 loss_fusion: 0.0746 loss: 0.3197 2022/10/05 13:11:40 - mmengine - INFO - Epoch(train) [7][3400/10520] lr: 1.0000e-04 eta: 1 day, 0:54:22 time: 0.8767 data_time: 0.1541 memory: 17203 loss_visual: 0.0741 loss_lang: 0.1422 loss_fusion: 0.0637 loss: 0.2800 2022/10/05 13:12:33 - mmengine - INFO - Epoch(train) [7][3500/10520] lr: 1.0000e-04 eta: 1 day, 0:52:59 time: 0.6614 data_time: 0.1216 memory: 17203 loss_visual: 0.0739 loss_lang: 0.1411 loss_fusion: 0.0639 loss: 0.2789 2022/10/05 13:13:26 - mmengine - INFO - Epoch(train) [7][3600/10520] lr: 1.0000e-04 eta: 1 day, 0:51:37 time: 0.4414 data_time: 0.0280 memory: 17203 loss_visual: 0.0847 loss_lang: 0.1502 loss_fusion: 0.0749 loss: 0.3098 2022/10/05 13:14:19 - mmengine - INFO - Epoch(train) [7][3700/10520] lr: 1.0000e-04 eta: 1 day, 0:50:15 time: 0.3671 data_time: 0.0293 memory: 17203 loss_visual: 0.0712 loss_lang: 0.1404 loss_fusion: 0.0615 loss: 0.2731 2022/10/05 13:15:13 - mmengine - INFO - Epoch(train) [7][3800/10520] lr: 1.0000e-04 eta: 1 day, 0:48:54 time: 0.3657 data_time: 0.0312 memory: 17203 loss_visual: 0.0711 loss_lang: 0.1415 loss_fusion: 0.0605 loss: 0.2732 2022/10/05 13:15:59 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 13:16:06 - mmengine - INFO - Epoch(train) [7][3900/10520] lr: 1.0000e-04 eta: 1 day, 0:47:33 time: 0.3495 data_time: 0.0031 memory: 17203 loss_visual: 0.0695 loss_lang: 0.1402 loss_fusion: 0.0613 loss: 0.2711 2022/10/05 13:16:59 - mmengine - INFO - Epoch(train) [7][4000/10520] lr: 1.0000e-04 eta: 1 day, 0:46:10 time: 0.3589 data_time: 0.0033 memory: 17203 loss_visual: 0.0766 loss_lang: 0.1430 loss_fusion: 0.0669 loss: 0.2865 2022/10/05 13:17:57 - mmengine - INFO - Epoch(train) [7][4100/10520] lr: 1.0000e-04 eta: 1 day, 0:45:00 time: 0.8393 data_time: 0.1484 memory: 17203 loss_visual: 0.0848 loss_lang: 0.1570 loss_fusion: 0.0745 loss: 0.3163 2022/10/05 13:18:50 - mmengine - INFO - Epoch(train) [7][4200/10520] lr: 1.0000e-04 eta: 1 day, 0:43:37 time: 0.8520 data_time: 0.1534 memory: 17203 loss_visual: 0.0792 loss_lang: 0.1501 loss_fusion: 0.0687 loss: 0.2979 2022/10/05 13:19:43 - mmengine - INFO - Epoch(train) [7][4300/10520] lr: 1.0000e-04 eta: 1 day, 0:42:15 time: 0.6666 data_time: 0.0988 memory: 17203 loss_visual: 0.0771 loss_lang: 0.1477 loss_fusion: 0.0676 loss: 0.2923 2022/10/05 13:20:36 - mmengine - INFO - Epoch(train) [7][4400/10520] lr: 1.0000e-04 eta: 1 day, 0:40:54 time: 0.4558 data_time: 0.0428 memory: 17203 loss_visual: 0.0815 loss_lang: 0.1508 loss_fusion: 0.0713 loss: 0.3036 2022/10/05 13:21:29 - mmengine - INFO - Epoch(train) [7][4500/10520] lr: 1.0000e-04 eta: 1 day, 0:39:33 time: 0.3712 data_time: 0.0311 memory: 17203 loss_visual: 0.0778 loss_lang: 0.1444 loss_fusion: 0.0680 loss: 0.2902 2022/10/05 13:22:22 - mmengine - INFO - Epoch(train) [7][4600/10520] lr: 1.0000e-04 eta: 1 day, 0:38:10 time: 0.3597 data_time: 0.0281 memory: 17203 loss_visual: 0.0715 loss_lang: 0.1394 loss_fusion: 0.0621 loss: 0.2730 2022/10/05 13:23:14 - mmengine - INFO - Epoch(train) [7][4700/10520] lr: 1.0000e-04 eta: 1 day, 0:36:47 time: 0.3477 data_time: 0.0034 memory: 17203 loss_visual: 0.0803 loss_lang: 0.1467 loss_fusion: 0.0697 loss: 0.2967 2022/10/05 13:24:06 - mmengine - INFO - Epoch(train) [7][4800/10520] lr: 1.0000e-04 eta: 1 day, 0:35:24 time: 0.3623 data_time: 0.0034 memory: 17203 loss_visual: 0.0763 loss_lang: 0.1501 loss_fusion: 0.0664 loss: 0.2928 2022/10/05 13:24:52 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 13:25:04 - mmengine - INFO - Epoch(train) [7][4900/10520] lr: 1.0000e-04 eta: 1 day, 0:34:12 time: 0.8485 data_time: 0.1330 memory: 17203 loss_visual: 0.0770 loss_lang: 0.1466 loss_fusion: 0.0673 loss: 0.2909 2022/10/05 13:25:57 - mmengine - INFO - Epoch(train) [7][5000/10520] lr: 1.0000e-04 eta: 1 day, 0:32:52 time: 0.8567 data_time: 0.1760 memory: 17203 loss_visual: 0.0795 loss_lang: 0.1483 loss_fusion: 0.0700 loss: 0.2978 2022/10/05 13:26:51 - mmengine - INFO - Epoch(train) [7][5100/10520] lr: 1.0000e-04 eta: 1 day, 0:31:32 time: 0.6877 data_time: 0.1209 memory: 17203 loss_visual: 0.0768 loss_lang: 0.1465 loss_fusion: 0.0669 loss: 0.2901 2022/10/05 13:27:45 - mmengine - INFO - Epoch(train) [7][5200/10520] lr: 1.0000e-04 eta: 1 day, 0:30:13 time: 0.4564 data_time: 0.0323 memory: 17203 loss_visual: 0.0763 loss_lang: 0.1441 loss_fusion: 0.0653 loss: 0.2857 2022/10/05 13:28:38 - mmengine - INFO - Epoch(train) [7][5300/10520] lr: 1.0000e-04 eta: 1 day, 0:28:52 time: 0.3749 data_time: 0.0327 memory: 17203 loss_visual: 0.0801 loss_lang: 0.1489 loss_fusion: 0.0692 loss: 0.2981 2022/10/05 13:29:30 - mmengine - INFO - Epoch(train) [7][5400/10520] lr: 1.0000e-04 eta: 1 day, 0:27:30 time: 0.3588 data_time: 0.0278 memory: 17203 loss_visual: 0.0774 loss_lang: 0.1428 loss_fusion: 0.0668 loss: 0.2871 2022/10/05 13:30:24 - mmengine - INFO - Epoch(train) [7][5500/10520] lr: 1.0000e-04 eta: 1 day, 0:26:11 time: 0.3460 data_time: 0.0034 memory: 17203 loss_visual: 0.0748 loss_lang: 0.1469 loss_fusion: 0.0663 loss: 0.2880 2022/10/05 13:31:18 - mmengine - INFO - Epoch(train) [7][5600/10520] lr: 1.0000e-04 eta: 1 day, 0:24:52 time: 0.3555 data_time: 0.0039 memory: 17203 loss_visual: 0.0730 loss_lang: 0.1426 loss_fusion: 0.0625 loss: 0.2781 2022/10/05 13:32:18 - mmengine - INFO - Epoch(train) [7][5700/10520] lr: 1.0000e-04 eta: 1 day, 0:23:45 time: 0.7693 data_time: 0.1392 memory: 17203 loss_visual: 0.0759 loss_lang: 0.1441 loss_fusion: 0.0662 loss: 0.2863 2022/10/05 13:33:14 - mmengine - INFO - Epoch(train) [7][5800/10520] lr: 1.0000e-04 eta: 1 day, 0:22:31 time: 0.8947 data_time: 0.1628 memory: 17203 loss_visual: 0.0867 loss_lang: 0.1605 loss_fusion: 0.0772 loss: 0.3243 2022/10/05 13:33:57 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 13:34:10 - mmengine - INFO - Epoch(train) [7][5900/10520] lr: 1.0000e-04 eta: 1 day, 0:21:18 time: 0.8693 data_time: 0.1301 memory: 17203 loss_visual: 0.0791 loss_lang: 0.1526 loss_fusion: 0.0694 loss: 0.3011 2022/10/05 13:35:06 - mmengine - INFO - Epoch(train) [7][6000/10520] lr: 1.0000e-04 eta: 1 day, 0:20:02 time: 0.5671 data_time: 0.0255 memory: 17203 loss_visual: 0.0683 loss_lang: 0.1402 loss_fusion: 0.0590 loss: 0.2675 2022/10/05 13:36:01 - mmengine - INFO - Epoch(train) [7][6100/10520] lr: 1.0000e-04 eta: 1 day, 0:18:48 time: 0.4403 data_time: 0.0297 memory: 17203 loss_visual: 0.0722 loss_lang: 0.1390 loss_fusion: 0.0621 loss: 0.2733 2022/10/05 13:36:58 - mmengine - INFO - Epoch(train) [7][6200/10520] lr: 1.0000e-04 eta: 1 day, 0:17:35 time: 0.3675 data_time: 0.0302 memory: 17203 loss_visual: 0.0689 loss_lang: 0.1414 loss_fusion: 0.0592 loss: 0.2695 2022/10/05 13:37:54 - mmengine - INFO - Epoch(train) [7][6300/10520] lr: 1.0000e-04 eta: 1 day, 0:16:20 time: 0.3577 data_time: 0.0034 memory: 17203 loss_visual: 0.0687 loss_lang: 0.1368 loss_fusion: 0.0595 loss: 0.2650 2022/10/05 13:38:50 - mmengine - INFO - Epoch(train) [7][6400/10520] lr: 1.0000e-04 eta: 1 day, 0:15:05 time: 0.3517 data_time: 0.0035 memory: 17203 loss_visual: 0.0763 loss_lang: 0.1464 loss_fusion: 0.0668 loss: 0.2895 2022/10/05 13:40:40 - mmengine - INFO - Epoch(train) [7][6500/10520] lr: 1.0000e-04 eta: 1 day, 0:15:42 time: 3.0981 data_time: 0.1504 memory: 17203 loss_visual: 0.0838 loss_lang: 0.1522 loss_fusion: 0.0738 loss: 0.3098 2022/10/05 13:43:34 - mmengine - INFO - Epoch(train) [7][6600/10520] lr: 1.0000e-04 eta: 1 day, 0:18:26 time: 1.3633 data_time: 0.1805 memory: 17203 loss_visual: 0.0735 loss_lang: 0.1449 loss_fusion: 0.0644 loss: 0.2827 2022/10/05 13:44:31 - mmengine - INFO - Epoch(train) [7][6700/10520] lr: 1.0000e-04 eta: 1 day, 0:17:12 time: 0.7915 data_time: 0.1256 memory: 17203 loss_visual: 0.0772 loss_lang: 0.1479 loss_fusion: 0.0666 loss: 0.2917 2022/10/05 13:45:27 - mmengine - INFO - Epoch(train) [7][6800/10520] lr: 1.0000e-04 eta: 1 day, 0:15:58 time: 0.5828 data_time: 0.0334 memory: 17203 loss_visual: 0.0745 loss_lang: 0.1430 loss_fusion: 0.0638 loss: 0.2813 2022/10/05 13:46:10 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 13:46:23 - mmengine - INFO - Epoch(train) [7][6900/10520] lr: 1.0000e-04 eta: 1 day, 0:14:44 time: 0.4074 data_time: 0.0303 memory: 17203 loss_visual: 0.0761 loss_lang: 0.1473 loss_fusion: 0.0675 loss: 0.2909 2022/10/05 13:47:20 - mmengine - INFO - Epoch(train) [7][7000/10520] lr: 1.0000e-04 eta: 1 day, 0:13:30 time: 0.3790 data_time: 0.0295 memory: 17203 loss_visual: 0.0762 loss_lang: 0.1437 loss_fusion: 0.0653 loss: 0.2852 2022/10/05 13:48:16 - mmengine - INFO - Epoch(train) [7][7100/10520] lr: 1.0000e-04 eta: 1 day, 0:12:16 time: 0.3577 data_time: 0.0040 memory: 17203 loss_visual: 0.0845 loss_lang: 0.1551 loss_fusion: 0.0764 loss: 0.3160 2022/10/05 13:49:11 - mmengine - INFO - Epoch(train) [7][7200/10520] lr: 1.0000e-04 eta: 1 day, 0:11:00 time: 0.3542 data_time: 0.0034 memory: 17203 loss_visual: 0.0727 loss_lang: 0.1427 loss_fusion: 0.0632 loss: 0.2785 2022/10/05 13:50:11 - mmengine - INFO - Epoch(train) [7][7300/10520] lr: 1.0000e-04 eta: 1 day, 0:09:54 time: 0.7703 data_time: 0.1427 memory: 17203 loss_visual: 0.0707 loss_lang: 0.1355 loss_fusion: 0.0605 loss: 0.2666 2022/10/05 13:51:08 - mmengine - INFO - Epoch(train) [7][7400/10520] lr: 1.0000e-04 eta: 1 day, 0:08:40 time: 0.9296 data_time: 0.1657 memory: 17203 loss_visual: 0.0791 loss_lang: 0.1506 loss_fusion: 0.0689 loss: 0.2986 2022/10/05 13:52:19 - mmengine - INFO - Epoch(train) [7][7500/10520] lr: 1.0000e-04 eta: 1 day, 0:07:56 time: 1.0088 data_time: 0.1121 memory: 17203 loss_visual: 0.0705 loss_lang: 0.1437 loss_fusion: 0.0617 loss: 0.2759 2022/10/05 13:55:43 - mmengine - INFO - Epoch(train) [7][7600/10520] lr: 1.0000e-04 eta: 1 day, 0:11:35 time: 0.5830 data_time: 0.0292 memory: 17203 loss_visual: 0.0745 loss_lang: 0.1440 loss_fusion: 0.0643 loss: 0.2829 2022/10/05 13:57:01 - mmengine - INFO - Epoch(train) [7][7700/10520] lr: 1.0000e-04 eta: 1 day, 0:11:03 time: 0.4283 data_time: 0.0320 memory: 17203 loss_visual: 0.0677 loss_lang: 0.1430 loss_fusion: 0.0578 loss: 0.2685 2022/10/05 13:57:57 - mmengine - INFO - Epoch(train) [7][7800/10520] lr: 1.0000e-04 eta: 1 day, 0:09:47 time: 0.3730 data_time: 0.0287 memory: 17203 loss_visual: 0.0758 loss_lang: 0.1434 loss_fusion: 0.0648 loss: 0.2840 2022/10/05 13:58:45 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 13:58:53 - mmengine - INFO - Epoch(train) [7][7900/10520] lr: 1.0000e-04 eta: 1 day, 0:08:32 time: 0.3541 data_time: 0.0030 memory: 17203 loss_visual: 0.0781 loss_lang: 0.1427 loss_fusion: 0.0676 loss: 0.2885 2022/10/05 13:59:49 - mmengine - INFO - Epoch(train) [7][8000/10520] lr: 1.0000e-04 eta: 1 day, 0:07:18 time: 0.3541 data_time: 0.0032 memory: 17203 loss_visual: 0.0780 loss_lang: 0.1456 loss_fusion: 0.0680 loss: 0.2915 2022/10/05 14:00:50 - mmengine - INFO - Epoch(train) [7][8100/10520] lr: 1.0000e-04 eta: 1 day, 0:06:13 time: 0.7574 data_time: 0.1048 memory: 17203 loss_visual: 0.0804 loss_lang: 0.1532 loss_fusion: 0.0708 loss: 0.3044 2022/10/05 14:01:47 - mmengine - INFO - Epoch(train) [7][8200/10520] lr: 1.0000e-04 eta: 1 day, 0:05:01 time: 0.9390 data_time: 0.1528 memory: 17203 loss_visual: 0.0780 loss_lang: 0.1491 loss_fusion: 0.0691 loss: 0.2961 2022/10/05 14:02:44 - mmengine - INFO - Epoch(train) [7][8300/10520] lr: 1.0000e-04 eta: 1 day, 0:03:47 time: 0.8537 data_time: 0.1153 memory: 17203 loss_visual: 0.0813 loss_lang: 0.1487 loss_fusion: 0.0706 loss: 0.3006 2022/10/05 14:03:39 - mmengine - INFO - Epoch(train) [7][8400/10520] lr: 1.0000e-04 eta: 1 day, 0:02:31 time: 0.5612 data_time: 0.0345 memory: 17203 loss_visual: 0.0737 loss_lang: 0.1432 loss_fusion: 0.0651 loss: 0.2820 2022/10/05 14:04:35 - mmengine - INFO - Epoch(train) [7][8500/10520] lr: 1.0000e-04 eta: 1 day, 0:01:15 time: 0.4002 data_time: 0.0299 memory: 17203 loss_visual: 0.0668 loss_lang: 0.1411 loss_fusion: 0.0567 loss: 0.2646 2022/10/05 14:05:31 - mmengine - INFO - Epoch(train) [7][8600/10520] lr: 1.0000e-04 eta: 1 day, 0:00:01 time: 0.3697 data_time: 0.0297 memory: 17203 loss_visual: 0.0703 loss_lang: 0.1404 loss_fusion: 0.0621 loss: 0.2728 2022/10/05 14:06:26 - mmengine - INFO - Epoch(train) [7][8700/10520] lr: 1.0000e-04 eta: 23:58:46 time: 0.3509 data_time: 0.0037 memory: 17203 loss_visual: 0.0778 loss_lang: 0.1462 loss_fusion: 0.0669 loss: 0.2909 2022/10/05 14:07:22 - mmengine - INFO - Epoch(train) [7][8800/10520] lr: 1.0000e-04 eta: 23:57:31 time: 0.3754 data_time: 0.0032 memory: 17203 loss_visual: 0.0767 loss_lang: 0.1430 loss_fusion: 0.0665 loss: 0.2862 2022/10/05 14:08:10 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 14:08:22 - mmengine - INFO - Epoch(train) [7][8900/10520] lr: 1.0000e-04 eta: 23:56:23 time: 0.7918 data_time: 0.1192 memory: 17203 loss_visual: 0.0813 loss_lang: 0.1559 loss_fusion: 0.0734 loss: 0.3106 2022/10/05 14:09:19 - mmengine - INFO - Epoch(train) [7][9000/10520] lr: 1.0000e-04 eta: 23:55:12 time: 0.8572 data_time: 0.1641 memory: 17203 loss_visual: 0.0811 loss_lang: 0.1518 loss_fusion: 0.0706 loss: 0.3034 2022/10/05 14:10:16 - mmengine - INFO - Epoch(train) [7][9100/10520] lr: 1.0000e-04 eta: 23:54:00 time: 0.8014 data_time: 0.1348 memory: 17203 loss_visual: 0.0770 loss_lang: 0.1444 loss_fusion: 0.0665 loss: 0.2878 2022/10/05 14:11:13 - mmengine - INFO - Epoch(train) [7][9200/10520] lr: 1.0000e-04 eta: 23:52:47 time: 0.5930 data_time: 0.0284 memory: 17203 loss_visual: 0.0746 loss_lang: 0.1475 loss_fusion: 0.0648 loss: 0.2869 2022/10/05 14:12:09 - mmengine - INFO - Epoch(train) [7][9300/10520] lr: 1.0000e-04 eta: 23:51:34 time: 0.4280 data_time: 0.0303 memory: 17203 loss_visual: 0.0706 loss_lang: 0.1393 loss_fusion: 0.0611 loss: 0.2710 2022/10/05 14:13:06 - mmengine - INFO - Epoch(train) [7][9400/10520] lr: 1.0000e-04 eta: 23:50:20 time: 0.3699 data_time: 0.0298 memory: 17203 loss_visual: 0.0771 loss_lang: 0.1485 loss_fusion: 0.0670 loss: 0.2925 2022/10/05 14:14:01 - mmengine - INFO - Epoch(train) [7][9500/10520] lr: 1.0000e-04 eta: 23:49:05 time: 0.3546 data_time: 0.0033 memory: 17203 loss_visual: 0.0676 loss_lang: 0.1368 loss_fusion: 0.0577 loss: 0.2621 2022/10/05 14:14:57 - mmengine - INFO - Epoch(train) [7][9600/10520] lr: 1.0000e-04 eta: 23:47:50 time: 0.3515 data_time: 0.0036 memory: 17203 loss_visual: 0.0658 loss_lang: 0.1323 loss_fusion: 0.0571 loss: 0.2553 2022/10/05 14:15:56 - mmengine - INFO - Epoch(train) [7][9700/10520] lr: 1.0000e-04 eta: 23:46:42 time: 0.7290 data_time: 0.1333 memory: 17203 loss_visual: 0.0824 loss_lang: 0.1480 loss_fusion: 0.0728 loss: 0.3032 2022/10/05 14:16:54 - mmengine - INFO - Epoch(train) [7][9800/10520] lr: 1.0000e-04 eta: 23:45:32 time: 0.9480 data_time: 0.1396 memory: 17203 loss_visual: 0.0785 loss_lang: 0.1431 loss_fusion: 0.0687 loss: 0.2903 2022/10/05 14:17:37 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 14:17:51 - mmengine - INFO - Epoch(train) [7][9900/10520] lr: 1.0000e-04 eta: 23:44:19 time: 0.8409 data_time: 0.1180 memory: 17203 loss_visual: 0.0690 loss_lang: 0.1396 loss_fusion: 0.0603 loss: 0.2689 2022/10/05 14:18:46 - mmengine - INFO - Epoch(train) [7][10000/10520] lr: 1.0000e-04 eta: 23:43:04 time: 0.5659 data_time: 0.0268 memory: 17203 loss_visual: 0.0827 loss_lang: 0.1467 loss_fusion: 0.0716 loss: 0.3011 2022/10/05 14:19:42 - mmengine - INFO - Epoch(train) [7][10100/10520] lr: 1.0000e-04 eta: 23:41:50 time: 0.4158 data_time: 0.0276 memory: 17203 loss_visual: 0.0756 loss_lang: 0.1475 loss_fusion: 0.0661 loss: 0.2892 2022/10/05 14:20:37 - mmengine - INFO - Epoch(train) [7][10200/10520] lr: 1.0000e-04 eta: 23:40:35 time: 0.3769 data_time: 0.0356 memory: 17203 loss_visual: 0.0762 loss_lang: 0.1418 loss_fusion: 0.0661 loss: 0.2842 2022/10/05 14:21:33 - mmengine - INFO - Epoch(train) [7][10300/10520] lr: 1.0000e-04 eta: 23:39:21 time: 0.3526 data_time: 0.0031 memory: 17203 loss_visual: 0.0823 loss_lang: 0.1519 loss_fusion: 0.0713 loss: 0.3055 2022/10/05 14:22:29 - mmengine - INFO - Epoch(train) [7][10400/10520] lr: 1.0000e-04 eta: 23:38:07 time: 0.3566 data_time: 0.0034 memory: 17203 loss_visual: 0.0655 loss_lang: 0.1328 loss_fusion: 0.0554 loss: 0.2537 2022/10/05 14:23:26 - mmengine - INFO - Epoch(train) [7][10500/10520] lr: 1.0000e-04 eta: 23:36:56 time: 0.5687 data_time: 0.0671 memory: 17203 loss_visual: 0.0811 loss_lang: 0.1490 loss_fusion: 0.0701 loss: 0.3002 2022/10/05 14:23:34 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 14:23:34 - mmengine - INFO - Saving checkpoint at 7 epochs 2022/10/05 14:23:49 - mmengine - INFO - Epoch(val) [7][100/959] eta: 0:00:42 time: 0.0495 data_time: 0.0009 memory: 17203 2022/10/05 14:23:54 - mmengine - INFO - Epoch(val) [7][200/959] eta: 0:00:38 time: 0.0504 data_time: 0.0016 memory: 734 2022/10/05 14:23:59 - mmengine - INFO - Epoch(val) [7][300/959] eta: 0:00:33 time: 0.0511 data_time: 0.0019 memory: 734 2022/10/05 14:24:03 - mmengine - INFO - Epoch(val) [7][400/959] eta: 0:00:26 time: 0.0466 data_time: 0.0011 memory: 734 2022/10/05 14:24:09 - mmengine - INFO - Epoch(val) [7][500/959] eta: 0:00:22 time: 0.0498 data_time: 0.0013 memory: 734 2022/10/05 14:24:14 - mmengine - INFO - Epoch(val) [7][600/959] eta: 0:00:16 time: 0.0461 data_time: 0.0010 memory: 734 2022/10/05 14:24:19 - mmengine - INFO - Epoch(val) [7][700/959] eta: 0:00:12 time: 0.0469 data_time: 0.0020 memory: 734 2022/10/05 14:24:23 - mmengine - INFO - Epoch(val) [7][800/959] eta: 0:00:03 time: 0.0242 data_time: 0.0007 memory: 734 2022/10/05 14:24:25 - mmengine - INFO - Epoch(val) [7][900/959] eta: 0:00:01 time: 0.0218 data_time: 0.0006 memory: 734 2022/10/05 14:24:26 - mmengine - INFO - Epoch(val) [7][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.8229 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9350 SVT/recog/word_acc_ignore_case_symbol: 0.9366 SVTP/recog/word_acc_ignore_case_symbol: 0.8574 IC13/recog/word_acc_ignore_case_symbol: 0.9310 IC15/recog/word_acc_ignore_case_symbol: 0.7805 2022/10/05 14:25:30 - mmengine - INFO - Epoch(train) [8][100/10520] lr: 1.0000e-04 eta: 23:35:36 time: 0.8272 data_time: 0.1601 memory: 17203 loss_visual: 0.0731 loss_lang: 0.1483 loss_fusion: 0.0626 loss: 0.2839 2022/10/05 14:26:23 - mmengine - INFO - Epoch(train) [8][200/10520] lr: 1.0000e-04 eta: 23:34:16 time: 0.8549 data_time: 0.1955 memory: 17203 loss_visual: 0.0749 loss_lang: 0.1387 loss_fusion: 0.0631 loss: 0.2766 2022/10/05 14:27:16 - mmengine - INFO - Epoch(train) [8][300/10520] lr: 1.0000e-04 eta: 23:32:57 time: 0.5615 data_time: 0.0414 memory: 17203 loss_visual: 0.0776 loss_lang: 0.1469 loss_fusion: 0.0677 loss: 0.2921 2022/10/05 14:27:49 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 14:28:10 - mmengine - INFO - Epoch(train) [8][400/10520] lr: 1.0000e-04 eta: 23:31:40 time: 0.4349 data_time: 0.0174 memory: 17203 loss_visual: 0.0792 loss_lang: 0.1487 loss_fusion: 0.0704 loss: 0.2983 2022/10/05 14:29:03 - mmengine - INFO - Epoch(train) [8][500/10520] lr: 1.0000e-04 eta: 23:30:22 time: 0.4125 data_time: 0.0033 memory: 17203 loss_visual: 0.0687 loss_lang: 0.1366 loss_fusion: 0.0602 loss: 0.2655 2022/10/05 14:29:56 - mmengine - INFO - Epoch(train) [8][600/10520] lr: 1.0000e-04 eta: 23:29:02 time: 0.3624 data_time: 0.0033 memory: 17203 loss_visual: 0.0663 loss_lang: 0.1344 loss_fusion: 0.0566 loss: 0.2572 2022/10/05 14:30:49 - mmengine - INFO - Epoch(train) [8][700/10520] lr: 1.0000e-04 eta: 23:27:43 time: 0.3449 data_time: 0.0033 memory: 17203 loss_visual: 0.0717 loss_lang: 0.1399 loss_fusion: 0.0616 loss: 0.2731 2022/10/05 14:31:42 - mmengine - INFO - Epoch(train) [8][800/10520] lr: 1.0000e-04 eta: 23:26:25 time: 0.3689 data_time: 0.0035 memory: 17203 loss_visual: 0.0766 loss_lang: 0.1496 loss_fusion: 0.0675 loss: 0.2937 2022/10/05 14:32:41 - mmengine - INFO - Epoch(train) [8][900/10520] lr: 1.0000e-04 eta: 23:25:17 time: 0.8137 data_time: 0.1660 memory: 17203 loss_visual: 0.0708 loss_lang: 0.1419 loss_fusion: 0.0610 loss: 0.2737 2022/10/05 14:33:34 - mmengine - INFO - Epoch(train) [8][1000/10520] lr: 1.0000e-04 eta: 23:23:58 time: 0.8403 data_time: 0.1842 memory: 17203 loss_visual: 0.0648 loss_lang: 0.1353 loss_fusion: 0.0547 loss: 0.2548 2022/10/05 14:34:28 - mmengine - INFO - Epoch(train) [8][1100/10520] lr: 1.0000e-04 eta: 23:22:42 time: 0.5878 data_time: 0.0419 memory: 17203 loss_visual: 0.0665 loss_lang: 0.1365 loss_fusion: 0.0572 loss: 0.2602 2022/10/05 14:35:22 - mmengine - INFO - Epoch(train) [8][1200/10520] lr: 1.0000e-04 eta: 23:21:25 time: 0.4341 data_time: 0.0149 memory: 17203 loss_visual: 0.0728 loss_lang: 0.1395 loss_fusion: 0.0645 loss: 0.2768 2022/10/05 14:36:15 - mmengine - INFO - Epoch(train) [8][1300/10520] lr: 1.0000e-04 eta: 23:20:07 time: 0.4367 data_time: 0.0029 memory: 17203 loss_visual: 0.0756 loss_lang: 0.1438 loss_fusion: 0.0660 loss: 0.2854 2022/10/05 14:36:48 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 14:37:09 - mmengine - INFO - Epoch(train) [8][1400/10520] lr: 1.0000e-04 eta: 23:18:50 time: 0.3779 data_time: 0.0033 memory: 17203 loss_visual: 0.0701 loss_lang: 0.1337 loss_fusion: 0.0601 loss: 0.2638 2022/10/05 14:38:02 - mmengine - INFO - Epoch(train) [8][1500/10520] lr: 1.0000e-04 eta: 23:17:33 time: 0.3800 data_time: 0.0210 memory: 17203 loss_visual: 0.0672 loss_lang: 0.1354 loss_fusion: 0.0582 loss: 0.2609 2022/10/05 14:38:55 - mmengine - INFO - Epoch(train) [8][1600/10520] lr: 1.0000e-04 eta: 23:16:14 time: 0.3388 data_time: 0.0031 memory: 17203 loss_visual: 0.0755 loss_lang: 0.1402 loss_fusion: 0.0649 loss: 0.2806 2022/10/05 14:39:53 - mmengine - INFO - Epoch(train) [8][1700/10520] lr: 1.0000e-04 eta: 23:15:05 time: 0.7867 data_time: 0.1680 memory: 17203 loss_visual: 0.0742 loss_lang: 0.1423 loss_fusion: 0.0657 loss: 0.2822 2022/10/05 14:40:47 - mmengine - INFO - Epoch(train) [8][1800/10520] lr: 1.0000e-04 eta: 23:13:48 time: 0.8624 data_time: 0.1813 memory: 17203 loss_visual: 0.0665 loss_lang: 0.1332 loss_fusion: 0.0567 loss: 0.2564 2022/10/05 14:41:40 - mmengine - INFO - Epoch(train) [8][1900/10520] lr: 1.0000e-04 eta: 23:12:31 time: 0.6117 data_time: 0.0433 memory: 17203 loss_visual: 0.0728 loss_lang: 0.1421 loss_fusion: 0.0626 loss: 0.2774 2022/10/05 14:42:34 - mmengine - INFO - Epoch(train) [8][2000/10520] lr: 1.0000e-04 eta: 23:11:15 time: 0.4739 data_time: 0.0163 memory: 17203 loss_visual: 0.0746 loss_lang: 0.1461 loss_fusion: 0.0638 loss: 0.2846 2022/10/05 14:43:27 - mmengine - INFO - Epoch(train) [8][2100/10520] lr: 1.0000e-04 eta: 23:09:57 time: 0.4264 data_time: 0.0029 memory: 17203 loss_visual: 0.0764 loss_lang: 0.1500 loss_fusion: 0.0671 loss: 0.2935 2022/10/05 14:44:20 - mmengine - INFO - Epoch(train) [8][2200/10520] lr: 1.0000e-04 eta: 23:08:40 time: 0.3731 data_time: 0.0032 memory: 17203 loss_visual: 0.0683 loss_lang: 0.1360 loss_fusion: 0.0589 loss: 0.2633 2022/10/05 14:45:14 - mmengine - INFO - Epoch(train) [8][2300/10520] lr: 1.0000e-04 eta: 23:07:22 time: 0.3625 data_time: 0.0032 memory: 17203 loss_visual: 0.0685 loss_lang: 0.1323 loss_fusion: 0.0588 loss: 0.2597 2022/10/05 14:45:47 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 14:46:07 - mmengine - INFO - Epoch(train) [8][2400/10520] lr: 1.0000e-04 eta: 23:06:06 time: 0.3643 data_time: 0.0030 memory: 17203 loss_visual: 0.0756 loss_lang: 0.1494 loss_fusion: 0.0662 loss: 0.2913 2022/10/05 14:47:05 - mmengine - INFO - Epoch(train) [8][2500/10520] lr: 1.0000e-04 eta: 23:04:57 time: 0.7832 data_time: 0.1636 memory: 17203 loss_visual: 0.0772 loss_lang: 0.1429 loss_fusion: 0.0670 loss: 0.2871 2022/10/05 14:47:59 - mmengine - INFO - Epoch(train) [8][2600/10520] lr: 1.0000e-04 eta: 23:03:40 time: 0.8638 data_time: 0.1810 memory: 17203 loss_visual: 0.0752 loss_lang: 0.1403 loss_fusion: 0.0651 loss: 0.2806 2022/10/05 14:48:52 - mmengine - INFO - Epoch(train) [8][2700/10520] lr: 1.0000e-04 eta: 23:02:23 time: 0.5991 data_time: 0.0475 memory: 17203 loss_visual: 0.0712 loss_lang: 0.1403 loss_fusion: 0.0618 loss: 0.2732 2022/10/05 14:49:46 - mmengine - INFO - Epoch(train) [8][2800/10520] lr: 1.0000e-04 eta: 23:01:08 time: 0.4286 data_time: 0.0169 memory: 17203 loss_visual: 0.0772 loss_lang: 0.1500 loss_fusion: 0.0682 loss: 0.2955 2022/10/05 14:50:40 - mmengine - INFO - Epoch(train) [8][2900/10520] lr: 1.0000e-04 eta: 22:59:51 time: 0.4241 data_time: 0.0034 memory: 17203 loss_visual: 0.0767 loss_lang: 0.1460 loss_fusion: 0.0679 loss: 0.2906 2022/10/05 14:51:33 - mmengine - INFO - Epoch(train) [8][3000/10520] lr: 1.0000e-04 eta: 22:58:35 time: 0.3716 data_time: 0.0035 memory: 17203 loss_visual: 0.0678 loss_lang: 0.1345 loss_fusion: 0.0583 loss: 0.2606 2022/10/05 14:52:26 - mmengine - INFO - Epoch(train) [8][3100/10520] lr: 1.0000e-04 eta: 22:57:17 time: 0.3420 data_time: 0.0029 memory: 17203 loss_visual: 0.0655 loss_lang: 0.1305 loss_fusion: 0.0559 loss: 0.2519 2022/10/05 14:53:19 - mmengine - INFO - Epoch(train) [8][3200/10520] lr: 1.0000e-04 eta: 22:56:00 time: 0.3412 data_time: 0.0030 memory: 17203 loss_visual: 0.0738 loss_lang: 0.1395 loss_fusion: 0.0639 loss: 0.2771 2022/10/05 14:54:16 - mmengine - INFO - Epoch(train) [8][3300/10520] lr: 1.0000e-04 eta: 22:54:51 time: 0.8156 data_time: 0.1621 memory: 17203 loss_visual: 0.0688 loss_lang: 0.1379 loss_fusion: 0.0610 loss: 0.2676 2022/10/05 14:54:45 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 14:55:10 - mmengine - INFO - Epoch(train) [8][3400/10520] lr: 1.0000e-04 eta: 22:53:34 time: 0.8583 data_time: 0.1978 memory: 17203 loss_visual: 0.0761 loss_lang: 0.1480 loss_fusion: 0.0677 loss: 0.2918 2022/10/05 14:56:02 - mmengine - INFO - Epoch(train) [8][3500/10520] lr: 1.0000e-04 eta: 22:52:16 time: 0.5797 data_time: 0.0440 memory: 17203 loss_visual: 0.0760 loss_lang: 0.1422 loss_fusion: 0.0667 loss: 0.2849 2022/10/05 14:56:56 - mmengine - INFO - Epoch(train) [8][3600/10520] lr: 1.0000e-04 eta: 22:51:01 time: 0.4313 data_time: 0.0156 memory: 17203 loss_visual: 0.0811 loss_lang: 0.1508 loss_fusion: 0.0711 loss: 0.3029 2022/10/05 14:57:49 - mmengine - INFO - Epoch(train) [8][3700/10520] lr: 1.0000e-04 eta: 22:49:44 time: 0.4139 data_time: 0.0031 memory: 17203 loss_visual: 0.0755 loss_lang: 0.1454 loss_fusion: 0.0677 loss: 0.2886 2022/10/05 14:58:43 - mmengine - INFO - Epoch(train) [8][3800/10520] lr: 1.0000e-04 eta: 22:48:29 time: 0.3792 data_time: 0.0040 memory: 17203 loss_visual: 0.0665 loss_lang: 0.1348 loss_fusion: 0.0571 loss: 0.2584 2022/10/05 14:59:36 - mmengine - INFO - Epoch(train) [8][3900/10520] lr: 1.0000e-04 eta: 22:47:13 time: 0.3438 data_time: 0.0031 memory: 17203 loss_visual: 0.0745 loss_lang: 0.1426 loss_fusion: 0.0651 loss: 0.2822 2022/10/05 15:00:30 - mmengine - INFO - Epoch(train) [8][4000/10520] lr: 1.0000e-04 eta: 22:45:58 time: 0.3377 data_time: 0.0032 memory: 17203 loss_visual: 0.0691 loss_lang: 0.1369 loss_fusion: 0.0589 loss: 0.2650 2022/10/05 15:01:27 - mmengine - INFO - Epoch(train) [8][4100/10520] lr: 1.0000e-04 eta: 22:44:47 time: 0.7888 data_time: 0.1568 memory: 17203 loss_visual: 0.0750 loss_lang: 0.1414 loss_fusion: 0.0653 loss: 0.2817 2022/10/05 15:02:21 - mmengine - INFO - Epoch(train) [8][4200/10520] lr: 1.0000e-04 eta: 22:43:32 time: 0.8418 data_time: 0.1910 memory: 17203 loss_visual: 0.0752 loss_lang: 0.1382 loss_fusion: 0.0658 loss: 0.2792 2022/10/05 15:03:15 - mmengine - INFO - Epoch(train) [8][4300/10520] lr: 1.0000e-04 eta: 22:42:18 time: 0.6095 data_time: 0.0432 memory: 17203 loss_visual: 0.0717 loss_lang: 0.1371 loss_fusion: 0.0627 loss: 0.2715 2022/10/05 15:03:48 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 15:04:09 - mmengine - INFO - Epoch(train) [8][4400/10520] lr: 1.0000e-04 eta: 22:41:02 time: 0.4627 data_time: 0.0140 memory: 17203 loss_visual: 0.0701 loss_lang: 0.1390 loss_fusion: 0.0608 loss: 0.2700 2022/10/05 15:05:01 - mmengine - INFO - Epoch(train) [8][4500/10520] lr: 1.0000e-04 eta: 22:39:44 time: 0.4153 data_time: 0.0029 memory: 17203 loss_visual: 0.0747 loss_lang: 0.1416 loss_fusion: 0.0650 loss: 0.2813 2022/10/05 15:05:54 - mmengine - INFO - Epoch(train) [8][4600/10520] lr: 1.0000e-04 eta: 22:38:28 time: 0.3733 data_time: 0.0030 memory: 17203 loss_visual: 0.0642 loss_lang: 0.1307 loss_fusion: 0.0551 loss: 0.2501 2022/10/05 15:06:47 - mmengine - INFO - Epoch(train) [8][4700/10520] lr: 1.0000e-04 eta: 22:37:11 time: 0.3443 data_time: 0.0031 memory: 17203 loss_visual: 0.0790 loss_lang: 0.1469 loss_fusion: 0.0685 loss: 0.2943 2022/10/05 15:07:39 - mmengine - INFO - Epoch(train) [8][4800/10520] lr: 1.0000e-04 eta: 22:35:55 time: 0.3405 data_time: 0.0031 memory: 17203 loss_visual: 0.0756 loss_lang: 0.1400 loss_fusion: 0.0648 loss: 0.2804 2022/10/05 15:11:52 - mmengine - INFO - Epoch(train) [8][4900/10520] lr: 1.0000e-04 eta: 22:40:13 time: 2.4124 data_time: 0.2217 memory: 17203 loss_visual: 0.0699 loss_lang: 0.1363 loss_fusion: 0.0607 loss: 0.2669 2022/10/05 15:12:46 - mmengine - INFO - Epoch(train) [8][5000/10520] lr: 1.0000e-04 eta: 22:38:59 time: 0.8384 data_time: 0.1751 memory: 17203 loss_visual: 0.0712 loss_lang: 0.1400 loss_fusion: 0.0620 loss: 0.2731 2022/10/05 15:13:38 - mmengine - INFO - Epoch(train) [8][5100/10520] lr: 1.0000e-04 eta: 22:37:40 time: 0.5635 data_time: 0.0437 memory: 17203 loss_visual: 0.0769 loss_lang: 0.1443 loss_fusion: 0.0669 loss: 0.2880 2022/10/05 15:14:31 - mmengine - INFO - Epoch(train) [8][5200/10520] lr: 1.0000e-04 eta: 22:36:23 time: 0.4329 data_time: 0.0156 memory: 17203 loss_visual: 0.0709 loss_lang: 0.1378 loss_fusion: 0.0610 loss: 0.2697 2022/10/05 15:15:24 - mmengine - INFO - Epoch(train) [8][5300/10520] lr: 1.0000e-04 eta: 22:35:06 time: 0.4013 data_time: 0.0032 memory: 17203 loss_visual: 0.0609 loss_lang: 0.1298 loss_fusion: 0.0511 loss: 0.2417 2022/10/05 15:15:56 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 15:16:16 - mmengine - INFO - Epoch(train) [8][5400/10520] lr: 1.0000e-04 eta: 22:33:49 time: 0.3913 data_time: 0.0036 memory: 17203 loss_visual: 0.0667 loss_lang: 0.1292 loss_fusion: 0.0578 loss: 0.2538 2022/10/05 15:17:09 - mmengine - INFO - Epoch(train) [8][5500/10520] lr: 1.0000e-04 eta: 22:32:32 time: 0.3617 data_time: 0.0031 memory: 17203 loss_visual: 0.0720 loss_lang: 0.1387 loss_fusion: 0.0628 loss: 0.2735 2022/10/05 15:18:02 - mmengine - INFO - Epoch(train) [8][5600/10520] lr: 1.0000e-04 eta: 22:31:15 time: 0.3380 data_time: 0.0031 memory: 17203 loss_visual: 0.0763 loss_lang: 0.1397 loss_fusion: 0.0657 loss: 0.2817 2022/10/05 15:18:59 - mmengine - INFO - Epoch(train) [8][5700/10520] lr: 1.0000e-04 eta: 22:30:05 time: 0.7653 data_time: 0.1723 memory: 17203 loss_visual: 0.0669 loss_lang: 0.1337 loss_fusion: 0.0574 loss: 0.2580 2022/10/05 15:19:53 - mmengine - INFO - Epoch(train) [8][5800/10520] lr: 1.0000e-04 eta: 22:28:51 time: 0.9017 data_time: 0.1806 memory: 17203 loss_visual: 0.0723 loss_lang: 0.1366 loss_fusion: 0.0632 loss: 0.2720 2022/10/05 15:20:47 - mmengine - INFO - Epoch(train) [8][5900/10520] lr: 1.0000e-04 eta: 22:27:36 time: 0.6138 data_time: 0.0453 memory: 17203 loss_visual: 0.0732 loss_lang: 0.1430 loss_fusion: 0.0628 loss: 0.2791 2022/10/05 15:21:40 - mmengine - INFO - Epoch(train) [8][6000/10520] lr: 1.0000e-04 eta: 22:26:21 time: 0.4449 data_time: 0.0153 memory: 17203 loss_visual: 0.0822 loss_lang: 0.1500 loss_fusion: 0.0734 loss: 0.3057 2022/10/05 15:22:34 - mmengine - INFO - Epoch(train) [8][6100/10520] lr: 1.0000e-04 eta: 22:25:05 time: 0.4291 data_time: 0.0034 memory: 17203 loss_visual: 0.0701 loss_lang: 0.1390 loss_fusion: 0.0613 loss: 0.2704 2022/10/05 15:23:26 - mmengine - INFO - Epoch(train) [8][6200/10520] lr: 1.0000e-04 eta: 22:23:48 time: 0.3919 data_time: 0.0033 memory: 17203 loss_visual: 0.0625 loss_lang: 0.1282 loss_fusion: 0.0537 loss: 0.2445 2022/10/05 15:24:19 - mmengine - INFO - Epoch(train) [8][6300/10520] lr: 1.0000e-04 eta: 22:22:31 time: 0.3469 data_time: 0.0033 memory: 17203 loss_visual: 0.0684 loss_lang: 0.1346 loss_fusion: 0.0588 loss: 0.2618 2022/10/05 15:24:52 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 15:25:12 - mmengine - INFO - Epoch(train) [8][6400/10520] lr: 1.0000e-04 eta: 22:21:16 time: 0.3382 data_time: 0.0032 memory: 17203 loss_visual: 0.0693 loss_lang: 0.1386 loss_fusion: 0.0605 loss: 0.2684 2022/10/05 15:26:10 - mmengine - INFO - Epoch(train) [8][6500/10520] lr: 1.0000e-04 eta: 22:20:07 time: 0.7657 data_time: 0.1724 memory: 17203 loss_visual: 0.0727 loss_lang: 0.1393 loss_fusion: 0.0638 loss: 0.2759 2022/10/05 15:27:04 - mmengine - INFO - Epoch(train) [8][6600/10520] lr: 1.0000e-04 eta: 22:18:53 time: 0.8873 data_time: 0.1995 memory: 17203 loss_visual: 0.0797 loss_lang: 0.1456 loss_fusion: 0.0707 loss: 0.2960 2022/10/05 15:27:56 - mmengine - INFO - Epoch(train) [8][6700/10520] lr: 1.0000e-04 eta: 22:17:37 time: 0.6028 data_time: 0.0592 memory: 17203 loss_visual: 0.0689 loss_lang: 0.1375 loss_fusion: 0.0596 loss: 0.2660 2022/10/05 15:28:50 - mmengine - INFO - Epoch(train) [8][6800/10520] lr: 1.0000e-04 eta: 22:16:22 time: 0.4524 data_time: 0.0141 memory: 17203 loss_visual: 0.0681 loss_lang: 0.1394 loss_fusion: 0.0597 loss: 0.2671 2022/10/05 15:29:43 - mmengine - INFO - Epoch(train) [8][6900/10520] lr: 1.0000e-04 eta: 22:15:06 time: 0.4037 data_time: 0.0033 memory: 17203 loss_visual: 0.0720 loss_lang: 0.1376 loss_fusion: 0.0624 loss: 0.2721 2022/10/05 15:30:36 - mmengine - INFO - Epoch(train) [8][7000/10520] lr: 1.0000e-04 eta: 22:13:51 time: 0.3677 data_time: 0.0044 memory: 17203 loss_visual: 0.0744 loss_lang: 0.1428 loss_fusion: 0.0641 loss: 0.2813 2022/10/05 15:31:30 - mmengine - INFO - Epoch(train) [8][7100/10520] lr: 1.0000e-04 eta: 22:12:36 time: 0.3584 data_time: 0.0034 memory: 17203 loss_visual: 0.0674 loss_lang: 0.1331 loss_fusion: 0.0595 loss: 0.2599 2022/10/05 15:32:23 - mmengine - INFO - Epoch(train) [8][7200/10520] lr: 1.0000e-04 eta: 22:11:20 time: 0.3601 data_time: 0.0032 memory: 17203 loss_visual: 0.0848 loss_lang: 0.1495 loss_fusion: 0.0752 loss: 0.3095 2022/10/05 15:33:20 - mmengine - INFO - Epoch(train) [8][7300/10520] lr: 1.0000e-04 eta: 22:10:11 time: 0.7657 data_time: 0.1674 memory: 17203 loss_visual: 0.0690 loss_lang: 0.1372 loss_fusion: 0.0598 loss: 0.2661 2022/10/05 15:33:49 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 15:34:14 - mmengine - INFO - Epoch(train) [8][7400/10520] lr: 1.0000e-04 eta: 22:08:58 time: 0.8660 data_time: 0.1827 memory: 17203 loss_visual: 0.0719 loss_lang: 0.1404 loss_fusion: 0.0627 loss: 0.2750 2022/10/05 15:35:08 - mmengine - INFO - Epoch(train) [8][7500/10520] lr: 1.0000e-04 eta: 22:07:44 time: 0.6369 data_time: 0.0428 memory: 17203 loss_visual: 0.0780 loss_lang: 0.1490 loss_fusion: 0.0683 loss: 0.2953 2022/10/05 15:36:01 - mmengine - INFO - Epoch(train) [8][7600/10520] lr: 1.0000e-04 eta: 22:06:29 time: 0.4580 data_time: 0.0146 memory: 17203 loss_visual: 0.0781 loss_lang: 0.1455 loss_fusion: 0.0694 loss: 0.2930 2022/10/05 15:36:54 - mmengine - INFO - Epoch(train) [8][7700/10520] lr: 1.0000e-04 eta: 22:05:14 time: 0.4130 data_time: 0.0031 memory: 17203 loss_visual: 0.0748 loss_lang: 0.1460 loss_fusion: 0.0654 loss: 0.2861 2022/10/05 15:37:48 - mmengine - INFO - Epoch(train) [8][7800/10520] lr: 1.0000e-04 eta: 22:03:59 time: 0.3678 data_time: 0.0037 memory: 17203 loss_visual: 0.0728 loss_lang: 0.1423 loss_fusion: 0.0632 loss: 0.2783 2022/10/05 15:38:40 - mmengine - INFO - Epoch(train) [8][7900/10520] lr: 1.0000e-04 eta: 22:02:44 time: 0.3449 data_time: 0.0038 memory: 17203 loss_visual: 0.0747 loss_lang: 0.1472 loss_fusion: 0.0651 loss: 0.2870 2022/10/05 15:39:33 - mmengine - INFO - Epoch(train) [8][8000/10520] lr: 1.0000e-04 eta: 22:01:28 time: 0.3402 data_time: 0.0034 memory: 17203 loss_visual: 0.0739 loss_lang: 0.1407 loss_fusion: 0.0642 loss: 0.2788 2022/10/05 15:40:31 - mmengine - INFO - Epoch(train) [8][8100/10520] lr: 1.0000e-04 eta: 22:00:21 time: 0.8152 data_time: 0.1873 memory: 17203 loss_visual: 0.0680 loss_lang: 0.1442 loss_fusion: 0.0593 loss: 0.2715 2022/10/05 15:41:25 - mmengine - INFO - Epoch(train) [8][8200/10520] lr: 1.0000e-04 eta: 21:59:07 time: 0.8560 data_time: 0.1891 memory: 17203 loss_visual: 0.0645 loss_lang: 0.1279 loss_fusion: 0.0545 loss: 0.2469 2022/10/05 15:42:20 - mmengine - INFO - Epoch(train) [8][8300/10520] lr: 1.0000e-04 eta: 21:57:55 time: 0.6041 data_time: 0.0427 memory: 17203 loss_visual: 0.0740 loss_lang: 0.1387 loss_fusion: 0.0651 loss: 0.2779 2022/10/05 15:42:54 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 15:43:15 - mmengine - INFO - Epoch(train) [8][8400/10520] lr: 1.0000e-04 eta: 21:56:44 time: 0.4700 data_time: 0.0149 memory: 17203 loss_visual: 0.0733 loss_lang: 0.1380 loss_fusion: 0.0634 loss: 0.2746 2022/10/05 15:44:08 - mmengine - INFO - Epoch(train) [8][8500/10520] lr: 1.0000e-04 eta: 21:55:28 time: 0.4346 data_time: 0.0032 memory: 17203 loss_visual: 0.0810 loss_lang: 0.1463 loss_fusion: 0.0703 loss: 0.2977 2022/10/05 15:45:01 - mmengine - INFO - Epoch(train) [8][8600/10520] lr: 1.0000e-04 eta: 21:54:13 time: 0.4084 data_time: 0.0043 memory: 17203 loss_visual: 0.0694 loss_lang: 0.1335 loss_fusion: 0.0607 loss: 0.2636 2022/10/05 15:45:54 - mmengine - INFO - Epoch(train) [8][8700/10520] lr: 1.0000e-04 eta: 21:52:59 time: 0.3492 data_time: 0.0031 memory: 17203 loss_visual: 0.0712 loss_lang: 0.1395 loss_fusion: 0.0616 loss: 0.2723 2022/10/05 15:46:48 - mmengine - INFO - Epoch(train) [8][8800/10520] lr: 1.0000e-04 eta: 21:51:45 time: 0.3392 data_time: 0.0034 memory: 17203 loss_visual: 0.0687 loss_lang: 0.1356 loss_fusion: 0.0603 loss: 0.2646 2022/10/05 15:47:45 - mmengine - INFO - Epoch(train) [8][8900/10520] lr: 1.0000e-04 eta: 21:50:36 time: 0.7541 data_time: 0.1660 memory: 17203 loss_visual: 0.0740 loss_lang: 0.1462 loss_fusion: 0.0648 loss: 0.2850 2022/10/05 15:48:38 - mmengine - INFO - Epoch(train) [8][9000/10520] lr: 1.0000e-04 eta: 21:49:22 time: 0.8403 data_time: 0.1883 memory: 17203 loss_visual: 0.0792 loss_lang: 0.1448 loss_fusion: 0.0699 loss: 0.2939 2022/10/05 15:49:32 - mmengine - INFO - Epoch(train) [8][9100/10520] lr: 1.0000e-04 eta: 21:48:09 time: 0.6290 data_time: 0.0567 memory: 17203 loss_visual: 0.0768 loss_lang: 0.1452 loss_fusion: 0.0656 loss: 0.2875 2022/10/05 15:50:26 - mmengine - INFO - Epoch(train) [8][9200/10520] lr: 1.0000e-04 eta: 21:46:56 time: 0.4325 data_time: 0.0162 memory: 17203 loss_visual: 0.0660 loss_lang: 0.1327 loss_fusion: 0.0565 loss: 0.2553 2022/10/05 15:51:19 - mmengine - INFO - Epoch(train) [8][9300/10520] lr: 1.0000e-04 eta: 21:45:41 time: 0.4426 data_time: 0.0034 memory: 17203 loss_visual: 0.0684 loss_lang: 0.1349 loss_fusion: 0.0593 loss: 0.2626 2022/10/05 15:51:57 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 15:52:21 - mmengine - INFO - Epoch(train) [8][9400/10520] lr: 1.0000e-04 eta: 21:44:40 time: 0.3782 data_time: 0.0033 memory: 17203 loss_visual: 0.0745 loss_lang: 0.1404 loss_fusion: 0.0657 loss: 0.2806 2022/10/05 15:56:33 - mmengine - INFO - Epoch(train) [8][9500/10520] lr: 1.0000e-04 eta: 21:48:30 time: 0.3729 data_time: 0.0032 memory: 17203 loss_visual: 0.0720 loss_lang: 0.1392 loss_fusion: 0.0623 loss: 0.2735 2022/10/05 15:57:27 - mmengine - INFO - Epoch(train) [8][9600/10520] lr: 1.0000e-04 eta: 21:47:18 time: 0.3513 data_time: 0.0030 memory: 17203 loss_visual: 0.0708 loss_lang: 0.1387 loss_fusion: 0.0611 loss: 0.2706 2022/10/05 15:58:26 - mmengine - INFO - Epoch(train) [8][9700/10520] lr: 1.0000e-04 eta: 21:46:12 time: 0.7870 data_time: 0.1687 memory: 17203 loss_visual: 0.0658 loss_lang: 0.1293 loss_fusion: 0.0561 loss: 0.2512 2022/10/05 15:59:20 - mmengine - INFO - Epoch(train) [8][9800/10520] lr: 1.0000e-04 eta: 21:44:59 time: 0.8471 data_time: 0.1907 memory: 17203 loss_visual: 0.0615 loss_lang: 0.1294 loss_fusion: 0.0534 loss: 0.2442 2022/10/05 16:00:15 - mmengine - INFO - Epoch(train) [8][9900/10520] lr: 1.0000e-04 eta: 21:43:46 time: 0.6241 data_time: 0.0478 memory: 17203 loss_visual: 0.0675 loss_lang: 0.1348 loss_fusion: 0.0586 loss: 0.2609 2022/10/05 16:01:09 - mmengine - INFO - Epoch(train) [8][10000/10520] lr: 1.0000e-04 eta: 21:42:33 time: 0.4632 data_time: 0.0150 memory: 17203 loss_visual: 0.0679 loss_lang: 0.1338 loss_fusion: 0.0589 loss: 0.2607 2022/10/05 16:02:02 - mmengine - INFO - Epoch(train) [8][10100/10520] lr: 1.0000e-04 eta: 21:41:19 time: 0.4041 data_time: 0.0035 memory: 17203 loss_visual: 0.0696 loss_lang: 0.1341 loss_fusion: 0.0602 loss: 0.2638 2022/10/05 16:02:57 - mmengine - INFO - Epoch(train) [8][10200/10520] lr: 1.0000e-04 eta: 21:40:06 time: 0.3978 data_time: 0.0037 memory: 17203 loss_visual: 0.0800 loss_lang: 0.1455 loss_fusion: 0.0704 loss: 0.2959 2022/10/05 16:03:50 - mmengine - INFO - Epoch(train) [8][10300/10520] lr: 1.0000e-04 eta: 21:38:52 time: 0.3965 data_time: 0.0111 memory: 17203 loss_visual: 0.0667 loss_lang: 0.1360 loss_fusion: 0.0575 loss: 0.2601 2022/10/05 16:04:24 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 16:04:43 - mmengine - INFO - Epoch(train) [8][10400/10520] lr: 1.0000e-04 eta: 21:37:38 time: 0.3419 data_time: 0.0034 memory: 17203 loss_visual: 0.0638 loss_lang: 0.1300 loss_fusion: 0.0544 loss: 0.2482 2022/10/05 16:05:38 - mmengine - INFO - Epoch(train) [8][10500/10520] lr: 1.0000e-04 eta: 21:36:26 time: 0.5898 data_time: 0.0974 memory: 17203 loss_visual: 0.0706 loss_lang: 0.1349 loss_fusion: 0.0612 loss: 0.2667 2022/10/05 16:05:45 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 16:05:45 - mmengine - INFO - Saving checkpoint at 8 epochs 2022/10/05 16:05:59 - mmengine - INFO - Epoch(val) [8][100/959] eta: 0:00:37 time: 0.0432 data_time: 0.0015 memory: 17203 2022/10/05 16:06:04 - mmengine - INFO - Epoch(val) [8][200/959] eta: 0:00:37 time: 0.0490 data_time: 0.0014 memory: 734 2022/10/05 16:06:09 - mmengine - INFO - Epoch(val) [8][300/959] eta: 0:00:34 time: 0.0526 data_time: 0.0017 memory: 734 2022/10/05 16:06:14 - mmengine - INFO - Epoch(val) [8][400/959] eta: 0:00:30 time: 0.0539 data_time: 0.0023 memory: 734 2022/10/05 16:06:19 - mmengine - INFO - Epoch(val) [8][500/959] eta: 0:00:23 time: 0.0520 data_time: 0.0018 memory: 734 2022/10/05 16:06:24 - mmengine - INFO - Epoch(val) [8][600/959] eta: 0:00:16 time: 0.0453 data_time: 0.0012 memory: 734 2022/10/05 16:06:29 - mmengine - INFO - Epoch(val) [8][700/959] eta: 0:00:12 time: 0.0485 data_time: 0.0011 memory: 734 2022/10/05 16:06:33 - mmengine - INFO - Epoch(val) [8][800/959] eta: 0:00:05 time: 0.0319 data_time: 0.0009 memory: 734 2022/10/05 16:06:36 - mmengine - INFO - Epoch(val) [8][900/959] eta: 0:00:01 time: 0.0214 data_time: 0.0005 memory: 734 2022/10/05 16:06:37 - mmengine - INFO - Epoch(val) [8][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.8194 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9397 SVT/recog/word_acc_ignore_case_symbol: 0.9258 SVTP/recog/word_acc_ignore_case_symbol: 0.8651 IC13/recog/word_acc_ignore_case_symbol: 0.9330 IC15/recog/word_acc_ignore_case_symbol: 0.7877 2022/10/05 16:07:40 - mmengine - INFO - Epoch(train) [9][100/10520] lr: 1.0000e-04 eta: 21:35:06 time: 0.7369 data_time: 0.1429 memory: 17203 loss_visual: 0.0772 loss_lang: 0.1431 loss_fusion: 0.0687 loss: 0.2890 2022/10/05 16:08:32 - mmengine - INFO - Epoch(train) [9][200/10520] lr: 1.0000e-04 eta: 21:33:51 time: 0.7456 data_time: 0.1628 memory: 17203 loss_visual: 0.0707 loss_lang: 0.1347 loss_fusion: 0.0616 loss: 0.2670 2022/10/05 16:09:25 - mmengine - INFO - Epoch(train) [9][300/10520] lr: 1.0000e-04 eta: 21:32:37 time: 0.6652 data_time: 0.1006 memory: 17203 loss_visual: 0.0688 loss_lang: 0.1351 loss_fusion: 0.0598 loss: 0.2637 2022/10/05 16:10:18 - mmengine - INFO - Epoch(train) [9][400/10520] lr: 1.0000e-04 eta: 21:31:22 time: 0.5335 data_time: 0.0275 memory: 17203 loss_visual: 0.0695 loss_lang: 0.1394 loss_fusion: 0.0596 loss: 0.2684 2022/10/05 16:11:11 - mmengine - INFO - Epoch(train) [9][500/10520] lr: 1.0000e-04 eta: 21:30:08 time: 0.4157 data_time: 0.0112 memory: 17203 loss_visual: 0.0596 loss_lang: 0.1271 loss_fusion: 0.0492 loss: 0.2360 2022/10/05 16:12:05 - mmengine - INFO - Epoch(train) [9][600/10520] lr: 1.0000e-04 eta: 21:28:55 time: 0.3789 data_time: 0.0107 memory: 17203 loss_visual: 0.0743 loss_lang: 0.1366 loss_fusion: 0.0647 loss: 0.2755 2022/10/05 16:12:58 - mmengine - INFO - Epoch(train) [9][700/10520] lr: 1.0000e-04 eta: 21:27:40 time: 0.3658 data_time: 0.0106 memory: 17203 loss_visual: 0.0716 loss_lang: 0.1343 loss_fusion: 0.0616 loss: 0.2674 2022/10/05 16:13:51 - mmengine - INFO - Epoch(train) [9][800/10520] lr: 1.0000e-04 eta: 21:26:25 time: 0.3588 data_time: 0.0030 memory: 17203 loss_visual: 0.0774 loss_lang: 0.1412 loss_fusion: 0.0682 loss: 0.2869 2022/10/05 16:14:14 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 16:14:46 - mmengine - INFO - Epoch(train) [9][900/10520] lr: 1.0000e-04 eta: 21:25:15 time: 0.6789 data_time: 0.1370 memory: 17203 loss_visual: 0.0739 loss_lang: 0.1422 loss_fusion: 0.0656 loss: 0.2816 2022/10/05 16:15:40 - mmengine - INFO - Epoch(train) [9][1000/10520] lr: 1.0000e-04 eta: 21:24:03 time: 0.8482 data_time: 0.1728 memory: 17203 loss_visual: 0.0660 loss_lang: 0.1333 loss_fusion: 0.0560 loss: 0.2553 2022/10/05 16:16:32 - mmengine - INFO - Epoch(train) [9][1100/10520] lr: 1.0000e-04 eta: 21:22:47 time: 0.6347 data_time: 0.0984 memory: 17203 loss_visual: 0.0821 loss_lang: 0.1463 loss_fusion: 0.0712 loss: 0.2996 2022/10/05 16:17:26 - mmengine - INFO - Epoch(train) [9][1200/10520] lr: 1.0000e-04 eta: 21:21:34 time: 0.5497 data_time: 0.0251 memory: 17203 loss_visual: 0.0746 loss_lang: 0.1438 loss_fusion: 0.0647 loss: 0.2830 2022/10/05 16:18:18 - mmengine - INFO - Epoch(train) [9][1300/10520] lr: 1.0000e-04 eta: 21:20:19 time: 0.4036 data_time: 0.0120 memory: 17203 loss_visual: 0.0672 loss_lang: 0.1392 loss_fusion: 0.0575 loss: 0.2639 2022/10/05 16:19:12 - mmengine - INFO - Epoch(train) [9][1400/10520] lr: 1.0000e-04 eta: 21:19:06 time: 0.3829 data_time: 0.0110 memory: 17203 loss_visual: 0.0702 loss_lang: 0.1364 loss_fusion: 0.0605 loss: 0.2671 2022/10/05 16:20:35 - mmengine - INFO - Epoch(train) [9][1500/10520] lr: 1.0000e-04 eta: 21:18:35 time: 2.7291 data_time: 0.0215 memory: 17203 loss_visual: 0.0656 loss_lang: 0.1299 loss_fusion: 0.0557 loss: 0.2512 2022/10/05 16:21:27 - mmengine - INFO - Epoch(train) [9][1600/10520] lr: 1.0000e-04 eta: 21:17:21 time: 0.3466 data_time: 0.0032 memory: 17203 loss_visual: 0.0720 loss_lang: 0.1364 loss_fusion: 0.0618 loss: 0.2702 2022/10/05 16:22:59 - mmengine - INFO - Epoch(train) [9][1700/10520] lr: 1.0000e-04 eta: 21:17:04 time: 0.6745 data_time: 0.1490 memory: 17203 loss_visual: 0.0698 loss_lang: 0.1324 loss_fusion: 0.0600 loss: 0.2623 2022/10/05 16:23:53 - mmengine - INFO - Epoch(train) [9][1800/10520] lr: 1.0000e-04 eta: 21:15:51 time: 0.8104 data_time: 0.1870 memory: 17203 loss_visual: 0.0758 loss_lang: 0.1436 loss_fusion: 0.0665 loss: 0.2859 2022/10/05 16:24:14 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 16:24:46 - mmengine - INFO - Epoch(train) [9][1900/10520] lr: 1.0000e-04 eta: 21:14:37 time: 0.6560 data_time: 0.1372 memory: 17203 loss_visual: 0.0713 loss_lang: 0.1335 loss_fusion: 0.0606 loss: 0.2654 2022/10/05 16:25:38 - mmengine - INFO - Epoch(train) [9][2000/10520] lr: 1.0000e-04 eta: 21:13:23 time: 0.5123 data_time: 0.0474 memory: 17203 loss_visual: 0.0671 loss_lang: 0.1307 loss_fusion: 0.0565 loss: 0.2543 2022/10/05 16:26:32 - mmengine - INFO - Epoch(train) [9][2100/10520] lr: 1.0000e-04 eta: 21:12:10 time: 0.4233 data_time: 0.0117 memory: 17203 loss_visual: 0.0715 loss_lang: 0.1379 loss_fusion: 0.0637 loss: 0.2731 2022/10/05 16:27:24 - mmengine - INFO - Epoch(train) [9][2200/10520] lr: 1.0000e-04 eta: 21:10:55 time: 0.3827 data_time: 0.0117 memory: 17203 loss_visual: 0.0705 loss_lang: 0.1355 loss_fusion: 0.0592 loss: 0.2651 2022/10/05 16:28:16 - mmengine - INFO - Epoch(train) [9][2300/10520] lr: 1.0000e-04 eta: 21:09:40 time: 0.3671 data_time: 0.0113 memory: 17203 loss_visual: 0.0680 loss_lang: 0.1359 loss_fusion: 0.0578 loss: 0.2617 2022/10/05 16:29:09 - mmengine - INFO - Epoch(train) [9][2400/10520] lr: 1.0000e-04 eta: 21:08:25 time: 0.3474 data_time: 0.0030 memory: 17203 loss_visual: 0.0688 loss_lang: 0.1334 loss_fusion: 0.0600 loss: 0.2622 2022/10/05 16:30:04 - mmengine - INFO - Epoch(train) [9][2500/10520] lr: 1.0000e-04 eta: 21:07:16 time: 0.6731 data_time: 0.1435 memory: 17203 loss_visual: 0.0722 loss_lang: 0.1391 loss_fusion: 0.0627 loss: 0.2741 2022/10/05 16:30:57 - mmengine - INFO - Epoch(train) [9][2600/10520] lr: 1.0000e-04 eta: 21:06:02 time: 0.7780 data_time: 0.1519 memory: 17203 loss_visual: 0.0685 loss_lang: 0.1284 loss_fusion: 0.0592 loss: 0.2561 2022/10/05 16:31:50 - mmengine - INFO - Epoch(train) [9][2700/10520] lr: 1.0000e-04 eta: 21:04:49 time: 0.6724 data_time: 0.1098 memory: 17203 loss_visual: 0.0717 loss_lang: 0.1330 loss_fusion: 0.0613 loss: 0.2661 2022/10/05 16:32:43 - mmengine - INFO - Epoch(train) [9][2800/10520] lr: 1.0000e-04 eta: 21:03:35 time: 0.5279 data_time: 0.0298 memory: 17203 loss_visual: 0.0725 loss_lang: 0.1408 loss_fusion: 0.0627 loss: 0.2761 2022/10/05 16:33:03 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 16:33:35 - mmengine - INFO - Epoch(train) [9][2900/10520] lr: 1.0000e-04 eta: 21:02:21 time: 0.3918 data_time: 0.0116 memory: 17203 loss_visual: 0.0627 loss_lang: 0.1284 loss_fusion: 0.0541 loss: 0.2452 2022/10/05 16:34:29 - mmengine - INFO - Epoch(train) [9][3000/10520] lr: 1.0000e-04 eta: 21:01:08 time: 0.3818 data_time: 0.0112 memory: 17203 loss_visual: 0.0587 loss_lang: 0.1276 loss_fusion: 0.0493 loss: 0.2356 2022/10/05 16:35:22 - mmengine - INFO - Epoch(train) [9][3100/10520] lr: 1.0000e-04 eta: 20:59:56 time: 0.3753 data_time: 0.0129 memory: 17203 loss_visual: 0.0665 loss_lang: 0.1303 loss_fusion: 0.0565 loss: 0.2532 2022/10/05 16:36:19 - mmengine - INFO - Epoch(train) [9][3200/10520] lr: 1.0000e-04 eta: 20:58:47 time: 0.3679 data_time: 0.0034 memory: 17203 loss_visual: 0.0681 loss_lang: 0.1382 loss_fusion: 0.0594 loss: 0.2658 2022/10/05 16:39:46 - mmengine - INFO - Epoch(train) [9][3300/10520] lr: 1.0000e-04 eta: 21:01:11 time: 0.6577 data_time: 0.1423 memory: 17203 loss_visual: 0.0717 loss_lang: 0.1358 loss_fusion: 0.0618 loss: 0.2693 2022/10/05 16:41:10 - mmengine - INFO - Epoch(train) [9][3400/10520] lr: 1.0000e-04 eta: 21:00:41 time: 0.7591 data_time: 0.1694 memory: 17203 loss_visual: 0.0645 loss_lang: 0.1289 loss_fusion: 0.0552 loss: 0.2486 2022/10/05 16:42:03 - mmengine - INFO - Epoch(train) [9][3500/10520] lr: 1.0000e-04 eta: 20:59:28 time: 0.6266 data_time: 0.1114 memory: 17203 loss_visual: 0.0694 loss_lang: 0.1340 loss_fusion: 0.0598 loss: 0.2631 2022/10/05 16:42:56 - mmengine - INFO - Epoch(train) [9][3600/10520] lr: 1.0000e-04 eta: 20:58:14 time: 0.4765 data_time: 0.0247 memory: 17203 loss_visual: 0.0721 loss_lang: 0.1364 loss_fusion: 0.0608 loss: 0.2694 2022/10/05 16:43:49 - mmengine - INFO - Epoch(train) [9][3700/10520] lr: 1.0000e-04 eta: 20:57:00 time: 0.3884 data_time: 0.0115 memory: 17203 loss_visual: 0.0722 loss_lang: 0.1404 loss_fusion: 0.0630 loss: 0.2757 2022/10/05 16:44:42 - mmengine - INFO - Epoch(train) [9][3800/10520] lr: 1.0000e-04 eta: 20:55:46 time: 0.3793 data_time: 0.0116 memory: 17203 loss_visual: 0.0633 loss_lang: 0.1278 loss_fusion: 0.0549 loss: 0.2460 2022/10/05 16:45:02 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 16:45:36 - mmengine - INFO - Epoch(train) [9][3900/10520] lr: 1.0000e-04 eta: 20:54:34 time: 0.3644 data_time: 0.0095 memory: 17203 loss_visual: 0.0740 loss_lang: 0.1437 loss_fusion: 0.0657 loss: 0.2834 2022/10/05 16:46:28 - mmengine - INFO - Epoch(train) [9][4000/10520] lr: 1.0000e-04 eta: 20:53:20 time: 0.3512 data_time: 0.0036 memory: 17203 loss_visual: 0.0688 loss_lang: 0.1334 loss_fusion: 0.0599 loss: 0.2622 2022/10/05 16:47:25 - mmengine - INFO - Epoch(train) [9][4100/10520] lr: 1.0000e-04 eta: 20:52:13 time: 0.6862 data_time: 0.1408 memory: 17203 loss_visual: 0.0779 loss_lang: 0.1435 loss_fusion: 0.0678 loss: 0.2891 2022/10/05 16:48:20 - mmengine - INFO - Epoch(train) [9][4200/10520] lr: 1.0000e-04 eta: 20:51:01 time: 0.7679 data_time: 0.1731 memory: 17203 loss_visual: 0.0717 loss_lang: 0.1355 loss_fusion: 0.0626 loss: 0.2698 2022/10/05 16:49:12 - mmengine - INFO - Epoch(train) [9][4300/10520] lr: 1.0000e-04 eta: 20:49:47 time: 0.6527 data_time: 0.1113 memory: 17203 loss_visual: 0.0613 loss_lang: 0.1264 loss_fusion: 0.0517 loss: 0.2394 2022/10/05 16:50:06 - mmengine - INFO - Epoch(train) [9][4400/10520] lr: 1.0000e-04 eta: 20:48:35 time: 0.5289 data_time: 0.0279 memory: 17203 loss_visual: 0.0729 loss_lang: 0.1419 loss_fusion: 0.0643 loss: 0.2791 2022/10/05 16:50:59 - mmengine - INFO - Epoch(train) [9][4500/10520] lr: 1.0000e-04 eta: 20:47:22 time: 0.4138 data_time: 0.0110 memory: 17203 loss_visual: 0.0655 loss_lang: 0.1339 loss_fusion: 0.0568 loss: 0.2562 2022/10/05 16:51:52 - mmengine - INFO - Epoch(train) [9][4600/10520] lr: 1.0000e-04 eta: 20:46:08 time: 0.3789 data_time: 0.0101 memory: 17203 loss_visual: 0.0698 loss_lang: 0.1355 loss_fusion: 0.0613 loss: 0.2666 2022/10/05 16:52:45 - mmengine - INFO - Epoch(train) [9][4700/10520] lr: 1.0000e-04 eta: 20:44:55 time: 0.3930 data_time: 0.0122 memory: 17203 loss_visual: 0.0742 loss_lang: 0.1393 loss_fusion: 0.0650 loss: 0.2786 2022/10/05 16:53:36 - mmengine - INFO - Epoch(train) [9][4800/10520] lr: 1.0000e-04 eta: 20:43:40 time: 0.3489 data_time: 0.0035 memory: 17203 loss_visual: 0.0668 loss_lang: 0.1284 loss_fusion: 0.0557 loss: 0.2510 2022/10/05 16:54:01 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 16:54:32 - mmengine - INFO - Epoch(train) [9][4900/10520] lr: 1.0000e-04 eta: 20:42:31 time: 0.6644 data_time: 0.1285 memory: 17203 loss_visual: 0.0558 loss_lang: 0.1188 loss_fusion: 0.0481 loss: 0.2227 2022/10/05 16:55:25 - mmengine - INFO - Epoch(train) [9][5000/10520] lr: 1.0000e-04 eta: 20:41:18 time: 0.7823 data_time: 0.1656 memory: 17203 loss_visual: 0.0729 loss_lang: 0.1398 loss_fusion: 0.0638 loss: 0.2766 2022/10/05 16:56:19 - mmengine - INFO - Epoch(train) [9][5100/10520] lr: 1.0000e-04 eta: 20:40:06 time: 0.7283 data_time: 0.1439 memory: 17203 loss_visual: 0.0654 loss_lang: 0.1308 loss_fusion: 0.0552 loss: 0.2514 2022/10/05 16:57:12 - mmengine - INFO - Epoch(train) [9][5200/10520] lr: 1.0000e-04 eta: 20:38:53 time: 0.5405 data_time: 0.0242 memory: 17203 loss_visual: 0.0723 loss_lang: 0.1396 loss_fusion: 0.0636 loss: 0.2755 2022/10/05 16:58:05 - mmengine - INFO - Epoch(train) [9][5300/10520] lr: 1.0000e-04 eta: 20:37:41 time: 0.4011 data_time: 0.0126 memory: 17203 loss_visual: 0.0610 loss_lang: 0.1274 loss_fusion: 0.0518 loss: 0.2401 2022/10/05 16:58:57 - mmengine - INFO - Epoch(train) [9][5400/10520] lr: 1.0000e-04 eta: 20:36:27 time: 0.3906 data_time: 0.0106 memory: 17203 loss_visual: 0.0676 loss_lang: 0.1350 loss_fusion: 0.0602 loss: 0.2628 2022/10/05 16:59:50 - mmengine - INFO - Epoch(train) [9][5500/10520] lr: 1.0000e-04 eta: 20:35:15 time: 0.3649 data_time: 0.0106 memory: 17203 loss_visual: 0.0715 loss_lang: 0.1374 loss_fusion: 0.0627 loss: 0.2717 2022/10/05 17:00:43 - mmengine - INFO - Epoch(train) [9][5600/10520] lr: 1.0000e-04 eta: 20:34:01 time: 0.3457 data_time: 0.0039 memory: 17203 loss_visual: 0.0729 loss_lang: 0.1379 loss_fusion: 0.0627 loss: 0.2734 2022/10/05 17:01:39 - mmengine - INFO - Epoch(train) [9][5700/10520] lr: 1.0000e-04 eta: 20:32:53 time: 0.6902 data_time: 0.1447 memory: 17203 loss_visual: 0.0737 loss_lang: 0.1355 loss_fusion: 0.0636 loss: 0.2729 2022/10/05 17:02:32 - mmengine - INFO - Epoch(train) [9][5800/10520] lr: 1.0000e-04 eta: 20:31:40 time: 0.7104 data_time: 0.1680 memory: 17203 loss_visual: 0.0705 loss_lang: 0.1381 loss_fusion: 0.0600 loss: 0.2686 2022/10/05 17:02:52 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 17:03:25 - mmengine - INFO - Epoch(train) [9][5900/10520] lr: 1.0000e-04 eta: 20:30:27 time: 0.6290 data_time: 0.1131 memory: 17203 loss_visual: 0.0716 loss_lang: 0.1327 loss_fusion: 0.0611 loss: 0.2654 2022/10/05 17:04:19 - mmengine - INFO - Epoch(train) [9][6000/10520] lr: 1.0000e-04 eta: 20:29:16 time: 0.5619 data_time: 0.0255 memory: 17203 loss_visual: 0.0753 loss_lang: 0.1404 loss_fusion: 0.0652 loss: 0.2808 2022/10/05 17:05:12 - mmengine - INFO - Epoch(train) [9][6100/10520] lr: 1.0000e-04 eta: 20:28:04 time: 0.4102 data_time: 0.0115 memory: 17203 loss_visual: 0.0694 loss_lang: 0.1352 loss_fusion: 0.0587 loss: 0.2633 2022/10/05 17:06:05 - mmengine - INFO - Epoch(train) [9][6200/10520] lr: 1.0000e-04 eta: 20:26:51 time: 0.3827 data_time: 0.0107 memory: 17203 loss_visual: 0.0649 loss_lang: 0.1279 loss_fusion: 0.0555 loss: 0.2483 2022/10/05 17:06:57 - mmengine - INFO - Epoch(train) [9][6300/10520] lr: 1.0000e-04 eta: 20:25:38 time: 0.3729 data_time: 0.0126 memory: 17203 loss_visual: 0.0667 loss_lang: 0.1318 loss_fusion: 0.0582 loss: 0.2567 2022/10/05 17:07:51 - mmengine - INFO - Epoch(train) [9][6400/10520] lr: 1.0000e-04 eta: 20:24:27 time: 0.3533 data_time: 0.0035 memory: 17203 loss_visual: 0.0771 loss_lang: 0.1487 loss_fusion: 0.0676 loss: 0.2933 2022/10/05 17:08:45 - mmengine - INFO - Epoch(train) [9][6500/10520] lr: 1.0000e-04 eta: 20:23:16 time: 0.6320 data_time: 0.1331 memory: 17203 loss_visual: 0.0681 loss_lang: 0.1349 loss_fusion: 0.0592 loss: 0.2622 2022/10/05 17:09:38 - mmengine - INFO - Epoch(train) [9][6600/10520] lr: 1.0000e-04 eta: 20:22:04 time: 0.7722 data_time: 0.1664 memory: 17203 loss_visual: 0.0679 loss_lang: 0.1330 loss_fusion: 0.0594 loss: 0.2603 2022/10/05 17:10:32 - mmengine - INFO - Epoch(train) [9][6700/10520] lr: 1.0000e-04 eta: 20:20:52 time: 0.6615 data_time: 0.1085 memory: 17203 loss_visual: 0.0722 loss_lang: 0.1375 loss_fusion: 0.0630 loss: 0.2727 2022/10/05 17:11:26 - mmengine - INFO - Epoch(train) [9][6800/10520] lr: 1.0000e-04 eta: 20:19:41 time: 0.5266 data_time: 0.0267 memory: 17203 loss_visual: 0.0710 loss_lang: 0.1352 loss_fusion: 0.0618 loss: 0.2680 2022/10/05 17:11:46 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 17:12:19 - mmengine - INFO - Epoch(train) [9][6900/10520] lr: 1.0000e-04 eta: 20:18:30 time: 0.3825 data_time: 0.0107 memory: 17203 loss_visual: 0.0712 loss_lang: 0.1421 loss_fusion: 0.0629 loss: 0.2762 2022/10/05 17:13:12 - mmengine - INFO - Epoch(train) [9][7000/10520] lr: 1.0000e-04 eta: 20:17:18 time: 0.3935 data_time: 0.0122 memory: 17203 loss_visual: 0.0666 loss_lang: 0.1343 loss_fusion: 0.0582 loss: 0.2591 2022/10/05 17:14:05 - mmengine - INFO - Epoch(train) [9][7100/10520] lr: 1.0000e-04 eta: 20:16:06 time: 0.3660 data_time: 0.0113 memory: 17203 loss_visual: 0.0755 loss_lang: 0.1345 loss_fusion: 0.0642 loss: 0.2742 2022/10/05 17:14:57 - mmengine - INFO - Epoch(train) [9][7200/10520] lr: 1.0000e-04 eta: 20:14:53 time: 0.3467 data_time: 0.0033 memory: 17203 loss_visual: 0.0645 loss_lang: 0.1315 loss_fusion: 0.0564 loss: 0.2524 2022/10/05 17:15:53 - mmengine - INFO - Epoch(train) [9][7300/10520] lr: 1.0000e-04 eta: 20:13:45 time: 0.6594 data_time: 0.1125 memory: 17203 loss_visual: 0.0621 loss_lang: 0.1299 loss_fusion: 0.0529 loss: 0.2449 2022/10/05 17:16:47 - mmengine - INFO - Epoch(train) [9][7400/10520] lr: 1.0000e-04 eta: 20:12:33 time: 0.7517 data_time: 0.1589 memory: 17203 loss_visual: 0.0696 loss_lang: 0.1390 loss_fusion: 0.0614 loss: 0.2700 2022/10/05 17:17:41 - mmengine - INFO - Epoch(train) [9][7500/10520] lr: 1.0000e-04 eta: 20:11:23 time: 0.6854 data_time: 0.1333 memory: 17203 loss_visual: 0.0675 loss_lang: 0.1321 loss_fusion: 0.0587 loss: 0.2583 2022/10/05 17:18:35 - mmengine - INFO - Epoch(train) [9][7600/10520] lr: 1.0000e-04 eta: 20:10:12 time: 0.5039 data_time: 0.0267 memory: 17203 loss_visual: 0.0679 loss_lang: 0.1372 loss_fusion: 0.0592 loss: 0.2643 2022/10/05 17:19:27 - mmengine - INFO - Epoch(train) [9][7700/10520] lr: 1.0000e-04 eta: 20:08:59 time: 0.3890 data_time: 0.0115 memory: 17203 loss_visual: 0.0610 loss_lang: 0.1277 loss_fusion: 0.0532 loss: 0.2419 2022/10/05 17:20:21 - mmengine - INFO - Epoch(train) [9][7800/10520] lr: 1.0000e-04 eta: 20:07:48 time: 0.3764 data_time: 0.0108 memory: 17203 loss_visual: 0.0626 loss_lang: 0.1289 loss_fusion: 0.0531 loss: 0.2446 2022/10/05 17:20:40 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 17:21:13 - mmengine - INFO - Epoch(train) [9][7900/10520] lr: 1.0000e-04 eta: 20:06:36 time: 0.3671 data_time: 0.0114 memory: 17203 loss_visual: 0.0648 loss_lang: 0.1341 loss_fusion: 0.0570 loss: 0.2559 2022/10/05 17:22:06 - mmengine - INFO - Epoch(train) [9][8000/10520] lr: 1.0000e-04 eta: 20:05:24 time: 0.3560 data_time: 0.0066 memory: 17203 loss_visual: 0.0669 loss_lang: 0.1308 loss_fusion: 0.0561 loss: 0.2538 2022/10/05 17:23:03 - mmengine - INFO - Epoch(train) [9][8100/10520] lr: 1.0000e-04 eta: 20:04:17 time: 0.6633 data_time: 0.1386 memory: 17203 loss_visual: 0.0653 loss_lang: 0.1294 loss_fusion: 0.0554 loss: 0.2501 2022/10/05 17:23:58 - mmengine - INFO - Epoch(train) [9][8200/10520] lr: 1.0000e-04 eta: 20:03:09 time: 0.8170 data_time: 0.1740 memory: 17203 loss_visual: 0.0665 loss_lang: 0.1312 loss_fusion: 0.0578 loss: 0.2555 2022/10/05 17:24:52 - mmengine - INFO - Epoch(train) [9][8300/10520] lr: 1.0000e-04 eta: 20:01:58 time: 0.6953 data_time: 0.1171 memory: 17203 loss_visual: 0.0646 loss_lang: 0.1321 loss_fusion: 0.0565 loss: 0.2532 2022/10/05 17:25:45 - mmengine - INFO - Epoch(train) [9][8400/10520] lr: 1.0000e-04 eta: 20:00:47 time: 0.4778 data_time: 0.0256 memory: 17203 loss_visual: 0.0668 loss_lang: 0.1333 loss_fusion: 0.0579 loss: 0.2580 2022/10/05 17:26:38 - mmengine - INFO - Epoch(train) [9][8500/10520] lr: 1.0000e-04 eta: 19:59:36 time: 0.3900 data_time: 0.0112 memory: 17203 loss_visual: 0.0660 loss_lang: 0.1346 loss_fusion: 0.0576 loss: 0.2582 2022/10/05 17:27:31 - mmengine - INFO - Epoch(train) [9][8600/10520] lr: 1.0000e-04 eta: 19:58:24 time: 0.3773 data_time: 0.0106 memory: 17203 loss_visual: 0.0685 loss_lang: 0.1327 loss_fusion: 0.0601 loss: 0.2612 2022/10/05 17:28:24 - mmengine - INFO - Epoch(train) [9][8700/10520] lr: 1.0000e-04 eta: 19:57:13 time: 0.3677 data_time: 0.0111 memory: 17203 loss_visual: 0.0641 loss_lang: 0.1315 loss_fusion: 0.0542 loss: 0.2498 2022/10/05 17:29:17 - mmengine - INFO - Epoch(train) [9][8800/10520] lr: 1.0000e-04 eta: 19:56:01 time: 0.3637 data_time: 0.0037 memory: 17203 loss_visual: 0.0705 loss_lang: 0.1364 loss_fusion: 0.0602 loss: 0.2671 2022/10/05 17:29:41 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 17:30:13 - mmengine - INFO - Epoch(train) [9][8900/10520] lr: 1.0000e-04 eta: 19:54:54 time: 0.6949 data_time: 0.1125 memory: 17203 loss_visual: 0.0740 loss_lang: 0.1400 loss_fusion: 0.0651 loss: 0.2791 2022/10/05 17:31:08 - mmengine - INFO - Epoch(train) [9][9000/10520] lr: 1.0000e-04 eta: 19:53:44 time: 0.7870 data_time: 0.1691 memory: 17203 loss_visual: 0.0717 loss_lang: 0.1368 loss_fusion: 0.0636 loss: 0.2720 2022/10/05 17:32:02 - mmengine - INFO - Epoch(train) [9][9100/10520] lr: 1.0000e-04 eta: 19:52:34 time: 0.7433 data_time: 0.1772 memory: 17203 loss_visual: 0.0685 loss_lang: 0.1337 loss_fusion: 0.0591 loss: 0.2613 2022/10/05 17:33:32 - mmengine - INFO - Epoch(train) [9][9200/10520] lr: 1.0000e-04 eta: 19:52:09 time: 0.4988 data_time: 0.0250 memory: 17203 loss_visual: 0.0671 loss_lang: 0.1317 loss_fusion: 0.0589 loss: 0.2577 2022/10/05 17:34:47 - mmengine - INFO - Epoch(train) [9][9300/10520] lr: 1.0000e-04 eta: 19:51:26 time: 0.4428 data_time: 0.0114 memory: 17203 loss_visual: 0.0741 loss_lang: 0.1421 loss_fusion: 0.0643 loss: 0.2805 2022/10/05 17:35:41 - mmengine - INFO - Epoch(train) [9][9400/10520] lr: 1.0000e-04 eta: 19:50:16 time: 0.3840 data_time: 0.0118 memory: 17203 loss_visual: 0.0592 loss_lang: 0.1260 loss_fusion: 0.0496 loss: 0.2348 2022/10/05 17:36:35 - mmengine - INFO - Epoch(train) [9][9500/10520] lr: 1.0000e-04 eta: 19:49:06 time: 0.3676 data_time: 0.0101 memory: 17203 loss_visual: 0.0679 loss_lang: 0.1296 loss_fusion: 0.0577 loss: 0.2552 2022/10/05 17:37:28 - mmengine - INFO - Epoch(train) [9][9600/10520] lr: 1.0000e-04 eta: 19:47:55 time: 0.3836 data_time: 0.0035 memory: 17203 loss_visual: 0.0766 loss_lang: 0.1433 loss_fusion: 0.0666 loss: 0.2864 2022/10/05 17:38:25 - mmengine - INFO - Epoch(train) [9][9700/10520] lr: 1.0000e-04 eta: 19:46:49 time: 0.6766 data_time: 0.1147 memory: 17203 loss_visual: 0.0615 loss_lang: 0.1285 loss_fusion: 0.0536 loss: 0.2436 2022/10/05 17:39:19 - mmengine - INFO - Epoch(train) [9][9800/10520] lr: 1.0000e-04 eta: 19:45:39 time: 0.7830 data_time: 0.2003 memory: 17203 loss_visual: 0.0700 loss_lang: 0.1348 loss_fusion: 0.0599 loss: 0.2646 2022/10/05 17:39:41 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 17:40:13 - mmengine - INFO - Epoch(train) [9][9900/10520] lr: 1.0000e-04 eta: 19:44:29 time: 0.7180 data_time: 0.1537 memory: 17203 loss_visual: 0.0651 loss_lang: 0.1286 loss_fusion: 0.0569 loss: 0.2506 2022/10/05 17:41:07 - mmengine - INFO - Epoch(train) [9][10000/10520] lr: 1.0000e-04 eta: 19:43:18 time: 0.5417 data_time: 0.0272 memory: 17203 loss_visual: 0.0764 loss_lang: 0.1412 loss_fusion: 0.0666 loss: 0.2842 2022/10/05 17:42:00 - mmengine - INFO - Epoch(train) [9][10100/10520] lr: 1.0000e-04 eta: 19:42:07 time: 0.3878 data_time: 0.0138 memory: 17203 loss_visual: 0.0702 loss_lang: 0.1369 loss_fusion: 0.0622 loss: 0.2693 2022/10/05 17:42:52 - mmengine - INFO - Epoch(train) [9][10200/10520] lr: 1.0000e-04 eta: 19:40:56 time: 0.3778 data_time: 0.0106 memory: 17203 loss_visual: 0.0747 loss_lang: 0.1416 loss_fusion: 0.0667 loss: 0.2831 2022/10/05 17:43:46 - mmengine - INFO - Epoch(train) [9][10300/10520] lr: 1.0000e-04 eta: 19:39:45 time: 0.3652 data_time: 0.0106 memory: 17203 loss_visual: 0.0636 loss_lang: 0.1265 loss_fusion: 0.0545 loss: 0.2446 2022/10/05 17:44:38 - mmengine - INFO - Epoch(train) [9][10400/10520] lr: 1.0000e-04 eta: 19:38:33 time: 0.3542 data_time: 0.0033 memory: 17203 loss_visual: 0.0676 loss_lang: 0.1306 loss_fusion: 0.0572 loss: 0.2554 2022/10/05 17:45:31 - mmengine - INFO - Epoch(train) [9][10500/10520] lr: 1.0000e-04 eta: 19:37:23 time: 0.5379 data_time: 0.0881 memory: 17203 loss_visual: 0.0697 loss_lang: 0.1338 loss_fusion: 0.0603 loss: 0.2637 2022/10/05 17:45:39 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 17:45:39 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/10/05 17:45:54 - mmengine - INFO - Epoch(val) [9][100/959] eta: 0:00:45 time: 0.0532 data_time: 0.0015 memory: 17203 2022/10/05 17:45:59 - mmengine - INFO - Epoch(val) [9][200/959] eta: 0:00:36 time: 0.0481 data_time: 0.0018 memory: 734 2022/10/05 17:46:04 - mmengine - INFO - Epoch(val) [9][300/959] eta: 0:00:36 time: 0.0561 data_time: 0.0026 memory: 734 2022/10/05 17:46:09 - mmengine - INFO - Epoch(val) [9][400/959] eta: 0:00:29 time: 0.0532 data_time: 0.0015 memory: 734 2022/10/05 17:46:14 - mmengine - INFO - Epoch(val) [9][500/959] eta: 0:00:25 time: 0.0550 data_time: 0.0029 memory: 734 2022/10/05 17:46:19 - mmengine - INFO - Epoch(val) [9][600/959] eta: 0:00:17 time: 0.0475 data_time: 0.0023 memory: 734 2022/10/05 17:46:24 - mmengine - INFO - Epoch(val) [9][700/959] eta: 0:00:14 time: 0.0547 data_time: 0.0019 memory: 734 2022/10/05 17:46:28 - mmengine - INFO - Epoch(val) [9][800/959] eta: 0:00:03 time: 0.0239 data_time: 0.0007 memory: 734 2022/10/05 17:46:30 - mmengine - INFO - Epoch(val) [9][900/959] eta: 0:00:01 time: 0.0215 data_time: 0.0006 memory: 734 2022/10/05 17:46:32 - mmengine - INFO - Epoch(val) [9][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.8056 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9320 SVT/recog/word_acc_ignore_case_symbol: 0.9474 SVTP/recog/word_acc_ignore_case_symbol: 0.8682 IC13/recog/word_acc_ignore_case_symbol: 0.9271 IC15/recog/word_acc_ignore_case_symbol: 0.7809 2022/10/05 17:47:37 - mmengine - INFO - Epoch(train) [10][100/10520] lr: 1.0000e-04 eta: 19:36:10 time: 0.8896 data_time: 0.1733 memory: 17203 loss_visual: 0.0726 loss_lang: 0.1327 loss_fusion: 0.0631 loss: 0.2684 2022/10/05 17:48:34 - mmengine - INFO - Epoch(train) [10][200/10520] lr: 1.0000e-04 eta: 19:35:03 time: 0.9077 data_time: 0.1832 memory: 17203 loss_visual: 0.0663 loss_lang: 0.1292 loss_fusion: 0.0576 loss: 0.2530 2022/10/05 17:49:30 - mmengine - INFO - Epoch(train) [10][300/10520] lr: 1.0000e-04 eta: 19:33:57 time: 0.8327 data_time: 0.1859 memory: 17203 loss_visual: 0.0620 loss_lang: 0.1249 loss_fusion: 0.0534 loss: 0.2404 2022/10/05 17:49:38 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 17:50:26 - mmengine - INFO - Epoch(train) [10][400/10520] lr: 1.0000e-04 eta: 19:32:50 time: 0.5028 data_time: 0.0336 memory: 17203 loss_visual: 0.0689 loss_lang: 0.1340 loss_fusion: 0.0595 loss: 0.2623 2022/10/05 17:51:22 - mmengine - INFO - Epoch(train) [10][500/10520] lr: 1.0000e-04 eta: 19:31:43 time: 0.3858 data_time: 0.0195 memory: 17203 loss_visual: 0.0642 loss_lang: 0.1270 loss_fusion: 0.0556 loss: 0.2468 2022/10/05 17:52:18 - mmengine - INFO - Epoch(train) [10][600/10520] lr: 1.0000e-04 eta: 19:30:35 time: 0.3773 data_time: 0.0033 memory: 17203 loss_visual: 0.0651 loss_lang: 0.1291 loss_fusion: 0.0568 loss: 0.2510 2022/10/05 17:53:14 - mmengine - INFO - Epoch(train) [10][700/10520] lr: 1.0000e-04 eta: 19:29:28 time: 0.3457 data_time: 0.0034 memory: 17203 loss_visual: 0.0632 loss_lang: 0.1298 loss_fusion: 0.0542 loss: 0.2471 2022/10/05 17:54:10 - mmengine - INFO - Epoch(train) [10][800/10520] lr: 1.0000e-04 eta: 19:28:21 time: 0.3672 data_time: 0.0039 memory: 17203 loss_visual: 0.0747 loss_lang: 0.1379 loss_fusion: 0.0647 loss: 0.2774 2022/10/05 17:55:10 - mmengine - INFO - Epoch(train) [10][900/10520] lr: 1.0000e-04 eta: 19:27:18 time: 0.7947 data_time: 0.1596 memory: 17203 loss_visual: 0.0742 loss_lang: 0.1395 loss_fusion: 0.0642 loss: 0.2780 2022/10/05 17:56:07 - mmengine - INFO - Epoch(train) [10][1000/10520] lr: 1.0000e-04 eta: 19:26:13 time: 0.9700 data_time: 0.1722 memory: 17203 loss_visual: 0.0709 loss_lang: 0.1351 loss_fusion: 0.0608 loss: 0.2668 2022/10/05 17:57:03 - mmengine - INFO - Epoch(train) [10][1100/10520] lr: 1.0000e-04 eta: 19:25:06 time: 0.7542 data_time: 0.1625 memory: 17203 loss_visual: 0.0691 loss_lang: 0.1348 loss_fusion: 0.0600 loss: 0.2639 2022/10/05 17:58:01 - mmengine - INFO - Epoch(train) [10][1200/10520] lr: 1.0000e-04 eta: 19:24:01 time: 0.6392 data_time: 0.0307 memory: 17203 loss_visual: 0.0736 loss_lang: 0.1356 loss_fusion: 0.0651 loss: 0.2743 2022/10/05 17:59:12 - mmengine - INFO - Epoch(train) [10][1300/10520] lr: 1.0000e-04 eta: 19:23:12 time: 0.4023 data_time: 0.0221 memory: 17203 loss_visual: 0.0661 loss_lang: 0.1279 loss_fusion: 0.0565 loss: 0.2504 2022/10/05 17:59:25 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 18:00:34 - mmengine - INFO - Epoch(train) [10][1400/10520] lr: 1.0000e-04 eta: 19:22:37 time: 0.3893 data_time: 0.0033 memory: 17203 loss_visual: 0.0692 loss_lang: 0.1319 loss_fusion: 0.0597 loss: 0.2608 2022/10/05 18:01:31 - mmengine - INFO - Epoch(train) [10][1500/10520] lr: 1.0000e-04 eta: 19:21:31 time: 0.4529 data_time: 0.0035 memory: 17203 loss_visual: 0.0701 loss_lang: 0.1405 loss_fusion: 0.0617 loss: 0.2723 2022/10/05 18:02:27 - mmengine - INFO - Epoch(train) [10][1600/10520] lr: 1.0000e-04 eta: 19:20:23 time: 0.3464 data_time: 0.0033 memory: 17203 loss_visual: 0.0683 loss_lang: 0.1355 loss_fusion: 0.0598 loss: 0.2636 2022/10/05 18:03:27 - mmengine - INFO - Epoch(train) [10][1700/10520] lr: 1.0000e-04 eta: 19:19:22 time: 0.8376 data_time: 0.1685 memory: 17203 loss_visual: 0.0728 loss_lang: 0.1341 loss_fusion: 0.0633 loss: 0.2702 2022/10/05 18:04:23 - mmengine - INFO - Epoch(train) [10][1800/10520] lr: 1.0000e-04 eta: 19:18:15 time: 0.9535 data_time: 0.1683 memory: 17203 loss_visual: 0.0682 loss_lang: 0.1318 loss_fusion: 0.0595 loss: 0.2594 2022/10/05 18:05:19 - mmengine - INFO - Epoch(train) [10][1900/10520] lr: 1.0000e-04 eta: 19:17:08 time: 0.8044 data_time: 0.1601 memory: 17203 loss_visual: 0.0683 loss_lang: 0.1330 loss_fusion: 0.0608 loss: 0.2621 2022/10/05 18:06:15 - mmengine - INFO - Epoch(train) [10][2000/10520] lr: 1.0000e-04 eta: 19:16:00 time: 0.5223 data_time: 0.0645 memory: 17203 loss_visual: 0.0648 loss_lang: 0.1295 loss_fusion: 0.0538 loss: 0.2481 2022/10/05 18:07:11 - mmengine - INFO - Epoch(train) [10][2100/10520] lr: 1.0000e-04 eta: 19:14:54 time: 0.3888 data_time: 0.0219 memory: 17203 loss_visual: 0.0752 loss_lang: 0.1390 loss_fusion: 0.0657 loss: 0.2800 2022/10/05 18:08:07 - mmengine - INFO - Epoch(train) [10][2200/10520] lr: 1.0000e-04 eta: 19:13:47 time: 0.3807 data_time: 0.0035 memory: 17203 loss_visual: 0.0756 loss_lang: 0.1388 loss_fusion: 0.0657 loss: 0.2801 2022/10/05 18:09:02 - mmengine - INFO - Epoch(train) [10][2300/10520] lr: 1.0000e-04 eta: 19:12:39 time: 0.3498 data_time: 0.0041 memory: 17203 loss_visual: 0.0703 loss_lang: 0.1317 loss_fusion: 0.0631 loss: 0.2651 2022/10/05 18:09:16 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 18:09:57 - mmengine - INFO - Epoch(train) [10][2400/10520] lr: 1.0000e-04 eta: 19:11:31 time: 0.3454 data_time: 0.0033 memory: 17203 loss_visual: 0.0599 loss_lang: 0.1214 loss_fusion: 0.0516 loss: 0.2328 2022/10/05 18:10:58 - mmengine - INFO - Epoch(train) [10][2500/10520] lr: 1.0000e-04 eta: 19:10:30 time: 0.7614 data_time: 0.1630 memory: 17203 loss_visual: 0.0676 loss_lang: 0.1330 loss_fusion: 0.0595 loss: 0.2601 2022/10/05 18:11:55 - mmengine - INFO - Epoch(train) [10][2600/10520] lr: 1.0000e-04 eta: 19:09:23 time: 0.8768 data_time: 0.1782 memory: 17203 loss_visual: 0.0619 loss_lang: 0.1262 loss_fusion: 0.0526 loss: 0.2407 2022/10/05 18:12:50 - mmengine - INFO - Epoch(train) [10][2700/10520] lr: 1.0000e-04 eta: 19:08:17 time: 0.7880 data_time: 0.1576 memory: 17203 loss_visual: 0.0665 loss_lang: 0.1271 loss_fusion: 0.0568 loss: 0.2505 2022/10/05 18:13:45 - mmengine - INFO - Epoch(train) [10][2800/10520] lr: 1.0000e-04 eta: 19:07:08 time: 0.5406 data_time: 0.0331 memory: 17203 loss_visual: 0.0615 loss_lang: 0.1231 loss_fusion: 0.0523 loss: 0.2369 2022/10/05 18:14:40 - mmengine - INFO - Epoch(train) [10][2900/10520] lr: 1.0000e-04 eta: 19:06:01 time: 0.4110 data_time: 0.0227 memory: 17203 loss_visual: 0.0602 loss_lang: 0.1192 loss_fusion: 0.0516 loss: 0.2310 2022/10/05 18:16:05 - mmengine - INFO - Epoch(train) [10][3000/10520] lr: 1.0000e-04 eta: 19:05:27 time: 0.3661 data_time: 0.0034 memory: 17203 loss_visual: 0.0618 loss_lang: 0.1231 loss_fusion: 0.0524 loss: 0.2373 2022/10/05 18:17:23 - mmengine - INFO - Epoch(train) [10][3100/10520] lr: 1.0000e-04 eta: 19:04:46 time: 0.3482 data_time: 0.0033 memory: 17203 loss_visual: 0.0657 loss_lang: 0.1297 loss_fusion: 0.0566 loss: 0.2521 2022/10/05 18:18:20 - mmengine - INFO - Epoch(train) [10][3200/10520] lr: 1.0000e-04 eta: 19:03:40 time: 0.3558 data_time: 0.0037 memory: 17203 loss_visual: 0.0672 loss_lang: 0.1298 loss_fusion: 0.0579 loss: 0.2548 2022/10/05 18:19:20 - mmengine - INFO - Epoch(train) [10][3300/10520] lr: 1.0000e-04 eta: 19:02:38 time: 0.7924 data_time: 0.1690 memory: 17203 loss_visual: 0.0743 loss_lang: 0.1398 loss_fusion: 0.0637 loss: 0.2778 2022/10/05 18:19:29 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 18:20:16 - mmengine - INFO - Epoch(train) [10][3400/10520] lr: 1.0000e-04 eta: 19:01:31 time: 0.9129 data_time: 0.1733 memory: 17203 loss_visual: 0.0659 loss_lang: 0.1279 loss_fusion: 0.0580 loss: 0.2518 2022/10/05 18:21:11 - mmengine - INFO - Epoch(train) [10][3500/10520] lr: 1.0000e-04 eta: 19:00:24 time: 0.7218 data_time: 0.1564 memory: 17203 loss_visual: 0.0679 loss_lang: 0.1293 loss_fusion: 0.0594 loss: 0.2566 2022/10/05 18:22:06 - mmengine - INFO - Epoch(train) [10][3600/10520] lr: 1.0000e-04 eta: 18:59:16 time: 0.5224 data_time: 0.0372 memory: 17203 loss_visual: 0.0729 loss_lang: 0.1306 loss_fusion: 0.0616 loss: 0.2652 2022/10/05 18:23:01 - mmengine - INFO - Epoch(train) [10][3700/10520] lr: 1.0000e-04 eta: 18:58:08 time: 0.4140 data_time: 0.0231 memory: 17203 loss_visual: 0.0773 loss_lang: 0.1376 loss_fusion: 0.0675 loss: 0.2823 2022/10/05 18:23:57 - mmengine - INFO - Epoch(train) [10][3800/10520] lr: 1.0000e-04 eta: 18:57:02 time: 0.4028 data_time: 0.0033 memory: 17203 loss_visual: 0.0610 loss_lang: 0.1261 loss_fusion: 0.0515 loss: 0.2387 2022/10/05 18:24:53 - mmengine - INFO - Epoch(train) [10][3900/10520] lr: 1.0000e-04 eta: 18:55:55 time: 0.3500 data_time: 0.0035 memory: 17203 loss_visual: 0.0632 loss_lang: 0.1289 loss_fusion: 0.0546 loss: 0.2466 2022/10/05 18:25:48 - mmengine - INFO - Epoch(train) [10][4000/10520] lr: 1.0000e-04 eta: 18:54:47 time: 0.3772 data_time: 0.0033 memory: 17203 loss_visual: 0.0591 loss_lang: 0.1242 loss_fusion: 0.0486 loss: 0.2320 2022/10/05 18:26:48 - mmengine - INFO - Epoch(train) [10][4100/10520] lr: 1.0000e-04 eta: 18:53:46 time: 0.8642 data_time: 0.2030 memory: 17203 loss_visual: 0.0684 loss_lang: 0.1330 loss_fusion: 0.0591 loss: 0.2606 2022/10/05 18:27:45 - mmengine - INFO - Epoch(train) [10][4200/10520] lr: 1.0000e-04 eta: 18:52:40 time: 0.9202 data_time: 0.1867 memory: 17203 loss_visual: 0.0668 loss_lang: 0.1299 loss_fusion: 0.0574 loss: 0.2541 2022/10/05 18:28:42 - mmengine - INFO - Epoch(train) [10][4300/10520] lr: 1.0000e-04 eta: 18:51:35 time: 0.7927 data_time: 0.1668 memory: 17203 loss_visual: 0.0662 loss_lang: 0.1287 loss_fusion: 0.0572 loss: 0.2521 2022/10/05 18:28:50 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 18:29:40 - mmengine - INFO - Epoch(train) [10][4400/10520] lr: 1.0000e-04 eta: 18:50:30 time: 0.5201 data_time: 0.0338 memory: 17203 loss_visual: 0.0700 loss_lang: 0.1310 loss_fusion: 0.0604 loss: 0.2614 2022/10/05 18:30:35 - mmengine - INFO - Epoch(train) [10][4500/10520] lr: 1.0000e-04 eta: 18:49:23 time: 0.4117 data_time: 0.0474 memory: 17203 loss_visual: 0.0736 loss_lang: 0.1373 loss_fusion: 0.0649 loss: 0.2758 2022/10/05 18:31:31 - mmengine - INFO - Epoch(train) [10][4600/10520] lr: 1.0000e-04 eta: 18:48:16 time: 0.3619 data_time: 0.0035 memory: 17203 loss_visual: 0.0671 loss_lang: 0.1288 loss_fusion: 0.0568 loss: 0.2528 2022/10/05 18:32:26 - mmengine - INFO - Epoch(train) [10][4700/10520] lr: 1.0000e-04 eta: 18:47:08 time: 0.3494 data_time: 0.0035 memory: 17203 loss_visual: 0.0653 loss_lang: 0.1335 loss_fusion: 0.0564 loss: 0.2552 2022/10/05 18:33:21 - mmengine - INFO - Epoch(train) [10][4800/10520] lr: 1.0000e-04 eta: 18:46:02 time: 0.3441 data_time: 0.0035 memory: 17203 loss_visual: 0.0670 loss_lang: 0.1351 loss_fusion: 0.0576 loss: 0.2597 2022/10/05 18:34:22 - mmengine - INFO - Epoch(train) [10][4900/10520] lr: 1.0000e-04 eta: 18:45:01 time: 0.8275 data_time: 0.1716 memory: 17203 loss_visual: 0.0738 loss_lang: 0.1419 loss_fusion: 0.0652 loss: 0.2809 2022/10/05 18:35:19 - mmengine - INFO - Epoch(train) [10][5000/10520] lr: 1.0000e-04 eta: 18:43:55 time: 0.9025 data_time: 0.1770 memory: 17203 loss_visual: 0.0628 loss_lang: 0.1255 loss_fusion: 0.0540 loss: 0.2424 2022/10/05 18:36:15 - mmengine - INFO - Epoch(train) [10][5100/10520] lr: 1.0000e-04 eta: 18:42:49 time: 0.7991 data_time: 0.1418 memory: 17203 loss_visual: 0.0748 loss_lang: 0.1381 loss_fusion: 0.0652 loss: 0.2780 2022/10/05 18:37:12 - mmengine - INFO - Epoch(train) [10][5200/10520] lr: 1.0000e-04 eta: 18:41:43 time: 0.5251 data_time: 0.0336 memory: 17203 loss_visual: 0.0629 loss_lang: 0.1276 loss_fusion: 0.0552 loss: 0.2456 2022/10/05 18:38:09 - mmengine - INFO - Epoch(train) [10][5300/10520] lr: 1.0000e-04 eta: 18:40:38 time: 0.3944 data_time: 0.0196 memory: 17203 loss_visual: 0.0670 loss_lang: 0.1306 loss_fusion: 0.0585 loss: 0.2561 2022/10/05 18:38:22 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 18:39:05 - mmengine - INFO - Epoch(train) [10][5400/10520] lr: 1.0000e-04 eta: 18:39:32 time: 0.3678 data_time: 0.0043 memory: 17203 loss_visual: 0.0699 loss_lang: 0.1307 loss_fusion: 0.0614 loss: 0.2621 2022/10/05 18:40:01 - mmengine - INFO - Epoch(train) [10][5500/10520] lr: 1.0000e-04 eta: 18:38:26 time: 0.3440 data_time: 0.0033 memory: 17203 loss_visual: 0.0632 loss_lang: 0.1260 loss_fusion: 0.0546 loss: 0.2438 2022/10/05 18:40:57 - mmengine - INFO - Epoch(train) [10][5600/10520] lr: 1.0000e-04 eta: 18:37:19 time: 0.3566 data_time: 0.0032 memory: 17203 loss_visual: 0.0708 loss_lang: 0.1346 loss_fusion: 0.0622 loss: 0.2676 2022/10/05 18:41:58 - mmengine - INFO - Epoch(train) [10][5700/10520] lr: 1.0000e-04 eta: 18:36:18 time: 0.7870 data_time: 0.1597 memory: 17203 loss_visual: 0.0681 loss_lang: 0.1329 loss_fusion: 0.0585 loss: 0.2596 2022/10/05 18:42:55 - mmengine - INFO - Epoch(train) [10][5800/10520] lr: 1.0000e-04 eta: 18:35:14 time: 0.9792 data_time: 0.1768 memory: 17203 loss_visual: 0.0677 loss_lang: 0.1304 loss_fusion: 0.0594 loss: 0.2576 2022/10/05 18:43:52 - mmengine - INFO - Epoch(train) [10][5900/10520] lr: 1.0000e-04 eta: 18:34:09 time: 0.7979 data_time: 0.1657 memory: 17203 loss_visual: 0.0587 loss_lang: 0.1229 loss_fusion: 0.0500 loss: 0.2316 2022/10/05 18:44:50 - mmengine - INFO - Epoch(train) [10][6000/10520] lr: 1.0000e-04 eta: 18:33:04 time: 0.5163 data_time: 0.0339 memory: 17203 loss_visual: 0.0621 loss_lang: 0.1253 loss_fusion: 0.0545 loss: 0.2419 2022/10/05 18:45:48 - mmengine - INFO - Epoch(train) [10][6100/10520] lr: 1.0000e-04 eta: 18:32:00 time: 0.3860 data_time: 0.0236 memory: 17203 loss_visual: 0.0712 loss_lang: 0.1337 loss_fusion: 0.0618 loss: 0.2666 2022/10/05 18:47:13 - mmengine - INFO - Epoch(train) [10][6200/10520] lr: 1.0000e-04 eta: 18:31:25 time: 0.3610 data_time: 0.0038 memory: 17203 loss_visual: 0.0678 loss_lang: 0.1320 loss_fusion: 0.0578 loss: 0.2576 2022/10/05 18:48:28 - mmengine - INFO - Epoch(train) [10][6300/10520] lr: 1.0000e-04 eta: 18:30:40 time: 0.3470 data_time: 0.0034 memory: 17203 loss_visual: 0.0625 loss_lang: 0.1280 loss_fusion: 0.0538 loss: 0.2444 2022/10/05 18:48:45 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 18:49:26 - mmengine - INFO - Epoch(train) [10][6400/10520] lr: 1.0000e-04 eta: 18:29:36 time: 0.3436 data_time: 0.0036 memory: 17203 loss_visual: 0.0626 loss_lang: 0.1263 loss_fusion: 0.0541 loss: 0.2430 2022/10/05 18:50:31 - mmengine - INFO - Epoch(train) [10][6500/10520] lr: 1.0000e-04 eta: 18:28:39 time: 0.8093 data_time: 0.1599 memory: 17203 loss_visual: 0.0712 loss_lang: 0.1388 loss_fusion: 0.0617 loss: 0.2717 2022/10/05 18:51:29 - mmengine - INFO - Epoch(train) [10][6600/10520] lr: 1.0000e-04 eta: 18:27:36 time: 0.9269 data_time: 0.1748 memory: 17203 loss_visual: 0.0770 loss_lang: 0.1397 loss_fusion: 0.0676 loss: 0.2843 2022/10/05 18:52:26 - mmengine - INFO - Epoch(train) [10][6700/10520] lr: 1.0000e-04 eta: 18:26:30 time: 0.7994 data_time: 0.1550 memory: 17203 loss_visual: 0.0579 loss_lang: 0.1213 loss_fusion: 0.0485 loss: 0.2277 2022/10/05 18:53:22 - mmengine - INFO - Epoch(train) [10][6800/10520] lr: 1.0000e-04 eta: 18:25:24 time: 0.5698 data_time: 0.0353 memory: 17203 loss_visual: 0.0665 loss_lang: 0.1280 loss_fusion: 0.0574 loss: 0.2519 2022/10/05 18:54:17 - mmengine - INFO - Epoch(train) [10][6900/10520] lr: 1.0000e-04 eta: 18:24:17 time: 0.3910 data_time: 0.0204 memory: 17203 loss_visual: 0.0723 loss_lang: 0.1294 loss_fusion: 0.0623 loss: 0.2640 2022/10/05 18:55:12 - mmengine - INFO - Epoch(train) [10][7000/10520] lr: 1.0000e-04 eta: 18:23:10 time: 0.3629 data_time: 0.0035 memory: 17203 loss_visual: 0.0613 loss_lang: 0.1239 loss_fusion: 0.0521 loss: 0.2374 2022/10/05 18:56:08 - mmengine - INFO - Epoch(train) [10][7100/10520] lr: 1.0000e-04 eta: 18:22:04 time: 0.3486 data_time: 0.0032 memory: 17203 loss_visual: 0.0605 loss_lang: 0.1231 loss_fusion: 0.0520 loss: 0.2355 2022/10/05 18:57:03 - mmengine - INFO - Epoch(train) [10][7200/10520] lr: 1.0000e-04 eta: 18:20:56 time: 0.3435 data_time: 0.0034 memory: 17203 loss_visual: 0.0676 loss_lang: 0.1321 loss_fusion: 0.0576 loss: 0.2573 2022/10/05 18:58:03 - mmengine - INFO - Epoch(train) [10][7300/10520] lr: 1.0000e-04 eta: 18:19:54 time: 0.8051 data_time: 0.1829 memory: 17203 loss_visual: 0.0606 loss_lang: 0.1254 loss_fusion: 0.0529 loss: 0.2389 2022/10/05 18:58:12 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 18:59:00 - mmengine - INFO - Epoch(train) [10][7400/10520] lr: 1.0000e-04 eta: 18:18:49 time: 0.9034 data_time: 0.1772 memory: 17203 loss_visual: 0.0645 loss_lang: 0.1315 loss_fusion: 0.0558 loss: 0.2517 2022/10/05 18:59:56 - mmengine - INFO - Epoch(train) [10][7500/10520] lr: 1.0000e-04 eta: 18:17:43 time: 0.7852 data_time: 0.1680 memory: 17203 loss_visual: 0.0699 loss_lang: 0.1358 loss_fusion: 0.0606 loss: 0.2664 2022/10/05 19:00:53 - mmengine - INFO - Epoch(train) [10][7600/10520] lr: 1.0000e-04 eta: 18:16:39 time: 0.4956 data_time: 0.0481 memory: 17203 loss_visual: 0.0689 loss_lang: 0.1321 loss_fusion: 0.0589 loss: 0.2599 2022/10/05 19:01:49 - mmengine - INFO - Epoch(train) [10][7700/10520] lr: 1.0000e-04 eta: 18:15:33 time: 0.4023 data_time: 0.0200 memory: 17203 loss_visual: 0.0634 loss_lang: 0.1232 loss_fusion: 0.0537 loss: 0.2403 2022/10/05 19:02:46 - mmengine - INFO - Epoch(train) [10][7800/10520] lr: 1.0000e-04 eta: 18:14:28 time: 0.3980 data_time: 0.0049 memory: 17203 loss_visual: 0.0699 loss_lang: 0.1351 loss_fusion: 0.0608 loss: 0.2658 2022/10/05 19:03:42 - mmengine - INFO - Epoch(train) [10][7900/10520] lr: 1.0000e-04 eta: 18:13:22 time: 0.3629 data_time: 0.0033 memory: 17203 loss_visual: 0.0648 loss_lang: 0.1315 loss_fusion: 0.0560 loss: 0.2523 2022/10/05 19:04:38 - mmengine - INFO - Epoch(train) [10][8000/10520] lr: 1.0000e-04 eta: 18:12:16 time: 0.3437 data_time: 0.0033 memory: 17203 loss_visual: 0.0614 loss_lang: 0.1255 loss_fusion: 0.0530 loss: 0.2399 2022/10/05 19:05:40 - mmengine - INFO - Epoch(train) [10][8100/10520] lr: 1.0000e-04 eta: 18:11:16 time: 0.8190 data_time: 0.1600 memory: 17203 loss_visual: 0.0744 loss_lang: 0.1377 loss_fusion: 0.0644 loss: 0.2765 2022/10/05 19:06:37 - mmengine - INFO - Epoch(train) [10][8200/10520] lr: 1.0000e-04 eta: 18:10:11 time: 0.9625 data_time: 0.1790 memory: 17203 loss_visual: 0.0611 loss_lang: 0.1240 loss_fusion: 0.0528 loss: 0.2378 2022/10/05 19:07:34 - mmengine - INFO - Epoch(train) [10][8300/10520] lr: 1.0000e-04 eta: 18:09:07 time: 0.8058 data_time: 0.1691 memory: 17203 loss_visual: 0.0676 loss_lang: 0.1302 loss_fusion: 0.0579 loss: 0.2557 2022/10/05 19:07:41 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 19:08:31 - mmengine - INFO - Epoch(train) [10][8400/10520] lr: 1.0000e-04 eta: 18:08:02 time: 0.5018 data_time: 0.0340 memory: 17203 loss_visual: 0.0586 loss_lang: 0.1250 loss_fusion: 0.0512 loss: 0.2347 2022/10/05 19:09:28 - mmengine - INFO - Epoch(train) [10][8500/10520] lr: 1.0000e-04 eta: 18:06:57 time: 0.3850 data_time: 0.0207 memory: 17203 loss_visual: 0.0650 loss_lang: 0.1276 loss_fusion: 0.0560 loss: 0.2487 2022/10/05 19:10:25 - mmengine - INFO - Epoch(train) [10][8600/10520] lr: 1.0000e-04 eta: 18:05:52 time: 0.4052 data_time: 0.0034 memory: 17203 loss_visual: 0.0647 loss_lang: 0.1276 loss_fusion: 0.0554 loss: 0.2477 2022/10/05 19:11:21 - mmengine - INFO - Epoch(train) [10][8700/10520] lr: 1.0000e-04 eta: 18:04:46 time: 0.3496 data_time: 0.0033 memory: 17203 loss_visual: 0.0655 loss_lang: 0.1328 loss_fusion: 0.0565 loss: 0.2548 2022/10/05 19:12:18 - mmengine - INFO - Epoch(train) [10][8800/10520] lr: 1.0000e-04 eta: 18:03:41 time: 0.3738 data_time: 0.0031 memory: 17203 loss_visual: 0.0660 loss_lang: 0.1279 loss_fusion: 0.0575 loss: 0.2514 2022/10/05 19:13:20 - mmengine - INFO - Epoch(train) [10][8900/10520] lr: 1.0000e-04 eta: 18:02:42 time: 0.8769 data_time: 0.1623 memory: 17203 loss_visual: 0.0675 loss_lang: 0.1313 loss_fusion: 0.0585 loss: 0.2573 2022/10/05 19:14:25 - mmengine - INFO - Epoch(train) [10][9000/10520] lr: 1.0000e-04 eta: 18:01:45 time: 1.0275 data_time: 0.1844 memory: 17203 loss_visual: 0.0586 loss_lang: 0.1219 loss_fusion: 0.0500 loss: 0.2305 2022/10/05 19:15:27 - mmengine - INFO - Epoch(train) [10][9100/10520] lr: 1.0000e-04 eta: 18:00:45 time: 0.8582 data_time: 0.1445 memory: 17203 loss_visual: 0.0622 loss_lang: 0.1186 loss_fusion: 0.0522 loss: 0.2329 2022/10/05 19:16:25 - mmengine - INFO - Epoch(train) [10][9200/10520] lr: 1.0000e-04 eta: 17:59:42 time: 0.5764 data_time: 0.0335 memory: 17203 loss_visual: 0.0766 loss_lang: 0.1396 loss_fusion: 0.0658 loss: 0.2820 2022/10/05 19:17:27 - mmengine - INFO - Epoch(train) [10][9300/10520] lr: 1.0000e-04 eta: 17:58:42 time: 0.4131 data_time: 0.0202 memory: 17203 loss_visual: 0.0781 loss_lang: 0.1430 loss_fusion: 0.0687 loss: 0.2898 2022/10/05 19:17:40 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 19:18:22 - mmengine - INFO - Epoch(train) [10][9400/10520] lr: 1.0000e-04 eta: 17:57:36 time: 0.3635 data_time: 0.0034 memory: 17203 loss_visual: 0.0695 loss_lang: 0.1327 loss_fusion: 0.0604 loss: 0.2626 2022/10/05 19:19:18 - mmengine - INFO - Epoch(train) [10][9500/10520] lr: 1.0000e-04 eta: 17:56:29 time: 0.3519 data_time: 0.0032 memory: 17203 loss_visual: 0.0640 loss_lang: 0.1279 loss_fusion: 0.0551 loss: 0.2469 2022/10/05 19:20:13 - mmengine - INFO - Epoch(train) [10][9600/10520] lr: 1.0000e-04 eta: 17:55:23 time: 0.3433 data_time: 0.0033 memory: 17203 loss_visual: 0.0623 loss_lang: 0.1229 loss_fusion: 0.0530 loss: 0.2382 2022/10/05 19:21:15 - mmengine - INFO - Epoch(train) [10][9700/10520] lr: 1.0000e-04 eta: 17:54:23 time: 0.8345 data_time: 0.1608 memory: 17203 loss_visual: 0.0776 loss_lang: 0.1414 loss_fusion: 0.0686 loss: 0.2876 2022/10/05 19:22:12 - mmengine - INFO - Epoch(train) [10][9800/10520] lr: 1.0000e-04 eta: 17:53:19 time: 0.8778 data_time: 0.1826 memory: 17203 loss_visual: 0.0663 loss_lang: 0.1275 loss_fusion: 0.0573 loss: 0.2511 2022/10/05 19:23:08 - mmengine - INFO - Epoch(train) [10][9900/10520] lr: 1.0000e-04 eta: 17:52:13 time: 0.8126 data_time: 0.1544 memory: 17203 loss_visual: 0.0680 loss_lang: 0.1278 loss_fusion: 0.0587 loss: 0.2545 2022/10/05 19:24:03 - mmengine - INFO - Epoch(train) [10][10000/10520] lr: 1.0000e-04 eta: 17:51:06 time: 0.5521 data_time: 0.0345 memory: 17203 loss_visual: 0.0604 loss_lang: 0.1256 loss_fusion: 0.0516 loss: 0.2377 2022/10/05 19:24:58 - mmengine - INFO - Epoch(train) [10][10100/10520] lr: 1.0000e-04 eta: 17:50:00 time: 0.3905 data_time: 0.0207 memory: 17203 loss_visual: 0.0696 loss_lang: 0.1330 loss_fusion: 0.0610 loss: 0.2636 2022/10/05 19:25:54 - mmengine - INFO - Epoch(train) [10][10200/10520] lr: 1.0000e-04 eta: 17:48:54 time: 0.3646 data_time: 0.0033 memory: 17203 loss_visual: 0.0752 loss_lang: 0.1343 loss_fusion: 0.0660 loss: 0.2756 2022/10/05 19:26:50 - mmengine - INFO - Epoch(train) [10][10300/10520] lr: 1.0000e-04 eta: 17:47:49 time: 0.3898 data_time: 0.0036 memory: 17203 loss_visual: 0.0680 loss_lang: 0.1301 loss_fusion: 0.0576 loss: 0.2557 2022/10/05 19:27:04 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 19:27:46 - mmengine - INFO - Epoch(train) [10][10400/10520] lr: 1.0000e-04 eta: 17:46:43 time: 0.3440 data_time: 0.0033 memory: 17203 loss_visual: 0.0573 loss_lang: 0.1192 loss_fusion: 0.0489 loss: 0.2254 2022/10/05 19:28:44 - mmengine - INFO - Epoch(train) [10][10500/10520] lr: 1.0000e-04 eta: 17:45:39 time: 0.5819 data_time: 0.0843 memory: 17203 loss_visual: 0.0652 loss_lang: 0.1297 loss_fusion: 0.0573 loss: 0.2522 2022/10/05 19:28:52 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 19:28:52 - mmengine - INFO - Saving checkpoint at 10 epochs 2022/10/05 19:29:06 - mmengine - INFO - Epoch(val) [10][100/959] eta: 0:00:40 time: 0.0475 data_time: 0.0018 memory: 17203 2022/10/05 19:29:11 - mmengine - INFO - Epoch(val) [10][200/959] eta: 0:00:35 time: 0.0470 data_time: 0.0011 memory: 734 2022/10/05 19:29:16 - mmengine - INFO - Epoch(val) [10][300/959] eta: 0:00:32 time: 0.0492 data_time: 0.0024 memory: 734 2022/10/05 19:29:20 - mmengine - INFO - Epoch(val) [10][400/959] eta: 0:00:26 time: 0.0479 data_time: 0.0019 memory: 734 2022/10/05 19:29:25 - mmengine - INFO - Epoch(val) [10][500/959] eta: 0:00:22 time: 0.0490 data_time: 0.0029 memory: 734 2022/10/05 19:29:30 - mmengine - INFO - Epoch(val) [10][600/959] eta: 0:00:15 time: 0.0431 data_time: 0.0010 memory: 734 2022/10/05 19:29:35 - mmengine - INFO - Epoch(val) [10][700/959] eta: 0:00:11 time: 0.0446 data_time: 0.0010 memory: 734 2022/10/05 19:29:39 - mmengine - INFO - Epoch(val) [10][800/959] eta: 0:00:04 time: 0.0310 data_time: 0.0008 memory: 734 2022/10/05 19:29:42 - mmengine - INFO - Epoch(val) [10][900/959] eta: 0:00:01 time: 0.0222 data_time: 0.0006 memory: 734 2022/10/05 19:29:44 - mmengine - INFO - Epoch(val) [10][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.8264 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9460 SVT/recog/word_acc_ignore_case_symbol: 0.9351 SVTP/recog/word_acc_ignore_case_symbol: 0.8729 IC13/recog/word_acc_ignore_case_symbol: 0.9350 IC15/recog/word_acc_ignore_case_symbol: 0.7944 2022/10/05 19:30:46 - mmengine - INFO - Epoch(train) [11][100/10520] lr: 1.0000e-04 eta: 17:44:23 time: 0.6683 data_time: 0.1936 memory: 17203 loss_visual: 0.0600 loss_lang: 0.1273 loss_fusion: 0.0514 loss: 0.2387 2022/10/05 19:31:41 - mmengine - INFO - Epoch(train) [11][200/10520] lr: 1.0000e-04 eta: 17:43:17 time: 0.8250 data_time: 0.2109 memory: 17203 loss_visual: 0.0661 loss_lang: 0.1314 loss_fusion: 0.0574 loss: 0.2548 2022/10/05 19:32:36 - mmengine - INFO - Epoch(train) [11][300/10520] lr: 1.0000e-04 eta: 17:42:11 time: 0.6095 data_time: 0.0304 memory: 17203 loss_visual: 0.0701 loss_lang: 0.1312 loss_fusion: 0.0612 loss: 0.2625 2022/10/05 19:33:31 - mmengine - INFO - Epoch(train) [11][400/10520] lr: 1.0000e-04 eta: 17:41:04 time: 0.5561 data_time: 0.0281 memory: 17203 loss_visual: 0.0719 loss_lang: 0.1374 loss_fusion: 0.0632 loss: 0.2725 2022/10/05 19:34:25 - mmengine - INFO - Epoch(train) [11][500/10520] lr: 1.0000e-04 eta: 17:39:56 time: 0.4264 data_time: 0.0038 memory: 17203 loss_visual: 0.0731 loss_lang: 0.1366 loss_fusion: 0.0652 loss: 0.2749 2022/10/05 19:35:20 - mmengine - INFO - Epoch(train) [11][600/10520] lr: 1.0000e-04 eta: 17:38:50 time: 0.4255 data_time: 0.0040 memory: 17203 loss_visual: 0.0701 loss_lang: 0.1350 loss_fusion: 0.0615 loss: 0.2665 2022/10/05 19:36:14 - mmengine - INFO - Epoch(train) [11][700/10520] lr: 1.0000e-04 eta: 17:37:43 time: 0.3779 data_time: 0.0033 memory: 17203 loss_visual: 0.0643 loss_lang: 0.1300 loss_fusion: 0.0559 loss: 0.2502 2022/10/05 19:37:10 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 19:37:10 - mmengine - INFO - Epoch(train) [11][800/10520] lr: 1.0000e-04 eta: 17:36:37 time: 0.4026 data_time: 0.0033 memory: 17203 loss_visual: 0.0641 loss_lang: 0.1261 loss_fusion: 0.0550 loss: 0.2453 2022/10/05 19:38:08 - mmengine - INFO - Epoch(train) [11][900/10520] lr: 1.0000e-04 eta: 17:35:33 time: 0.7069 data_time: 0.1620 memory: 17203 loss_visual: 0.0584 loss_lang: 0.1252 loss_fusion: 0.0508 loss: 0.2345 2022/10/05 19:39:04 - mmengine - INFO - Epoch(train) [11][1000/10520] lr: 1.0000e-04 eta: 17:34:28 time: 0.8289 data_time: 0.2030 memory: 17203 loss_visual: 0.0704 loss_lang: 0.1362 loss_fusion: 0.0617 loss: 0.2683 2022/10/05 19:39:59 - mmengine - INFO - Epoch(train) [11][1100/10520] lr: 1.0000e-04 eta: 17:33:22 time: 0.6649 data_time: 0.0298 memory: 17203 loss_visual: 0.0629 loss_lang: 0.1278 loss_fusion: 0.0539 loss: 0.2447 2022/10/05 19:40:54 - mmengine - INFO - Epoch(train) [11][1200/10520] lr: 1.0000e-04 eta: 17:32:16 time: 0.5403 data_time: 0.0324 memory: 17203 loss_visual: 0.0711 loss_lang: 0.1325 loss_fusion: 0.0612 loss: 0.2647 2022/10/05 19:41:50 - mmengine - INFO - Epoch(train) [11][1300/10520] lr: 1.0000e-04 eta: 17:31:10 time: 0.4465 data_time: 0.0040 memory: 17203 loss_visual: 0.0678 loss_lang: 0.1322 loss_fusion: 0.0604 loss: 0.2603 2022/10/05 19:42:56 - mmengine - INFO - Epoch(train) [11][1400/10520] lr: 1.0000e-04 eta: 17:30:15 time: 0.4491 data_time: 0.0036 memory: 17203 loss_visual: 0.0630 loss_lang: 0.1312 loss_fusion: 0.0543 loss: 0.2485 2022/10/05 19:43:53 - mmengine - INFO - Epoch(train) [11][1500/10520] lr: 1.0000e-04 eta: 17:29:10 time: 0.3995 data_time: 0.0032 memory: 17203 loss_visual: 0.0677 loss_lang: 0.1332 loss_fusion: 0.0605 loss: 0.2613 2022/10/05 19:44:47 - mmengine - INFO - Epoch(train) [11][1600/10520] lr: 1.0000e-04 eta: 17:28:03 time: 0.3640 data_time: 0.0035 memory: 17203 loss_visual: 0.0598 loss_lang: 0.1203 loss_fusion: 0.0521 loss: 0.2322 2022/10/05 19:45:45 - mmengine - INFO - Epoch(train) [11][1700/10520] lr: 1.0000e-04 eta: 17:27:00 time: 0.6917 data_time: 0.1506 memory: 17203 loss_visual: 0.0608 loss_lang: 0.1282 loss_fusion: 0.0520 loss: 0.2410 2022/10/05 19:46:41 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 19:46:41 - mmengine - INFO - Epoch(train) [11][1800/10520] lr: 1.0000e-04 eta: 17:25:55 time: 0.7953 data_time: 0.1925 memory: 17203 loss_visual: 0.0635 loss_lang: 0.1260 loss_fusion: 0.0554 loss: 0.2449 2022/10/05 19:47:37 - mmengine - INFO - Epoch(train) [11][1900/10520] lr: 1.0000e-04 eta: 17:24:49 time: 0.5902 data_time: 0.0289 memory: 17203 loss_visual: 0.0708 loss_lang: 0.1312 loss_fusion: 0.0612 loss: 0.2632 2022/10/05 19:48:33 - mmengine - INFO - Epoch(train) [11][2000/10520] lr: 1.0000e-04 eta: 17:23:44 time: 0.6460 data_time: 0.0422 memory: 17203 loss_visual: 0.0630 loss_lang: 0.1232 loss_fusion: 0.0525 loss: 0.2387 2022/10/05 19:49:31 - mmengine - INFO - Epoch(train) [11][2100/10520] lr: 1.0000e-04 eta: 17:22:41 time: 0.4502 data_time: 0.0037 memory: 17203 loss_visual: 0.0635 loss_lang: 0.1253 loss_fusion: 0.0548 loss: 0.2437 2022/10/05 19:50:28 - mmengine - INFO - Epoch(train) [11][2200/10520] lr: 1.0000e-04 eta: 17:21:37 time: 0.4235 data_time: 0.0035 memory: 17203 loss_visual: 0.0707 loss_lang: 0.1318 loss_fusion: 0.0611 loss: 0.2635 2022/10/05 19:51:28 - mmengine - INFO - Epoch(train) [11][2300/10520] lr: 1.0000e-04 eta: 17:20:36 time: 0.3752 data_time: 0.0034 memory: 17203 loss_visual: 0.0693 loss_lang: 0.1305 loss_fusion: 0.0586 loss: 0.2584 2022/10/05 19:52:24 - mmengine - INFO - Epoch(train) [11][2400/10520] lr: 1.0000e-04 eta: 17:19:30 time: 0.3612 data_time: 0.0031 memory: 17203 loss_visual: 0.0671 loss_lang: 0.1319 loss_fusion: 0.0589 loss: 0.2580 2022/10/05 19:53:23 - mmengine - INFO - Epoch(train) [11][2500/10520] lr: 1.0000e-04 eta: 17:18:28 time: 0.6902 data_time: 0.1582 memory: 17203 loss_visual: 0.0700 loss_lang: 0.1350 loss_fusion: 0.0628 loss: 0.2678 2022/10/05 19:54:20 - mmengine - INFO - Epoch(train) [11][2600/10520] lr: 1.0000e-04 eta: 17:17:24 time: 0.8387 data_time: 0.1926 memory: 17203 loss_visual: 0.0654 loss_lang: 0.1300 loss_fusion: 0.0566 loss: 0.2520 2022/10/05 19:55:16 - mmengine - INFO - Epoch(train) [11][2700/10520] lr: 1.0000e-04 eta: 17:16:19 time: 0.6127 data_time: 0.0314 memory: 17203 loss_visual: 0.0656 loss_lang: 0.1277 loss_fusion: 0.0566 loss: 0.2499 2022/10/05 19:56:12 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 19:56:12 - mmengine - INFO - Epoch(train) [11][2800/10520] lr: 1.0000e-04 eta: 17:15:14 time: 0.5540 data_time: 0.0294 memory: 17203 loss_visual: 0.0606 loss_lang: 0.1212 loss_fusion: 0.0505 loss: 0.2323 2022/10/05 19:57:10 - mmengine - INFO - Epoch(train) [11][2900/10520] lr: 1.0000e-04 eta: 17:14:10 time: 0.4735 data_time: 0.0039 memory: 17203 loss_visual: 0.0715 loss_lang: 0.1381 loss_fusion: 0.0620 loss: 0.2716 2022/10/05 19:58:06 - mmengine - INFO - Epoch(train) [11][3000/10520] lr: 1.0000e-04 eta: 17:13:05 time: 0.4625 data_time: 0.0039 memory: 17203 loss_visual: 0.0719 loss_lang: 0.1334 loss_fusion: 0.0640 loss: 0.2693 2022/10/05 19:59:02 - mmengine - INFO - Epoch(train) [11][3100/10520] lr: 1.0000e-04 eta: 17:12:00 time: 0.3818 data_time: 0.0043 memory: 17203 loss_visual: 0.0612 loss_lang: 0.1195 loss_fusion: 0.0517 loss: 0.2323 2022/10/05 19:59:58 - mmengine - INFO - Epoch(train) [11][3200/10520] lr: 1.0000e-04 eta: 17:10:55 time: 0.3623 data_time: 0.0035 memory: 17203 loss_visual: 0.0602 loss_lang: 0.1254 loss_fusion: 0.0524 loss: 0.2381 2022/10/05 20:00:58 - mmengine - INFO - Epoch(train) [11][3300/10520] lr: 1.0000e-04 eta: 17:09:54 time: 0.7076 data_time: 0.1665 memory: 17203 loss_visual: 0.0653 loss_lang: 0.1214 loss_fusion: 0.0565 loss: 0.2432 2022/10/05 20:01:56 - mmengine - INFO - Epoch(train) [11][3400/10520] lr: 1.0000e-04 eta: 17:08:51 time: 0.8365 data_time: 0.1886 memory: 17203 loss_visual: 0.0680 loss_lang: 0.1398 loss_fusion: 0.0611 loss: 0.2689 2022/10/05 20:02:53 - mmengine - INFO - Epoch(train) [11][3500/10520] lr: 1.0000e-04 eta: 17:07:47 time: 0.6424 data_time: 0.0302 memory: 17203 loss_visual: 0.0581 loss_lang: 0.1187 loss_fusion: 0.0487 loss: 0.2255 2022/10/05 20:03:50 - mmengine - INFO - Epoch(train) [11][3600/10520] lr: 1.0000e-04 eta: 17:06:42 time: 0.5645 data_time: 0.0301 memory: 17203 loss_visual: 0.0755 loss_lang: 0.1422 loss_fusion: 0.0660 loss: 0.2837 2022/10/05 20:04:46 - mmengine - INFO - Epoch(train) [11][3700/10520] lr: 1.0000e-04 eta: 17:05:38 time: 0.4362 data_time: 0.0039 memory: 17203 loss_visual: 0.0621 loss_lang: 0.1310 loss_fusion: 0.0538 loss: 0.2470 2022/10/05 20:05:43 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 20:05:43 - mmengine - INFO - Epoch(train) [11][3800/10520] lr: 1.0000e-04 eta: 17:04:33 time: 0.4489 data_time: 0.0202 memory: 17203 loss_visual: 0.0584 loss_lang: 0.1232 loss_fusion: 0.0496 loss: 0.2312 2022/10/05 20:06:39 - mmengine - INFO - Epoch(train) [11][3900/10520] lr: 1.0000e-04 eta: 17:03:29 time: 0.3972 data_time: 0.0037 memory: 17203 loss_visual: 0.0672 loss_lang: 0.1333 loss_fusion: 0.0581 loss: 0.2587 2022/10/05 20:07:35 - mmengine - INFO - Epoch(train) [11][4000/10520] lr: 1.0000e-04 eta: 17:02:23 time: 0.3691 data_time: 0.0038 memory: 17203 loss_visual: 0.0735 loss_lang: 0.1357 loss_fusion: 0.0630 loss: 0.2721 2022/10/05 20:08:34 - mmengine - INFO - Epoch(train) [11][4100/10520] lr: 1.0000e-04 eta: 17:01:21 time: 0.6850 data_time: 0.1602 memory: 17203 loss_visual: 0.0765 loss_lang: 0.1442 loss_fusion: 0.0679 loss: 0.2887 2022/10/05 20:09:31 - mmengine - INFO - Epoch(train) [11][4200/10520] lr: 1.0000e-04 eta: 17:00:17 time: 0.8421 data_time: 0.1797 memory: 17203 loss_visual: 0.0737 loss_lang: 0.1353 loss_fusion: 0.0647 loss: 0.2737 2022/10/05 20:10:27 - mmengine - INFO - Epoch(train) [11][4300/10520] lr: 1.0000e-04 eta: 16:59:12 time: 0.6318 data_time: 0.0311 memory: 17203 loss_visual: 0.0629 loss_lang: 0.1235 loss_fusion: 0.0535 loss: 0.2399 2022/10/05 20:11:23 - mmengine - INFO - Epoch(train) [11][4400/10520] lr: 1.0000e-04 eta: 16:58:08 time: 0.5693 data_time: 0.0289 memory: 17203 loss_visual: 0.0644 loss_lang: 0.1285 loss_fusion: 0.0555 loss: 0.2484 2022/10/05 20:12:20 - mmengine - INFO - Epoch(train) [11][4500/10520] lr: 1.0000e-04 eta: 16:57:04 time: 0.4315 data_time: 0.0036 memory: 17203 loss_visual: 0.0551 loss_lang: 0.1153 loss_fusion: 0.0457 loss: 0.2162 2022/10/05 20:13:16 - mmengine - INFO - Epoch(train) [11][4600/10520] lr: 1.0000e-04 eta: 16:55:59 time: 0.4437 data_time: 0.0039 memory: 17203 loss_visual: 0.0690 loss_lang: 0.1334 loss_fusion: 0.0617 loss: 0.2641 2022/10/05 20:14:11 - mmengine - INFO - Epoch(train) [11][4700/10520] lr: 1.0000e-04 eta: 16:54:54 time: 0.3815 data_time: 0.0033 memory: 17203 loss_visual: 0.0695 loss_lang: 0.1311 loss_fusion: 0.0605 loss: 0.2611 2022/10/05 20:15:08 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 20:15:08 - mmengine - INFO - Epoch(train) [11][4800/10520] lr: 1.0000e-04 eta: 16:53:49 time: 0.3595 data_time: 0.0035 memory: 17203 loss_visual: 0.0663 loss_lang: 0.1282 loss_fusion: 0.0593 loss: 0.2538 2022/10/05 20:16:06 - mmengine - INFO - Epoch(train) [11][4900/10520] lr: 1.0000e-04 eta: 16:52:46 time: 0.6953 data_time: 0.1585 memory: 17203 loss_visual: 0.0668 loss_lang: 0.1331 loss_fusion: 0.0571 loss: 0.2570 2022/10/05 20:17:01 - mmengine - INFO - Epoch(train) [11][5000/10520] lr: 1.0000e-04 eta: 16:51:41 time: 0.8038 data_time: 0.1854 memory: 17203 loss_visual: 0.0697 loss_lang: 0.1336 loss_fusion: 0.0609 loss: 0.2642 2022/10/05 20:17:56 - mmengine - INFO - Epoch(train) [11][5100/10520] lr: 1.0000e-04 eta: 16:50:35 time: 0.6149 data_time: 0.0297 memory: 17203 loss_visual: 0.0540 loss_lang: 0.1163 loss_fusion: 0.0465 loss: 0.2168 2022/10/05 20:18:52 - mmengine - INFO - Epoch(train) [11][5200/10520] lr: 1.0000e-04 eta: 16:49:31 time: 0.5563 data_time: 0.0300 memory: 17203 loss_visual: 0.0652 loss_lang: 0.1305 loss_fusion: 0.0559 loss: 0.2517 2022/10/05 20:19:50 - mmengine - INFO - Epoch(train) [11][5300/10520] lr: 1.0000e-04 eta: 16:48:27 time: 0.4384 data_time: 0.0037 memory: 17203 loss_visual: 0.0644 loss_lang: 0.1286 loss_fusion: 0.0556 loss: 0.2486 2022/10/05 20:20:45 - mmengine - INFO - Epoch(train) [11][5400/10520] lr: 1.0000e-04 eta: 16:47:22 time: 0.4580 data_time: 0.0049 memory: 17203 loss_visual: 0.0648 loss_lang: 0.1282 loss_fusion: 0.0563 loss: 0.2493 2022/10/05 20:21:41 - mmengine - INFO - Epoch(train) [11][5500/10520] lr: 1.0000e-04 eta: 16:46:17 time: 0.3753 data_time: 0.0032 memory: 17203 loss_visual: 0.0623 loss_lang: 0.1251 loss_fusion: 0.0537 loss: 0.2410 2022/10/05 20:22:36 - mmengine - INFO - Epoch(train) [11][5600/10520] lr: 1.0000e-04 eta: 16:45:12 time: 0.3662 data_time: 0.0035 memory: 17203 loss_visual: 0.0696 loss_lang: 0.1310 loss_fusion: 0.0613 loss: 0.2619 2022/10/05 20:23:34 - mmengine - INFO - Epoch(train) [11][5700/10520] lr: 1.0000e-04 eta: 16:44:09 time: 0.6802 data_time: 0.1688 memory: 17203 loss_visual: 0.0612 loss_lang: 0.1173 loss_fusion: 0.0524 loss: 0.2310 2022/10/05 20:24:30 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 20:24:30 - mmengine - INFO - Epoch(train) [11][5800/10520] lr: 1.0000e-04 eta: 16:43:05 time: 0.8034 data_time: 0.1934 memory: 17203 loss_visual: 0.0751 loss_lang: 0.1400 loss_fusion: 0.0656 loss: 0.2808 2022/10/05 20:25:26 - mmengine - INFO - Epoch(train) [11][5900/10520] lr: 1.0000e-04 eta: 16:42:00 time: 0.6502 data_time: 0.0298 memory: 17203 loss_visual: 0.0571 loss_lang: 0.1184 loss_fusion: 0.0493 loss: 0.2248 2022/10/05 20:26:22 - mmengine - INFO - Epoch(train) [11][6000/10520] lr: 1.0000e-04 eta: 16:40:55 time: 0.5592 data_time: 0.0313 memory: 17203 loss_visual: 0.0618 loss_lang: 0.1206 loss_fusion: 0.0527 loss: 0.2351 2022/10/05 20:27:17 - mmengine - INFO - Epoch(train) [11][6100/10520] lr: 1.0000e-04 eta: 16:39:49 time: 0.4525 data_time: 0.0035 memory: 17203 loss_visual: 0.0643 loss_lang: 0.1299 loss_fusion: 0.0560 loss: 0.2502 2022/10/05 20:28:11 - mmengine - INFO - Epoch(train) [11][6200/10520] lr: 1.0000e-04 eta: 16:38:43 time: 0.4187 data_time: 0.0038 memory: 17203 loss_visual: 0.0627 loss_lang: 0.1262 loss_fusion: 0.0533 loss: 0.2421 2022/10/05 20:29:06 - mmengine - INFO - Epoch(train) [11][6300/10520] lr: 1.0000e-04 eta: 16:37:38 time: 0.4133 data_time: 0.0070 memory: 17203 loss_visual: 0.0692 loss_lang: 0.1317 loss_fusion: 0.0599 loss: 0.2608 2022/10/05 20:30:01 - mmengine - INFO - Epoch(train) [11][6400/10520] lr: 1.0000e-04 eta: 16:36:33 time: 0.3750 data_time: 0.0038 memory: 17203 loss_visual: 0.0703 loss_lang: 0.1335 loss_fusion: 0.0623 loss: 0.2662 2022/10/05 20:31:00 - mmengine - INFO - Epoch(train) [11][6500/10520] lr: 1.0000e-04 eta: 16:35:30 time: 0.6751 data_time: 0.1648 memory: 17203 loss_visual: 0.0679 loss_lang: 0.1316 loss_fusion: 0.0596 loss: 0.2591 2022/10/05 20:31:56 - mmengine - INFO - Epoch(train) [11][6600/10520] lr: 1.0000e-04 eta: 16:34:26 time: 0.8217 data_time: 0.1956 memory: 17203 loss_visual: 0.0674 loss_lang: 0.1310 loss_fusion: 0.0580 loss: 0.2564 2022/10/05 20:32:52 - mmengine - INFO - Epoch(train) [11][6700/10520] lr: 1.0000e-04 eta: 16:33:21 time: 0.6054 data_time: 0.0313 memory: 17203 loss_visual: 0.0654 loss_lang: 0.1300 loss_fusion: 0.0567 loss: 0.2521 2022/10/05 20:33:48 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 20:33:48 - mmengine - INFO - Epoch(train) [11][6800/10520] lr: 1.0000e-04 eta: 16:32:17 time: 0.5746 data_time: 0.0323 memory: 17203 loss_visual: 0.0661 loss_lang: 0.1266 loss_fusion: 0.0568 loss: 0.2495 2022/10/05 20:34:42 - mmengine - INFO - Epoch(train) [11][6900/10520] lr: 1.0000e-04 eta: 16:31:11 time: 0.4588 data_time: 0.0034 memory: 17203 loss_visual: 0.0663 loss_lang: 0.1283 loss_fusion: 0.0576 loss: 0.2522 2022/10/05 20:35:37 - mmengine - INFO - Epoch(train) [11][7000/10520] lr: 1.0000e-04 eta: 16:30:06 time: 0.4327 data_time: 0.0033 memory: 17203 loss_visual: 0.0635 loss_lang: 0.1281 loss_fusion: 0.0554 loss: 0.2470 2022/10/05 20:36:33 - mmengine - INFO - Epoch(train) [11][7100/10520] lr: 1.0000e-04 eta: 16:29:01 time: 0.3944 data_time: 0.0038 memory: 17203 loss_visual: 0.0774 loss_lang: 0.1399 loss_fusion: 0.0680 loss: 0.2853 2022/10/05 20:37:28 - mmengine - INFO - Epoch(train) [11][7200/10520] lr: 1.0000e-04 eta: 16:27:55 time: 0.3597 data_time: 0.0043 memory: 17203 loss_visual: 0.0615 loss_lang: 0.1231 loss_fusion: 0.0531 loss: 0.2377 2022/10/05 20:38:26 - mmengine - INFO - Epoch(train) [11][7300/10520] lr: 1.0000e-04 eta: 16:26:53 time: 0.7171 data_time: 0.1749 memory: 17203 loss_visual: 0.0616 loss_lang: 0.1261 loss_fusion: 0.0532 loss: 0.2409 2022/10/05 20:39:23 - mmengine - INFO - Epoch(train) [11][7400/10520] lr: 1.0000e-04 eta: 16:25:49 time: 0.8372 data_time: 0.2230 memory: 17203 loss_visual: 0.0615 loss_lang: 0.1222 loss_fusion: 0.0529 loss: 0.2366 2022/10/05 20:40:19 - mmengine - INFO - Epoch(train) [11][7500/10520] lr: 1.0000e-04 eta: 16:24:45 time: 0.6174 data_time: 0.0298 memory: 17203 loss_visual: 0.0735 loss_lang: 0.1366 loss_fusion: 0.0663 loss: 0.2764 2022/10/05 20:41:15 - mmengine - INFO - Epoch(train) [11][7600/10520] lr: 1.0000e-04 eta: 16:23:41 time: 0.5661 data_time: 0.0332 memory: 17203 loss_visual: 0.0600 loss_lang: 0.1239 loss_fusion: 0.0510 loss: 0.2349 2022/10/05 20:42:12 - mmengine - INFO - Epoch(train) [11][7700/10520] lr: 1.0000e-04 eta: 16:22:37 time: 0.4505 data_time: 0.0036 memory: 17203 loss_visual: 0.0586 loss_lang: 0.1208 loss_fusion: 0.0489 loss: 0.2283 2022/10/05 20:43:06 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 20:43:06 - mmengine - INFO - Epoch(train) [11][7800/10520] lr: 1.0000e-04 eta: 16:21:31 time: 0.4309 data_time: 0.0042 memory: 17203 loss_visual: 0.0647 loss_lang: 0.1266 loss_fusion: 0.0569 loss: 0.2482 2022/10/05 20:44:00 - mmengine - INFO - Epoch(train) [11][7900/10520] lr: 1.0000e-04 eta: 16:20:26 time: 0.3782 data_time: 0.0038 memory: 17203 loss_visual: 0.0617 loss_lang: 0.1256 loss_fusion: 0.0532 loss: 0.2406 2022/10/05 20:44:55 - mmengine - INFO - Epoch(train) [11][8000/10520] lr: 1.0000e-04 eta: 16:19:20 time: 0.3612 data_time: 0.0039 memory: 17203 loss_visual: 0.0639 loss_lang: 0.1279 loss_fusion: 0.0548 loss: 0.2467 2022/10/05 20:45:53 - mmengine - INFO - Epoch(train) [11][8100/10520] lr: 1.0000e-04 eta: 16:18:17 time: 0.6694 data_time: 0.1941 memory: 17203 loss_visual: 0.0693 loss_lang: 0.1339 loss_fusion: 0.0606 loss: 0.2639 2022/10/05 20:46:49 - mmengine - INFO - Epoch(train) [11][8200/10520] lr: 1.0000e-04 eta: 16:17:13 time: 0.8331 data_time: 0.2098 memory: 17203 loss_visual: 0.0666 loss_lang: 0.1286 loss_fusion: 0.0580 loss: 0.2532 2022/10/05 20:47:44 - mmengine - INFO - Epoch(train) [11][8300/10520] lr: 1.0000e-04 eta: 16:16:08 time: 0.6279 data_time: 0.0326 memory: 17203 loss_visual: 0.0654 loss_lang: 0.1305 loss_fusion: 0.0562 loss: 0.2521 2022/10/05 20:48:39 - mmengine - INFO - Epoch(train) [11][8400/10520] lr: 1.0000e-04 eta: 16:15:03 time: 0.5800 data_time: 0.0406 memory: 17203 loss_visual: 0.0623 loss_lang: 0.1301 loss_fusion: 0.0554 loss: 0.2478 2022/10/05 20:49:34 - mmengine - INFO - Epoch(train) [11][8500/10520] lr: 1.0000e-04 eta: 16:13:58 time: 0.4283 data_time: 0.0044 memory: 17203 loss_visual: 0.0618 loss_lang: 0.1215 loss_fusion: 0.0534 loss: 0.2367 2022/10/05 20:50:29 - mmengine - INFO - Epoch(train) [11][8600/10520] lr: 1.0000e-04 eta: 16:12:53 time: 0.4250 data_time: 0.0044 memory: 17203 loss_visual: 0.0733 loss_lang: 0.1376 loss_fusion: 0.0640 loss: 0.2748 2022/10/05 20:51:23 - mmengine - INFO - Epoch(train) [11][8700/10520] lr: 1.0000e-04 eta: 16:11:47 time: 0.3785 data_time: 0.0047 memory: 17203 loss_visual: 0.0687 loss_lang: 0.1274 loss_fusion: 0.0591 loss: 0.2553 2022/10/05 20:52:18 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 20:52:18 - mmengine - INFO - Epoch(train) [11][8800/10520] lr: 1.0000e-04 eta: 16:10:42 time: 0.3816 data_time: 0.0046 memory: 17203 loss_visual: 0.0673 loss_lang: 0.1311 loss_fusion: 0.0585 loss: 0.2569 2022/10/05 20:54:56 - mmengine - INFO - Epoch(train) [11][8900/10520] lr: 1.0000e-04 eta: 16:11:04 time: 10.6822 data_time: 0.2272 memory: 17203 loss_visual: 0.0711 loss_lang: 0.1306 loss_fusion: 0.0619 loss: 0.2636 2022/10/05 20:55:54 - mmengine - INFO - Epoch(train) [11][9000/10520] lr: 1.0000e-04 eta: 16:10:01 time: 0.7845 data_time: 0.2330 memory: 17203 loss_visual: 0.0652 loss_lang: 0.1290 loss_fusion: 0.0576 loss: 0.2518 2022/10/05 20:56:50 - mmengine - INFO - Epoch(train) [11][9100/10520] lr: 1.0000e-04 eta: 16:08:57 time: 0.6292 data_time: 0.0357 memory: 17203 loss_visual: 0.0555 loss_lang: 0.1176 loss_fusion: 0.0474 loss: 0.2205 2022/10/05 20:57:46 - mmengine - INFO - Epoch(train) [11][9200/10520] lr: 1.0000e-04 eta: 16:07:52 time: 0.5592 data_time: 0.0345 memory: 17203 loss_visual: 0.0717 loss_lang: 0.1362 loss_fusion: 0.0626 loss: 0.2705 2022/10/05 20:58:41 - mmengine - INFO - Epoch(train) [11][9300/10520] lr: 1.0000e-04 eta: 16:06:47 time: 0.4406 data_time: 0.0040 memory: 17203 loss_visual: 0.0601 loss_lang: 0.1219 loss_fusion: 0.0522 loss: 0.2342 2022/10/05 20:59:36 - mmengine - INFO - Epoch(train) [11][9400/10520] lr: 1.0000e-04 eta: 16:05:42 time: 0.4242 data_time: 0.0045 memory: 17203 loss_visual: 0.0578 loss_lang: 0.1236 loss_fusion: 0.0503 loss: 0.2317 2022/10/05 21:00:31 - mmengine - INFO - Epoch(train) [11][9500/10520] lr: 1.0000e-04 eta: 16:04:38 time: 0.3809 data_time: 0.0045 memory: 17203 loss_visual: 0.0568 loss_lang: 0.1181 loss_fusion: 0.0486 loss: 0.2235 2022/10/05 21:01:27 - mmengine - INFO - Epoch(train) [11][9600/10520] lr: 1.0000e-04 eta: 16:03:33 time: 0.3620 data_time: 0.0047 memory: 17203 loss_visual: 0.0618 loss_lang: 0.1257 loss_fusion: 0.0526 loss: 0.2401 2022/10/05 21:02:25 - mmengine - INFO - Epoch(train) [11][9700/10520] lr: 1.0000e-04 eta: 16:02:30 time: 0.6624 data_time: 0.2069 memory: 17203 loss_visual: 0.0650 loss_lang: 0.1271 loss_fusion: 0.0547 loss: 0.2469 2022/10/05 21:03:22 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 21:03:22 - mmengine - INFO - Epoch(train) [11][9800/10520] lr: 1.0000e-04 eta: 16:01:27 time: 0.8354 data_time: 0.2605 memory: 17203 loss_visual: 0.0657 loss_lang: 0.1308 loss_fusion: 0.0569 loss: 0.2534 2022/10/05 21:04:17 - mmengine - INFO - Epoch(train) [11][9900/10520] lr: 1.0000e-04 eta: 16:00:22 time: 0.6292 data_time: 0.0328 memory: 17203 loss_visual: 0.0674 loss_lang: 0.1273 loss_fusion: 0.0586 loss: 0.2533 2022/10/05 21:05:13 - mmengine - INFO - Epoch(train) [11][10000/10520] lr: 1.0000e-04 eta: 15:59:18 time: 0.5704 data_time: 0.0349 memory: 17203 loss_visual: 0.0673 loss_lang: 0.1330 loss_fusion: 0.0597 loss: 0.2600 2022/10/05 21:06:07 - mmengine - INFO - Epoch(train) [11][10100/10520] lr: 1.0000e-04 eta: 15:58:12 time: 0.4485 data_time: 0.0054 memory: 17203 loss_visual: 0.0680 loss_lang: 0.1306 loss_fusion: 0.0578 loss: 0.2564 2022/10/05 21:07:03 - mmengine - INFO - Epoch(train) [11][10200/10520] lr: 1.0000e-04 eta: 15:57:08 time: 0.4212 data_time: 0.0052 memory: 17203 loss_visual: 0.0671 loss_lang: 0.1335 loss_fusion: 0.0584 loss: 0.2590 2022/10/05 21:07:58 - mmengine - INFO - Epoch(train) [11][10300/10520] lr: 1.0000e-04 eta: 15:56:03 time: 0.3830 data_time: 0.0050 memory: 17203 loss_visual: 0.0596 loss_lang: 0.1235 loss_fusion: 0.0503 loss: 0.2335 2022/10/05 21:08:53 - mmengine - INFO - Epoch(train) [11][10400/10520] lr: 1.0000e-04 eta: 15:54:58 time: 0.3616 data_time: 0.0045 memory: 17203 loss_visual: 0.0611 loss_lang: 0.1228 loss_fusion: 0.0523 loss: 0.2362 2022/10/05 21:09:49 - mmengine - INFO - Epoch(train) [11][10500/10520] lr: 1.0000e-04 eta: 15:53:54 time: 0.5451 data_time: 0.1358 memory: 17203 loss_visual: 0.0698 loss_lang: 0.1323 loss_fusion: 0.0612 loss: 0.2633 2022/10/05 21:09:57 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 21:09:57 - mmengine - INFO - Saving checkpoint at 11 epochs 2022/10/05 21:10:14 - mmengine - INFO - Epoch(val) [11][100/959] eta: 0:00:45 time: 0.0535 data_time: 0.0071 memory: 17203 2022/10/05 21:10:19 - mmengine - INFO - Epoch(val) [11][200/959] eta: 0:00:44 time: 0.0589 data_time: 0.0022 memory: 734 2022/10/05 21:10:25 - mmengine - INFO - Epoch(val) [11][300/959] eta: 0:00:36 time: 0.0559 data_time: 0.0074 memory: 734 2022/10/05 21:10:30 - mmengine - INFO - Epoch(val) [11][400/959] eta: 0:00:27 time: 0.0486 data_time: 0.0046 memory: 734 2022/10/05 21:10:35 - mmengine - INFO - Epoch(val) [11][500/959] eta: 0:00:23 time: 0.0508 data_time: 0.0047 memory: 734 2022/10/05 21:10:41 - mmengine - INFO - Epoch(val) [11][600/959] eta: 0:00:17 time: 0.0492 data_time: 0.0060 memory: 734 2022/10/05 21:10:45 - mmengine - INFO - Epoch(val) [11][700/959] eta: 0:00:06 time: 0.0234 data_time: 0.0006 memory: 734 2022/10/05 21:10:48 - mmengine - INFO - Epoch(val) [11][800/959] eta: 0:00:22 time: 0.1401 data_time: 0.0007 memory: 734 2022/10/05 21:10:50 - mmengine - INFO - Epoch(val) [11][900/959] eta: 0:00:01 time: 0.0221 data_time: 0.0006 memory: 734 2022/10/05 21:10:52 - mmengine - INFO - Epoch(val) [11][959/959] 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: 0.9304 SVTP/recog/word_acc_ignore_case_symbol: 0.8775 IC13/recog/word_acc_ignore_case_symbol: 0.9251 IC15/recog/word_acc_ignore_case_symbol: 0.7954 2022/10/05 21:11:56 - mmengine - INFO - Epoch(train) [12][100/10520] lr: 1.0000e-04 eta: 15:52:41 time: 0.8492 data_time: 0.1753 memory: 17203 loss_visual: 0.0622 loss_lang: 0.1252 loss_fusion: 0.0530 loss: 0.2405 2022/10/05 21:12:52 - mmengine - INFO - Epoch(train) [12][200/10520] lr: 1.0000e-04 eta: 15:51:37 time: 0.9620 data_time: 0.2038 memory: 17203 loss_visual: 0.0593 loss_lang: 0.1218 loss_fusion: 0.0498 loss: 0.2309 2022/10/05 21:13:34 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 21:13:47 - mmengine - INFO - Epoch(train) [12][300/10520] lr: 1.0000e-04 eta: 15:50:32 time: 0.6199 data_time: 0.0505 memory: 17203 loss_visual: 0.0575 loss_lang: 0.1207 loss_fusion: 0.0495 loss: 0.2278 2022/10/05 21:14:42 - mmengine - INFO - Epoch(train) [12][400/10520] lr: 1.0000e-04 eta: 15:49:27 time: 0.4160 data_time: 0.0347 memory: 17203 loss_visual: 0.0557 loss_lang: 0.1254 loss_fusion: 0.0468 loss: 0.2279 2022/10/05 21:15:37 - mmengine - INFO - Epoch(train) [12][500/10520] lr: 1.0000e-04 eta: 15:48:22 time: 0.3875 data_time: 0.0300 memory: 17203 loss_visual: 0.0589 loss_lang: 0.1205 loss_fusion: 0.0513 loss: 0.2307 2022/10/05 21:16:32 - mmengine - INFO - Epoch(train) [12][600/10520] lr: 1.0000e-04 eta: 15:47:18 time: 0.3669 data_time: 0.0132 memory: 17203 loss_visual: 0.0686 loss_lang: 0.1278 loss_fusion: 0.0593 loss: 0.2558 2022/10/05 21:17:28 - mmengine - INFO - Epoch(train) [12][700/10520] lr: 1.0000e-04 eta: 15:46:13 time: 0.3762 data_time: 0.0049 memory: 17203 loss_visual: 0.0668 loss_lang: 0.1306 loss_fusion: 0.0588 loss: 0.2563 2022/10/05 21:18:23 - mmengine - INFO - Epoch(train) [12][800/10520] lr: 1.0000e-04 eta: 15:45:08 time: 0.3614 data_time: 0.0046 memory: 17203 loss_visual: 0.0647 loss_lang: 0.1245 loss_fusion: 0.0560 loss: 0.2452 2022/10/05 21:19:22 - mmengine - INFO - Epoch(train) [12][900/10520] lr: 1.0000e-04 eta: 15:44:07 time: 0.8364 data_time: 0.1988 memory: 17203 loss_visual: 0.0692 loss_lang: 0.1315 loss_fusion: 0.0612 loss: 0.2618 2022/10/05 21:20:18 - mmengine - INFO - Epoch(train) [12][1000/10520] lr: 1.0000e-04 eta: 15:43:03 time: 0.8696 data_time: 0.1985 memory: 17203 loss_visual: 0.0687 loss_lang: 0.1324 loss_fusion: 0.0580 loss: 0.2591 2022/10/05 21:21:13 - mmengine - INFO - Epoch(train) [12][1100/10520] lr: 1.0000e-04 eta: 15:41:59 time: 0.6418 data_time: 0.0437 memory: 17203 loss_visual: 0.0645 loss_lang: 0.1245 loss_fusion: 0.0565 loss: 0.2455 2022/10/05 21:22:08 - mmengine - INFO - Epoch(train) [12][1200/10520] lr: 1.0000e-04 eta: 15:40:54 time: 0.4503 data_time: 0.0307 memory: 17203 loss_visual: 0.0645 loss_lang: 0.1273 loss_fusion: 0.0559 loss: 0.2476 2022/10/05 21:22:49 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 21:23:03 - mmengine - INFO - Epoch(train) [12][1300/10520] lr: 1.0000e-04 eta: 15:39:49 time: 0.3743 data_time: 0.0263 memory: 17203 loss_visual: 0.0577 loss_lang: 0.1207 loss_fusion: 0.0498 loss: 0.2281 2022/10/05 21:23:56 - mmengine - INFO - Epoch(train) [12][1400/10520] lr: 1.0000e-04 eta: 15:38:43 time: 0.3678 data_time: 0.0117 memory: 17203 loss_visual: 0.0611 loss_lang: 0.1262 loss_fusion: 0.0524 loss: 0.2398 2022/10/05 21:24:50 - mmengine - INFO - Epoch(train) [12][1500/10520] lr: 1.0000e-04 eta: 15:37:37 time: 0.3590 data_time: 0.0042 memory: 17203 loss_visual: 0.0594 loss_lang: 0.1229 loss_fusion: 0.0498 loss: 0.2321 2022/10/05 21:25:45 - mmengine - INFO - Epoch(train) [12][1600/10520] lr: 1.0000e-04 eta: 15:36:32 time: 0.3972 data_time: 0.0037 memory: 17203 loss_visual: 0.0590 loss_lang: 0.1218 loss_fusion: 0.0508 loss: 0.2316 2022/10/05 21:26:43 - mmengine - INFO - Epoch(train) [12][1700/10520] lr: 1.0000e-04 eta: 15:35:30 time: 0.8211 data_time: 0.1421 memory: 17203 loss_visual: 0.0613 loss_lang: 0.1229 loss_fusion: 0.0531 loss: 0.2373 2022/10/05 21:27:38 - mmengine - INFO - Epoch(train) [12][1800/10520] lr: 1.0000e-04 eta: 15:34:25 time: 0.9068 data_time: 0.1846 memory: 17203 loss_visual: 0.0581 loss_lang: 0.1143 loss_fusion: 0.0483 loss: 0.2207 2022/10/05 21:28:33 - mmengine - INFO - Epoch(train) [12][1900/10520] lr: 1.0000e-04 eta: 15:33:21 time: 0.6293 data_time: 0.0429 memory: 17203 loss_visual: 0.0677 loss_lang: 0.1305 loss_fusion: 0.0584 loss: 0.2567 2022/10/05 21:29:28 - mmengine - INFO - Epoch(train) [12][2000/10520] lr: 1.0000e-04 eta: 15:32:16 time: 0.4103 data_time: 0.0279 memory: 17203 loss_visual: 0.0580 loss_lang: 0.1215 loss_fusion: 0.0494 loss: 0.2289 2022/10/05 21:30:22 - mmengine - INFO - Epoch(train) [12][2100/10520] lr: 1.0000e-04 eta: 15:31:11 time: 0.3854 data_time: 0.0245 memory: 17203 loss_visual: 0.0617 loss_lang: 0.1217 loss_fusion: 0.0528 loss: 0.2361 2022/10/05 21:31:16 - mmengine - INFO - Epoch(train) [12][2200/10520] lr: 1.0000e-04 eta: 15:30:06 time: 0.3778 data_time: 0.0112 memory: 17203 loss_visual: 0.0668 loss_lang: 0.1286 loss_fusion: 0.0586 loss: 0.2540 2022/10/05 21:32:02 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 21:32:10 - mmengine - INFO - Epoch(train) [12][2300/10520] lr: 1.0000e-04 eta: 15:29:00 time: 0.3858 data_time: 0.0038 memory: 17203 loss_visual: 0.0593 loss_lang: 0.1218 loss_fusion: 0.0505 loss: 0.2316 2022/10/05 21:33:04 - mmengine - INFO - Epoch(train) [12][2400/10520] lr: 1.0000e-04 eta: 15:27:55 time: 0.3625 data_time: 0.0042 memory: 17203 loss_visual: 0.0758 loss_lang: 0.1370 loss_fusion: 0.0666 loss: 0.2794 2022/10/05 21:34:04 - mmengine - INFO - Epoch(train) [12][2500/10520] lr: 1.0000e-04 eta: 15:26:54 time: 0.8253 data_time: 0.1485 memory: 17203 loss_visual: 0.0653 loss_lang: 0.1313 loss_fusion: 0.0552 loss: 0.2517 2022/10/05 21:34:59 - mmengine - INFO - Epoch(train) [12][2600/10520] lr: 1.0000e-04 eta: 15:25:50 time: 0.9119 data_time: 0.1656 memory: 17203 loss_visual: 0.0655 loss_lang: 0.1296 loss_fusion: 0.0571 loss: 0.2521 2022/10/05 21:35:55 - mmengine - INFO - Epoch(train) [12][2700/10520] lr: 1.0000e-04 eta: 15:24:46 time: 0.7036 data_time: 0.0625 memory: 17203 loss_visual: 0.0567 loss_lang: 0.1211 loss_fusion: 0.0492 loss: 0.2270 2022/10/05 21:36:50 - mmengine - INFO - Epoch(train) [12][2800/10520] lr: 1.0000e-04 eta: 15:23:42 time: 0.4188 data_time: 0.0294 memory: 17203 loss_visual: 0.0642 loss_lang: 0.1294 loss_fusion: 0.0567 loss: 0.2503 2022/10/05 21:37:45 - mmengine - INFO - Epoch(train) [12][2900/10520] lr: 1.0000e-04 eta: 15:22:37 time: 0.3705 data_time: 0.0250 memory: 17203 loss_visual: 0.0714 loss_lang: 0.1314 loss_fusion: 0.0627 loss: 0.2654 2022/10/05 21:38:40 - mmengine - INFO - Epoch(train) [12][3000/10520] lr: 1.0000e-04 eta: 15:21:33 time: 0.3956 data_time: 0.0117 memory: 17203 loss_visual: 0.0660 loss_lang: 0.1252 loss_fusion: 0.0557 loss: 0.2469 2022/10/05 21:39:34 - mmengine - INFO - Epoch(train) [12][3100/10520] lr: 1.0000e-04 eta: 15:20:28 time: 0.3889 data_time: 0.0038 memory: 17203 loss_visual: 0.0692 loss_lang: 0.1345 loss_fusion: 0.0608 loss: 0.2645 2022/10/05 21:40:30 - mmengine - INFO - Epoch(train) [12][3200/10520] lr: 1.0000e-04 eta: 15:19:24 time: 0.3646 data_time: 0.0035 memory: 17203 loss_visual: 0.0690 loss_lang: 0.1306 loss_fusion: 0.0614 loss: 0.2610 2022/10/05 21:41:17 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 21:41:29 - mmengine - INFO - Epoch(train) [12][3300/10520] lr: 1.0000e-04 eta: 15:18:23 time: 0.8678 data_time: 0.1725 memory: 17203 loss_visual: 0.0711 loss_lang: 0.1340 loss_fusion: 0.0628 loss: 0.2680 2022/10/05 21:42:25 - mmengine - INFO - Epoch(train) [12][3400/10520] lr: 1.0000e-04 eta: 15:17:19 time: 0.9099 data_time: 0.1693 memory: 17203 loss_visual: 0.0611 loss_lang: 0.1234 loss_fusion: 0.0538 loss: 0.2383 2022/10/05 21:43:19 - mmengine - INFO - Epoch(train) [12][3500/10520] lr: 1.0000e-04 eta: 15:16:14 time: 0.6367 data_time: 0.0397 memory: 17203 loss_visual: 0.0598 loss_lang: 0.1237 loss_fusion: 0.0502 loss: 0.2337 2022/10/05 21:44:14 - mmengine - INFO - Epoch(train) [12][3600/10520] lr: 1.0000e-04 eta: 15:15:10 time: 0.4408 data_time: 0.0327 memory: 17203 loss_visual: 0.0626 loss_lang: 0.1250 loss_fusion: 0.0530 loss: 0.2406 2022/10/05 21:45:09 - mmengine - INFO - Epoch(train) [12][3700/10520] lr: 1.0000e-04 eta: 15:14:05 time: 0.3654 data_time: 0.0209 memory: 17203 loss_visual: 0.0649 loss_lang: 0.1264 loss_fusion: 0.0561 loss: 0.2474 2022/10/05 21:46:04 - mmengine - INFO - Epoch(train) [12][3800/10520] lr: 1.0000e-04 eta: 15:13:01 time: 0.3688 data_time: 0.0106 memory: 17203 loss_visual: 0.0667 loss_lang: 0.1291 loss_fusion: 0.0590 loss: 0.2548 2022/10/05 21:46:59 - mmengine - INFO - Epoch(train) [12][3900/10520] lr: 1.0000e-04 eta: 15:11:57 time: 0.3887 data_time: 0.0035 memory: 17203 loss_visual: 0.0643 loss_lang: 0.1234 loss_fusion: 0.0541 loss: 0.2418 2022/10/05 21:47:54 - mmengine - INFO - Epoch(train) [12][4000/10520] lr: 1.0000e-04 eta: 15:10:52 time: 0.3702 data_time: 0.0042 memory: 17203 loss_visual: 0.0691 loss_lang: 0.1315 loss_fusion: 0.0605 loss: 0.2612 2022/10/05 21:48:53 - mmengine - INFO - Epoch(train) [12][4100/10520] lr: 1.0000e-04 eta: 15:09:52 time: 0.8564 data_time: 0.1459 memory: 17203 loss_visual: 0.0583 loss_lang: 0.1224 loss_fusion: 0.0500 loss: 0.2307 2022/10/05 21:49:49 - mmengine - INFO - Epoch(train) [12][4200/10520] lr: 1.0000e-04 eta: 15:08:47 time: 0.9009 data_time: 0.1628 memory: 17203 loss_visual: 0.0610 loss_lang: 0.1223 loss_fusion: 0.0527 loss: 0.2360 2022/10/05 21:50:30 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 21:50:44 - mmengine - INFO - Epoch(train) [12][4300/10520] lr: 1.0000e-04 eta: 15:07:43 time: 0.7024 data_time: 0.0399 memory: 17203 loss_visual: 0.0500 loss_lang: 0.1120 loss_fusion: 0.0418 loss: 0.2038 2022/10/05 21:51:38 - mmengine - INFO - Epoch(train) [12][4400/10520] lr: 1.0000e-04 eta: 15:06:39 time: 0.4530 data_time: 0.0285 memory: 17203 loss_visual: 0.0666 loss_lang: 0.1267 loss_fusion: 0.0577 loss: 0.2509 2022/10/05 21:52:33 - mmengine - INFO - Epoch(train) [12][4500/10520] lr: 1.0000e-04 eta: 15:05:35 time: 0.3748 data_time: 0.0299 memory: 17203 loss_visual: 0.0655 loss_lang: 0.1314 loss_fusion: 0.0578 loss: 0.2547 2022/10/05 21:53:28 - mmengine - INFO - Epoch(train) [12][4600/10520] lr: 1.0000e-04 eta: 15:04:30 time: 0.3674 data_time: 0.0135 memory: 17203 loss_visual: 0.0581 loss_lang: 0.1217 loss_fusion: 0.0507 loss: 0.2306 2022/10/05 21:54:24 - mmengine - INFO - Epoch(train) [12][4700/10520] lr: 1.0000e-04 eta: 15:03:26 time: 0.3708 data_time: 0.0037 memory: 17203 loss_visual: 0.0501 loss_lang: 0.1147 loss_fusion: 0.0424 loss: 0.2073 2022/10/05 21:55:18 - mmengine - INFO - Epoch(train) [12][4800/10520] lr: 1.0000e-04 eta: 15:02:22 time: 0.3998 data_time: 0.0037 memory: 17203 loss_visual: 0.0781 loss_lang: 0.1380 loss_fusion: 0.0690 loss: 0.2851 2022/10/05 21:56:17 - mmengine - INFO - Epoch(train) [12][4900/10520] lr: 1.0000e-04 eta: 15:01:20 time: 0.7764 data_time: 0.1462 memory: 17203 loss_visual: 0.0685 loss_lang: 0.1297 loss_fusion: 0.0593 loss: 0.2576 2022/10/05 21:57:12 - mmengine - INFO - Epoch(train) [12][5000/10520] lr: 1.0000e-04 eta: 15:00:17 time: 0.9070 data_time: 0.1564 memory: 17203 loss_visual: 0.0726 loss_lang: 0.1391 loss_fusion: 0.0636 loss: 0.2753 2022/10/05 21:58:08 - mmengine - INFO - Epoch(train) [12][5100/10520] lr: 1.0000e-04 eta: 14:59:13 time: 0.7135 data_time: 0.0398 memory: 17203 loss_visual: 0.0587 loss_lang: 0.1205 loss_fusion: 0.0494 loss: 0.2286 2022/10/05 21:59:01 - mmengine - INFO - Epoch(train) [12][5200/10520] lr: 1.0000e-04 eta: 14:58:08 time: 0.4360 data_time: 0.0265 memory: 17203 loss_visual: 0.0605 loss_lang: 0.1252 loss_fusion: 0.0526 loss: 0.2383 2022/10/05 21:59:43 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 21:59:56 - mmengine - INFO - Epoch(train) [12][5300/10520] lr: 1.0000e-04 eta: 14:57:03 time: 0.3743 data_time: 0.0238 memory: 17203 loss_visual: 0.0697 loss_lang: 0.1314 loss_fusion: 0.0599 loss: 0.2611 2022/10/05 22:00:50 - mmengine - INFO - Epoch(train) [12][5400/10520] lr: 1.0000e-04 eta: 14:55:59 time: 0.3810 data_time: 0.0106 memory: 17203 loss_visual: 0.0610 loss_lang: 0.1216 loss_fusion: 0.0519 loss: 0.2345 2022/10/05 22:01:45 - mmengine - INFO - Epoch(train) [12][5500/10520] lr: 1.0000e-04 eta: 14:54:55 time: 0.3706 data_time: 0.0032 memory: 17203 loss_visual: 0.0684 loss_lang: 0.1295 loss_fusion: 0.0600 loss: 0.2579 2022/10/05 22:02:41 - mmengine - INFO - Epoch(train) [12][5600/10520] lr: 1.0000e-04 eta: 14:53:51 time: 0.3746 data_time: 0.0034 memory: 17203 loss_visual: 0.0566 loss_lang: 0.1222 loss_fusion: 0.0472 loss: 0.2260 2022/10/05 22:03:41 - mmengine - INFO - Epoch(train) [12][5700/10520] lr: 1.0000e-04 eta: 14:52:51 time: 0.8432 data_time: 0.1488 memory: 17203 loss_visual: 0.0573 loss_lang: 0.1248 loss_fusion: 0.0494 loss: 0.2315 2022/10/05 22:04:37 - mmengine - INFO - Epoch(train) [12][5800/10520] lr: 1.0000e-04 eta: 14:51:48 time: 0.9231 data_time: 0.1693 memory: 17203 loss_visual: 0.0642 loss_lang: 0.1267 loss_fusion: 0.0555 loss: 0.2464 2022/10/05 22:05:33 - mmengine - INFO - Epoch(train) [12][5900/10520] lr: 1.0000e-04 eta: 14:50:44 time: 0.6817 data_time: 0.0441 memory: 17203 loss_visual: 0.0710 loss_lang: 0.1364 loss_fusion: 0.0634 loss: 0.2709 2022/10/05 22:06:27 - mmengine - INFO - Epoch(train) [12][6000/10520] lr: 1.0000e-04 eta: 14:49:39 time: 0.4593 data_time: 0.0308 memory: 17203 loss_visual: 0.0735 loss_lang: 0.1336 loss_fusion: 0.0637 loss: 0.2708 2022/10/05 22:07:21 - mmengine - INFO - Epoch(train) [12][6100/10520] lr: 1.0000e-04 eta: 14:48:35 time: 0.3858 data_time: 0.0233 memory: 17203 loss_visual: 0.0636 loss_lang: 0.1260 loss_fusion: 0.0533 loss: 0.2428 2022/10/05 22:08:16 - mmengine - INFO - Epoch(train) [12][6200/10520] lr: 1.0000e-04 eta: 14:47:31 time: 0.4137 data_time: 0.0114 memory: 17203 loss_visual: 0.0551 loss_lang: 0.1206 loss_fusion: 0.0469 loss: 0.2226 2022/10/05 22:09:04 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 22:09:11 - mmengine - INFO - Epoch(train) [12][6300/10520] lr: 1.0000e-04 eta: 14:46:27 time: 0.3729 data_time: 0.0035 memory: 17203 loss_visual: 0.0616 loss_lang: 0.1265 loss_fusion: 0.0550 loss: 0.2431 2022/10/05 22:10:07 - mmengine - INFO - Epoch(train) [12][6400/10520] lr: 1.0000e-04 eta: 14:45:24 time: 0.3787 data_time: 0.0036 memory: 17203 loss_visual: 0.0575 loss_lang: 0.1192 loss_fusion: 0.0481 loss: 0.2248 2022/10/05 22:11:06 - mmengine - INFO - Epoch(train) [12][6500/10520] lr: 1.0000e-04 eta: 14:44:23 time: 0.8328 data_time: 0.1290 memory: 17203 loss_visual: 0.0606 loss_lang: 0.1210 loss_fusion: 0.0515 loss: 0.2331 2022/10/05 22:12:02 - mmengine - INFO - Epoch(train) [12][6600/10520] lr: 1.0000e-04 eta: 14:43:19 time: 0.9013 data_time: 0.1745 memory: 17203 loss_visual: 0.0657 loss_lang: 0.1312 loss_fusion: 0.0567 loss: 0.2537 2022/10/05 22:12:57 - mmengine - INFO - Epoch(train) [12][6700/10520] lr: 1.0000e-04 eta: 14:42:16 time: 0.6755 data_time: 0.0409 memory: 17203 loss_visual: 0.0623 loss_lang: 0.1204 loss_fusion: 0.0530 loss: 0.2357 2022/10/05 22:13:52 - mmengine - INFO - Epoch(train) [12][6800/10520] lr: 1.0000e-04 eta: 14:41:12 time: 0.4371 data_time: 0.0287 memory: 17203 loss_visual: 0.0599 loss_lang: 0.1214 loss_fusion: 0.0510 loss: 0.2323 2022/10/05 22:14:46 - mmengine - INFO - Epoch(train) [12][6900/10520] lr: 1.0000e-04 eta: 14:40:07 time: 0.3849 data_time: 0.0237 memory: 17203 loss_visual: 0.0569 loss_lang: 0.1228 loss_fusion: 0.0493 loss: 0.2290 2022/10/05 22:15:42 - mmengine - INFO - Epoch(train) [12][7000/10520] lr: 1.0000e-04 eta: 14:39:04 time: 0.3692 data_time: 0.0110 memory: 17203 loss_visual: 0.0700 loss_lang: 0.1323 loss_fusion: 0.0613 loss: 0.2635 2022/10/05 22:16:37 - mmengine - INFO - Epoch(train) [12][7100/10520] lr: 1.0000e-04 eta: 14:38:00 time: 0.4007 data_time: 0.0077 memory: 17203 loss_visual: 0.0549 loss_lang: 0.1155 loss_fusion: 0.0461 loss: 0.2165 2022/10/05 22:17:32 - mmengine - INFO - Epoch(train) [12][7200/10520] lr: 1.0000e-04 eta: 14:36:56 time: 0.3891 data_time: 0.0040 memory: 17203 loss_visual: 0.0621 loss_lang: 0.1245 loss_fusion: 0.0539 loss: 0.2405 2022/10/05 22:18:19 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 22:18:31 - mmengine - INFO - Epoch(train) [12][7300/10520] lr: 1.0000e-04 eta: 14:35:55 time: 0.8001 data_time: 0.1420 memory: 17203 loss_visual: 0.0672 loss_lang: 0.1326 loss_fusion: 0.0586 loss: 0.2583 2022/10/05 22:19:27 - mmengine - INFO - Epoch(train) [12][7400/10520] lr: 1.0000e-04 eta: 14:34:52 time: 0.9218 data_time: 0.1774 memory: 17203 loss_visual: 0.0631 loss_lang: 0.1248 loss_fusion: 0.0540 loss: 0.2419 2022/10/05 22:20:22 - mmengine - INFO - Epoch(train) [12][7500/10520] lr: 1.0000e-04 eta: 14:33:48 time: 0.7105 data_time: 0.0398 memory: 17203 loss_visual: 0.0644 loss_lang: 0.1264 loss_fusion: 0.0557 loss: 0.2465 2022/10/05 22:21:16 - mmengine - INFO - Epoch(train) [12][7600/10520] lr: 1.0000e-04 eta: 14:32:44 time: 0.4572 data_time: 0.0302 memory: 17203 loss_visual: 0.0553 loss_lang: 0.1205 loss_fusion: 0.0474 loss: 0.2231 2022/10/05 22:22:11 - mmengine - INFO - Epoch(train) [12][7700/10520] lr: 1.0000e-04 eta: 14:31:40 time: 0.3749 data_time: 0.0247 memory: 17203 loss_visual: 0.0700 loss_lang: 0.1326 loss_fusion: 0.0612 loss: 0.2637 2022/10/05 22:23:06 - mmengine - INFO - Epoch(train) [12][7800/10520] lr: 1.0000e-04 eta: 14:30:36 time: 0.3824 data_time: 0.0111 memory: 17203 loss_visual: 0.0615 loss_lang: 0.1229 loss_fusion: 0.0523 loss: 0.2367 2022/10/05 22:24:01 - mmengine - INFO - Epoch(train) [12][7900/10520] lr: 1.0000e-04 eta: 14:29:32 time: 0.3776 data_time: 0.0035 memory: 17203 loss_visual: 0.0676 loss_lang: 0.1279 loss_fusion: 0.0587 loss: 0.2541 2022/10/05 22:24:57 - mmengine - INFO - Epoch(train) [12][8000/10520] lr: 1.0000e-04 eta: 14:28:29 time: 0.3941 data_time: 0.0032 memory: 17203 loss_visual: 0.0625 loss_lang: 0.1234 loss_fusion: 0.0534 loss: 0.2393 2022/10/05 22:25:56 - mmengine - INFO - Epoch(train) [12][8100/10520] lr: 1.0000e-04 eta: 14:27:28 time: 0.8656 data_time: 0.1559 memory: 17203 loss_visual: 0.0665 loss_lang: 0.1289 loss_fusion: 0.0578 loss: 0.2532 2022/10/05 22:26:51 - mmengine - INFO - Epoch(train) [12][8200/10520] lr: 1.0000e-04 eta: 14:26:25 time: 0.8891 data_time: 0.1695 memory: 17203 loss_visual: 0.0628 loss_lang: 0.1280 loss_fusion: 0.0542 loss: 0.2450 2022/10/05 22:27:33 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 22:27:47 - mmengine - INFO - Epoch(train) [12][8300/10520] lr: 1.0000e-04 eta: 14:25:22 time: 0.6970 data_time: 0.0396 memory: 17203 loss_visual: 0.0565 loss_lang: 0.1184 loss_fusion: 0.0481 loss: 0.2229 2022/10/05 22:28:41 - mmengine - INFO - Epoch(train) [12][8400/10520] lr: 1.0000e-04 eta: 14:24:18 time: 0.4348 data_time: 0.0292 memory: 17203 loss_visual: 0.0601 loss_lang: 0.1231 loss_fusion: 0.0509 loss: 0.2341 2022/10/05 22:29:35 - mmengine - INFO - Epoch(train) [12][8500/10520] lr: 1.0000e-04 eta: 14:23:13 time: 0.3670 data_time: 0.0231 memory: 17203 loss_visual: 0.0647 loss_lang: 0.1221 loss_fusion: 0.0548 loss: 0.2416 2022/10/05 22:30:29 - mmengine - INFO - Epoch(train) [12][8600/10520] lr: 1.0000e-04 eta: 14:22:09 time: 0.3704 data_time: 0.0107 memory: 17203 loss_visual: 0.0667 loss_lang: 0.1259 loss_fusion: 0.0583 loss: 0.2510 2022/10/05 22:31:23 - mmengine - INFO - Epoch(train) [12][8700/10520] lr: 1.0000e-04 eta: 14:21:05 time: 0.3913 data_time: 0.0034 memory: 17203 loss_visual: 0.0631 loss_lang: 0.1275 loss_fusion: 0.0538 loss: 0.2445 2022/10/05 22:32:18 - mmengine - INFO - Epoch(train) [12][8800/10520] lr: 1.0000e-04 eta: 14:20:01 time: 0.3942 data_time: 0.0049 memory: 17203 loss_visual: 0.0619 loss_lang: 0.1231 loss_fusion: 0.0521 loss: 0.2371 2022/10/05 22:33:19 - mmengine - INFO - Epoch(train) [12][8900/10520] lr: 1.0000e-04 eta: 14:19:01 time: 0.8407 data_time: 0.1544 memory: 17203 loss_visual: 0.0584 loss_lang: 0.1182 loss_fusion: 0.0499 loss: 0.2265 2022/10/05 22:34:16 - mmengine - INFO - Epoch(train) [12][9000/10520] lr: 1.0000e-04 eta: 14:17:59 time: 0.9164 data_time: 0.1864 memory: 17203 loss_visual: 0.0572 loss_lang: 0.1188 loss_fusion: 0.0492 loss: 0.2252 2022/10/05 22:35:11 - mmengine - INFO - Epoch(train) [12][9100/10520] lr: 1.0000e-04 eta: 14:16:56 time: 0.7064 data_time: 0.0427 memory: 17203 loss_visual: 0.0632 loss_lang: 0.1222 loss_fusion: 0.0551 loss: 0.2405 2022/10/05 22:36:06 - mmengine - INFO - Epoch(train) [12][9200/10520] lr: 1.0000e-04 eta: 14:15:52 time: 0.4166 data_time: 0.0403 memory: 17203 loss_visual: 0.0637 loss_lang: 0.1200 loss_fusion: 0.0552 loss: 0.2390 2022/10/05 22:36:48 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 22:37:01 - mmengine - INFO - Epoch(train) [12][9300/10520] lr: 1.0000e-04 eta: 14:14:48 time: 0.3952 data_time: 0.0272 memory: 17203 loss_visual: 0.0663 loss_lang: 0.1292 loss_fusion: 0.0579 loss: 0.2534 2022/10/05 22:37:56 - mmengine - INFO - Epoch(train) [12][9400/10520] lr: 1.0000e-04 eta: 14:13:45 time: 0.3879 data_time: 0.0114 memory: 17203 loss_visual: 0.0568 loss_lang: 0.1212 loss_fusion: 0.0486 loss: 0.2265 2022/10/05 22:38:51 - mmengine - INFO - Epoch(train) [12][9500/10520] lr: 1.0000e-04 eta: 14:12:42 time: 0.3722 data_time: 0.0039 memory: 17203 loss_visual: 0.0549 loss_lang: 0.1184 loss_fusion: 0.0471 loss: 0.2204 2022/10/05 22:39:47 - mmengine - INFO - Epoch(train) [12][9600/10520] lr: 1.0000e-04 eta: 14:11:39 time: 0.3699 data_time: 0.0037 memory: 17203 loss_visual: 0.0527 loss_lang: 0.1160 loss_fusion: 0.0453 loss: 0.2140 2022/10/05 22:40:46 - mmengine - INFO - Epoch(train) [12][9700/10520] lr: 1.0000e-04 eta: 14:10:38 time: 0.8667 data_time: 0.1266 memory: 17203 loss_visual: 0.0550 loss_lang: 0.1177 loss_fusion: 0.0470 loss: 0.2196 2022/10/05 22:41:42 - mmengine - INFO - Epoch(train) [12][9800/10520] lr: 1.0000e-04 eta: 14:09:35 time: 0.9545 data_time: 0.2041 memory: 17203 loss_visual: 0.0599 loss_lang: 0.1214 loss_fusion: 0.0519 loss: 0.2331 2022/10/05 22:42:37 - mmengine - INFO - Epoch(train) [12][9900/10520] lr: 1.0000e-04 eta: 14:08:32 time: 0.6511 data_time: 0.0436 memory: 17203 loss_visual: 0.0573 loss_lang: 0.1173 loss_fusion: 0.0486 loss: 0.2232 2022/10/05 22:43:32 - mmengine - INFO - Epoch(train) [12][10000/10520] lr: 1.0000e-04 eta: 14:07:28 time: 0.4228 data_time: 0.0445 memory: 17203 loss_visual: 0.0694 loss_lang: 0.1336 loss_fusion: 0.0616 loss: 0.2646 2022/10/05 22:44:26 - mmengine - INFO - Epoch(train) [12][10100/10520] lr: 1.0000e-04 eta: 14:06:24 time: 0.3947 data_time: 0.0263 memory: 17203 loss_visual: 0.0599 loss_lang: 0.1218 loss_fusion: 0.0526 loss: 0.2343 2022/10/05 22:45:21 - mmengine - INFO - Epoch(train) [12][10200/10520] lr: 1.0000e-04 eta: 14:05:21 time: 0.3687 data_time: 0.0117 memory: 17203 loss_visual: 0.0642 loss_lang: 0.1259 loss_fusion: 0.0563 loss: 0.2464 2022/10/05 22:46:08 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 22:46:16 - mmengine - INFO - Epoch(train) [12][10300/10520] lr: 1.0000e-04 eta: 14:04:17 time: 0.4002 data_time: 0.0038 memory: 17203 loss_visual: 0.0602 loss_lang: 0.1244 loss_fusion: 0.0510 loss: 0.2357 2022/10/05 22:47:10 - mmengine - INFO - Epoch(train) [12][10400/10520] lr: 1.0000e-04 eta: 14:03:13 time: 0.3725 data_time: 0.0032 memory: 17203 loss_visual: 0.0692 loss_lang: 0.1289 loss_fusion: 0.0589 loss: 0.2569 2022/10/05 22:48:06 - mmengine - INFO - Epoch(train) [12][10500/10520] lr: 1.0000e-04 eta: 14:02:10 time: 0.5635 data_time: 0.0737 memory: 17203 loss_visual: 0.0589 loss_lang: 0.1196 loss_fusion: 0.0509 loss: 0.2293 2022/10/05 22:48:13 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 22:48:13 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/10/05 22:48:30 - mmengine - INFO - Epoch(val) [12][100/959] eta: 0:00:41 time: 0.0478 data_time: 0.0023 memory: 17203 2022/10/05 22:48:35 - mmengine - INFO - Epoch(val) [12][200/959] eta: 0:00:37 time: 0.0495 data_time: 0.0022 memory: 734 2022/10/05 22:48:40 - mmengine - INFO - Epoch(val) [12][300/959] eta: 0:00:29 time: 0.0454 data_time: 0.0010 memory: 734 2022/10/05 22:48:45 - mmengine - INFO - Epoch(val) [12][400/959] eta: 0:00:33 time: 0.0597 data_time: 0.0020 memory: 734 2022/10/05 22:48:49 - mmengine - INFO - Epoch(val) [12][500/959] eta: 0:00:21 time: 0.0464 data_time: 0.0016 memory: 734 2022/10/05 22:48:54 - mmengine - INFO - Epoch(val) [12][600/959] eta: 0:00:16 time: 0.0463 data_time: 0.0018 memory: 734 2022/10/05 22:48:59 - mmengine - INFO - Epoch(val) [12][700/959] eta: 0:00:13 time: 0.0531 data_time: 0.0020 memory: 734 2022/10/05 22:49:02 - mmengine - INFO - Epoch(val) [12][800/959] eta: 0:00:03 time: 0.0223 data_time: 0.0006 memory: 734 2022/10/05 22:49:04 - mmengine - INFO - Epoch(val) [12][900/959] eta: 0:00:01 time: 0.0213 data_time: 0.0005 memory: 734 2022/10/05 22:49:07 - mmengine - INFO - Epoch(val) [12][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.8472 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9460 SVT/recog/word_acc_ignore_case_symbol: 0.9366 SVTP/recog/word_acc_ignore_case_symbol: 0.8729 IC13/recog/word_acc_ignore_case_symbol: 0.9429 IC15/recog/word_acc_ignore_case_symbol: 0.7992 2022/10/05 22:50:08 - mmengine - INFO - Epoch(train) [13][100/10520] lr: 1.0000e-04 eta: 14:00:56 time: 0.7241 data_time: 0.1607 memory: 17203 loss_visual: 0.0638 loss_lang: 0.1255 loss_fusion: 0.0556 loss: 0.2448 2022/10/05 22:51:03 - mmengine - INFO - Epoch(train) [13][200/10520] lr: 1.0000e-04 eta: 13:59:53 time: 0.8307 data_time: 0.2198 memory: 17203 loss_visual: 0.0673 loss_lang: 0.1374 loss_fusion: 0.0597 loss: 0.2644 2022/10/05 22:51:57 - mmengine - INFO - Epoch(train) [13][300/10520] lr: 1.0000e-04 eta: 13:58:49 time: 0.7152 data_time: 0.0421 memory: 17203 loss_visual: 0.0591 loss_lang: 0.1191 loss_fusion: 0.0509 loss: 0.2292 2022/10/05 22:52:50 - mmengine - INFO - Epoch(train) [13][400/10520] lr: 1.0000e-04 eta: 13:57:44 time: 0.5138 data_time: 0.0747 memory: 17203 loss_visual: 0.0632 loss_lang: 0.1213 loss_fusion: 0.0533 loss: 0.2378 2022/10/05 22:53:43 - mmengine - INFO - Epoch(train) [13][500/10520] lr: 1.0000e-04 eta: 13:56:40 time: 0.3856 data_time: 0.0107 memory: 17203 loss_visual: 0.0648 loss_lang: 0.1301 loss_fusion: 0.0568 loss: 0.2516 2022/10/05 22:54:37 - mmengine - INFO - Epoch(train) [13][600/10520] lr: 1.0000e-04 eta: 13:55:35 time: 0.3426 data_time: 0.0034 memory: 17203 loss_visual: 0.0606 loss_lang: 0.1224 loss_fusion: 0.0520 loss: 0.2349 2022/10/05 22:55:30 - mmengine - INFO - Epoch(train) [13][700/10520] lr: 1.0000e-04 eta: 13:54:31 time: 0.3613 data_time: 0.0035 memory: 17203 loss_visual: 0.0590 loss_lang: 0.1204 loss_fusion: 0.0494 loss: 0.2288 2022/10/05 22:56:03 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 22:56:23 - mmengine - INFO - Epoch(train) [13][800/10520] lr: 1.0000e-04 eta: 13:53:26 time: 0.3956 data_time: 0.0115 memory: 17203 loss_visual: 0.0675 loss_lang: 0.1300 loss_fusion: 0.0598 loss: 0.2572 2022/10/05 22:57:21 - mmengine - INFO - Epoch(train) [13][900/10520] lr: 1.0000e-04 eta: 13:52:25 time: 0.7408 data_time: 0.1518 memory: 17203 loss_visual: 0.0581 loss_lang: 0.1207 loss_fusion: 0.0502 loss: 0.2290 2022/10/05 22:58:16 - mmengine - INFO - Epoch(train) [13][1000/10520] lr: 1.0000e-04 eta: 13:51:22 time: 0.8739 data_time: 0.1838 memory: 17203 loss_visual: 0.0637 loss_lang: 0.1246 loss_fusion: 0.0545 loss: 0.2428 2022/10/05 22:59:11 - mmengine - INFO - Epoch(train) [13][1100/10520] lr: 1.0000e-04 eta: 13:50:18 time: 0.7895 data_time: 0.0711 memory: 17203 loss_visual: 0.0553 loss_lang: 0.1182 loss_fusion: 0.0470 loss: 0.2205 2022/10/05 23:00:04 - mmengine - INFO - Epoch(train) [13][1200/10520] lr: 1.0000e-04 eta: 13:49:14 time: 0.5095 data_time: 0.0497 memory: 17203 loss_visual: 0.0643 loss_lang: 0.1289 loss_fusion: 0.0564 loss: 0.2496 2022/10/05 23:00:58 - mmengine - INFO - Epoch(train) [13][1300/10520] lr: 1.0000e-04 eta: 13:48:10 time: 0.3955 data_time: 0.0112 memory: 17203 loss_visual: 0.0604 loss_lang: 0.1264 loss_fusion: 0.0509 loss: 0.2376 2022/10/05 23:01:51 - mmengine - INFO - Epoch(train) [13][1400/10520] lr: 1.0000e-04 eta: 13:47:06 time: 0.3703 data_time: 0.0289 memory: 17203 loss_visual: 0.0545 loss_lang: 0.1181 loss_fusion: 0.0471 loss: 0.2197 2022/10/05 23:02:45 - mmengine - INFO - Epoch(train) [13][1500/10520] lr: 1.0000e-04 eta: 13:46:02 time: 0.3607 data_time: 0.0037 memory: 17203 loss_visual: 0.0571 loss_lang: 0.1152 loss_fusion: 0.0478 loss: 0.2202 2022/10/05 23:03:39 - mmengine - INFO - Epoch(train) [13][1600/10520] lr: 1.0000e-04 eta: 13:44:58 time: 0.3730 data_time: 0.0140 memory: 17203 loss_visual: 0.0684 loss_lang: 0.1282 loss_fusion: 0.0605 loss: 0.2571 2022/10/05 23:04:35 - mmengine - INFO - Epoch(train) [13][1700/10520] lr: 1.0000e-04 eta: 13:43:56 time: 0.7169 data_time: 0.1464 memory: 17203 loss_visual: 0.0711 loss_lang: 0.1343 loss_fusion: 0.0617 loss: 0.2670 2022/10/05 23:05:06 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 23:05:31 - mmengine - INFO - Epoch(train) [13][1800/10520] lr: 1.0000e-04 eta: 13:42:53 time: 0.8467 data_time: 0.2151 memory: 17203 loss_visual: 0.0602 loss_lang: 0.1263 loss_fusion: 0.0525 loss: 0.2390 2022/10/05 23:06:24 - mmengine - INFO - Epoch(train) [13][1900/10520] lr: 1.0000e-04 eta: 13:41:49 time: 0.7262 data_time: 0.0470 memory: 17203 loss_visual: 0.0612 loss_lang: 0.1192 loss_fusion: 0.0520 loss: 0.2323 2022/10/05 23:07:18 - mmengine - INFO - Epoch(train) [13][2000/10520] lr: 1.0000e-04 eta: 13:40:44 time: 0.5120 data_time: 0.0534 memory: 17203 loss_visual: 0.0575 loss_lang: 0.1219 loss_fusion: 0.0496 loss: 0.2290 2022/10/05 23:08:11 - mmengine - INFO - Epoch(train) [13][2100/10520] lr: 1.0000e-04 eta: 13:39:40 time: 0.3963 data_time: 0.0113 memory: 17203 loss_visual: 0.0656 loss_lang: 0.1246 loss_fusion: 0.0573 loss: 0.2475 2022/10/05 23:09:04 - mmengine - INFO - Epoch(train) [13][2200/10520] lr: 1.0000e-04 eta: 13:38:36 time: 0.3373 data_time: 0.0035 memory: 17203 loss_visual: 0.0664 loss_lang: 0.1241 loss_fusion: 0.0576 loss: 0.2480 2022/10/05 23:09:57 - mmengine - INFO - Epoch(train) [13][2300/10520] lr: 1.0000e-04 eta: 13:37:32 time: 0.3907 data_time: 0.0033 memory: 17203 loss_visual: 0.0633 loss_lang: 0.1237 loss_fusion: 0.0541 loss: 0.2411 2022/10/05 23:10:51 - mmengine - INFO - Epoch(train) [13][2400/10520] lr: 1.0000e-04 eta: 13:36:28 time: 0.3770 data_time: 0.0113 memory: 17203 loss_visual: 0.0577 loss_lang: 0.1178 loss_fusion: 0.0479 loss: 0.2235 2022/10/05 23:11:47 - mmengine - INFO - Epoch(train) [13][2500/10520] lr: 1.0000e-04 eta: 13:35:25 time: 0.7118 data_time: 0.1458 memory: 17203 loss_visual: 0.0612 loss_lang: 0.1247 loss_fusion: 0.0534 loss: 0.2393 2022/10/05 23:12:42 - mmengine - INFO - Epoch(train) [13][2600/10520] lr: 1.0000e-04 eta: 13:34:23 time: 0.8663 data_time: 0.2025 memory: 17203 loss_visual: 0.0684 loss_lang: 0.1338 loss_fusion: 0.0579 loss: 0.2601 2022/10/05 23:13:36 - mmengine - INFO - Epoch(train) [13][2700/10520] lr: 1.0000e-04 eta: 13:33:19 time: 0.7276 data_time: 0.0464 memory: 17203 loss_visual: 0.0624 loss_lang: 0.1224 loss_fusion: 0.0535 loss: 0.2383 2022/10/05 23:14:08 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 23:14:30 - mmengine - INFO - Epoch(train) [13][2800/10520] lr: 1.0000e-04 eta: 13:32:15 time: 0.5234 data_time: 0.0553 memory: 17203 loss_visual: 0.0674 loss_lang: 0.1293 loss_fusion: 0.0596 loss: 0.2563 2022/10/05 23:15:23 - mmengine - INFO - Epoch(train) [13][2900/10520] lr: 1.0000e-04 eta: 13:31:11 time: 0.3931 data_time: 0.0106 memory: 17203 loss_visual: 0.0552 loss_lang: 0.1172 loss_fusion: 0.0482 loss: 0.2207 2022/10/05 23:16:17 - mmengine - INFO - Epoch(train) [13][3000/10520] lr: 1.0000e-04 eta: 13:30:07 time: 0.3385 data_time: 0.0044 memory: 17203 loss_visual: 0.0592 loss_lang: 0.1186 loss_fusion: 0.0521 loss: 0.2300 2022/10/05 23:17:10 - mmengine - INFO - Epoch(train) [13][3100/10520] lr: 1.0000e-04 eta: 13:29:03 time: 0.3683 data_time: 0.0056 memory: 17203 loss_visual: 0.0697 loss_lang: 0.1317 loss_fusion: 0.0603 loss: 0.2617 2022/10/05 23:18:04 - mmengine - INFO - Epoch(train) [13][3200/10520] lr: 1.0000e-04 eta: 13:27:59 time: 0.3761 data_time: 0.0115 memory: 17203 loss_visual: 0.0575 loss_lang: 0.1227 loss_fusion: 0.0495 loss: 0.2297 2022/10/05 23:19:01 - mmengine - INFO - Epoch(train) [13][3300/10520] lr: 1.0000e-04 eta: 13:26:58 time: 0.7616 data_time: 0.1448 memory: 17203 loss_visual: 0.0652 loss_lang: 0.1291 loss_fusion: 0.0564 loss: 0.2508 2022/10/05 23:19:56 - mmengine - INFO - Epoch(train) [13][3400/10520] lr: 1.0000e-04 eta: 13:25:55 time: 0.8281 data_time: 0.1871 memory: 17203 loss_visual: 0.0564 loss_lang: 0.1222 loss_fusion: 0.0482 loss: 0.2268 2022/10/05 23:20:50 - mmengine - INFO - Epoch(train) [13][3500/10520] lr: 1.0000e-04 eta: 13:24:51 time: 0.7248 data_time: 0.0653 memory: 17203 loss_visual: 0.0654 loss_lang: 0.1253 loss_fusion: 0.0555 loss: 0.2462 2022/10/05 23:21:43 - mmengine - INFO - Epoch(train) [13][3600/10520] lr: 1.0000e-04 eta: 13:23:47 time: 0.5508 data_time: 0.0500 memory: 17203 loss_visual: 0.0615 loss_lang: 0.1221 loss_fusion: 0.0523 loss: 0.2359 2022/10/05 23:22:37 - mmengine - INFO - Epoch(train) [13][3700/10520] lr: 1.0000e-04 eta: 13:22:43 time: 0.3912 data_time: 0.0127 memory: 17203 loss_visual: 0.0554 loss_lang: 0.1158 loss_fusion: 0.0474 loss: 0.2186 2022/10/05 23:23:10 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 23:23:30 - mmengine - INFO - Epoch(train) [13][3800/10520] lr: 1.0000e-04 eta: 13:21:40 time: 0.3386 data_time: 0.0034 memory: 17203 loss_visual: 0.0628 loss_lang: 0.1215 loss_fusion: 0.0537 loss: 0.2380 2022/10/05 23:24:24 - mmengine - INFO - Epoch(train) [13][3900/10520] lr: 1.0000e-04 eta: 13:20:36 time: 0.3625 data_time: 0.0035 memory: 17203 loss_visual: 0.0646 loss_lang: 0.1224 loss_fusion: 0.0561 loss: 0.2431 2022/10/05 23:25:17 - mmengine - INFO - Epoch(train) [13][4000/10520] lr: 1.0000e-04 eta: 13:19:32 time: 0.3809 data_time: 0.0118 memory: 17203 loss_visual: 0.0574 loss_lang: 0.1219 loss_fusion: 0.0483 loss: 0.2276 2022/10/05 23:26:14 - mmengine - INFO - Epoch(train) [13][4100/10520] lr: 1.0000e-04 eta: 13:18:30 time: 0.7494 data_time: 0.1357 memory: 17203 loss_visual: 0.0610 loss_lang: 0.1244 loss_fusion: 0.0520 loss: 0.2374 2022/10/05 23:27:08 - mmengine - INFO - Epoch(train) [13][4200/10520] lr: 1.0000e-04 eta: 13:17:27 time: 0.8058 data_time: 0.1874 memory: 17203 loss_visual: 0.0669 loss_lang: 0.1269 loss_fusion: 0.0585 loss: 0.2522 2022/10/05 23:28:03 - mmengine - INFO - Epoch(train) [13][4300/10520] lr: 1.0000e-04 eta: 13:16:24 time: 0.7459 data_time: 0.0446 memory: 17203 loss_visual: 0.0600 loss_lang: 0.1249 loss_fusion: 0.0515 loss: 0.2364 2022/10/05 23:28:56 - mmengine - INFO - Epoch(train) [13][4400/10520] lr: 1.0000e-04 eta: 13:15:20 time: 0.5516 data_time: 0.0744 memory: 17203 loss_visual: 0.0579 loss_lang: 0.1191 loss_fusion: 0.0492 loss: 0.2261 2022/10/05 23:29:49 - mmengine - INFO - Epoch(train) [13][4500/10520] lr: 1.0000e-04 eta: 13:14:16 time: 0.4196 data_time: 0.0106 memory: 17203 loss_visual: 0.0641 loss_lang: 0.1229 loss_fusion: 0.0541 loss: 0.2410 2022/10/05 23:30:43 - mmengine - INFO - Epoch(train) [13][4600/10520] lr: 1.0000e-04 eta: 13:13:13 time: 0.3386 data_time: 0.0038 memory: 17203 loss_visual: 0.0569 loss_lang: 0.1158 loss_fusion: 0.0488 loss: 0.2215 2022/10/05 23:31:37 - mmengine - INFO - Epoch(train) [13][4700/10520] lr: 1.0000e-04 eta: 13:12:09 time: 0.3612 data_time: 0.0037 memory: 17203 loss_visual: 0.0599 loss_lang: 0.1203 loss_fusion: 0.0517 loss: 0.2319 2022/10/05 23:32:09 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 23:32:30 - mmengine - INFO - Epoch(train) [13][4800/10520] lr: 1.0000e-04 eta: 13:11:05 time: 0.3831 data_time: 0.0109 memory: 17203 loss_visual: 0.0489 loss_lang: 0.1101 loss_fusion: 0.0408 loss: 0.1998 2022/10/05 23:33:27 - mmengine - INFO - Epoch(train) [13][4900/10520] lr: 1.0000e-04 eta: 13:10:04 time: 0.7088 data_time: 0.1431 memory: 17203 loss_visual: 0.0639 loss_lang: 0.1259 loss_fusion: 0.0551 loss: 0.2449 2022/10/05 23:34:22 - mmengine - INFO - Epoch(train) [13][5000/10520] lr: 1.0000e-04 eta: 13:09:01 time: 0.8761 data_time: 0.2062 memory: 17203 loss_visual: 0.0641 loss_lang: 0.1286 loss_fusion: 0.0559 loss: 0.2486 2022/10/05 23:35:16 - mmengine - INFO - Epoch(train) [13][5100/10520] lr: 1.0000e-04 eta: 13:07:58 time: 0.7081 data_time: 0.0425 memory: 17203 loss_visual: 0.0581 loss_lang: 0.1242 loss_fusion: 0.0513 loss: 0.2337 2022/10/05 23:36:10 - mmengine - INFO - Epoch(train) [13][5200/10520] lr: 1.0000e-04 eta: 13:06:55 time: 0.5242 data_time: 0.0515 memory: 17203 loss_visual: 0.0653 loss_lang: 0.1292 loss_fusion: 0.0573 loss: 0.2518 2022/10/05 23:37:03 - mmengine - INFO - Epoch(train) [13][5300/10520] lr: 1.0000e-04 eta: 13:05:51 time: 0.3903 data_time: 0.0125 memory: 17203 loss_visual: 0.0609 loss_lang: 0.1233 loss_fusion: 0.0525 loss: 0.2367 2022/10/05 23:37:57 - mmengine - INFO - Epoch(train) [13][5400/10520] lr: 1.0000e-04 eta: 13:04:48 time: 0.3803 data_time: 0.0036 memory: 17203 loss_visual: 0.0524 loss_lang: 0.1131 loss_fusion: 0.0442 loss: 0.2097 2022/10/05 23:38:51 - mmengine - INFO - Epoch(train) [13][5500/10520] lr: 1.0000e-04 eta: 13:03:44 time: 0.3612 data_time: 0.0033 memory: 17203 loss_visual: 0.0605 loss_lang: 0.1202 loss_fusion: 0.0519 loss: 0.2327 2022/10/05 23:39:44 - mmengine - INFO - Epoch(train) [13][5600/10520] lr: 1.0000e-04 eta: 13:02:40 time: 0.3849 data_time: 0.0112 memory: 17203 loss_visual: 0.0637 loss_lang: 0.1213 loss_fusion: 0.0549 loss: 0.2399 2022/10/05 23:40:42 - mmengine - INFO - Epoch(train) [13][5700/10520] lr: 1.0000e-04 eta: 13:01:39 time: 0.7549 data_time: 0.1490 memory: 17203 loss_visual: 0.0613 loss_lang: 0.1247 loss_fusion: 0.0522 loss: 0.2382 2022/10/05 23:41:11 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 23:41:37 - mmengine - INFO - Epoch(train) [13][5800/10520] lr: 1.0000e-04 eta: 13:00:37 time: 0.8524 data_time: 0.2060 memory: 17203 loss_visual: 0.0659 loss_lang: 0.1267 loss_fusion: 0.0575 loss: 0.2502 2022/10/05 23:42:30 - mmengine - INFO - Epoch(train) [13][5900/10520] lr: 1.0000e-04 eta: 12:59:33 time: 0.7015 data_time: 0.0451 memory: 17203 loss_visual: 0.0603 loss_lang: 0.1209 loss_fusion: 0.0522 loss: 0.2335 2022/10/05 23:43:24 - mmengine - INFO - Epoch(train) [13][6000/10520] lr: 1.0000e-04 eta: 12:58:30 time: 0.5011 data_time: 0.0539 memory: 17203 loss_visual: 0.0637 loss_lang: 0.1246 loss_fusion: 0.0558 loss: 0.2440 2022/10/05 23:44:17 - mmengine - INFO - Epoch(train) [13][6100/10520] lr: 1.0000e-04 eta: 12:57:26 time: 0.4081 data_time: 0.0103 memory: 17203 loss_visual: 0.0558 loss_lang: 0.1144 loss_fusion: 0.0483 loss: 0.2185 2022/10/05 23:45:11 - mmengine - INFO - Epoch(train) [13][6200/10520] lr: 1.0000e-04 eta: 12:56:23 time: 0.3423 data_time: 0.0036 memory: 17203 loss_visual: 0.0601 loss_lang: 0.1200 loss_fusion: 0.0535 loss: 0.2336 2022/10/05 23:46:04 - mmengine - INFO - Epoch(train) [13][6300/10520] lr: 1.0000e-04 eta: 12:55:19 time: 0.3807 data_time: 0.0035 memory: 17203 loss_visual: 0.0529 loss_lang: 0.1134 loss_fusion: 0.0441 loss: 0.2104 2022/10/05 23:46:57 - mmengine - INFO - Epoch(train) [13][6400/10520] lr: 1.0000e-04 eta: 12:54:16 time: 0.3668 data_time: 0.0120 memory: 17203 loss_visual: 0.0638 loss_lang: 0.1244 loss_fusion: 0.0560 loss: 0.2442 2022/10/05 23:47:55 - mmengine - INFO - Epoch(train) [13][6500/10520] lr: 1.0000e-04 eta: 12:53:14 time: 0.7242 data_time: 0.1469 memory: 17203 loss_visual: 0.0561 loss_lang: 0.1186 loss_fusion: 0.0476 loss: 0.2223 2022/10/05 23:48:49 - mmengine - INFO - Epoch(train) [13][6600/10520] lr: 1.0000e-04 eta: 12:52:12 time: 0.8939 data_time: 0.1910 memory: 17203 loss_visual: 0.0599 loss_lang: 0.1182 loss_fusion: 0.0503 loss: 0.2284 2022/10/05 23:49:44 - mmengine - INFO - Epoch(train) [13][6700/10520] lr: 1.0000e-04 eta: 12:51:09 time: 0.7130 data_time: 0.0652 memory: 17203 loss_visual: 0.0696 loss_lang: 0.1262 loss_fusion: 0.0606 loss: 0.2565 2022/10/05 23:50:17 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 23:50:38 - mmengine - INFO - Epoch(train) [13][6800/10520] lr: 1.0000e-04 eta: 12:50:06 time: 0.5411 data_time: 0.0541 memory: 17203 loss_visual: 0.0577 loss_lang: 0.1177 loss_fusion: 0.0498 loss: 0.2252 2022/10/05 23:51:32 - mmengine - INFO - Epoch(train) [13][6900/10520] lr: 1.0000e-04 eta: 12:49:03 time: 0.3948 data_time: 0.0157 memory: 17203 loss_visual: 0.0671 loss_lang: 0.1231 loss_fusion: 0.0568 loss: 0.2469 2022/10/05 23:52:25 - mmengine - INFO - Epoch(train) [13][7000/10520] lr: 1.0000e-04 eta: 12:48:00 time: 0.3442 data_time: 0.0036 memory: 17203 loss_visual: 0.0559 loss_lang: 0.1198 loss_fusion: 0.0484 loss: 0.2242 2022/10/05 23:53:19 - mmengine - INFO - Epoch(train) [13][7100/10520] lr: 1.0000e-04 eta: 12:46:56 time: 0.3848 data_time: 0.0049 memory: 17203 loss_visual: 0.0664 loss_lang: 0.1281 loss_fusion: 0.0571 loss: 0.2516 2022/10/05 23:54:13 - mmengine - INFO - Epoch(train) [13][7200/10520] lr: 1.0000e-04 eta: 12:45:53 time: 0.4065 data_time: 0.0121 memory: 17203 loss_visual: 0.0614 loss_lang: 0.1277 loss_fusion: 0.0531 loss: 0.2422 2022/10/05 23:55:12 - mmengine - INFO - Epoch(train) [13][7300/10520] lr: 1.0000e-04 eta: 12:44:53 time: 0.7480 data_time: 0.1296 memory: 17203 loss_visual: 0.0606 loss_lang: 0.1222 loss_fusion: 0.0513 loss: 0.2340 2022/10/05 23:56:07 - mmengine - INFO - Epoch(train) [13][7400/10520] lr: 1.0000e-04 eta: 12:43:51 time: 0.8726 data_time: 0.2079 memory: 17203 loss_visual: 0.0569 loss_lang: 0.1212 loss_fusion: 0.0488 loss: 0.2270 2022/10/05 23:57:02 - mmengine - INFO - Epoch(train) [13][7500/10520] lr: 1.0000e-04 eta: 12:42:48 time: 0.6927 data_time: 0.0453 memory: 17203 loss_visual: 0.0603 loss_lang: 0.1198 loss_fusion: 0.0513 loss: 0.2313 2022/10/05 23:57:56 - mmengine - INFO - Epoch(train) [13][7600/10520] lr: 1.0000e-04 eta: 12:41:45 time: 0.5490 data_time: 0.0568 memory: 17203 loss_visual: 0.0560 loss_lang: 0.1163 loss_fusion: 0.0489 loss: 0.2212 2022/10/05 23:58:49 - mmengine - INFO - Epoch(train) [13][7700/10520] lr: 1.0000e-04 eta: 12:40:42 time: 0.4039 data_time: 0.0140 memory: 17203 loss_visual: 0.0587 loss_lang: 0.1193 loss_fusion: 0.0507 loss: 0.2287 2022/10/05 23:59:23 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/05 23:59:42 - mmengine - INFO - Epoch(train) [13][7800/10520] lr: 1.0000e-04 eta: 12:39:39 time: 0.3394 data_time: 0.0034 memory: 17203 loss_visual: 0.0581 loss_lang: 0.1182 loss_fusion: 0.0500 loss: 0.2263 2022/10/06 00:03:25 - mmengine - INFO - Epoch(train) [13][7900/10520] lr: 1.0000e-04 eta: 12:40:12 time: 0.3967 data_time: 0.0033 memory: 17203 loss_visual: 0.0576 loss_lang: 0.1182 loss_fusion: 0.0500 loss: 0.2258 2022/10/06 00:06:01 - mmengine - INFO - Epoch(train) [13][8000/10520] lr: 1.0000e-04 eta: 12:39:09 time: 0.3979 data_time: 0.0126 memory: 17203 loss_visual: 0.0680 loss_lang: 0.1293 loss_fusion: 0.0598 loss: 0.2570 2022/10/06 00:06:57 - mmengine - INFO - Epoch(train) [13][8100/10520] lr: 1.0000e-04 eta: 12:39:04 time: 0.6849 data_time: 0.1556 memory: 17203 loss_visual: 0.0618 loss_lang: 0.1245 loss_fusion: 0.0544 loss: 0.2407 2022/10/06 00:07:51 - mmengine - INFO - Epoch(train) [13][8200/10520] lr: 1.0000e-04 eta: 12:38:01 time: 0.8117 data_time: 0.1864 memory: 17203 loss_visual: 0.0672 loss_lang: 0.1283 loss_fusion: 0.0582 loss: 0.2537 2022/10/06 00:08:45 - mmengine - INFO - Epoch(train) [13][8300/10520] lr: 1.0000e-04 eta: 12:36:58 time: 0.7438 data_time: 0.0428 memory: 17203 loss_visual: 0.0657 loss_lang: 0.1317 loss_fusion: 0.0566 loss: 0.2540 2022/10/06 00:09:40 - mmengine - INFO - Epoch(train) [13][8400/10520] lr: 1.0000e-04 eta: 12:35:55 time: 0.6228 data_time: 0.1365 memory: 17203 loss_visual: 0.0496 loss_lang: 0.1066 loss_fusion: 0.0412 loss: 0.1974 2022/10/06 00:10:33 - mmengine - INFO - Epoch(train) [13][8500/10520] lr: 1.0000e-04 eta: 12:34:51 time: 0.4178 data_time: 0.0112 memory: 17203 loss_visual: 0.0573 loss_lang: 0.1185 loss_fusion: 0.0485 loss: 0.2244 2022/10/06 00:11:27 - mmengine - INFO - Epoch(train) [13][8600/10520] lr: 1.0000e-04 eta: 12:33:48 time: 0.3398 data_time: 0.0035 memory: 17203 loss_visual: 0.0551 loss_lang: 0.1177 loss_fusion: 0.0477 loss: 0.2205 2022/10/06 00:12:21 - mmengine - INFO - Epoch(train) [13][8700/10520] lr: 1.0000e-04 eta: 12:32:45 time: 0.3616 data_time: 0.0033 memory: 17203 loss_visual: 0.0553 loss_lang: 0.1174 loss_fusion: 0.0475 loss: 0.2202 2022/10/06 00:12:54 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 00:13:14 - mmengine - INFO - Epoch(train) [13][8800/10520] lr: 1.0000e-04 eta: 12:31:41 time: 0.3762 data_time: 0.0134 memory: 17203 loss_visual: 0.0622 loss_lang: 0.1210 loss_fusion: 0.0538 loss: 0.2370 2022/10/06 00:14:12 - mmengine - INFO - Epoch(train) [13][8900/10520] lr: 1.0000e-04 eta: 12:30:40 time: 0.7314 data_time: 0.1545 memory: 17203 loss_visual: 0.0681 loss_lang: 0.1345 loss_fusion: 0.0599 loss: 0.2625 2022/10/06 00:15:06 - mmengine - INFO - Epoch(train) [13][9000/10520] lr: 1.0000e-04 eta: 12:29:38 time: 0.8341 data_time: 0.1962 memory: 17203 loss_visual: 0.0654 loss_lang: 0.1278 loss_fusion: 0.0563 loss: 0.2495 2022/10/06 00:16:01 - mmengine - INFO - Epoch(train) [13][9100/10520] lr: 1.0000e-04 eta: 12:28:35 time: 0.7282 data_time: 0.0472 memory: 17203 loss_visual: 0.0624 loss_lang: 0.1247 loss_fusion: 0.0549 loss: 0.2420 2022/10/06 00:16:54 - mmengine - INFO - Epoch(train) [13][9200/10520] lr: 1.0000e-04 eta: 12:27:31 time: 0.5601 data_time: 0.0757 memory: 17203 loss_visual: 0.0580 loss_lang: 0.1158 loss_fusion: 0.0498 loss: 0.2235 2022/10/06 00:17:51 - mmengine - INFO - Epoch(train) [13][9300/10520] lr: 1.0000e-04 eta: 12:26:30 time: 0.4186 data_time: 0.0114 memory: 17203 loss_visual: 0.0611 loss_lang: 0.1248 loss_fusion: 0.0535 loss: 0.2394 2022/10/06 00:18:46 - mmengine - INFO - Epoch(train) [13][9400/10520] lr: 1.0000e-04 eta: 12:25:27 time: 0.3418 data_time: 0.0037 memory: 17203 loss_visual: 0.0576 loss_lang: 0.1223 loss_fusion: 0.0491 loss: 0.2289 2022/10/06 00:19:40 - mmengine - INFO - Epoch(train) [13][9500/10520] lr: 1.0000e-04 eta: 12:24:24 time: 0.3770 data_time: 0.0036 memory: 17203 loss_visual: 0.0640 loss_lang: 0.1250 loss_fusion: 0.0553 loss: 0.2443 2022/10/06 00:20:34 - mmengine - INFO - Epoch(train) [13][9600/10520] lr: 1.0000e-04 eta: 12:23:21 time: 0.3772 data_time: 0.0117 memory: 17203 loss_visual: 0.0610 loss_lang: 0.1242 loss_fusion: 0.0537 loss: 0.2388 2022/10/06 00:21:33 - mmengine - INFO - Epoch(train) [13][9700/10520] lr: 1.0000e-04 eta: 12:22:21 time: 0.7421 data_time: 0.1295 memory: 17203 loss_visual: 0.0658 loss_lang: 0.1263 loss_fusion: 0.0562 loss: 0.2482 2022/10/06 00:22:03 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 00:22:29 - mmengine - INFO - Epoch(train) [13][9800/10520] lr: 1.0000e-04 eta: 12:21:19 time: 0.8746 data_time: 0.1860 memory: 17203 loss_visual: 0.0576 loss_lang: 0.1211 loss_fusion: 0.0493 loss: 0.2279 2022/10/06 00:23:23 - mmengine - INFO - Epoch(train) [13][9900/10520] lr: 1.0000e-04 eta: 12:20:16 time: 0.7131 data_time: 0.0651 memory: 17203 loss_visual: 0.0626 loss_lang: 0.1177 loss_fusion: 0.0542 loss: 0.2345 2022/10/06 00:24:19 - mmengine - INFO - Epoch(train) [13][10000/10520] lr: 1.0000e-04 eta: 12:19:14 time: 0.5714 data_time: 0.0542 memory: 17203 loss_visual: 0.0613 loss_lang: 0.1221 loss_fusion: 0.0528 loss: 0.2362 2022/10/06 00:25:13 - mmengine - INFO - Epoch(train) [13][10100/10520] lr: 1.0000e-04 eta: 12:18:11 time: 0.4181 data_time: 0.0112 memory: 17203 loss_visual: 0.0588 loss_lang: 0.1214 loss_fusion: 0.0511 loss: 0.2313 2022/10/06 00:26:07 - mmengine - INFO - Epoch(train) [13][10200/10520] lr: 1.0000e-04 eta: 12:17:08 time: 0.3403 data_time: 0.0037 memory: 17203 loss_visual: 0.0591 loss_lang: 0.1185 loss_fusion: 0.0517 loss: 0.2293 2022/10/06 00:27:01 - mmengine - INFO - Epoch(train) [13][10300/10520] lr: 1.0000e-04 eta: 12:16:05 time: 0.3638 data_time: 0.0035 memory: 17203 loss_visual: 0.0597 loss_lang: 0.1247 loss_fusion: 0.0511 loss: 0.2355 2022/10/06 00:27:55 - mmengine - INFO - Epoch(train) [13][10400/10520] lr: 1.0000e-04 eta: 12:15:02 time: 0.3789 data_time: 0.0092 memory: 17203 loss_visual: 0.0640 loss_lang: 0.1256 loss_fusion: 0.0551 loss: 0.2447 2022/10/06 00:28:51 - mmengine - INFO - Epoch(train) [13][10500/10520] lr: 1.0000e-04 eta: 12:14:00 time: 0.5987 data_time: 0.0995 memory: 17203 loss_visual: 0.0603 loss_lang: 0.1204 loss_fusion: 0.0518 loss: 0.2326 2022/10/06 00:28:59 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 00:28:59 - mmengine - INFO - Saving checkpoint at 13 epochs 2022/10/06 00:29:14 - mmengine - INFO - Epoch(val) [13][100/959] eta: 0:00:41 time: 0.0480 data_time: 0.0012 memory: 17203 2022/10/06 00:29:19 - mmengine - INFO - Epoch(val) [13][200/959] eta: 0:00:37 time: 0.0490 data_time: 0.0025 memory: 734 2022/10/06 00:29:24 - mmengine - INFO - Epoch(val) [13][300/959] eta: 0:00:31 time: 0.0484 data_time: 0.0020 memory: 734 2022/10/06 00:29:29 - mmengine - INFO - Epoch(val) [13][400/959] eta: 0:00:29 time: 0.0525 data_time: 0.0044 memory: 734 2022/10/06 00:29:34 - mmengine - INFO - Epoch(val) [13][500/959] eta: 0:00:21 time: 0.0478 data_time: 0.0021 memory: 734 2022/10/06 00:29:39 - mmengine - INFO - Epoch(val) [13][600/959] eta: 0:00:21 time: 0.0594 data_time: 0.0031 memory: 734 2022/10/06 00:29:44 - mmengine - INFO - Epoch(val) [13][700/959] eta: 0:00:11 time: 0.0459 data_time: 0.0020 memory: 734 2022/10/06 00:29:47 - mmengine - INFO - Epoch(val) [13][800/959] eta: 0:00:04 time: 0.0259 data_time: 0.0006 memory: 734 2022/10/06 00:29:49 - mmengine - INFO - Epoch(val) [13][900/959] eta: 0:00:01 time: 0.0215 data_time: 0.0006 memory: 734 2022/10/06 00:29:51 - mmengine - INFO - Epoch(val) [13][959/959] 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: 0.9320 SVTP/recog/word_acc_ignore_case_symbol: 0.8744 IC13/recog/word_acc_ignore_case_symbol: 0.9507 IC15/recog/word_acc_ignore_case_symbol: 0.7987 2022/10/06 00:30:57 - mmengine - INFO - Epoch(train) [14][100/10520] lr: 1.0000e-04 eta: 12:12:50 time: 0.7963 data_time: 0.2098 memory: 17203 loss_visual: 0.0639 loss_lang: 0.1287 loss_fusion: 0.0555 loss: 0.2480 2022/10/06 00:31:55 - mmengine - INFO - Epoch(train) [14][200/10520] lr: 1.0000e-04 eta: 12:11:49 time: 0.9412 data_time: 0.1789 memory: 17203 loss_visual: 0.0598 loss_lang: 0.1168 loss_fusion: 0.0512 loss: 0.2278 2022/10/06 00:32:17 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 00:32:52 - mmengine - INFO - Epoch(train) [14][300/10520] lr: 1.0000e-04 eta: 12:10:48 time: 0.7794 data_time: 0.1002 memory: 17203 loss_visual: 0.0594 loss_lang: 0.1208 loss_fusion: 0.0520 loss: 0.2321 2022/10/06 00:33:50 - mmengine - INFO - Epoch(train) [14][400/10520] lr: 1.0000e-04 eta: 12:09:47 time: 0.5046 data_time: 0.0038 memory: 17203 loss_visual: 0.0577 loss_lang: 0.1168 loss_fusion: 0.0492 loss: 0.2236 2022/10/06 00:34:46 - mmengine - INFO - Epoch(train) [14][500/10520] lr: 1.0000e-04 eta: 12:08:45 time: 0.4201 data_time: 0.0036 memory: 17203 loss_visual: 0.0581 loss_lang: 0.1190 loss_fusion: 0.0489 loss: 0.2260 2022/10/06 00:35:43 - mmengine - INFO - Epoch(train) [14][600/10520] lr: 1.0000e-04 eta: 12:07:44 time: 0.3757 data_time: 0.0036 memory: 17203 loss_visual: 0.0617 loss_lang: 0.1200 loss_fusion: 0.0533 loss: 0.2351 2022/10/06 00:36:39 - mmengine - INFO - Epoch(train) [14][700/10520] lr: 1.0000e-04 eta: 12:06:42 time: 0.3644 data_time: 0.0110 memory: 17203 loss_visual: 0.0577 loss_lang: 0.1204 loss_fusion: 0.0500 loss: 0.2281 2022/10/06 00:37:35 - mmengine - INFO - Epoch(train) [14][800/10520] lr: 1.0000e-04 eta: 12:05:40 time: 0.3655 data_time: 0.0126 memory: 17203 loss_visual: 0.0661 loss_lang: 0.1270 loss_fusion: 0.0579 loss: 0.2510 2022/10/06 00:38:36 - mmengine - INFO - Epoch(train) [14][900/10520] lr: 1.0000e-04 eta: 12:04:41 time: 0.7450 data_time: 0.1991 memory: 17203 loss_visual: 0.0557 loss_lang: 0.1168 loss_fusion: 0.0472 loss: 0.2197 2022/10/06 00:39:35 - mmengine - INFO - Epoch(train) [14][1000/10520] lr: 1.0000e-04 eta: 12:03:41 time: 0.9546 data_time: 0.1980 memory: 17203 loss_visual: 0.0583 loss_lang: 0.1205 loss_fusion: 0.0508 loss: 0.2296 2022/10/06 00:40:32 - mmengine - INFO - Epoch(train) [14][1100/10520] lr: 1.0000e-04 eta: 12:02:40 time: 0.7748 data_time: 0.0682 memory: 17203 loss_visual: 0.0569 loss_lang: 0.1168 loss_fusion: 0.0493 loss: 0.2230 2022/10/06 00:41:28 - mmengine - INFO - Epoch(train) [14][1200/10520] lr: 1.0000e-04 eta: 12:01:38 time: 0.5495 data_time: 0.0040 memory: 17203 loss_visual: 0.0563 loss_lang: 0.1152 loss_fusion: 0.0482 loss: 0.2198 2022/10/06 00:41:49 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 00:42:24 - mmengine - INFO - Epoch(train) [14][1300/10520] lr: 1.0000e-04 eta: 12:00:36 time: 0.4279 data_time: 0.0036 memory: 17203 loss_visual: 0.0651 loss_lang: 0.1279 loss_fusion: 0.0570 loss: 0.2501 2022/10/06 00:43:21 - mmengine - INFO - Epoch(train) [14][1400/10520] lr: 1.0000e-04 eta: 11:59:35 time: 0.3622 data_time: 0.0037 memory: 17203 loss_visual: 0.0708 loss_lang: 0.1306 loss_fusion: 0.0623 loss: 0.2637 2022/10/06 00:44:18 - mmengine - INFO - Epoch(train) [14][1500/10520] lr: 1.0000e-04 eta: 11:58:34 time: 0.3655 data_time: 0.0134 memory: 17203 loss_visual: 0.0610 loss_lang: 0.1232 loss_fusion: 0.0523 loss: 0.2364 2022/10/06 00:45:14 - mmengine - INFO - Epoch(train) [14][1600/10520] lr: 1.0000e-04 eta: 11:57:32 time: 0.3699 data_time: 0.0115 memory: 17203 loss_visual: 0.0610 loss_lang: 0.1222 loss_fusion: 0.0535 loss: 0.2368 2022/10/06 00:46:14 - mmengine - INFO - Epoch(train) [14][1700/10520] lr: 1.0000e-04 eta: 11:56:32 time: 0.7631 data_time: 0.1841 memory: 17203 loss_visual: 0.0577 loss_lang: 0.1194 loss_fusion: 0.0499 loss: 0.2270 2022/10/06 00:47:13 - mmengine - INFO - Epoch(train) [14][1800/10520] lr: 1.0000e-04 eta: 11:55:32 time: 0.9325 data_time: 0.1871 memory: 17203 loss_visual: 0.0618 loss_lang: 0.1226 loss_fusion: 0.0536 loss: 0.2380 2022/10/06 00:48:10 - mmengine - INFO - Epoch(train) [14][1900/10520] lr: 1.0000e-04 eta: 11:54:31 time: 0.8083 data_time: 0.0743 memory: 17203 loss_visual: 0.0577 loss_lang: 0.1215 loss_fusion: 0.0507 loss: 0.2299 2022/10/06 00:49:25 - mmengine - INFO - Epoch(train) [14][2000/10520] lr: 1.0000e-04 eta: 11:53:39 time: 0.8528 data_time: 0.0037 memory: 17203 loss_visual: 0.0667 loss_lang: 0.1246 loss_fusion: 0.0588 loss: 0.2501 2022/10/06 00:50:34 - mmengine - INFO - Epoch(train) [14][2100/10520] lr: 1.0000e-04 eta: 11:52:44 time: 0.4108 data_time: 0.0039 memory: 17203 loss_visual: 0.0629 loss_lang: 0.1260 loss_fusion: 0.0557 loss: 0.2445 2022/10/06 00:52:15 - mmengine - INFO - Epoch(train) [14][2200/10520] lr: 1.0000e-04 eta: 11:52:05 time: 0.3981 data_time: 0.0038 memory: 17203 loss_visual: 0.0543 loss_lang: 0.1151 loss_fusion: 0.0472 loss: 0.2167 2022/10/06 00:52:36 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 00:53:12 - mmengine - INFO - Epoch(train) [14][2300/10520] lr: 1.0000e-04 eta: 11:51:04 time: 0.3687 data_time: 0.0115 memory: 17203 loss_visual: 0.0558 loss_lang: 0.1152 loss_fusion: 0.0474 loss: 0.2183 2022/10/06 00:54:10 - mmengine - INFO - Epoch(train) [14][2400/10520] lr: 1.0000e-04 eta: 11:50:03 time: 0.3667 data_time: 0.0135 memory: 17203 loss_visual: 0.0639 loss_lang: 0.1245 loss_fusion: 0.0567 loss: 0.2450 2022/10/06 00:55:11 - mmengine - INFO - Epoch(train) [14][2500/10520] lr: 1.0000e-04 eta: 11:49:04 time: 0.7715 data_time: 0.1873 memory: 17203 loss_visual: 0.0658 loss_lang: 0.1215 loss_fusion: 0.0565 loss: 0.2438 2022/10/06 00:56:10 - mmengine - INFO - Epoch(train) [14][2600/10520] lr: 1.0000e-04 eta: 11:48:04 time: 0.9373 data_time: 0.1739 memory: 17203 loss_visual: 0.0650 loss_lang: 0.1259 loss_fusion: 0.0553 loss: 0.2462 2022/10/06 00:57:07 - mmengine - INFO - Epoch(train) [14][2700/10520] lr: 1.0000e-04 eta: 11:47:03 time: 0.7979 data_time: 0.0722 memory: 17203 loss_visual: 0.0631 loss_lang: 0.1251 loss_fusion: 0.0547 loss: 0.2429 2022/10/06 00:58:04 - mmengine - INFO - Epoch(train) [14][2800/10520] lr: 1.0000e-04 eta: 11:46:01 time: 0.5014 data_time: 0.0040 memory: 17203 loss_visual: 0.0570 loss_lang: 0.1184 loss_fusion: 0.0489 loss: 0.2243 2022/10/06 00:59:00 - mmengine - INFO - Epoch(train) [14][2900/10520] lr: 1.0000e-04 eta: 11:45:00 time: 0.4223 data_time: 0.0041 memory: 17203 loss_visual: 0.0556 loss_lang: 0.1164 loss_fusion: 0.0478 loss: 0.2198 2022/10/06 00:59:56 - mmengine - INFO - Epoch(train) [14][3000/10520] lr: 1.0000e-04 eta: 11:43:58 time: 0.3547 data_time: 0.0035 memory: 17203 loss_visual: 0.0603 loss_lang: 0.1240 loss_fusion: 0.0515 loss: 0.2358 2022/10/06 01:00:53 - mmengine - INFO - Epoch(train) [14][3100/10520] lr: 1.0000e-04 eta: 11:42:57 time: 0.3903 data_time: 0.0116 memory: 17203 loss_visual: 0.0593 loss_lang: 0.1201 loss_fusion: 0.0505 loss: 0.2299 2022/10/06 01:01:49 - mmengine - INFO - Epoch(train) [14][3200/10520] lr: 1.0000e-04 eta: 11:41:55 time: 0.3656 data_time: 0.0125 memory: 17203 loss_visual: 0.0615 loss_lang: 0.1216 loss_fusion: 0.0538 loss: 0.2369 2022/10/06 01:02:17 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 01:02:50 - mmengine - INFO - Epoch(train) [14][3300/10520] lr: 1.0000e-04 eta: 11:40:56 time: 0.7356 data_time: 0.2018 memory: 17203 loss_visual: 0.0636 loss_lang: 0.1277 loss_fusion: 0.0546 loss: 0.2459 2022/10/06 01:03:49 - mmengine - INFO - Epoch(train) [14][3400/10520] lr: 1.0000e-04 eta: 11:39:55 time: 0.9845 data_time: 0.1956 memory: 17203 loss_visual: 0.0628 loss_lang: 0.1206 loss_fusion: 0.0538 loss: 0.2373 2022/10/06 01:04:46 - mmengine - INFO - Epoch(train) [14][3500/10520] lr: 1.0000e-04 eta: 11:38:55 time: 0.8509 data_time: 0.1068 memory: 17203 loss_visual: 0.0600 loss_lang: 0.1216 loss_fusion: 0.0519 loss: 0.2336 2022/10/06 01:05:43 - mmengine - INFO - Epoch(train) [14][3600/10520] lr: 1.0000e-04 eta: 11:37:53 time: 0.5257 data_time: 0.0038 memory: 17203 loss_visual: 0.0641 loss_lang: 0.1257 loss_fusion: 0.0559 loss: 0.2457 2022/10/06 01:06:39 - mmengine - INFO - Epoch(train) [14][3700/10520] lr: 1.0000e-04 eta: 11:36:52 time: 0.4312 data_time: 0.0035 memory: 17203 loss_visual: 0.0627 loss_lang: 0.1241 loss_fusion: 0.0553 loss: 0.2422 2022/10/06 01:07:36 - mmengine - INFO - Epoch(train) [14][3800/10520] lr: 1.0000e-04 eta: 11:35:50 time: 0.3563 data_time: 0.0036 memory: 17203 loss_visual: 0.0594 loss_lang: 0.1255 loss_fusion: 0.0514 loss: 0.2363 2022/10/06 01:08:33 - mmengine - INFO - Epoch(train) [14][3900/10520] lr: 1.0000e-04 eta: 11:34:49 time: 0.3887 data_time: 0.0114 memory: 17203 loss_visual: 0.0632 loss_lang: 0.1241 loss_fusion: 0.0566 loss: 0.2439 2022/10/06 01:09:29 - mmengine - INFO - Epoch(train) [14][4000/10520] lr: 1.0000e-04 eta: 11:33:48 time: 0.3625 data_time: 0.0120 memory: 17203 loss_visual: 0.0533 loss_lang: 0.1123 loss_fusion: 0.0464 loss: 0.2120 2022/10/06 01:10:30 - mmengine - INFO - Epoch(train) [14][4100/10520] lr: 1.0000e-04 eta: 11:32:48 time: 0.7246 data_time: 0.1881 memory: 17203 loss_visual: 0.0610 loss_lang: 0.1212 loss_fusion: 0.0527 loss: 0.2349 2022/10/06 01:11:28 - mmengine - INFO - Epoch(train) [14][4200/10520] lr: 1.0000e-04 eta: 11:31:48 time: 0.9229 data_time: 0.1878 memory: 17203 loss_visual: 0.0645 loss_lang: 0.1236 loss_fusion: 0.0567 loss: 0.2448 2022/10/06 01:11:50 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 01:12:25 - mmengine - INFO - Epoch(train) [14][4300/10520] lr: 1.0000e-04 eta: 11:30:47 time: 0.8334 data_time: 0.0614 memory: 17203 loss_visual: 0.0666 loss_lang: 0.1238 loss_fusion: 0.0593 loss: 0.2496 2022/10/06 01:13:22 - mmengine - INFO - Epoch(train) [14][4400/10520] lr: 1.0000e-04 eta: 11:29:46 time: 0.5004 data_time: 0.0082 memory: 17203 loss_visual: 0.0609 loss_lang: 0.1186 loss_fusion: 0.0520 loss: 0.2315 2022/10/06 01:14:19 - mmengine - INFO - Epoch(train) [14][4500/10520] lr: 1.0000e-04 eta: 11:28:44 time: 0.4262 data_time: 0.0035 memory: 17203 loss_visual: 0.0630 loss_lang: 0.1257 loss_fusion: 0.0557 loss: 0.2444 2022/10/06 01:15:15 - mmengine - INFO - Epoch(train) [14][4600/10520] lr: 1.0000e-04 eta: 11:27:43 time: 0.3588 data_time: 0.0034 memory: 17203 loss_visual: 0.0554 loss_lang: 0.1183 loss_fusion: 0.0480 loss: 0.2216 2022/10/06 01:16:12 - mmengine - INFO - Epoch(train) [14][4700/10520] lr: 1.0000e-04 eta: 11:26:42 time: 0.3623 data_time: 0.0128 memory: 17203 loss_visual: 0.0607 loss_lang: 0.1248 loss_fusion: 0.0513 loss: 0.2368 2022/10/06 01:17:09 - mmengine - INFO - Epoch(train) [14][4800/10520] lr: 1.0000e-04 eta: 11:25:41 time: 0.3772 data_time: 0.0119 memory: 17203 loss_visual: 0.0625 loss_lang: 0.1202 loss_fusion: 0.0538 loss: 0.2365 2022/10/06 01:18:11 - mmengine - INFO - Epoch(train) [14][4900/10520] lr: 1.0000e-04 eta: 11:24:42 time: 0.7903 data_time: 0.2365 memory: 17203 loss_visual: 0.0640 loss_lang: 0.1220 loss_fusion: 0.0536 loss: 0.2396 2022/10/06 01:19:11 - mmengine - INFO - Epoch(train) [14][5000/10520] lr: 1.0000e-04 eta: 11:23:42 time: 0.9106 data_time: 0.1849 memory: 17203 loss_visual: 0.0619 loss_lang: 0.1187 loss_fusion: 0.0533 loss: 0.2338 2022/10/06 01:20:08 - mmengine - INFO - Epoch(train) [14][5100/10520] lr: 1.0000e-04 eta: 11:22:41 time: 0.7897 data_time: 0.0843 memory: 17203 loss_visual: 0.0545 loss_lang: 0.1159 loss_fusion: 0.0460 loss: 0.2163 2022/10/06 01:21:05 - mmengine - INFO - Epoch(train) [14][5200/10520] lr: 1.0000e-04 eta: 11:21:40 time: 0.5171 data_time: 0.0039 memory: 17203 loss_visual: 0.0586 loss_lang: 0.1192 loss_fusion: 0.0502 loss: 0.2280 2022/10/06 01:21:26 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 01:22:01 - mmengine - INFO - Epoch(train) [14][5300/10520] lr: 1.0000e-04 eta: 11:20:38 time: 0.4034 data_time: 0.0037 memory: 17203 loss_visual: 0.0596 loss_lang: 0.1247 loss_fusion: 0.0525 loss: 0.2369 2022/10/06 01:22:58 - mmengine - INFO - Epoch(train) [14][5400/10520] lr: 1.0000e-04 eta: 11:19:37 time: 0.4027 data_time: 0.0036 memory: 17203 loss_visual: 0.0600 loss_lang: 0.1239 loss_fusion: 0.0509 loss: 0.2347 2022/10/06 01:23:55 - mmengine - INFO - Epoch(train) [14][5500/10520] lr: 1.0000e-04 eta: 11:18:36 time: 0.4047 data_time: 0.0123 memory: 17203 loss_visual: 0.0628 loss_lang: 0.1241 loss_fusion: 0.0531 loss: 0.2399 2022/10/06 01:24:52 - mmengine - INFO - Epoch(train) [14][5600/10520] lr: 1.0000e-04 eta: 11:17:35 time: 0.3815 data_time: 0.0095 memory: 17203 loss_visual: 0.0597 loss_lang: 0.1210 loss_fusion: 0.0512 loss: 0.2319 2022/10/06 01:25:52 - mmengine - INFO - Epoch(train) [14][5700/10520] lr: 1.0000e-04 eta: 11:16:36 time: 0.7627 data_time: 0.2047 memory: 17203 loss_visual: 0.0562 loss_lang: 0.1160 loss_fusion: 0.0488 loss: 0.2210 2022/10/06 01:26:51 - mmengine - INFO - Epoch(train) [14][5800/10520] lr: 1.0000e-04 eta: 11:15:35 time: 0.9601 data_time: 0.1892 memory: 17203 loss_visual: 0.0563 loss_lang: 0.1188 loss_fusion: 0.0491 loss: 0.2242 2022/10/06 01:27:49 - mmengine - INFO - Epoch(train) [14][5900/10520] lr: 1.0000e-04 eta: 11:14:35 time: 0.8291 data_time: 0.0751 memory: 17203 loss_visual: 0.0584 loss_lang: 0.1209 loss_fusion: 0.0500 loss: 0.2292 2022/10/06 01:28:46 - mmengine - INFO - Epoch(train) [14][6000/10520] lr: 1.0000e-04 eta: 11:13:33 time: 0.5403 data_time: 0.0211 memory: 17203 loss_visual: 0.0607 loss_lang: 0.1219 loss_fusion: 0.0527 loss: 0.2353 2022/10/06 01:29:42 - mmengine - INFO - Epoch(train) [14][6100/10520] lr: 1.0000e-04 eta: 11:12:32 time: 0.4262 data_time: 0.0039 memory: 17203 loss_visual: 0.0620 loss_lang: 0.1202 loss_fusion: 0.0535 loss: 0.2357 2022/10/06 01:30:39 - mmengine - INFO - Epoch(train) [14][6200/10520] lr: 1.0000e-04 eta: 11:11:31 time: 0.3925 data_time: 0.0034 memory: 17203 loss_visual: 0.0566 loss_lang: 0.1142 loss_fusion: 0.0471 loss: 0.2180 2022/10/06 01:31:01 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 01:31:35 - mmengine - INFO - Epoch(train) [14][6300/10520] lr: 1.0000e-04 eta: 11:10:30 time: 0.3600 data_time: 0.0102 memory: 17203 loss_visual: 0.0524 loss_lang: 0.1140 loss_fusion: 0.0462 loss: 0.2126 2022/10/06 01:32:32 - mmengine - INFO - Epoch(train) [14][6400/10520] lr: 1.0000e-04 eta: 11:09:29 time: 0.4064 data_time: 0.0128 memory: 17203 loss_visual: 0.0551 loss_lang: 0.1160 loss_fusion: 0.0472 loss: 0.2182 2022/10/06 01:33:33 - mmengine - INFO - Epoch(train) [14][6500/10520] lr: 1.0000e-04 eta: 11:08:30 time: 0.7829 data_time: 0.2123 memory: 17203 loss_visual: 0.0521 loss_lang: 0.1129 loss_fusion: 0.0446 loss: 0.2096 2022/10/06 01:34:33 - mmengine - INFO - Epoch(train) [14][6600/10520] lr: 1.0000e-04 eta: 11:07:30 time: 0.9236 data_time: 0.1852 memory: 17203 loss_visual: 0.0641 loss_lang: 0.1235 loss_fusion: 0.0556 loss: 0.2433 2022/10/06 01:35:30 - mmengine - INFO - Epoch(train) [14][6700/10520] lr: 1.0000e-04 eta: 11:06:29 time: 0.8022 data_time: 0.0901 memory: 17203 loss_visual: 0.0624 loss_lang: 0.1211 loss_fusion: 0.0544 loss: 0.2380 2022/10/06 01:36:28 - mmengine - INFO - Epoch(train) [14][6800/10520] lr: 1.0000e-04 eta: 11:05:28 time: 0.5011 data_time: 0.0037 memory: 17203 loss_visual: 0.0742 loss_lang: 0.1415 loss_fusion: 0.0647 loss: 0.2803 2022/10/06 01:37:25 - mmengine - INFO - Epoch(train) [14][6900/10520] lr: 1.0000e-04 eta: 11:04:27 time: 0.4080 data_time: 0.0041 memory: 17203 loss_visual: 0.0653 loss_lang: 0.1271 loss_fusion: 0.0560 loss: 0.2483 2022/10/06 01:38:22 - mmengine - INFO - Epoch(train) [14][7000/10520] lr: 1.0000e-04 eta: 11:03:26 time: 0.3772 data_time: 0.0037 memory: 17203 loss_visual: 0.0517 loss_lang: 0.1089 loss_fusion: 0.0442 loss: 0.2048 2022/10/06 01:39:19 - mmengine - INFO - Epoch(train) [14][7100/10520] lr: 1.0000e-04 eta: 11:02:25 time: 0.3598 data_time: 0.0109 memory: 17203 loss_visual: 0.0566 loss_lang: 0.1179 loss_fusion: 0.0478 loss: 0.2222 2022/10/06 01:40:17 - mmengine - INFO - Epoch(train) [14][7200/10520] lr: 1.0000e-04 eta: 11:01:25 time: 0.3829 data_time: 0.0113 memory: 17203 loss_visual: 0.0641 loss_lang: 0.1269 loss_fusion: 0.0557 loss: 0.2466 2022/10/06 01:40:44 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 01:41:18 - mmengine - INFO - Epoch(train) [14][7300/10520] lr: 1.0000e-04 eta: 11:00:25 time: 0.7635 data_time: 0.2055 memory: 17203 loss_visual: 0.0576 loss_lang: 0.1159 loss_fusion: 0.0488 loss: 0.2223 2022/10/06 01:42:17 - mmengine - INFO - Epoch(train) [14][7400/10520] lr: 1.0000e-04 eta: 10:59:25 time: 0.9498 data_time: 0.1799 memory: 17203 loss_visual: 0.0696 loss_lang: 0.1308 loss_fusion: 0.0605 loss: 0.2609 2022/10/06 01:43:14 - mmengine - INFO - Epoch(train) [14][7500/10520] lr: 1.0000e-04 eta: 10:58:24 time: 0.8178 data_time: 0.0889 memory: 17203 loss_visual: 0.0583 loss_lang: 0.1186 loss_fusion: 0.0500 loss: 0.2269 2022/10/06 01:44:11 - mmengine - INFO - Epoch(train) [14][7600/10520] lr: 1.0000e-04 eta: 10:57:23 time: 0.4849 data_time: 0.0038 memory: 17203 loss_visual: 0.0599 loss_lang: 0.1175 loss_fusion: 0.0500 loss: 0.2274 2022/10/06 01:45:08 - mmengine - INFO - Epoch(train) [14][7700/10520] lr: 1.0000e-04 eta: 10:56:22 time: 0.4129 data_time: 0.0037 memory: 17203 loss_visual: 0.0533 loss_lang: 0.1124 loss_fusion: 0.0450 loss: 0.2107 2022/10/06 01:46:05 - mmengine - INFO - Epoch(train) [14][7800/10520] lr: 1.0000e-04 eta: 10:55:22 time: 0.3915 data_time: 0.0049 memory: 17203 loss_visual: 0.0535 loss_lang: 0.1155 loss_fusion: 0.0463 loss: 0.2153 2022/10/06 01:47:02 - mmengine - INFO - Epoch(train) [14][7900/10520] lr: 1.0000e-04 eta: 10:54:21 time: 0.3712 data_time: 0.0104 memory: 17203 loss_visual: 0.0616 loss_lang: 0.1247 loss_fusion: 0.0531 loss: 0.2394 2022/10/06 01:47:59 - mmengine - INFO - Epoch(train) [14][8000/10520] lr: 1.0000e-04 eta: 10:53:19 time: 0.3785 data_time: 0.0119 memory: 17203 loss_visual: 0.0580 loss_lang: 0.1195 loss_fusion: 0.0491 loss: 0.2267 2022/10/06 01:49:00 - mmengine - INFO - Epoch(train) [14][8100/10520] lr: 1.0000e-04 eta: 10:52:20 time: 0.7499 data_time: 0.1974 memory: 17203 loss_visual: 0.0577 loss_lang: 0.1218 loss_fusion: 0.0495 loss: 0.2290 2022/10/06 01:50:01 - mmengine - INFO - Epoch(train) [14][8200/10520] lr: 1.0000e-04 eta: 10:51:21 time: 0.9973 data_time: 0.1855 memory: 17203 loss_visual: 0.0517 loss_lang: 0.1111 loss_fusion: 0.0437 loss: 0.2066 2022/10/06 01:50:23 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 01:51:00 - mmengine - INFO - Epoch(train) [14][8300/10520] lr: 1.0000e-04 eta: 10:50:21 time: 0.9214 data_time: 0.0903 memory: 17203 loss_visual: 0.0665 loss_lang: 0.1250 loss_fusion: 0.0584 loss: 0.2499 2022/10/06 01:51:57 - mmengine - INFO - Epoch(train) [14][8400/10520] lr: 1.0000e-04 eta: 10:49:20 time: 0.5458 data_time: 0.0038 memory: 17203 loss_visual: 0.0596 loss_lang: 0.1218 loss_fusion: 0.0506 loss: 0.2321 2022/10/06 01:52:55 - mmengine - INFO - Epoch(train) [14][8500/10520] lr: 1.0000e-04 eta: 10:48:19 time: 0.4348 data_time: 0.0040 memory: 17203 loss_visual: 0.0612 loss_lang: 0.1192 loss_fusion: 0.0512 loss: 0.2316 2022/10/06 01:53:51 - mmengine - INFO - Epoch(train) [14][8600/10520] lr: 1.0000e-04 eta: 10:47:18 time: 0.3557 data_time: 0.0038 memory: 17203 loss_visual: 0.0597 loss_lang: 0.1254 loss_fusion: 0.0524 loss: 0.2375 2022/10/06 01:54:49 - mmengine - INFO - Epoch(train) [14][8700/10520] lr: 1.0000e-04 eta: 10:46:18 time: 0.3789 data_time: 0.0126 memory: 17203 loss_visual: 0.0651 loss_lang: 0.1235 loss_fusion: 0.0569 loss: 0.2455 2022/10/06 01:55:46 - mmengine - INFO - Epoch(train) [14][8800/10520] lr: 1.0000e-04 eta: 10:45:17 time: 0.4009 data_time: 0.0122 memory: 17203 loss_visual: 0.0598 loss_lang: 0.1235 loss_fusion: 0.0527 loss: 0.2360 2022/10/06 01:56:46 - mmengine - INFO - Epoch(train) [14][8900/10520] lr: 1.0000e-04 eta: 10:44:17 time: 0.7323 data_time: 0.1997 memory: 17203 loss_visual: 0.0587 loss_lang: 0.1183 loss_fusion: 0.0515 loss: 0.2285 2022/10/06 01:57:45 - mmengine - INFO - Epoch(train) [14][9000/10520] lr: 1.0000e-04 eta: 10:43:17 time: 0.9471 data_time: 0.1799 memory: 17203 loss_visual: 0.0558 loss_lang: 0.1161 loss_fusion: 0.0474 loss: 0.2193 2022/10/06 01:58:43 - mmengine - INFO - Epoch(train) [14][9100/10520] lr: 1.0000e-04 eta: 10:42:17 time: 0.8527 data_time: 0.0895 memory: 17203 loss_visual: 0.0626 loss_lang: 0.1192 loss_fusion: 0.0525 loss: 0.2343 2022/10/06 01:59:40 - mmengine - INFO - Epoch(train) [14][9200/10520] lr: 1.0000e-04 eta: 10:41:16 time: 0.5031 data_time: 0.0232 memory: 17203 loss_visual: 0.0585 loss_lang: 0.1201 loss_fusion: 0.0508 loss: 0.2293 2022/10/06 02:00:01 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 02:01:07 - mmengine - INFO - Epoch(train) [14][9300/10520] lr: 1.0000e-04 eta: 10:40:28 time: 0.6290 data_time: 0.0066 memory: 17203 loss_visual: 0.0643 loss_lang: 0.1266 loss_fusion: 0.0563 loss: 0.2472 2022/10/06 02:02:37 - mmengine - INFO - Epoch(train) [14][9400/10520] lr: 1.0000e-04 eta: 10:39:42 time: 0.4563 data_time: 0.0067 memory: 17203 loss_visual: 0.0599 loss_lang: 0.1220 loss_fusion: 0.0525 loss: 0.2345 2022/10/06 02:04:15 - mmengine - INFO - Epoch(train) [14][9500/10520] lr: 1.0000e-04 eta: 10:38:59 time: 0.5880 data_time: 0.0169 memory: 17203 loss_visual: 0.0539 loss_lang: 0.1130 loss_fusion: 0.0461 loss: 0.2129 2022/10/06 02:05:39 - mmengine - INFO - Epoch(train) [14][9600/10520] lr: 1.0000e-04 eta: 10:38:09 time: 0.5341 data_time: 0.0171 memory: 17203 loss_visual: 0.0586 loss_lang: 0.1182 loss_fusion: 0.0494 loss: 0.2261 2022/10/06 02:07:22 - mmengine - INFO - Epoch(train) [14][9700/10520] lr: 1.0000e-04 eta: 10:37:28 time: 1.1445 data_time: 0.2867 memory: 17203 loss_visual: 0.0595 loss_lang: 0.1201 loss_fusion: 0.0504 loss: 0.2301 2022/10/06 02:08:59 - mmengine - INFO - Epoch(train) [14][9800/10520] lr: 1.0000e-04 eta: 10:36:45 time: 1.7767 data_time: 0.2582 memory: 17203 loss_visual: 0.0638 loss_lang: 0.1278 loss_fusion: 0.0546 loss: 0.2463 2022/10/06 02:10:32 - mmengine - INFO - Epoch(train) [14][9900/10520] lr: 1.0000e-04 eta: 10:36:00 time: 1.3859 data_time: 0.1373 memory: 17203 loss_visual: 0.0585 loss_lang: 0.1230 loss_fusion: 0.0500 loss: 0.2315 2022/10/06 02:12:11 - mmengine - INFO - Epoch(train) [14][10000/10520] lr: 1.0000e-04 eta: 10:35:17 time: 1.0034 data_time: 0.0072 memory: 17203 loss_visual: 0.0656 loss_lang: 0.1237 loss_fusion: 0.0574 loss: 0.2467 2022/10/06 02:13:40 - mmengine - INFO - Epoch(train) [14][10100/10520] lr: 1.0000e-04 eta: 10:34:29 time: 0.5666 data_time: 0.0075 memory: 17203 loss_visual: 0.0553 loss_lang: 0.1190 loss_fusion: 0.0467 loss: 0.2209 2022/10/06 02:15:17 - mmengine - INFO - Epoch(train) [14][10200/10520] lr: 1.0000e-04 eta: 10:33:46 time: 0.5024 data_time: 0.0070 memory: 17203 loss_visual: 0.0708 loss_lang: 0.1298 loss_fusion: 0.0619 loss: 0.2626 2022/10/06 02:15:50 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 02:16:48 - mmengine - INFO - Epoch(train) [14][10300/10520] lr: 1.0000e-04 eta: 10:32:59 time: 0.6628 data_time: 0.0186 memory: 17203 loss_visual: 0.0620 loss_lang: 0.1210 loss_fusion: 0.0535 loss: 0.2366 2022/10/06 02:18:20 - mmengine - INFO - Epoch(train) [14][10400/10520] lr: 1.0000e-04 eta: 10:32:12 time: 0.4756 data_time: 0.0193 memory: 17203 loss_visual: 0.0645 loss_lang: 0.1249 loss_fusion: 0.0572 loss: 0.2466 2022/10/06 02:19:48 - mmengine - INFO - Epoch(train) [14][10500/10520] lr: 1.0000e-04 eta: 10:31:25 time: 0.6004 data_time: 0.1133 memory: 17203 loss_visual: 0.0559 loss_lang: 0.1216 loss_fusion: 0.0489 loss: 0.2264 2022/10/06 02:19:57 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 02:19:57 - mmengine - INFO - Saving checkpoint at 14 epochs 2022/10/06 02:20:12 - mmengine - INFO - Epoch(val) [14][100/959] eta: 0:00:45 time: 0.0525 data_time: 0.0013 memory: 17203 2022/10/06 02:20:17 - mmengine - INFO - Epoch(val) [14][200/959] eta: 0:00:53 time: 0.0699 data_time: 0.0022 memory: 734 2022/10/06 02:20:22 - mmengine - INFO - Epoch(val) [14][300/959] eta: 0:00:32 time: 0.0496 data_time: 0.0019 memory: 734 2022/10/06 02:20:27 - mmengine - INFO - Epoch(val) [14][400/959] eta: 0:00:29 time: 0.0529 data_time: 0.0023 memory: 734 2022/10/06 02:20:31 - mmengine - INFO - Epoch(val) [14][500/959] eta: 0:00:21 time: 0.0471 data_time: 0.0012 memory: 734 2022/10/06 02:20:36 - mmengine - INFO - Epoch(val) [14][600/959] eta: 0:00:16 time: 0.0468 data_time: 0.0035 memory: 734 2022/10/06 02:20:41 - mmengine - INFO - Epoch(val) [14][700/959] eta: 0:00:13 time: 0.0509 data_time: 0.0024 memory: 734 2022/10/06 02:20:45 - mmengine - INFO - Epoch(val) [14][800/959] eta: 0:00:03 time: 0.0231 data_time: 0.0006 memory: 734 2022/10/06 02:20:48 - mmengine - INFO - Epoch(val) [14][900/959] eta: 0:00:01 time: 0.0220 data_time: 0.0005 memory: 734 2022/10/06 02:20:49 - mmengine - INFO - Epoch(val) [14][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.8576 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9460 SVT/recog/word_acc_ignore_case_symbol: 0.9335 SVTP/recog/word_acc_ignore_case_symbol: 0.8791 IC13/recog/word_acc_ignore_case_symbol: 0.9389 IC15/recog/word_acc_ignore_case_symbol: 0.8002 2022/10/06 02:21:53 - mmengine - INFO - Epoch(train) [15][100/10520] lr: 1.0000e-04 eta: 10:30:12 time: 0.7533 data_time: 0.2152 memory: 17203 loss_visual: 0.0623 loss_lang: 0.1211 loss_fusion: 0.0541 loss: 0.2375 2022/10/06 02:22:48 - mmengine - INFO - Epoch(train) [15][200/10520] lr: 1.0000e-04 eta: 10:29:11 time: 0.8853 data_time: 0.1767 memory: 17203 loss_visual: 0.0670 loss_lang: 0.1300 loss_fusion: 0.0596 loss: 0.2566 2022/10/06 02:23:41 - mmengine - INFO - Epoch(train) [15][300/10520] lr: 1.0000e-04 eta: 10:28:08 time: 0.6823 data_time: 0.1407 memory: 17203 loss_visual: 0.0602 loss_lang: 0.1183 loss_fusion: 0.0519 loss: 0.2304 2022/10/06 02:24:35 - mmengine - INFO - Epoch(train) [15][400/10520] lr: 1.0000e-04 eta: 10:27:05 time: 0.3875 data_time: 0.0119 memory: 17203 loss_visual: 0.0666 loss_lang: 0.1257 loss_fusion: 0.0580 loss: 0.2502 2022/10/06 02:25:29 - mmengine - INFO - Epoch(train) [15][500/10520] lr: 1.0000e-04 eta: 10:26:03 time: 0.4068 data_time: 0.0119 memory: 17203 loss_visual: 0.0618 loss_lang: 0.1275 loss_fusion: 0.0543 loss: 0.2435 2022/10/06 02:26:23 - mmengine - INFO - Epoch(train) [15][600/10520] lr: 1.0000e-04 eta: 10:25:00 time: 0.3981 data_time: 0.0118 memory: 17203 loss_visual: 0.0560 loss_lang: 0.1203 loss_fusion: 0.0477 loss: 0.2240 2022/10/06 02:27:18 - mmengine - INFO - Epoch(train) [15][700/10520] lr: 1.0000e-04 eta: 10:23:58 time: 0.3980 data_time: 0.0187 memory: 17203 loss_visual: 0.0599 loss_lang: 0.1193 loss_fusion: 0.0515 loss: 0.2306 2022/10/06 02:27:31 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 02:28:12 - mmengine - INFO - Epoch(train) [15][800/10520] lr: 1.0000e-04 eta: 10:22:55 time: 0.3790 data_time: 0.0035 memory: 17203 loss_visual: 0.0545 loss_lang: 0.1178 loss_fusion: 0.0461 loss: 0.2184 2022/10/06 02:29:10 - mmengine - INFO - Epoch(train) [15][900/10520] lr: 1.0000e-04 eta: 10:21:54 time: 0.7860 data_time: 0.1819 memory: 17203 loss_visual: 0.0601 loss_lang: 0.1160 loss_fusion: 0.0514 loss: 0.2275 2022/10/06 02:30:05 - mmengine - INFO - Epoch(train) [15][1000/10520] lr: 1.0000e-04 eta: 10:20:52 time: 0.8426 data_time: 0.1666 memory: 17203 loss_visual: 0.0572 loss_lang: 0.1189 loss_fusion: 0.0479 loss: 0.2239 2022/10/06 02:30:59 - mmengine - INFO - Epoch(train) [15][1100/10520] lr: 1.0000e-04 eta: 10:19:50 time: 0.7086 data_time: 0.1571 memory: 17203 loss_visual: 0.0617 loss_lang: 0.1220 loss_fusion: 0.0528 loss: 0.2365 2022/10/06 02:31:53 - mmengine - INFO - Epoch(train) [15][1200/10520] lr: 1.0000e-04 eta: 10:18:47 time: 0.3887 data_time: 0.0117 memory: 17203 loss_visual: 0.0626 loss_lang: 0.1237 loss_fusion: 0.0548 loss: 0.2411 2022/10/06 02:32:46 - mmengine - INFO - Epoch(train) [15][1300/10520] lr: 1.0000e-04 eta: 10:17:45 time: 0.3941 data_time: 0.0114 memory: 17203 loss_visual: 0.0568 loss_lang: 0.1179 loss_fusion: 0.0479 loss: 0.2226 2022/10/06 02:33:41 - mmengine - INFO - Epoch(train) [15][1400/10520] lr: 1.0000e-04 eta: 10:16:42 time: 0.4220 data_time: 0.0118 memory: 17203 loss_visual: 0.0648 loss_lang: 0.1276 loss_fusion: 0.0564 loss: 0.2488 2022/10/06 02:34:35 - mmengine - INFO - Epoch(train) [15][1500/10520] lr: 1.0000e-04 eta: 10:15:40 time: 0.4052 data_time: 0.0118 memory: 17203 loss_visual: 0.0642 loss_lang: 0.1251 loss_fusion: 0.0553 loss: 0.2447 2022/10/06 02:35:28 - mmengine - INFO - Epoch(train) [15][1600/10520] lr: 1.0000e-04 eta: 10:14:37 time: 0.4020 data_time: 0.0037 memory: 17203 loss_visual: 0.0637 loss_lang: 0.1190 loss_fusion: 0.0535 loss: 0.2362 2022/10/06 02:36:27 - mmengine - INFO - Epoch(train) [15][1700/10520] lr: 1.0000e-04 eta: 10:13:37 time: 0.7542 data_time: 0.1727 memory: 17203 loss_visual: 0.0700 loss_lang: 0.1299 loss_fusion: 0.0603 loss: 0.2602 2022/10/06 02:36:37 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 02:37:23 - mmengine - INFO - Epoch(train) [15][1800/10520] lr: 1.0000e-04 eta: 10:12:36 time: 0.9068 data_time: 0.1702 memory: 17203 loss_visual: 0.0609 loss_lang: 0.1190 loss_fusion: 0.0517 loss: 0.2317 2022/10/06 02:38:17 - mmengine - INFO - Epoch(train) [15][1900/10520] lr: 1.0000e-04 eta: 10:11:33 time: 0.6822 data_time: 0.1451 memory: 17203 loss_visual: 0.0608 loss_lang: 0.1248 loss_fusion: 0.0511 loss: 0.2367 2022/10/06 02:39:12 - mmengine - INFO - Epoch(train) [15][2000/10520] lr: 1.0000e-04 eta: 10:10:31 time: 0.4249 data_time: 0.0422 memory: 17203 loss_visual: 0.0526 loss_lang: 0.1176 loss_fusion: 0.0459 loss: 0.2161 2022/10/06 02:40:06 - mmengine - INFO - Epoch(train) [15][2100/10520] lr: 1.0000e-04 eta: 10:09:29 time: 0.3976 data_time: 0.0117 memory: 17203 loss_visual: 0.0502 loss_lang: 0.1083 loss_fusion: 0.0425 loss: 0.2010 2022/10/06 02:40:59 - mmengine - INFO - Epoch(train) [15][2200/10520] lr: 1.0000e-04 eta: 10:08:26 time: 0.4214 data_time: 0.0137 memory: 17203 loss_visual: 0.0531 loss_lang: 0.1150 loss_fusion: 0.0458 loss: 0.2139 2022/10/06 02:41:55 - mmengine - INFO - Epoch(train) [15][2300/10520] lr: 1.0000e-04 eta: 10:07:24 time: 0.3879 data_time: 0.0115 memory: 17203 loss_visual: 0.0569 loss_lang: 0.1162 loss_fusion: 0.0479 loss: 0.2211 2022/10/06 02:42:48 - mmengine - INFO - Epoch(train) [15][2400/10520] lr: 1.0000e-04 eta: 10:06:22 time: 0.3902 data_time: 0.0033 memory: 17203 loss_visual: 0.0616 loss_lang: 0.1226 loss_fusion: 0.0537 loss: 0.2379 2022/10/06 02:43:47 - mmengine - INFO - Epoch(train) [15][2500/10520] lr: 1.0000e-04 eta: 10:05:21 time: 0.7390 data_time: 0.1669 memory: 17203 loss_visual: 0.0520 loss_lang: 0.1102 loss_fusion: 0.0429 loss: 0.2051 2022/10/06 02:44:42 - mmengine - INFO - Epoch(train) [15][2600/10520] lr: 1.0000e-04 eta: 10:04:19 time: 0.8777 data_time: 0.1695 memory: 17203 loss_visual: 0.0550 loss_lang: 0.1144 loss_fusion: 0.0458 loss: 0.2153 2022/10/06 02:45:36 - mmengine - INFO - Epoch(train) [15][2700/10520] lr: 1.0000e-04 eta: 10:03:17 time: 0.7148 data_time: 0.1404 memory: 17203 loss_visual: 0.0595 loss_lang: 0.1198 loss_fusion: 0.0520 loss: 0.2313 2022/10/06 02:45:44 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 02:46:30 - mmengine - INFO - Epoch(train) [15][2800/10520] lr: 1.0000e-04 eta: 10:02:15 time: 0.3919 data_time: 0.0117 memory: 17203 loss_visual: 0.0640 loss_lang: 0.1256 loss_fusion: 0.0558 loss: 0.2454 2022/10/06 02:47:24 - mmengine - INFO - Epoch(train) [15][2900/10520] lr: 1.0000e-04 eta: 10:01:13 time: 0.4468 data_time: 0.0378 memory: 17203 loss_visual: 0.0682 loss_lang: 0.1310 loss_fusion: 0.0601 loss: 0.2593 2022/10/06 02:48:18 - mmengine - INFO - Epoch(train) [15][3000/10520] lr: 1.0000e-04 eta: 10:00:10 time: 0.3989 data_time: 0.0113 memory: 17203 loss_visual: 0.0600 loss_lang: 0.1192 loss_fusion: 0.0512 loss: 0.2303 2022/10/06 02:49:12 - mmengine - INFO - Epoch(train) [15][3100/10520] lr: 1.0000e-04 eta: 9:59:08 time: 0.4052 data_time: 0.0114 memory: 17203 loss_visual: 0.0634 loss_lang: 0.1237 loss_fusion: 0.0554 loss: 0.2425 2022/10/06 02:50:06 - mmengine - INFO - Epoch(train) [15][3200/10520] lr: 1.0000e-04 eta: 9:58:06 time: 0.4078 data_time: 0.0033 memory: 17203 loss_visual: 0.0639 loss_lang: 0.1240 loss_fusion: 0.0553 loss: 0.2432 2022/10/06 02:51:03 - mmengine - INFO - Epoch(train) [15][3300/10520] lr: 1.0000e-04 eta: 9:57:05 time: 0.7379 data_time: 0.1705 memory: 17203 loss_visual: 0.0634 loss_lang: 0.1249 loss_fusion: 0.0558 loss: 0.2442 2022/10/06 02:51:59 - mmengine - INFO - Epoch(train) [15][3400/10520] lr: 1.0000e-04 eta: 9:56:03 time: 0.8736 data_time: 0.2100 memory: 17203 loss_visual: 0.0607 loss_lang: 0.1250 loss_fusion: 0.0503 loss: 0.2360 2022/10/06 02:52:52 - mmengine - INFO - Epoch(train) [15][3500/10520] lr: 1.0000e-04 eta: 9:55:01 time: 0.6821 data_time: 0.1424 memory: 17203 loss_visual: 0.0641 loss_lang: 0.1207 loss_fusion: 0.0564 loss: 0.2413 2022/10/06 02:53:46 - mmengine - INFO - Epoch(train) [15][3600/10520] lr: 1.0000e-04 eta: 9:53:58 time: 0.3892 data_time: 0.0122 memory: 17203 loss_visual: 0.0631 loss_lang: 0.1197 loss_fusion: 0.0543 loss: 0.2371 2022/10/06 02:54:39 - mmengine - INFO - Epoch(train) [15][3700/10520] lr: 1.0000e-04 eta: 9:52:56 time: 0.4006 data_time: 0.0121 memory: 17203 loss_visual: 0.0559 loss_lang: 0.1135 loss_fusion: 0.0465 loss: 0.2159 2022/10/06 02:54:52 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 02:55:33 - mmengine - INFO - Epoch(train) [15][3800/10520] lr: 1.0000e-04 eta: 9:51:54 time: 0.3866 data_time: 0.0122 memory: 17203 loss_visual: 0.0562 loss_lang: 0.1197 loss_fusion: 0.0471 loss: 0.2231 2022/10/06 02:56:27 - mmengine - INFO - Epoch(train) [15][3900/10520] lr: 1.0000e-04 eta: 9:50:52 time: 0.4086 data_time: 0.0117 memory: 17203 loss_visual: 0.0596 loss_lang: 0.1184 loss_fusion: 0.0515 loss: 0.2295 2022/10/06 02:57:20 - mmengine - INFO - Epoch(train) [15][4000/10520] lr: 1.0000e-04 eta: 9:49:49 time: 0.3856 data_time: 0.0037 memory: 17203 loss_visual: 0.0584 loss_lang: 0.1224 loss_fusion: 0.0514 loss: 0.2322 2022/10/06 02:58:18 - mmengine - INFO - Epoch(train) [15][4100/10520] lr: 1.0000e-04 eta: 9:48:48 time: 0.7096 data_time: 0.1716 memory: 17203 loss_visual: 0.0643 loss_lang: 0.1276 loss_fusion: 0.0578 loss: 0.2498 2022/10/06 02:59:13 - mmengine - INFO - Epoch(train) [15][4200/10520] lr: 1.0000e-04 eta: 9:47:47 time: 0.8857 data_time: 0.1647 memory: 17203 loss_visual: 0.0574 loss_lang: 0.1168 loss_fusion: 0.0490 loss: 0.2232 2022/10/06 03:00:08 - mmengine - INFO - Epoch(train) [15][4300/10520] lr: 1.0000e-04 eta: 9:46:45 time: 0.6861 data_time: 0.1490 memory: 17203 loss_visual: 0.0538 loss_lang: 0.1132 loss_fusion: 0.0460 loss: 0.2130 2022/10/06 03:01:02 - mmengine - INFO - Epoch(train) [15][4400/10520] lr: 1.0000e-04 eta: 9:45:43 time: 0.3844 data_time: 0.0109 memory: 17203 loss_visual: 0.0540 loss_lang: 0.1131 loss_fusion: 0.0458 loss: 0.2128 2022/10/06 03:01:56 - mmengine - INFO - Epoch(train) [15][4500/10520] lr: 1.0000e-04 eta: 9:44:41 time: 0.3989 data_time: 0.0107 memory: 17203 loss_visual: 0.0543 loss_lang: 0.1117 loss_fusion: 0.0462 loss: 0.2122 2022/10/06 03:02:50 - mmengine - INFO - Epoch(train) [15][4600/10520] lr: 1.0000e-04 eta: 9:43:39 time: 0.3793 data_time: 0.0120 memory: 17203 loss_visual: 0.0619 loss_lang: 0.1219 loss_fusion: 0.0532 loss: 0.2370 2022/10/06 03:03:44 - mmengine - INFO - Epoch(train) [15][4700/10520] lr: 1.0000e-04 eta: 9:42:36 time: 0.4227 data_time: 0.0118 memory: 17203 loss_visual: 0.0601 loss_lang: 0.1206 loss_fusion: 0.0524 loss: 0.2330 2022/10/06 03:03:57 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 03:04:38 - mmengine - INFO - Epoch(train) [15][4800/10520] lr: 1.0000e-04 eta: 9:41:34 time: 0.3874 data_time: 0.0034 memory: 17203 loss_visual: 0.0691 loss_lang: 0.1269 loss_fusion: 0.0598 loss: 0.2558 2022/10/06 03:05:34 - mmengine - INFO - Epoch(train) [15][4900/10520] lr: 1.0000e-04 eta: 9:40:33 time: 0.7295 data_time: 0.1630 memory: 17203 loss_visual: 0.0581 loss_lang: 0.1188 loss_fusion: 0.0495 loss: 0.2265 2022/10/06 03:06:30 - mmengine - INFO - Epoch(train) [15][5000/10520] lr: 1.0000e-04 eta: 9:39:32 time: 0.8852 data_time: 0.1717 memory: 17203 loss_visual: 0.0612 loss_lang: 0.1227 loss_fusion: 0.0528 loss: 0.2367 2022/10/06 03:07:24 - mmengine - INFO - Epoch(train) [15][5100/10520] lr: 1.0000e-04 eta: 9:38:30 time: 0.6798 data_time: 0.1181 memory: 17203 loss_visual: 0.0589 loss_lang: 0.1166 loss_fusion: 0.0511 loss: 0.2265 2022/10/06 03:08:18 - mmengine - INFO - Epoch(train) [15][5200/10520] lr: 1.0000e-04 eta: 9:37:27 time: 0.3999 data_time: 0.0128 memory: 17203 loss_visual: 0.0628 loss_lang: 0.1264 loss_fusion: 0.0563 loss: 0.2455 2022/10/06 03:09:12 - mmengine - INFO - Epoch(train) [15][5300/10520] lr: 1.0000e-04 eta: 9:36:26 time: 0.3982 data_time: 0.0113 memory: 17203 loss_visual: 0.0596 loss_lang: 0.1218 loss_fusion: 0.0516 loss: 0.2329 2022/10/06 03:10:06 - mmengine - INFO - Epoch(train) [15][5400/10520] lr: 1.0000e-04 eta: 9:35:23 time: 0.3885 data_time: 0.0121 memory: 17203 loss_visual: 0.0493 loss_lang: 0.1117 loss_fusion: 0.0426 loss: 0.2035 2022/10/06 03:11:00 - mmengine - INFO - Epoch(train) [15][5500/10520] lr: 1.0000e-04 eta: 9:34:22 time: 0.3922 data_time: 0.0116 memory: 17203 loss_visual: 0.0526 loss_lang: 0.1135 loss_fusion: 0.0444 loss: 0.2105 2022/10/06 03:11:54 - mmengine - INFO - Epoch(train) [15][5600/10520] lr: 1.0000e-04 eta: 9:33:20 time: 0.4483 data_time: 0.0044 memory: 17203 loss_visual: 0.0601 loss_lang: 0.1222 loss_fusion: 0.0520 loss: 0.2342 2022/10/06 03:12:52 - mmengine - INFO - Epoch(train) [15][5700/10520] lr: 1.0000e-04 eta: 9:32:19 time: 0.7211 data_time: 0.1797 memory: 17203 loss_visual: 0.0549 loss_lang: 0.1139 loss_fusion: 0.0470 loss: 0.2158 2022/10/06 03:13:01 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 03:13:47 - mmengine - INFO - Epoch(train) [15][5800/10520] lr: 1.0000e-04 eta: 9:31:17 time: 0.8579 data_time: 0.1909 memory: 17203 loss_visual: 0.0536 loss_lang: 0.1174 loss_fusion: 0.0466 loss: 0.2176 2022/10/06 03:14:41 - mmengine - INFO - Epoch(train) [15][5900/10520] lr: 1.0000e-04 eta: 9:30:15 time: 0.6631 data_time: 0.1520 memory: 17203 loss_visual: 0.0527 loss_lang: 0.1126 loss_fusion: 0.0447 loss: 0.2101 2022/10/06 03:15:35 - mmengine - INFO - Epoch(train) [15][6000/10520] lr: 1.0000e-04 eta: 9:29:13 time: 0.3869 data_time: 0.0133 memory: 17203 loss_visual: 0.0516 loss_lang: 0.1110 loss_fusion: 0.0446 loss: 0.2071 2022/10/06 03:16:29 - mmengine - INFO - Epoch(train) [15][6100/10520] lr: 1.0000e-04 eta: 9:28:12 time: 0.4276 data_time: 0.0120 memory: 17203 loss_visual: 0.0600 loss_lang: 0.1189 loss_fusion: 0.0520 loss: 0.2308 2022/10/06 03:17:22 - mmengine - INFO - Epoch(train) [15][6200/10520] lr: 1.0000e-04 eta: 9:27:09 time: 0.3906 data_time: 0.0144 memory: 17203 loss_visual: 0.0542 loss_lang: 0.1150 loss_fusion: 0.0468 loss: 0.2160 2022/10/06 03:18:16 - mmengine - INFO - Epoch(train) [15][6300/10520] lr: 1.0000e-04 eta: 9:26:07 time: 0.4036 data_time: 0.0112 memory: 17203 loss_visual: 0.0637 loss_lang: 0.1256 loss_fusion: 0.0561 loss: 0.2454 2022/10/06 03:19:10 - mmengine - INFO - Epoch(train) [15][6400/10520] lr: 1.0000e-04 eta: 9:25:05 time: 0.4102 data_time: 0.0037 memory: 17203 loss_visual: 0.0593 loss_lang: 0.1201 loss_fusion: 0.0512 loss: 0.2306 2022/10/06 03:20:08 - mmengine - INFO - Epoch(train) [15][6500/10520] lr: 1.0000e-04 eta: 9:24:05 time: 0.7647 data_time: 0.1762 memory: 17203 loss_visual: 0.0522 loss_lang: 0.1133 loss_fusion: 0.0451 loss: 0.2107 2022/10/06 03:21:03 - mmengine - INFO - Epoch(train) [15][6600/10520] lr: 1.0000e-04 eta: 9:23:03 time: 0.8810 data_time: 0.1795 memory: 17203 loss_visual: 0.0575 loss_lang: 0.1182 loss_fusion: 0.0492 loss: 0.2248 2022/10/06 03:21:57 - mmengine - INFO - Epoch(train) [15][6700/10520] lr: 1.0000e-04 eta: 9:22:02 time: 0.6896 data_time: 0.1425 memory: 17203 loss_visual: 0.0560 loss_lang: 0.1169 loss_fusion: 0.0477 loss: 0.2206 2022/10/06 03:22:05 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 03:22:52 - mmengine - INFO - Epoch(train) [15][6800/10520] lr: 1.0000e-04 eta: 9:21:00 time: 0.3899 data_time: 0.0111 memory: 17203 loss_visual: 0.0581 loss_lang: 0.1160 loss_fusion: 0.0500 loss: 0.2241 2022/10/06 03:23:45 - mmengine - INFO - Epoch(train) [15][6900/10520] lr: 1.0000e-04 eta: 9:19:58 time: 0.4120 data_time: 0.0250 memory: 17203 loss_visual: 0.0627 loss_lang: 0.1245 loss_fusion: 0.0561 loss: 0.2432 2022/10/06 03:24:39 - mmengine - INFO - Epoch(train) [15][7000/10520] lr: 1.0000e-04 eta: 9:18:56 time: 0.4107 data_time: 0.0118 memory: 17203 loss_visual: 0.0641 loss_lang: 0.1206 loss_fusion: 0.0553 loss: 0.2399 2022/10/06 03:25:33 - mmengine - INFO - Epoch(train) [15][7100/10520] lr: 1.0000e-04 eta: 9:17:54 time: 0.4126 data_time: 0.0123 memory: 17203 loss_visual: 0.0589 loss_lang: 0.1158 loss_fusion: 0.0495 loss: 0.2242 2022/10/06 03:26:26 - mmengine - INFO - Epoch(train) [15][7200/10520] lr: 1.0000e-04 eta: 9:16:52 time: 0.3866 data_time: 0.0037 memory: 17203 loss_visual: 0.0646 loss_lang: 0.1273 loss_fusion: 0.0565 loss: 0.2484 2022/10/06 03:27:23 - mmengine - INFO - Epoch(train) [15][7300/10520] lr: 1.0000e-04 eta: 9:15:51 time: 0.7227 data_time: 0.1714 memory: 17203 loss_visual: 0.0553 loss_lang: 0.1175 loss_fusion: 0.0481 loss: 0.2210 2022/10/06 03:28:18 - mmengine - INFO - Epoch(train) [15][7400/10520] lr: 1.0000e-04 eta: 9:14:50 time: 0.8928 data_time: 0.2088 memory: 17203 loss_visual: 0.0531 loss_lang: 0.1125 loss_fusion: 0.0450 loss: 0.2106 2022/10/06 03:29:13 - mmengine - INFO - Epoch(train) [15][7500/10520] lr: 1.0000e-04 eta: 9:13:48 time: 0.6939 data_time: 0.1234 memory: 17203 loss_visual: 0.0575 loss_lang: 0.1166 loss_fusion: 0.0490 loss: 0.2232 2022/10/06 03:30:06 - mmengine - INFO - Epoch(train) [15][7600/10520] lr: 1.0000e-04 eta: 9:12:46 time: 0.4014 data_time: 0.0121 memory: 17203 loss_visual: 0.0517 loss_lang: 0.1127 loss_fusion: 0.0434 loss: 0.2078 2022/10/06 03:31:01 - mmengine - INFO - Epoch(train) [15][7700/10520] lr: 1.0000e-04 eta: 9:11:44 time: 0.3948 data_time: 0.0133 memory: 17203 loss_visual: 0.0668 loss_lang: 0.1251 loss_fusion: 0.0570 loss: 0.2489 2022/10/06 03:31:13 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 03:31:54 - mmengine - INFO - Epoch(train) [15][7800/10520] lr: 1.0000e-04 eta: 9:10:42 time: 0.4078 data_time: 0.0109 memory: 17203 loss_visual: 0.0595 loss_lang: 0.1182 loss_fusion: 0.0505 loss: 0.2282 2022/10/06 03:32:50 - mmengine - INFO - Epoch(train) [15][7900/10520] lr: 1.0000e-04 eta: 9:09:41 time: 0.4328 data_time: 0.0153 memory: 17203 loss_visual: 0.0583 loss_lang: 0.1220 loss_fusion: 0.0505 loss: 0.2309 2022/10/06 03:33:44 - mmengine - INFO - Epoch(train) [15][8000/10520] lr: 1.0000e-04 eta: 9:08:40 time: 0.4402 data_time: 0.0043 memory: 17203 loss_visual: 0.0602 loss_lang: 0.1180 loss_fusion: 0.0522 loss: 0.2304 2022/10/06 03:34:43 - mmengine - INFO - Epoch(train) [15][8100/10520] lr: 1.0000e-04 eta: 9:07:39 time: 0.7363 data_time: 0.1743 memory: 17203 loss_visual: 0.0567 loss_lang: 0.1118 loss_fusion: 0.0488 loss: 0.2173 2022/10/06 03:35:38 - mmengine - INFO - Epoch(train) [15][8200/10520] lr: 1.0000e-04 eta: 9:06:38 time: 0.9163 data_time: 0.1679 memory: 17203 loss_visual: 0.0564 loss_lang: 0.1128 loss_fusion: 0.0482 loss: 0.2173 2022/10/06 03:36:33 - mmengine - INFO - Epoch(train) [15][8300/10520] lr: 1.0000e-04 eta: 9:05:37 time: 0.6939 data_time: 0.1596 memory: 17203 loss_visual: 0.0574 loss_lang: 0.1144 loss_fusion: 0.0502 loss: 0.2220 2022/10/06 03:37:27 - mmengine - INFO - Epoch(train) [15][8400/10520] lr: 1.0000e-04 eta: 9:04:35 time: 0.4058 data_time: 0.0311 memory: 17203 loss_visual: 0.0608 loss_lang: 0.1196 loss_fusion: 0.0531 loss: 0.2335 2022/10/06 03:38:20 - mmengine - INFO - Epoch(train) [15][8500/10520] lr: 1.0000e-04 eta: 9:03:33 time: 0.4079 data_time: 0.0134 memory: 17203 loss_visual: 0.0605 loss_lang: 0.1235 loss_fusion: 0.0527 loss: 0.2367 2022/10/06 03:39:14 - mmengine - INFO - Epoch(train) [15][8600/10520] lr: 1.0000e-04 eta: 9:02:31 time: 0.3847 data_time: 0.0115 memory: 17203 loss_visual: 0.0577 loss_lang: 0.1211 loss_fusion: 0.0486 loss: 0.2274 2022/10/06 03:40:08 - mmengine - INFO - Epoch(train) [15][8700/10520] lr: 1.0000e-04 eta: 9:01:30 time: 0.4174 data_time: 0.0122 memory: 17203 loss_visual: 0.0622 loss_lang: 0.1187 loss_fusion: 0.0540 loss: 0.2348 2022/10/06 03:40:21 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 03:41:02 - mmengine - INFO - Epoch(train) [15][8800/10520] lr: 1.0000e-04 eta: 9:00:28 time: 0.3872 data_time: 0.0036 memory: 17203 loss_visual: 0.0527 loss_lang: 0.1139 loss_fusion: 0.0459 loss: 0.2126 2022/10/06 03:41:59 - mmengine - INFO - Epoch(train) [15][8900/10520] lr: 1.0000e-04 eta: 8:59:27 time: 0.7654 data_time: 0.1748 memory: 17203 loss_visual: 0.0615 loss_lang: 0.1264 loss_fusion: 0.0526 loss: 0.2405 2022/10/06 03:42:54 - mmengine - INFO - Epoch(train) [15][9000/10520] lr: 1.0000e-04 eta: 8:58:26 time: 0.8724 data_time: 0.1804 memory: 17203 loss_visual: 0.0574 loss_lang: 0.1179 loss_fusion: 0.0499 loss: 0.2252 2022/10/06 03:43:49 - mmengine - INFO - Epoch(train) [15][9100/10520] lr: 1.0000e-04 eta: 8:57:24 time: 0.6980 data_time: 0.1429 memory: 17203 loss_visual: 0.0543 loss_lang: 0.1149 loss_fusion: 0.0463 loss: 0.2156 2022/10/06 03:44:43 - mmengine - INFO - Epoch(train) [15][9200/10520] lr: 1.0000e-04 eta: 8:56:23 time: 0.3853 data_time: 0.0139 memory: 17203 loss_visual: 0.0571 loss_lang: 0.1164 loss_fusion: 0.0497 loss: 0.2232 2022/10/06 03:45:37 - mmengine - INFO - Epoch(train) [15][9300/10520] lr: 1.0000e-04 eta: 8:55:21 time: 0.4323 data_time: 0.0224 memory: 17203 loss_visual: 0.0601 loss_lang: 0.1215 loss_fusion: 0.0519 loss: 0.2335 2022/10/06 03:46:30 - mmengine - INFO - Epoch(train) [15][9400/10520] lr: 1.0000e-04 eta: 8:54:19 time: 0.3900 data_time: 0.0148 memory: 17203 loss_visual: 0.0559 loss_lang: 0.1147 loss_fusion: 0.0480 loss: 0.2186 2022/10/06 03:47:26 - mmengine - INFO - Epoch(train) [15][9500/10520] lr: 1.0000e-04 eta: 8:53:18 time: 0.3986 data_time: 0.0139 memory: 17203 loss_visual: 0.0569 loss_lang: 0.1142 loss_fusion: 0.0484 loss: 0.2195 2022/10/06 03:48:19 - mmengine - INFO - Epoch(train) [15][9600/10520] lr: 1.0000e-04 eta: 8:52:16 time: 0.3858 data_time: 0.0039 memory: 17203 loss_visual: 0.0571 loss_lang: 0.1202 loss_fusion: 0.0496 loss: 0.2269 2022/10/06 03:49:17 - mmengine - INFO - Epoch(train) [15][9700/10520] lr: 1.0000e-04 eta: 8:51:16 time: 0.7416 data_time: 0.1774 memory: 17203 loss_visual: 0.0552 loss_lang: 0.1145 loss_fusion: 0.0461 loss: 0.2159 2022/10/06 03:49:26 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 03:50:12 - mmengine - INFO - Epoch(train) [15][9800/10520] lr: 1.0000e-04 eta: 8:50:15 time: 0.8580 data_time: 0.1812 memory: 17203 loss_visual: 0.0630 loss_lang: 0.1190 loss_fusion: 0.0548 loss: 0.2367 2022/10/06 03:51:05 - mmengine - INFO - Epoch(train) [15][9900/10520] lr: 1.0000e-04 eta: 8:49:13 time: 0.6848 data_time: 0.1222 memory: 17203 loss_visual: 0.0534 loss_lang: 0.1132 loss_fusion: 0.0453 loss: 0.2119 2022/10/06 03:52:00 - mmengine - INFO - Epoch(train) [15][10000/10520] lr: 1.0000e-04 eta: 8:48:12 time: 0.3862 data_time: 0.0126 memory: 17203 loss_visual: 0.0536 loss_lang: 0.1137 loss_fusion: 0.0462 loss: 0.2135 2022/10/06 03:52:53 - mmengine - INFO - Epoch(train) [15][10100/10520] lr: 1.0000e-04 eta: 8:47:10 time: 0.4140 data_time: 0.0134 memory: 17203 loss_visual: 0.0580 loss_lang: 0.1182 loss_fusion: 0.0504 loss: 0.2266 2022/10/06 03:53:47 - mmengine - INFO - Epoch(train) [15][10200/10520] lr: 1.0000e-04 eta: 8:46:08 time: 0.3859 data_time: 0.0131 memory: 17203 loss_visual: 0.0533 loss_lang: 0.1151 loss_fusion: 0.0454 loss: 0.2138 2022/10/06 03:54:41 - mmengine - INFO - Epoch(train) [15][10300/10520] lr: 1.0000e-04 eta: 8:45:07 time: 0.3903 data_time: 0.0134 memory: 17203 loss_visual: 0.0675 loss_lang: 0.1286 loss_fusion: 0.0599 loss: 0.2561 2022/10/06 03:55:35 - mmengine - INFO - Epoch(train) [15][10400/10520] lr: 1.0000e-04 eta: 8:44:05 time: 0.4095 data_time: 0.0043 memory: 17203 loss_visual: 0.0556 loss_lang: 0.1150 loss_fusion: 0.0472 loss: 0.2177 2022/10/06 03:56:30 - mmengine - INFO - Epoch(train) [15][10500/10520] lr: 1.0000e-04 eta: 8:43:04 time: 0.5880 data_time: 0.1147 memory: 17203 loss_visual: 0.0513 loss_lang: 0.1116 loss_fusion: 0.0436 loss: 0.2065 2022/10/06 03:56:38 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 03:56:38 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/10/06 03:56:53 - mmengine - INFO - Epoch(val) [15][100/959] eta: 0:00:42 time: 0.0500 data_time: 0.0015 memory: 17203 2022/10/06 03:56:58 - mmengine - INFO - Epoch(val) [15][200/959] eta: 0:00:36 time: 0.0477 data_time: 0.0023 memory: 734 2022/10/06 03:57:03 - mmengine - INFO - Epoch(val) [15][300/959] eta: 0:00:30 time: 0.0467 data_time: 0.0019 memory: 734 2022/10/06 03:57:08 - mmengine - INFO - Epoch(val) [15][400/959] eta: 0:00:27 time: 0.0488 data_time: 0.0018 memory: 734 2022/10/06 03:57:12 - mmengine - INFO - Epoch(val) [15][500/959] eta: 0:00:19 time: 0.0436 data_time: 0.0015 memory: 734 2022/10/06 03:57:17 - mmengine - INFO - Epoch(val) [15][600/959] eta: 0:00:15 time: 0.0429 data_time: 0.0011 memory: 734 2022/10/06 03:57:22 - mmengine - INFO - Epoch(val) [15][700/959] eta: 0:00:12 time: 0.0482 data_time: 0.0031 memory: 734 2022/10/06 03:57:26 - mmengine - INFO - Epoch(val) [15][800/959] eta: 0:00:04 time: 0.0281 data_time: 0.0034 memory: 734 2022/10/06 03:57:28 - mmengine - INFO - Epoch(val) [15][900/959] eta: 0:00:01 time: 0.0212 data_time: 0.0005 memory: 734 2022/10/06 03:57:30 - mmengine - INFO - Epoch(val) [15][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.8611 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9497 SVT/recog/word_acc_ignore_case_symbol: 0.9274 SVTP/recog/word_acc_ignore_case_symbol: 0.8822 IC13/recog/word_acc_ignore_case_symbol: 0.9478 IC15/recog/word_acc_ignore_case_symbol: 0.8045 2022/10/06 03:58:32 - mmengine - INFO - Epoch(train) [16][100/10520] lr: 1.0000e-04 eta: 8:41:52 time: 0.7902 data_time: 0.2213 memory: 17203 loss_visual: 0.0521 loss_lang: 0.1168 loss_fusion: 0.0453 loss: 0.2142 2022/10/06 03:59:27 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 03:59:27 - mmengine - INFO - Epoch(train) [16][200/10520] lr: 1.0000e-04 eta: 8:40:50 time: 0.8643 data_time: 0.2355 memory: 17203 loss_visual: 0.0559 loss_lang: 0.1176 loss_fusion: 0.0480 loss: 0.2215 2022/10/06 04:00:21 - mmengine - INFO - Epoch(train) [16][300/10520] lr: 1.0000e-04 eta: 8:39:49 time: 0.5708 data_time: 0.0060 memory: 17203 loss_visual: 0.0625 loss_lang: 0.1247 loss_fusion: 0.0551 loss: 0.2422 2022/10/06 04:01:16 - mmengine - INFO - Epoch(train) [16][400/10520] lr: 1.0000e-04 eta: 8:38:48 time: 0.4160 data_time: 0.0205 memory: 17203 loss_visual: 0.0581 loss_lang: 0.1179 loss_fusion: 0.0507 loss: 0.2267 2022/10/06 04:02:12 - mmengine - INFO - Epoch(train) [16][500/10520] lr: 1.0000e-04 eta: 8:37:47 time: 0.4737 data_time: 0.0160 memory: 17203 loss_visual: 0.0539 loss_lang: 0.1132 loss_fusion: 0.0455 loss: 0.2127 2022/10/06 04:03:06 - mmengine - INFO - Epoch(train) [16][600/10520] lr: 1.0000e-04 eta: 8:36:45 time: 0.4340 data_time: 0.0218 memory: 17203 loss_visual: 0.0540 loss_lang: 0.1116 loss_fusion: 0.0471 loss: 0.2127 2022/10/06 04:03:59 - mmengine - INFO - Epoch(train) [16][700/10520] lr: 1.0000e-04 eta: 8:35:44 time: 0.3626 data_time: 0.0051 memory: 17203 loss_visual: 0.0580 loss_lang: 0.1161 loss_fusion: 0.0495 loss: 0.2237 2022/10/06 04:04:54 - mmengine - INFO - Epoch(train) [16][800/10520] lr: 1.0000e-04 eta: 8:34:42 time: 0.3955 data_time: 0.0046 memory: 17203 loss_visual: 0.0648 loss_lang: 0.1215 loss_fusion: 0.0566 loss: 0.2428 2022/10/06 04:05:52 - mmengine - INFO - Epoch(train) [16][900/10520] lr: 1.0000e-04 eta: 8:33:42 time: 0.7784 data_time: 0.2451 memory: 17203 loss_visual: 0.0592 loss_lang: 0.1253 loss_fusion: 0.0534 loss: 0.2378 2022/10/06 04:06:48 - mmengine - INFO - Epoch(train) [16][1000/10520] lr: 1.0000e-04 eta: 8:32:41 time: 0.8107 data_time: 0.1815 memory: 17203 loss_visual: 0.0552 loss_lang: 0.1146 loss_fusion: 0.0478 loss: 0.2177 2022/10/06 04:07:42 - mmengine - INFO - Epoch(train) [16][1100/10520] lr: 1.0000e-04 eta: 8:31:40 time: 0.6110 data_time: 0.0050 memory: 17203 loss_visual: 0.0635 loss_lang: 0.1218 loss_fusion: 0.0549 loss: 0.2403 2022/10/06 04:08:37 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 04:08:37 - mmengine - INFO - Epoch(train) [16][1200/10520] lr: 1.0000e-04 eta: 8:30:39 time: 0.4291 data_time: 0.0189 memory: 17203 loss_visual: 0.0688 loss_lang: 0.1313 loss_fusion: 0.0596 loss: 0.2598 2022/10/06 04:09:33 - mmengine - INFO - Epoch(train) [16][1300/10520] lr: 1.0000e-04 eta: 8:29:38 time: 0.4713 data_time: 0.0189 memory: 17203 loss_visual: 0.0560 loss_lang: 0.1138 loss_fusion: 0.0486 loss: 0.2184 2022/10/06 04:10:28 - mmengine - INFO - Epoch(train) [16][1400/10520] lr: 1.0000e-04 eta: 8:28:37 time: 0.4323 data_time: 0.0230 memory: 17203 loss_visual: 0.0606 loss_lang: 0.1197 loss_fusion: 0.0518 loss: 0.2321 2022/10/06 04:11:23 - mmengine - INFO - Epoch(train) [16][1500/10520] lr: 1.0000e-04 eta: 8:27:36 time: 0.3569 data_time: 0.0046 memory: 17203 loss_visual: 0.0538 loss_lang: 0.1167 loss_fusion: 0.0457 loss: 0.2163 2022/10/06 04:12:17 - mmengine - INFO - Epoch(train) [16][1600/10520] lr: 1.0000e-04 eta: 8:26:34 time: 0.3615 data_time: 0.0045 memory: 17203 loss_visual: 0.0629 loss_lang: 0.1230 loss_fusion: 0.0542 loss: 0.2401 2022/10/06 04:13:15 - mmengine - INFO - Epoch(train) [16][1700/10520] lr: 1.0000e-04 eta: 8:25:34 time: 0.7511 data_time: 0.2025 memory: 17203 loss_visual: 0.0501 loss_lang: 0.1105 loss_fusion: 0.0426 loss: 0.2032 2022/10/06 04:14:10 - mmengine - INFO - Epoch(train) [16][1800/10520] lr: 1.0000e-04 eta: 8:24:33 time: 0.8350 data_time: 0.1959 memory: 17203 loss_visual: 0.0514 loss_lang: 0.1093 loss_fusion: 0.0438 loss: 0.2046 2022/10/06 04:15:05 - mmengine - INFO - Epoch(train) [16][1900/10520] lr: 1.0000e-04 eta: 8:23:32 time: 0.5461 data_time: 0.0049 memory: 17203 loss_visual: 0.0584 loss_lang: 0.1247 loss_fusion: 0.0513 loss: 0.2344 2022/10/06 04:16:01 - mmengine - INFO - Epoch(train) [16][2000/10520] lr: 1.0000e-04 eta: 8:22:32 time: 0.4297 data_time: 0.0269 memory: 17203 loss_visual: 0.0619 loss_lang: 0.1213 loss_fusion: 0.0539 loss: 0.2371 2022/10/06 04:16:56 - mmengine - INFO - Epoch(train) [16][2100/10520] lr: 1.0000e-04 eta: 8:21:30 time: 0.4656 data_time: 0.0259 memory: 17203 loss_visual: 0.0623 loss_lang: 0.1231 loss_fusion: 0.0548 loss: 0.2402 2022/10/06 04:17:50 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 04:17:50 - mmengine - INFO - Epoch(train) [16][2200/10520] lr: 1.0000e-04 eta: 8:20:29 time: 0.4150 data_time: 0.0256 memory: 17203 loss_visual: 0.0580 loss_lang: 0.1140 loss_fusion: 0.0491 loss: 0.2211 2022/10/06 04:18:44 - mmengine - INFO - Epoch(train) [16][2300/10520] lr: 1.0000e-04 eta: 8:19:28 time: 0.3474 data_time: 0.0049 memory: 17203 loss_visual: 0.0537 loss_lang: 0.1161 loss_fusion: 0.0466 loss: 0.2164 2022/10/06 04:19:38 - mmengine - INFO - Epoch(train) [16][2400/10520] lr: 1.0000e-04 eta: 8:18:26 time: 0.3653 data_time: 0.0047 memory: 17203 loss_visual: 0.0554 loss_lang: 0.1169 loss_fusion: 0.0474 loss: 0.2196 2022/10/06 04:20:36 - mmengine - INFO - Epoch(train) [16][2500/10520] lr: 1.0000e-04 eta: 8:17:26 time: 0.7614 data_time: 0.1798 memory: 17203 loss_visual: 0.0611 loss_lang: 0.1211 loss_fusion: 0.0528 loss: 0.2350 2022/10/06 04:21:32 - mmengine - INFO - Epoch(train) [16][2600/10520] lr: 1.0000e-04 eta: 8:16:26 time: 0.8710 data_time: 0.2076 memory: 17203 loss_visual: 0.0590 loss_lang: 0.1260 loss_fusion: 0.0512 loss: 0.2361 2022/10/06 04:22:26 - mmengine - INFO - Epoch(train) [16][2700/10520] lr: 1.0000e-04 eta: 8:15:24 time: 0.5737 data_time: 0.0047 memory: 17203 loss_visual: 0.0576 loss_lang: 0.1164 loss_fusion: 0.0500 loss: 0.2240 2022/10/06 04:23:22 - mmengine - INFO - Epoch(train) [16][2800/10520] lr: 1.0000e-04 eta: 8:14:24 time: 0.4435 data_time: 0.0246 memory: 17203 loss_visual: 0.0569 loss_lang: 0.1132 loss_fusion: 0.0479 loss: 0.2179 2022/10/06 04:24:16 - mmengine - INFO - Epoch(train) [16][2900/10520] lr: 1.0000e-04 eta: 8:13:22 time: 0.4912 data_time: 0.0272 memory: 17203 loss_visual: 0.0623 loss_lang: 0.1223 loss_fusion: 0.0531 loss: 0.2376 2022/10/06 04:25:10 - mmengine - INFO - Epoch(train) [16][3000/10520] lr: 1.0000e-04 eta: 8:12:21 time: 0.4361 data_time: 0.0285 memory: 17203 loss_visual: 0.0530 loss_lang: 0.1130 loss_fusion: 0.0457 loss: 0.2117 2022/10/06 04:26:06 - mmengine - INFO - Epoch(train) [16][3100/10520] lr: 1.0000e-04 eta: 8:11:20 time: 0.3696 data_time: 0.0048 memory: 17203 loss_visual: 0.0656 loss_lang: 0.1252 loss_fusion: 0.0565 loss: 0.2473 2022/10/06 04:27:01 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 04:27:01 - mmengine - INFO - Epoch(train) [16][3200/10520] lr: 1.0000e-04 eta: 8:10:19 time: 0.3841 data_time: 0.0052 memory: 17203 loss_visual: 0.0551 loss_lang: 0.1144 loss_fusion: 0.0480 loss: 0.2174 2022/10/06 04:28:00 - mmengine - INFO - Epoch(train) [16][3300/10520] lr: 1.0000e-04 eta: 8:09:20 time: 0.7846 data_time: 0.2004 memory: 17203 loss_visual: 0.0598 loss_lang: 0.1247 loss_fusion: 0.0513 loss: 0.2358 2022/10/06 04:28:55 - mmengine - INFO - Epoch(train) [16][3400/10520] lr: 1.0000e-04 eta: 8:08:19 time: 0.8403 data_time: 0.2144 memory: 17203 loss_visual: 0.0582 loss_lang: 0.1144 loss_fusion: 0.0503 loss: 0.2229 2022/10/06 04:29:50 - mmengine - INFO - Epoch(train) [16][3500/10520] lr: 1.0000e-04 eta: 8:07:18 time: 0.6266 data_time: 0.0050 memory: 17203 loss_visual: 0.0613 loss_lang: 0.1201 loss_fusion: 0.0529 loss: 0.2343 2022/10/06 04:30:45 - mmengine - INFO - Epoch(train) [16][3600/10520] lr: 1.0000e-04 eta: 8:06:17 time: 0.4431 data_time: 0.0320 memory: 17203 loss_visual: 0.0568 loss_lang: 0.1206 loss_fusion: 0.0494 loss: 0.2267 2022/10/06 04:31:40 - mmengine - INFO - Epoch(train) [16][3700/10520] lr: 1.0000e-04 eta: 8:05:16 time: 0.5382 data_time: 0.0620 memory: 17203 loss_visual: 0.0533 loss_lang: 0.1104 loss_fusion: 0.0448 loss: 0.2084 2022/10/06 04:32:34 - mmengine - INFO - Epoch(train) [16][3800/10520] lr: 1.0000e-04 eta: 8:04:15 time: 0.4482 data_time: 0.0300 memory: 17203 loss_visual: 0.0631 loss_lang: 0.1219 loss_fusion: 0.0544 loss: 0.2394 2022/10/06 04:33:28 - mmengine - INFO - Epoch(train) [16][3900/10520] lr: 1.0000e-04 eta: 8:03:14 time: 0.3495 data_time: 0.0046 memory: 17203 loss_visual: 0.0547 loss_lang: 0.1142 loss_fusion: 0.0469 loss: 0.2158 2022/10/06 04:34:23 - mmengine - INFO - Epoch(train) [16][4000/10520] lr: 1.0000e-04 eta: 8:02:13 time: 0.3701 data_time: 0.0047 memory: 17203 loss_visual: 0.0572 loss_lang: 0.1201 loss_fusion: 0.0505 loss: 0.2279 2022/10/06 04:35:21 - mmengine - INFO - Epoch(train) [16][4100/10520] lr: 1.0000e-04 eta: 8:01:13 time: 0.7241 data_time: 0.2199 memory: 17203 loss_visual: 0.0615 loss_lang: 0.1217 loss_fusion: 0.0532 loss: 0.2365 2022/10/06 04:36:16 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 04:36:16 - mmengine - INFO - Epoch(train) [16][4200/10520] lr: 1.0000e-04 eta: 8:00:12 time: 0.8088 data_time: 0.2027 memory: 17203 loss_visual: 0.0619 loss_lang: 0.1202 loss_fusion: 0.0538 loss: 0.2360 2022/10/06 04:37:11 - mmengine - INFO - Epoch(train) [16][4300/10520] lr: 1.0000e-04 eta: 7:59:11 time: 0.5665 data_time: 0.0053 memory: 17203 loss_visual: 0.0565 loss_lang: 0.1142 loss_fusion: 0.0476 loss: 0.2183 2022/10/06 04:38:07 - mmengine - INFO - Epoch(train) [16][4400/10520] lr: 1.0000e-04 eta: 7:58:10 time: 0.4857 data_time: 0.0343 memory: 17203 loss_visual: 0.0625 loss_lang: 0.1217 loss_fusion: 0.0543 loss: 0.2385 2022/10/06 04:39:03 - mmengine - INFO - Epoch(train) [16][4500/10520] lr: 1.0000e-04 eta: 7:57:10 time: 0.4986 data_time: 0.0363 memory: 17203 loss_visual: 0.0570 loss_lang: 0.1189 loss_fusion: 0.0491 loss: 0.2250 2022/10/06 04:39:58 - mmengine - INFO - Epoch(train) [16][4600/10520] lr: 1.0000e-04 eta: 7:56:09 time: 0.4471 data_time: 0.0316 memory: 17203 loss_visual: 0.0585 loss_lang: 0.1185 loss_fusion: 0.0509 loss: 0.2279 2022/10/06 04:40:53 - mmengine - INFO - Epoch(train) [16][4700/10520] lr: 1.0000e-04 eta: 7:55:08 time: 0.3750 data_time: 0.0049 memory: 17203 loss_visual: 0.0459 loss_lang: 0.1064 loss_fusion: 0.0395 loss: 0.1918 2022/10/06 04:41:47 - mmengine - INFO - Epoch(train) [16][4800/10520] lr: 1.0000e-04 eta: 7:54:07 time: 0.3608 data_time: 0.0047 memory: 17203 loss_visual: 0.0549 loss_lang: 0.1123 loss_fusion: 0.0460 loss: 0.2133 2022/10/06 04:42:45 - mmengine - INFO - Epoch(train) [16][4900/10520] lr: 1.0000e-04 eta: 7:53:07 time: 0.7218 data_time: 0.1992 memory: 17203 loss_visual: 0.0604 loss_lang: 0.1154 loss_fusion: 0.0526 loss: 0.2284 2022/10/06 04:43:41 - mmengine - INFO - Epoch(train) [16][5000/10520] lr: 1.0000e-04 eta: 7:52:06 time: 0.8230 data_time: 0.2054 memory: 17203 loss_visual: 0.0573 loss_lang: 0.1185 loss_fusion: 0.0495 loss: 0.2253 2022/10/06 04:44:36 - mmengine - INFO - Epoch(train) [16][5100/10520] lr: 1.0000e-04 eta: 7:51:05 time: 0.6019 data_time: 0.0042 memory: 17203 loss_visual: 0.0533 loss_lang: 0.1168 loss_fusion: 0.0458 loss: 0.2160 2022/10/06 04:45:31 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 04:45:31 - mmengine - INFO - Epoch(train) [16][5200/10520] lr: 1.0000e-04 eta: 7:50:05 time: 0.4608 data_time: 0.0601 memory: 17203 loss_visual: 0.0592 loss_lang: 0.1188 loss_fusion: 0.0520 loss: 0.2300 2022/10/06 04:46:27 - mmengine - INFO - Epoch(train) [16][5300/10520] lr: 1.0000e-04 eta: 7:49:04 time: 0.5108 data_time: 0.0785 memory: 17203 loss_visual: 0.0579 loss_lang: 0.1173 loss_fusion: 0.0509 loss: 0.2260 2022/10/06 04:47:22 - mmengine - INFO - Epoch(train) [16][5400/10520] lr: 1.0000e-04 eta: 7:48:03 time: 0.4763 data_time: 0.0787 memory: 17203 loss_visual: 0.0557 loss_lang: 0.1169 loss_fusion: 0.0475 loss: 0.2201 2022/10/06 04:48:17 - mmengine - INFO - Epoch(train) [16][5500/10520] lr: 1.0000e-04 eta: 7:47:02 time: 0.3424 data_time: 0.0049 memory: 17203 loss_visual: 0.0556 loss_lang: 0.1124 loss_fusion: 0.0478 loss: 0.2157 2022/10/06 04:49:11 - mmengine - INFO - Epoch(train) [16][5600/10520] lr: 1.0000e-04 eta: 7:46:01 time: 0.3899 data_time: 0.0046 memory: 17203 loss_visual: 0.0546 loss_lang: 0.1172 loss_fusion: 0.0457 loss: 0.2175 2022/10/06 04:50:11 - mmengine - INFO - Epoch(train) [16][5700/10520] lr: 1.0000e-04 eta: 7:45:02 time: 0.7825 data_time: 0.2218 memory: 17203 loss_visual: 0.0617 loss_lang: 0.1215 loss_fusion: 0.0539 loss: 0.2371 2022/10/06 04:51:07 - mmengine - INFO - Epoch(train) [16][5800/10520] lr: 1.0000e-04 eta: 7:44:02 time: 0.8144 data_time: 0.1563 memory: 17203 loss_visual: 0.0551 loss_lang: 0.1180 loss_fusion: 0.0482 loss: 0.2212 2022/10/06 04:52:02 - mmengine - INFO - Epoch(train) [16][5900/10520] lr: 1.0000e-04 eta: 7:43:01 time: 0.5882 data_time: 0.0046 memory: 17203 loss_visual: 0.0599 loss_lang: 0.1184 loss_fusion: 0.0527 loss: 0.2310 2022/10/06 04:52:56 - mmengine - INFO - Epoch(train) [16][6000/10520] lr: 1.0000e-04 eta: 7:42:00 time: 0.4398 data_time: 0.0281 memory: 17203 loss_visual: 0.0563 loss_lang: 0.1158 loss_fusion: 0.0488 loss: 0.2208 2022/10/06 04:53:51 - mmengine - INFO - Epoch(train) [16][6100/10520] lr: 1.0000e-04 eta: 7:40:59 time: 0.5043 data_time: 0.0303 memory: 17203 loss_visual: 0.0603 loss_lang: 0.1220 loss_fusion: 0.0525 loss: 0.2348 2022/10/06 04:54:44 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 04:54:44 - mmengine - INFO - Epoch(train) [16][6200/10520] lr: 1.0000e-04 eta: 7:39:58 time: 0.4611 data_time: 0.0413 memory: 17203 loss_visual: 0.0520 loss_lang: 0.1067 loss_fusion: 0.0437 loss: 0.2024 2022/10/06 04:55:38 - mmengine - INFO - Epoch(train) [16][6300/10520] lr: 1.0000e-04 eta: 7:38:56 time: 0.3469 data_time: 0.0034 memory: 17203 loss_visual: 0.0521 loss_lang: 0.1112 loss_fusion: 0.0448 loss: 0.2081 2022/10/06 04:56:32 - mmengine - INFO - Epoch(train) [16][6400/10520] lr: 1.0000e-04 eta: 7:37:56 time: 0.3837 data_time: 0.0033 memory: 17203 loss_visual: 0.0518 loss_lang: 0.1086 loss_fusion: 0.0443 loss: 0.2047 2022/10/06 04:57:29 - mmengine - INFO - Epoch(train) [16][6500/10520] lr: 1.0000e-04 eta: 7:36:55 time: 0.7474 data_time: 0.1809 memory: 17203 loss_visual: 0.0542 loss_lang: 0.1097 loss_fusion: 0.0473 loss: 0.2113 2022/10/06 04:58:24 - mmengine - INFO - Epoch(train) [16][6600/10520] lr: 1.0000e-04 eta: 7:35:55 time: 0.8357 data_time: 0.1747 memory: 17203 loss_visual: 0.0579 loss_lang: 0.1181 loss_fusion: 0.0487 loss: 0.2246 2022/10/06 04:59:18 - mmengine - INFO - Epoch(train) [16][6700/10520] lr: 1.0000e-04 eta: 7:34:54 time: 0.5959 data_time: 0.0038 memory: 17203 loss_visual: 0.0561 loss_lang: 0.1155 loss_fusion: 0.0478 loss: 0.2195 2022/10/06 05:00:13 - mmengine - INFO - Epoch(train) [16][6800/10520] lr: 1.0000e-04 eta: 7:33:53 time: 0.4297 data_time: 0.0362 memory: 17203 loss_visual: 0.0607 loss_lang: 0.1171 loss_fusion: 0.0525 loss: 0.2303 2022/10/06 05:01:07 - mmengine - INFO - Epoch(train) [16][6900/10520] lr: 1.0000e-04 eta: 7:32:52 time: 0.4682 data_time: 0.0250 memory: 17203 loss_visual: 0.0563 loss_lang: 0.1191 loss_fusion: 0.0500 loss: 0.2254 2022/10/06 05:02:02 - mmengine - INFO - Epoch(train) [16][7000/10520] lr: 1.0000e-04 eta: 7:31:51 time: 0.4487 data_time: 0.0507 memory: 17203 loss_visual: 0.0563 loss_lang: 0.1162 loss_fusion: 0.0480 loss: 0.2205 2022/10/06 05:02:55 - mmengine - INFO - Epoch(train) [16][7100/10520] lr: 1.0000e-04 eta: 7:30:50 time: 0.3775 data_time: 0.0035 memory: 17203 loss_visual: 0.0563 loss_lang: 0.1157 loss_fusion: 0.0501 loss: 0.2222 2022/10/06 05:03:50 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 05:03:50 - mmengine - INFO - Epoch(train) [16][7200/10520] lr: 1.0000e-04 eta: 7:29:49 time: 0.3965 data_time: 0.0032 memory: 17203 loss_visual: 0.0632 loss_lang: 0.1242 loss_fusion: 0.0559 loss: 0.2434 2022/10/06 05:04:47 - mmengine - INFO - Epoch(train) [16][7300/10520] lr: 1.0000e-04 eta: 7:28:49 time: 0.7347 data_time: 0.1873 memory: 17203 loss_visual: 0.0621 loss_lang: 0.1205 loss_fusion: 0.0540 loss: 0.2367 2022/10/06 05:05:42 - mmengine - INFO - Epoch(train) [16][7400/10520] lr: 1.0000e-04 eta: 7:27:48 time: 0.7971 data_time: 0.1809 memory: 17203 loss_visual: 0.0545 loss_lang: 0.1166 loss_fusion: 0.0476 loss: 0.2187 2022/10/06 05:06:36 - mmengine - INFO - Epoch(train) [16][7500/10520] lr: 1.0000e-04 eta: 7:26:48 time: 0.5992 data_time: 0.0035 memory: 17203 loss_visual: 0.0557 loss_lang: 0.1169 loss_fusion: 0.0483 loss: 0.2209 2022/10/06 05:07:31 - mmengine - INFO - Epoch(train) [16][7600/10520] lr: 1.0000e-04 eta: 7:25:47 time: 0.4823 data_time: 0.0264 memory: 17203 loss_visual: 0.0595 loss_lang: 0.1183 loss_fusion: 0.0515 loss: 0.2293 2022/10/06 05:08:25 - mmengine - INFO - Epoch(train) [16][7700/10520] lr: 1.0000e-04 eta: 7:24:46 time: 0.4739 data_time: 0.0329 memory: 17203 loss_visual: 0.0585 loss_lang: 0.1177 loss_fusion: 0.0496 loss: 0.2258 2022/10/06 05:09:20 - mmengine - INFO - Epoch(train) [16][7800/10520] lr: 1.0000e-04 eta: 7:23:45 time: 0.4276 data_time: 0.0279 memory: 17203 loss_visual: 0.0487 loss_lang: 0.1077 loss_fusion: 0.0398 loss: 0.1961 2022/10/06 05:10:14 - mmengine - INFO - Epoch(train) [16][7900/10520] lr: 1.0000e-04 eta: 7:22:44 time: 0.3480 data_time: 0.0033 memory: 17203 loss_visual: 0.0649 loss_lang: 0.1243 loss_fusion: 0.0552 loss: 0.2443 2022/10/06 05:11:08 - mmengine - INFO - Epoch(train) [16][8000/10520] lr: 1.0000e-04 eta: 7:21:43 time: 0.3614 data_time: 0.0033 memory: 17203 loss_visual: 0.0550 loss_lang: 0.1151 loss_fusion: 0.0473 loss: 0.2174 2022/10/06 05:12:05 - mmengine - INFO - Epoch(train) [16][8100/10520] lr: 1.0000e-04 eta: 7:20:43 time: 0.7097 data_time: 0.1543 memory: 17203 loss_visual: 0.0513 loss_lang: 0.1112 loss_fusion: 0.0444 loss: 0.2069 2022/10/06 05:13:00 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 05:13:00 - mmengine - INFO - Epoch(train) [16][8200/10520] lr: 1.0000e-04 eta: 7:19:43 time: 0.7809 data_time: 0.1700 memory: 17203 loss_visual: 0.0530 loss_lang: 0.1129 loss_fusion: 0.0466 loss: 0.2126 2022/10/06 05:13:55 - mmengine - INFO - Epoch(train) [16][8300/10520] lr: 1.0000e-04 eta: 7:18:42 time: 0.5727 data_time: 0.0034 memory: 17203 loss_visual: 0.0580 loss_lang: 0.1166 loss_fusion: 0.0501 loss: 0.2247 2022/10/06 05:14:49 - mmengine - INFO - Epoch(train) [16][8400/10520] lr: 1.0000e-04 eta: 7:17:41 time: 0.4401 data_time: 0.0250 memory: 17203 loss_visual: 0.0555 loss_lang: 0.1173 loss_fusion: 0.0470 loss: 0.2199 2022/10/06 05:15:44 - mmengine - INFO - Epoch(train) [16][8500/10520] lr: 1.0000e-04 eta: 7:16:41 time: 0.4992 data_time: 0.0255 memory: 17203 loss_visual: 0.0638 loss_lang: 0.1230 loss_fusion: 0.0559 loss: 0.2427 2022/10/06 05:16:37 - mmengine - INFO - Epoch(train) [16][8600/10520] lr: 1.0000e-04 eta: 7:15:40 time: 0.4600 data_time: 0.0293 memory: 17203 loss_visual: 0.0689 loss_lang: 0.1253 loss_fusion: 0.0610 loss: 0.2552 2022/10/06 05:17:32 - mmengine - INFO - Epoch(train) [16][8700/10520] lr: 1.0000e-04 eta: 7:14:39 time: 0.3455 data_time: 0.0037 memory: 17203 loss_visual: 0.0533 loss_lang: 0.1142 loss_fusion: 0.0448 loss: 0.2123 2022/10/06 05:18:26 - mmengine - INFO - Epoch(train) [16][8800/10520] lr: 1.0000e-04 eta: 7:13:38 time: 0.3817 data_time: 0.0047 memory: 17203 loss_visual: 0.0458 loss_lang: 0.1054 loss_fusion: 0.0383 loss: 0.1894 2022/10/06 05:19:24 - mmengine - INFO - Epoch(train) [16][8900/10520] lr: 1.0000e-04 eta: 7:12:38 time: 0.7376 data_time: 0.1801 memory: 17203 loss_visual: 0.0653 loss_lang: 0.1209 loss_fusion: 0.0560 loss: 0.2421 2022/10/06 05:20:20 - mmengine - INFO - Epoch(train) [16][9000/10520] lr: 1.0000e-04 eta: 7:11:38 time: 0.8299 data_time: 0.1680 memory: 17203 loss_visual: 0.0488 loss_lang: 0.1028 loss_fusion: 0.0410 loss: 0.1925 2022/10/06 05:21:14 - mmengine - INFO - Epoch(train) [16][9100/10520] lr: 1.0000e-04 eta: 7:10:37 time: 0.5659 data_time: 0.0032 memory: 17203 loss_visual: 0.0608 loss_lang: 0.1191 loss_fusion: 0.0532 loss: 0.2331 2022/10/06 05:22:09 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 05:22:09 - mmengine - INFO - Epoch(train) [16][9200/10520] lr: 1.0000e-04 eta: 7:09:37 time: 0.4918 data_time: 0.0264 memory: 17203 loss_visual: 0.0540 loss_lang: 0.1123 loss_fusion: 0.0467 loss: 0.2130 2022/10/06 05:23:16 - mmengine - INFO - Epoch(train) [16][9300/10520] lr: 1.0000e-04 eta: 7:08:39 time: 0.5712 data_time: 0.0262 memory: 17203 loss_visual: 0.0564 loss_lang: 0.1144 loss_fusion: 0.0497 loss: 0.2205 2022/10/06 05:24:13 - mmengine - INFO - Epoch(train) [16][9400/10520] lr: 1.0000e-04 eta: 7:07:39 time: 0.4372 data_time: 0.0454 memory: 17203 loss_visual: 0.0622 loss_lang: 0.1206 loss_fusion: 0.0548 loss: 0.2376 2022/10/06 05:25:08 - mmengine - INFO - Epoch(train) [16][9500/10520] lr: 1.0000e-04 eta: 7:06:39 time: 0.3548 data_time: 0.0033 memory: 17203 loss_visual: 0.0621 loss_lang: 0.1292 loss_fusion: 0.0534 loss: 0.2447 2022/10/06 05:26:05 - mmengine - INFO - Epoch(train) [16][9600/10520] lr: 1.0000e-04 eta: 7:05:39 time: 0.3586 data_time: 0.0032 memory: 17203 loss_visual: 0.0552 loss_lang: 0.1157 loss_fusion: 0.0476 loss: 0.2185 2022/10/06 05:27:02 - mmengine - INFO - Epoch(train) [16][9700/10520] lr: 1.0000e-04 eta: 7:04:39 time: 0.7301 data_time: 0.1752 memory: 17203 loss_visual: 0.0577 loss_lang: 0.1185 loss_fusion: 0.0496 loss: 0.2258 2022/10/06 05:27:57 - mmengine - INFO - Epoch(train) [16][9800/10520] lr: 1.0000e-04 eta: 7:03:38 time: 0.7846 data_time: 0.1814 memory: 17203 loss_visual: 0.0586 loss_lang: 0.1220 loss_fusion: 0.0497 loss: 0.2303 2022/10/06 05:28:52 - mmengine - INFO - Epoch(train) [16][9900/10520] lr: 1.0000e-04 eta: 7:02:38 time: 0.5988 data_time: 0.0034 memory: 17203 loss_visual: 0.0606 loss_lang: 0.1194 loss_fusion: 0.0507 loss: 0.2307 2022/10/06 05:29:45 - mmengine - INFO - Epoch(train) [16][10000/10520] lr: 1.0000e-04 eta: 7:01:37 time: 0.4326 data_time: 0.0299 memory: 17203 loss_visual: 0.0532 loss_lang: 0.1142 loss_fusion: 0.0452 loss: 0.2126 2022/10/06 05:30:40 - mmengine - INFO - Epoch(train) [16][10100/10520] lr: 1.0000e-04 eta: 7:00:36 time: 0.4749 data_time: 0.0261 memory: 17203 loss_visual: 0.0589 loss_lang: 0.1177 loss_fusion: 0.0503 loss: 0.2268 2022/10/06 05:31:34 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 05:31:34 - mmengine - INFO - Epoch(train) [16][10200/10520] lr: 1.0000e-04 eta: 6:59:36 time: 0.4338 data_time: 0.0258 memory: 17203 loss_visual: 0.0551 loss_lang: 0.1209 loss_fusion: 0.0479 loss: 0.2239 2022/10/06 05:32:29 - mmengine - INFO - Epoch(train) [16][10300/10520] lr: 1.0000e-04 eta: 6:58:35 time: 0.3529 data_time: 0.0045 memory: 17203 loss_visual: 0.0532 loss_lang: 0.1087 loss_fusion: 0.0457 loss: 0.2076 2022/10/06 05:33:24 - mmengine - INFO - Epoch(train) [16][10400/10520] lr: 1.0000e-04 eta: 6:57:34 time: 0.3819 data_time: 0.0031 memory: 17203 loss_visual: 0.0541 loss_lang: 0.1140 loss_fusion: 0.0473 loss: 0.2154 2022/10/06 05:34:19 - mmengine - INFO - Epoch(train) [16][10500/10520] lr: 1.0000e-04 eta: 6:56:34 time: 0.5862 data_time: 0.1035 memory: 17203 loss_visual: 0.0508 loss_lang: 0.1073 loss_fusion: 0.0434 loss: 0.2015 2022/10/06 05:34:27 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 05:34:27 - mmengine - INFO - Saving checkpoint at 16 epochs 2022/10/06 05:34:45 - mmengine - INFO - Epoch(val) [16][100/959] eta: 0:00:36 time: 0.0429 data_time: 0.0009 memory: 17203 2022/10/06 05:34:50 - mmengine - INFO - Epoch(val) [16][200/959] eta: 0:00:33 time: 0.0442 data_time: 0.0010 memory: 734 2022/10/06 05:34:55 - mmengine - INFO - Epoch(val) [16][300/959] eta: 0:00:30 time: 0.0466 data_time: 0.0017 memory: 734 2022/10/06 05:35:00 - mmengine - INFO - Epoch(val) [16][400/959] eta: 0:00:25 time: 0.0462 data_time: 0.0020 memory: 734 2022/10/06 05:35:04 - mmengine - INFO - Epoch(val) [16][500/959] eta: 0:00:20 time: 0.0448 data_time: 0.0022 memory: 734 2022/10/06 05:35:09 - mmengine - INFO - Epoch(val) [16][600/959] eta: 0:00:15 time: 0.0435 data_time: 0.0017 memory: 734 2022/10/06 05:35:14 - mmengine - INFO - Epoch(val) [16][700/959] eta: 0:00:08 time: 0.0330 data_time: 0.0009 memory: 734 2022/10/06 05:35:16 - mmengine - INFO - Epoch(val) [16][800/959] eta: 0:00:03 time: 0.0214 data_time: 0.0006 memory: 734 2022/10/06 05:35:19 - mmengine - INFO - Epoch(val) [16][900/959] eta: 0:00:01 time: 0.0223 data_time: 0.0005 memory: 734 2022/10/06 05:35:21 - mmengine - INFO - Epoch(val) [16][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.8750 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9517 SVT/recog/word_acc_ignore_case_symbol: 0.9351 SVTP/recog/word_acc_ignore_case_symbol: 0.8837 IC13/recog/word_acc_ignore_case_symbol: 0.9537 IC15/recog/word_acc_ignore_case_symbol: 0.8036 2022/10/06 05:36:24 - mmengine - INFO - Epoch(train) [17][100/10520] lr: 1.0000e-05 eta: 6:55:23 time: 0.7804 data_time: 0.1636 memory: 17203 loss_visual: 0.0534 loss_lang: 0.1128 loss_fusion: 0.0464 loss: 0.2126 2022/10/06 05:37:21 - mmengine - INFO - Epoch(train) [17][200/10520] lr: 1.0000e-05 eta: 6:54:23 time: 0.8976 data_time: 0.1874 memory: 17203 loss_visual: 0.0551 loss_lang: 0.1115 loss_fusion: 0.0463 loss: 0.2129 2022/10/06 05:38:16 - mmengine - INFO - Epoch(train) [17][300/10520] lr: 1.0000e-05 eta: 6:53:22 time: 0.6588 data_time: 0.1422 memory: 17203 loss_visual: 0.0480 loss_lang: 0.1067 loss_fusion: 0.0410 loss: 0.1958 2022/10/06 05:39:12 - mmengine - INFO - Epoch(train) [17][400/10520] lr: 1.0000e-05 eta: 6:52:22 time: 0.4260 data_time: 0.0034 memory: 17203 loss_visual: 0.0489 loss_lang: 0.1056 loss_fusion: 0.0421 loss: 0.1966 2022/10/06 05:40:07 - mmengine - INFO - Epoch(train) [17][500/10520] lr: 1.0000e-05 eta: 6:51:22 time: 0.4477 data_time: 0.0034 memory: 17203 loss_visual: 0.0519 loss_lang: 0.1098 loss_fusion: 0.0446 loss: 0.2063 2022/10/06 05:41:02 - mmengine - INFO - Epoch(train) [17][600/10520] lr: 1.0000e-05 eta: 6:50:21 time: 0.3978 data_time: 0.0038 memory: 17203 loss_visual: 0.0563 loss_lang: 0.1122 loss_fusion: 0.0496 loss: 0.2181 2022/10/06 05:41:50 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 05:41:58 - mmengine - INFO - Epoch(train) [17][700/10520] lr: 1.0000e-05 eta: 6:49:21 time: 0.3685 data_time: 0.0033 memory: 17203 loss_visual: 0.0528 loss_lang: 0.1155 loss_fusion: 0.0458 loss: 0.2141 2022/10/06 05:42:54 - mmengine - INFO - Epoch(train) [17][800/10520] lr: 1.0000e-05 eta: 6:48:21 time: 0.3620 data_time: 0.0031 memory: 17203 loss_visual: 0.0540 loss_lang: 0.1093 loss_fusion: 0.0457 loss: 0.2089 2022/10/06 05:43:52 - mmengine - INFO - Epoch(train) [17][900/10520] lr: 1.0000e-05 eta: 6:47:21 time: 0.8188 data_time: 0.1924 memory: 17203 loss_visual: 0.0515 loss_lang: 0.1125 loss_fusion: 0.0428 loss: 0.2068 2022/10/06 05:44:48 - mmengine - INFO - Epoch(train) [17][1000/10520] lr: 1.0000e-05 eta: 6:46:21 time: 0.8460 data_time: 0.1858 memory: 17203 loss_visual: 0.0516 loss_lang: 0.1085 loss_fusion: 0.0445 loss: 0.2046 2022/10/06 05:45:43 - mmengine - INFO - Epoch(train) [17][1100/10520] lr: 1.0000e-05 eta: 6:45:21 time: 0.6816 data_time: 0.1513 memory: 17203 loss_visual: 0.0517 loss_lang: 0.1064 loss_fusion: 0.0436 loss: 0.2016 2022/10/06 05:46:37 - mmengine - INFO - Epoch(train) [17][1200/10520] lr: 1.0000e-05 eta: 6:44:20 time: 0.4538 data_time: 0.0033 memory: 17203 loss_visual: 0.0510 loss_lang: 0.1087 loss_fusion: 0.0430 loss: 0.2026 2022/10/06 05:47:32 - mmengine - INFO - Epoch(train) [17][1300/10520] lr: 1.0000e-05 eta: 6:43:19 time: 0.4075 data_time: 0.0031 memory: 17203 loss_visual: 0.0579 loss_lang: 0.1120 loss_fusion: 0.0501 loss: 0.2200 2022/10/06 05:48:26 - mmengine - INFO - Epoch(train) [17][1400/10520] lr: 1.0000e-05 eta: 6:42:19 time: 0.3902 data_time: 0.0032 memory: 17203 loss_visual: 0.0517 loss_lang: 0.1118 loss_fusion: 0.0441 loss: 0.2076 2022/10/06 05:49:20 - mmengine - INFO - Epoch(train) [17][1500/10520] lr: 1.0000e-05 eta: 6:41:18 time: 0.3781 data_time: 0.0033 memory: 17203 loss_visual: 0.0518 loss_lang: 0.1123 loss_fusion: 0.0446 loss: 0.2087 2022/10/06 05:50:15 - mmengine - INFO - Epoch(train) [17][1600/10520] lr: 1.0000e-05 eta: 6:40:18 time: 0.3634 data_time: 0.0036 memory: 17203 loss_visual: 0.0501 loss_lang: 0.1094 loss_fusion: 0.0437 loss: 0.2033 2022/10/06 05:51:02 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 05:51:14 - mmengine - INFO - Epoch(train) [17][1700/10520] lr: 1.0000e-05 eta: 6:39:18 time: 0.7847 data_time: 0.1814 memory: 17203 loss_visual: 0.0456 loss_lang: 0.1027 loss_fusion: 0.0392 loss: 0.1875 2022/10/06 05:52:10 - mmengine - INFO - Epoch(train) [17][1800/10520] lr: 1.0000e-05 eta: 6:38:18 time: 0.9062 data_time: 0.1858 memory: 17203 loss_visual: 0.0544 loss_lang: 0.1130 loss_fusion: 0.0474 loss: 0.2148 2022/10/06 05:53:05 - mmengine - INFO - Epoch(train) [17][1900/10520] lr: 1.0000e-05 eta: 6:37:18 time: 0.6452 data_time: 0.1346 memory: 17203 loss_visual: 0.0575 loss_lang: 0.1143 loss_fusion: 0.0497 loss: 0.2215 2022/10/06 05:54:00 - mmengine - INFO - Epoch(train) [17][2000/10520] lr: 1.0000e-05 eta: 6:36:18 time: 0.4605 data_time: 0.0034 memory: 17203 loss_visual: 0.0540 loss_lang: 0.1074 loss_fusion: 0.0461 loss: 0.2076 2022/10/06 05:54:55 - mmengine - INFO - Epoch(train) [17][2100/10520] lr: 1.0000e-05 eta: 6:35:17 time: 0.4341 data_time: 0.0031 memory: 17203 loss_visual: 0.0539 loss_lang: 0.1129 loss_fusion: 0.0469 loss: 0.2137 2022/10/06 05:55:49 - mmengine - INFO - Epoch(train) [17][2200/10520] lr: 1.0000e-05 eta: 6:34:17 time: 0.3827 data_time: 0.0034 memory: 17203 loss_visual: 0.0553 loss_lang: 0.1116 loss_fusion: 0.0478 loss: 0.2148 2022/10/06 05:56:45 - mmengine - INFO - Epoch(train) [17][2300/10520] lr: 1.0000e-05 eta: 6:33:17 time: 0.3685 data_time: 0.0038 memory: 17203 loss_visual: 0.0479 loss_lang: 0.1103 loss_fusion: 0.0408 loss: 0.1990 2022/10/06 05:57:39 - mmengine - INFO - Epoch(train) [17][2400/10520] lr: 1.0000e-05 eta: 6:32:16 time: 0.3682 data_time: 0.0031 memory: 17203 loss_visual: 0.0540 loss_lang: 0.1093 loss_fusion: 0.0457 loss: 0.2090 2022/10/06 05:58:38 - mmengine - INFO - Epoch(train) [17][2500/10520] lr: 1.0000e-05 eta: 6:31:17 time: 0.8265 data_time: 0.1923 memory: 17203 loss_visual: 0.0531 loss_lang: 0.1081 loss_fusion: 0.0468 loss: 0.2080 2022/10/06 05:59:35 - mmengine - INFO - Epoch(train) [17][2600/10520] lr: 1.0000e-05 eta: 6:30:17 time: 0.8687 data_time: 0.1854 memory: 17203 loss_visual: 0.0458 loss_lang: 0.1059 loss_fusion: 0.0394 loss: 0.1911 2022/10/06 06:00:20 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 06:00:33 - mmengine - INFO - Epoch(train) [17][2700/10520] lr: 1.0000e-05 eta: 6:29:17 time: 0.6910 data_time: 0.1285 memory: 17203 loss_visual: 0.0504 loss_lang: 0.1061 loss_fusion: 0.0426 loss: 0.1991 2022/10/06 06:01:27 - mmengine - INFO - Epoch(train) [17][2800/10520] lr: 1.0000e-05 eta: 6:28:17 time: 0.4313 data_time: 0.0036 memory: 17203 loss_visual: 0.0519 loss_lang: 0.1104 loss_fusion: 0.0441 loss: 0.2065 2022/10/06 06:02:22 - mmengine - INFO - Epoch(train) [17][2900/10520] lr: 1.0000e-05 eta: 6:27:16 time: 0.4176 data_time: 0.0032 memory: 17203 loss_visual: 0.0526 loss_lang: 0.1107 loss_fusion: 0.0444 loss: 0.2077 2022/10/06 06:03:17 - mmengine - INFO - Epoch(train) [17][3000/10520] lr: 1.0000e-05 eta: 6:26:16 time: 0.3819 data_time: 0.0032 memory: 17203 loss_visual: 0.0500 loss_lang: 0.1070 loss_fusion: 0.0421 loss: 0.1991 2022/10/06 06:04:11 - mmengine - INFO - Epoch(train) [17][3100/10520] lr: 1.0000e-05 eta: 6:25:16 time: 0.3760 data_time: 0.0032 memory: 17203 loss_visual: 0.0487 loss_lang: 0.1062 loss_fusion: 0.0408 loss: 0.1957 2022/10/06 06:05:06 - mmengine - INFO - Epoch(train) [17][3200/10520] lr: 1.0000e-05 eta: 6:24:15 time: 0.3855 data_time: 0.0029 memory: 17203 loss_visual: 0.0423 loss_lang: 0.1002 loss_fusion: 0.0351 loss: 0.1776 2022/10/06 06:06:04 - mmengine - INFO - Epoch(train) [17][3300/10520] lr: 1.0000e-05 eta: 6:23:16 time: 0.8257 data_time: 0.1855 memory: 17203 loss_visual: 0.0549 loss_lang: 0.1125 loss_fusion: 0.0465 loss: 0.2139 2022/10/06 06:07:01 - mmengine - INFO - Epoch(train) [17][3400/10520] lr: 1.0000e-05 eta: 6:22:16 time: 0.8355 data_time: 0.1874 memory: 17203 loss_visual: 0.0458 loss_lang: 0.1012 loss_fusion: 0.0391 loss: 0.1862 2022/10/06 06:07:56 - mmengine - INFO - Epoch(train) [17][3500/10520] lr: 1.0000e-05 eta: 6:21:16 time: 0.7239 data_time: 0.1610 memory: 17203 loss_visual: 0.0599 loss_lang: 0.1211 loss_fusion: 0.0517 loss: 0.2327 2022/10/06 06:08:51 - mmengine - INFO - Epoch(train) [17][3600/10520] lr: 1.0000e-05 eta: 6:20:15 time: 0.4388 data_time: 0.0036 memory: 17203 loss_visual: 0.0528 loss_lang: 0.1085 loss_fusion: 0.0443 loss: 0.2056 2022/10/06 06:09:32 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 06:09:45 - mmengine - INFO - Epoch(train) [17][3700/10520] lr: 1.0000e-05 eta: 6:19:15 time: 0.4312 data_time: 0.0031 memory: 17203 loss_visual: 0.0595 loss_lang: 0.1201 loss_fusion: 0.0504 loss: 0.2300 2022/10/06 06:10:40 - mmengine - INFO - Epoch(train) [17][3800/10520] lr: 1.0000e-05 eta: 6:18:15 time: 0.4043 data_time: 0.0034 memory: 17203 loss_visual: 0.0428 loss_lang: 0.0985 loss_fusion: 0.0350 loss: 0.1763 2022/10/06 06:11:34 - mmengine - INFO - Epoch(train) [17][3900/10520] lr: 1.0000e-05 eta: 6:17:14 time: 0.3688 data_time: 0.0033 memory: 17203 loss_visual: 0.0549 loss_lang: 0.1094 loss_fusion: 0.0470 loss: 0.2113 2022/10/06 06:12:28 - mmengine - INFO - Epoch(train) [17][4000/10520] lr: 1.0000e-05 eta: 6:16:14 time: 0.3673 data_time: 0.0033 memory: 17203 loss_visual: 0.0514 loss_lang: 0.1020 loss_fusion: 0.0426 loss: 0.1960 2022/10/06 06:13:27 - mmengine - INFO - Epoch(train) [17][4100/10520] lr: 1.0000e-05 eta: 6:15:14 time: 0.7865 data_time: 0.1754 memory: 17203 loss_visual: 0.0452 loss_lang: 0.1014 loss_fusion: 0.0379 loss: 0.1844 2022/10/06 06:14:23 - mmengine - INFO - Epoch(train) [17][4200/10520] lr: 1.0000e-05 eta: 6:14:14 time: 0.9178 data_time: 0.1883 memory: 17203 loss_visual: 0.0482 loss_lang: 0.1030 loss_fusion: 0.0400 loss: 0.1911 2022/10/06 06:15:18 - mmengine - INFO - Epoch(train) [17][4300/10520] lr: 1.0000e-05 eta: 6:13:14 time: 0.6932 data_time: 0.1445 memory: 17203 loss_visual: 0.0488 loss_lang: 0.1112 loss_fusion: 0.0435 loss: 0.2035 2022/10/06 06:16:12 - mmengine - INFO - Epoch(train) [17][4400/10520] lr: 1.0000e-05 eta: 6:12:14 time: 0.4453 data_time: 0.0032 memory: 17203 loss_visual: 0.0517 loss_lang: 0.1093 loss_fusion: 0.0446 loss: 0.2056 2022/10/06 06:17:07 - mmengine - INFO - Epoch(train) [17][4500/10520] lr: 1.0000e-05 eta: 6:11:13 time: 0.4075 data_time: 0.0032 memory: 17203 loss_visual: 0.0516 loss_lang: 0.1129 loss_fusion: 0.0442 loss: 0.2087 2022/10/06 06:18:02 - mmengine - INFO - Epoch(train) [17][4600/10520] lr: 1.0000e-05 eta: 6:10:13 time: 0.4155 data_time: 0.0033 memory: 17203 loss_visual: 0.0483 loss_lang: 0.1066 loss_fusion: 0.0405 loss: 0.1954 2022/10/06 06:18:49 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 06:18:57 - mmengine - INFO - Epoch(train) [17][4700/10520] lr: 1.0000e-05 eta: 6:09:13 time: 0.3709 data_time: 0.0032 memory: 17203 loss_visual: 0.0494 loss_lang: 0.1051 loss_fusion: 0.0424 loss: 0.1969 2022/10/06 06:19:51 - mmengine - INFO - Epoch(train) [17][4800/10520] lr: 1.0000e-05 eta: 6:08:13 time: 0.3805 data_time: 0.0061 memory: 17203 loss_visual: 0.0490 loss_lang: 0.1067 loss_fusion: 0.0412 loss: 0.1969 2022/10/06 06:20:50 - mmengine - INFO - Epoch(train) [17][4900/10520] lr: 1.0000e-05 eta: 6:07:13 time: 0.7955 data_time: 0.1889 memory: 17203 loss_visual: 0.0636 loss_lang: 0.1241 loss_fusion: 0.0562 loss: 0.2438 2022/10/06 06:21:46 - mmengine - INFO - Epoch(train) [17][5000/10520] lr: 1.0000e-05 eta: 6:06:13 time: 0.8256 data_time: 0.1881 memory: 17203 loss_visual: 0.0534 loss_lang: 0.1080 loss_fusion: 0.0457 loss: 0.2071 2022/10/06 06:22:41 - mmengine - INFO - Epoch(train) [17][5100/10520] lr: 1.0000e-05 eta: 6:05:13 time: 0.6623 data_time: 0.1431 memory: 17203 loss_visual: 0.0517 loss_lang: 0.1123 loss_fusion: 0.0442 loss: 0.2081 2022/10/06 06:23:37 - mmengine - INFO - Epoch(train) [17][5200/10520] lr: 1.0000e-05 eta: 6:04:13 time: 0.4217 data_time: 0.0032 memory: 17203 loss_visual: 0.0490 loss_lang: 0.1072 loss_fusion: 0.0405 loss: 0.1967 2022/10/06 06:24:32 - mmengine - INFO - Epoch(train) [17][5300/10520] lr: 1.0000e-05 eta: 6:03:13 time: 0.4090 data_time: 0.0034 memory: 17203 loss_visual: 0.0507 loss_lang: 0.1121 loss_fusion: 0.0438 loss: 0.2065 2022/10/06 06:25:26 - mmengine - INFO - Epoch(train) [17][5400/10520] lr: 1.0000e-05 eta: 6:02:13 time: 0.3836 data_time: 0.0032 memory: 17203 loss_visual: 0.0458 loss_lang: 0.1018 loss_fusion: 0.0375 loss: 0.1851 2022/10/06 06:26:22 - mmengine - INFO - Epoch(train) [17][5500/10520] lr: 1.0000e-05 eta: 6:01:13 time: 0.3920 data_time: 0.0034 memory: 17203 loss_visual: 0.0448 loss_lang: 0.0993 loss_fusion: 0.0386 loss: 0.1827 2022/10/06 06:27:16 - mmengine - INFO - Epoch(train) [17][5600/10520] lr: 1.0000e-05 eta: 6:00:12 time: 0.3672 data_time: 0.0035 memory: 17203 loss_visual: 0.0523 loss_lang: 0.1071 loss_fusion: 0.0435 loss: 0.2029 2022/10/06 06:28:03 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 06:28:15 - mmengine - INFO - Epoch(train) [17][5700/10520] lr: 1.0000e-05 eta: 5:59:13 time: 0.7664 data_time: 0.2101 memory: 17203 loss_visual: 0.0487 loss_lang: 0.1059 loss_fusion: 0.0415 loss: 0.1962 2022/10/06 06:29:10 - mmengine - INFO - Epoch(train) [17][5800/10520] lr: 1.0000e-05 eta: 5:58:13 time: 0.7988 data_time: 0.1918 memory: 17203 loss_visual: 0.0551 loss_lang: 0.1102 loss_fusion: 0.0483 loss: 0.2136 2022/10/06 06:30:05 - mmengine - INFO - Epoch(train) [17][5900/10520] lr: 1.0000e-05 eta: 5:57:13 time: 0.6994 data_time: 0.1400 memory: 17203 loss_visual: 0.0514 loss_lang: 0.1128 loss_fusion: 0.0440 loss: 0.2083 2022/10/06 06:31:00 - mmengine - INFO - Epoch(train) [17][6000/10520] lr: 1.0000e-05 eta: 5:56:13 time: 0.4492 data_time: 0.0034 memory: 17203 loss_visual: 0.0521 loss_lang: 0.1059 loss_fusion: 0.0440 loss: 0.2020 2022/10/06 06:31:55 - mmengine - INFO - Epoch(train) [17][6100/10520] lr: 1.0000e-05 eta: 5:55:13 time: 0.4349 data_time: 0.0034 memory: 17203 loss_visual: 0.0489 loss_lang: 0.1062 loss_fusion: 0.0421 loss: 0.1972 2022/10/06 06:32:49 - mmengine - INFO - Epoch(train) [17][6200/10520] lr: 1.0000e-05 eta: 5:54:12 time: 0.3815 data_time: 0.0035 memory: 17203 loss_visual: 0.0540 loss_lang: 0.1139 loss_fusion: 0.0482 loss: 0.2160 2022/10/06 06:33:44 - mmengine - INFO - Epoch(train) [17][6300/10520] lr: 1.0000e-05 eta: 5:53:12 time: 0.3714 data_time: 0.0034 memory: 17203 loss_visual: 0.0527 loss_lang: 0.1098 loss_fusion: 0.0459 loss: 0.2084 2022/10/06 06:34:39 - mmengine - INFO - Epoch(train) [17][6400/10520] lr: 1.0000e-05 eta: 5:52:12 time: 0.3675 data_time: 0.0032 memory: 17203 loss_visual: 0.0480 loss_lang: 0.1094 loss_fusion: 0.0410 loss: 0.1984 2022/10/06 06:35:38 - mmengine - INFO - Epoch(train) [17][6500/10520] lr: 1.0000e-05 eta: 5:51:13 time: 0.8113 data_time: 0.1926 memory: 17203 loss_visual: 0.0507 loss_lang: 0.1068 loss_fusion: 0.0419 loss: 0.1994 2022/10/06 06:36:33 - mmengine - INFO - Epoch(train) [17][6600/10520] lr: 1.0000e-05 eta: 5:50:13 time: 0.8757 data_time: 0.1820 memory: 17203 loss_visual: 0.0470 loss_lang: 0.1052 loss_fusion: 0.0401 loss: 0.1924 2022/10/06 06:37:15 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 06:37:28 - mmengine - INFO - Epoch(train) [17][6700/10520] lr: 1.0000e-05 eta: 5:49:13 time: 0.6877 data_time: 0.1511 memory: 17203 loss_visual: 0.0506 loss_lang: 0.1128 loss_fusion: 0.0448 loss: 0.2082 2022/10/06 06:38:23 - mmengine - INFO - Epoch(train) [17][6800/10520] lr: 1.0000e-05 eta: 5:48:12 time: 0.4331 data_time: 0.0031 memory: 17203 loss_visual: 0.0521 loss_lang: 0.1101 loss_fusion: 0.0446 loss: 0.2068 2022/10/06 06:39:17 - mmengine - INFO - Epoch(train) [17][6900/10520] lr: 1.0000e-05 eta: 5:47:12 time: 0.4130 data_time: 0.0034 memory: 17203 loss_visual: 0.0534 loss_lang: 0.1095 loss_fusion: 0.0469 loss: 0.2098 2022/10/06 06:40:12 - mmengine - INFO - Epoch(train) [17][7000/10520] lr: 1.0000e-05 eta: 5:46:12 time: 0.3959 data_time: 0.0038 memory: 17203 loss_visual: 0.0475 loss_lang: 0.1065 loss_fusion: 0.0403 loss: 0.1943 2022/10/06 06:41:07 - mmengine - INFO - Epoch(train) [17][7100/10520] lr: 1.0000e-05 eta: 5:45:12 time: 0.4110 data_time: 0.0034 memory: 17203 loss_visual: 0.0518 loss_lang: 0.1119 loss_fusion: 0.0445 loss: 0.2082 2022/10/06 06:42:01 - mmengine - INFO - Epoch(train) [17][7200/10520] lr: 1.0000e-05 eta: 5:44:12 time: 0.3680 data_time: 0.0034 memory: 17203 loss_visual: 0.0503 loss_lang: 0.1058 loss_fusion: 0.0423 loss: 0.1984 2022/10/06 06:43:00 - mmengine - INFO - Epoch(train) [17][7300/10520] lr: 1.0000e-05 eta: 5:43:13 time: 0.8184 data_time: 0.1946 memory: 17203 loss_visual: 0.0569 loss_lang: 0.1165 loss_fusion: 0.0484 loss: 0.2217 2022/10/06 06:43:55 - mmengine - INFO - Epoch(train) [17][7400/10520] lr: 1.0000e-05 eta: 5:42:13 time: 0.8242 data_time: 0.1871 memory: 17203 loss_visual: 0.0535 loss_lang: 0.1106 loss_fusion: 0.0464 loss: 0.2105 2022/10/06 06:44:49 - mmengine - INFO - Epoch(train) [17][7500/10520] lr: 1.0000e-05 eta: 5:41:12 time: 0.6252 data_time: 0.1481 memory: 17203 loss_visual: 0.0482 loss_lang: 0.1060 loss_fusion: 0.0418 loss: 0.1960 2022/10/06 06:45:44 - mmengine - INFO - Epoch(train) [17][7600/10520] lr: 1.0000e-05 eta: 5:40:12 time: 0.4283 data_time: 0.0034 memory: 17203 loss_visual: 0.0486 loss_lang: 0.1035 loss_fusion: 0.0410 loss: 0.1932 2022/10/06 06:46:27 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 06:46:40 - mmengine - INFO - Epoch(train) [17][7700/10520] lr: 1.0000e-05 eta: 5:39:12 time: 0.4024 data_time: 0.0033 memory: 17203 loss_visual: 0.0418 loss_lang: 0.0953 loss_fusion: 0.0359 loss: 0.1730 2022/10/06 06:47:34 - mmengine - INFO - Epoch(train) [17][7800/10520] lr: 1.0000e-05 eta: 5:38:12 time: 0.3832 data_time: 0.0034 memory: 17203 loss_visual: 0.0489 loss_lang: 0.1097 loss_fusion: 0.0423 loss: 0.2008 2022/10/06 06:48:29 - mmengine - INFO - Epoch(train) [17][7900/10520] lr: 1.0000e-05 eta: 5:37:12 time: 0.3908 data_time: 0.0033 memory: 17203 loss_visual: 0.0520 loss_lang: 0.1089 loss_fusion: 0.0445 loss: 0.2054 2022/10/06 06:49:24 - mmengine - INFO - Epoch(train) [17][8000/10520] lr: 1.0000e-05 eta: 5:36:12 time: 0.3906 data_time: 0.0038 memory: 17203 loss_visual: 0.0538 loss_lang: 0.1114 loss_fusion: 0.0463 loss: 0.2115 2022/10/06 06:50:23 - mmengine - INFO - Epoch(train) [17][8100/10520] lr: 1.0000e-05 eta: 5:35:13 time: 0.8138 data_time: 0.1768 memory: 17203 loss_visual: 0.0520 loss_lang: 0.1086 loss_fusion: 0.0461 loss: 0.2067 2022/10/06 06:51:18 - mmengine - INFO - Epoch(train) [17][8200/10520] lr: 1.0000e-05 eta: 5:34:13 time: 0.9191 data_time: 0.1825 memory: 17203 loss_visual: 0.0515 loss_lang: 0.1080 loss_fusion: 0.0442 loss: 0.2036 2022/10/06 06:52:14 - mmengine - INFO - Epoch(train) [17][8300/10520] lr: 1.0000e-05 eta: 5:33:13 time: 0.7108 data_time: 0.1487 memory: 17203 loss_visual: 0.0451 loss_lang: 0.1042 loss_fusion: 0.0381 loss: 0.1873 2022/10/06 06:53:09 - mmengine - INFO - Epoch(train) [17][8400/10520] lr: 1.0000e-05 eta: 5:32:13 time: 0.4746 data_time: 0.0032 memory: 17203 loss_visual: 0.0461 loss_lang: 0.1070 loss_fusion: 0.0394 loss: 0.1925 2022/10/06 06:54:04 - mmengine - INFO - Epoch(train) [17][8500/10520] lr: 1.0000e-05 eta: 5:31:13 time: 0.4132 data_time: 0.0046 memory: 17203 loss_visual: 0.0430 loss_lang: 0.1050 loss_fusion: 0.0352 loss: 0.1833 2022/10/06 06:54:58 - mmengine - INFO - Epoch(train) [17][8600/10520] lr: 1.0000e-05 eta: 5:30:13 time: 0.3865 data_time: 0.0033 memory: 17203 loss_visual: 0.0562 loss_lang: 0.1164 loss_fusion: 0.0501 loss: 0.2227 2022/10/06 06:55:45 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 06:55:53 - mmengine - INFO - Epoch(train) [17][8700/10520] lr: 1.0000e-05 eta: 5:29:13 time: 0.3751 data_time: 0.0035 memory: 17203 loss_visual: 0.0511 loss_lang: 0.1078 loss_fusion: 0.0442 loss: 0.2032 2022/10/06 06:56:47 - mmengine - INFO - Epoch(train) [17][8800/10520] lr: 1.0000e-05 eta: 5:28:13 time: 0.3697 data_time: 0.0035 memory: 17203 loss_visual: 0.0567 loss_lang: 0.1120 loss_fusion: 0.0483 loss: 0.2170 2022/10/06 06:57:46 - mmengine - INFO - Epoch(train) [17][8900/10520] lr: 1.0000e-05 eta: 5:27:14 time: 0.8171 data_time: 0.1948 memory: 17203 loss_visual: 0.0490 loss_lang: 0.1057 loss_fusion: 0.0416 loss: 0.1963 2022/10/06 06:58:42 - mmengine - INFO - Epoch(train) [17][9000/10520] lr: 1.0000e-05 eta: 5:26:14 time: 0.8848 data_time: 0.1900 memory: 17203 loss_visual: 0.0475 loss_lang: 0.1036 loss_fusion: 0.0391 loss: 0.1902 2022/10/06 06:59:38 - mmengine - INFO - Epoch(train) [17][9100/10520] lr: 1.0000e-05 eta: 5:25:14 time: 0.6915 data_time: 0.1464 memory: 17203 loss_visual: 0.0465 loss_lang: 0.1029 loss_fusion: 0.0397 loss: 0.1891 2022/10/06 07:00:33 - mmengine - INFO - Epoch(train) [17][9200/10520] lr: 1.0000e-05 eta: 5:24:14 time: 0.4552 data_time: 0.0036 memory: 17203 loss_visual: 0.0516 loss_lang: 0.1082 loss_fusion: 0.0452 loss: 0.2050 2022/10/06 07:01:27 - mmengine - INFO - Epoch(train) [17][9300/10520] lr: 1.0000e-05 eta: 5:23:14 time: 0.4324 data_time: 0.0123 memory: 17203 loss_visual: 0.0443 loss_lang: 0.0991 loss_fusion: 0.0369 loss: 0.1803 2022/10/06 07:02:22 - mmengine - INFO - Epoch(train) [17][9400/10520] lr: 1.0000e-05 eta: 5:22:14 time: 0.3830 data_time: 0.0031 memory: 17203 loss_visual: 0.0516 loss_lang: 0.1072 loss_fusion: 0.0442 loss: 0.2030 2022/10/06 07:03:17 - mmengine - INFO - Epoch(train) [17][9500/10520] lr: 1.0000e-05 eta: 5:21:14 time: 0.3719 data_time: 0.0035 memory: 17203 loss_visual: 0.0528 loss_lang: 0.1098 loss_fusion: 0.0459 loss: 0.2084 2022/10/06 07:04:11 - mmengine - INFO - Epoch(train) [17][9600/10520] lr: 1.0000e-05 eta: 5:20:14 time: 0.3839 data_time: 0.0030 memory: 17203 loss_visual: 0.0457 loss_lang: 0.1039 loss_fusion: 0.0393 loss: 0.1889 2022/10/06 07:04:58 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 07:05:09 - mmengine - INFO - Epoch(train) [17][9700/10520] lr: 1.0000e-05 eta: 5:19:15 time: 0.7914 data_time: 0.1953 memory: 17203 loss_visual: 0.0503 loss_lang: 0.1095 loss_fusion: 0.0440 loss: 0.2039 2022/10/06 07:06:04 - mmengine - INFO - Epoch(train) [17][9800/10520] lr: 1.0000e-05 eta: 5:18:15 time: 0.8431 data_time: 0.2078 memory: 17203 loss_visual: 0.0452 loss_lang: 0.0997 loss_fusion: 0.0386 loss: 0.1835 2022/10/06 07:06:58 - mmengine - INFO - Epoch(train) [17][9900/10520] lr: 1.0000e-05 eta: 5:17:15 time: 0.6190 data_time: 0.1383 memory: 17203 loss_visual: 0.0538 loss_lang: 0.1160 loss_fusion: 0.0476 loss: 0.2174 2022/10/06 07:07:54 - mmengine - INFO - Epoch(train) [17][10000/10520] lr: 1.0000e-05 eta: 5:16:15 time: 0.4391 data_time: 0.0033 memory: 17203 loss_visual: 0.0523 loss_lang: 0.1093 loss_fusion: 0.0438 loss: 0.2054 2022/10/06 07:08:48 - mmengine - INFO - Epoch(train) [17][10100/10520] lr: 1.0000e-05 eta: 5:15:15 time: 0.4354 data_time: 0.0217 memory: 17203 loss_visual: 0.0487 loss_lang: 0.1077 loss_fusion: 0.0409 loss: 0.1973 2022/10/06 07:09:42 - mmengine - INFO - Epoch(train) [17][10200/10520] lr: 1.0000e-05 eta: 5:14:15 time: 0.3842 data_time: 0.0033 memory: 17203 loss_visual: 0.0450 loss_lang: 0.1003 loss_fusion: 0.0378 loss: 0.1831 2022/10/06 07:10:36 - mmengine - INFO - Epoch(train) [17][10300/10520] lr: 1.0000e-05 eta: 5:13:15 time: 0.4042 data_time: 0.0068 memory: 17203 loss_visual: 0.0560 loss_lang: 0.1135 loss_fusion: 0.0485 loss: 0.2179 2022/10/06 07:11:31 - mmengine - INFO - Epoch(train) [17][10400/10520] lr: 1.0000e-05 eta: 5:12:15 time: 0.3685 data_time: 0.0031 memory: 17203 loss_visual: 0.0453 loss_lang: 0.0992 loss_fusion: 0.0389 loss: 0.1834 2022/10/06 07:12:26 - mmengine - INFO - Epoch(train) [17][10500/10520] lr: 1.0000e-05 eta: 5:11:15 time: 0.5900 data_time: 0.1030 memory: 17203 loss_visual: 0.0531 loss_lang: 0.1072 loss_fusion: 0.0449 loss: 0.2052 2022/10/06 07:12:33 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 07:12:34 - mmengine - INFO - Saving checkpoint at 17 epochs 2022/10/06 07:12:48 - mmengine - INFO - Epoch(val) [17][100/959] eta: 0:00:39 time: 0.0455 data_time: 0.0011 memory: 17203 2022/10/06 07:12:53 - mmengine - INFO - Epoch(val) [17][200/959] eta: 0:00:37 time: 0.0496 data_time: 0.0025 memory: 734 2022/10/06 07:12:58 - mmengine - INFO - Epoch(val) [17][300/959] eta: 0:00:30 time: 0.0459 data_time: 0.0030 memory: 734 2022/10/06 07:13:03 - mmengine - INFO - Epoch(val) [17][400/959] eta: 0:00:28 time: 0.0507 data_time: 0.0018 memory: 734 2022/10/06 07:13:08 - mmengine - INFO - Epoch(val) [17][500/959] eta: 0:00:22 time: 0.0500 data_time: 0.0024 memory: 734 2022/10/06 07:13:13 - mmengine - INFO - Epoch(val) [17][600/959] eta: 0:00:18 time: 0.0505 data_time: 0.0016 memory: 734 2022/10/06 07:13:18 - mmengine - INFO - Epoch(val) [17][700/959] eta: 0:00:11 time: 0.0458 data_time: 0.0022 memory: 734 2022/10/06 07:13:22 - mmengine - INFO - Epoch(val) [17][800/959] eta: 0:00:03 time: 0.0246 data_time: 0.0006 memory: 734 2022/10/06 07:13:24 - mmengine - INFO - Epoch(val) [17][900/959] eta: 0:00:01 time: 0.0222 data_time: 0.0006 memory: 734 2022/10/06 07:13:26 - mmengine - INFO - Epoch(val) [17][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.8785 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9583 SVT/recog/word_acc_ignore_case_symbol: 0.9320 SVTP/recog/word_acc_ignore_case_symbol: 0.8884 IC13/recog/word_acc_ignore_case_symbol: 0.9567 IC15/recog/word_acc_ignore_case_symbol: 0.8117 2022/10/06 07:14:32 - mmengine - INFO - Epoch(train) [18][100/10520] lr: 1.0000e-05 eta: 5:10:04 time: 0.7775 data_time: 0.0800 memory: 17203 loss_visual: 0.0526 loss_lang: 0.1056 loss_fusion: 0.0444 loss: 0.2026 2022/10/06 07:15:03 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 07:15:28 - mmengine - INFO - Epoch(train) [18][200/10520] lr: 1.0000e-05 eta: 5:09:05 time: 0.8299 data_time: 0.1199 memory: 17203 loss_visual: 0.0554 loss_lang: 0.1163 loss_fusion: 0.0480 loss: 0.2196 2022/10/06 07:16:25 - mmengine - INFO - Epoch(train) [18][300/10520] lr: 1.0000e-05 eta: 5:08:05 time: 0.7521 data_time: 0.1413 memory: 17203 loss_visual: 0.0521 loss_lang: 0.1119 loss_fusion: 0.0445 loss: 0.2085 2022/10/06 07:17:22 - mmengine - INFO - Epoch(train) [18][400/10520] lr: 1.0000e-05 eta: 5:07:06 time: 0.5354 data_time: 0.1088 memory: 17203 loss_visual: 0.0497 loss_lang: 0.1031 loss_fusion: 0.0418 loss: 0.1946 2022/10/06 07:18:18 - mmengine - INFO - Epoch(train) [18][500/10520] lr: 1.0000e-05 eta: 5:06:06 time: 0.4633 data_time: 0.0038 memory: 17203 loss_visual: 0.0529 loss_lang: 0.1093 loss_fusion: 0.0458 loss: 0.2079 2022/10/06 07:19:14 - mmengine - INFO - Epoch(train) [18][600/10520] lr: 1.0000e-05 eta: 5:05:06 time: 0.3727 data_time: 0.0036 memory: 17203 loss_visual: 0.0462 loss_lang: 0.0987 loss_fusion: 0.0386 loss: 0.1835 2022/10/06 07:20:11 - mmengine - INFO - Epoch(train) [18][700/10520] lr: 1.0000e-05 eta: 5:04:07 time: 0.3589 data_time: 0.0031 memory: 17203 loss_visual: 0.0525 loss_lang: 0.1072 loss_fusion: 0.0447 loss: 0.2044 2022/10/06 07:21:09 - mmengine - INFO - Epoch(train) [18][800/10520] lr: 1.0000e-05 eta: 5:03:08 time: 0.3776 data_time: 0.0033 memory: 17203 loss_visual: 0.0493 loss_lang: 0.1085 loss_fusion: 0.0417 loss: 0.1995 2022/10/06 07:22:10 - mmengine - INFO - Epoch(train) [18][900/10520] lr: 1.0000e-05 eta: 5:02:09 time: 0.8253 data_time: 0.0845 memory: 17203 loss_visual: 0.0492 loss_lang: 0.1058 loss_fusion: 0.0424 loss: 0.1975 2022/10/06 07:23:05 - mmengine - INFO - Epoch(train) [18][1000/10520] lr: 1.0000e-05 eta: 5:01:09 time: 0.8257 data_time: 0.1254 memory: 17203 loss_visual: 0.0482 loss_lang: 0.1062 loss_fusion: 0.0404 loss: 0.1948 2022/10/06 07:24:02 - mmengine - INFO - Epoch(train) [18][1100/10520] lr: 1.0000e-05 eta: 5:00:09 time: 0.8068 data_time: 0.1454 memory: 17203 loss_visual: 0.0500 loss_lang: 0.1060 loss_fusion: 0.0433 loss: 0.1993 2022/10/06 07:24:36 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 07:24:59 - mmengine - INFO - Epoch(train) [18][1200/10520] lr: 1.0000e-05 eta: 4:59:10 time: 0.5270 data_time: 0.0810 memory: 17203 loss_visual: 0.0454 loss_lang: 0.0996 loss_fusion: 0.0383 loss: 0.1833 2022/10/06 07:25:54 - mmengine - INFO - Epoch(train) [18][1300/10520] lr: 1.0000e-05 eta: 4:58:10 time: 0.4117 data_time: 0.0033 memory: 17203 loss_visual: 0.0517 loss_lang: 0.1116 loss_fusion: 0.0453 loss: 0.2086 2022/10/06 07:26:50 - mmengine - INFO - Epoch(train) [18][1400/10520] lr: 1.0000e-05 eta: 4:57:10 time: 0.3865 data_time: 0.0033 memory: 17203 loss_visual: 0.0580 loss_lang: 0.1165 loss_fusion: 0.0506 loss: 0.2251 2022/10/06 07:27:45 - mmengine - INFO - Epoch(train) [18][1500/10520] lr: 1.0000e-05 eta: 4:56:11 time: 0.3583 data_time: 0.0034 memory: 17203 loss_visual: 0.0495 loss_lang: 0.1108 loss_fusion: 0.0429 loss: 0.2033 2022/10/06 07:28:42 - mmengine - INFO - Epoch(train) [18][1600/10520] lr: 1.0000e-05 eta: 4:55:11 time: 0.3648 data_time: 0.0027 memory: 17203 loss_visual: 0.0473 loss_lang: 0.1050 loss_fusion: 0.0402 loss: 0.1925 2022/10/06 07:29:43 - mmengine - INFO - Epoch(train) [18][1700/10520] lr: 1.0000e-05 eta: 4:54:12 time: 0.8060 data_time: 0.0549 memory: 17203 loss_visual: 0.0511 loss_lang: 0.1095 loss_fusion: 0.0440 loss: 0.2046 2022/10/06 07:30:39 - mmengine - INFO - Epoch(train) [18][1800/10520] lr: 1.0000e-05 eta: 4:53:13 time: 0.8864 data_time: 0.1481 memory: 17203 loss_visual: 0.0479 loss_lang: 0.1056 loss_fusion: 0.0408 loss: 0.1944 2022/10/06 07:31:36 - mmengine - INFO - Epoch(train) [18][1900/10520] lr: 1.0000e-05 eta: 4:52:13 time: 0.7791 data_time: 0.1503 memory: 17203 loss_visual: 0.0482 loss_lang: 0.1020 loss_fusion: 0.0409 loss: 0.1910 2022/10/06 07:32:32 - mmengine - INFO - Epoch(train) [18][2000/10520] lr: 1.0000e-05 eta: 4:51:14 time: 0.5334 data_time: 0.0813 memory: 17203 loss_visual: 0.0449 loss_lang: 0.0997 loss_fusion: 0.0383 loss: 0.1829 2022/10/06 07:33:27 - mmengine - INFO - Epoch(train) [18][2100/10520] lr: 1.0000e-05 eta: 4:50:14 time: 0.4313 data_time: 0.0033 memory: 17203 loss_visual: 0.0497 loss_lang: 0.1030 loss_fusion: 0.0437 loss: 0.1965 2022/10/06 07:34:03 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 07:34:24 - mmengine - INFO - Epoch(train) [18][2200/10520] lr: 1.0000e-05 eta: 4:49:14 time: 0.3846 data_time: 0.0036 memory: 17203 loss_visual: 0.0546 loss_lang: 0.1104 loss_fusion: 0.0460 loss: 0.2110 2022/10/06 07:35:21 - mmengine - INFO - Epoch(train) [18][2300/10520] lr: 1.0000e-05 eta: 4:48:15 time: 0.4173 data_time: 0.0233 memory: 17203 loss_visual: 0.0503 loss_lang: 0.1090 loss_fusion: 0.0422 loss: 0.2014 2022/10/06 07:36:18 - mmengine - INFO - Epoch(train) [18][2400/10520] lr: 1.0000e-05 eta: 4:47:15 time: 0.4089 data_time: 0.0044 memory: 17203 loss_visual: 0.0551 loss_lang: 0.1140 loss_fusion: 0.0475 loss: 0.2167 2022/10/06 07:37:19 - mmengine - INFO - Epoch(train) [18][2500/10520] lr: 1.0000e-05 eta: 4:46:17 time: 0.7732 data_time: 0.0647 memory: 17203 loss_visual: 0.0542 loss_lang: 0.1129 loss_fusion: 0.0462 loss: 0.2132 2022/10/06 07:38:16 - mmengine - INFO - Epoch(train) [18][2600/10520] lr: 1.0000e-05 eta: 4:45:17 time: 0.8681 data_time: 0.1108 memory: 17203 loss_visual: 0.0502 loss_lang: 0.1125 loss_fusion: 0.0426 loss: 0.2053 2022/10/06 07:39:12 - mmengine - INFO - Epoch(train) [18][2700/10520] lr: 1.0000e-05 eta: 4:44:18 time: 0.7583 data_time: 0.1208 memory: 17203 loss_visual: 0.0468 loss_lang: 0.1019 loss_fusion: 0.0397 loss: 0.1884 2022/10/06 07:40:09 - mmengine - INFO - Epoch(train) [18][2800/10520] lr: 1.0000e-05 eta: 4:43:18 time: 0.5547 data_time: 0.0897 memory: 17203 loss_visual: 0.0502 loss_lang: 0.1122 loss_fusion: 0.0438 loss: 0.2062 2022/10/06 07:41:04 - mmengine - INFO - Epoch(train) [18][2900/10520] lr: 1.0000e-05 eta: 4:42:18 time: 0.4076 data_time: 0.0032 memory: 17203 loss_visual: 0.0501 loss_lang: 0.1053 loss_fusion: 0.0428 loss: 0.1982 2022/10/06 07:42:00 - mmengine - INFO - Epoch(train) [18][3000/10520] lr: 1.0000e-05 eta: 4:41:19 time: 0.3988 data_time: 0.0167 memory: 17203 loss_visual: 0.0496 loss_lang: 0.1057 loss_fusion: 0.0427 loss: 0.1980 2022/10/06 07:42:56 - mmengine - INFO - Epoch(train) [18][3100/10520] lr: 1.0000e-05 eta: 4:40:19 time: 0.3711 data_time: 0.0038 memory: 17203 loss_visual: 0.0574 loss_lang: 0.1168 loss_fusion: 0.0493 loss: 0.2235 2022/10/06 07:43:30 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 07:43:52 - mmengine - INFO - Epoch(train) [18][3200/10520] lr: 1.0000e-05 eta: 4:39:20 time: 0.3896 data_time: 0.0037 memory: 17203 loss_visual: 0.0444 loss_lang: 0.1007 loss_fusion: 0.0376 loss: 0.1828 2022/10/06 07:44:52 - mmengine - INFO - Epoch(train) [18][3300/10520] lr: 1.0000e-05 eta: 4:38:21 time: 0.8126 data_time: 0.0704 memory: 17203 loss_visual: 0.0510 loss_lang: 0.1075 loss_fusion: 0.0440 loss: 0.2025 2022/10/06 07:45:49 - mmengine - INFO - Epoch(train) [18][3400/10520] lr: 1.0000e-05 eta: 4:37:21 time: 0.8325 data_time: 0.1122 memory: 17203 loss_visual: 0.0525 loss_lang: 0.1101 loss_fusion: 0.0435 loss: 0.2062 2022/10/06 07:46:46 - mmengine - INFO - Epoch(train) [18][3500/10520] lr: 1.0000e-05 eta: 4:36:22 time: 0.8101 data_time: 0.1200 memory: 17203 loss_visual: 0.0465 loss_lang: 0.1010 loss_fusion: 0.0386 loss: 0.1861 2022/10/06 07:47:42 - mmengine - INFO - Epoch(train) [18][3600/10520] lr: 1.0000e-05 eta: 4:35:22 time: 0.5343 data_time: 0.0829 memory: 17203 loss_visual: 0.0517 loss_lang: 0.1047 loss_fusion: 0.0438 loss: 0.2001 2022/10/06 07:48:38 - mmengine - INFO - Epoch(train) [18][3700/10520] lr: 1.0000e-05 eta: 4:34:23 time: 0.4480 data_time: 0.0035 memory: 17203 loss_visual: 0.0565 loss_lang: 0.1102 loss_fusion: 0.0487 loss: 0.2154 2022/10/06 07:49:34 - mmengine - INFO - Epoch(train) [18][3800/10520] lr: 1.0000e-05 eta: 4:33:23 time: 0.3932 data_time: 0.0033 memory: 17203 loss_visual: 0.0498 loss_lang: 0.1036 loss_fusion: 0.0415 loss: 0.1949 2022/10/06 07:50:30 - mmengine - INFO - Epoch(train) [18][3900/10520] lr: 1.0000e-05 eta: 4:32:23 time: 0.3652 data_time: 0.0033 memory: 17203 loss_visual: 0.0458 loss_lang: 0.1002 loss_fusion: 0.0383 loss: 0.1843 2022/10/06 07:51:26 - mmengine - INFO - Epoch(train) [18][4000/10520] lr: 1.0000e-05 eta: 4:31:24 time: 0.3696 data_time: 0.0032 memory: 17203 loss_visual: 0.0531 loss_lang: 0.1110 loss_fusion: 0.0446 loss: 0.2087 2022/10/06 07:52:26 - mmengine - INFO - Epoch(train) [18][4100/10520] lr: 1.0000e-05 eta: 4:30:25 time: 0.7602 data_time: 0.0535 memory: 17203 loss_visual: 0.0499 loss_lang: 0.1053 loss_fusion: 0.0429 loss: 0.1981 2022/10/06 07:52:57 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 07:53:23 - mmengine - INFO - Epoch(train) [18][4200/10520] lr: 1.0000e-05 eta: 4:29:26 time: 0.8658 data_time: 0.1124 memory: 17203 loss_visual: 0.0518 loss_lang: 0.1045 loss_fusion: 0.0438 loss: 0.2001 2022/10/06 07:54:19 - mmengine - INFO - Epoch(train) [18][4300/10520] lr: 1.0000e-05 eta: 4:28:26 time: 0.7645 data_time: 0.1422 memory: 17203 loss_visual: 0.0499 loss_lang: 0.1055 loss_fusion: 0.0427 loss: 0.1980 2022/10/06 07:55:16 - mmengine - INFO - Epoch(train) [18][4400/10520] lr: 1.0000e-05 eta: 4:27:27 time: 0.5034 data_time: 0.0797 memory: 17203 loss_visual: 0.0434 loss_lang: 0.1039 loss_fusion: 0.0375 loss: 0.1848 2022/10/06 07:56:12 - mmengine - INFO - Epoch(train) [18][4500/10520] lr: 1.0000e-05 eta: 4:26:27 time: 0.4277 data_time: 0.0039 memory: 17203 loss_visual: 0.0451 loss_lang: 0.1018 loss_fusion: 0.0385 loss: 0.1854 2022/10/06 07:57:07 - mmengine - INFO - Epoch(train) [18][4600/10520] lr: 1.0000e-05 eta: 4:25:27 time: 0.3906 data_time: 0.0038 memory: 17203 loss_visual: 0.0552 loss_lang: 0.1094 loss_fusion: 0.0486 loss: 0.2132 2022/10/06 07:58:03 - mmengine - INFO - Epoch(train) [18][4700/10520] lr: 1.0000e-05 eta: 4:24:28 time: 0.3774 data_time: 0.0040 memory: 17203 loss_visual: 0.0494 loss_lang: 0.1081 loss_fusion: 0.0428 loss: 0.2003 2022/10/06 07:58:59 - mmengine - INFO - Epoch(train) [18][4800/10520] lr: 1.0000e-05 eta: 4:23:28 time: 0.3793 data_time: 0.0034 memory: 17203 loss_visual: 0.0480 loss_lang: 0.1081 loss_fusion: 0.0413 loss: 0.1974 2022/10/06 07:59:59 - mmengine - INFO - Epoch(train) [18][4900/10520] lr: 1.0000e-05 eta: 4:22:29 time: 0.7718 data_time: 0.0765 memory: 17203 loss_visual: 0.0502 loss_lang: 0.1042 loss_fusion: 0.0424 loss: 0.1968 2022/10/06 08:00:56 - mmengine - INFO - Epoch(train) [18][5000/10520] lr: 1.0000e-05 eta: 4:21:30 time: 0.9326 data_time: 0.1038 memory: 17203 loss_visual: 0.0524 loss_lang: 0.1087 loss_fusion: 0.0445 loss: 0.2055 2022/10/06 08:01:53 - mmengine - INFO - Epoch(train) [18][5100/10520] lr: 1.0000e-05 eta: 4:20:31 time: 0.7785 data_time: 0.1287 memory: 17203 loss_visual: 0.0427 loss_lang: 0.0999 loss_fusion: 0.0361 loss: 0.1787 2022/10/06 08:02:27 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 08:02:49 - mmengine - INFO - Epoch(train) [18][5200/10520] lr: 1.0000e-05 eta: 4:19:31 time: 0.5052 data_time: 0.0812 memory: 17203 loss_visual: 0.0444 loss_lang: 0.0982 loss_fusion: 0.0368 loss: 0.1794 2022/10/06 08:03:45 - mmengine - INFO - Epoch(train) [18][5300/10520] lr: 1.0000e-05 eta: 4:18:32 time: 0.4333 data_time: 0.0034 memory: 17203 loss_visual: 0.0482 loss_lang: 0.1042 loss_fusion: 0.0409 loss: 0.1933 2022/10/06 08:04:40 - mmengine - INFO - Epoch(train) [18][5400/10520] lr: 1.0000e-05 eta: 4:17:32 time: 0.3941 data_time: 0.0032 memory: 17203 loss_visual: 0.0525 loss_lang: 0.1052 loss_fusion: 0.0433 loss: 0.2010 2022/10/06 08:05:36 - mmengine - INFO - Epoch(train) [18][5500/10520] lr: 1.0000e-05 eta: 4:16:32 time: 0.3717 data_time: 0.0033 memory: 17203 loss_visual: 0.0535 loss_lang: 0.1122 loss_fusion: 0.0463 loss: 0.2120 2022/10/06 08:06:31 - mmengine - INFO - Epoch(train) [18][5600/10520] lr: 1.0000e-05 eta: 4:15:33 time: 0.3716 data_time: 0.0037 memory: 17203 loss_visual: 0.0467 loss_lang: 0.1046 loss_fusion: 0.0404 loss: 0.1917 2022/10/06 08:07:32 - mmengine - INFO - Epoch(train) [18][5700/10520] lr: 1.0000e-05 eta: 4:14:34 time: 0.8033 data_time: 0.0695 memory: 17203 loss_visual: 0.0526 loss_lang: 0.1098 loss_fusion: 0.0461 loss: 0.2085 2022/10/06 08:08:28 - mmengine - INFO - Epoch(train) [18][5800/10520] lr: 1.0000e-05 eta: 4:13:35 time: 0.8579 data_time: 0.1204 memory: 17203 loss_visual: 0.0467 loss_lang: 0.1033 loss_fusion: 0.0394 loss: 0.1895 2022/10/06 08:09:26 - mmengine - INFO - Epoch(train) [18][5900/10520] lr: 1.0000e-05 eta: 4:12:35 time: 0.8187 data_time: 0.1244 memory: 17203 loss_visual: 0.0503 loss_lang: 0.1073 loss_fusion: 0.0431 loss: 0.2007 2022/10/06 08:10:23 - mmengine - INFO - Epoch(train) [18][6000/10520] lr: 1.0000e-05 eta: 4:11:36 time: 0.6051 data_time: 0.0812 memory: 17203 loss_visual: 0.0499 loss_lang: 0.1053 loss_fusion: 0.0427 loss: 0.1980 2022/10/06 08:11:18 - mmengine - INFO - Epoch(train) [18][6100/10520] lr: 1.0000e-05 eta: 4:10:36 time: 0.4185 data_time: 0.0035 memory: 17203 loss_visual: 0.0517 loss_lang: 0.1109 loss_fusion: 0.0442 loss: 0.2068 2022/10/06 08:11:53 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 08:12:14 - mmengine - INFO - Epoch(train) [18][6200/10520] lr: 1.0000e-05 eta: 4:09:37 time: 0.4015 data_time: 0.0034 memory: 17203 loss_visual: 0.0502 loss_lang: 0.1066 loss_fusion: 0.0433 loss: 0.2001 2022/10/06 08:13:10 - mmengine - INFO - Epoch(train) [18][6300/10520] lr: 1.0000e-05 eta: 4:08:37 time: 0.3646 data_time: 0.0032 memory: 17203 loss_visual: 0.0522 loss_lang: 0.1085 loss_fusion: 0.0453 loss: 0.2061 2022/10/06 08:14:07 - mmengine - INFO - Epoch(train) [18][6400/10520] lr: 1.0000e-05 eta: 4:07:38 time: 0.3733 data_time: 0.0035 memory: 17203 loss_visual: 0.0431 loss_lang: 0.0988 loss_fusion: 0.0360 loss: 0.1778 2022/10/06 08:15:07 - mmengine - INFO - Epoch(train) [18][6500/10520] lr: 1.0000e-05 eta: 4:06:39 time: 0.8037 data_time: 0.0481 memory: 17203 loss_visual: 0.0475 loss_lang: 0.1064 loss_fusion: 0.0403 loss: 0.1942 2022/10/06 08:16:04 - mmengine - INFO - Epoch(train) [18][6600/10520] lr: 1.0000e-05 eta: 4:05:40 time: 0.8583 data_time: 0.1096 memory: 17203 loss_visual: 0.0497 loss_lang: 0.1062 loss_fusion: 0.0428 loss: 0.1987 2022/10/06 08:17:01 - mmengine - INFO - Epoch(train) [18][6700/10520] lr: 1.0000e-05 eta: 4:04:40 time: 0.7739 data_time: 0.1444 memory: 17203 loss_visual: 0.0454 loss_lang: 0.1016 loss_fusion: 0.0384 loss: 0.1853 2022/10/06 08:17:58 - mmengine - INFO - Epoch(train) [18][6800/10520] lr: 1.0000e-05 eta: 4:03:41 time: 0.5125 data_time: 0.0833 memory: 17203 loss_visual: 0.0545 loss_lang: 0.1114 loss_fusion: 0.0478 loss: 0.2138 2022/10/06 08:18:55 - mmengine - INFO - Epoch(train) [18][6900/10520] lr: 1.0000e-05 eta: 4:02:42 time: 0.4567 data_time: 0.0036 memory: 17203 loss_visual: 0.0443 loss_lang: 0.0998 loss_fusion: 0.0357 loss: 0.1797 2022/10/06 08:19:51 - mmengine - INFO - Epoch(train) [18][7000/10520] lr: 1.0000e-05 eta: 4:01:42 time: 0.3695 data_time: 0.0034 memory: 17203 loss_visual: 0.0467 loss_lang: 0.0969 loss_fusion: 0.0384 loss: 0.1820 2022/10/06 08:20:47 - mmengine - INFO - Epoch(train) [18][7100/10520] lr: 1.0000e-05 eta: 4:00:43 time: 0.3684 data_time: 0.0036 memory: 17203 loss_visual: 0.0436 loss_lang: 0.0978 loss_fusion: 0.0348 loss: 0.1762 2022/10/06 08:21:22 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 08:21:43 - mmengine - INFO - Epoch(train) [18][7200/10520] lr: 1.0000e-05 eta: 3:59:43 time: 0.3755 data_time: 0.0033 memory: 17203 loss_visual: 0.0503 loss_lang: 0.1025 loss_fusion: 0.0423 loss: 0.1952 2022/10/06 08:22:44 - mmengine - INFO - Epoch(train) [18][7300/10520] lr: 1.0000e-05 eta: 3:58:45 time: 0.8072 data_time: 0.0793 memory: 17203 loss_visual: 0.0553 loss_lang: 0.1144 loss_fusion: 0.0464 loss: 0.2161 2022/10/06 08:23:41 - mmengine - INFO - Epoch(train) [18][7400/10520] lr: 1.0000e-05 eta: 3:57:45 time: 0.8899 data_time: 0.1055 memory: 17203 loss_visual: 0.0562 loss_lang: 0.1145 loss_fusion: 0.0482 loss: 0.2190 2022/10/06 08:24:39 - mmengine - INFO - Epoch(train) [18][7500/10520] lr: 1.0000e-05 eta: 3:56:46 time: 0.7633 data_time: 0.1250 memory: 17203 loss_visual: 0.0573 loss_lang: 0.1184 loss_fusion: 0.0509 loss: 0.2267 2022/10/06 08:25:35 - mmengine - INFO - Epoch(train) [18][7600/10520] lr: 1.0000e-05 eta: 3:55:47 time: 0.5669 data_time: 0.0858 memory: 17203 loss_visual: 0.0462 loss_lang: 0.1041 loss_fusion: 0.0398 loss: 0.1901 2022/10/06 08:26:32 - mmengine - INFO - Epoch(train) [18][7700/10520] lr: 1.0000e-05 eta: 3:54:47 time: 0.4111 data_time: 0.0035 memory: 17203 loss_visual: 0.0513 loss_lang: 0.1062 loss_fusion: 0.0434 loss: 0.2010 2022/10/06 08:27:27 - mmengine - INFO - Epoch(train) [18][7800/10520] lr: 1.0000e-05 eta: 3:53:48 time: 0.3682 data_time: 0.0036 memory: 17203 loss_visual: 0.0459 loss_lang: 0.1024 loss_fusion: 0.0384 loss: 0.1867 2022/10/06 08:28:24 - mmengine - INFO - Epoch(train) [18][7900/10520] lr: 1.0000e-05 eta: 3:52:48 time: 0.3634 data_time: 0.0036 memory: 17203 loss_visual: 0.0465 loss_lang: 0.1044 loss_fusion: 0.0393 loss: 0.1901 2022/10/06 08:29:20 - mmengine - INFO - Epoch(train) [18][8000/10520] lr: 1.0000e-05 eta: 3:51:49 time: 0.3658 data_time: 0.0036 memory: 17203 loss_visual: 0.0445 loss_lang: 0.1006 loss_fusion: 0.0377 loss: 0.1827 2022/10/06 08:30:20 - mmengine - INFO - Epoch(train) [18][8100/10520] lr: 1.0000e-05 eta: 3:50:50 time: 0.7810 data_time: 0.0704 memory: 17203 loss_visual: 0.0537 loss_lang: 0.1122 loss_fusion: 0.0453 loss: 0.2112 2022/10/06 08:30:50 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 08:31:16 - mmengine - INFO - Epoch(train) [18][8200/10520] lr: 1.0000e-05 eta: 3:49:51 time: 0.8655 data_time: 0.1141 memory: 17203 loss_visual: 0.0479 loss_lang: 0.1036 loss_fusion: 0.0406 loss: 0.1922 2022/10/06 08:32:12 - mmengine - INFO - Epoch(train) [18][8300/10520] lr: 1.0000e-05 eta: 3:48:51 time: 0.7739 data_time: 0.1199 memory: 17203 loss_visual: 0.0527 loss_lang: 0.1123 loss_fusion: 0.0454 loss: 0.2103 2022/10/06 08:33:09 - mmengine - INFO - Epoch(train) [18][8400/10520] lr: 1.0000e-05 eta: 3:47:52 time: 0.5357 data_time: 0.0830 memory: 17203 loss_visual: 0.0526 loss_lang: 0.1152 loss_fusion: 0.0453 loss: 0.2131 2022/10/06 08:34:05 - mmengine - INFO - Epoch(train) [18][8500/10520] lr: 1.0000e-05 eta: 3:46:53 time: 0.4537 data_time: 0.0035 memory: 17203 loss_visual: 0.0447 loss_lang: 0.1039 loss_fusion: 0.0376 loss: 0.1862 2022/10/06 08:35:02 - mmengine - INFO - Epoch(train) [18][8600/10520] lr: 1.0000e-05 eta: 3:45:53 time: 0.3662 data_time: 0.0037 memory: 17203 loss_visual: 0.0467 loss_lang: 0.1013 loss_fusion: 0.0389 loss: 0.1869 2022/10/06 08:35:58 - mmengine - INFO - Epoch(train) [18][8700/10520] lr: 1.0000e-05 eta: 3:44:54 time: 0.3614 data_time: 0.0033 memory: 17203 loss_visual: 0.0455 loss_lang: 0.1033 loss_fusion: 0.0381 loss: 0.1869 2022/10/06 08:36:54 - mmengine - INFO - Epoch(train) [18][8800/10520] lr: 1.0000e-05 eta: 3:43:54 time: 0.3929 data_time: 0.0037 memory: 17203 loss_visual: 0.0525 loss_lang: 0.1031 loss_fusion: 0.0441 loss: 0.1998 2022/10/06 08:37:54 - mmengine - INFO - Epoch(train) [18][8900/10520] lr: 1.0000e-05 eta: 3:42:56 time: 0.8011 data_time: 0.0507 memory: 17203 loss_visual: 0.0514 loss_lang: 0.1106 loss_fusion: 0.0430 loss: 0.2051 2022/10/06 08:38:51 - mmengine - INFO - Epoch(train) [18][9000/10520] lr: 1.0000e-05 eta: 3:41:56 time: 0.8619 data_time: 0.1145 memory: 17203 loss_visual: 0.0434 loss_lang: 0.1011 loss_fusion: 0.0363 loss: 0.1808 2022/10/06 08:39:48 - mmengine - INFO - Epoch(train) [18][9100/10520] lr: 1.0000e-05 eta: 3:40:57 time: 0.7856 data_time: 0.1722 memory: 17203 loss_visual: 0.0516 loss_lang: 0.1093 loss_fusion: 0.0450 loss: 0.2060 2022/10/06 08:40:22 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 08:40:45 - mmengine - INFO - Epoch(train) [18][9200/10520] lr: 1.0000e-05 eta: 3:39:58 time: 0.5727 data_time: 0.0787 memory: 17203 loss_visual: 0.0504 loss_lang: 0.1110 loss_fusion: 0.0426 loss: 0.2040 2022/10/06 08:41:41 - mmengine - INFO - Epoch(train) [18][9300/10520] lr: 1.0000e-05 eta: 3:38:58 time: 0.4136 data_time: 0.0035 memory: 17203 loss_visual: 0.0538 loss_lang: 0.1120 loss_fusion: 0.0466 loss: 0.2124 2022/10/06 08:42:37 - mmengine - INFO - Epoch(train) [18][9400/10520] lr: 1.0000e-05 eta: 3:37:59 time: 0.3918 data_time: 0.0034 memory: 17203 loss_visual: 0.0478 loss_lang: 0.1048 loss_fusion: 0.0407 loss: 0.1933 2022/10/06 08:43:33 - mmengine - INFO - Epoch(train) [18][9500/10520] lr: 1.0000e-05 eta: 3:37:00 time: 0.3768 data_time: 0.0048 memory: 17203 loss_visual: 0.0479 loss_lang: 0.1048 loss_fusion: 0.0399 loss: 0.1926 2022/10/06 08:44:29 - mmengine - INFO - Epoch(train) [18][9600/10520] lr: 1.0000e-05 eta: 3:36:00 time: 0.3836 data_time: 0.0033 memory: 17203 loss_visual: 0.0520 loss_lang: 0.1084 loss_fusion: 0.0460 loss: 0.2064 2022/10/06 08:45:30 - mmengine - INFO - Epoch(train) [18][9700/10520] lr: 1.0000e-05 eta: 3:35:01 time: 0.8010 data_time: 0.0780 memory: 17203 loss_visual: 0.0509 loss_lang: 0.1090 loss_fusion: 0.0432 loss: 0.2032 2022/10/06 08:46:26 - mmengine - INFO - Epoch(train) [18][9800/10520] lr: 1.0000e-05 eta: 3:34:02 time: 0.8846 data_time: 0.1339 memory: 17203 loss_visual: 0.0477 loss_lang: 0.1065 loss_fusion: 0.0413 loss: 0.1956 2022/10/06 08:47:23 - mmengine - INFO - Epoch(train) [18][9900/10520] lr: 1.0000e-05 eta: 3:33:03 time: 0.7764 data_time: 0.1253 memory: 17203 loss_visual: 0.0547 loss_lang: 0.1131 loss_fusion: 0.0478 loss: 0.2156 2022/10/06 08:48:19 - mmengine - INFO - Epoch(train) [18][10000/10520] lr: 1.0000e-05 eta: 3:32:03 time: 0.5222 data_time: 0.0810 memory: 17203 loss_visual: 0.0530 loss_lang: 0.1078 loss_fusion: 0.0450 loss: 0.2058 2022/10/06 08:49:16 - mmengine - INFO - Epoch(train) [18][10100/10520] lr: 1.0000e-05 eta: 3:31:04 time: 0.4542 data_time: 0.0032 memory: 17203 loss_visual: 0.0459 loss_lang: 0.1004 loss_fusion: 0.0390 loss: 0.1853 2022/10/06 08:49:51 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 08:50:13 - mmengine - INFO - Epoch(train) [18][10200/10520] lr: 1.0000e-05 eta: 3:30:05 time: 0.3675 data_time: 0.0037 memory: 17203 loss_visual: 0.0512 loss_lang: 0.1098 loss_fusion: 0.0438 loss: 0.2048 2022/10/06 08:51:10 - mmengine - INFO - Epoch(train) [18][10300/10520] lr: 1.0000e-05 eta: 3:29:06 time: 0.3623 data_time: 0.0032 memory: 17203 loss_visual: 0.0521 loss_lang: 0.1082 loss_fusion: 0.0453 loss: 0.2056 2022/10/06 08:52:07 - mmengine - INFO - Epoch(train) [18][10400/10520] lr: 1.0000e-05 eta: 3:28:06 time: 0.3839 data_time: 0.0033 memory: 17203 loss_visual: 0.0506 loss_lang: 0.1095 loss_fusion: 0.0445 loss: 0.2047 2022/10/06 08:53:05 - mmengine - INFO - Epoch(train) [18][10500/10520] lr: 1.0000e-05 eta: 3:27:07 time: 0.5934 data_time: 0.0652 memory: 17203 loss_visual: 0.0507 loss_lang: 0.1093 loss_fusion: 0.0425 loss: 0.2025 2022/10/06 08:53:13 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 08:53:13 - mmengine - INFO - Saving checkpoint at 18 epochs 2022/10/06 08:53:31 - mmengine - INFO - Epoch(val) [18][100/959] eta: 0:00:36 time: 0.0425 data_time: 0.0023 memory: 17203 2022/10/06 08:53:36 - mmengine - INFO - Epoch(val) [18][200/959] eta: 0:00:36 time: 0.0484 data_time: 0.0015 memory: 734 2022/10/06 08:53:40 - mmengine - INFO - Epoch(val) [18][300/959] eta: 0:00:30 time: 0.0466 data_time: 0.0038 memory: 734 2022/10/06 08:53:45 - mmengine - INFO - Epoch(val) [18][400/959] eta: 0:00:24 time: 0.0434 data_time: 0.0018 memory: 734 2022/10/06 08:53:50 - mmengine - INFO - Epoch(val) [18][500/959] eta: 0:00:22 time: 0.0488 data_time: 0.0025 memory: 734 2022/10/06 08:53:55 - mmengine - INFO - Epoch(val) [18][600/959] eta: 0:00:20 time: 0.0559 data_time: 0.0027 memory: 734 2022/10/06 08:54:00 - mmengine - INFO - Epoch(val) [18][700/959] eta: 0:00:10 time: 0.0399 data_time: 0.0017 memory: 734 2022/10/06 08:54:02 - mmengine - INFO - Epoch(val) [18][800/959] eta: 0:00:03 time: 0.0217 data_time: 0.0006 memory: 734 2022/10/06 08:54:05 - mmengine - INFO - Epoch(val) [18][900/959] eta: 0:00:01 time: 0.0219 data_time: 0.0006 memory: 734 2022/10/06 08:54:07 - mmengine - INFO - Epoch(val) [18][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.8785 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9603 SVT/recog/word_acc_ignore_case_symbol: 0.9382 SVTP/recog/word_acc_ignore_case_symbol: 0.8868 IC13/recog/word_acc_ignore_case_symbol: 0.9547 IC15/recog/word_acc_ignore_case_symbol: 0.8122 2022/10/06 08:55:09 - mmengine - INFO - Epoch(train) [19][100/10520] lr: 1.0000e-06 eta: 3:25:56 time: 0.7991 data_time: 0.1270 memory: 17203 loss_visual: 0.0582 loss_lang: 0.1177 loss_fusion: 0.0511 loss: 0.2270 2022/10/06 08:56:01 - mmengine - INFO - Epoch(train) [19][200/10520] lr: 1.0000e-06 eta: 3:24:57 time: 0.7958 data_time: 0.1639 memory: 17203 loss_visual: 0.0514 loss_lang: 0.1065 loss_fusion: 0.0441 loss: 0.2020 2022/10/06 08:56:52 - mmengine - INFO - Epoch(train) [19][300/10520] lr: 1.0000e-06 eta: 3:23:57 time: 0.5781 data_time: 0.0664 memory: 17203 loss_visual: 0.0559 loss_lang: 0.1123 loss_fusion: 0.0480 loss: 0.2162 2022/10/06 08:57:44 - mmengine - INFO - Epoch(train) [19][400/10520] lr: 1.0000e-06 eta: 3:22:57 time: 0.3637 data_time: 0.0120 memory: 17203 loss_visual: 0.0508 loss_lang: 0.1090 loss_fusion: 0.0425 loss: 0.2022 2022/10/06 08:58:37 - mmengine - INFO - Epoch(train) [19][500/10520] lr: 1.0000e-06 eta: 3:21:57 time: 0.4042 data_time: 0.0262 memory: 17203 loss_visual: 0.0518 loss_lang: 0.1091 loss_fusion: 0.0442 loss: 0.2051 2022/10/06 08:59:29 - mmengine - INFO - Epoch(train) [19][600/10520] lr: 1.0000e-06 eta: 3:20:58 time: 0.4119 data_time: 0.0209 memory: 17203 loss_visual: 0.0511 loss_lang: 0.1044 loss_fusion: 0.0441 loss: 0.1995 2022/10/06 08:59:48 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 09:00:20 - mmengine - INFO - Epoch(train) [19][700/10520] lr: 1.0000e-06 eta: 3:19:58 time: 0.3918 data_time: 0.0160 memory: 17203 loss_visual: 0.0514 loss_lang: 0.1076 loss_fusion: 0.0452 loss: 0.2042 2022/10/06 09:01:11 - mmengine - INFO - Epoch(train) [19][800/10520] lr: 1.0000e-06 eta: 3:18:58 time: 0.3823 data_time: 0.0272 memory: 17203 loss_visual: 0.0476 loss_lang: 0.1051 loss_fusion: 0.0402 loss: 0.1928 2022/10/06 09:02:07 - mmengine - INFO - Epoch(train) [19][900/10520] lr: 1.0000e-06 eta: 3:17:59 time: 0.7816 data_time: 0.1520 memory: 17203 loss_visual: 0.0492 loss_lang: 0.1068 loss_fusion: 0.0426 loss: 0.1985 2022/10/06 09:03:00 - mmengine - INFO - Epoch(train) [19][1000/10520] lr: 1.0000e-06 eta: 3:16:59 time: 0.8299 data_time: 0.1867 memory: 17203 loss_visual: 0.0474 loss_lang: 0.1028 loss_fusion: 0.0400 loss: 0.1902 2022/10/06 09:03:52 - mmengine - INFO - Epoch(train) [19][1100/10520] lr: 1.0000e-06 eta: 3:15:59 time: 0.5745 data_time: 0.0856 memory: 17203 loss_visual: 0.0453 loss_lang: 0.1011 loss_fusion: 0.0395 loss: 0.1859 2022/10/06 09:04:44 - mmengine - INFO - Epoch(train) [19][1200/10520] lr: 1.0000e-06 eta: 3:15:00 time: 0.3648 data_time: 0.0111 memory: 17203 loss_visual: 0.0456 loss_lang: 0.1009 loss_fusion: 0.0381 loss: 0.1847 2022/10/06 09:05:36 - mmengine - INFO - Epoch(train) [19][1300/10520] lr: 1.0000e-06 eta: 3:14:00 time: 0.3620 data_time: 0.0247 memory: 17203 loss_visual: 0.0451 loss_lang: 0.0970 loss_fusion: 0.0382 loss: 0.1803 2022/10/06 09:06:29 - mmengine - INFO - Epoch(train) [19][1400/10520] lr: 1.0000e-06 eta: 3:13:00 time: 0.4346 data_time: 0.0167 memory: 17203 loss_visual: 0.0485 loss_lang: 0.1096 loss_fusion: 0.0416 loss: 0.1996 2022/10/06 09:07:22 - mmengine - INFO - Epoch(train) [19][1500/10520] lr: 1.0000e-06 eta: 3:12:01 time: 0.3904 data_time: 0.0162 memory: 17203 loss_visual: 0.0455 loss_lang: 0.1050 loss_fusion: 0.0387 loss: 0.1892 2022/10/06 09:08:14 - mmengine - INFO - Epoch(train) [19][1600/10520] lr: 1.0000e-06 eta: 3:11:01 time: 0.3880 data_time: 0.0252 memory: 17203 loss_visual: 0.0567 loss_lang: 0.1141 loss_fusion: 0.0488 loss: 0.2196 2022/10/06 09:08:39 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 09:09:10 - mmengine - INFO - Epoch(train) [19][1700/10520] lr: 1.0000e-06 eta: 3:10:02 time: 0.7721 data_time: 0.1534 memory: 17203 loss_visual: 0.0515 loss_lang: 0.1091 loss_fusion: 0.0434 loss: 0.2040 2022/10/06 09:10:02 - mmengine - INFO - Epoch(train) [19][1800/10520] lr: 1.0000e-06 eta: 3:09:02 time: 0.7892 data_time: 0.1703 memory: 17203 loss_visual: 0.0550 loss_lang: 0.1112 loss_fusion: 0.0480 loss: 0.2143 2022/10/06 09:10:54 - mmengine - INFO - Epoch(train) [19][1900/10520] lr: 1.0000e-06 eta: 3:08:02 time: 0.5970 data_time: 0.0875 memory: 17203 loss_visual: 0.0487 loss_lang: 0.1063 loss_fusion: 0.0433 loss: 0.1982 2022/10/06 09:11:46 - mmengine - INFO - Epoch(train) [19][2000/10520] lr: 1.0000e-06 eta: 3:07:03 time: 0.3698 data_time: 0.0109 memory: 17203 loss_visual: 0.0495 loss_lang: 0.1090 loss_fusion: 0.0426 loss: 0.2012 2022/10/06 09:12:38 - mmengine - INFO - Epoch(train) [19][2100/10520] lr: 1.0000e-06 eta: 3:06:03 time: 0.3680 data_time: 0.0289 memory: 17203 loss_visual: 0.0432 loss_lang: 0.0986 loss_fusion: 0.0361 loss: 0.1779 2022/10/06 09:13:30 - mmengine - INFO - Epoch(train) [19][2200/10520] lr: 1.0000e-06 eta: 3:05:04 time: 0.4405 data_time: 0.0181 memory: 17203 loss_visual: 0.0514 loss_lang: 0.1045 loss_fusion: 0.0445 loss: 0.2003 2022/10/06 09:14:23 - mmengine - INFO - Epoch(train) [19][2300/10520] lr: 1.0000e-06 eta: 3:04:04 time: 0.4122 data_time: 0.0159 memory: 17203 loss_visual: 0.0505 loss_lang: 0.1106 loss_fusion: 0.0451 loss: 0.2063 2022/10/06 09:15:15 - mmengine - INFO - Epoch(train) [19][2400/10520] lr: 1.0000e-06 eta: 3:03:04 time: 0.3744 data_time: 0.0244 memory: 17203 loss_visual: 0.0511 loss_lang: 0.1066 loss_fusion: 0.0435 loss: 0.2012 2022/10/06 09:16:11 - mmengine - INFO - Epoch(train) [19][2500/10520] lr: 1.0000e-06 eta: 3:02:05 time: 0.8038 data_time: 0.1578 memory: 17203 loss_visual: 0.0476 loss_lang: 0.1022 loss_fusion: 0.0411 loss: 0.1909 2022/10/06 09:17:04 - mmengine - INFO - Epoch(train) [19][2600/10520] lr: 1.0000e-06 eta: 3:01:06 time: 0.8533 data_time: 0.2002 memory: 17203 loss_visual: 0.0413 loss_lang: 0.0979 loss_fusion: 0.0337 loss: 0.1728 2022/10/06 09:17:24 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 09:17:57 - mmengine - INFO - Epoch(train) [19][2700/10520] lr: 1.0000e-06 eta: 3:00:06 time: 0.5814 data_time: 0.0967 memory: 17203 loss_visual: 0.0497 loss_lang: 0.1086 loss_fusion: 0.0425 loss: 0.2008 2022/10/06 09:18:49 - mmengine - INFO - Epoch(train) [19][2800/10520] lr: 1.0000e-06 eta: 2:59:07 time: 0.3789 data_time: 0.0114 memory: 17203 loss_visual: 0.0445 loss_lang: 0.1071 loss_fusion: 0.0380 loss: 0.1896 2022/10/06 09:19:40 - mmengine - INFO - Epoch(train) [19][2900/10520] lr: 1.0000e-06 eta: 2:58:07 time: 0.3675 data_time: 0.0251 memory: 17203 loss_visual: 0.0441 loss_lang: 0.1023 loss_fusion: 0.0367 loss: 0.1831 2022/10/06 09:20:33 - mmengine - INFO - Epoch(train) [19][3000/10520] lr: 1.0000e-06 eta: 2:57:07 time: 0.4061 data_time: 0.0208 memory: 17203 loss_visual: 0.0516 loss_lang: 0.1097 loss_fusion: 0.0442 loss: 0.2056 2022/10/06 09:21:25 - mmengine - INFO - Epoch(train) [19][3100/10520] lr: 1.0000e-06 eta: 2:56:08 time: 0.3946 data_time: 0.0165 memory: 17203 loss_visual: 0.0482 loss_lang: 0.1053 loss_fusion: 0.0401 loss: 0.1936 2022/10/06 09:22:17 - mmengine - INFO - Epoch(train) [19][3200/10520] lr: 1.0000e-06 eta: 2:55:08 time: 0.3935 data_time: 0.0473 memory: 17203 loss_visual: 0.0551 loss_lang: 0.1101 loss_fusion: 0.0480 loss: 0.2132 2022/10/06 09:23:13 - mmengine - INFO - Epoch(train) [19][3300/10520] lr: 1.0000e-06 eta: 2:54:09 time: 0.7888 data_time: 0.1815 memory: 17203 loss_visual: 0.0575 loss_lang: 0.1117 loss_fusion: 0.0501 loss: 0.2192 2022/10/06 09:24:05 - mmengine - INFO - Epoch(train) [19][3400/10520] lr: 1.0000e-06 eta: 2:53:10 time: 0.8330 data_time: 0.1476 memory: 17203 loss_visual: 0.0507 loss_lang: 0.1073 loss_fusion: 0.0446 loss: 0.2025 2022/10/06 09:24:56 - mmengine - INFO - Epoch(train) [19][3500/10520] lr: 1.0000e-06 eta: 2:52:10 time: 0.5529 data_time: 0.0903 memory: 17203 loss_visual: 0.0501 loss_lang: 0.1017 loss_fusion: 0.0416 loss: 0.1934 2022/10/06 09:25:49 - mmengine - INFO - Epoch(train) [19][3600/10520] lr: 1.0000e-06 eta: 2:51:11 time: 0.3929 data_time: 0.0111 memory: 17203 loss_visual: 0.0492 loss_lang: 0.1098 loss_fusion: 0.0422 loss: 0.2012 2022/10/06 09:26:09 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 09:26:41 - mmengine - INFO - Epoch(train) [19][3700/10520] lr: 1.0000e-06 eta: 2:50:11 time: 0.3854 data_time: 0.0450 memory: 17203 loss_visual: 0.0487 loss_lang: 0.1015 loss_fusion: 0.0413 loss: 0.1915 2022/10/06 09:27:33 - mmengine - INFO - Epoch(train) [19][3800/10520] lr: 1.0000e-06 eta: 2:49:12 time: 0.3951 data_time: 0.0194 memory: 17203 loss_visual: 0.0481 loss_lang: 0.0989 loss_fusion: 0.0407 loss: 0.1877 2022/10/06 09:28:26 - mmengine - INFO - Epoch(train) [19][3900/10520] lr: 1.0000e-06 eta: 2:48:12 time: 0.3945 data_time: 0.0215 memory: 17203 loss_visual: 0.0523 loss_lang: 0.1103 loss_fusion: 0.0450 loss: 0.2076 2022/10/06 09:29:18 - mmengine - INFO - Epoch(train) [19][4000/10520] lr: 1.0000e-06 eta: 2:47:13 time: 0.3940 data_time: 0.0281 memory: 17203 loss_visual: 0.0518 loss_lang: 0.1116 loss_fusion: 0.0446 loss: 0.2080 2022/10/06 09:30:14 - mmengine - INFO - Epoch(train) [19][4100/10520] lr: 1.0000e-06 eta: 2:46:14 time: 0.7640 data_time: 0.1652 memory: 17203 loss_visual: 0.0565 loss_lang: 0.1167 loss_fusion: 0.0492 loss: 0.2224 2022/10/06 09:31:08 - mmengine - INFO - Epoch(train) [19][4200/10520] lr: 1.0000e-06 eta: 2:45:14 time: 0.8288 data_time: 0.2034 memory: 17203 loss_visual: 0.0429 loss_lang: 0.0985 loss_fusion: 0.0370 loss: 0.1784 2022/10/06 09:32:01 - mmengine - INFO - Epoch(train) [19][4300/10520] lr: 1.0000e-06 eta: 2:44:15 time: 0.5886 data_time: 0.1028 memory: 17203 loss_visual: 0.0460 loss_lang: 0.1020 loss_fusion: 0.0386 loss: 0.1866 2022/10/06 09:32:53 - mmengine - INFO - Epoch(train) [19][4400/10520] lr: 1.0000e-06 eta: 2:43:15 time: 0.3648 data_time: 0.0118 memory: 17203 loss_visual: 0.0517 loss_lang: 0.1087 loss_fusion: 0.0446 loss: 0.2050 2022/10/06 09:33:46 - mmengine - INFO - Epoch(train) [19][4500/10520] lr: 1.0000e-06 eta: 2:42:16 time: 0.3760 data_time: 0.0224 memory: 17203 loss_visual: 0.0465 loss_lang: 0.1021 loss_fusion: 0.0389 loss: 0.1875 2022/10/06 09:34:39 - mmengine - INFO - Epoch(train) [19][4600/10520] lr: 1.0000e-06 eta: 2:41:17 time: 0.4269 data_time: 0.0168 memory: 17203 loss_visual: 0.0531 loss_lang: 0.1119 loss_fusion: 0.0458 loss: 0.2108 2022/10/06 09:34:58 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 09:35:31 - mmengine - INFO - Epoch(train) [19][4700/10520] lr: 1.0000e-06 eta: 2:40:17 time: 0.4367 data_time: 0.0207 memory: 17203 loss_visual: 0.0447 loss_lang: 0.1020 loss_fusion: 0.0383 loss: 0.1850 2022/10/06 09:36:22 - mmengine - INFO - Epoch(train) [19][4800/10520] lr: 1.0000e-06 eta: 2:39:18 time: 0.4035 data_time: 0.0296 memory: 17203 loss_visual: 0.0476 loss_lang: 0.1041 loss_fusion: 0.0385 loss: 0.1902 2022/10/06 09:37:19 - mmengine - INFO - Epoch(train) [19][4900/10520] lr: 1.0000e-06 eta: 2:38:19 time: 0.7855 data_time: 0.1504 memory: 17203 loss_visual: 0.0473 loss_lang: 0.1012 loss_fusion: 0.0398 loss: 0.1883 2022/10/06 09:38:11 - mmengine - INFO - Epoch(train) [19][5000/10520] lr: 1.0000e-06 eta: 2:37:19 time: 0.7872 data_time: 0.1717 memory: 17203 loss_visual: 0.0505 loss_lang: 0.1053 loss_fusion: 0.0434 loss: 0.1992 2022/10/06 09:39:04 - mmengine - INFO - Epoch(train) [19][5100/10520] lr: 1.0000e-06 eta: 2:36:20 time: 0.6130 data_time: 0.0807 memory: 17203 loss_visual: 0.0475 loss_lang: 0.1067 loss_fusion: 0.0404 loss: 0.1946 2022/10/06 09:39:56 - mmengine - INFO - Epoch(train) [19][5200/10520] lr: 1.0000e-06 eta: 2:35:20 time: 0.3868 data_time: 0.0121 memory: 17203 loss_visual: 0.0425 loss_lang: 0.0984 loss_fusion: 0.0359 loss: 0.1768 2022/10/06 09:40:47 - mmengine - INFO - Epoch(train) [19][5300/10520] lr: 1.0000e-06 eta: 2:34:21 time: 0.3778 data_time: 0.0291 memory: 17203 loss_visual: 0.0531 loss_lang: 0.1062 loss_fusion: 0.0461 loss: 0.2054 2022/10/06 09:41:39 - mmengine - INFO - Epoch(train) [19][5400/10520] lr: 1.0000e-06 eta: 2:33:22 time: 0.3903 data_time: 0.0182 memory: 17203 loss_visual: 0.0352 loss_lang: 0.0915 loss_fusion: 0.0295 loss: 0.1562 2022/10/06 09:42:32 - mmengine - INFO - Epoch(train) [19][5500/10520] lr: 1.0000e-06 eta: 2:32:22 time: 0.4091 data_time: 0.0242 memory: 17203 loss_visual: 0.0449 loss_lang: 0.0979 loss_fusion: 0.0373 loss: 0.1800 2022/10/06 09:43:24 - mmengine - INFO - Epoch(train) [19][5600/10520] lr: 1.0000e-06 eta: 2:31:23 time: 0.3835 data_time: 0.0250 memory: 17203 loss_visual: 0.0392 loss_lang: 0.0953 loss_fusion: 0.0321 loss: 0.1667 2022/10/06 09:43:49 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 09:44:21 - mmengine - INFO - Epoch(train) [19][5700/10520] lr: 1.0000e-06 eta: 2:30:24 time: 0.8428 data_time: 0.1313 memory: 17203 loss_visual: 0.0505 loss_lang: 0.1068 loss_fusion: 0.0436 loss: 0.2009 2022/10/06 09:45:13 - mmengine - INFO - Epoch(train) [19][5800/10520] lr: 1.0000e-06 eta: 2:29:25 time: 0.7931 data_time: 0.1720 memory: 17203 loss_visual: 0.0478 loss_lang: 0.1044 loss_fusion: 0.0416 loss: 0.1939 2022/10/06 09:46:05 - mmengine - INFO - Epoch(train) [19][5900/10520] lr: 1.0000e-06 eta: 2:28:25 time: 0.5640 data_time: 0.0773 memory: 17203 loss_visual: 0.0432 loss_lang: 0.1046 loss_fusion: 0.0361 loss: 0.1839 2022/10/06 09:46:57 - mmengine - INFO - Epoch(train) [19][6000/10520] lr: 1.0000e-06 eta: 2:27:26 time: 0.3672 data_time: 0.0121 memory: 17203 loss_visual: 0.0571 loss_lang: 0.1130 loss_fusion: 0.0490 loss: 0.2191 2022/10/06 09:47:48 - mmengine - INFO - Epoch(train) [19][6100/10520] lr: 1.0000e-06 eta: 2:26:26 time: 0.3606 data_time: 0.0240 memory: 17203 loss_visual: 0.0549 loss_lang: 0.1098 loss_fusion: 0.0474 loss: 0.2120 2022/10/06 09:48:40 - mmengine - INFO - Epoch(train) [19][6200/10520] lr: 1.0000e-06 eta: 2:25:27 time: 0.4302 data_time: 0.0183 memory: 17203 loss_visual: 0.0450 loss_lang: 0.1022 loss_fusion: 0.0385 loss: 0.1857 2022/10/06 09:49:33 - mmengine - INFO - Epoch(train) [19][6300/10520] lr: 1.0000e-06 eta: 2:24:28 time: 0.4252 data_time: 0.0183 memory: 17203 loss_visual: 0.0558 loss_lang: 0.1113 loss_fusion: 0.0479 loss: 0.2149 2022/10/06 09:50:25 - mmengine - INFO - Epoch(train) [19][6400/10520] lr: 1.0000e-06 eta: 2:23:29 time: 0.3769 data_time: 0.0253 memory: 17203 loss_visual: 0.0508 loss_lang: 0.1091 loss_fusion: 0.0434 loss: 0.2033 2022/10/06 09:51:22 - mmengine - INFO - Epoch(train) [19][6500/10520] lr: 1.0000e-06 eta: 2:22:30 time: 0.8029 data_time: 0.1656 memory: 17203 loss_visual: 0.0467 loss_lang: 0.1007 loss_fusion: 0.0394 loss: 0.1868 2022/10/06 09:52:14 - mmengine - INFO - Epoch(train) [19][6600/10520] lr: 1.0000e-06 eta: 2:21:30 time: 0.8051 data_time: 0.1635 memory: 17203 loss_visual: 0.0484 loss_lang: 0.1023 loss_fusion: 0.0403 loss: 0.1909 2022/10/06 09:52:33 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 09:53:06 - mmengine - INFO - Epoch(train) [19][6700/10520] lr: 1.0000e-06 eta: 2:20:31 time: 0.5833 data_time: 0.0736 memory: 17203 loss_visual: 0.0460 loss_lang: 0.1025 loss_fusion: 0.0398 loss: 0.1883 2022/10/06 09:53:59 - mmengine - INFO - Epoch(train) [19][6800/10520] lr: 1.0000e-06 eta: 2:19:32 time: 0.3675 data_time: 0.0121 memory: 17203 loss_visual: 0.0494 loss_lang: 0.1054 loss_fusion: 0.0428 loss: 0.1976 2022/10/06 09:54:52 - mmengine - INFO - Epoch(train) [19][6900/10520] lr: 1.0000e-06 eta: 2:18:33 time: 0.3603 data_time: 0.0239 memory: 17203 loss_visual: 0.0529 loss_lang: 0.1140 loss_fusion: 0.0455 loss: 0.2125 2022/10/06 09:55:44 - mmengine - INFO - Epoch(train) [19][7000/10520] lr: 1.0000e-06 eta: 2:17:33 time: 0.4009 data_time: 0.0204 memory: 17203 loss_visual: 0.0521 loss_lang: 0.1110 loss_fusion: 0.0439 loss: 0.2069 2022/10/06 09:56:36 - mmengine - INFO - Epoch(train) [19][7100/10520] lr: 1.0000e-06 eta: 2:16:34 time: 0.3917 data_time: 0.0174 memory: 17203 loss_visual: 0.0490 loss_lang: 0.1051 loss_fusion: 0.0420 loss: 0.1961 2022/10/06 09:57:28 - mmengine - INFO - Epoch(train) [19][7200/10520] lr: 1.0000e-06 eta: 2:15:35 time: 0.3762 data_time: 0.0260 memory: 17203 loss_visual: 0.0558 loss_lang: 0.1137 loss_fusion: 0.0474 loss: 0.2169 2022/10/06 09:58:25 - mmengine - INFO - Epoch(train) [19][7300/10520] lr: 1.0000e-06 eta: 2:14:36 time: 0.7926 data_time: 0.1575 memory: 17203 loss_visual: 0.0519 loss_lang: 0.1074 loss_fusion: 0.0448 loss: 0.2041 2022/10/06 09:59:17 - mmengine - INFO - Epoch(train) [19][7400/10520] lr: 1.0000e-06 eta: 2:13:37 time: 0.8449 data_time: 0.1645 memory: 17203 loss_visual: 0.0464 loss_lang: 0.1038 loss_fusion: 0.0399 loss: 0.1901 2022/10/06 10:00:08 - mmengine - INFO - Epoch(train) [19][7500/10520] lr: 1.0000e-06 eta: 2:12:37 time: 0.5720 data_time: 0.0755 memory: 17203 loss_visual: 0.0504 loss_lang: 0.1129 loss_fusion: 0.0423 loss: 0.2056 2022/10/06 10:01:00 - mmengine - INFO - Epoch(train) [19][7600/10520] lr: 1.0000e-06 eta: 2:11:38 time: 0.3671 data_time: 0.0121 memory: 17203 loss_visual: 0.0488 loss_lang: 0.1072 loss_fusion: 0.0422 loss: 0.1982 2022/10/06 10:01:21 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 10:01:52 - mmengine - INFO - Epoch(train) [19][7700/10520] lr: 1.0000e-06 eta: 2:10:39 time: 0.3678 data_time: 0.0244 memory: 17203 loss_visual: 0.0480 loss_lang: 0.1037 loss_fusion: 0.0419 loss: 0.1937 2022/10/06 10:02:44 - mmengine - INFO - Epoch(train) [19][7800/10520] lr: 1.0000e-06 eta: 2:09:40 time: 0.4066 data_time: 0.0173 memory: 17203 loss_visual: 0.0478 loss_lang: 0.1051 loss_fusion: 0.0408 loss: 0.1937 2022/10/06 10:03:36 - mmengine - INFO - Epoch(train) [19][7900/10520] lr: 1.0000e-06 eta: 2:08:40 time: 0.4521 data_time: 0.0168 memory: 17203 loss_visual: 0.0496 loss_lang: 0.1077 loss_fusion: 0.0432 loss: 0.2005 2022/10/06 10:04:28 - mmengine - INFO - Epoch(train) [19][8000/10520] lr: 1.0000e-06 eta: 2:07:41 time: 0.3767 data_time: 0.0253 memory: 17203 loss_visual: 0.0506 loss_lang: 0.1070 loss_fusion: 0.0445 loss: 0.2022 2022/10/06 10:05:23 - mmengine - INFO - Epoch(train) [19][8100/10520] lr: 1.0000e-06 eta: 2:06:42 time: 0.7532 data_time: 0.1576 memory: 17203 loss_visual: 0.0479 loss_lang: 0.1051 loss_fusion: 0.0404 loss: 0.1933 2022/10/06 10:06:16 - mmengine - INFO - Epoch(train) [19][8200/10520] lr: 1.0000e-06 eta: 2:05:43 time: 0.7987 data_time: 0.1866 memory: 17203 loss_visual: 0.0549 loss_lang: 0.1081 loss_fusion: 0.0471 loss: 0.2101 2022/10/06 10:07:08 - mmengine - INFO - Epoch(train) [19][8300/10520] lr: 1.0000e-06 eta: 2:04:44 time: 0.6157 data_time: 0.0717 memory: 17203 loss_visual: 0.0513 loss_lang: 0.1106 loss_fusion: 0.0441 loss: 0.2061 2022/10/06 10:08:01 - mmengine - INFO - Epoch(train) [19][8400/10520] lr: 1.0000e-06 eta: 2:03:45 time: 0.3617 data_time: 0.0118 memory: 17203 loss_visual: 0.0406 loss_lang: 0.0939 loss_fusion: 0.0338 loss: 0.1683 2022/10/06 10:08:53 - mmengine - INFO - Epoch(train) [19][8500/10520] lr: 1.0000e-06 eta: 2:02:46 time: 0.3857 data_time: 0.0287 memory: 17203 loss_visual: 0.0465 loss_lang: 0.1025 loss_fusion: 0.0402 loss: 0.1893 2022/10/06 10:09:45 - mmengine - INFO - Epoch(train) [19][8600/10520] lr: 1.0000e-06 eta: 2:01:46 time: 0.4018 data_time: 0.0187 memory: 17203 loss_visual: 0.0508 loss_lang: 0.1041 loss_fusion: 0.0433 loss: 0.1982 2022/10/06 10:10:05 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 10:10:38 - mmengine - INFO - Epoch(train) [19][8700/10520] lr: 1.0000e-06 eta: 2:00:47 time: 0.3929 data_time: 0.0160 memory: 17203 loss_visual: 0.0526 loss_lang: 0.1073 loss_fusion: 0.0444 loss: 0.2043 2022/10/06 10:11:30 - mmengine - INFO - Epoch(train) [19][8800/10520] lr: 1.0000e-06 eta: 1:59:48 time: 0.3778 data_time: 0.0302 memory: 17203 loss_visual: 0.0501 loss_lang: 0.1075 loss_fusion: 0.0434 loss: 0.2010 2022/10/06 10:12:27 - mmengine - INFO - Epoch(train) [19][8900/10520] lr: 1.0000e-06 eta: 1:58:49 time: 0.8135 data_time: 0.1918 memory: 17203 loss_visual: 0.0502 loss_lang: 0.1110 loss_fusion: 0.0435 loss: 0.2047 2022/10/06 10:13:20 - mmengine - INFO - Epoch(train) [19][9000/10520] lr: 1.0000e-06 eta: 1:57:50 time: 0.8135 data_time: 0.2133 memory: 17203 loss_visual: 0.0437 loss_lang: 0.1006 loss_fusion: 0.0375 loss: 0.1818 2022/10/06 10:14:13 - mmengine - INFO - Epoch(train) [19][9100/10520] lr: 1.0000e-06 eta: 1:56:51 time: 0.5565 data_time: 0.0863 memory: 17203 loss_visual: 0.0551 loss_lang: 0.1171 loss_fusion: 0.0472 loss: 0.2195 2022/10/06 10:15:06 - mmengine - INFO - Epoch(train) [19][9200/10520] lr: 1.0000e-06 eta: 1:55:52 time: 0.3690 data_time: 0.0139 memory: 17203 loss_visual: 0.0480 loss_lang: 0.1051 loss_fusion: 0.0417 loss: 0.1948 2022/10/06 10:15:58 - mmengine - INFO - Epoch(train) [19][9300/10520] lr: 1.0000e-06 eta: 1:54:53 time: 0.4100 data_time: 0.0317 memory: 17203 loss_visual: 0.0592 loss_lang: 0.1198 loss_fusion: 0.0521 loss: 0.2311 2022/10/06 10:16:51 - mmengine - INFO - Epoch(train) [19][9400/10520] lr: 1.0000e-06 eta: 1:53:54 time: 0.3944 data_time: 0.0227 memory: 17203 loss_visual: 0.0427 loss_lang: 0.0996 loss_fusion: 0.0363 loss: 0.1786 2022/10/06 10:17:43 - mmengine - INFO - Epoch(train) [19][9500/10520] lr: 1.0000e-06 eta: 1:52:55 time: 0.3986 data_time: 0.0212 memory: 17203 loss_visual: 0.0519 loss_lang: 0.1040 loss_fusion: 0.0438 loss: 0.1997 2022/10/06 10:18:35 - mmengine - INFO - Epoch(train) [19][9600/10520] lr: 1.0000e-06 eta: 1:51:56 time: 0.3873 data_time: 0.0309 memory: 17203 loss_visual: 0.0483 loss_lang: 0.1055 loss_fusion: 0.0413 loss: 0.1951 2022/10/06 10:18:59 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 10:19:31 - mmengine - INFO - Epoch(train) [19][9700/10520] lr: 1.0000e-06 eta: 1:50:57 time: 0.7897 data_time: 0.1724 memory: 17203 loss_visual: 0.0558 loss_lang: 0.1118 loss_fusion: 0.0493 loss: 0.2169 2022/10/06 10:20:24 - mmengine - INFO - Epoch(train) [19][9800/10520] lr: 1.0000e-06 eta: 1:49:58 time: 0.8198 data_time: 0.1716 memory: 17203 loss_visual: 0.0490 loss_lang: 0.1095 loss_fusion: 0.0421 loss: 0.2006 2022/10/06 10:21:15 - mmengine - INFO - Epoch(train) [19][9900/10520] lr: 1.0000e-06 eta: 1:48:59 time: 0.5704 data_time: 0.0750 memory: 17203 loss_visual: 0.0482 loss_lang: 0.1006 loss_fusion: 0.0410 loss: 0.1898 2022/10/06 10:22:07 - mmengine - INFO - Epoch(train) [19][10000/10520] lr: 1.0000e-06 eta: 1:48:00 time: 0.3882 data_time: 0.0145 memory: 17203 loss_visual: 0.0461 loss_lang: 0.1031 loss_fusion: 0.0383 loss: 0.1875 2022/10/06 10:22:59 - mmengine - INFO - Epoch(train) [19][10100/10520] lr: 1.0000e-06 eta: 1:47:01 time: 0.3635 data_time: 0.0260 memory: 17203 loss_visual: 0.0550 loss_lang: 0.1107 loss_fusion: 0.0468 loss: 0.2124 2022/10/06 10:23:51 - mmengine - INFO - Epoch(train) [19][10200/10520] lr: 1.0000e-06 eta: 1:46:01 time: 0.4018 data_time: 0.0178 memory: 17203 loss_visual: 0.0497 loss_lang: 0.1071 loss_fusion: 0.0428 loss: 0.1996 2022/10/06 10:24:43 - mmengine - INFO - Epoch(train) [19][10300/10520] lr: 1.0000e-06 eta: 1:45:02 time: 0.4092 data_time: 0.0162 memory: 17203 loss_visual: 0.0488 loss_lang: 0.1059 loss_fusion: 0.0419 loss: 0.1966 2022/10/06 10:25:36 - mmengine - INFO - Epoch(train) [19][10400/10520] lr: 1.0000e-06 eta: 1:44:03 time: 0.3764 data_time: 0.0281 memory: 17203 loss_visual: 0.0525 loss_lang: 0.1054 loss_fusion: 0.0442 loss: 0.2020 2022/10/06 10:26:29 - mmengine - INFO - Epoch(train) [19][10500/10520] lr: 1.0000e-06 eta: 1:43:04 time: 0.5754 data_time: 0.0830 memory: 17203 loss_visual: 0.0500 loss_lang: 0.1090 loss_fusion: 0.0422 loss: 0.2012 2022/10/06 10:26:36 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 10:26:36 - mmengine - INFO - Saving checkpoint at 19 epochs 2022/10/06 10:26:51 - mmengine - INFO - Epoch(val) [19][100/959] eta: 0:00:42 time: 0.0494 data_time: 0.0019 memory: 17203 2022/10/06 10:26:55 - mmengine - INFO - Epoch(val) [19][200/959] eta: 0:00:35 time: 0.0468 data_time: 0.0030 memory: 734 2022/10/06 10:27:00 - mmengine - INFO - Epoch(val) [19][300/959] eta: 0:00:27 time: 0.0420 data_time: 0.0018 memory: 734 2022/10/06 10:27:05 - mmengine - INFO - Epoch(val) [19][400/959] eta: 0:00:26 time: 0.0467 data_time: 0.0014 memory: 734 2022/10/06 10:27:10 - mmengine - INFO - Epoch(val) [19][500/959] eta: 0:00:20 time: 0.0456 data_time: 0.0019 memory: 734 2022/10/06 10:27:15 - mmengine - INFO - Epoch(val) [19][600/959] eta: 0:00:16 time: 0.0468 data_time: 0.0016 memory: 734 2022/10/06 10:27:20 - mmengine - INFO - Epoch(val) [19][700/959] eta: 0:00:12 time: 0.0481 data_time: 0.0015 memory: 734 2022/10/06 10:27:24 - mmengine - INFO - Epoch(val) [19][800/959] eta: 0:00:03 time: 0.0241 data_time: 0.0006 memory: 734 2022/10/06 10:27:26 - mmengine - INFO - Epoch(val) [19][900/959] eta: 0:00:01 time: 0.0220 data_time: 0.0006 memory: 734 2022/10/06 10:27:28 - mmengine - INFO - Epoch(val) [19][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.8750 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9593 SVT/recog/word_acc_ignore_case_symbol: 0.9366 SVTP/recog/word_acc_ignore_case_symbol: 0.8884 IC13/recog/word_acc_ignore_case_symbol: 0.9567 IC15/recog/word_acc_ignore_case_symbol: 0.8113 2022/10/06 10:28:32 - mmengine - INFO - Epoch(train) [20][100/10520] lr: 1.0000e-06 eta: 1:41:54 time: 0.7918 data_time: 0.2015 memory: 17203 loss_visual: 0.0501 loss_lang: 0.1086 loss_fusion: 0.0438 loss: 0.2025 2022/10/06 10:28:41 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 10:29:29 - mmengine - INFO - Epoch(train) [20][200/10520] lr: 1.0000e-06 eta: 1:40:55 time: 0.9262 data_time: 0.2208 memory: 17203 loss_visual: 0.0485 loss_lang: 0.1077 loss_fusion: 0.0400 loss: 0.1962 2022/10/06 10:30:26 - mmengine - INFO - Epoch(train) [20][300/10520] lr: 1.0000e-06 eta: 1:39:56 time: 0.7870 data_time: 0.0727 memory: 17203 loss_visual: 0.0532 loss_lang: 0.1070 loss_fusion: 0.0464 loss: 0.2067 2022/10/06 10:31:21 - mmengine - INFO - Epoch(train) [20][400/10520] lr: 1.0000e-06 eta: 1:38:58 time: 0.4689 data_time: 0.0611 memory: 17203 loss_visual: 0.0504 loss_lang: 0.1080 loss_fusion: 0.0429 loss: 0.2014 2022/10/06 10:32:17 - mmengine - INFO - Epoch(train) [20][500/10520] lr: 1.0000e-06 eta: 1:37:59 time: 0.3793 data_time: 0.0034 memory: 17203 loss_visual: 0.0552 loss_lang: 0.1082 loss_fusion: 0.0468 loss: 0.2102 2022/10/06 10:33:13 - mmengine - INFO - Epoch(train) [20][600/10520] lr: 1.0000e-06 eta: 1:37:00 time: 0.3681 data_time: 0.0031 memory: 17203 loss_visual: 0.0466 loss_lang: 0.1050 loss_fusion: 0.0394 loss: 0.1909 2022/10/06 10:34:09 - mmengine - INFO - Epoch(train) [20][700/10520] lr: 1.0000e-06 eta: 1:36:01 time: 0.3718 data_time: 0.0032 memory: 17203 loss_visual: 0.0468 loss_lang: 0.1028 loss_fusion: 0.0411 loss: 0.1907 2022/10/06 10:35:06 - mmengine - INFO - Epoch(train) [20][800/10520] lr: 1.0000e-06 eta: 1:35:02 time: 0.3645 data_time: 0.0036 memory: 17203 loss_visual: 0.0483 loss_lang: 0.1028 loss_fusion: 0.0407 loss: 0.1918 2022/10/06 10:36:06 - mmengine - INFO - Epoch(train) [20][900/10520] lr: 1.0000e-06 eta: 1:34:04 time: 0.8026 data_time: 0.1329 memory: 17203 loss_visual: 0.0425 loss_lang: 0.0971 loss_fusion: 0.0367 loss: 0.1763 2022/10/06 10:37:05 - mmengine - INFO - Epoch(train) [20][1000/10520] lr: 1.0000e-06 eta: 1:33:05 time: 1.0540 data_time: 0.2305 memory: 17203 loss_visual: 0.0533 loss_lang: 0.1085 loss_fusion: 0.0458 loss: 0.2076 2022/10/06 10:38:01 - mmengine - INFO - Epoch(train) [20][1100/10520] lr: 1.0000e-06 eta: 1:32:06 time: 0.7937 data_time: 0.0382 memory: 17203 loss_visual: 0.0491 loss_lang: 0.1019 loss_fusion: 0.0416 loss: 0.1927 2022/10/06 10:38:09 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 10:38:58 - mmengine - INFO - Epoch(train) [20][1200/10520] lr: 1.0000e-06 eta: 1:31:08 time: 0.4656 data_time: 0.0552 memory: 17203 loss_visual: 0.0491 loss_lang: 0.1062 loss_fusion: 0.0413 loss: 0.1966 2022/10/06 10:39:55 - mmengine - INFO - Epoch(train) [20][1300/10520] lr: 1.0000e-06 eta: 1:30:09 time: 0.3627 data_time: 0.0033 memory: 17203 loss_visual: 0.0463 loss_lang: 0.0993 loss_fusion: 0.0394 loss: 0.1850 2022/10/06 10:40:51 - mmengine - INFO - Epoch(train) [20][1400/10520] lr: 1.0000e-06 eta: 1:29:10 time: 0.3693 data_time: 0.0033 memory: 17203 loss_visual: 0.0515 loss_lang: 0.1105 loss_fusion: 0.0446 loss: 0.2066 2022/10/06 10:41:47 - mmengine - INFO - Epoch(train) [20][1500/10520] lr: 1.0000e-06 eta: 1:28:11 time: 0.3753 data_time: 0.0034 memory: 17203 loss_visual: 0.0525 loss_lang: 0.1099 loss_fusion: 0.0442 loss: 0.2066 2022/10/06 10:42:44 - mmengine - INFO - Epoch(train) [20][1600/10520] lr: 1.0000e-06 eta: 1:27:13 time: 0.3622 data_time: 0.0032 memory: 17203 loss_visual: 0.0478 loss_lang: 0.1048 loss_fusion: 0.0398 loss: 0.1925 2022/10/06 10:43:46 - mmengine - INFO - Epoch(train) [20][1700/10520] lr: 1.0000e-06 eta: 1:26:14 time: 0.8228 data_time: 0.1594 memory: 17203 loss_visual: 0.0446 loss_lang: 0.0998 loss_fusion: 0.0383 loss: 0.1827 2022/10/06 10:44:44 - mmengine - INFO - Epoch(train) [20][1800/10520] lr: 1.0000e-06 eta: 1:25:15 time: 1.0286 data_time: 0.2070 memory: 17203 loss_visual: 0.0482 loss_lang: 0.1050 loss_fusion: 0.0408 loss: 0.1940 2022/10/06 10:45:41 - mmengine - INFO - Epoch(train) [20][1900/10520] lr: 1.0000e-06 eta: 1:24:17 time: 0.7831 data_time: 0.0549 memory: 17203 loss_visual: 0.0452 loss_lang: 0.1001 loss_fusion: 0.0379 loss: 0.1832 2022/10/06 10:46:38 - mmengine - INFO - Epoch(train) [20][2000/10520] lr: 1.0000e-06 eta: 1:23:18 time: 0.4396 data_time: 0.0505 memory: 17203 loss_visual: 0.0507 loss_lang: 0.1059 loss_fusion: 0.0431 loss: 0.1997 2022/10/06 10:47:35 - mmengine - INFO - Epoch(train) [20][2100/10520] lr: 1.0000e-06 eta: 1:22:19 time: 0.3806 data_time: 0.0035 memory: 17203 loss_visual: 0.0474 loss_lang: 0.1009 loss_fusion: 0.0409 loss: 0.1891 2022/10/06 10:47:49 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 10:48:32 - mmengine - INFO - Epoch(train) [20][2200/10520] lr: 1.0000e-06 eta: 1:21:20 time: 0.3850 data_time: 0.0033 memory: 17203 loss_visual: 0.0429 loss_lang: 0.0989 loss_fusion: 0.0358 loss: 0.1776 2022/10/06 10:49:29 - mmengine - INFO - Epoch(train) [20][2300/10520] lr: 1.0000e-06 eta: 1:20:22 time: 0.3698 data_time: 0.0034 memory: 17203 loss_visual: 0.0429 loss_lang: 0.0984 loss_fusion: 0.0352 loss: 0.1765 2022/10/06 10:50:26 - mmengine - INFO - Epoch(train) [20][2400/10520] lr: 1.0000e-06 eta: 1:19:23 time: 0.3826 data_time: 0.0032 memory: 17203 loss_visual: 0.0481 loss_lang: 0.1039 loss_fusion: 0.0410 loss: 0.1930 2022/10/06 10:51:26 - mmengine - INFO - Epoch(train) [20][2500/10520] lr: 1.0000e-06 eta: 1:18:24 time: 0.7621 data_time: 0.1707 memory: 17203 loss_visual: 0.0492 loss_lang: 0.1080 loss_fusion: 0.0409 loss: 0.1981 2022/10/06 10:52:26 - mmengine - INFO - Epoch(train) [20][2600/10520] lr: 1.0000e-06 eta: 1:17:26 time: 1.0457 data_time: 0.2228 memory: 17203 loss_visual: 0.0446 loss_lang: 0.0964 loss_fusion: 0.0378 loss: 0.1787 2022/10/06 10:53:22 - mmengine - INFO - Epoch(train) [20][2700/10520] lr: 1.0000e-06 eta: 1:16:27 time: 0.7805 data_time: 0.0382 memory: 17203 loss_visual: 0.0503 loss_lang: 0.1059 loss_fusion: 0.0425 loss: 0.1986 2022/10/06 10:54:18 - mmengine - INFO - Epoch(train) [20][2800/10520] lr: 1.0000e-06 eta: 1:15:28 time: 0.4549 data_time: 0.0394 memory: 17203 loss_visual: 0.0467 loss_lang: 0.1015 loss_fusion: 0.0396 loss: 0.1878 2022/10/06 10:55:14 - mmengine - INFO - Epoch(train) [20][2900/10520] lr: 1.0000e-06 eta: 1:14:29 time: 0.3782 data_time: 0.0032 memory: 17203 loss_visual: 0.0491 loss_lang: 0.1079 loss_fusion: 0.0425 loss: 0.1994 2022/10/06 10:56:11 - mmengine - INFO - Epoch(train) [20][3000/10520] lr: 1.0000e-06 eta: 1:13:31 time: 0.3808 data_time: 0.0033 memory: 17203 loss_visual: 0.0447 loss_lang: 0.0995 loss_fusion: 0.0375 loss: 0.1817 2022/10/06 10:57:08 - mmengine - INFO - Epoch(train) [20][3100/10520] lr: 1.0000e-06 eta: 1:12:32 time: 0.3724 data_time: 0.0033 memory: 17203 loss_visual: 0.0471 loss_lang: 0.1040 loss_fusion: 0.0399 loss: 0.1910 2022/10/06 10:57:22 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 10:58:05 - mmengine - INFO - Epoch(train) [20][3200/10520] lr: 1.0000e-06 eta: 1:11:33 time: 0.3624 data_time: 0.0035 memory: 17203 loss_visual: 0.0474 loss_lang: 0.1006 loss_fusion: 0.0397 loss: 0.1877 2022/10/06 10:59:04 - mmengine - INFO - Epoch(train) [20][3300/10520] lr: 1.0000e-06 eta: 1:10:35 time: 0.7717 data_time: 0.1551 memory: 17203 loss_visual: 0.0491 loss_lang: 0.1060 loss_fusion: 0.0416 loss: 0.1967 2022/10/06 11:00:02 - mmengine - INFO - Epoch(train) [20][3400/10520] lr: 1.0000e-06 eta: 1:09:36 time: 1.0050 data_time: 0.1776 memory: 17203 loss_visual: 0.0525 loss_lang: 0.1126 loss_fusion: 0.0455 loss: 0.2106 2022/10/06 11:00:59 - mmengine - INFO - Epoch(train) [20][3500/10520] lr: 1.0000e-06 eta: 1:08:37 time: 0.7932 data_time: 0.0425 memory: 17203 loss_visual: 0.0548 loss_lang: 0.1125 loss_fusion: 0.0470 loss: 0.2143 2022/10/06 11:01:55 - mmengine - INFO - Epoch(train) [20][3600/10520] lr: 1.0000e-06 eta: 1:07:38 time: 0.4529 data_time: 0.0401 memory: 17203 loss_visual: 0.0495 loss_lang: 0.1044 loss_fusion: 0.0412 loss: 0.1951 2022/10/06 11:02:50 - mmengine - INFO - Epoch(train) [20][3700/10520] lr: 1.0000e-06 eta: 1:06:40 time: 0.3696 data_time: 0.0034 memory: 17203 loss_visual: 0.0560 loss_lang: 0.1121 loss_fusion: 0.0495 loss: 0.2175 2022/10/06 11:03:48 - mmengine - INFO - Epoch(train) [20][3800/10520] lr: 1.0000e-06 eta: 1:05:41 time: 0.3999 data_time: 0.0144 memory: 17203 loss_visual: 0.0505 loss_lang: 0.1069 loss_fusion: 0.0451 loss: 0.2025 2022/10/06 11:04:44 - mmengine - INFO - Epoch(train) [20][3900/10520] lr: 1.0000e-06 eta: 1:04:42 time: 0.3666 data_time: 0.0031 memory: 17203 loss_visual: 0.0521 loss_lang: 0.1095 loss_fusion: 0.0441 loss: 0.2056 2022/10/06 11:05:40 - mmengine - INFO - Epoch(train) [20][4000/10520] lr: 1.0000e-06 eta: 1:03:44 time: 0.3884 data_time: 0.0037 memory: 17203 loss_visual: 0.0471 loss_lang: 0.1043 loss_fusion: 0.0391 loss: 0.1905 2022/10/06 11:06:40 - mmengine - INFO - Epoch(train) [20][4100/10520] lr: 1.0000e-06 eta: 1:02:45 time: 0.7930 data_time: 0.1352 memory: 17203 loss_visual: 0.0503 loss_lang: 0.1090 loss_fusion: 0.0441 loss: 0.2034 2022/10/06 11:06:50 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 11:07:39 - mmengine - INFO - Epoch(train) [20][4200/10520] lr: 1.0000e-06 eta: 1:01:46 time: 1.0501 data_time: 0.1938 memory: 17203 loss_visual: 0.0531 loss_lang: 0.1075 loss_fusion: 0.0452 loss: 0.2058 2022/10/06 11:08:35 - mmengine - INFO - Epoch(train) [20][4300/10520] lr: 1.0000e-06 eta: 1:00:48 time: 0.7523 data_time: 0.0560 memory: 17203 loss_visual: 0.0472 loss_lang: 0.1038 loss_fusion: 0.0405 loss: 0.1916 2022/10/06 11:09:33 - mmengine - INFO - Epoch(train) [20][4400/10520] lr: 1.0000e-06 eta: 0:59:49 time: 0.5081 data_time: 0.0407 memory: 17203 loss_visual: 0.0461 loss_lang: 0.1020 loss_fusion: 0.0388 loss: 0.1870 2022/10/06 11:10:30 - mmengine - INFO - Epoch(train) [20][4500/10520] lr: 1.0000e-06 eta: 0:58:50 time: 0.4096 data_time: 0.0087 memory: 17203 loss_visual: 0.0528 loss_lang: 0.1088 loss_fusion: 0.0451 loss: 0.2068 2022/10/06 11:11:25 - mmengine - INFO - Epoch(train) [20][4600/10520] lr: 1.0000e-06 eta: 0:57:51 time: 0.3916 data_time: 0.0035 memory: 17203 loss_visual: 0.0488 loss_lang: 0.1040 loss_fusion: 0.0397 loss: 0.1926 2022/10/06 11:12:21 - mmengine - INFO - Epoch(train) [20][4700/10520] lr: 1.0000e-06 eta: 0:56:53 time: 0.3820 data_time: 0.0033 memory: 17203 loss_visual: 0.0508 loss_lang: 0.1067 loss_fusion: 0.0429 loss: 0.2004 2022/10/06 11:13:19 - mmengine - INFO - Epoch(train) [20][4800/10520] lr: 1.0000e-06 eta: 0:55:54 time: 0.3565 data_time: 0.0033 memory: 17203 loss_visual: 0.0483 loss_lang: 0.1066 loss_fusion: 0.0415 loss: 0.1964 2022/10/06 11:14:19 - mmengine - INFO - Epoch(train) [20][4900/10520] lr: 1.0000e-06 eta: 0:54:55 time: 0.7728 data_time: 0.1367 memory: 17203 loss_visual: 0.0475 loss_lang: 0.1038 loss_fusion: 0.0400 loss: 0.1913 2022/10/06 11:15:18 - mmengine - INFO - Epoch(train) [20][5000/10520] lr: 1.0000e-06 eta: 0:53:57 time: 1.0070 data_time: 0.1603 memory: 17203 loss_visual: 0.0534 loss_lang: 0.1103 loss_fusion: 0.0463 loss: 0.2100 2022/10/06 11:16:13 - mmengine - INFO - Epoch(train) [20][5100/10520] lr: 1.0000e-06 eta: 0:52:58 time: 0.7462 data_time: 0.0594 memory: 17203 loss_visual: 0.0529 loss_lang: 0.1137 loss_fusion: 0.0459 loss: 0.2124 2022/10/06 11:16:21 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 11:17:09 - mmengine - INFO - Epoch(train) [20][5200/10520] lr: 1.0000e-06 eta: 0:51:59 time: 0.4410 data_time: 0.0456 memory: 17203 loss_visual: 0.0493 loss_lang: 0.1073 loss_fusion: 0.0425 loss: 0.1991 2022/10/06 11:18:06 - mmengine - INFO - Epoch(train) [20][5300/10520] lr: 1.0000e-06 eta: 0:51:01 time: 0.3678 data_time: 0.0035 memory: 17203 loss_visual: 0.0476 loss_lang: 0.1050 loss_fusion: 0.0407 loss: 0.1932 2022/10/06 11:19:03 - mmengine - INFO - Epoch(train) [20][5400/10520] lr: 1.0000e-06 eta: 0:50:02 time: 0.3670 data_time: 0.0037 memory: 17203 loss_visual: 0.0465 loss_lang: 0.1042 loss_fusion: 0.0388 loss: 0.1895 2022/10/06 11:19:59 - mmengine - INFO - Epoch(train) [20][5500/10520] lr: 1.0000e-06 eta: 0:49:03 time: 0.3632 data_time: 0.0032 memory: 17203 loss_visual: 0.0497 loss_lang: 0.1049 loss_fusion: 0.0416 loss: 0.1961 2022/10/06 11:20:56 - mmengine - INFO - Epoch(train) [20][5600/10520] lr: 1.0000e-06 eta: 0:48:05 time: 0.3603 data_time: 0.0033 memory: 17203 loss_visual: 0.0540 loss_lang: 0.1091 loss_fusion: 0.0470 loss: 0.2101 2022/10/06 11:21:56 - mmengine - INFO - Epoch(train) [20][5700/10520] lr: 1.0000e-06 eta: 0:47:06 time: 0.7743 data_time: 0.1440 memory: 17203 loss_visual: 0.0495 loss_lang: 0.1028 loss_fusion: 0.0413 loss: 0.1937 2022/10/06 11:22:54 - mmengine - INFO - Epoch(train) [20][5800/10520] lr: 1.0000e-06 eta: 0:46:07 time: 0.9930 data_time: 0.1621 memory: 17203 loss_visual: 0.0478 loss_lang: 0.1033 loss_fusion: 0.0402 loss: 0.1913 2022/10/06 11:23:51 - mmengine - INFO - Epoch(train) [20][5900/10520] lr: 1.0000e-06 eta: 0:45:09 time: 0.7734 data_time: 0.0455 memory: 17203 loss_visual: 0.0511 loss_lang: 0.1106 loss_fusion: 0.0443 loss: 0.2060 2022/10/06 11:24:47 - mmengine - INFO - Epoch(train) [20][6000/10520] lr: 1.0000e-06 eta: 0:44:10 time: 0.4257 data_time: 0.0412 memory: 17203 loss_visual: 0.0463 loss_lang: 0.0977 loss_fusion: 0.0389 loss: 0.1829 2022/10/06 11:25:43 - mmengine - INFO - Epoch(train) [20][6100/10520] lr: 1.0000e-06 eta: 0:43:11 time: 0.3864 data_time: 0.0155 memory: 17203 loss_visual: 0.0487 loss_lang: 0.1056 loss_fusion: 0.0414 loss: 0.1956 2022/10/06 11:25:57 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 11:26:39 - mmengine - INFO - Epoch(train) [20][6200/10520] lr: 1.0000e-06 eta: 0:42:13 time: 0.3765 data_time: 0.0037 memory: 17203 loss_visual: 0.0425 loss_lang: 0.0941 loss_fusion: 0.0352 loss: 0.1718 2022/10/06 11:27:35 - mmengine - INFO - Epoch(train) [20][6300/10520] lr: 1.0000e-06 eta: 0:41:14 time: 0.3689 data_time: 0.0039 memory: 17203 loss_visual: 0.0513 loss_lang: 0.1053 loss_fusion: 0.0447 loss: 0.2014 2022/10/06 11:28:31 - mmengine - INFO - Epoch(train) [20][6400/10520] lr: 1.0000e-06 eta: 0:40:15 time: 0.3834 data_time: 0.0032 memory: 17203 loss_visual: 0.0506 loss_lang: 0.1065 loss_fusion: 0.0432 loss: 0.2004 2022/10/06 11:29:32 - mmengine - INFO - Epoch(train) [20][6500/10520] lr: 1.0000e-06 eta: 0:39:17 time: 0.7845 data_time: 0.1559 memory: 17203 loss_visual: 0.0412 loss_lang: 0.0978 loss_fusion: 0.0337 loss: 0.1727 2022/10/06 11:30:30 - mmengine - INFO - Epoch(train) [20][6600/10520] lr: 1.0000e-06 eta: 0:38:18 time: 0.9630 data_time: 0.1985 memory: 17203 loss_visual: 0.0487 loss_lang: 0.1087 loss_fusion: 0.0423 loss: 0.1997 2022/10/06 11:31:27 - mmengine - INFO - Epoch(train) [20][6700/10520] lr: 1.0000e-06 eta: 0:37:19 time: 0.8388 data_time: 0.0629 memory: 17203 loss_visual: 0.0526 loss_lang: 0.1088 loss_fusion: 0.0463 loss: 0.2077 2022/10/06 11:32:23 - mmengine - INFO - Epoch(train) [20][6800/10520] lr: 1.0000e-06 eta: 0:36:21 time: 0.4706 data_time: 0.0422 memory: 17203 loss_visual: 0.0527 loss_lang: 0.1079 loss_fusion: 0.0456 loss: 0.2063 2022/10/06 11:33:18 - mmengine - INFO - Epoch(train) [20][6900/10520] lr: 1.0000e-06 eta: 0:35:22 time: 0.3724 data_time: 0.0037 memory: 17203 loss_visual: 0.0478 loss_lang: 0.1030 loss_fusion: 0.0386 loss: 0.1894 2022/10/06 11:34:14 - mmengine - INFO - Epoch(train) [20][7000/10520] lr: 1.0000e-06 eta: 0:34:23 time: 0.4235 data_time: 0.0034 memory: 17203 loss_visual: 0.0460 loss_lang: 0.1052 loss_fusion: 0.0395 loss: 0.1906 2022/10/06 11:35:11 - mmengine - INFO - Epoch(train) [20][7100/10520] lr: 1.0000e-06 eta: 0:33:25 time: 0.3898 data_time: 0.0037 memory: 17203 loss_visual: 0.0538 loss_lang: 0.1135 loss_fusion: 0.0459 loss: 0.2133 2022/10/06 11:35:24 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 11:36:08 - mmengine - INFO - Epoch(train) [20][7200/10520] lr: 1.0000e-06 eta: 0:32:26 time: 0.3855 data_time: 0.0035 memory: 17203 loss_visual: 0.0539 loss_lang: 0.1115 loss_fusion: 0.0461 loss: 0.2115 2022/10/06 11:37:10 - mmengine - INFO - Epoch(train) [20][7300/10520] lr: 1.0000e-06 eta: 0:31:27 time: 0.8393 data_time: 0.1434 memory: 17203 loss_visual: 0.0489 loss_lang: 0.1084 loss_fusion: 0.0429 loss: 0.2001 2022/10/06 11:38:08 - mmengine - INFO - Epoch(train) [20][7400/10520] lr: 1.0000e-06 eta: 0:30:29 time: 0.9823 data_time: 0.1950 memory: 17203 loss_visual: 0.0449 loss_lang: 0.1029 loss_fusion: 0.0391 loss: 0.1870 2022/10/06 11:39:05 - mmengine - INFO - Epoch(train) [20][7500/10520] lr: 1.0000e-06 eta: 0:29:30 time: 0.8390 data_time: 0.0531 memory: 17203 loss_visual: 0.0455 loss_lang: 0.1030 loss_fusion: 0.0387 loss: 0.1871 2022/10/06 11:40:00 - mmengine - INFO - Epoch(train) [20][7600/10520] lr: 1.0000e-06 eta: 0:28:31 time: 0.4348 data_time: 0.0426 memory: 17203 loss_visual: 0.0460 loss_lang: 0.1070 loss_fusion: 0.0384 loss: 0.1913 2022/10/06 11:40:58 - mmengine - INFO - Epoch(train) [20][7700/10520] lr: 1.0000e-06 eta: 0:27:33 time: 0.3794 data_time: 0.0036 memory: 17203 loss_visual: 0.0454 loss_lang: 0.1073 loss_fusion: 0.0385 loss: 0.1912 2022/10/06 11:41:55 - mmengine - INFO - Epoch(train) [20][7800/10520] lr: 1.0000e-06 eta: 0:26:34 time: 0.3962 data_time: 0.0034 memory: 17203 loss_visual: 0.0496 loss_lang: 0.1058 loss_fusion: 0.0416 loss: 0.1971 2022/10/06 11:42:53 - mmengine - INFO - Epoch(train) [20][7900/10520] lr: 1.0000e-06 eta: 0:25:35 time: 0.3800 data_time: 0.0033 memory: 17203 loss_visual: 0.0528 loss_lang: 0.1118 loss_fusion: 0.0469 loss: 0.2115 2022/10/06 11:43:49 - mmengine - INFO - Epoch(train) [20][8000/10520] lr: 1.0000e-06 eta: 0:24:37 time: 0.3813 data_time: 0.0039 memory: 17203 loss_visual: 0.0496 loss_lang: 0.1097 loss_fusion: 0.0410 loss: 0.2003 2022/10/06 11:44:50 - mmengine - INFO - Epoch(train) [20][8100/10520] lr: 1.0000e-06 eta: 0:23:38 time: 0.7855 data_time: 0.1462 memory: 17203 loss_visual: 0.0522 loss_lang: 0.1097 loss_fusion: 0.0444 loss: 0.2062 2022/10/06 11:44:59 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 11:45:48 - mmengine - INFO - Epoch(train) [20][8200/10520] lr: 1.0000e-06 eta: 0:22:40 time: 1.0139 data_time: 0.1903 memory: 17203 loss_visual: 0.0469 loss_lang: 0.1067 loss_fusion: 0.0393 loss: 0.1929 2022/10/06 11:46:46 - mmengine - INFO - Epoch(train) [20][8300/10520] lr: 1.0000e-06 eta: 0:21:41 time: 0.7874 data_time: 0.0441 memory: 17203 loss_visual: 0.0510 loss_lang: 0.1041 loss_fusion: 0.0452 loss: 0.2003 2022/10/06 11:47:43 - mmengine - INFO - Epoch(train) [20][8400/10520] lr: 1.0000e-06 eta: 0:20:42 time: 0.4617 data_time: 0.0635 memory: 17203 loss_visual: 0.0478 loss_lang: 0.1029 loss_fusion: 0.0397 loss: 0.1904 2022/10/06 11:48:39 - mmengine - INFO - Epoch(train) [20][8500/10520] lr: 1.0000e-06 eta: 0:19:44 time: 0.3620 data_time: 0.0034 memory: 17203 loss_visual: 0.0602 loss_lang: 0.1177 loss_fusion: 0.0527 loss: 0.2306 2022/10/06 11:49:36 - mmengine - INFO - Epoch(train) [20][8600/10520] lr: 1.0000e-06 eta: 0:18:45 time: 0.4343 data_time: 0.0037 memory: 17203 loss_visual: 0.0488 loss_lang: 0.1062 loss_fusion: 0.0425 loss: 0.1975 2022/10/06 11:50:32 - mmengine - INFO - Epoch(train) [20][8700/10520] lr: 1.0000e-06 eta: 0:17:46 time: 0.3787 data_time: 0.0036 memory: 17203 loss_visual: 0.0474 loss_lang: 0.1059 loss_fusion: 0.0404 loss: 0.1938 2022/10/06 11:51:28 - mmengine - INFO - Epoch(train) [20][8800/10520] lr: 1.0000e-06 eta: 0:16:48 time: 0.3629 data_time: 0.0035 memory: 17203 loss_visual: 0.0494 loss_lang: 0.1035 loss_fusion: 0.0412 loss: 0.1941 2022/10/06 11:52:29 - mmengine - INFO - Epoch(train) [20][8900/10520] lr: 1.0000e-06 eta: 0:15:49 time: 0.7732 data_time: 0.1488 memory: 17203 loss_visual: 0.0482 loss_lang: 0.1059 loss_fusion: 0.0402 loss: 0.1943 2022/10/06 11:53:28 - mmengine - INFO - Epoch(train) [20][9000/10520] lr: 1.0000e-06 eta: 0:14:51 time: 1.0010 data_time: 0.1825 memory: 17203 loss_visual: 0.0506 loss_lang: 0.1073 loss_fusion: 0.0430 loss: 0.2009 2022/10/06 11:54:24 - mmengine - INFO - Epoch(train) [20][9100/10520] lr: 1.0000e-06 eta: 0:13:52 time: 0.7607 data_time: 0.0491 memory: 17203 loss_visual: 0.0428 loss_lang: 0.0977 loss_fusion: 0.0360 loss: 0.1766 2022/10/06 11:54:31 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 11:55:19 - mmengine - INFO - Epoch(train) [20][9200/10520] lr: 1.0000e-06 eta: 0:12:53 time: 0.4453 data_time: 0.0468 memory: 17203 loss_visual: 0.0417 loss_lang: 0.1001 loss_fusion: 0.0346 loss: 0.1764 2022/10/06 11:56:16 - mmengine - INFO - Epoch(train) [20][9300/10520] lr: 1.0000e-06 eta: 0:11:55 time: 0.3898 data_time: 0.0038 memory: 17203 loss_visual: 0.0485 loss_lang: 0.1059 loss_fusion: 0.0414 loss: 0.1957 2022/10/06 11:57:12 - mmengine - INFO - Epoch(train) [20][9400/10520] lr: 1.0000e-06 eta: 0:10:56 time: 0.3712 data_time: 0.0041 memory: 17203 loss_visual: 0.0419 loss_lang: 0.0931 loss_fusion: 0.0333 loss: 0.1683 2022/10/06 11:58:08 - mmengine - INFO - Epoch(train) [20][9500/10520] lr: 1.0000e-06 eta: 0:09:57 time: 0.3672 data_time: 0.0034 memory: 17203 loss_visual: 0.0454 loss_lang: 0.1036 loss_fusion: 0.0390 loss: 0.1880 2022/10/06 11:59:05 - mmengine - INFO - Epoch(train) [20][9600/10520] lr: 1.0000e-06 eta: 0:08:59 time: 0.3608 data_time: 0.0045 memory: 17203 loss_visual: 0.0475 loss_lang: 0.1004 loss_fusion: 0.0410 loss: 0.1888 2022/10/06 12:00:06 - mmengine - INFO - Epoch(train) [20][9700/10520] lr: 1.0000e-06 eta: 0:08:00 time: 0.9327 data_time: 0.1708 memory: 17203 loss_visual: 0.0530 loss_lang: 0.1062 loss_fusion: 0.0457 loss: 0.2049 2022/10/06 12:01:04 - mmengine - INFO - Epoch(train) [20][9800/10520] lr: 1.0000e-06 eta: 0:07:02 time: 0.9786 data_time: 0.2092 memory: 17203 loss_visual: 0.0482 loss_lang: 0.1030 loss_fusion: 0.0419 loss: 0.1930 2022/10/06 12:02:00 - mmengine - INFO - Epoch(train) [20][9900/10520] lr: 1.0000e-06 eta: 0:06:03 time: 0.8129 data_time: 0.0454 memory: 17203 loss_visual: 0.0506 loss_lang: 0.1098 loss_fusion: 0.0437 loss: 0.2041 2022/10/06 12:02:56 - mmengine - INFO - Epoch(train) [20][10000/10520] lr: 1.0000e-06 eta: 0:05:04 time: 0.4602 data_time: 0.0468 memory: 17203 loss_visual: 0.0480 loss_lang: 0.1084 loss_fusion: 0.0410 loss: 0.1974 2022/10/06 12:03:52 - mmengine - INFO - Epoch(train) [20][10100/10520] lr: 1.0000e-06 eta: 0:04:06 time: 0.3612 data_time: 0.0037 memory: 17203 loss_visual: 0.0543 loss_lang: 0.1126 loss_fusion: 0.0490 loss: 0.2159 2022/10/06 12:04:06 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 12:04:49 - mmengine - INFO - Epoch(train) [20][10200/10520] lr: 1.0000e-06 eta: 0:03:07 time: 0.3727 data_time: 0.0037 memory: 17203 loss_visual: 0.0526 loss_lang: 0.1048 loss_fusion: 0.0443 loss: 0.2017 2022/10/06 12:05:45 - mmengine - INFO - Epoch(train) [20][10300/10520] lr: 1.0000e-06 eta: 0:02:08 time: 0.4111 data_time: 0.0036 memory: 17203 loss_visual: 0.0504 loss_lang: 0.1057 loss_fusion: 0.0432 loss: 0.1993 2022/10/06 12:06:40 - mmengine - INFO - Epoch(train) [20][10400/10520] lr: 1.0000e-06 eta: 0:01:10 time: 0.3593 data_time: 0.0035 memory: 17203 loss_visual: 0.0531 loss_lang: 0.1070 loss_fusion: 0.0460 loss: 0.2060 2022/10/06 12:07:38 - mmengine - INFO - Epoch(train) [20][10500/10520] lr: 1.0000e-06 eta: 0:00:11 time: 0.5722 data_time: 0.1112 memory: 17203 loss_visual: 0.0455 loss_lang: 0.1021 loss_fusion: 0.0376 loss: 0.1853 2022/10/06 12:07:47 - mmengine - INFO - Exp name: abinet_20e_st-an_mj_20221005_012617 2022/10/06 12:07:47 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/10/06 12:08:01 - mmengine - INFO - Epoch(val) [20][100/959] eta: 0:00:46 time: 0.0539 data_time: 0.0020 memory: 17203 2022/10/06 12:08:06 - mmengine - INFO - Epoch(val) [20][200/959] eta: 0:00:38 time: 0.0503 data_time: 0.0020 memory: 734 2022/10/06 12:08:11 - mmengine - INFO - Epoch(val) [20][300/959] eta: 0:00:27 time: 0.0423 data_time: 0.0017 memory: 734 2022/10/06 12:08:16 - mmengine - INFO - Epoch(val) [20][400/959] eta: 0:00:26 time: 0.0476 data_time: 0.0019 memory: 734 2022/10/06 12:08:21 - mmengine - INFO - Epoch(val) [20][500/959] eta: 0:00:20 time: 0.0452 data_time: 0.0011 memory: 734 2022/10/06 12:08:26 - mmengine - INFO - Epoch(val) [20][600/959] eta: 0:00:16 time: 0.0466 data_time: 0.0011 memory: 734 2022/10/06 12:08:31 - mmengine - INFO - Epoch(val) [20][700/959] eta: 0:00:12 time: 0.0476 data_time: 0.0011 memory: 734 2022/10/06 12:08:35 - mmengine - INFO - Epoch(val) [20][800/959] eta: 0:00:04 time: 0.0274 data_time: 0.0008 memory: 734 2022/10/06 12:08:37 - mmengine - INFO - Epoch(val) [20][900/959] eta: 0:00:01 time: 0.0232 data_time: 0.0007 memory: 734 2022/10/06 12:08:39 - mmengine - INFO - Epoch(val) [20][959/959] CUTE80/recog/word_acc_ignore_case_symbol: 0.8750 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9583 SVT/recog/word_acc_ignore_case_symbol: 0.9382 SVTP/recog/word_acc_ignore_case_symbol: 0.8868 IC13/recog/word_acc_ignore_case_symbol: 0.9537 IC15/recog/word_acc_ignore_case_symbol: 0.8113