2022/09/16 10:33:22 - 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: 1632857166 GPU 0: 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: 1 ------------------------------------------------------------ 2022/09/16 10:33:23 - 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_nrtr_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=0.0003)) train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=6, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [dict(type='MultiStepLR', milestones=[3, 4], end=6)] file_client_args = dict(backend='disk') dictionary = dict( type='Dictionary', dict_file= 'configs/textrecog/nrtr/../../../dicts/english_digits_symbols.txt', with_padding=True, with_unknown=True, same_start_end=True, with_start=True, with_end=True) model = dict( type='NRTR', backbone=dict( type='ResNet31OCR', layers=[1, 2, 5, 3], channels=[32, 64, 128, 256, 512, 512], stage4_pool_cfg=dict(kernel_size=(2, 1), stride=(2, 1)), last_stage_pool=False), encoder=dict(type='NRTREncoder'), decoder=dict( type='NRTRDecoder', module_loss=dict( type='CEModuleLoss', ignore_first_char=True, flatten=True), postprocessor=dict(type='AttentionPostprocessor'), dictionary=dict( type='Dictionary', dict_file= 'configs/textrecog/nrtr/../../../dicts/english_digits_symbols.txt', with_padding=True, with_unknown=True, same_start_end=True, with_start=True, with_end=True), max_seq_len=30), data_preprocessor=dict( type='TextRecogDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375])) train_pipeline = [ dict( type='LoadImageFromFile', file_client_args=dict(backend='disk'), ignore_empty=True, min_size=2), dict(type='LoadOCRAnnotations', with_text=True), dict( type='RescaleToHeight', height=32, min_width=32, max_width=160, width_divisor=4), dict(type='PadToWidth', width=160), dict( type='PackTextRecogInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio')) ] test_pipeline = [ dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')), dict( type='RescaleToHeight', height=32, min_width=32, max_width=160, width_divisor=16), dict(type='PadToWidth', width=160), dict(type='LoadOCRAnnotations', with_text=True), dict( type='PackTextRecogInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio')) ] train_list = [ dict( type='OCRDataset', data_root='data/rec/Syn90k/', data_prefix=dict(img_path='mnt/ramdisk/max/90kDICT32px'), ann_file='train_labels.json', test_mode=False, pipeline=None), dict( type='OCRDataset', data_root='data/rec/SynthText/', data_prefix=dict(img_path='synthtext/SynthText_patch_horizontal'), ann_file='train_labels.json', test_mode=False, pipeline=None) ] test_list = [ dict( type='OCRDataset', data_root='data/rec/ct80/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/IIIT5K/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/svt/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/svtp/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/icdar_2013/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/icdar_2015/', ann_file='test_labels.json', test_mode=True, pipeline=None) ] train_dataset = dict( type='ConcatDataset', datasets=[ dict( type='OCRDataset', data_root='data/rec/Syn90k/', data_prefix=dict(img_path='mnt/ramdisk/max/90kDICT32px'), ann_file='train_labels.json', test_mode=False, pipeline=None), dict( type='OCRDataset', data_root='data/rec/SynthText/', data_prefix=dict(img_path='synthtext/SynthText_patch_horizontal'), ann_file='train_labels.json', test_mode=False, pipeline=None) ], pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='disk'), ignore_empty=True, min_size=2), dict(type='LoadOCRAnnotations', with_text=True), dict( type='RescaleToHeight', height=32, min_width=32, max_width=160, width_divisor=4), dict(type='PadToWidth', width=160), dict( type='PackTextRecogInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio')) ]) test_dataset = dict( type='ConcatDataset', datasets=[ dict( type='OCRDataset', data_root='data/rec/ct80/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/IIIT5K/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/svt/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/svtp/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/icdar_2013/', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='data/rec/icdar_2015/', ann_file='test_labels.json', test_mode=True, pipeline=None) ], pipeline=[ dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')), dict( type='RescaleToHeight', height=32, min_width=32, max_width=160, width_divisor=16), dict(type='PadToWidth', width=160), dict(type='LoadOCRAnnotations', with_text=True), dict( type='PackTextRecogInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio')) ]) train_dataloader = dict( batch_size=384, num_workers=24, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='ConcatDataset', datasets=[ dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/Syn90k', data_prefix=dict(img_path='mnt/ramdisk/max/90kDICT32px'), ann_file='train_labels.json', test_mode=False, pipeline=None), dict( type='OCRDataset', data_root= 'openmmlab:s3://openmmlab/datasets/ocr/recog/SynthText', data_prefix=dict( img_path='synthtext/SynthText_patch_horizontal'), ann_file='train_labels.json', test_mode=False, pipeline=None) ], pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='petrel'), ignore_empty=True, min_size=2), dict(type='LoadOCRAnnotations', with_text=True), dict( type='RescaleToHeight', height=32, min_width=32, max_width=160, width_divisor=4), dict(type='PadToWidth', width=160), dict( type='PackTextRecogInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio')) ])) test_dataloader = dict( batch_size=1, num_workers=4, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='ConcatDataset', datasets=[ dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/ct80', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/IIIT5K', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/svt', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/svtp', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root= 'openmmlab:s3://openmmlab/datasets/ocr/recog/icdar_2013', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root= 'openmmlab:s3://openmmlab/datasets/ocr/recog/icdar_2015', ann_file='test_labels.json', test_mode=True, pipeline=None) ], pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='petrel')), dict( type='RescaleToHeight', height=32, min_width=32, max_width=160, width_divisor=16), dict(type='PadToWidth', width=160), dict(type='LoadOCRAnnotations', with_text=True), dict( type='PackTextRecogInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio')) ])) val_dataloader = dict( batch_size=1, num_workers=4, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='ConcatDataset', datasets=[ dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/ct80', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/IIIT5K', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/svt', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/recog/svtp', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root= 'openmmlab:s3://openmmlab/datasets/ocr/recog/icdar_2013', ann_file='test_labels.json', test_mode=True, pipeline=None), dict( type='OCRDataset', data_root= 'openmmlab:s3://openmmlab/datasets/ocr/recog/icdar_2015', ann_file='test_labels.json', test_mode=True, pipeline=None) ], pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='petrel')), dict( type='RescaleToHeight', height=32, min_width=32, max_width=160, width_divisor=16), dict(type='PadToWidth', width=160), dict(type='LoadOCRAnnotations', with_text=True), dict( type='PackTextRecogInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio')) ])) launcher = 'slurm' work_dir = './work_dirs/nrtr_resnet31-1by8-1by4_6e_st_mj' Name of parameter - Initialization information backbone.conv1_1.weight - torch.Size([32, 3, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.conv1_1.bias - torch.Size([32]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.bn1_1.weight - torch.Size([32]): UniformInit: a=0, b=1, bias=0 backbone.bn1_1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of NRTR backbone.conv1_2.weight - torch.Size([64, 32, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.conv1_2.bias - torch.Size([64]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.bn1_2.weight - torch.Size([64]): UniformInit: a=0, b=1, bias=0 backbone.bn1_2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of NRTR backbone.block2.0.conv1.weight - torch.Size([128, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block2.0.conv2.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block2.0.bn1.weight - torch.Size([128]): UniformInit: a=0, b=1, bias=0 backbone.block2.0.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of NRTR backbone.block2.0.bn2.weight - torch.Size([128]): UniformInit: a=0, b=1, bias=0 backbone.block2.0.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of NRTR backbone.block2.0.downsample.0.weight - torch.Size([128, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block2.0.downsample.1.weight - torch.Size([128]): UniformInit: a=0, b=1, bias=0 backbone.block2.0.downsample.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of NRTR backbone.conv2.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.conv2.bias - torch.Size([128]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.bn2.weight - torch.Size([128]): UniformInit: a=0, b=1, bias=0 backbone.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of NRTR backbone.block3.0.conv1.weight - torch.Size([256, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block3.0.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block3.0.bn1.weight - torch.Size([256]): UniformInit: a=0, b=1, bias=0 backbone.block3.0.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR backbone.block3.0.bn2.weight - torch.Size([256]): UniformInit: a=0, b=1, bias=0 backbone.block3.0.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR backbone.block3.0.downsample.0.weight - torch.Size([256, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block3.0.downsample.1.weight - torch.Size([256]): UniformInit: a=0, b=1, bias=0 backbone.block3.0.downsample.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR backbone.block3.1.conv1.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block3.1.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block3.1.bn1.weight - torch.Size([256]): UniformInit: a=0, b=1, bias=0 backbone.block3.1.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR backbone.block3.1.bn2.weight - torch.Size([256]): UniformInit: a=0, b=1, bias=0 backbone.block3.1.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR backbone.conv3.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.conv3.bias - torch.Size([256]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.bn3.weight - torch.Size([256]): UniformInit: a=0, b=1, bias=0 backbone.bn3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR backbone.block4.0.conv1.weight - torch.Size([512, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block4.0.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block4.0.bn1.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.block4.0.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.block4.0.bn2.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.block4.0.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.block4.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block4.0.downsample.1.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.block4.0.downsample.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.block4.1.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block4.1.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block4.1.bn1.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.block4.1.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.block4.1.bn2.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.block4.1.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.block4.2.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block4.2.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block4.2.bn1.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.block4.2.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.block4.2.bn2.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.block4.2.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.block4.3.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block4.3.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block4.3.bn1.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.block4.3.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.block4.3.bn2.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.block4.3.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.block4.4.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block4.4.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block4.4.bn1.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.block4.4.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.block4.4.bn2.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.block4.4.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.conv4.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.conv4.bias - torch.Size([512]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.bn4.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.bn4.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.block5.0.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block5.0.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block5.0.bn1.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.block5.0.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.block5.0.bn2.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.block5.0.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.block5.1.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block5.1.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block5.1.bn1.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.block5.1.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.block5.1.bn2.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.block5.1.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.block5.2.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block5.2.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.block5.2.bn1.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.block5.2.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.block5.2.bn2.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.block5.2.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR backbone.conv5.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.conv5.bias - torch.Size([512]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.bn5.weight - torch.Size([512]): UniformInit: a=0, b=1, bias=0 backbone.bn5.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.0.attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.0.attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.0.attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.0.attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.0.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.0.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.0.mlp.w_1.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.0.mlp.w_1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.0.mlp.w_2.weight - torch.Size([512, 256]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.0.mlp.w_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.0.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.0.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.1.attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.1.attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.1.attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.1.attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.1.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.1.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.1.mlp.w_1.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.1.mlp.w_1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.1.mlp.w_2.weight - torch.Size([512, 256]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.1.mlp.w_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.1.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.1.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.2.attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.2.attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.2.attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.2.attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.2.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.2.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.2.mlp.w_1.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.2.mlp.w_1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.2.mlp.w_2.weight - torch.Size([512, 256]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.2.mlp.w_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.2.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.2.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.3.attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.3.attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.3.attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.3.attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.3.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.3.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.3.mlp.w_1.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.3.mlp.w_1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.3.mlp.w_2.weight - torch.Size([512, 256]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.3.mlp.w_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.3.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.3.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.4.attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.4.attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.4.attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.4.attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.4.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.4.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.4.mlp.w_1.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.4.mlp.w_1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.4.mlp.w_2.weight - torch.Size([512, 256]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.4.mlp.w_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.4.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.4.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.5.attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.5.attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.5.attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.5.attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.5.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.5.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.5.mlp.w_1.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.5.mlp.w_1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.5.mlp.w_2.weight - torch.Size([512, 256]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.5.mlp.w_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.5.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_stack.5.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_norm.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR encoder.layer_norm.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.trg_word_emb.weight - torch.Size([93, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.norm3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.norm3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.self_attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.self_attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.self_attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.self_attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.enc_attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.enc_attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.enc_attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.enc_attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.mlp.w_1.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.mlp.w_1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.mlp.w_2.weight - torch.Size([512, 256]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.0.mlp.w_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.norm3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.norm3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.self_attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.self_attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.self_attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.self_attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.enc_attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.enc_attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.enc_attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.enc_attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.mlp.w_1.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.mlp.w_1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.mlp.w_2.weight - torch.Size([512, 256]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.1.mlp.w_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.norm3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.norm3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.self_attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.self_attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.self_attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.self_attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.enc_attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.enc_attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.enc_attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.enc_attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.mlp.w_1.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.mlp.w_1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.mlp.w_2.weight - torch.Size([512, 256]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.2.mlp.w_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.norm3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.norm3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.self_attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.self_attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.self_attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.self_attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.enc_attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.enc_attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.enc_attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.enc_attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.mlp.w_1.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.mlp.w_1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.mlp.w_2.weight - torch.Size([512, 256]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.3.mlp.w_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.norm3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.norm3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.self_attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.self_attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.self_attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.self_attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.enc_attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.enc_attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.enc_attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.enc_attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.mlp.w_1.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.mlp.w_1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.mlp.w_2.weight - torch.Size([512, 256]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.4.mlp.w_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.norm3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.norm3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.self_attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.self_attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.self_attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.self_attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.enc_attn.linear_q.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.enc_attn.linear_k.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.enc_attn.linear_v.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.enc_attn.fc.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.mlp.w_1.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.mlp.w_1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.mlp.w_2.weight - torch.Size([512, 256]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_stack.5.mlp.w_2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_norm.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.layer_norm.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of NRTR decoder.classifier.weight - torch.Size([93, 512]): The value is the same before and after calling `init_weights` of NRTR decoder.classifier.bias - torch.Size([93]): The value is the same before and after calling `init_weights` of NRTR 2022/09/16 10:38:04 - mmengine - INFO - Checkpoints will be saved to sproject:s3://1.0.0rc0_nrtr_retest/nrtr_resnet31-1by8-1by4_6e_st_mj by PetrelBackend. 2022/09/16 11:09:13 - mmengine - INFO - Epoch(train) [1][100/42151] lr: 3.0000e-04 eta: 54 days, 16:10:02 time: 0.7079 data_time: 0.0866 memory: 28727 loss_ce: 0.6028 loss: 0.6028 2022/09/16 11:10:23 - mmengine - INFO - Epoch(train) [1][200/42151] lr: 3.0000e-04 eta: 28 days, 8:32:16 time: 0.9418 data_time: 0.3730 memory: 28726 loss_ce: 0.5531 loss: 0.5531 2022/09/16 11:11:30 - mmengine - INFO - Epoch(train) [1][300/42151] lr: 3.0000e-04 eta: 19 days, 13:13:02 time: 0.6441 data_time: 0.0911 memory: 28726 loss_ce: 0.5245 loss: 0.5245 2022/09/16 11:12:40 - mmengine - INFO - Epoch(train) [1][400/42151] lr: 3.0000e-04 eta: 15 days, 3:56:55 time: 0.6755 data_time: 0.0813 memory: 28726 loss_ce: 0.4969 loss: 0.4969 2022/09/16 11:13:48 - mmengine - INFO - Epoch(train) [1][500/42151] lr: 3.0000e-04 eta: 12 days, 12:40:39 time: 0.6453 data_time: 0.0728 memory: 28726 loss_ce: 0.4128 loss: 0.4128 2022/09/16 11:14:56 - mmengine - INFO - Epoch(train) [1][600/42151] lr: 3.0000e-04 eta: 10 days, 18:25:02 time: 0.5689 data_time: 0.0294 memory: 28726 loss_ce: 0.2487 loss: 0.2487 2022/09/16 11:16:05 - mmengine - INFO - Epoch(train) [1][700/42151] lr: 3.0000e-04 eta: 9 days, 12:16:30 time: 0.6659 data_time: 0.1218 memory: 28726 loss_ce: 0.1641 loss: 0.1641 2022/09/16 11:17:14 - mmengine - INFO - Epoch(train) [1][800/42151] lr: 3.0000e-04 eta: 8 days, 13:44:06 time: 0.7518 data_time: 0.1984 memory: 28726 loss_ce: 0.1317 loss: 0.1317 2022/09/16 11:18:23 - mmengine - INFO - Epoch(train) [1][900/42151] lr: 3.0000e-04 eta: 7 days, 20:08:04 time: 0.6111 data_time: 0.0655 memory: 28726 loss_ce: 0.1151 loss: 0.1151 2022/09/16 11:19:32 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 11:19:32 - mmengine - INFO - Epoch(train) [1][1000/42151] lr: 3.0000e-04 eta: 7 days, 6:04:51 time: 0.6421 data_time: 0.1053 memory: 28726 loss_ce: 0.1017 loss: 0.1017 2022/09/16 11:20:40 - mmengine - INFO - Epoch(train) [1][1100/42151] lr: 3.0000e-04 eta: 6 days, 18:32:50 time: 0.6498 data_time: 0.1134 memory: 28726 loss_ce: 0.0991 loss: 0.0991 2022/09/16 11:21:49 - mmengine - INFO - Epoch(train) [1][1200/42151] lr: 3.0000e-04 eta: 6 days, 8:57:46 time: 0.5784 data_time: 0.0396 memory: 28726 loss_ce: 0.0935 loss: 0.0935 2022/09/16 11:22:59 - mmengine - INFO - Epoch(train) [1][1300/42151] lr: 3.0000e-04 eta: 6 days, 0:51:47 time: 0.6717 data_time: 0.1343 memory: 28726 loss_ce: 0.0874 loss: 0.0874 2022/09/16 11:24:07 - mmengine - INFO - Epoch(train) [1][1400/42151] lr: 3.0000e-04 eta: 5 days, 17:52:54 time: 0.7614 data_time: 0.1796 memory: 28726 loss_ce: 0.0827 loss: 0.0827 2022/09/16 11:25:15 - mmengine - INFO - Epoch(train) [1][1500/42151] lr: 3.0000e-04 eta: 5 days, 11:48:11 time: 0.6184 data_time: 0.0787 memory: 28726 loss_ce: 0.0798 loss: 0.0798 2022/09/16 11:26:25 - mmengine - INFO - Epoch(train) [1][1600/42151] lr: 3.0000e-04 eta: 5 days, 6:34:19 time: 0.6952 data_time: 0.1454 memory: 28726 loss_ce: 0.0736 loss: 0.0736 2022/09/16 11:27:34 - mmengine - INFO - Epoch(train) [1][1700/42151] lr: 3.0000e-04 eta: 5 days, 1:54:22 time: 0.6667 data_time: 0.1258 memory: 28726 loss_ce: 0.0738 loss: 0.0738 2022/09/16 11:28:43 - mmengine - INFO - Epoch(train) [1][1800/42151] lr: 3.0000e-04 eta: 4 days, 21:45:49 time: 0.5798 data_time: 0.0398 memory: 28726 loss_ce: 0.0679 loss: 0.0679 2022/09/16 11:29:54 - mmengine - INFO - Epoch(train) [1][1900/42151] lr: 3.0000e-04 eta: 4 days, 18:06:21 time: 0.6617 data_time: 0.1166 memory: 28726 loss_ce: 0.0649 loss: 0.0649 2022/09/16 11:31:02 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 11:31:02 - mmengine - INFO - Epoch(train) [1][2000/42151] lr: 3.0000e-04 eta: 4 days, 14:44:54 time: 0.7361 data_time: 0.1590 memory: 28726 loss_ce: 0.0673 loss: 0.0673 2022/09/16 11:32:10 - mmengine - INFO - Epoch(train) [1][2100/42151] lr: 3.0000e-04 eta: 4 days, 11:40:53 time: 0.6091 data_time: 0.0581 memory: 28726 loss_ce: 0.0676 loss: 0.0676 2022/09/16 11:33:19 - mmengine - INFO - Epoch(train) [1][2200/42151] lr: 3.0000e-04 eta: 4 days, 8:56:47 time: 0.6664 data_time: 0.1124 memory: 28726 loss_ce: 0.0663 loss: 0.0663 2022/09/16 11:34:29 - mmengine - INFO - Epoch(train) [1][2300/42151] lr: 3.0000e-04 eta: 4 days, 6:27:25 time: 0.7264 data_time: 0.1238 memory: 28726 loss_ce: 0.0631 loss: 0.0631 2022/09/16 11:35:37 - mmengine - INFO - Epoch(train) [1][2400/42151] lr: 3.0000e-04 eta: 4 days, 4:06:59 time: 0.5723 data_time: 0.0365 memory: 28726 loss_ce: 0.0648 loss: 0.0648 2022/09/16 11:36:47 - mmengine - INFO - Epoch(train) [1][2500/42151] lr: 3.0000e-04 eta: 4 days, 2:01:09 time: 0.6935 data_time: 0.1578 memory: 28726 loss_ce: 0.0575 loss: 0.0575 2022/09/16 11:37:57 - mmengine - INFO - Epoch(train) [1][2600/42151] lr: 3.0000e-04 eta: 4 days, 0:04:09 time: 0.7868 data_time: 0.1953 memory: 28726 loss_ce: 0.0585 loss: 0.0585 2022/09/16 11:39:05 - mmengine - INFO - Epoch(train) [1][2700/42151] lr: 3.0000e-04 eta: 3 days, 22:14:39 time: 0.6218 data_time: 0.0836 memory: 28726 loss_ce: 0.0591 loss: 0.0591 2022/09/16 11:40:14 - mmengine - INFO - Epoch(train) [1][2800/42151] lr: 3.0000e-04 eta: 3 days, 20:31:54 time: 0.6295 data_time: 0.0766 memory: 28726 loss_ce: 0.0571 loss: 0.0571 2022/09/16 11:41:26 - mmengine - INFO - Epoch(train) [1][2900/42151] lr: 3.0000e-04 eta: 3 days, 19:02:06 time: 0.7357 data_time: 0.0901 memory: 28726 loss_ce: 0.0578 loss: 0.0578 2022/09/16 11:42:35 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 11:42:35 - mmengine - INFO - Epoch(train) [1][3000/42151] lr: 3.0000e-04 eta: 3 days, 17:33:32 time: 0.5898 data_time: 0.0493 memory: 28726 loss_ce: 0.0613 loss: 0.0613 2022/09/16 11:43:44 - mmengine - INFO - Epoch(train) [1][3100/42151] lr: 3.0000e-04 eta: 3 days, 16:11:45 time: 0.6830 data_time: 0.1351 memory: 28726 loss_ce: 0.0600 loss: 0.0600 2022/09/16 11:44:57 - mmengine - INFO - Epoch(train) [1][3200/42151] lr: 3.0000e-04 eta: 3 days, 14:58:56 time: 0.8660 data_time: 0.1915 memory: 28726 loss_ce: 0.0567 loss: 0.0567 2022/09/16 11:46:12 - mmengine - INFO - Epoch(train) [1][3300/42151] lr: 3.0000e-04 eta: 3 days, 13:53:36 time: 0.6152 data_time: 0.0481 memory: 28726 loss_ce: 0.0518 loss: 0.0518 2022/09/16 11:47:22 - mmengine - INFO - Epoch(train) [1][3400/42151] lr: 3.0000e-04 eta: 3 days, 12:45:39 time: 0.6590 data_time: 0.1156 memory: 28726 loss_ce: 0.0541 loss: 0.0541 2022/09/16 11:48:32 - mmengine - INFO - Epoch(train) [1][3500/42151] lr: 3.0000e-04 eta: 3 days, 11:41:06 time: 0.6590 data_time: 0.1214 memory: 28726 loss_ce: 0.0547 loss: 0.0547 2022/09/16 11:49:41 - mmengine - INFO - Epoch(train) [1][3600/42151] lr: 3.0000e-04 eta: 3 days, 10:39:07 time: 0.5902 data_time: 0.0499 memory: 28726 loss_ce: 0.0539 loss: 0.0539 2022/09/16 11:50:52 - mmengine - INFO - Epoch(train) [1][3700/42151] lr: 3.0000e-04 eta: 3 days, 9:42:35 time: 0.6753 data_time: 0.1327 memory: 28726 loss_ce: 0.0548 loss: 0.0548 2022/09/16 11:52:01 - mmengine - INFO - Epoch(train) [1][3800/42151] lr: 3.0000e-04 eta: 3 days, 8:47:09 time: 0.7438 data_time: 0.1835 memory: 28726 loss_ce: 0.0499 loss: 0.0499 2022/09/16 11:53:10 - mmengine - INFO - Epoch(train) [1][3900/42151] lr: 3.0000e-04 eta: 3 days, 7:54:35 time: 0.6350 data_time: 0.0679 memory: 28726 loss_ce: 0.0493 loss: 0.0493 2022/09/16 11:54:29 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 11:54:29 - mmengine - INFO - Epoch(train) [1][4000/42151] lr: 3.0000e-04 eta: 3 days, 7:14:48 time: 0.7398 data_time: 0.1585 memory: 28726 loss_ce: 0.0486 loss: 0.0486 2022/09/16 11:55:38 - mmengine - INFO - Epoch(train) [1][4100/42151] lr: 3.0000e-04 eta: 3 days, 6:26:43 time: 0.6508 data_time: 0.1134 memory: 28726 loss_ce: 0.0479 loss: 0.0479 2022/09/16 11:56:47 - mmengine - INFO - Epoch(train) [1][4200/42151] lr: 3.0000e-04 eta: 3 days, 5:40:46 time: 0.6106 data_time: 0.0404 memory: 28726 loss_ce: 0.0491 loss: 0.0491 2022/09/16 11:57:56 - mmengine - INFO - Epoch(train) [1][4300/42151] lr: 3.0000e-04 eta: 3 days, 4:57:24 time: 0.6795 data_time: 0.1084 memory: 28726 loss_ce: 0.0461 loss: 0.0461 2022/09/16 11:59:10 - mmengine - INFO - Epoch(train) [1][4400/42151] lr: 3.0000e-04 eta: 3 days, 4:20:29 time: 0.7769 data_time: 0.1708 memory: 28726 loss_ce: 0.0493 loss: 0.0493 2022/09/16 12:00:25 - mmengine - INFO - Epoch(train) [1][4500/42151] lr: 3.0000e-04 eta: 3 days, 3:45:19 time: 0.6517 data_time: 0.0974 memory: 28726 loss_ce: 0.0491 loss: 0.0491 2022/09/16 12:01:33 - mmengine - INFO - Epoch(train) [1][4600/42151] lr: 3.0000e-04 eta: 3 days, 3:06:29 time: 0.6620 data_time: 0.0882 memory: 28726 loss_ce: 0.0447 loss: 0.0447 2022/09/16 12:02:42 - mmengine - INFO - Epoch(train) [1][4700/42151] lr: 3.0000e-04 eta: 3 days, 2:29:05 time: 0.7506 data_time: 0.1490 memory: 28726 loss_ce: 0.0478 loss: 0.0478 2022/09/16 12:03:54 - mmengine - INFO - Epoch(train) [1][4800/42151] lr: 3.0000e-04 eta: 3 days, 1:56:26 time: 0.6031 data_time: 0.0392 memory: 28726 loss_ce: 0.0468 loss: 0.0468 2022/09/16 12:05:04 - mmengine - INFO - Epoch(train) [1][4900/42151] lr: 3.0000e-04 eta: 3 days, 1:23:25 time: 0.6574 data_time: 0.1178 memory: 28726 loss_ce: 0.0480 loss: 0.0480 2022/09/16 12:06:13 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 12:06:13 - mmengine - INFO - Epoch(train) [1][5000/42151] lr: 3.0000e-04 eta: 3 days, 0:50:09 time: 0.7487 data_time: 0.2087 memory: 28726 loss_ce: 0.0462 loss: 0.0462 2022/09/16 12:07:26 - mmengine - INFO - Epoch(train) [1][5100/42151] lr: 3.0000e-04 eta: 3 days, 0:21:51 time: 0.6317 data_time: 0.0748 memory: 28726 loss_ce: 0.0439 loss: 0.0439 2022/09/16 12:08:36 - mmengine - INFO - Epoch(train) [1][5200/42151] lr: 3.0000e-04 eta: 2 days, 23:52:40 time: 0.7075 data_time: 0.1232 memory: 28726 loss_ce: 0.0443 loss: 0.0443 2022/09/16 12:09:45 - mmengine - INFO - Epoch(train) [1][5300/42151] lr: 3.0000e-04 eta: 2 days, 23:23:19 time: 0.6830 data_time: 0.1454 memory: 28726 loss_ce: 0.0411 loss: 0.0411 2022/09/16 12:10:54 - mmengine - INFO - Epoch(train) [1][5400/42151] lr: 3.0000e-04 eta: 2 days, 22:54:35 time: 0.6214 data_time: 0.0861 memory: 28726 loss_ce: 0.0426 loss: 0.0426 2022/09/16 12:12:03 - mmengine - INFO - Epoch(train) [1][5500/42151] lr: 3.0000e-04 eta: 2 days, 22:27:16 time: 0.6235 data_time: 0.0866 memory: 28726 loss_ce: 0.0427 loss: 0.0427 2022/09/16 12:13:11 - mmengine - INFO - Epoch(train) [1][5600/42151] lr: 3.0000e-04 eta: 2 days, 22:00:34 time: 0.7521 data_time: 0.1952 memory: 28726 loss_ce: 0.0439 loss: 0.0439 2022/09/16 12:14:19 - mmengine - INFO - Epoch(train) [1][5700/42151] lr: 3.0000e-04 eta: 2 days, 21:34:09 time: 0.6382 data_time: 0.0619 memory: 28726 loss_ce: 0.0467 loss: 0.0467 2022/09/16 12:15:28 - mmengine - INFO - Epoch(train) [1][5800/42151] lr: 3.0000e-04 eta: 2 days, 21:09:43 time: 0.6577 data_time: 0.1193 memory: 28726 loss_ce: 0.0427 loss: 0.0427 2022/09/16 12:16:38 - mmengine - INFO - Epoch(train) [1][5900/42151] lr: 3.0000e-04 eta: 2 days, 20:46:10 time: 0.6781 data_time: 0.1075 memory: 28726 loss_ce: 0.0434 loss: 0.0434 2022/09/16 12:17:47 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 12:17:47 - mmengine - INFO - Epoch(train) [1][6000/42151] lr: 3.0000e-04 eta: 2 days, 20:23:36 time: 0.6264 data_time: 0.0668 memory: 28726 loss_ce: 0.0418 loss: 0.0418 2022/09/16 12:18:58 - mmengine - INFO - Epoch(train) [1][6100/42151] lr: 3.0000e-04 eta: 2 days, 20:01:56 time: 0.6695 data_time: 0.1223 memory: 28726 loss_ce: 0.0427 loss: 0.0427 2022/09/16 12:20:06 - mmengine - INFO - Epoch(train) [1][6200/42151] lr: 3.0000e-04 eta: 2 days, 19:39:49 time: 0.7221 data_time: 0.1854 memory: 28726 loss_ce: 0.0453 loss: 0.0453 2022/09/16 12:21:14 - mmengine - INFO - Epoch(train) [1][6300/42151] lr: 3.0000e-04 eta: 2 days, 19:18:16 time: 0.6373 data_time: 0.0958 memory: 28726 loss_ce: 0.0398 loss: 0.0398 2022/09/16 12:22:24 - mmengine - INFO - Epoch(train) [1][6400/42151] lr: 3.0000e-04 eta: 2 days, 18:58:08 time: 0.6609 data_time: 0.0991 memory: 28726 loss_ce: 0.0454 loss: 0.0454 2022/09/16 12:23:33 - mmengine - INFO - Epoch(train) [1][6500/42151] lr: 3.0000e-04 eta: 2 days, 18:38:22 time: 0.7312 data_time: 0.1589 memory: 28726 loss_ce: 0.0446 loss: 0.0446 2022/09/16 12:24:42 - mmengine - INFO - Epoch(train) [1][6600/42151] lr: 3.0000e-04 eta: 2 days, 18:19:07 time: 0.7250 data_time: 0.1304 memory: 28726 loss_ce: 0.0414 loss: 0.0414 2022/09/16 12:25:51 - mmengine - INFO - Epoch(train) [1][6700/42151] lr: 3.0000e-04 eta: 2 days, 18:00:34 time: 0.6388 data_time: 0.0745 memory: 28726 loss_ce: 0.0425 loss: 0.0425 2022/09/16 12:27:00 - mmengine - INFO - Epoch(train) [1][6800/42151] lr: 3.0000e-04 eta: 2 days, 17:42:14 time: 0.7616 data_time: 0.2210 memory: 28726 loss_ce: 0.0413 loss: 0.0413 2022/09/16 12:28:09 - mmengine - INFO - Epoch(train) [1][6900/42151] lr: 3.0000e-04 eta: 2 days, 17:24:23 time: 0.6576 data_time: 0.1192 memory: 28726 loss_ce: 0.0429 loss: 0.0429 2022/09/16 12:29:20 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 12:29:20 - mmengine - INFO - Epoch(train) [1][7000/42151] lr: 3.0000e-04 eta: 2 days, 17:08:18 time: 0.7031 data_time: 0.1668 memory: 28726 loss_ce: 0.0383 loss: 0.0383 2022/09/16 12:30:29 - mmengine - INFO - Epoch(train) [1][7100/42151] lr: 3.0000e-04 eta: 2 days, 16:51:40 time: 0.6865 data_time: 0.1460 memory: 28726 loss_ce: 0.0369 loss: 0.0369 2022/09/16 12:31:37 - mmengine - INFO - Epoch(train) [1][7200/42151] lr: 3.0000e-04 eta: 2 days, 16:34:42 time: 0.6187 data_time: 0.0787 memory: 28726 loss_ce: 0.0359 loss: 0.0359 2022/09/16 12:32:46 - mmengine - INFO - Epoch(train) [1][7300/42151] lr: 3.0000e-04 eta: 2 days, 16:18:50 time: 0.6421 data_time: 0.0833 memory: 28726 loss_ce: 0.0398 loss: 0.0398 2022/09/16 12:33:55 - mmengine - INFO - Epoch(train) [1][7400/42151] lr: 3.0000e-04 eta: 2 days, 16:03:23 time: 0.7082 data_time: 0.1640 memory: 28726 loss_ce: 0.0382 loss: 0.0382 2022/09/16 12:35:04 - mmengine - INFO - Epoch(train) [1][7500/42151] lr: 3.0000e-04 eta: 2 days, 15:48:23 time: 0.6513 data_time: 0.1024 memory: 28726 loss_ce: 0.0377 loss: 0.0377 2022/09/16 12:36:14 - mmengine - INFO - Epoch(train) [1][7600/42151] lr: 3.0000e-04 eta: 2 days, 15:34:02 time: 0.7033 data_time: 0.1617 memory: 28726 loss_ce: 0.0382 loss: 0.0382 2022/09/16 12:37:24 - mmengine - INFO - Epoch(train) [1][7700/42151] lr: 3.0000e-04 eta: 2 days, 15:19:48 time: 0.7188 data_time: 0.1432 memory: 28726 loss_ce: 0.0395 loss: 0.0395 2022/09/16 12:38:32 - mmengine - INFO - Epoch(train) [1][7800/42151] lr: 3.0000e-04 eta: 2 days, 15:05:33 time: 0.6570 data_time: 0.1027 memory: 28726 loss_ce: 0.0343 loss: 0.0343 2022/09/16 12:39:42 - mmengine - INFO - Epoch(train) [1][7900/42151] lr: 3.0000e-04 eta: 2 days, 14:51:52 time: 0.6509 data_time: 0.1076 memory: 28726 loss_ce: 0.0381 loss: 0.0381 2022/09/16 12:40:51 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 12:40:51 - mmengine - INFO - Epoch(train) [1][8000/42151] lr: 3.0000e-04 eta: 2 days, 14:38:31 time: 0.6965 data_time: 0.1165 memory: 28726 loss_ce: 0.0375 loss: 0.0375 2022/09/16 12:42:00 - mmengine - INFO - Epoch(train) [1][8100/42151] lr: 3.0000e-04 eta: 2 days, 14:25:25 time: 0.6597 data_time: 0.1207 memory: 28726 loss_ce: 0.0406 loss: 0.0406 2022/09/16 12:43:09 - mmengine - INFO - Epoch(train) [1][8200/42151] lr: 3.0000e-04 eta: 2 days, 14:12:46 time: 0.7438 data_time: 0.1374 memory: 28726 loss_ce: 0.0403 loss: 0.0403 2022/09/16 12:44:18 - mmengine - INFO - Epoch(train) [1][8300/42151] lr: 3.0000e-04 eta: 2 days, 13:59:48 time: 0.6901 data_time: 0.1521 memory: 28726 loss_ce: 0.0387 loss: 0.0387 2022/09/16 12:45:25 - mmengine - INFO - Epoch(train) [1][8400/42151] lr: 3.0000e-04 eta: 2 days, 13:46:47 time: 0.6188 data_time: 0.0866 memory: 28726 loss_ce: 0.0371 loss: 0.0371 2022/09/16 12:46:34 - mmengine - INFO - Epoch(train) [1][8500/42151] lr: 3.0000e-04 eta: 2 days, 13:34:30 time: 0.6398 data_time: 0.1031 memory: 28726 loss_ce: 0.0367 loss: 0.0367 2022/09/16 12:47:42 - mmengine - INFO - Epoch(train) [1][8600/42151] lr: 3.0000e-04 eta: 2 days, 13:22:38 time: 0.7219 data_time: 0.1772 memory: 28726 loss_ce: 0.0371 loss: 0.0371 2022/09/16 12:48:51 - mmengine - INFO - Epoch(train) [1][8700/42151] lr: 3.0000e-04 eta: 2 days, 13:10:55 time: 0.6720 data_time: 0.0703 memory: 28726 loss_ce: 0.0364 loss: 0.0364 2022/09/16 12:50:00 - mmengine - INFO - Epoch(train) [1][8800/42151] lr: 3.0000e-04 eta: 2 days, 12:59:35 time: 0.6533 data_time: 0.1169 memory: 28726 loss_ce: 0.0350 loss: 0.0350 2022/09/16 12:51:09 - mmengine - INFO - Epoch(train) [1][8900/42151] lr: 3.0000e-04 eta: 2 days, 12:48:27 time: 0.6920 data_time: 0.1489 memory: 28726 loss_ce: 0.0354 loss: 0.0354 2022/09/16 12:52:18 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 12:52:18 - mmengine - INFO - Epoch(train) [1][9000/42151] lr: 3.0000e-04 eta: 2 days, 12:37:33 time: 0.6578 data_time: 0.1177 memory: 28726 loss_ce: 0.0348 loss: 0.0348 2022/09/16 12:53:27 - mmengine - INFO - Epoch(train) [1][9100/42151] lr: 3.0000e-04 eta: 2 days, 12:27:01 time: 0.6433 data_time: 0.1050 memory: 28726 loss_ce: 0.0322 loss: 0.0322 2022/09/16 12:54:37 - mmengine - INFO - Epoch(train) [1][9200/42151] lr: 3.0000e-04 eta: 2 days, 12:16:49 time: 0.7491 data_time: 0.2123 memory: 28726 loss_ce: 0.0357 loss: 0.0357 2022/09/16 12:55:45 - mmengine - INFO - Epoch(train) [1][9300/42151] lr: 3.0000e-04 eta: 2 days, 12:06:32 time: 0.6477 data_time: 0.0684 memory: 28726 loss_ce: 0.0370 loss: 0.0370 2022/09/16 12:56:55 - mmengine - INFO - Epoch(train) [1][9400/42151] lr: 3.0000e-04 eta: 2 days, 11:56:37 time: 0.6565 data_time: 0.1213 memory: 28726 loss_ce: 0.0378 loss: 0.0378 2022/09/16 12:58:04 - mmengine - INFO - Epoch(train) [1][9500/42151] lr: 3.0000e-04 eta: 2 days, 11:46:41 time: 0.6756 data_time: 0.1080 memory: 28726 loss_ce: 0.0355 loss: 0.0355 2022/09/16 12:59:12 - mmengine - INFO - Epoch(train) [1][9600/42151] lr: 3.0000e-04 eta: 2 days, 11:36:47 time: 0.6357 data_time: 0.0631 memory: 28726 loss_ce: 0.0373 loss: 0.0373 2022/09/16 13:00:22 - mmengine - INFO - Epoch(train) [1][9700/42151] lr: 3.0000e-04 eta: 2 days, 11:27:42 time: 0.6592 data_time: 0.1161 memory: 28726 loss_ce: 0.0351 loss: 0.0351 2022/09/16 13:01:32 - mmengine - INFO - Epoch(train) [1][9800/42151] lr: 3.0000e-04 eta: 2 days, 11:18:42 time: 0.7304 data_time: 0.1768 memory: 28726 loss_ce: 0.0377 loss: 0.0377 2022/09/16 13:02:40 - mmengine - INFO - Epoch(train) [1][9900/42151] lr: 3.0000e-04 eta: 2 days, 11:09:17 time: 0.6396 data_time: 0.0972 memory: 28726 loss_ce: 0.0382 loss: 0.0382 2022/09/16 13:03:50 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 13:03:50 - mmengine - INFO - Epoch(train) [1][10000/42151] lr: 3.0000e-04 eta: 2 days, 11:00:28 time: 0.6841 data_time: 0.1481 memory: 28726 loss_ce: 0.0357 loss: 0.0357 2022/09/16 13:04:59 - mmengine - INFO - Epoch(train) [1][10100/42151] lr: 3.0000e-04 eta: 2 days, 10:51:53 time: 0.7136 data_time: 0.1197 memory: 28726 loss_ce: 0.0342 loss: 0.0342 2022/09/16 13:06:09 - mmengine - INFO - Epoch(train) [1][10200/42151] lr: 3.0000e-04 eta: 2 days, 10:43:29 time: 0.6925 data_time: 0.1539 memory: 28726 loss_ce: 0.0325 loss: 0.0325 2022/09/16 13:07:18 - mmengine - INFO - Epoch(train) [1][10300/42151] lr: 3.0000e-04 eta: 2 days, 10:34:49 time: 0.6533 data_time: 0.1187 memory: 28726 loss_ce: 0.0331 loss: 0.0331 2022/09/16 13:08:28 - mmengine - INFO - Epoch(train) [1][10400/42151] lr: 3.0000e-04 eta: 2 days, 10:26:59 time: 0.7313 data_time: 0.1579 memory: 28726 loss_ce: 0.0330 loss: 0.0330 2022/09/16 13:09:38 - mmengine - INFO - Epoch(train) [1][10500/42151] lr: 3.0000e-04 eta: 2 days, 10:18:51 time: 0.6988 data_time: 0.1154 memory: 28726 loss_ce: 0.0326 loss: 0.0326 2022/09/16 13:10:46 - mmengine - INFO - Epoch(train) [1][10600/42151] lr: 3.0000e-04 eta: 2 days, 10:10:36 time: 0.6979 data_time: 0.1615 memory: 28726 loss_ce: 0.0344 loss: 0.0344 2022/09/16 13:11:55 - mmengine - INFO - Epoch(train) [1][10700/42151] lr: 3.0000e-04 eta: 2 days, 10:02:34 time: 0.6484 data_time: 0.0847 memory: 28726 loss_ce: 0.0336 loss: 0.0336 2022/09/16 13:13:06 - mmengine - INFO - Epoch(train) [1][10800/42151] lr: 3.0000e-04 eta: 2 days, 9:55:14 time: 0.6985 data_time: 0.1526 memory: 28726 loss_ce: 0.0365 loss: 0.0365 2022/09/16 13:14:16 - mmengine - INFO - Epoch(train) [1][10900/42151] lr: 3.0000e-04 eta: 2 days, 9:47:50 time: 0.6935 data_time: 0.1174 memory: 28726 loss_ce: 0.0374 loss: 0.0374 2022/09/16 13:15:26 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 13:15:26 - mmengine - INFO - Epoch(train) [1][11000/42151] lr: 3.0000e-04 eta: 2 days, 9:40:34 time: 0.6974 data_time: 0.0821 memory: 28726 loss_ce: 0.0343 loss: 0.0343 2022/09/16 13:16:35 - mmengine - INFO - Epoch(train) [1][11100/42151] lr: 3.0000e-04 eta: 2 days, 9:33:04 time: 0.6695 data_time: 0.1020 memory: 28726 loss_ce: 0.0358 loss: 0.0358 2022/09/16 13:17:45 - mmengine - INFO - Epoch(train) [1][11200/42151] lr: 3.0000e-04 eta: 2 days, 9:26:04 time: 0.7220 data_time: 0.1457 memory: 28726 loss_ce: 0.0342 loss: 0.0342 2022/09/16 13:18:56 - mmengine - INFO - Epoch(train) [1][11300/42151] lr: 3.0000e-04 eta: 2 days, 9:19:10 time: 0.7370 data_time: 0.1664 memory: 28726 loss_ce: 0.0320 loss: 0.0320 2022/09/16 13:20:05 - mmengine - INFO - Epoch(train) [1][11400/42151] lr: 3.0000e-04 eta: 2 days, 9:12:07 time: 0.7649 data_time: 0.2246 memory: 28726 loss_ce: 0.0315 loss: 0.0315 2022/09/16 13:21:14 - mmengine - INFO - Epoch(train) [1][11500/42151] lr: 3.0000e-04 eta: 2 days, 9:05:01 time: 0.6643 data_time: 0.0808 memory: 28726 loss_ce: 0.0297 loss: 0.0297 2022/09/16 13:22:24 - mmengine - INFO - Epoch(train) [1][11600/42151] lr: 3.0000e-04 eta: 2 days, 8:58:17 time: 0.6305 data_time: 0.0944 memory: 28726 loss_ce: 0.0329 loss: 0.0329 2022/09/16 13:23:32 - mmengine - INFO - Epoch(train) [1][11700/42151] lr: 3.0000e-04 eta: 2 days, 8:51:08 time: 0.6455 data_time: 0.0837 memory: 28726 loss_ce: 0.0346 loss: 0.0346 2022/09/16 13:24:42 - mmengine - INFO - Epoch(train) [1][11800/42151] lr: 3.0000e-04 eta: 2 days, 8:44:30 time: 0.7409 data_time: 0.1890 memory: 28726 loss_ce: 0.0330 loss: 0.0330 2022/09/16 13:25:51 - mmengine - INFO - Epoch(train) [1][11900/42151] lr: 3.0000e-04 eta: 2 days, 8:37:59 time: 0.7008 data_time: 0.1443 memory: 28726 loss_ce: 0.0282 loss: 0.0282 2022/09/16 13:27:00 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 13:27:00 - mmengine - INFO - Epoch(train) [1][12000/42151] lr: 3.0000e-04 eta: 2 days, 8:31:10 time: 0.6308 data_time: 0.0765 memory: 28726 loss_ce: 0.0351 loss: 0.0351 2022/09/16 13:28:07 - mmengine - INFO - Epoch(train) [1][12100/42151] lr: 3.0000e-04 eta: 2 days, 8:24:07 time: 0.6047 data_time: 0.0650 memory: 28726 loss_ce: 0.0349 loss: 0.0349 2022/09/16 13:29:16 - mmengine - INFO - Epoch(train) [1][12200/42151] lr: 3.0000e-04 eta: 2 days, 8:17:41 time: 0.6698 data_time: 0.1330 memory: 28726 loss_ce: 0.0327 loss: 0.0327 2022/09/16 13:30:25 - mmengine - INFO - Epoch(train) [1][12300/42151] lr: 3.0000e-04 eta: 2 days, 8:11:17 time: 0.6637 data_time: 0.1244 memory: 28726 loss_ce: 0.0323 loss: 0.0323 2022/09/16 13:31:35 - mmengine - INFO - Epoch(train) [1][12400/42151] lr: 3.0000e-04 eta: 2 days, 8:05:20 time: 0.7785 data_time: 0.1894 memory: 28726 loss_ce: 0.0336 loss: 0.0336 2022/09/16 13:32:43 - mmengine - INFO - Epoch(train) [1][12500/42151] lr: 3.0000e-04 eta: 2 days, 7:58:51 time: 0.6949 data_time: 0.1557 memory: 28726 loss_ce: 0.0315 loss: 0.0315 2022/09/16 13:33:52 - mmengine - INFO - Epoch(train) [1][12600/42151] lr: 3.0000e-04 eta: 2 days, 7:52:41 time: 0.6501 data_time: 0.0882 memory: 28726 loss_ce: 0.0324 loss: 0.0324 2022/09/16 13:35:01 - mmengine - INFO - Epoch(train) [1][12700/42151] lr: 3.0000e-04 eta: 2 days, 7:46:31 time: 0.6322 data_time: 0.0979 memory: 28726 loss_ce: 0.0344 loss: 0.0344 2022/09/16 13:36:10 - mmengine - INFO - Epoch(train) [1][12800/42151] lr: 3.0000e-04 eta: 2 days, 7:40:32 time: 0.6953 data_time: 0.1289 memory: 28726 loss_ce: 0.0323 loss: 0.0323 2022/09/16 13:37:19 - mmengine - INFO - Epoch(train) [1][12900/42151] lr: 3.0000e-04 eta: 2 days, 7:34:46 time: 0.6535 data_time: 0.1095 memory: 28726 loss_ce: 0.0286 loss: 0.0286 2022/09/16 13:38:28 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 13:38:28 - mmengine - INFO - Epoch(train) [1][13000/42151] lr: 3.0000e-04 eta: 2 days, 7:29:02 time: 0.7158 data_time: 0.1550 memory: 28726 loss_ce: 0.0308 loss: 0.0308 2022/09/16 13:39:38 - mmengine - INFO - Epoch(train) [1][13100/42151] lr: 3.0000e-04 eta: 2 days, 7:23:37 time: 0.7603 data_time: 0.1438 memory: 28726 loss_ce: 0.0309 loss: 0.0309 2022/09/16 13:40:47 - mmengine - INFO - Epoch(train) [1][13200/42151] lr: 3.0000e-04 eta: 2 days, 7:18:01 time: 0.6166 data_time: 0.0820 memory: 28726 loss_ce: 0.0295 loss: 0.0295 2022/09/16 13:41:57 - mmengine - INFO - Epoch(train) [1][13300/42151] lr: 3.0000e-04 eta: 2 days, 7:12:27 time: 0.6401 data_time: 0.1005 memory: 28726 loss_ce: 0.0314 loss: 0.0314 2022/09/16 13:43:06 - mmengine - INFO - Epoch(train) [1][13400/42151] lr: 3.0000e-04 eta: 2 days, 7:07:05 time: 0.6894 data_time: 0.1320 memory: 28726 loss_ce: 0.0295 loss: 0.0295 2022/09/16 13:44:15 - mmengine - INFO - Epoch(train) [1][13500/42151] lr: 3.0000e-04 eta: 2 days, 7:01:36 time: 0.6579 data_time: 0.1227 memory: 28726 loss_ce: 0.0305 loss: 0.0305 2022/09/16 13:45:24 - mmengine - INFO - Epoch(train) [1][13600/42151] lr: 3.0000e-04 eta: 2 days, 6:56:10 time: 0.6901 data_time: 0.1312 memory: 28726 loss_ce: 0.0328 loss: 0.0328 2022/09/16 13:46:33 - mmengine - INFO - Epoch(train) [1][13700/42151] lr: 3.0000e-04 eta: 2 days, 6:50:43 time: 0.6917 data_time: 0.1538 memory: 28726 loss_ce: 0.0310 loss: 0.0310 2022/09/16 13:47:43 - mmengine - INFO - Epoch(train) [1][13800/42151] lr: 3.0000e-04 eta: 2 days, 6:45:52 time: 0.6589 data_time: 0.1141 memory: 28726 loss_ce: 0.0281 loss: 0.0281 2022/09/16 13:48:52 - mmengine - INFO - Epoch(train) [1][13900/42151] lr: 3.0000e-04 eta: 2 days, 6:40:30 time: 0.6418 data_time: 0.0846 memory: 28726 loss_ce: 0.0336 loss: 0.0336 2022/09/16 13:50:03 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 13:50:03 - mmengine - INFO - Epoch(train) [1][14000/42151] lr: 3.0000e-04 eta: 2 days, 6:35:53 time: 0.6983 data_time: 0.0963 memory: 28726 loss_ce: 0.0294 loss: 0.0294 2022/09/16 13:51:12 - mmengine - INFO - Epoch(train) [1][14100/42151] lr: 3.0000e-04 eta: 2 days, 6:30:59 time: 0.6581 data_time: 0.1195 memory: 28726 loss_ce: 0.0292 loss: 0.0292 2022/09/16 13:52:23 - mmengine - INFO - Epoch(train) [1][14200/42151] lr: 3.0000e-04 eta: 2 days, 6:26:12 time: 0.7445 data_time: 0.1785 memory: 28726 loss_ce: 0.0265 loss: 0.0265 2022/09/16 13:53:31 - mmengine - INFO - Epoch(train) [1][14300/42151] lr: 3.0000e-04 eta: 2 days, 6:21:05 time: 0.6878 data_time: 0.1443 memory: 28726 loss_ce: 0.0294 loss: 0.0294 2022/09/16 13:54:40 - mmengine - INFO - Epoch(train) [1][14400/42151] lr: 3.0000e-04 eta: 2 days, 6:16:06 time: 0.6598 data_time: 0.0841 memory: 28726 loss_ce: 0.0308 loss: 0.0308 2022/09/16 13:55:49 - mmengine - INFO - Epoch(train) [1][14500/42151] lr: 3.0000e-04 eta: 2 days, 6:11:16 time: 0.6299 data_time: 0.0745 memory: 28726 loss_ce: 0.0301 loss: 0.0301 2022/09/16 13:56:59 - mmengine - INFO - Epoch(train) [1][14600/42151] lr: 3.0000e-04 eta: 2 days, 6:06:44 time: 0.6748 data_time: 0.1298 memory: 28726 loss_ce: 0.0281 loss: 0.0281 2022/09/16 13:58:09 - mmengine - INFO - Epoch(train) [1][14700/42151] lr: 3.0000e-04 eta: 2 days, 6:02:11 time: 0.6631 data_time: 0.1224 memory: 28726 loss_ce: 0.0290 loss: 0.0290 2022/09/16 13:59:23 - mmengine - INFO - Epoch(train) [1][14800/42151] lr: 3.0000e-04 eta: 2 days, 5:58:37 time: 1.0624 data_time: 0.2333 memory: 28726 loss_ce: 0.0307 loss: 0.0307 2022/09/16 14:00:32 - mmengine - INFO - Epoch(train) [1][14900/42151] lr: 3.0000e-04 eta: 2 days, 5:53:49 time: 0.7335 data_time: 0.1682 memory: 28726 loss_ce: 0.0283 loss: 0.0283 2022/09/16 14:01:41 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 14:01:41 - mmengine - INFO - Epoch(train) [1][15000/42151] lr: 3.0000e-04 eta: 2 days, 5:49:14 time: 0.6298 data_time: 0.0866 memory: 28726 loss_ce: 0.0306 loss: 0.0306 2022/09/16 14:02:51 - mmengine - INFO - Epoch(train) [1][15100/42151] lr: 3.0000e-04 eta: 2 days, 5:44:56 time: 0.7242 data_time: 0.1256 memory: 28726 loss_ce: 0.0277 loss: 0.0277 2022/09/16 14:04:01 - mmengine - INFO - Epoch(train) [1][15200/42151] lr: 3.0000e-04 eta: 2 days, 5:40:31 time: 0.6886 data_time: 0.0985 memory: 28726 loss_ce: 0.0296 loss: 0.0296 2022/09/16 14:05:09 - mmengine - INFO - Epoch(train) [1][15300/42151] lr: 3.0000e-04 eta: 2 days, 5:35:49 time: 0.6682 data_time: 0.1338 memory: 28726 loss_ce: 0.0278 loss: 0.0278 2022/09/16 14:06:18 - mmengine - INFO - Epoch(train) [1][15400/42151] lr: 3.0000e-04 eta: 2 days, 5:31:24 time: 0.7541 data_time: 0.1829 memory: 28726 loss_ce: 0.0288 loss: 0.0288 2022/09/16 14:07:28 - mmengine - INFO - Epoch(train) [1][15500/42151] lr: 3.0000e-04 eta: 2 days, 5:27:04 time: 0.7156 data_time: 0.1729 memory: 28726 loss_ce: 0.0274 loss: 0.0274 2022/09/16 14:08:38 - mmengine - INFO - Epoch(train) [1][15600/42151] lr: 3.0000e-04 eta: 2 days, 5:22:51 time: 0.6088 data_time: 0.0726 memory: 28726 loss_ce: 0.0276 loss: 0.0276 2022/09/16 14:09:47 - mmengine - INFO - Epoch(train) [1][15700/42151] lr: 3.0000e-04 eta: 2 days, 5:18:36 time: 0.6537 data_time: 0.1105 memory: 28726 loss_ce: 0.0279 loss: 0.0279 2022/09/16 14:10:56 - mmengine - INFO - Epoch(train) [1][15800/42151] lr: 3.0000e-04 eta: 2 days, 5:14:17 time: 0.6436 data_time: 0.1060 memory: 28726 loss_ce: 0.0328 loss: 0.0328 2022/09/16 14:12:04 - mmengine - INFO - Epoch(train) [1][15900/42151] lr: 3.0000e-04 eta: 2 days, 5:09:42 time: 0.6494 data_time: 0.1164 memory: 28726 loss_ce: 0.0312 loss: 0.0312 2022/09/16 14:13:13 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 14:13:13 - mmengine - INFO - Epoch(train) [1][16000/42151] lr: 3.0000e-04 eta: 2 days, 5:05:30 time: 0.7103 data_time: 0.1689 memory: 28726 loss_ce: 0.0292 loss: 0.0292 2022/09/16 14:14:23 - mmengine - INFO - Epoch(train) [1][16100/42151] lr: 3.0000e-04 eta: 2 days, 5:01:25 time: 0.7632 data_time: 0.1918 memory: 28726 loss_ce: 0.0283 loss: 0.0283 2022/09/16 14:15:31 - mmengine - INFO - Epoch(train) [1][16200/42151] lr: 3.0000e-04 eta: 2 days, 4:57:09 time: 0.6298 data_time: 0.0957 memory: 28726 loss_ce: 0.0265 loss: 0.0265 2022/09/16 14:16:39 - mmengine - INFO - Epoch(train) [1][16300/42151] lr: 3.0000e-04 eta: 2 days, 4:52:46 time: 0.6204 data_time: 0.0811 memory: 28726 loss_ce: 0.0306 loss: 0.0306 2022/09/16 14:17:48 - mmengine - INFO - Epoch(train) [1][16400/42151] lr: 3.0000e-04 eta: 2 days, 4:48:40 time: 0.6712 data_time: 0.1312 memory: 28726 loss_ce: 0.0272 loss: 0.0272 2022/09/16 14:18:57 - mmengine - INFO - Epoch(train) [1][16500/42151] lr: 3.0000e-04 eta: 2 days, 4:44:35 time: 0.6838 data_time: 0.1482 memory: 28726 loss_ce: 0.0261 loss: 0.0261 2022/09/16 14:20:06 - mmengine - INFO - Epoch(train) [1][16600/42151] lr: 3.0000e-04 eta: 2 days, 4:40:33 time: 0.7197 data_time: 0.1845 memory: 28726 loss_ce: 0.0268 loss: 0.0268 2022/09/16 14:21:16 - mmengine - INFO - Epoch(train) [1][16700/42151] lr: 3.0000e-04 eta: 2 days, 4:36:48 time: 0.7583 data_time: 0.2254 memory: 28726 loss_ce: 0.0262 loss: 0.0262 2022/09/16 14:22:25 - mmengine - INFO - Epoch(train) [1][16800/42151] lr: 3.0000e-04 eta: 2 days, 4:32:54 time: 0.6479 data_time: 0.1088 memory: 28726 loss_ce: 0.0288 loss: 0.0288 2022/09/16 14:23:34 - mmengine - INFO - Epoch(train) [1][16900/42151] lr: 3.0000e-04 eta: 2 days, 4:28:54 time: 0.6660 data_time: 0.0815 memory: 28726 loss_ce: 0.0293 loss: 0.0293 2022/09/16 14:24:42 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 14:24:42 - mmengine - INFO - Epoch(train) [1][17000/42151] lr: 3.0000e-04 eta: 2 days, 4:24:47 time: 0.6402 data_time: 0.1043 memory: 28726 loss_ce: 0.0284 loss: 0.0284 2022/09/16 14:25:50 - mmengine - INFO - Epoch(train) [1][17100/42151] lr: 3.0000e-04 eta: 2 days, 4:20:39 time: 0.6436 data_time: 0.1079 memory: 28726 loss_ce: 0.0296 loss: 0.0296 2022/09/16 14:27:00 - mmengine - INFO - Epoch(train) [1][17200/42151] lr: 3.0000e-04 eta: 2 days, 4:17:00 time: 0.7229 data_time: 0.1820 memory: 28726 loss_ce: 0.0286 loss: 0.0286 2022/09/16 14:28:08 - mmengine - INFO - Epoch(train) [1][17300/42151] lr: 3.0000e-04 eta: 2 days, 4:13:07 time: 0.7383 data_time: 0.1917 memory: 28726 loss_ce: 0.0287 loss: 0.0287 2022/09/16 14:29:17 - mmengine - INFO - Epoch(train) [1][17400/42151] lr: 3.0000e-04 eta: 2 days, 4:09:14 time: 0.6517 data_time: 0.1176 memory: 28726 loss_ce: 0.0284 loss: 0.0284 2022/09/16 14:30:25 - mmengine - INFO - Epoch(train) [1][17500/42151] lr: 3.0000e-04 eta: 2 days, 4:05:13 time: 0.6294 data_time: 0.0904 memory: 28726 loss_ce: 0.0297 loss: 0.0297 2022/09/16 14:31:33 - mmengine - INFO - Epoch(train) [1][17600/42151] lr: 3.0000e-04 eta: 2 days, 4:01:28 time: 0.6750 data_time: 0.1247 memory: 28726 loss_ce: 0.0296 loss: 0.0296 2022/09/16 14:32:43 - mmengine - INFO - Epoch(train) [1][17700/42151] lr: 3.0000e-04 eta: 2 days, 3:57:57 time: 0.7252 data_time: 0.1509 memory: 28726 loss_ce: 0.0274 loss: 0.0274 2022/09/16 14:33:52 - mmengine - INFO - Epoch(train) [1][17800/42151] lr: 3.0000e-04 eta: 2 days, 3:54:20 time: 0.7321 data_time: 0.1828 memory: 28726 loss_ce: 0.0267 loss: 0.0267 2022/09/16 14:35:00 - mmengine - INFO - Epoch(train) [1][17900/42151] lr: 3.0000e-04 eta: 2 days, 3:50:30 time: 0.7110 data_time: 0.1762 memory: 28726 loss_ce: 0.0294 loss: 0.0294 2022/09/16 14:36:08 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 14:36:08 - mmengine - INFO - Epoch(train) [1][18000/42151] lr: 3.0000e-04 eta: 2 days, 3:46:38 time: 0.6297 data_time: 0.0926 memory: 28726 loss_ce: 0.0291 loss: 0.0291 2022/09/16 14:37:16 - mmengine - INFO - Epoch(train) [1][18100/42151] lr: 3.0000e-04 eta: 2 days, 3:42:48 time: 0.6243 data_time: 0.0838 memory: 28726 loss_ce: 0.0250 loss: 0.0250 2022/09/16 14:38:24 - mmengine - INFO - Epoch(train) [1][18200/42151] lr: 3.0000e-04 eta: 2 days, 3:39:11 time: 0.6536 data_time: 0.1120 memory: 28726 loss_ce: 0.0288 loss: 0.0288 2022/09/16 14:39:32 - mmengine - INFO - Epoch(train) [1][18300/42151] lr: 3.0000e-04 eta: 2 days, 3:35:25 time: 0.6449 data_time: 0.1082 memory: 28726 loss_ce: 0.0264 loss: 0.0264 2022/09/16 14:40:40 - mmengine - INFO - Epoch(train) [1][18400/42151] lr: 3.0000e-04 eta: 2 days, 3:31:48 time: 0.7222 data_time: 0.1753 memory: 28726 loss_ce: 0.0265 loss: 0.0265 2022/09/16 14:41:50 - mmengine - INFO - Epoch(train) [1][18500/42151] lr: 3.0000e-04 eta: 2 days, 3:28:23 time: 0.7544 data_time: 0.2071 memory: 28726 loss_ce: 0.0302 loss: 0.0302 2022/09/16 14:42:58 - mmengine - INFO - Epoch(train) [1][18600/42151] lr: 3.0000e-04 eta: 2 days, 3:24:55 time: 0.6380 data_time: 0.0914 memory: 28726 loss_ce: 0.0264 loss: 0.0264 2022/09/16 14:44:07 - mmengine - INFO - Epoch(train) [1][18700/42151] lr: 3.0000e-04 eta: 2 days, 3:21:27 time: 0.6192 data_time: 0.0792 memory: 28726 loss_ce: 0.0260 loss: 0.0260 2022/09/16 14:45:15 - mmengine - INFO - Epoch(train) [1][18800/42151] lr: 3.0000e-04 eta: 2 days, 3:17:55 time: 0.6616 data_time: 0.1256 memory: 28726 loss_ce: 0.0304 loss: 0.0304 2022/09/16 14:46:24 - mmengine - INFO - Epoch(train) [1][18900/42151] lr: 3.0000e-04 eta: 2 days, 3:14:33 time: 0.6998 data_time: 0.1301 memory: 28726 loss_ce: 0.0275 loss: 0.0275 2022/09/16 14:47:33 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 14:47:33 - mmengine - INFO - Epoch(train) [1][19000/42151] lr: 3.0000e-04 eta: 2 days, 3:11:12 time: 0.7462 data_time: 0.2072 memory: 28726 loss_ce: 0.0257 loss: 0.0257 2022/09/16 14:48:41 - mmengine - INFO - Epoch(train) [1][19100/42151] lr: 3.0000e-04 eta: 2 days, 3:07:43 time: 0.7074 data_time: 0.1663 memory: 28726 loss_ce: 0.0253 loss: 0.0253 2022/09/16 14:49:49 - mmengine - INFO - Epoch(train) [1][19200/42151] lr: 3.0000e-04 eta: 2 days, 3:04:12 time: 0.6236 data_time: 0.0817 memory: 28726 loss_ce: 0.0279 loss: 0.0279 2022/09/16 14:50:57 - mmengine - INFO - Epoch(train) [1][19300/42151] lr: 3.0000e-04 eta: 2 days, 3:00:43 time: 0.6266 data_time: 0.0894 memory: 28726 loss_ce: 0.0261 loss: 0.0261 2022/09/16 14:52:06 - mmengine - INFO - Epoch(train) [1][19400/42151] lr: 3.0000e-04 eta: 2 days, 2:57:28 time: 0.6830 data_time: 0.1253 memory: 28726 loss_ce: 0.0246 loss: 0.0246 2022/09/16 14:53:15 - mmengine - INFO - Epoch(train) [1][19500/42151] lr: 3.0000e-04 eta: 2 days, 2:54:13 time: 0.6510 data_time: 0.1154 memory: 28726 loss_ce: 0.0281 loss: 0.0281 2022/09/16 14:54:24 - mmengine - INFO - Epoch(train) [1][19600/42151] lr: 3.0000e-04 eta: 2 days, 2:50:55 time: 0.7489 data_time: 0.2020 memory: 28726 loss_ce: 0.0266 loss: 0.0266 2022/09/16 14:55:32 - mmengine - INFO - Epoch(train) [1][19700/42151] lr: 3.0000e-04 eta: 2 days, 2:47:44 time: 0.7130 data_time: 0.1731 memory: 28726 loss_ce: 0.0263 loss: 0.0263 2022/09/16 14:56:41 - mmengine - INFO - Epoch(train) [1][19800/42151] lr: 3.0000e-04 eta: 2 days, 2:44:26 time: 0.6407 data_time: 0.0911 memory: 28726 loss_ce: 0.0286 loss: 0.0286 2022/09/16 14:57:49 - mmengine - INFO - Epoch(train) [1][19900/42151] lr: 3.0000e-04 eta: 2 days, 2:41:08 time: 0.6332 data_time: 0.0917 memory: 28726 loss_ce: 0.0247 loss: 0.0247 2022/09/16 14:58:58 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 14:58:58 - mmengine - INFO - Epoch(train) [1][20000/42151] lr: 3.0000e-04 eta: 2 days, 2:38:00 time: 0.6678 data_time: 0.1312 memory: 28726 loss_ce: 0.0266 loss: 0.0266 2022/09/16 15:00:07 - mmengine - INFO - Epoch(train) [1][20100/42151] lr: 3.0000e-04 eta: 2 days, 2:34:52 time: 0.6462 data_time: 0.1114 memory: 28726 loss_ce: 0.0279 loss: 0.0279 2022/09/16 15:01:16 - mmengine - INFO - Epoch(train) [1][20200/42151] lr: 3.0000e-04 eta: 2 days, 2:31:47 time: 0.7652 data_time: 0.1948 memory: 28726 loss_ce: 0.0264 loss: 0.0264 2022/09/16 15:02:23 - mmengine - INFO - Epoch(train) [1][20300/42151] lr: 3.0000e-04 eta: 2 days, 2:28:30 time: 0.7260 data_time: 0.1878 memory: 28726 loss_ce: 0.0251 loss: 0.0251 2022/09/16 15:03:33 - mmengine - INFO - Epoch(train) [1][20400/42151] lr: 3.0000e-04 eta: 2 days, 2:25:31 time: 0.6501 data_time: 0.1084 memory: 28726 loss_ce: 0.0268 loss: 0.0268 2022/09/16 15:04:40 - mmengine - INFO - Epoch(train) [1][20500/42151] lr: 3.0000e-04 eta: 2 days, 2:22:16 time: 0.6234 data_time: 0.0835 memory: 28726 loss_ce: 0.0286 loss: 0.0286 2022/09/16 15:05:49 - mmengine - INFO - Epoch(train) [1][20600/42151] lr: 3.0000e-04 eta: 2 days, 2:19:12 time: 0.6932 data_time: 0.1259 memory: 28726 loss_ce: 0.0266 loss: 0.0266 2022/09/16 15:06:58 - mmengine - INFO - Epoch(train) [1][20700/42151] lr: 3.0000e-04 eta: 2 days, 2:16:08 time: 0.7020 data_time: 0.1478 memory: 28726 loss_ce: 0.0264 loss: 0.0264 2022/09/16 15:08:07 - mmengine - INFO - Epoch(train) [1][20800/42151] lr: 3.0000e-04 eta: 2 days, 2:13:11 time: 0.7605 data_time: 0.2076 memory: 28726 loss_ce: 0.0253 loss: 0.0253 2022/09/16 15:09:14 - mmengine - INFO - Epoch(train) [1][20900/42151] lr: 3.0000e-04 eta: 2 days, 2:10:01 time: 0.6813 data_time: 0.1452 memory: 28726 loss_ce: 0.0255 loss: 0.0255 2022/09/16 15:10:23 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 15:10:23 - mmengine - INFO - Epoch(train) [1][21000/42151] lr: 3.0000e-04 eta: 2 days, 2:07:04 time: 0.6606 data_time: 0.1130 memory: 28726 loss_ce: 0.0239 loss: 0.0239 2022/09/16 15:11:32 - mmengine - INFO - Epoch(train) [1][21100/42151] lr: 3.0000e-04 eta: 2 days, 2:04:05 time: 0.6179 data_time: 0.0832 memory: 28726 loss_ce: 0.0271 loss: 0.0271 2022/09/16 15:12:41 - mmengine - INFO - Epoch(train) [1][21200/42151] lr: 3.0000e-04 eta: 2 days, 2:01:15 time: 0.6450 data_time: 0.1024 memory: 28726 loss_ce: 0.0237 loss: 0.0237 2022/09/16 15:13:50 - mmengine - INFO - Epoch(train) [1][21300/42151] lr: 3.0000e-04 eta: 2 days, 1:58:17 time: 0.6693 data_time: 0.1275 memory: 28726 loss_ce: 0.0241 loss: 0.0241 2022/09/16 15:14:59 - mmengine - INFO - Epoch(train) [1][21400/42151] lr: 3.0000e-04 eta: 2 days, 1:55:31 time: 0.7527 data_time: 0.2041 memory: 28726 loss_ce: 0.0262 loss: 0.0262 2022/09/16 15:16:08 - mmengine - INFO - Epoch(train) [1][21500/42151] lr: 3.0000e-04 eta: 2 days, 1:52:38 time: 0.7714 data_time: 0.1817 memory: 28726 loss_ce: 0.0258 loss: 0.0258 2022/09/16 15:17:17 - mmengine - INFO - Epoch(train) [1][21600/42151] lr: 3.0000e-04 eta: 2 days, 1:49:44 time: 0.6438 data_time: 0.0953 memory: 28726 loss_ce: 0.0262 loss: 0.0262 2022/09/16 15:18:26 - mmengine - INFO - Epoch(train) [1][21700/42151] lr: 3.0000e-04 eta: 2 days, 1:46:56 time: 0.6519 data_time: 0.0930 memory: 28726 loss_ce: 0.0235 loss: 0.0235 2022/09/16 15:19:35 - mmengine - INFO - Epoch(train) [1][21800/42151] lr: 3.0000e-04 eta: 2 days, 1:44:10 time: 0.6593 data_time: 0.1202 memory: 28726 loss_ce: 0.0286 loss: 0.0286 2022/09/16 15:20:43 - mmengine - INFO - Epoch(train) [1][21900/42151] lr: 3.0000e-04 eta: 2 days, 1:41:11 time: 0.6535 data_time: 0.1160 memory: 28726 loss_ce: 0.0258 loss: 0.0258 2022/09/16 15:21:52 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 15:21:52 - mmengine - INFO - Epoch(train) [1][22000/42151] lr: 3.0000e-04 eta: 2 days, 1:38:29 time: 0.7442 data_time: 0.2085 memory: 28726 loss_ce: 0.0254 loss: 0.0254 2022/09/16 15:23:00 - mmengine - INFO - Epoch(train) [1][22100/42151] lr: 3.0000e-04 eta: 2 days, 1:35:31 time: 0.6892 data_time: 0.1510 memory: 28726 loss_ce: 0.0256 loss: 0.0256 2022/09/16 15:24:09 - mmengine - INFO - Epoch(train) [1][22200/42151] lr: 3.0000e-04 eta: 2 days, 1:32:45 time: 0.6383 data_time: 0.1026 memory: 28726 loss_ce: 0.0251 loss: 0.0251 2022/09/16 15:25:17 - mmengine - INFO - Epoch(train) [1][22300/42151] lr: 3.0000e-04 eta: 2 days, 1:29:54 time: 0.6105 data_time: 0.0787 memory: 28726 loss_ce: 0.0271 loss: 0.0271 2022/09/16 15:26:25 - mmengine - INFO - Epoch(train) [1][22400/42151] lr: 3.0000e-04 eta: 2 days, 1:26:58 time: 0.6312 data_time: 0.0954 memory: 28726 loss_ce: 0.0237 loss: 0.0237 2022/09/16 15:27:33 - mmengine - INFO - Epoch(train) [1][22500/42151] lr: 3.0000e-04 eta: 2 days, 1:24:08 time: 0.6710 data_time: 0.1266 memory: 28726 loss_ce: 0.0255 loss: 0.0255 2022/09/16 15:28:42 - mmengine - INFO - Epoch(train) [1][22600/42151] lr: 3.0000e-04 eta: 2 days, 1:21:26 time: 0.7117 data_time: 0.1754 memory: 28726 loss_ce: 0.0242 loss: 0.0242 2022/09/16 15:29:50 - mmengine - INFO - Epoch(train) [1][22700/42151] lr: 3.0000e-04 eta: 2 days, 1:18:35 time: 0.6808 data_time: 0.1399 memory: 28726 loss_ce: 0.0282 loss: 0.0282 2022/09/16 15:30:58 - mmengine - INFO - Epoch(train) [1][22800/42151] lr: 3.0000e-04 eta: 2 days, 1:15:49 time: 0.6372 data_time: 0.0949 memory: 28726 loss_ce: 0.0230 loss: 0.0230 2022/09/16 15:32:06 - mmengine - INFO - Epoch(train) [1][22900/42151] lr: 3.0000e-04 eta: 2 days, 1:13:00 time: 0.6374 data_time: 0.0849 memory: 28726 loss_ce: 0.0254 loss: 0.0254 2022/09/16 15:33:15 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 15:33:15 - mmengine - INFO - Epoch(train) [1][23000/42151] lr: 3.0000e-04 eta: 2 days, 1:10:19 time: 0.6532 data_time: 0.1173 memory: 28726 loss_ce: 0.0268 loss: 0.0268 2022/09/16 15:34:23 - mmengine - INFO - Epoch(train) [1][23100/42151] lr: 3.0000e-04 eta: 2 days, 1:07:38 time: 0.6361 data_time: 0.1014 memory: 28726 loss_ce: 0.0270 loss: 0.0270 2022/09/16 15:35:31 - mmengine - INFO - Epoch(train) [1][23200/42151] lr: 3.0000e-04 eta: 2 days, 1:04:54 time: 0.7144 data_time: 0.1818 memory: 28726 loss_ce: 0.0262 loss: 0.0262 2022/09/16 15:36:39 - mmengine - INFO - Epoch(train) [1][23300/42151] lr: 3.0000e-04 eta: 2 days, 1:02:11 time: 0.7050 data_time: 0.1699 memory: 28726 loss_ce: 0.0256 loss: 0.0256 2022/09/16 15:37:47 - mmengine - INFO - Epoch(train) [1][23400/42151] lr: 3.0000e-04 eta: 2 days, 0:59:27 time: 0.6386 data_time: 0.0979 memory: 28726 loss_ce: 0.0264 loss: 0.0264 2022/09/16 15:38:57 - mmengine - INFO - Epoch(train) [1][23500/42151] lr: 3.0000e-04 eta: 2 days, 0:56:58 time: 0.6812 data_time: 0.1116 memory: 28726 loss_ce: 0.0264 loss: 0.0264 2022/09/16 15:40:06 - mmengine - INFO - Epoch(train) [1][23600/42151] lr: 3.0000e-04 eta: 2 days, 0:54:21 time: 0.6596 data_time: 0.1068 memory: 28726 loss_ce: 0.0244 loss: 0.0244 2022/09/16 15:41:45 - mmengine - INFO - Epoch(train) [1][23700/42151] lr: 3.0000e-04 eta: 2 days, 0:56:45 time: 3.8026 data_time: 3.2184 memory: 28726 loss_ce: 0.0258 loss: 0.0258 2022/09/16 15:42:59 - mmengine - INFO - Epoch(train) [1][23800/42151] lr: 3.0000e-04 eta: 2 days, 0:54:56 time: 0.5450 data_time: 0.0045 memory: 28726 loss_ce: 0.0251 loss: 0.0251 2022/09/16 15:44:14 - mmengine - INFO - Epoch(train) [1][23900/42151] lr: 3.0000e-04 eta: 2 days, 0:53:20 time: 0.6469 data_time: 0.1076 memory: 28726 loss_ce: 0.0251 loss: 0.0251 2022/09/16 15:45:22 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 15:45:22 - mmengine - INFO - Epoch(train) [1][24000/42151] lr: 3.0000e-04 eta: 2 days, 0:50:42 time: 0.8281 data_time: 0.2208 memory: 28726 loss_ce: 0.0262 loss: 0.0262 2022/09/16 15:46:36 - mmengine - INFO - Epoch(train) [1][24100/42151] lr: 3.0000e-04 eta: 2 days, 0:49:03 time: 0.6753 data_time: 0.1306 memory: 28726 loss_ce: 0.0224 loss: 0.0224 2022/09/16 15:47:44 - mmengine - INFO - Epoch(train) [1][24200/42151] lr: 3.0000e-04 eta: 2 days, 0:46:15 time: 0.5396 data_time: 0.0044 memory: 28726 loss_ce: 0.0260 loss: 0.0260 2022/09/16 15:48:52 - mmengine - INFO - Epoch(train) [1][24300/42151] lr: 3.0000e-04 eta: 2 days, 0:43:43 time: 0.7586 data_time: 0.1673 memory: 28726 loss_ce: 0.0253 loss: 0.0253 2022/09/16 15:50:01 - mmengine - INFO - Epoch(train) [1][24400/42151] lr: 3.0000e-04 eta: 2 days, 0:41:07 time: 0.7018 data_time: 0.1575 memory: 28726 loss_ce: 0.0221 loss: 0.0221 2022/09/16 15:51:10 - mmengine - INFO - Epoch(train) [1][24500/42151] lr: 3.0000e-04 eta: 2 days, 0:38:40 time: 0.8371 data_time: 0.2787 memory: 28726 loss_ce: 0.0244 loss: 0.0244 2022/09/16 15:52:18 - mmengine - INFO - Epoch(train) [1][24600/42151] lr: 3.0000e-04 eta: 2 days, 0:36:02 time: 0.6987 data_time: 0.1514 memory: 28726 loss_ce: 0.0241 loss: 0.0241 2022/09/16 15:53:24 - mmengine - INFO - Epoch(train) [1][24700/42151] lr: 3.0000e-04 eta: 2 days, 0:33:06 time: 0.5442 data_time: 0.0049 memory: 28726 loss_ce: 0.0244 loss: 0.0244 2022/09/16 15:54:33 - mmengine - INFO - Epoch(train) [1][24800/42151] lr: 3.0000e-04 eta: 2 days, 0:30:47 time: 0.6986 data_time: 0.1602 memory: 28726 loss_ce: 0.0226 loss: 0.0226 2022/09/16 15:55:42 - mmengine - INFO - Epoch(train) [1][24900/42151] lr: 3.0000e-04 eta: 2 days, 0:28:17 time: 0.7914 data_time: 0.2518 memory: 28726 loss_ce: 0.0245 loss: 0.0245 2022/09/16 15:56:50 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 15:56:50 - mmengine - INFO - Epoch(train) [1][25000/42151] lr: 3.0000e-04 eta: 2 days, 0:25:39 time: 0.6417 data_time: 0.1012 memory: 28726 loss_ce: 0.0230 loss: 0.0230 2022/09/16 15:58:02 - mmengine - INFO - Epoch(train) [1][25100/42151] lr: 3.0000e-04 eta: 2 days, 0:23:48 time: 0.6119 data_time: 0.0274 memory: 28726 loss_ce: 0.0246 loss: 0.0246 2022/09/16 15:59:08 - mmengine - INFO - Epoch(train) [1][25200/42151] lr: 3.0000e-04 eta: 2 days, 0:20:53 time: 0.7204 data_time: 0.1735 memory: 28726 loss_ce: 0.0234 loss: 0.0234 2022/09/16 16:00:17 - mmengine - INFO - Epoch(train) [1][25300/42151] lr: 3.0000e-04 eta: 2 days, 0:18:32 time: 0.6787 data_time: 0.0963 memory: 28726 loss_ce: 0.0224 loss: 0.0224 2022/09/16 16:01:25 - mmengine - INFO - Epoch(train) [1][25400/42151] lr: 3.0000e-04 eta: 2 days, 0:16:02 time: 0.6737 data_time: 0.1284 memory: 28726 loss_ce: 0.0252 loss: 0.0252 2022/09/16 16:02:33 - mmengine - INFO - Epoch(train) [1][25500/42151] lr: 3.0000e-04 eta: 2 days, 0:13:23 time: 0.7179 data_time: 0.1759 memory: 28726 loss_ce: 0.0254 loss: 0.0254 2022/09/16 16:03:41 - mmengine - INFO - Epoch(train) [1][25600/42151] lr: 3.0000e-04 eta: 2 days, 0:10:54 time: 0.7377 data_time: 0.1928 memory: 28726 loss_ce: 0.0227 loss: 0.0227 2022/09/16 16:04:49 - mmengine - INFO - Epoch(train) [1][25700/42151] lr: 3.0000e-04 eta: 2 days, 0:08:24 time: 0.6691 data_time: 0.1262 memory: 28726 loss_ce: 0.0240 loss: 0.0240 2022/09/16 16:05:58 - mmengine - INFO - Epoch(train) [1][25800/42151] lr: 3.0000e-04 eta: 2 days, 0:06:04 time: 0.6813 data_time: 0.1390 memory: 28726 loss_ce: 0.0249 loss: 0.0249 2022/09/16 16:07:07 - mmengine - INFO - Epoch(train) [1][25900/42151] lr: 3.0000e-04 eta: 2 days, 0:03:47 time: 0.6460 data_time: 0.0972 memory: 28726 loss_ce: 0.0232 loss: 0.0232 2022/09/16 16:08:14 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 16:08:14 - mmengine - INFO - Epoch(train) [1][26000/42151] lr: 3.0000e-04 eta: 2 days, 0:01:13 time: 0.6883 data_time: 0.1497 memory: 28726 loss_ce: 0.0284 loss: 0.0284 2022/09/16 16:09:23 - mmengine - INFO - Epoch(train) [1][26100/42151] lr: 3.0000e-04 eta: 1 day, 23:58:48 time: 0.7572 data_time: 0.1835 memory: 28726 loss_ce: 0.0239 loss: 0.0239 2022/09/16 16:10:33 - mmengine - INFO - Epoch(train) [1][26200/42151] lr: 3.0000e-04 eta: 1 day, 23:56:41 time: 0.7874 data_time: 0.1906 memory: 28726 loss_ce: 0.0215 loss: 0.0215 2022/09/16 16:11:41 - mmengine - INFO - Epoch(train) [1][26300/42151] lr: 3.0000e-04 eta: 1 day, 23:54:17 time: 0.6600 data_time: 0.1172 memory: 28726 loss_ce: 0.0236 loss: 0.0236 2022/09/16 16:12:49 - mmengine - INFO - Epoch(train) [1][26400/42151] lr: 3.0000e-04 eta: 1 day, 23:51:50 time: 0.6599 data_time: 0.1228 memory: 28726 loss_ce: 0.0212 loss: 0.0212 2022/09/16 16:13:58 - mmengine - INFO - Epoch(train) [1][26500/42151] lr: 3.0000e-04 eta: 1 day, 23:49:31 time: 0.6023 data_time: 0.0642 memory: 28726 loss_ce: 0.0227 loss: 0.0227 2022/09/16 16:15:06 - mmengine - INFO - Epoch(train) [1][26600/42151] lr: 3.0000e-04 eta: 1 day, 23:47:09 time: 0.6916 data_time: 0.1524 memory: 28726 loss_ce: 0.0233 loss: 0.0233 2022/09/16 16:16:14 - mmengine - INFO - Epoch(train) [1][26700/42151] lr: 3.0000e-04 eta: 1 day, 23:44:48 time: 0.7386 data_time: 0.1986 memory: 28726 loss_ce: 0.0233 loss: 0.0233 2022/09/16 16:17:23 - mmengine - INFO - Epoch(train) [1][26800/42151] lr: 3.0000e-04 eta: 1 day, 23:42:31 time: 0.6838 data_time: 0.1438 memory: 28726 loss_ce: 0.0230 loss: 0.0230 2022/09/16 16:18:31 - mmengine - INFO - Epoch(train) [1][26900/42151] lr: 3.0000e-04 eta: 1 day, 23:40:07 time: 0.6706 data_time: 0.1261 memory: 28726 loss_ce: 0.0223 loss: 0.0223 2022/09/16 16:19:40 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 16:19:40 - mmengine - INFO - Epoch(train) [1][27000/42151] lr: 3.0000e-04 eta: 1 day, 23:37:49 time: 0.6965 data_time: 0.1287 memory: 28726 loss_ce: 0.0235 loss: 0.0235 2022/09/16 16:20:47 - mmengine - INFO - Epoch(train) [1][27100/42151] lr: 3.0000e-04 eta: 1 day, 23:35:22 time: 0.6258 data_time: 0.0883 memory: 28726 loss_ce: 0.0245 loss: 0.0245 2022/09/16 16:21:55 - mmengine - INFO - Epoch(train) [1][27200/42151] lr: 3.0000e-04 eta: 1 day, 23:33:05 time: 0.6913 data_time: 0.1500 memory: 28726 loss_ce: 0.0198 loss: 0.0198 2022/09/16 16:23:04 - mmengine - INFO - Epoch(train) [1][27300/42151] lr: 3.0000e-04 eta: 1 day, 23:30:49 time: 0.7656 data_time: 0.2161 memory: 28726 loss_ce: 0.0238 loss: 0.0238 2022/09/16 16:24:13 - mmengine - INFO - Epoch(train) [1][27400/42151] lr: 3.0000e-04 eta: 1 day, 23:28:39 time: 0.6872 data_time: 0.1506 memory: 28726 loss_ce: 0.0212 loss: 0.0212 2022/09/16 16:25:22 - mmengine - INFO - Epoch(train) [1][27500/42151] lr: 3.0000e-04 eta: 1 day, 23:26:25 time: 0.6610 data_time: 0.1189 memory: 28726 loss_ce: 0.0218 loss: 0.0218 2022/09/16 16:26:31 - mmengine - INFO - Epoch(train) [1][27600/42151] lr: 3.0000e-04 eta: 1 day, 23:24:10 time: 0.6852 data_time: 0.1295 memory: 28726 loss_ce: 0.0230 loss: 0.0230 2022/09/16 16:27:40 - mmengine - INFO - Epoch(train) [1][27700/42151] lr: 3.0000e-04 eta: 1 day, 23:22:00 time: 0.6395 data_time: 0.0998 memory: 28726 loss_ce: 0.0218 loss: 0.0218 2022/09/16 16:28:48 - mmengine - INFO - Epoch(train) [1][27800/42151] lr: 3.0000e-04 eta: 1 day, 23:19:47 time: 0.6684 data_time: 0.1016 memory: 28726 loss_ce: 0.0216 loss: 0.0216 2022/09/16 16:29:57 - mmengine - INFO - Epoch(train) [1][27900/42151] lr: 3.0000e-04 eta: 1 day, 23:17:34 time: 0.6999 data_time: 0.1650 memory: 28726 loss_ce: 0.0238 loss: 0.0238 2022/09/16 16:31:06 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 16:31:06 - mmengine - INFO - Epoch(train) [1][28000/42151] lr: 3.0000e-04 eta: 1 day, 23:15:28 time: 0.7121 data_time: 0.1546 memory: 28726 loss_ce: 0.0233 loss: 0.0233 2022/09/16 16:32:14 - mmengine - INFO - Epoch(train) [1][28100/42151] lr: 3.0000e-04 eta: 1 day, 23:13:12 time: 0.6911 data_time: 0.1484 memory: 28726 loss_ce: 0.0209 loss: 0.0209 2022/09/16 16:33:22 - mmengine - INFO - Epoch(train) [1][28200/42151] lr: 3.0000e-04 eta: 1 day, 23:10:51 time: 0.6811 data_time: 0.0992 memory: 28726 loss_ce: 0.0232 loss: 0.0232 2022/09/16 16:34:31 - mmengine - INFO - Epoch(train) [1][28300/42151] lr: 3.0000e-04 eta: 1 day, 23:08:44 time: 0.6024 data_time: 0.0653 memory: 28726 loss_ce: 0.0244 loss: 0.0244 2022/09/16 16:35:41 - mmengine - INFO - Epoch(train) [1][28400/42151] lr: 3.0000e-04 eta: 1 day, 23:06:42 time: 0.6954 data_time: 0.1289 memory: 28726 loss_ce: 0.0239 loss: 0.0239 2022/09/16 16:36:49 - mmengine - INFO - Epoch(train) [1][28500/42151] lr: 3.0000e-04 eta: 1 day, 23:04:32 time: 0.7515 data_time: 0.2141 memory: 28726 loss_ce: 0.0208 loss: 0.0208 2022/09/16 16:38:00 - mmengine - INFO - Epoch(train) [1][28600/42151] lr: 3.0000e-04 eta: 1 day, 23:02:38 time: 0.7088 data_time: 0.1614 memory: 28726 loss_ce: 0.0223 loss: 0.0223 2022/09/16 16:39:09 - mmengine - INFO - Epoch(train) [1][28700/42151] lr: 3.0000e-04 eta: 1 day, 23:00:30 time: 0.7257 data_time: 0.1638 memory: 28726 loss_ce: 0.0265 loss: 0.0265 2022/09/16 16:40:16 - mmengine - INFO - Epoch(train) [1][28800/42151] lr: 3.0000e-04 eta: 1 day, 22:58:13 time: 0.7033 data_time: 0.1237 memory: 28726 loss_ce: 0.0225 loss: 0.0225 2022/09/16 16:41:26 - mmengine - INFO - Epoch(train) [1][28900/42151] lr: 3.0000e-04 eta: 1 day, 22:56:12 time: 0.6957 data_time: 0.1144 memory: 28726 loss_ce: 0.0214 loss: 0.0214 2022/09/16 16:42:35 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 16:42:35 - mmengine - INFO - Epoch(train) [1][29000/42151] lr: 3.0000e-04 eta: 1 day, 22:54:09 time: 0.6828 data_time: 0.1295 memory: 28726 loss_ce: 0.0240 loss: 0.0240 2022/09/16 16:43:45 - mmengine - INFO - Epoch(train) [1][29100/42151] lr: 3.0000e-04 eta: 1 day, 22:52:10 time: 0.7155 data_time: 0.1726 memory: 28726 loss_ce: 0.0213 loss: 0.0213 2022/09/16 16:44:56 - mmengine - INFO - Epoch(train) [1][29200/42151] lr: 3.0000e-04 eta: 1 day, 22:50:18 time: 0.7669 data_time: 0.2084 memory: 28726 loss_ce: 0.0214 loss: 0.0214 2022/09/16 16:46:03 - mmengine - INFO - Epoch(train) [1][29300/42151] lr: 3.0000e-04 eta: 1 day, 22:48:04 time: 0.6676 data_time: 0.0832 memory: 28726 loss_ce: 0.0219 loss: 0.0219 2022/09/16 16:47:13 - mmengine - INFO - Epoch(train) [1][29400/42151] lr: 3.0000e-04 eta: 1 day, 22:46:04 time: 0.7625 data_time: 0.1978 memory: 28726 loss_ce: 0.0235 loss: 0.0235 2022/09/16 16:48:22 - mmengine - INFO - Epoch(train) [1][29500/42151] lr: 3.0000e-04 eta: 1 day, 22:43:59 time: 0.6637 data_time: 0.1229 memory: 28726 loss_ce: 0.0221 loss: 0.0221 2022/09/16 16:49:31 - mmengine - INFO - Epoch(train) [1][29600/42151] lr: 3.0000e-04 eta: 1 day, 22:41:59 time: 0.6104 data_time: 0.0339 memory: 28726 loss_ce: 0.0211 loss: 0.0211 2022/09/16 16:50:39 - mmengine - INFO - Epoch(train) [1][29700/42151] lr: 3.0000e-04 eta: 1 day, 22:39:52 time: 0.6106 data_time: 0.0481 memory: 28726 loss_ce: 0.0250 loss: 0.0250 2022/09/16 16:51:49 - mmengine - INFO - Epoch(train) [1][29800/42151] lr: 3.0000e-04 eta: 1 day, 22:37:54 time: 0.7439 data_time: 0.1957 memory: 28726 loss_ce: 0.0221 loss: 0.0221 2022/09/16 16:52:57 - mmengine - INFO - Epoch(train) [1][29900/42151] lr: 3.0000e-04 eta: 1 day, 22:35:48 time: 0.6093 data_time: 0.0735 memory: 28726 loss_ce: 0.0216 loss: 0.0216 2022/09/16 16:54:08 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 16:54:08 - mmengine - INFO - Epoch(train) [1][30000/42151] lr: 3.0000e-04 eta: 1 day, 22:33:59 time: 0.7756 data_time: 0.2346 memory: 28726 loss_ce: 0.0238 loss: 0.0238 2022/09/16 16:55:18 - mmengine - INFO - Epoch(train) [1][30100/42151] lr: 3.0000e-04 eta: 1 day, 22:32:03 time: 0.7076 data_time: 0.1287 memory: 28726 loss_ce: 0.0217 loss: 0.0217 2022/09/16 16:56:26 - mmengine - INFO - Epoch(train) [1][30200/42151] lr: 3.0000e-04 eta: 1 day, 22:29:58 time: 0.6113 data_time: 0.0689 memory: 28726 loss_ce: 0.0235 loss: 0.0235 2022/09/16 16:57:34 - mmengine - INFO - Epoch(train) [1][30300/42151] lr: 3.0000e-04 eta: 1 day, 22:27:50 time: 0.6232 data_time: 0.0886 memory: 28726 loss_ce: 0.0235 loss: 0.0235 2022/09/16 16:58:43 - mmengine - INFO - Epoch(train) [1][30400/42151] lr: 3.0000e-04 eta: 1 day, 22:25:50 time: 0.7350 data_time: 0.1952 memory: 28726 loss_ce: 0.0232 loss: 0.0232 2022/09/16 16:59:53 - mmengine - INFO - Epoch(train) [1][30500/42151] lr: 3.0000e-04 eta: 1 day, 22:23:52 time: 0.6865 data_time: 0.1082 memory: 28726 loss_ce: 0.0233 loss: 0.0233 2022/09/16 17:01:01 - mmengine - INFO - Epoch(train) [1][30600/42151] lr: 3.0000e-04 eta: 1 day, 22:21:51 time: 0.7041 data_time: 0.1680 memory: 28726 loss_ce: 0.0204 loss: 0.0204 2022/09/16 17:02:11 - mmengine - INFO - Epoch(train) [1][30700/42151] lr: 3.0000e-04 eta: 1 day, 22:19:53 time: 0.6746 data_time: 0.1369 memory: 28726 loss_ce: 0.0226 loss: 0.0226 2022/09/16 17:03:19 - mmengine - INFO - Epoch(train) [1][30800/42151] lr: 3.0000e-04 eta: 1 day, 22:17:53 time: 0.6507 data_time: 0.1064 memory: 28726 loss_ce: 0.0224 loss: 0.0224 2022/09/16 17:04:28 - mmengine - INFO - Epoch(train) [1][30900/42151] lr: 3.0000e-04 eta: 1 day, 22:15:54 time: 0.6397 data_time: 0.0746 memory: 28726 loss_ce: 0.0216 loss: 0.0216 2022/09/16 17:05:38 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 17:05:38 - mmengine - INFO - Epoch(train) [1][31000/42151] lr: 3.0000e-04 eta: 1 day, 22:13:58 time: 0.7120 data_time: 0.1707 memory: 28726 loss_ce: 0.0223 loss: 0.0223 2022/09/16 17:06:46 - mmengine - INFO - Epoch(train) [1][31100/42151] lr: 3.0000e-04 eta: 1 day, 22:11:54 time: 0.6554 data_time: 0.1125 memory: 28726 loss_ce: 0.0223 loss: 0.0223 2022/09/16 17:07:56 - mmengine - INFO - Epoch(train) [1][31200/42151] lr: 3.0000e-04 eta: 1 day, 22:10:04 time: 0.7852 data_time: 0.2421 memory: 28726 loss_ce: 0.0224 loss: 0.0224 2022/09/16 17:09:05 - mmengine - INFO - Epoch(train) [1][31300/42151] lr: 3.0000e-04 eta: 1 day, 22:08:08 time: 0.6689 data_time: 0.1272 memory: 28726 loss_ce: 0.0224 loss: 0.0224 2022/09/16 17:10:14 - mmengine - INFO - Epoch(train) [1][31400/42151] lr: 3.0000e-04 eta: 1 day, 22:06:08 time: 0.6240 data_time: 0.0621 memory: 28726 loss_ce: 0.0205 loss: 0.0205 2022/09/16 17:11:23 - mmengine - INFO - Epoch(train) [1][31500/42151] lr: 3.0000e-04 eta: 1 day, 22:04:13 time: 0.6337 data_time: 0.0588 memory: 28726 loss_ce: 0.0203 loss: 0.0203 2022/09/16 17:12:32 - mmengine - INFO - Epoch(train) [1][31600/42151] lr: 3.0000e-04 eta: 1 day, 22:02:18 time: 0.7192 data_time: 0.1567 memory: 28726 loss_ce: 0.0206 loss: 0.0206 2022/09/16 17:13:39 - mmengine - INFO - Epoch(train) [1][31700/42151] lr: 3.0000e-04 eta: 1 day, 22:00:11 time: 0.6485 data_time: 0.1084 memory: 28726 loss_ce: 0.0224 loss: 0.0224 2022/09/16 17:14:49 - mmengine - INFO - Epoch(train) [1][31800/42151] lr: 3.0000e-04 eta: 1 day, 21:58:19 time: 0.7557 data_time: 0.1871 memory: 28726 loss_ce: 0.0218 loss: 0.0218 2022/09/16 17:15:57 - mmengine - INFO - Epoch(train) [1][31900/42151] lr: 3.0000e-04 eta: 1 day, 21:56:20 time: 0.6876 data_time: 0.1236 memory: 28726 loss_ce: 0.0220 loss: 0.0220 2022/09/16 17:17:06 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 17:17:07 - mmengine - INFO - Epoch(train) [1][32000/42151] lr: 3.0000e-04 eta: 1 day, 21:54:24 time: 0.6367 data_time: 0.0701 memory: 28726 loss_ce: 0.0210 loss: 0.0210 2022/09/16 17:18:15 - mmengine - INFO - Epoch(train) [1][32100/42151] lr: 3.0000e-04 eta: 1 day, 21:52:26 time: 0.6356 data_time: 0.0898 memory: 28726 loss_ce: 0.0214 loss: 0.0214 2022/09/16 17:19:24 - mmengine - INFO - Epoch(train) [1][32200/42151] lr: 3.0000e-04 eta: 1 day, 21:50:32 time: 0.7631 data_time: 0.2231 memory: 28726 loss_ce: 0.0224 loss: 0.0224 2022/09/16 17:20:32 - mmengine - INFO - Epoch(train) [1][32300/42151] lr: 3.0000e-04 eta: 1 day, 21:48:28 time: 0.6378 data_time: 0.0997 memory: 28726 loss_ce: 0.0199 loss: 0.0199 2022/09/16 17:21:41 - mmengine - INFO - Epoch(train) [1][32400/42151] lr: 3.0000e-04 eta: 1 day, 21:46:40 time: 0.7580 data_time: 0.1940 memory: 28726 loss_ce: 0.0201 loss: 0.0201 2022/09/16 17:22:49 - mmengine - INFO - Epoch(train) [1][32500/42151] lr: 3.0000e-04 eta: 1 day, 21:44:36 time: 0.6879 data_time: 0.1157 memory: 28726 loss_ce: 0.0203 loss: 0.0203 2022/09/16 17:23:58 - mmengine - INFO - Epoch(train) [1][32600/42151] lr: 3.0000e-04 eta: 1 day, 21:42:41 time: 0.6488 data_time: 0.0863 memory: 28726 loss_ce: 0.0210 loss: 0.0210 2022/09/16 17:25:06 - mmengine - INFO - Epoch(train) [1][32700/42151] lr: 3.0000e-04 eta: 1 day, 21:40:46 time: 0.6474 data_time: 0.0936 memory: 28726 loss_ce: 0.0214 loss: 0.0214 2022/09/16 17:26:16 - mmengine - INFO - Epoch(train) [1][32800/42151] lr: 3.0000e-04 eta: 1 day, 21:38:55 time: 0.7568 data_time: 0.2138 memory: 28726 loss_ce: 0.0228 loss: 0.0228 2022/09/16 17:27:24 - mmengine - INFO - Epoch(train) [1][32900/42151] lr: 3.0000e-04 eta: 1 day, 21:36:56 time: 0.6375 data_time: 0.0990 memory: 28726 loss_ce: 0.0211 loss: 0.0211 2022/09/16 17:28:32 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 17:28:32 - mmengine - INFO - Epoch(train) [1][33000/42151] lr: 3.0000e-04 eta: 1 day, 21:35:01 time: 0.7156 data_time: 0.1666 memory: 28726 loss_ce: 0.0231 loss: 0.0231 2022/09/16 17:29:42 - mmengine - INFO - Epoch(train) [1][33100/42151] lr: 3.0000e-04 eta: 1 day, 21:33:13 time: 0.6556 data_time: 0.1221 memory: 28726 loss_ce: 0.0233 loss: 0.0233 2022/09/16 17:30:50 - mmengine - INFO - Epoch(train) [1][33200/42151] lr: 3.0000e-04 eta: 1 day, 21:31:15 time: 0.6507 data_time: 0.1101 memory: 28726 loss_ce: 0.0224 loss: 0.0224 2022/09/16 17:31:58 - mmengine - INFO - Epoch(train) [1][33300/42151] lr: 3.0000e-04 eta: 1 day, 21:29:18 time: 0.6050 data_time: 0.0558 memory: 28726 loss_ce: 0.0238 loss: 0.0238 2022/09/16 17:33:08 - mmengine - INFO - Epoch(train) [1][33400/42151] lr: 3.0000e-04 eta: 1 day, 21:27:31 time: 0.7181 data_time: 0.1758 memory: 28726 loss_ce: 0.0201 loss: 0.0201 2022/09/16 17:34:17 - mmengine - INFO - Epoch(train) [1][33500/42151] lr: 3.0000e-04 eta: 1 day, 21:25:40 time: 0.7097 data_time: 0.1180 memory: 28726 loss_ce: 0.0219 loss: 0.0219 2022/09/16 17:35:26 - mmengine - INFO - Epoch(train) [1][33600/42151] lr: 3.0000e-04 eta: 1 day, 21:23:49 time: 0.7502 data_time: 0.2147 memory: 28726 loss_ce: 0.0229 loss: 0.0229 2022/09/16 17:36:36 - mmengine - INFO - Epoch(train) [1][33700/42151] lr: 3.0000e-04 eta: 1 day, 21:22:05 time: 0.6844 data_time: 0.1321 memory: 28726 loss_ce: 0.0205 loss: 0.0205 2022/09/16 17:37:46 - mmengine - INFO - Epoch(train) [1][33800/42151] lr: 3.0000e-04 eta: 1 day, 21:20:25 time: 0.6398 data_time: 0.0677 memory: 28726 loss_ce: 0.0197 loss: 0.0197 2022/09/16 17:38:56 - mmengine - INFO - Epoch(train) [1][33900/42151] lr: 3.0000e-04 eta: 1 day, 21:18:39 time: 0.6328 data_time: 0.0581 memory: 28726 loss_ce: 0.0220 loss: 0.0220 2022/09/16 17:40:05 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 17:40:05 - mmengine - INFO - Epoch(train) [1][34000/42151] lr: 3.0000e-04 eta: 1 day, 21:16:50 time: 0.7844 data_time: 0.1951 memory: 28726 loss_ce: 0.0231 loss: 0.0231 2022/09/16 17:41:14 - mmengine - INFO - Epoch(train) [1][34100/42151] lr: 3.0000e-04 eta: 1 day, 21:15:02 time: 0.6485 data_time: 0.1092 memory: 28726 loss_ce: 0.0215 loss: 0.0215 2022/09/16 17:42:25 - mmengine - INFO - Epoch(train) [1][34200/42151] lr: 3.0000e-04 eta: 1 day, 21:13:20 time: 0.7454 data_time: 0.1800 memory: 28726 loss_ce: 0.0214 loss: 0.0214 2022/09/16 17:43:33 - mmengine - INFO - Epoch(train) [1][34300/42151] lr: 3.0000e-04 eta: 1 day, 21:11:26 time: 0.6945 data_time: 0.1150 memory: 28726 loss_ce: 0.0230 loss: 0.0230 2022/09/16 17:44:42 - mmengine - INFO - Epoch(train) [1][34400/42151] lr: 3.0000e-04 eta: 1 day, 21:09:38 time: 0.6168 data_time: 0.0749 memory: 28726 loss_ce: 0.0225 loss: 0.0225 2022/09/16 17:45:51 - mmengine - INFO - Epoch(train) [1][34500/42151] lr: 3.0000e-04 eta: 1 day, 21:07:50 time: 0.6516 data_time: 0.1135 memory: 28726 loss_ce: 0.0243 loss: 0.0243 2022/09/16 17:47:00 - mmengine - INFO - Epoch(train) [1][34600/42151] lr: 3.0000e-04 eta: 1 day, 21:06:04 time: 0.6798 data_time: 0.1420 memory: 28726 loss_ce: 0.0203 loss: 0.0203 2022/09/16 17:48:12 - mmengine - INFO - Epoch(train) [1][34700/42151] lr: 3.0000e-04 eta: 1 day, 21:04:30 time: 0.8066 data_time: 0.2597 memory: 28726 loss_ce: 0.0223 loss: 0.0223 2022/09/16 17:49:20 - mmengine - INFO - Epoch(train) [1][34800/42151] lr: 3.0000e-04 eta: 1 day, 21:02:37 time: 0.8011 data_time: 0.2621 memory: 28726 loss_ce: 0.0202 loss: 0.0202 2022/09/16 17:50:30 - mmengine - INFO - Epoch(train) [1][34900/42151] lr: 3.0000e-04 eta: 1 day, 21:00:53 time: 0.7374 data_time: 0.1504 memory: 28726 loss_ce: 0.0224 loss: 0.0224 2022/09/16 17:51:40 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 17:51:40 - mmengine - INFO - Epoch(train) [1][35000/42151] lr: 3.0000e-04 eta: 1 day, 20:59:15 time: 0.7136 data_time: 0.1406 memory: 28726 loss_ce: 0.0235 loss: 0.0235 2022/09/16 17:52:47 - mmengine - INFO - Epoch(train) [1][35100/42151] lr: 3.0000e-04 eta: 1 day, 20:57:14 time: 0.6259 data_time: 0.0896 memory: 28726 loss_ce: 0.0232 loss: 0.0232 2022/09/16 17:53:53 - mmengine - INFO - Epoch(train) [1][35200/42151] lr: 3.0000e-04 eta: 1 day, 20:55:10 time: 0.6590 data_time: 0.1089 memory: 28726 loss_ce: 0.0219 loss: 0.0219 2022/09/16 17:55:02 - mmengine - INFO - Epoch(train) [1][35300/42151] lr: 3.0000e-04 eta: 1 day, 20:53:21 time: 0.7870 data_time: 0.2255 memory: 28726 loss_ce: 0.0227 loss: 0.0227 2022/09/16 17:56:09 - mmengine - INFO - Epoch(train) [1][35400/42151] lr: 3.0000e-04 eta: 1 day, 20:51:24 time: 0.7147 data_time: 0.1784 memory: 28726 loss_ce: 0.0214 loss: 0.0214 2022/09/16 17:57:17 - mmengine - INFO - Epoch(train) [1][35500/42151] lr: 3.0000e-04 eta: 1 day, 20:49:28 time: 0.6972 data_time: 0.1589 memory: 28726 loss_ce: 0.0211 loss: 0.0211 2022/09/16 17:58:25 - mmengine - INFO - Epoch(train) [1][35600/42151] lr: 3.0000e-04 eta: 1 day, 20:47:39 time: 0.6106 data_time: 0.0452 memory: 28726 loss_ce: 0.0214 loss: 0.0214 2022/09/16 17:59:34 - mmengine - INFO - Epoch(train) [1][35700/42151] lr: 3.0000e-04 eta: 1 day, 20:45:51 time: 0.7072 data_time: 0.1122 memory: 28726 loss_ce: 0.0219 loss: 0.0219 2022/09/16 18:00:49 - mmengine - INFO - Epoch(train) [1][35800/42151] lr: 3.0000e-04 eta: 1 day, 20:44:41 time: 0.6647 data_time: 0.1276 memory: 28726 loss_ce: 0.0204 loss: 0.0204 2022/09/16 18:01:57 - mmengine - INFO - Epoch(train) [1][35900/42151] lr: 3.0000e-04 eta: 1 day, 20:42:52 time: 0.6064 data_time: 0.0422 memory: 28726 loss_ce: 0.0200 loss: 0.0200 2022/09/16 18:03:06 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 18:03:06 - mmengine - INFO - Epoch(train) [1][36000/42151] lr: 3.0000e-04 eta: 1 day, 20:41:06 time: 0.6459 data_time: 0.1038 memory: 28726 loss_ce: 0.0231 loss: 0.0231 2022/09/16 18:04:15 - mmengine - INFO - Epoch(train) [1][36100/42151] lr: 3.0000e-04 eta: 1 day, 20:39:17 time: 0.6978 data_time: 0.1381 memory: 28726 loss_ce: 0.0216 loss: 0.0216 2022/09/16 18:05:24 - mmengine - INFO - Epoch(train) [1][36200/42151] lr: 3.0000e-04 eta: 1 day, 20:37:33 time: 0.7202 data_time: 0.1689 memory: 28726 loss_ce: 0.0201 loss: 0.0201 2022/09/16 18:06:33 - mmengine - INFO - Epoch(train) [1][36300/42151] lr: 3.0000e-04 eta: 1 day, 20:35:50 time: 0.7858 data_time: 0.2209 memory: 28726 loss_ce: 0.0190 loss: 0.0190 2022/09/16 18:07:42 - mmengine - INFO - Epoch(train) [1][36400/42151] lr: 3.0000e-04 eta: 1 day, 20:34:04 time: 0.5774 data_time: 0.0383 memory: 28726 loss_ce: 0.0191 loss: 0.0191 2022/09/16 18:08:51 - mmengine - INFO - Epoch(train) [1][36500/42151] lr: 3.0000e-04 eta: 1 day, 20:32:19 time: 0.6290 data_time: 0.0565 memory: 28726 loss_ce: 0.0206 loss: 0.0206 2022/09/16 18:10:01 - mmengine - INFO - Epoch(train) [1][36600/42151] lr: 3.0000e-04 eta: 1 day, 20:30:41 time: 0.7045 data_time: 0.1216 memory: 28726 loss_ce: 0.0201 loss: 0.0201 2022/09/16 18:11:09 - mmengine - INFO - Epoch(train) [1][36700/42151] lr: 3.0000e-04 eta: 1 day, 20:28:54 time: 0.6713 data_time: 0.0800 memory: 28726 loss_ce: 0.0212 loss: 0.0212 2022/09/16 18:12:19 - mmengine - INFO - Epoch(train) [1][36800/42151] lr: 3.0000e-04 eta: 1 day, 20:27:13 time: 0.6424 data_time: 0.1088 memory: 28726 loss_ce: 0.0222 loss: 0.0222 2022/09/16 18:13:27 - mmengine - INFO - Epoch(train) [1][36900/42151] lr: 3.0000e-04 eta: 1 day, 20:25:26 time: 0.7281 data_time: 0.1837 memory: 28726 loss_ce: 0.0200 loss: 0.0200 2022/09/16 18:14:36 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 18:14:36 - mmengine - INFO - Epoch(train) [1][37000/42151] lr: 3.0000e-04 eta: 1 day, 20:23:39 time: 0.6131 data_time: 0.0487 memory: 28726 loss_ce: 0.0215 loss: 0.0215 2022/09/16 18:15:44 - mmengine - INFO - Epoch(train) [1][37100/42151] lr: 3.0000e-04 eta: 1 day, 20:21:53 time: 0.6671 data_time: 0.1238 memory: 28726 loss_ce: 0.0203 loss: 0.0203 2022/09/16 18:16:54 - mmengine - INFO - Epoch(train) [1][37200/42151] lr: 3.0000e-04 eta: 1 day, 20:20:13 time: 0.6864 data_time: 0.1485 memory: 28726 loss_ce: 0.0210 loss: 0.0210 2022/09/16 18:18:03 - mmengine - INFO - Epoch(train) [1][37300/42151] lr: 3.0000e-04 eta: 1 day, 20:18:29 time: 0.6439 data_time: 0.0890 memory: 28726 loss_ce: 0.0194 loss: 0.0194 2022/09/16 18:19:12 - mmengine - INFO - Epoch(train) [1][37400/42151] lr: 3.0000e-04 eta: 1 day, 20:16:47 time: 0.6914 data_time: 0.1541 memory: 28726 loss_ce: 0.0199 loss: 0.0199 2022/09/16 18:20:20 - mmengine - INFO - Epoch(train) [1][37500/42151] lr: 3.0000e-04 eta: 1 day, 20:15:03 time: 0.7216 data_time: 0.1777 memory: 28726 loss_ce: 0.0209 loss: 0.0209 2022/09/16 18:21:30 - mmengine - INFO - Epoch(train) [1][37600/42151] lr: 3.0000e-04 eta: 1 day, 20:13:22 time: 0.5501 data_time: 0.0051 memory: 28726 loss_ce: 0.0201 loss: 0.0201 2022/09/16 18:22:38 - mmengine - INFO - Epoch(train) [1][37700/42151] lr: 3.0000e-04 eta: 1 day, 20:11:38 time: 0.6197 data_time: 0.0826 memory: 28726 loss_ce: 0.0211 loss: 0.0211 2022/09/16 18:23:47 - mmengine - INFO - Epoch(train) [1][37800/42151] lr: 3.0000e-04 eta: 1 day, 20:09:54 time: 0.7018 data_time: 0.1579 memory: 28726 loss_ce: 0.0190 loss: 0.0190 2022/09/16 18:24:58 - mmengine - INFO - Epoch(train) [1][37900/42151] lr: 3.0000e-04 eta: 1 day, 20:08:22 time: 0.8410 data_time: 0.2642 memory: 28726 loss_ce: 0.0196 loss: 0.0196 2022/09/16 18:26:05 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 18:26:05 - mmengine - INFO - Epoch(train) [1][38000/42151] lr: 3.0000e-04 eta: 1 day, 20:06:28 time: 0.8108 data_time: 0.2282 memory: 28726 loss_ce: 0.0190 loss: 0.0190 2022/09/16 18:27:14 - mmengine - INFO - Epoch(train) [1][38100/42151] lr: 3.0000e-04 eta: 1 day, 20:04:48 time: 0.7444 data_time: 0.1975 memory: 28726 loss_ce: 0.0181 loss: 0.0181 2022/09/16 18:28:23 - mmengine - INFO - Epoch(train) [1][38200/42151] lr: 3.0000e-04 eta: 1 day, 20:03:06 time: 0.6499 data_time: 0.0657 memory: 28726 loss_ce: 0.0191 loss: 0.0191 2022/09/16 18:29:30 - mmengine - INFO - Epoch(train) [1][38300/42151] lr: 3.0000e-04 eta: 1 day, 20:01:17 time: 0.6459 data_time: 0.1047 memory: 28726 loss_ce: 0.0185 loss: 0.0185 2022/09/16 18:30:39 - mmengine - INFO - Epoch(train) [1][38400/42151] lr: 3.0000e-04 eta: 1 day, 19:59:34 time: 0.6621 data_time: 0.1208 memory: 28726 loss_ce: 0.0222 loss: 0.0222 2022/09/16 18:31:49 - mmengine - INFO - Epoch(train) [1][38500/42151] lr: 3.0000e-04 eta: 1 day, 19:58:01 time: 0.7177 data_time: 0.1781 memory: 28726 loss_ce: 0.0194 loss: 0.0194 2022/09/16 18:32:59 - mmengine - INFO - Epoch(train) [1][38600/42151] lr: 3.0000e-04 eta: 1 day, 19:56:21 time: 0.7221 data_time: 0.1404 memory: 28726 loss_ce: 0.0193 loss: 0.0193 2022/09/16 18:34:07 - mmengine - INFO - Epoch(train) [1][38700/42151] lr: 3.0000e-04 eta: 1 day, 19:54:38 time: 0.6841 data_time: 0.1155 memory: 28726 loss_ce: 0.0201 loss: 0.0201 2022/09/16 18:35:17 - mmengine - INFO - Epoch(train) [1][38800/42151] lr: 3.0000e-04 eta: 1 day, 19:53:01 time: 0.6955 data_time: 0.1455 memory: 28726 loss_ce: 0.0207 loss: 0.0207 2022/09/16 18:36:26 - mmengine - INFO - Epoch(train) [1][38900/42151] lr: 3.0000e-04 eta: 1 day, 19:51:20 time: 0.6875 data_time: 0.1089 memory: 28726 loss_ce: 0.0187 loss: 0.0187 2022/09/16 18:37:35 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 18:37:35 - mmengine - INFO - Epoch(train) [1][39000/42151] lr: 3.0000e-04 eta: 1 day, 19:49:43 time: 0.7092 data_time: 0.1337 memory: 28726 loss_ce: 0.0204 loss: 0.0204 2022/09/16 18:38:43 - mmengine - INFO - Epoch(train) [1][39100/42151] lr: 3.0000e-04 eta: 1 day, 19:47:59 time: 0.6648 data_time: 0.1249 memory: 28726 loss_ce: 0.0177 loss: 0.0177 2022/09/16 18:39:53 - mmengine - INFO - Epoch(train) [1][39200/42151] lr: 3.0000e-04 eta: 1 day, 19:46:22 time: 0.7668 data_time: 0.2017 memory: 28726 loss_ce: 0.0205 loss: 0.0205 2022/09/16 18:41:01 - mmengine - INFO - Epoch(train) [1][39300/42151] lr: 3.0000e-04 eta: 1 day, 19:44:39 time: 0.7513 data_time: 0.2119 memory: 28726 loss_ce: 0.0209 loss: 0.0209 2022/09/16 18:42:10 - mmengine - INFO - Epoch(train) [1][39400/42151] lr: 3.0000e-04 eta: 1 day, 19:42:58 time: 0.6044 data_time: 0.0622 memory: 28726 loss_ce: 0.0195 loss: 0.0195 2022/09/16 18:43:18 - mmengine - INFO - Epoch(train) [1][39500/42151] lr: 3.0000e-04 eta: 1 day, 19:41:11 time: 0.6371 data_time: 0.0656 memory: 28726 loss_ce: 0.0209 loss: 0.0209 2022/09/16 18:44:26 - mmengine - INFO - Epoch(train) [1][39600/42151] lr: 3.0000e-04 eta: 1 day, 19:39:32 time: 0.8468 data_time: 0.3092 memory: 28726 loss_ce: 0.0205 loss: 0.0205 2022/09/16 18:45:34 - mmengine - INFO - Epoch(train) [1][39700/42151] lr: 3.0000e-04 eta: 1 day, 19:37:45 time: 0.7620 data_time: 0.1972 memory: 28726 loss_ce: 0.0184 loss: 0.0184 2022/09/16 18:46:41 - mmengine - INFO - Epoch(train) [1][39800/42151] lr: 3.0000e-04 eta: 1 day, 19:35:57 time: 0.5907 data_time: 0.0529 memory: 28726 loss_ce: 0.0216 loss: 0.0216 2022/09/16 18:47:50 - mmengine - INFO - Epoch(train) [1][39900/42151] lr: 3.0000e-04 eta: 1 day, 19:34:15 time: 0.6239 data_time: 0.0491 memory: 28726 loss_ce: 0.0180 loss: 0.0180 2022/09/16 18:48:58 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 18:48:58 - mmengine - INFO - Epoch(train) [1][40000/42151] lr: 3.0000e-04 eta: 1 day, 19:32:33 time: 0.5870 data_time: 0.0523 memory: 28726 loss_ce: 0.0191 loss: 0.0191 2022/09/16 18:50:07 - mmengine - INFO - Epoch(train) [1][40100/42151] lr: 3.0000e-04 eta: 1 day, 19:30:53 time: 0.6283 data_time: 0.0928 memory: 28726 loss_ce: 0.0208 loss: 0.0208 2022/09/16 18:51:16 - mmengine - INFO - Epoch(train) [1][40200/42151] lr: 3.0000e-04 eta: 1 day, 19:29:17 time: 0.8050 data_time: 0.2364 memory: 28726 loss_ce: 0.0209 loss: 0.0209 2022/09/16 18:52:25 - mmengine - INFO - Epoch(train) [1][40300/42151] lr: 3.0000e-04 eta: 1 day, 19:27:36 time: 0.8257 data_time: 0.2604 memory: 28726 loss_ce: 0.0204 loss: 0.0204 2022/09/16 18:53:34 - mmengine - INFO - Epoch(train) [1][40400/42151] lr: 3.0000e-04 eta: 1 day, 19:26:00 time: 0.7496 data_time: 0.2094 memory: 28726 loss_ce: 0.0199 loss: 0.0199 2022/09/16 18:54:41 - mmengine - INFO - Epoch(train) [1][40500/42151] lr: 3.0000e-04 eta: 1 day, 19:24:14 time: 0.5689 data_time: 0.0276 memory: 28726 loss_ce: 0.0199 loss: 0.0199 2022/09/16 18:55:50 - mmengine - INFO - Epoch(train) [1][40600/42151] lr: 3.0000e-04 eta: 1 day, 19:22:33 time: 0.5718 data_time: 0.0331 memory: 28726 loss_ce: 0.0216 loss: 0.0216 2022/09/16 18:56:58 - mmengine - INFO - Epoch(train) [1][40700/42151] lr: 3.0000e-04 eta: 1 day, 19:20:55 time: 0.6564 data_time: 0.1182 memory: 28726 loss_ce: 0.0177 loss: 0.0177 2022/09/16 18:58:08 - mmengine - INFO - Epoch(train) [1][40800/42151] lr: 3.0000e-04 eta: 1 day, 19:19:22 time: 0.7716 data_time: 0.1935 memory: 28726 loss_ce: 0.0196 loss: 0.0196 2022/09/16 18:59:17 - mmengine - INFO - Epoch(train) [1][40900/42151] lr: 3.0000e-04 eta: 1 day, 19:17:44 time: 0.7637 data_time: 0.2046 memory: 28726 loss_ce: 0.0191 loss: 0.0191 2022/09/16 19:00:25 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 19:00:25 - mmengine - INFO - Epoch(train) [1][41000/42151] lr: 3.0000e-04 eta: 1 day, 19:16:01 time: 0.6345 data_time: 0.0964 memory: 28726 loss_ce: 0.0191 loss: 0.0191 2022/09/16 19:01:35 - mmengine - INFO - Epoch(train) [1][41100/42151] lr: 3.0000e-04 eta: 1 day, 19:14:28 time: 0.6385 data_time: 0.0949 memory: 28726 loss_ce: 0.0203 loss: 0.0203 2022/09/16 19:02:45 - mmengine - INFO - Epoch(train) [1][41200/42151] lr: 3.0000e-04 eta: 1 day, 19:12:58 time: 0.6482 data_time: 0.1057 memory: 28726 loss_ce: 0.0212 loss: 0.0212 2022/09/16 19:03:54 - mmengine - INFO - Epoch(train) [1][41300/42151] lr: 3.0000e-04 eta: 1 day, 19:11:24 time: 0.7018 data_time: 0.1058 memory: 28726 loss_ce: 0.0205 loss: 0.0205 2022/09/16 19:05:04 - mmengine - INFO - Epoch(train) [1][41400/42151] lr: 3.0000e-04 eta: 1 day, 19:09:52 time: 0.7128 data_time: 0.1756 memory: 28726 loss_ce: 0.0181 loss: 0.0181 2022/09/16 19:06:13 - mmengine - INFO - Epoch(train) [1][41500/42151] lr: 3.0000e-04 eta: 1 day, 19:08:16 time: 0.7221 data_time: 0.1368 memory: 28726 loss_ce: 0.0214 loss: 0.0214 2022/09/16 19:07:22 - mmengine - INFO - Epoch(train) [1][41600/42151] lr: 3.0000e-04 eta: 1 day, 19:06:38 time: 0.6946 data_time: 0.1283 memory: 28726 loss_ce: 0.0207 loss: 0.0207 2022/09/16 19:08:31 - mmengine - INFO - Epoch(train) [1][41700/42151] lr: 3.0000e-04 eta: 1 day, 19:04:59 time: 0.6560 data_time: 0.0872 memory: 28726 loss_ce: 0.0196 loss: 0.0196 2022/09/16 19:09:39 - mmengine - INFO - Epoch(train) [1][41800/42151] lr: 3.0000e-04 eta: 1 day, 19:03:18 time: 0.6228 data_time: 0.0768 memory: 28726 loss_ce: 0.0211 loss: 0.0211 2022/09/16 19:10:48 - mmengine - INFO - Epoch(train) [1][41900/42151] lr: 3.0000e-04 eta: 1 day, 19:01:46 time: 0.6906 data_time: 0.1490 memory: 28726 loss_ce: 0.0218 loss: 0.0218 2022/09/16 19:11:58 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 19:11:58 - mmengine - INFO - Epoch(train) [1][42000/42151] lr: 3.0000e-04 eta: 1 day, 19:00:15 time: 0.7065 data_time: 0.1609 memory: 28726 loss_ce: 0.0205 loss: 0.0205 2022/09/16 19:13:07 - mmengine - INFO - Epoch(train) [1][42100/42151] lr: 3.0000e-04 eta: 1 day, 18:58:38 time: 0.7050 data_time: 0.1600 memory: 28726 loss_ce: 0.0200 loss: 0.0200 2022/09/16 19:13:43 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 19:13:43 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/09/16 19:19:21 - mmengine - INFO - Epoch(val) [1][100/7672] eta: 1:50:33 time: 0.8761 data_time: 0.0064 memory: 28726 2022/09/16 19:20:49 - mmengine - INFO - Epoch(val) [1][200/7672] eta: 1:54:21 time: 0.9183 data_time: 0.0009 memory: 1303 2022/09/16 19:22:09 - mmengine - INFO - Epoch(val) [1][300/7672] eta: 0:24:18 time: 0.1978 data_time: 0.0008 memory: 1303 2022/09/16 19:22:30 - mmengine - INFO - Epoch(val) [1][400/7672] eta: 0:25:31 time: 0.2106 data_time: 0.0008 memory: 1303 2022/09/16 19:22:52 - mmengine - INFO - Epoch(val) [1][500/7672] eta: 0:26:50 time: 0.2246 data_time: 0.0011 memory: 1303 2022/09/16 19:23:14 - mmengine - INFO - Epoch(val) [1][600/7672] eta: 0:24:44 time: 0.2099 data_time: 0.0008 memory: 1303 2022/09/16 19:23:35 - mmengine - INFO - Epoch(val) [1][700/7672] eta: 0:26:57 time: 0.2320 data_time: 0.0009 memory: 1303 2022/09/16 19:23:56 - mmengine - INFO - Epoch(val) [1][800/7672] eta: 0:23:21 time: 0.2039 data_time: 0.0008 memory: 1303 2022/09/16 19:24:18 - mmengine - INFO - Epoch(val) [1][900/7672] eta: 0:25:00 time: 0.2215 data_time: 0.0009 memory: 1303 2022/09/16 19:24:40 - mmengine - INFO - Epoch(val) [1][1000/7672] eta: 0:22:18 time: 0.2006 data_time: 0.0008 memory: 1303 2022/09/16 19:25:01 - mmengine - INFO - Epoch(val) [1][1100/7672] eta: 0:21:41 time: 0.1980 data_time: 0.0008 memory: 1303 2022/09/16 19:25:23 - mmengine - INFO - Epoch(val) [1][1200/7672] eta: 0:25:49 time: 0.2395 data_time: 0.0009 memory: 1303 2022/09/16 19:25:44 - mmengine - INFO - Epoch(val) [1][1300/7672] eta: 0:21:41 time: 0.2043 data_time: 0.0007 memory: 1303 2022/09/16 19:26:05 - mmengine - INFO - Epoch(val) [1][1400/7672] eta: 0:21:03 time: 0.2015 data_time: 0.0008 memory: 1303 2022/09/16 19:26:27 - mmengine - INFO - Epoch(val) [1][1500/7672] eta: 0:20:23 time: 0.1982 data_time: 0.0008 memory: 1303 2022/09/16 19:26:48 - mmengine - INFO - Epoch(val) [1][1600/7672] eta: 0:21:05 time: 0.2085 data_time: 0.0018 memory: 1303 2022/09/16 19:27:10 - mmengine - INFO - Epoch(val) [1][1700/7672] eta: 0:20:26 time: 0.2053 data_time: 0.0008 memory: 1303 2022/09/16 19:27:31 - mmengine - INFO - Epoch(val) [1][1800/7672] eta: 0:20:27 time: 0.2090 data_time: 0.0008 memory: 1303 2022/09/16 19:27:54 - mmengine - INFO - Epoch(val) [1][1900/7672] eta: 0:19:56 time: 0.2073 data_time: 0.0008 memory: 1303 2022/09/16 19:28:15 - mmengine - INFO - Epoch(val) [1][2000/7672] eta: 0:22:22 time: 0.2366 data_time: 0.0008 memory: 1303 2022/09/16 19:28:36 - mmengine - INFO - Epoch(val) [1][2100/7672] eta: 0:18:34 time: 0.2000 data_time: 0.0008 memory: 1303 2022/09/16 19:28:59 - mmengine - INFO - Epoch(val) [1][2200/7672] eta: 0:24:42 time: 0.2709 data_time: 0.0034 memory: 1303 2022/09/16 19:29:19 - mmengine - INFO - Epoch(val) [1][2300/7672] eta: 0:18:20 time: 0.2048 data_time: 0.0008 memory: 1303 2022/09/16 19:29:40 - mmengine - INFO - Epoch(val) [1][2400/7672] eta: 0:17:24 time: 0.1981 data_time: 0.0008 memory: 1303 2022/09/16 19:30:03 - mmengine - INFO - Epoch(val) [1][2500/7672] eta: 0:20:37 time: 0.2393 data_time: 0.0009 memory: 1303 2022/09/16 19:30:23 - mmengine - INFO - Epoch(val) [1][2600/7672] eta: 0:17:18 time: 0.2048 data_time: 0.0008 memory: 1303 2022/09/16 19:30:45 - mmengine - INFO - Epoch(val) [1][2700/7672] eta: 0:16:37 time: 0.2007 data_time: 0.0010 memory: 1303 2022/09/16 19:31:12 - mmengine - INFO - Epoch(val) [1][2800/7672] eta: 0:16:13 time: 0.1999 data_time: 0.0008 memory: 1303 2022/09/16 19:31:34 - mmengine - INFO - Epoch(val) [1][2900/7672] eta: 0:20:10 time: 0.2536 data_time: 0.0009 memory: 1303 2022/09/16 19:31:55 - mmengine - INFO - Epoch(val) [1][3000/7672] eta: 0:19:56 time: 0.2561 data_time: 0.0009 memory: 1303 2022/09/16 19:32:17 - mmengine - INFO - Epoch(val) [1][3100/7672] eta: 0:17:36 time: 0.2310 data_time: 0.0008 memory: 1303 2022/09/16 19:32:38 - mmengine - INFO - Epoch(val) [1][3200/7672] eta: 0:15:05 time: 0.2026 data_time: 0.0014 memory: 1303 2022/09/16 19:33:00 - mmengine - INFO - Epoch(val) [1][3300/7672] eta: 0:18:41 time: 0.2565 data_time: 0.0009 memory: 1303 2022/09/16 19:33:21 - mmengine - INFO - Epoch(val) [1][3400/7672] eta: 0:16:49 time: 0.2363 data_time: 0.0010 memory: 1303 2022/09/16 19:33:43 - mmengine - INFO - Epoch(val) [1][3500/7672] eta: 0:15:04 time: 0.2167 data_time: 0.0013 memory: 1303 2022/09/16 19:34:04 - mmengine - INFO - Epoch(val) [1][3600/7672] eta: 0:13:33 time: 0.1997 data_time: 0.0008 memory: 1303 2022/09/16 19:34:24 - mmengine - INFO - Epoch(val) [1][3700/7672] eta: 0:14:33 time: 0.2200 data_time: 0.0009 memory: 1303 2022/09/16 19:34:45 - mmengine - INFO - Epoch(val) [1][3800/7672] eta: 0:13:20 time: 0.2068 data_time: 0.0008 memory: 1303 2022/09/16 19:35:07 - mmengine - INFO - Epoch(val) [1][3900/7672] eta: 0:12:54 time: 0.2055 data_time: 0.0008 memory: 1303 2022/09/16 19:35:28 - mmengine - INFO - Epoch(val) [1][4000/7672] eta: 0:13:17 time: 0.2172 data_time: 0.0008 memory: 1303 2022/09/16 19:35:49 - mmengine - INFO - Epoch(val) [1][4100/7672] eta: 0:13:13 time: 0.2220 data_time: 0.0012 memory: 1303 2022/09/16 19:36:12 - mmengine - INFO - Epoch(val) [1][4200/7672] eta: 0:14:28 time: 0.2502 data_time: 0.0009 memory: 1303 2022/09/16 19:36:33 - mmengine - INFO - Epoch(val) [1][4300/7672] eta: 0:11:45 time: 0.2091 data_time: 0.0008 memory: 1303 2022/09/16 19:36:54 - mmengine - INFO - Epoch(val) [1][4400/7672] eta: 0:11:05 time: 0.2034 data_time: 0.0008 memory: 1303 2022/09/16 19:37:16 - mmengine - INFO - Epoch(val) [1][4500/7672] eta: 0:10:25 time: 0.1972 data_time: 0.0009 memory: 1303 2022/09/16 19:37:37 - mmengine - INFO - Epoch(val) [1][4600/7672] eta: 0:10:41 time: 0.2088 data_time: 0.0007 memory: 1303 2022/09/16 19:37:59 - mmengine - INFO - Epoch(val) [1][4700/7672] eta: 0:10:40 time: 0.2155 data_time: 0.0021 memory: 1303 2022/09/16 19:38:21 - mmengine - INFO - Epoch(val) [1][4800/7672] eta: 0:09:45 time: 0.2039 data_time: 0.0014 memory: 1303 2022/09/16 19:38:42 - mmengine - INFO - Epoch(val) [1][4900/7672] eta: 0:10:42 time: 0.2316 data_time: 0.0016 memory: 1303 2022/09/16 19:39:03 - mmengine - INFO - Epoch(val) [1][5000/7672] eta: 0:09:14 time: 0.2075 data_time: 0.0007 memory: 1303 2022/09/16 19:39:24 - mmengine - INFO - Epoch(val) [1][5100/7672] eta: 0:08:35 time: 0.2005 data_time: 0.0008 memory: 1303 2022/09/16 19:39:46 - mmengine - INFO - Epoch(val) [1][5200/7672] eta: 0:09:56 time: 0.2411 data_time: 0.0010 memory: 1303 2022/09/16 19:40:07 - mmengine - INFO - Epoch(val) [1][5300/7672] eta: 0:08:53 time: 0.2249 data_time: 0.0008 memory: 1303 2022/09/16 19:40:28 - mmengine - INFO - Epoch(val) [1][5400/7672] eta: 0:07:43 time: 0.2041 data_time: 0.0007 memory: 1303 2022/09/16 19:40:49 - mmengine - INFO - Epoch(val) [1][5500/7672] eta: 0:08:25 time: 0.2329 data_time: 0.0009 memory: 1303 2022/09/16 19:41:11 - mmengine - INFO - Epoch(val) [1][5600/7672] eta: 0:07:18 time: 0.2117 data_time: 0.0025 memory: 1303 2022/09/16 19:41:33 - mmengine - INFO - Epoch(val) [1][5700/7672] eta: 0:07:40 time: 0.2333 data_time: 0.0012 memory: 1303 2022/09/16 19:41:54 - mmengine - INFO - Epoch(val) [1][5800/7672] eta: 0:06:21 time: 0.2037 data_time: 0.0008 memory: 1303 2022/09/16 19:42:15 - mmengine - INFO - Epoch(val) [1][5900/7672] eta: 0:05:56 time: 0.2009 data_time: 0.0008 memory: 1303 2022/09/16 19:42:37 - mmengine - INFO - Epoch(val) [1][6000/7672] eta: 0:05:35 time: 0.2004 data_time: 0.0010 memory: 1303 2022/09/16 19:42:58 - mmengine - INFO - Epoch(val) [1][6100/7672] eta: 0:05:15 time: 0.2008 data_time: 0.0007 memory: 1303 2022/09/16 19:43:21 - mmengine - INFO - Epoch(val) [1][6200/7672] eta: 0:05:18 time: 0.2162 data_time: 0.0023 memory: 1303 2022/09/16 19:43:43 - mmengine - INFO - Epoch(val) [1][6300/7672] eta: 0:04:46 time: 0.2089 data_time: 0.0008 memory: 1303 2022/09/16 19:44:04 - mmengine - INFO - Epoch(val) [1][6400/7672] eta: 0:04:16 time: 0.2015 data_time: 0.0008 memory: 1303 2022/09/16 19:44:25 - mmengine - INFO - Epoch(val) [1][6500/7672] eta: 0:03:56 time: 0.2015 data_time: 0.0008 memory: 1303 2022/09/16 19:44:47 - mmengine - INFO - Epoch(val) [1][6600/7672] eta: 0:03:35 time: 0.2007 data_time: 0.0007 memory: 1303 2022/09/16 19:45:08 - mmengine - INFO - Epoch(val) [1][6700/7672] eta: 0:03:11 time: 0.1974 data_time: 0.0007 memory: 1303 2022/09/16 19:45:30 - mmengine - INFO - Epoch(val) [1][6800/7672] eta: 0:03:01 time: 0.2076 data_time: 0.0009 memory: 1303 2022/09/16 19:45:51 - mmengine - INFO - Epoch(val) [1][6900/7672] eta: 0:02:38 time: 0.2057 data_time: 0.0014 memory: 1303 2022/09/16 19:46:12 - mmengine - INFO - Epoch(val) [1][7000/7672] eta: 0:02:42 time: 0.2425 data_time: 0.0009 memory: 1303 2022/09/16 19:46:34 - mmengine - INFO - Epoch(val) [1][7100/7672] eta: 0:01:55 time: 0.2011 data_time: 0.0011 memory: 1303 2022/09/16 19:46:54 - mmengine - INFO - Epoch(val) [1][7200/7672] eta: 0:01:35 time: 0.2029 data_time: 0.0008 memory: 1303 2022/09/16 19:47:16 - mmengine - INFO - Epoch(val) [1][7300/7672] eta: 0:01:25 time: 0.2290 data_time: 0.0009 memory: 1303 2022/09/16 19:47:37 - mmengine - INFO - Epoch(val) [1][7400/7672] eta: 0:00:53 time: 0.1962 data_time: 0.0008 memory: 1303 2022/09/16 19:47:58 - mmengine - INFO - Epoch(val) [1][7500/7672] eta: 0:00:35 time: 0.2049 data_time: 0.0007 memory: 1303 2022/09/16 19:48:20 - mmengine - INFO - Epoch(val) [1][7600/7672] eta: 0:00:15 time: 0.2087 data_time: 0.0009 memory: 1303 2022/09/16 19:48:36 - mmengine - INFO - Epoch(val) [1][7672/7672] CUTE80/recog/word_acc_ignore_case_symbol: 0.8299 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9353 SVT/recog/word_acc_ignore_case_symbol: 0.8671 SVTP/recog/word_acc_ignore_case_symbol: 0.7209 IC13/recog/word_acc_ignore_case_symbol: 0.9202 IC15/recog/word_acc_ignore_case_symbol: 0.6822 2022/09/16 19:49:53 - mmengine - INFO - Epoch(train) [2][100/42151] lr: 3.0000e-04 eta: 1 day, 18:56:39 time: 0.8125 data_time: 0.2444 memory: 28727 loss_ce: 0.0184 loss: 0.0184 2022/09/16 19:51:02 - mmengine - INFO - Epoch(train) [2][200/42151] lr: 3.0000e-04 eta: 1 day, 18:55:04 time: 0.8261 data_time: 0.2869 memory: 28726 loss_ce: 0.0183 loss: 0.0183 2022/09/16 19:52:10 - mmengine - INFO - Epoch(train) [2][300/42151] lr: 3.0000e-04 eta: 1 day, 18:53:24 time: 0.7199 data_time: 0.1777 memory: 28726 loss_ce: 0.0191 loss: 0.0191 2022/09/16 19:53:18 - mmengine - INFO - Epoch(train) [2][400/42151] lr: 3.0000e-04 eta: 1 day, 18:51:45 time: 0.7521 data_time: 0.2092 memory: 28726 loss_ce: 0.0201 loss: 0.0201 2022/09/16 19:54:26 - mmengine - INFO - Epoch(train) [2][500/42151] lr: 3.0000e-04 eta: 1 day, 18:50:03 time: 0.5738 data_time: 0.0372 memory: 28726 loss_ce: 0.0175 loss: 0.0175 2022/09/16 19:55:35 - mmengine - INFO - Epoch(train) [2][600/42151] lr: 3.0000e-04 eta: 1 day, 18:48:27 time: 0.6438 data_time: 0.1074 memory: 28726 loss_ce: 0.0182 loss: 0.0182 2022/09/16 19:56:44 - mmengine - INFO - Epoch(train) [2][700/42151] lr: 3.0000e-04 eta: 1 day, 18:46:56 time: 0.7211 data_time: 0.1032 memory: 28726 loss_ce: 0.0181 loss: 0.0181 2022/09/16 19:57:52 - mmengine - INFO - Epoch(train) [2][800/42151] lr: 3.0000e-04 eta: 1 day, 18:45:17 time: 0.6807 data_time: 0.1355 memory: 28726 loss_ce: 0.0190 loss: 0.0190 2022/09/16 19:58:26 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 19:59:00 - mmengine - INFO - Epoch(train) [2][900/42151] lr: 3.0000e-04 eta: 1 day, 18:43:35 time: 0.6520 data_time: 0.0704 memory: 28726 loss_ce: 0.0187 loss: 0.0187 2022/09/16 20:00:07 - mmengine - INFO - Epoch(train) [2][1000/42151] lr: 3.0000e-04 eta: 1 day, 18:41:52 time: 0.6840 data_time: 0.1474 memory: 28726 loss_ce: 0.0177 loss: 0.0177 2022/09/16 20:01:15 - mmengine - INFO - Epoch(train) [2][1100/42151] lr: 3.0000e-04 eta: 1 day, 18:40:14 time: 0.6490 data_time: 0.1142 memory: 28726 loss_ce: 0.0169 loss: 0.0169 2022/09/16 20:02:25 - mmengine - INFO - Epoch(train) [2][1200/42151] lr: 3.0000e-04 eta: 1 day, 18:38:41 time: 0.7152 data_time: 0.1806 memory: 28726 loss_ce: 0.0167 loss: 0.0167 2022/09/16 20:03:33 - mmengine - INFO - Epoch(train) [2][1300/42151] lr: 3.0000e-04 eta: 1 day, 18:37:05 time: 0.7117 data_time: 0.1438 memory: 28726 loss_ce: 0.0185 loss: 0.0185 2022/09/16 20:04:41 - mmengine - INFO - Epoch(train) [2][1400/42151] lr: 3.0000e-04 eta: 1 day, 18:35:25 time: 0.6021 data_time: 0.0615 memory: 28726 loss_ce: 0.0167 loss: 0.0167 2022/09/16 20:05:49 - mmengine - INFO - Epoch(train) [2][1500/42151] lr: 3.0000e-04 eta: 1 day, 18:33:49 time: 0.6810 data_time: 0.1219 memory: 28726 loss_ce: 0.0197 loss: 0.0197 2022/09/16 20:06:58 - mmengine - INFO - Epoch(train) [2][1600/42151] lr: 3.0000e-04 eta: 1 day, 18:32:16 time: 0.7335 data_time: 0.1763 memory: 28726 loss_ce: 0.0180 loss: 0.0180 2022/09/16 20:08:08 - mmengine - INFO - Epoch(train) [2][1700/42151] lr: 3.0000e-04 eta: 1 day, 18:30:45 time: 0.6751 data_time: 0.1119 memory: 28726 loss_ce: 0.0159 loss: 0.0159 2022/09/16 20:09:16 - mmengine - INFO - Epoch(train) [2][1800/42151] lr: 3.0000e-04 eta: 1 day, 18:29:07 time: 0.6824 data_time: 0.1453 memory: 28726 loss_ce: 0.0186 loss: 0.0186 2022/09/16 20:09:50 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 20:10:25 - mmengine - INFO - Epoch(train) [2][1900/42151] lr: 3.0000e-04 eta: 1 day, 18:27:32 time: 0.6749 data_time: 0.1022 memory: 28726 loss_ce: 0.0177 loss: 0.0177 2022/09/16 20:11:33 - mmengine - INFO - Epoch(train) [2][2000/42151] lr: 3.0000e-04 eta: 1 day, 18:25:57 time: 0.6863 data_time: 0.1016 memory: 28726 loss_ce: 0.0174 loss: 0.0174 2022/09/16 20:12:42 - mmengine - INFO - Epoch(train) [2][2100/42151] lr: 3.0000e-04 eta: 1 day, 18:24:25 time: 0.6607 data_time: 0.0803 memory: 28726 loss_ce: 0.0167 loss: 0.0167 2022/09/16 20:13:52 - mmengine - INFO - Epoch(train) [2][2200/42151] lr: 3.0000e-04 eta: 1 day, 18:22:53 time: 0.6974 data_time: 0.1064 memory: 28726 loss_ce: 0.0191 loss: 0.0191 2022/09/16 20:15:00 - mmengine - INFO - Epoch(train) [2][2300/42151] lr: 3.0000e-04 eta: 1 day, 18:21:16 time: 0.7016 data_time: 0.1268 memory: 28726 loss_ce: 0.0190 loss: 0.0190 2022/09/16 20:16:08 - mmengine - INFO - Epoch(train) [2][2400/42151] lr: 3.0000e-04 eta: 1 day, 18:19:39 time: 0.7289 data_time: 0.1663 memory: 28726 loss_ce: 0.0179 loss: 0.0179 2022/09/16 20:17:16 - mmengine - INFO - Epoch(train) [2][2500/42151] lr: 3.0000e-04 eta: 1 day, 18:18:04 time: 0.6586 data_time: 0.1248 memory: 28726 loss_ce: 0.0189 loss: 0.0189 2022/09/16 20:18:24 - mmengine - INFO - Epoch(train) [2][2600/42151] lr: 3.0000e-04 eta: 1 day, 18:16:25 time: 0.6348 data_time: 0.1013 memory: 28726 loss_ce: 0.0175 loss: 0.0175 2022/09/16 20:19:33 - mmengine - INFO - Epoch(train) [2][2700/42151] lr: 3.0000e-04 eta: 1 day, 18:14:55 time: 0.6535 data_time: 0.0817 memory: 28726 loss_ce: 0.0213 loss: 0.0213 2022/09/16 20:20:42 - mmengine - INFO - Epoch(train) [2][2800/42151] lr: 3.0000e-04 eta: 1 day, 18:13:22 time: 0.7019 data_time: 0.1066 memory: 28726 loss_ce: 0.0196 loss: 0.0196 2022/09/16 20:21:16 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 20:21:51 - mmengine - INFO - Epoch(train) [2][2900/42151] lr: 3.0000e-04 eta: 1 day, 18:11:49 time: 0.6804 data_time: 0.1095 memory: 28726 loss_ce: 0.0183 loss: 0.0183 2022/09/16 20:22:59 - mmengine - INFO - Epoch(train) [2][3000/42151] lr: 3.0000e-04 eta: 1 day, 18:10:13 time: 0.7147 data_time: 0.1452 memory: 28726 loss_ce: 0.0206 loss: 0.0206 2022/09/16 20:24:06 - mmengine - INFO - Epoch(train) [2][3100/42151] lr: 3.0000e-04 eta: 1 day, 18:08:34 time: 0.7162 data_time: 0.1634 memory: 28726 loss_ce: 0.0199 loss: 0.0199 2022/09/16 20:25:14 - mmengine - INFO - Epoch(train) [2][3200/42151] lr: 3.0000e-04 eta: 1 day, 18:06:57 time: 0.6409 data_time: 0.1015 memory: 28726 loss_ce: 0.0177 loss: 0.0177 2022/09/16 20:26:23 - mmengine - INFO - Epoch(train) [2][3300/42151] lr: 3.0000e-04 eta: 1 day, 18:05:25 time: 0.6335 data_time: 0.0739 memory: 28726 loss_ce: 0.0163 loss: 0.0163 2022/09/16 20:27:33 - mmengine - INFO - Epoch(train) [2][3400/42151] lr: 3.0000e-04 eta: 1 day, 18:03:55 time: 0.7410 data_time: 0.1236 memory: 28726 loss_ce: 0.0169 loss: 0.0169 2022/09/16 20:28:41 - mmengine - INFO - Epoch(train) [2][3500/42151] lr: 3.0000e-04 eta: 1 day, 18:02:22 time: 0.6789 data_time: 0.1172 memory: 28726 loss_ce: 0.0168 loss: 0.0168 2022/09/16 20:29:51 - mmengine - INFO - Epoch(train) [2][3600/42151] lr: 3.0000e-04 eta: 1 day, 18:00:55 time: 0.7398 data_time: 0.1833 memory: 28726 loss_ce: 0.0187 loss: 0.0187 2022/09/16 20:31:00 - mmengine - INFO - Epoch(train) [2][3700/42151] lr: 3.0000e-04 eta: 1 day, 17:59:24 time: 0.6687 data_time: 0.1282 memory: 28726 loss_ce: 0.0190 loss: 0.0190 2022/09/16 20:32:10 - mmengine - INFO - Epoch(train) [2][3800/42151] lr: 3.0000e-04 eta: 1 day, 17:57:57 time: 0.6591 data_time: 0.1195 memory: 28726 loss_ce: 0.0196 loss: 0.0196 2022/09/16 20:32:45 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 20:33:20 - mmengine - INFO - Epoch(train) [2][3900/42151] lr: 3.0000e-04 eta: 1 day, 17:56:30 time: 0.6643 data_time: 0.1190 memory: 28726 loss_ce: 0.0166 loss: 0.0166 2022/09/16 20:34:30 - mmengine - INFO - Epoch(train) [2][4000/42151] lr: 3.0000e-04 eta: 1 day, 17:55:05 time: 0.8148 data_time: 0.2366 memory: 28726 loss_ce: 0.0206 loss: 0.0206 2022/09/16 20:35:40 - mmengine - INFO - Epoch(train) [2][4100/42151] lr: 3.0000e-04 eta: 1 day, 17:53:39 time: 0.8221 data_time: 0.2804 memory: 28726 loss_ce: 0.0178 loss: 0.0178 2022/09/16 20:36:49 - mmengine - INFO - Epoch(train) [2][4200/42151] lr: 3.0000e-04 eta: 1 day, 17:52:06 time: 0.8523 data_time: 0.3112 memory: 28726 loss_ce: 0.0183 loss: 0.0183 2022/09/16 20:37:57 - mmengine - INFO - Epoch(train) [2][4300/42151] lr: 3.0000e-04 eta: 1 day, 17:50:31 time: 0.6238 data_time: 0.0408 memory: 28726 loss_ce: 0.0168 loss: 0.0168 2022/09/16 20:39:06 - mmengine - INFO - Epoch(train) [2][4400/42151] lr: 3.0000e-04 eta: 1 day, 17:48:58 time: 0.6721 data_time: 0.0940 memory: 28726 loss_ce: 0.0185 loss: 0.0185 2022/09/16 20:40:14 - mmengine - INFO - Epoch(train) [2][4500/42151] lr: 3.0000e-04 eta: 1 day, 17:47:26 time: 0.6128 data_time: 0.0769 memory: 28726 loss_ce: 0.0177 loss: 0.0177 2022/09/16 20:41:23 - mmengine - INFO - Epoch(train) [2][4600/42151] lr: 3.0000e-04 eta: 1 day, 17:45:57 time: 0.7189 data_time: 0.1508 memory: 28726 loss_ce: 0.0166 loss: 0.0166 2022/09/16 20:42:32 - mmengine - INFO - Epoch(train) [2][4700/42151] lr: 3.0000e-04 eta: 1 day, 17:44:26 time: 0.6983 data_time: 0.1618 memory: 28726 loss_ce: 0.0184 loss: 0.0184 2022/09/16 20:43:40 - mmengine - INFO - Epoch(train) [2][4800/42151] lr: 3.0000e-04 eta: 1 day, 17:42:52 time: 0.8257 data_time: 0.2843 memory: 28726 loss_ce: 0.0177 loss: 0.0177 2022/09/16 20:44:14 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 20:44:55 - mmengine - INFO - Epoch(train) [2][4900/42151] lr: 3.0000e-04 eta: 1 day, 17:41:48 time: 0.5799 data_time: 0.0091 memory: 28726 loss_ce: 0.0200 loss: 0.0200 2022/09/16 20:46:03 - mmengine - INFO - Epoch(train) [2][5000/42151] lr: 3.0000e-04 eta: 1 day, 17:40:13 time: 0.8860 data_time: 0.3464 memory: 28726 loss_ce: 0.0192 loss: 0.0192 2022/09/16 20:47:11 - mmengine - INFO - Epoch(train) [2][5100/42151] lr: 3.0000e-04 eta: 1 day, 17:38:38 time: 0.6937 data_time: 0.1207 memory: 28726 loss_ce: 0.0169 loss: 0.0169 2022/09/16 20:48:18 - mmengine - INFO - Epoch(train) [2][5200/42151] lr: 3.0000e-04 eta: 1 day, 17:36:59 time: 0.6617 data_time: 0.0844 memory: 28726 loss_ce: 0.0164 loss: 0.0164 2022/09/16 20:49:26 - mmengine - INFO - Epoch(train) [2][5300/42151] lr: 3.0000e-04 eta: 1 day, 17:35:25 time: 0.6659 data_time: 0.1279 memory: 28726 loss_ce: 0.0176 loss: 0.0176 2022/09/16 20:50:35 - mmengine - INFO - Epoch(train) [2][5400/42151] lr: 3.0000e-04 eta: 1 day, 17:33:54 time: 0.7349 data_time: 0.1963 memory: 28726 loss_ce: 0.0174 loss: 0.0174 2022/09/16 20:51:43 - mmengine - INFO - Epoch(train) [2][5500/42151] lr: 3.0000e-04 eta: 1 day, 17:32:20 time: 0.7244 data_time: 0.1796 memory: 28726 loss_ce: 0.0202 loss: 0.0202 2022/09/16 20:52:51 - mmengine - INFO - Epoch(train) [2][5600/42151] lr: 3.0000e-04 eta: 1 day, 17:30:45 time: 0.5968 data_time: 0.0261 memory: 28726 loss_ce: 0.0204 loss: 0.0204 2022/09/16 20:54:00 - mmengine - INFO - Epoch(train) [2][5700/42151] lr: 3.0000e-04 eta: 1 day, 17:29:17 time: 0.6687 data_time: 0.1289 memory: 28726 loss_ce: 0.0211 loss: 0.0211 2022/09/16 20:55:07 - mmengine - INFO - Epoch(train) [2][5800/42151] lr: 3.0000e-04 eta: 1 day, 17:27:41 time: 0.7147 data_time: 0.1596 memory: 28726 loss_ce: 0.0193 loss: 0.0193 2022/09/16 20:55:41 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 20:56:15 - mmengine - INFO - Epoch(train) [2][5900/42151] lr: 3.0000e-04 eta: 1 day, 17:26:08 time: 0.6684 data_time: 0.1109 memory: 28726 loss_ce: 0.0177 loss: 0.0177 2022/09/16 20:57:24 - mmengine - INFO - Epoch(train) [2][6000/42151] lr: 3.0000e-04 eta: 1 day, 17:24:38 time: 0.7546 data_time: 0.2122 memory: 28726 loss_ce: 0.0197 loss: 0.0197 2022/09/16 20:58:32 - mmengine - INFO - Epoch(train) [2][6100/42151] lr: 3.0000e-04 eta: 1 day, 17:23:03 time: 0.7355 data_time: 0.1884 memory: 28726 loss_ce: 0.0172 loss: 0.0172 2022/09/16 20:59:42 - mmengine - INFO - Epoch(train) [2][6200/42151] lr: 3.0000e-04 eta: 1 day, 17:21:38 time: 0.6849 data_time: 0.1480 memory: 28726 loss_ce: 0.0200 loss: 0.0200 2022/09/16 21:00:50 - mmengine - INFO - Epoch(train) [2][6300/42151] lr: 3.0000e-04 eta: 1 day, 17:20:07 time: 0.6299 data_time: 0.0854 memory: 28726 loss_ce: 0.0181 loss: 0.0181 2022/09/16 21:01:59 - mmengine - INFO - Epoch(train) [2][6400/42151] lr: 3.0000e-04 eta: 1 day, 17:18:39 time: 0.6429 data_time: 0.1022 memory: 28726 loss_ce: 0.0196 loss: 0.0196 2022/09/16 21:03:08 - mmengine - INFO - Epoch(train) [2][6500/42151] lr: 3.0000e-04 eta: 1 day, 17:17:10 time: 0.7221 data_time: 0.1843 memory: 28726 loss_ce: 0.0183 loss: 0.0183 2022/09/16 21:04:17 - mmengine - INFO - Epoch(train) [2][6600/42151] lr: 3.0000e-04 eta: 1 day, 17:15:41 time: 0.7583 data_time: 0.1907 memory: 28726 loss_ce: 0.0175 loss: 0.0175 2022/09/16 21:05:26 - mmengine - INFO - Epoch(train) [2][6700/42151] lr: 3.0000e-04 eta: 1 day, 17:14:10 time: 0.5714 data_time: 0.0318 memory: 28726 loss_ce: 0.0184 loss: 0.0184 2022/09/16 21:06:34 - mmengine - INFO - Epoch(train) [2][6800/42151] lr: 3.0000e-04 eta: 1 day, 17:12:41 time: 0.6855 data_time: 0.1419 memory: 28726 loss_ce: 0.0197 loss: 0.0197 2022/09/16 21:07:08 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 21:07:43 - mmengine - INFO - Epoch(train) [2][6900/42151] lr: 3.0000e-04 eta: 1 day, 17:11:09 time: 0.6818 data_time: 0.1229 memory: 28726 loss_ce: 0.0170 loss: 0.0170 2022/09/16 21:08:52 - mmengine - INFO - Epoch(train) [2][7000/42151] lr: 3.0000e-04 eta: 1 day, 17:09:40 time: 0.6393 data_time: 0.0669 memory: 28726 loss_ce: 0.0194 loss: 0.0194 2022/09/16 21:10:02 - mmengine - INFO - Epoch(train) [2][7100/42151] lr: 3.0000e-04 eta: 1 day, 17:08:18 time: 0.8011 data_time: 0.2307 memory: 28726 loss_ce: 0.0174 loss: 0.0174 2022/09/16 21:11:11 - mmengine - INFO - Epoch(train) [2][7200/42151] lr: 3.0000e-04 eta: 1 day, 17:06:48 time: 0.7003 data_time: 0.1480 memory: 28726 loss_ce: 0.0188 loss: 0.0188 2022/09/16 21:12:20 - mmengine - INFO - Epoch(train) [2][7300/42151] lr: 3.0000e-04 eta: 1 day, 17:05:20 time: 0.6190 data_time: 0.0797 memory: 28726 loss_ce: 0.0170 loss: 0.0170 2022/09/16 21:13:28 - mmengine - INFO - Epoch(train) [2][7400/42151] lr: 3.0000e-04 eta: 1 day, 17:03:50 time: 0.6200 data_time: 0.0543 memory: 28726 loss_ce: 0.0181 loss: 0.0181 2022/09/16 21:14:38 - mmengine - INFO - Epoch(train) [2][7500/42151] lr: 3.0000e-04 eta: 1 day, 17:02:25 time: 0.8356 data_time: 0.2893 memory: 28726 loss_ce: 0.0173 loss: 0.0173 2022/09/16 21:15:46 - mmengine - INFO - Epoch(train) [2][7600/42151] lr: 3.0000e-04 eta: 1 day, 17:00:54 time: 0.7184 data_time: 0.1813 memory: 28726 loss_ce: 0.0155 loss: 0.0155 2022/09/16 21:16:57 - mmengine - INFO - Epoch(train) [2][7700/42151] lr: 3.0000e-04 eta: 1 day, 16:59:34 time: 0.8471 data_time: 0.3024 memory: 28726 loss_ce: 0.0171 loss: 0.0171 2022/09/16 21:18:12 - mmengine - INFO - Epoch(train) [2][7800/42151] lr: 3.0000e-04 eta: 1 day, 16:58:29 time: 0.5463 data_time: 0.0069 memory: 28726 loss_ce: 0.0191 loss: 0.0191 2022/09/16 21:18:49 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 21:19:20 - mmengine - INFO - Epoch(train) [2][7900/42151] lr: 3.0000e-04 eta: 1 day, 16:57:01 time: 0.6623 data_time: 0.1239 memory: 28726 loss_ce: 0.0165 loss: 0.0165 2022/09/16 21:20:28 - mmengine - INFO - Epoch(train) [2][8000/42151] lr: 3.0000e-04 eta: 1 day, 16:55:26 time: 0.6801 data_time: 0.1474 memory: 28726 loss_ce: 0.0182 loss: 0.0182 2022/09/16 21:21:37 - mmengine - INFO - Epoch(train) [2][8100/42151] lr: 3.0000e-04 eta: 1 day, 16:53:58 time: 0.6394 data_time: 0.0999 memory: 28726 loss_ce: 0.0184 loss: 0.0184 2022/09/16 21:22:45 - mmengine - INFO - Epoch(train) [2][8200/42151] lr: 3.0000e-04 eta: 1 day, 16:52:28 time: 0.6878 data_time: 0.1477 memory: 28726 loss_ce: 0.0191 loss: 0.0191 2022/09/16 21:23:55 - mmengine - INFO - Epoch(train) [2][8300/42151] lr: 3.0000e-04 eta: 1 day, 16:51:04 time: 0.5921 data_time: 0.0518 memory: 28726 loss_ce: 0.0164 loss: 0.0164 2022/09/16 21:25:05 - mmengine - INFO - Epoch(train) [2][8400/42151] lr: 3.0000e-04 eta: 1 day, 16:49:41 time: 0.7093 data_time: 0.1738 memory: 28726 loss_ce: 0.0178 loss: 0.0178 2022/09/16 21:26:13 - mmengine - INFO - Epoch(train) [2][8500/42151] lr: 3.0000e-04 eta: 1 day, 16:48:12 time: 0.6861 data_time: 0.1316 memory: 28726 loss_ce: 0.0173 loss: 0.0173 2022/09/16 21:27:23 - mmengine - INFO - Epoch(train) [2][8600/42151] lr: 3.0000e-04 eta: 1 day, 16:46:45 time: 0.6571 data_time: 0.1168 memory: 28726 loss_ce: 0.0195 loss: 0.0195 2022/09/16 21:28:32 - mmengine - INFO - Epoch(train) [2][8700/42151] lr: 3.0000e-04 eta: 1 day, 16:45:18 time: 0.6996 data_time: 0.1598 memory: 28726 loss_ce: 0.0188 loss: 0.0188 2022/09/16 21:29:40 - mmengine - INFO - Epoch(train) [2][8800/42151] lr: 3.0000e-04 eta: 1 day, 16:43:49 time: 0.7990 data_time: 0.2439 memory: 28726 loss_ce: 0.0187 loss: 0.0187 2022/09/16 21:30:15 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 21:30:49 - mmengine - INFO - Epoch(train) [2][8900/42151] lr: 3.0000e-04 eta: 1 day, 16:42:22 time: 0.7652 data_time: 0.2266 memory: 28726 loss_ce: 0.0199 loss: 0.0199 2022/09/16 21:31:57 - mmengine - INFO - Epoch(train) [2][9000/42151] lr: 3.0000e-04 eta: 1 day, 16:40:53 time: 0.6656 data_time: 0.1236 memory: 28726 loss_ce: 0.0181 loss: 0.0181 2022/09/16 21:33:06 - mmengine - INFO - Epoch(train) [2][9100/42151] lr: 3.0000e-04 eta: 1 day, 16:39:26 time: 0.5715 data_time: 0.0331 memory: 28726 loss_ce: 0.0190 loss: 0.0190 2022/09/16 21:34:16 - mmengine - INFO - Epoch(train) [2][9200/42151] lr: 3.0000e-04 eta: 1 day, 16:38:01 time: 0.6318 data_time: 0.0635 memory: 28726 loss_ce: 0.0164 loss: 0.0164 2022/09/16 21:35:26 - mmengine - INFO - Epoch(train) [2][9300/42151] lr: 3.0000e-04 eta: 1 day, 16:36:38 time: 0.7244 data_time: 0.1823 memory: 28726 loss_ce: 0.0184 loss: 0.0184 2022/09/16 21:36:35 - mmengine - INFO - Epoch(train) [2][9400/42151] lr: 3.0000e-04 eta: 1 day, 16:35:11 time: 0.7165 data_time: 0.1751 memory: 28726 loss_ce: 0.0186 loss: 0.0186 2022/09/16 21:37:43 - mmengine - INFO - Epoch(train) [2][9500/42151] lr: 3.0000e-04 eta: 1 day, 16:33:43 time: 0.7306 data_time: 0.1956 memory: 28726 loss_ce: 0.0169 loss: 0.0169 2022/09/16 21:38:51 - mmengine - INFO - Epoch(train) [2][9600/42151] lr: 3.0000e-04 eta: 1 day, 16:32:13 time: 0.7027 data_time: 0.1655 memory: 28726 loss_ce: 0.0167 loss: 0.0167 2022/09/16 21:40:00 - mmengine - INFO - Epoch(train) [2][9700/42151] lr: 3.0000e-04 eta: 1 day, 16:30:46 time: 0.6745 data_time: 0.1239 memory: 28726 loss_ce: 0.0172 loss: 0.0172 2022/09/16 21:41:09 - mmengine - INFO - Epoch(train) [2][9800/42151] lr: 3.0000e-04 eta: 1 day, 16:29:20 time: 0.6848 data_time: 0.1050 memory: 28726 loss_ce: 0.0189 loss: 0.0189 2022/09/16 21:41:43 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 21:42:18 - mmengine - INFO - Epoch(train) [2][9900/42151] lr: 3.0000e-04 eta: 1 day, 16:27:53 time: 0.6546 data_time: 0.1165 memory: 28726 loss_ce: 0.0181 loss: 0.0181 2022/09/16 21:43:27 - mmengine - INFO - Epoch(train) [2][10000/42151] lr: 3.0000e-04 eta: 1 day, 16:26:27 time: 0.7263 data_time: 0.1865 memory: 28726 loss_ce: 0.0175 loss: 0.0175 2022/09/16 21:44:36 - mmengine - INFO - Epoch(train) [2][10100/42151] lr: 3.0000e-04 eta: 1 day, 16:25:02 time: 0.7786 data_time: 0.1604 memory: 28726 loss_ce: 0.0151 loss: 0.0151 2022/09/16 21:45:45 - mmengine - INFO - Epoch(train) [2][10200/42151] lr: 3.0000e-04 eta: 1 day, 16:23:36 time: 0.7934 data_time: 0.2217 memory: 28726 loss_ce: 0.0179 loss: 0.0179 2022/09/16 21:46:53 - mmengine - INFO - Epoch(train) [2][10300/42151] lr: 3.0000e-04 eta: 1 day, 16:22:06 time: 0.6522 data_time: 0.1095 memory: 28726 loss_ce: 0.0183 loss: 0.0183 2022/09/16 21:48:02 - mmengine - INFO - Epoch(train) [2][10400/42151] lr: 3.0000e-04 eta: 1 day, 16:20:37 time: 0.5890 data_time: 0.0490 memory: 28726 loss_ce: 0.0196 loss: 0.0196 2022/09/16 21:49:12 - mmengine - INFO - Epoch(train) [2][10500/42151] lr: 3.0000e-04 eta: 1 day, 16:19:14 time: 0.7740 data_time: 0.2325 memory: 28726 loss_ce: 0.0170 loss: 0.0170 2022/09/16 21:50:21 - mmengine - INFO - Epoch(train) [2][10600/42151] lr: 3.0000e-04 eta: 1 day, 16:17:51 time: 0.7834 data_time: 0.2149 memory: 28726 loss_ce: 0.0168 loss: 0.0168 2022/09/16 21:51:28 - mmengine - INFO - Epoch(train) [2][10700/42151] lr: 3.0000e-04 eta: 1 day, 16:16:19 time: 0.7289 data_time: 0.1659 memory: 28726 loss_ce: 0.0183 loss: 0.0183 2022/09/16 21:52:36 - mmengine - INFO - Epoch(train) [2][10800/42151] lr: 3.0000e-04 eta: 1 day, 16:14:48 time: 0.6142 data_time: 0.0617 memory: 28726 loss_ce: 0.0182 loss: 0.0182 2022/09/16 21:53:10 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 21:53:45 - mmengine - INFO - Epoch(train) [2][10900/42151] lr: 3.0000e-04 eta: 1 day, 16:13:21 time: 0.6415 data_time: 0.1042 memory: 28726 loss_ce: 0.0166 loss: 0.0166 2022/09/16 21:54:54 - mmengine - INFO - Epoch(train) [2][11000/42151] lr: 3.0000e-04 eta: 1 day, 16:11:57 time: 0.6856 data_time: 0.1159 memory: 28726 loss_ce: 0.0203 loss: 0.0203 2022/09/16 21:56:05 - mmengine - INFO - Epoch(train) [2][11100/42151] lr: 3.0000e-04 eta: 1 day, 16:10:38 time: 0.8098 data_time: 0.2236 memory: 28726 loss_ce: 0.0184 loss: 0.0184 2022/09/16 21:57:13 - mmengine - INFO - Epoch(train) [2][11200/42151] lr: 3.0000e-04 eta: 1 day, 16:09:10 time: 0.6756 data_time: 0.1036 memory: 28726 loss_ce: 0.0159 loss: 0.0159 2022/09/16 21:58:22 - mmengine - INFO - Epoch(train) [2][11300/42151] lr: 3.0000e-04 eta: 1 day, 16:07:42 time: 0.7440 data_time: 0.1591 memory: 28726 loss_ce: 0.0192 loss: 0.0192 2022/09/16 21:59:30 - mmengine - INFO - Epoch(train) [2][11400/42151] lr: 3.0000e-04 eta: 1 day, 16:06:16 time: 0.6313 data_time: 0.0936 memory: 28726 loss_ce: 0.0185 loss: 0.0185 2022/09/16 22:00:40 - mmengine - INFO - Epoch(train) [2][11500/42151] lr: 3.0000e-04 eta: 1 day, 16:04:53 time: 0.6294 data_time: 0.0652 memory: 28726 loss_ce: 0.0182 loss: 0.0182 2022/09/16 22:01:50 - mmengine - INFO - Epoch(train) [2][11600/42151] lr: 3.0000e-04 eta: 1 day, 16:03:31 time: 0.6565 data_time: 0.1116 memory: 28726 loss_ce: 0.0169 loss: 0.0169 2022/09/16 22:03:00 - mmengine - INFO - Epoch(train) [2][11700/42151] lr: 3.0000e-04 eta: 1 day, 16:02:10 time: 0.8309 data_time: 0.2727 memory: 28726 loss_ce: 0.0178 loss: 0.0178 2022/09/16 22:04:07 - mmengine - INFO - Epoch(train) [2][11800/42151] lr: 3.0000e-04 eta: 1 day, 16:00:40 time: 0.7140 data_time: 0.1678 memory: 28726 loss_ce: 0.0185 loss: 0.0185 2022/09/16 22:04:41 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 22:05:16 - mmengine - INFO - Epoch(train) [2][11900/42151] lr: 3.0000e-04 eta: 1 day, 15:59:11 time: 0.7303 data_time: 0.1692 memory: 28726 loss_ce: 0.0183 loss: 0.0183 2022/09/16 22:06:24 - mmengine - INFO - Epoch(train) [2][12000/42151] lr: 3.0000e-04 eta: 1 day, 15:57:46 time: 0.6446 data_time: 0.0693 memory: 28726 loss_ce: 0.0174 loss: 0.0174 2022/09/16 22:07:34 - mmengine - INFO - Epoch(train) [2][12100/42151] lr: 3.0000e-04 eta: 1 day, 15:56:24 time: 0.6473 data_time: 0.1127 memory: 28726 loss_ce: 0.0182 loss: 0.0182 2022/09/16 22:08:44 - mmengine - INFO - Epoch(train) [2][12200/42151] lr: 3.0000e-04 eta: 1 day, 15:55:03 time: 0.6544 data_time: 0.1173 memory: 28726 loss_ce: 0.0176 loss: 0.0176 2022/09/16 22:09:55 - mmengine - INFO - Epoch(train) [2][12300/42151] lr: 3.0000e-04 eta: 1 day, 15:53:43 time: 0.7474 data_time: 0.2056 memory: 28726 loss_ce: 0.0184 loss: 0.0184 2022/09/16 22:11:05 - mmengine - INFO - Epoch(train) [2][12400/42151] lr: 3.0000e-04 eta: 1 day, 15:52:22 time: 0.6504 data_time: 0.1133 memory: 28726 loss_ce: 0.0155 loss: 0.0155 2022/09/16 22:12:15 - mmengine - INFO - Epoch(train) [2][12500/42151] lr: 3.0000e-04 eta: 1 day, 15:51:01 time: 0.7347 data_time: 0.1977 memory: 28726 loss_ce: 0.0160 loss: 0.0160 2022/09/16 22:13:24 - mmengine - INFO - Epoch(train) [2][12600/42151] lr: 3.0000e-04 eta: 1 day, 15:49:38 time: 0.7294 data_time: 0.1928 memory: 28726 loss_ce: 0.0181 loss: 0.0181 2022/09/16 22:14:33 - mmengine - INFO - Epoch(train) [2][12700/42151] lr: 3.0000e-04 eta: 1 day, 15:48:13 time: 0.5806 data_time: 0.0417 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/16 22:15:43 - mmengine - INFO - Epoch(train) [2][12800/42151] lr: 3.0000e-04 eta: 1 day, 15:46:54 time: 0.6726 data_time: 0.1011 memory: 28726 loss_ce: 0.0181 loss: 0.0181 2022/09/16 22:16:18 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 22:16:53 - mmengine - INFO - Epoch(train) [2][12900/42151] lr: 3.0000e-04 eta: 1 day, 15:45:33 time: 0.7380 data_time: 0.1850 memory: 28726 loss_ce: 0.0192 loss: 0.0192 2022/09/16 22:18:04 - mmengine - INFO - Epoch(train) [2][13000/42151] lr: 3.0000e-04 eta: 1 day, 15:44:14 time: 0.7692 data_time: 0.2244 memory: 28726 loss_ce: 0.0167 loss: 0.0167 2022/09/16 22:19:13 - mmengine - INFO - Epoch(train) [2][13100/42151] lr: 3.0000e-04 eta: 1 day, 15:42:49 time: 0.7356 data_time: 0.1674 memory: 28726 loss_ce: 0.0182 loss: 0.0182 2022/09/16 22:20:22 - mmengine - INFO - Epoch(train) [2][13200/42151] lr: 3.0000e-04 eta: 1 day, 15:41:24 time: 0.5866 data_time: 0.0492 memory: 28726 loss_ce: 0.0171 loss: 0.0171 2022/09/16 22:21:31 - mmengine - INFO - Epoch(train) [2][13300/42151] lr: 3.0000e-04 eta: 1 day, 15:40:02 time: 0.6500 data_time: 0.1022 memory: 28726 loss_ce: 0.0165 loss: 0.0165 2022/09/16 22:22:44 - mmengine - INFO - Epoch(train) [2][13400/42151] lr: 3.0000e-04 eta: 1 day, 15:38:52 time: 0.6744 data_time: 0.1363 memory: 28726 loss_ce: 0.0167 loss: 0.0167 2022/09/16 22:23:55 - mmengine - INFO - Epoch(train) [2][13500/42151] lr: 3.0000e-04 eta: 1 day, 15:37:33 time: 0.6863 data_time: 0.1408 memory: 28726 loss_ce: 0.0180 loss: 0.0180 2022/09/16 22:25:06 - mmengine - INFO - Epoch(train) [2][13600/42151] lr: 3.0000e-04 eta: 1 day, 15:36:17 time: 0.8025 data_time: 0.2018 memory: 28726 loss_ce: 0.0161 loss: 0.0161 2022/09/16 22:26:15 - mmengine - INFO - Epoch(train) [2][13700/42151] lr: 3.0000e-04 eta: 1 day, 15:34:51 time: 0.7457 data_time: 0.1965 memory: 28726 loss_ce: 0.0166 loss: 0.0166 2022/09/16 22:27:23 - mmengine - INFO - Epoch(train) [2][13800/42151] lr: 3.0000e-04 eta: 1 day, 15:33:24 time: 0.6528 data_time: 0.1147 memory: 28726 loss_ce: 0.0159 loss: 0.0159 2022/09/16 22:27:56 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 22:28:31 - mmengine - INFO - Epoch(train) [2][13900/42151] lr: 3.0000e-04 eta: 1 day, 15:31:59 time: 0.6115 data_time: 0.0695 memory: 28726 loss_ce: 0.0156 loss: 0.0156 2022/09/16 22:29:42 - mmengine - INFO - Epoch(train) [2][14000/42151] lr: 3.0000e-04 eta: 1 day, 15:30:40 time: 0.6699 data_time: 0.1246 memory: 28726 loss_ce: 0.0178 loss: 0.0178 2022/09/16 22:30:50 - mmengine - INFO - Epoch(train) [2][14100/42151] lr: 3.0000e-04 eta: 1 day, 15:29:14 time: 0.7057 data_time: 0.1321 memory: 28726 loss_ce: 0.0173 loss: 0.0173 2022/09/16 22:31:59 - mmengine - INFO - Epoch(train) [2][14200/42151] lr: 3.0000e-04 eta: 1 day, 15:27:50 time: 0.6706 data_time: 0.1336 memory: 28726 loss_ce: 0.0163 loss: 0.0163 2022/09/16 22:33:08 - mmengine - INFO - Epoch(train) [2][14300/42151] lr: 3.0000e-04 eta: 1 day, 15:26:26 time: 0.7498 data_time: 0.1925 memory: 28726 loss_ce: 0.0185 loss: 0.0185 2022/09/16 22:34:18 - mmengine - INFO - Epoch(train) [2][14400/42151] lr: 3.0000e-04 eta: 1 day, 15:25:04 time: 0.6739 data_time: 0.1158 memory: 28726 loss_ce: 0.0174 loss: 0.0174 2022/09/16 22:35:27 - mmengine - INFO - Epoch(train) [2][14500/42151] lr: 3.0000e-04 eta: 1 day, 15:23:42 time: 0.6660 data_time: 0.1134 memory: 28726 loss_ce: 0.0160 loss: 0.0160 2022/09/16 22:36:36 - mmengine - INFO - Epoch(train) [2][14600/42151] lr: 3.0000e-04 eta: 1 day, 15:22:18 time: 0.6630 data_time: 0.0838 memory: 28726 loss_ce: 0.0184 loss: 0.0184 2022/09/16 22:37:45 - mmengine - INFO - Epoch(train) [2][14700/42151] lr: 3.0000e-04 eta: 1 day, 15:20:54 time: 0.6622 data_time: 0.1232 memory: 28726 loss_ce: 0.0180 loss: 0.0180 2022/09/16 22:38:54 - mmengine - INFO - Epoch(train) [2][14800/42151] lr: 3.0000e-04 eta: 1 day, 15:19:32 time: 0.7009 data_time: 0.1647 memory: 28726 loss_ce: 0.0173 loss: 0.0173 2022/09/16 22:39:28 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 22:40:03 - mmengine - INFO - Epoch(train) [2][14900/42151] lr: 3.0000e-04 eta: 1 day, 15:18:07 time: 0.6564 data_time: 0.1221 memory: 28726 loss_ce: 0.0172 loss: 0.0172 2022/09/16 22:41:13 - mmengine - INFO - Epoch(train) [2][15000/42151] lr: 3.0000e-04 eta: 1 day, 15:16:46 time: 0.6860 data_time: 0.1250 memory: 28726 loss_ce: 0.0161 loss: 0.0161 2022/09/16 22:42:21 - mmengine - INFO - Epoch(train) [2][15100/42151] lr: 3.0000e-04 eta: 1 day, 15:15:21 time: 0.6252 data_time: 0.0777 memory: 28726 loss_ce: 0.0147 loss: 0.0147 2022/09/16 22:43:30 - mmengine - INFO - Epoch(train) [2][15200/42151] lr: 3.0000e-04 eta: 1 day, 15:13:58 time: 0.6947 data_time: 0.0992 memory: 28726 loss_ce: 0.0178 loss: 0.0178 2022/09/16 22:44:39 - mmengine - INFO - Epoch(train) [2][15300/42151] lr: 3.0000e-04 eta: 1 day, 15:12:34 time: 0.6674 data_time: 0.1306 memory: 28726 loss_ce: 0.0165 loss: 0.0165 2022/09/16 22:45:48 - mmengine - INFO - Epoch(train) [2][15400/42151] lr: 3.0000e-04 eta: 1 day, 15:11:12 time: 0.7216 data_time: 0.1538 memory: 28726 loss_ce: 0.0182 loss: 0.0182 2022/09/16 22:46:56 - mmengine - INFO - Epoch(train) [2][15500/42151] lr: 3.0000e-04 eta: 1 day, 15:09:45 time: 0.6881 data_time: 0.1429 memory: 28726 loss_ce: 0.0188 loss: 0.0188 2022/09/16 22:48:06 - mmengine - INFO - Epoch(train) [2][15600/42151] lr: 3.0000e-04 eta: 1 day, 15:08:24 time: 0.6646 data_time: 0.1248 memory: 28726 loss_ce: 0.0191 loss: 0.0191 2022/09/16 22:49:14 - mmengine - INFO - Epoch(train) [2][15700/42151] lr: 3.0000e-04 eta: 1 day, 15:07:00 time: 0.6527 data_time: 0.0848 memory: 28726 loss_ce: 0.0163 loss: 0.0163 2022/09/16 22:50:24 - mmengine - INFO - Epoch(train) [2][15800/42151] lr: 3.0000e-04 eta: 1 day, 15:05:37 time: 0.7001 data_time: 0.0995 memory: 28726 loss_ce: 0.0180 loss: 0.0180 2022/09/16 22:50:57 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 22:51:32 - mmengine - INFO - Epoch(train) [2][15900/42151] lr: 3.0000e-04 eta: 1 day, 15:04:12 time: 0.6996 data_time: 0.1488 memory: 28726 loss_ce: 0.0173 loss: 0.0173 2022/09/16 22:52:41 - mmengine - INFO - Epoch(train) [2][16000/42151] lr: 3.0000e-04 eta: 1 day, 15:02:50 time: 0.7213 data_time: 0.1656 memory: 28726 loss_ce: 0.0161 loss: 0.0161 2022/09/16 22:53:50 - mmengine - INFO - Epoch(train) [2][16100/42151] lr: 3.0000e-04 eta: 1 day, 15:01:25 time: 0.6975 data_time: 0.1595 memory: 28726 loss_ce: 0.0169 loss: 0.0169 2022/09/16 22:54:59 - mmengine - INFO - Epoch(train) [2][16200/42151] lr: 3.0000e-04 eta: 1 day, 15:00:05 time: 0.6895 data_time: 0.1287 memory: 28726 loss_ce: 0.0172 loss: 0.0172 2022/09/16 22:56:08 - mmengine - INFO - Epoch(train) [2][16300/42151] lr: 3.0000e-04 eta: 1 day, 14:58:40 time: 0.6503 data_time: 0.0985 memory: 28726 loss_ce: 0.0165 loss: 0.0165 2022/09/16 22:57:17 - mmengine - INFO - Epoch(train) [2][16400/42151] lr: 3.0000e-04 eta: 1 day, 14:57:18 time: 0.6471 data_time: 0.0796 memory: 28726 loss_ce: 0.0173 loss: 0.0173 2022/09/16 22:58:26 - mmengine - INFO - Epoch(train) [2][16500/42151] lr: 3.0000e-04 eta: 1 day, 14:55:56 time: 0.6615 data_time: 0.1189 memory: 28726 loss_ce: 0.0159 loss: 0.0159 2022/09/16 22:59:35 - mmengine - INFO - Epoch(train) [2][16600/42151] lr: 3.0000e-04 eta: 1 day, 14:54:33 time: 0.7214 data_time: 0.1603 memory: 28726 loss_ce: 0.0180 loss: 0.0180 2022/09/16 23:00:42 - mmengine - INFO - Epoch(train) [2][16700/42151] lr: 3.0000e-04 eta: 1 day, 14:53:04 time: 0.7054 data_time: 0.1654 memory: 28726 loss_ce: 0.0170 loss: 0.0170 2022/09/16 23:01:52 - mmengine - INFO - Epoch(train) [2][16800/42151] lr: 3.0000e-04 eta: 1 day, 14:51:43 time: 0.6839 data_time: 0.1363 memory: 28726 loss_ce: 0.0173 loss: 0.0173 2022/09/16 23:02:26 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 23:03:01 - mmengine - INFO - Epoch(train) [2][16900/42151] lr: 3.0000e-04 eta: 1 day, 14:50:22 time: 0.6691 data_time: 0.1294 memory: 28726 loss_ce: 0.0172 loss: 0.0172 2022/09/16 23:04:11 - mmengine - INFO - Epoch(train) [2][17000/42151] lr: 3.0000e-04 eta: 1 day, 14:49:02 time: 0.6445 data_time: 0.0755 memory: 28726 loss_ce: 0.0181 loss: 0.0181 2022/09/16 23:05:20 - mmengine - INFO - Epoch(train) [2][17100/42151] lr: 3.0000e-04 eta: 1 day, 14:47:39 time: 0.6848 data_time: 0.1461 memory: 28726 loss_ce: 0.0191 loss: 0.0191 2022/09/16 23:06:29 - mmengine - INFO - Epoch(train) [2][17200/42151] lr: 3.0000e-04 eta: 1 day, 14:46:17 time: 0.7428 data_time: 0.1782 memory: 28726 loss_ce: 0.0169 loss: 0.0169 2022/09/16 23:07:37 - mmengine - INFO - Epoch(train) [2][17300/42151] lr: 3.0000e-04 eta: 1 day, 14:44:53 time: 0.6944 data_time: 0.1547 memory: 28726 loss_ce: 0.0158 loss: 0.0158 2022/09/16 23:08:47 - mmengine - INFO - Epoch(train) [2][17400/42151] lr: 3.0000e-04 eta: 1 day, 14:43:34 time: 0.6856 data_time: 0.1469 memory: 28726 loss_ce: 0.0163 loss: 0.0163 2022/09/16 23:09:56 - mmengine - INFO - Epoch(train) [2][17500/42151] lr: 3.0000e-04 eta: 1 day, 14:42:10 time: 0.6404 data_time: 0.1044 memory: 28726 loss_ce: 0.0180 loss: 0.0180 2022/09/16 23:11:04 - mmengine - INFO - Epoch(train) [2][17600/42151] lr: 3.0000e-04 eta: 1 day, 14:40:45 time: 0.6366 data_time: 0.0737 memory: 28726 loss_ce: 0.0177 loss: 0.0177 2022/09/16 23:12:14 - mmengine - INFO - Epoch(train) [2][17700/42151] lr: 3.0000e-04 eta: 1 day, 14:39:25 time: 0.6861 data_time: 0.1389 memory: 28726 loss_ce: 0.0144 loss: 0.0144 2022/09/16 23:13:23 - mmengine - INFO - Epoch(train) [2][17800/42151] lr: 3.0000e-04 eta: 1 day, 14:38:04 time: 0.7542 data_time: 0.1772 memory: 28726 loss_ce: 0.0161 loss: 0.0161 2022/09/16 23:13:56 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 23:14:31 - mmengine - INFO - Epoch(train) [2][17900/42151] lr: 3.0000e-04 eta: 1 day, 14:36:38 time: 0.6983 data_time: 0.1524 memory: 28726 loss_ce: 0.0164 loss: 0.0164 2022/09/16 23:15:39 - mmengine - INFO - Epoch(train) [2][18000/42151] lr: 3.0000e-04 eta: 1 day, 14:35:15 time: 0.6588 data_time: 0.1228 memory: 28726 loss_ce: 0.0169 loss: 0.0169 2022/09/16 23:16:47 - mmengine - INFO - Epoch(train) [2][18100/42151] lr: 3.0000e-04 eta: 1 day, 14:33:48 time: 0.6405 data_time: 0.1055 memory: 28726 loss_ce: 0.0174 loss: 0.0174 2022/09/16 23:17:55 - mmengine - INFO - Epoch(train) [2][18200/42151] lr: 3.0000e-04 eta: 1 day, 14:32:22 time: 0.6526 data_time: 0.0817 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/16 23:19:03 - mmengine - INFO - Epoch(train) [2][18300/42151] lr: 3.0000e-04 eta: 1 day, 14:31:00 time: 0.7001 data_time: 0.1529 memory: 28726 loss_ce: 0.0165 loss: 0.0165 2022/09/16 23:20:13 - mmengine - INFO - Epoch(train) [2][18400/42151] lr: 3.0000e-04 eta: 1 day, 14:29:39 time: 0.7070 data_time: 0.1283 memory: 28726 loss_ce: 0.0172 loss: 0.0172 2022/09/16 23:21:21 - mmengine - INFO - Epoch(train) [2][18500/42151] lr: 3.0000e-04 eta: 1 day, 14:28:14 time: 0.6719 data_time: 0.1321 memory: 28726 loss_ce: 0.0174 loss: 0.0174 2022/09/16 23:22:30 - mmengine - INFO - Epoch(train) [2][18600/42151] lr: 3.0000e-04 eta: 1 day, 14:26:52 time: 0.6617 data_time: 0.1208 memory: 28726 loss_ce: 0.0163 loss: 0.0163 2022/09/16 23:23:37 - mmengine - INFO - Epoch(train) [2][18700/42151] lr: 3.0000e-04 eta: 1 day, 14:25:27 time: 0.6536 data_time: 0.1084 memory: 28726 loss_ce: 0.0175 loss: 0.0175 2022/09/16 23:24:46 - mmengine - INFO - Epoch(train) [2][18800/42151] lr: 3.0000e-04 eta: 1 day, 14:24:03 time: 0.6513 data_time: 0.0773 memory: 28726 loss_ce: 0.0150 loss: 0.0150 2022/09/16 23:25:20 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 23:25:55 - mmengine - INFO - Epoch(train) [2][18900/42151] lr: 3.0000e-04 eta: 1 day, 14:22:42 time: 0.6596 data_time: 0.1203 memory: 28726 loss_ce: 0.0164 loss: 0.0164 2022/09/16 23:27:05 - mmengine - INFO - Epoch(train) [2][19000/42151] lr: 3.0000e-04 eta: 1 day, 14:21:23 time: 0.7431 data_time: 0.1505 memory: 28726 loss_ce: 0.0168 loss: 0.0168 2022/09/16 23:28:13 - mmengine - INFO - Epoch(train) [2][19100/42151] lr: 3.0000e-04 eta: 1 day, 14:19:59 time: 0.6851 data_time: 0.1394 memory: 28726 loss_ce: 0.0177 loss: 0.0177 2022/09/16 23:29:22 - mmengine - INFO - Epoch(train) [2][19200/42151] lr: 3.0000e-04 eta: 1 day, 14:18:36 time: 0.6569 data_time: 0.1139 memory: 28726 loss_ce: 0.0172 loss: 0.0172 2022/09/16 23:30:29 - mmengine - INFO - Epoch(train) [2][19300/42151] lr: 3.0000e-04 eta: 1 day, 14:17:10 time: 0.6729 data_time: 0.1286 memory: 28726 loss_ce: 0.0186 loss: 0.0186 2022/09/16 23:31:37 - mmengine - INFO - Epoch(train) [2][19400/42151] lr: 3.0000e-04 eta: 1 day, 14:15:45 time: 0.6384 data_time: 0.0735 memory: 28726 loss_ce: 0.0179 loss: 0.0179 2022/09/16 23:32:46 - mmengine - INFO - Epoch(train) [2][19500/42151] lr: 3.0000e-04 eta: 1 day, 14:14:23 time: 0.7090 data_time: 0.1337 memory: 28726 loss_ce: 0.0171 loss: 0.0171 2022/09/16 23:33:55 - mmengine - INFO - Epoch(train) [2][19600/42151] lr: 3.0000e-04 eta: 1 day, 14:13:03 time: 0.6968 data_time: 0.1333 memory: 28726 loss_ce: 0.0163 loss: 0.0163 2022/09/16 23:35:03 - mmengine - INFO - Epoch(train) [2][19700/42151] lr: 3.0000e-04 eta: 1 day, 14:11:39 time: 0.6833 data_time: 0.1417 memory: 28726 loss_ce: 0.0166 loss: 0.0166 2022/09/16 23:36:12 - mmengine - INFO - Epoch(train) [2][19800/42151] lr: 3.0000e-04 eta: 1 day, 14:10:16 time: 0.6665 data_time: 0.1280 memory: 28726 loss_ce: 0.0164 loss: 0.0164 2022/09/16 23:36:45 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 23:37:20 - mmengine - INFO - Epoch(train) [2][19900/42151] lr: 3.0000e-04 eta: 1 day, 14:08:51 time: 0.6623 data_time: 0.1198 memory: 28726 loss_ce: 0.0167 loss: 0.0167 2022/09/16 23:38:29 - mmengine - INFO - Epoch(train) [2][20000/42151] lr: 3.0000e-04 eta: 1 day, 14:07:33 time: 0.6686 data_time: 0.0852 memory: 28726 loss_ce: 0.0153 loss: 0.0153 2022/09/16 23:39:38 - mmengine - INFO - Epoch(train) [2][20100/42151] lr: 3.0000e-04 eta: 1 day, 14:06:10 time: 0.6613 data_time: 0.1202 memory: 28726 loss_ce: 0.0182 loss: 0.0182 2022/09/16 23:40:48 - mmengine - INFO - Epoch(train) [2][20200/42151] lr: 3.0000e-04 eta: 1 day, 14:04:51 time: 0.7436 data_time: 0.1787 memory: 28726 loss_ce: 0.0194 loss: 0.0194 2022/09/16 23:41:59 - mmengine - INFO - Epoch(train) [2][20300/42151] lr: 3.0000e-04 eta: 1 day, 14:03:39 time: 0.7921 data_time: 0.1910 memory: 28726 loss_ce: 0.0177 loss: 0.0177 2022/09/16 23:43:09 - mmengine - INFO - Epoch(train) [2][20400/42151] lr: 3.0000e-04 eta: 1 day, 14:02:20 time: 0.6872 data_time: 0.1206 memory: 28726 loss_ce: 0.0176 loss: 0.0176 2022/09/16 23:44:18 - mmengine - INFO - Epoch(train) [2][20500/42151] lr: 3.0000e-04 eta: 1 day, 14:00:58 time: 0.6603 data_time: 0.0885 memory: 28726 loss_ce: 0.0155 loss: 0.0155 2022/09/16 23:45:27 - mmengine - INFO - Epoch(train) [2][20600/42151] lr: 3.0000e-04 eta: 1 day, 13:59:39 time: 0.6751 data_time: 0.1231 memory: 28726 loss_ce: 0.0169 loss: 0.0169 2022/09/16 23:46:37 - mmengine - INFO - Epoch(train) [2][20700/42151] lr: 3.0000e-04 eta: 1 day, 13:58:19 time: 0.6730 data_time: 0.1063 memory: 28726 loss_ce: 0.0160 loss: 0.0160 2022/09/16 23:47:46 - mmengine - INFO - Epoch(train) [2][20800/42151] lr: 3.0000e-04 eta: 1 day, 13:57:00 time: 0.7204 data_time: 0.1457 memory: 28726 loss_ce: 0.0168 loss: 0.0168 2022/09/16 23:48:21 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/16 23:48:57 - mmengine - INFO - Epoch(train) [2][20900/42151] lr: 3.0000e-04 eta: 1 day, 13:55:43 time: 0.6728 data_time: 0.1018 memory: 28726 loss_ce: 0.0165 loss: 0.0165 2022/09/16 23:50:07 - mmengine - INFO - Epoch(train) [2][21000/42151] lr: 3.0000e-04 eta: 1 day, 13:54:27 time: 0.6811 data_time: 0.1450 memory: 28726 loss_ce: 0.0170 loss: 0.0170 2022/09/16 23:51:16 - mmengine - INFO - Epoch(train) [2][21100/42151] lr: 3.0000e-04 eta: 1 day, 13:53:06 time: 0.6591 data_time: 0.0996 memory: 28726 loss_ce: 0.0179 loss: 0.0179 2022/09/16 23:52:25 - mmengine - INFO - Epoch(train) [2][21200/42151] lr: 3.0000e-04 eta: 1 day, 13:51:45 time: 0.6681 data_time: 0.1272 memory: 28726 loss_ce: 0.0158 loss: 0.0158 2022/09/16 23:53:34 - mmengine - INFO - Epoch(train) [2][21300/42151] lr: 3.0000e-04 eta: 1 day, 13:50:25 time: 0.7199 data_time: 0.1502 memory: 28726 loss_ce: 0.0183 loss: 0.0183 2022/09/16 23:54:44 - mmengine - INFO - Epoch(train) [2][21400/42151] lr: 3.0000e-04 eta: 1 day, 13:49:06 time: 0.6997 data_time: 0.1617 memory: 28726 loss_ce: 0.0167 loss: 0.0167 2022/09/16 23:55:53 - mmengine - INFO - Epoch(train) [2][21500/42151] lr: 3.0000e-04 eta: 1 day, 13:47:45 time: 0.6560 data_time: 0.1175 memory: 28726 loss_ce: 0.0157 loss: 0.0157 2022/09/16 23:57:02 - mmengine - INFO - Epoch(train) [2][21600/42151] lr: 3.0000e-04 eta: 1 day, 13:46:24 time: 0.6574 data_time: 0.1165 memory: 28726 loss_ce: 0.0148 loss: 0.0148 2022/09/16 23:58:12 - mmengine - INFO - Epoch(train) [2][21700/42151] lr: 3.0000e-04 eta: 1 day, 13:45:07 time: 0.6705 data_time: 0.0929 memory: 28726 loss_ce: 0.0161 loss: 0.0161 2022/09/16 23:59:21 - mmengine - INFO - Epoch(train) [2][21800/42151] lr: 3.0000e-04 eta: 1 day, 13:43:48 time: 0.6593 data_time: 0.0931 memory: 28726 loss_ce: 0.0169 loss: 0.0169 2022/09/16 23:59:56 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 00:00:36 - mmengine - INFO - Epoch(train) [2][21900/42151] lr: 3.0000e-04 eta: 1 day, 13:42:45 time: 0.6908 data_time: 0.1262 memory: 28726 loss_ce: 0.0165 loss: 0.0165 2022/09/17 00:01:46 - mmengine - INFO - Epoch(train) [2][22000/42151] lr: 3.0000e-04 eta: 1 day, 13:41:26 time: 0.7129 data_time: 0.1748 memory: 28726 loss_ce: 0.0160 loss: 0.0160 2022/09/17 00:02:55 - mmengine - INFO - Epoch(train) [2][22100/42151] lr: 3.0000e-04 eta: 1 day, 13:40:07 time: 0.7055 data_time: 0.1533 memory: 28726 loss_ce: 0.0149 loss: 0.0149 2022/09/17 00:04:05 - mmengine - INFO - Epoch(train) [2][22200/42151] lr: 3.0000e-04 eta: 1 day, 13:38:50 time: 0.6700 data_time: 0.1277 memory: 28726 loss_ce: 0.0183 loss: 0.0183 2022/09/17 00:05:15 - mmengine - INFO - Epoch(train) [2][22300/42151] lr: 3.0000e-04 eta: 1 day, 13:37:32 time: 0.6763 data_time: 0.0912 memory: 28726 loss_ce: 0.0172 loss: 0.0172 2022/09/17 00:06:25 - mmengine - INFO - Epoch(train) [2][22400/42151] lr: 3.0000e-04 eta: 1 day, 13:36:13 time: 0.6755 data_time: 0.0856 memory: 28726 loss_ce: 0.0170 loss: 0.0170 2022/09/17 00:07:34 - mmengine - INFO - Epoch(train) [2][22500/42151] lr: 3.0000e-04 eta: 1 day, 13:34:53 time: 0.6937 data_time: 0.1309 memory: 28726 loss_ce: 0.0156 loss: 0.0156 2022/09/17 00:08:44 - mmengine - INFO - Epoch(train) [2][22600/42151] lr: 3.0000e-04 eta: 1 day, 13:33:38 time: 0.7475 data_time: 0.2090 memory: 28726 loss_ce: 0.0167 loss: 0.0167 2022/09/17 00:09:53 - mmengine - INFO - Epoch(train) [2][22700/42151] lr: 3.0000e-04 eta: 1 day, 13:32:16 time: 0.6987 data_time: 0.1476 memory: 28726 loss_ce: 0.0121 loss: 0.0121 2022/09/17 00:11:02 - mmengine - INFO - Epoch(train) [2][22800/42151] lr: 3.0000e-04 eta: 1 day, 13:30:57 time: 0.6564 data_time: 0.1230 memory: 28726 loss_ce: 0.0177 loss: 0.0177 2022/09/17 00:11:37 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 00:12:12 - mmengine - INFO - Epoch(train) [2][22900/42151] lr: 3.0000e-04 eta: 1 day, 13:29:39 time: 0.6801 data_time: 0.0891 memory: 28726 loss_ce: 0.0151 loss: 0.0151 2022/09/17 00:13:23 - mmengine - INFO - Epoch(train) [2][23000/42151] lr: 3.0000e-04 eta: 1 day, 13:28:23 time: 0.6713 data_time: 0.0939 memory: 28726 loss_ce: 0.0169 loss: 0.0169 2022/09/17 00:14:33 - mmengine - INFO - Epoch(train) [2][23100/42151] lr: 3.0000e-04 eta: 1 day, 13:27:06 time: 0.7234 data_time: 0.1504 memory: 28726 loss_ce: 0.0155 loss: 0.0155 2022/09/17 00:15:44 - mmengine - INFO - Epoch(train) [2][23200/42151] lr: 3.0000e-04 eta: 1 day, 13:25:52 time: 0.7408 data_time: 0.2034 memory: 28726 loss_ce: 0.0158 loss: 0.0158 2022/09/17 00:16:53 - mmengine - INFO - Epoch(train) [2][23300/42151] lr: 3.0000e-04 eta: 1 day, 13:24:32 time: 0.6939 data_time: 0.1622 memory: 28726 loss_ce: 0.0145 loss: 0.0145 2022/09/17 00:18:02 - mmengine - INFO - Epoch(train) [2][23400/42151] lr: 3.0000e-04 eta: 1 day, 13:23:14 time: 0.6948 data_time: 0.1593 memory: 28726 loss_ce: 0.0158 loss: 0.0158 2022/09/17 00:19:12 - mmengine - INFO - Epoch(train) [2][23500/42151] lr: 3.0000e-04 eta: 1 day, 13:21:55 time: 0.6335 data_time: 0.0729 memory: 28726 loss_ce: 0.0162 loss: 0.0162 2022/09/17 00:20:21 - mmengine - INFO - Epoch(train) [2][23600/42151] lr: 3.0000e-04 eta: 1 day, 13:20:35 time: 0.6605 data_time: 0.0842 memory: 28726 loss_ce: 0.0169 loss: 0.0169 2022/09/17 00:21:32 - mmengine - INFO - Epoch(train) [2][23700/42151] lr: 3.0000e-04 eta: 1 day, 13:19:20 time: 0.7482 data_time: 0.1482 memory: 28726 loss_ce: 0.0171 loss: 0.0171 2022/09/17 00:22:43 - mmengine - INFO - Epoch(train) [2][23800/42151] lr: 3.0000e-04 eta: 1 day, 13:18:08 time: 0.7590 data_time: 0.2206 memory: 28726 loss_ce: 0.0156 loss: 0.0156 2022/09/17 00:23:18 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 00:23:52 - mmengine - INFO - Epoch(train) [2][23900/42151] lr: 3.0000e-04 eta: 1 day, 13:16:48 time: 0.7192 data_time: 0.1879 memory: 28726 loss_ce: 0.0146 loss: 0.0146 2022/09/17 00:25:02 - mmengine - INFO - Epoch(train) [2][24000/42151] lr: 3.0000e-04 eta: 1 day, 13:15:29 time: 0.6703 data_time: 0.1389 memory: 28726 loss_ce: 0.0169 loss: 0.0169 2022/09/17 00:26:12 - mmengine - INFO - Epoch(train) [2][24100/42151] lr: 3.0000e-04 eta: 1 day, 13:14:13 time: 0.6586 data_time: 0.0762 memory: 28726 loss_ce: 0.0166 loss: 0.0166 2022/09/17 00:27:22 - mmengine - INFO - Epoch(train) [2][24200/42151] lr: 3.0000e-04 eta: 1 day, 13:12:55 time: 0.6910 data_time: 0.0918 memory: 28726 loss_ce: 0.0151 loss: 0.0151 2022/09/17 00:28:32 - mmengine - INFO - Epoch(train) [2][24300/42151] lr: 3.0000e-04 eta: 1 day, 13:11:39 time: 0.7124 data_time: 0.1461 memory: 28726 loss_ce: 0.0168 loss: 0.0168 2022/09/17 00:29:42 - mmengine - INFO - Epoch(train) [2][24400/42151] lr: 3.0000e-04 eta: 1 day, 13:10:22 time: 0.7648 data_time: 0.2320 memory: 28726 loss_ce: 0.0164 loss: 0.0164 2022/09/17 00:30:49 - mmengine - INFO - Epoch(train) [2][24500/42151] lr: 3.0000e-04 eta: 1 day, 13:08:57 time: 0.7128 data_time: 0.1836 memory: 28726 loss_ce: 0.0173 loss: 0.0173 2022/09/17 00:31:58 - mmengine - INFO - Epoch(train) [2][24600/42151] lr: 3.0000e-04 eta: 1 day, 13:07:37 time: 0.7324 data_time: 0.1882 memory: 28726 loss_ce: 0.0169 loss: 0.0169 2022/09/17 00:33:07 - mmengine - INFO - Epoch(train) [2][24700/42151] lr: 3.0000e-04 eta: 1 day, 13:06:18 time: 0.6434 data_time: 0.0709 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/17 00:34:16 - mmengine - INFO - Epoch(train) [2][24800/42151] lr: 3.0000e-04 eta: 1 day, 13:04:58 time: 0.6409 data_time: 0.0802 memory: 28726 loss_ce: 0.0162 loss: 0.0162 2022/09/17 00:34:50 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 00:35:26 - mmengine - INFO - Epoch(train) [2][24900/42151] lr: 3.0000e-04 eta: 1 day, 13:03:42 time: 0.7219 data_time: 0.1559 memory: 28726 loss_ce: 0.0154 loss: 0.0154 2022/09/17 00:36:37 - mmengine - INFO - Epoch(train) [2][25000/42151] lr: 3.0000e-04 eta: 1 day, 13:02:27 time: 0.7456 data_time: 0.2016 memory: 28726 loss_ce: 0.0177 loss: 0.0177 2022/09/17 00:37:46 - mmengine - INFO - Epoch(train) [2][25100/42151] lr: 3.0000e-04 eta: 1 day, 13:01:07 time: 0.6816 data_time: 0.1381 memory: 28726 loss_ce: 0.0186 loss: 0.0186 2022/09/17 00:38:54 - mmengine - INFO - Epoch(train) [2][25200/42151] lr: 3.0000e-04 eta: 1 day, 12:59:45 time: 0.5808 data_time: 0.0523 memory: 28726 loss_ce: 0.0167 loss: 0.0167 2022/09/17 00:40:03 - mmengine - INFO - Epoch(train) [2][25300/42151] lr: 3.0000e-04 eta: 1 day, 12:58:27 time: 0.5836 data_time: 0.0500 memory: 28726 loss_ce: 0.0168 loss: 0.0168 2022/09/17 00:41:14 - mmengine - INFO - Epoch(train) [2][25400/42151] lr: 3.0000e-04 eta: 1 day, 12:57:13 time: 0.7204 data_time: 0.1668 memory: 28726 loss_ce: 0.0168 loss: 0.0168 2022/09/17 00:42:24 - mmengine - INFO - Epoch(train) [2][25500/42151] lr: 3.0000e-04 eta: 1 day, 12:55:54 time: 0.6503 data_time: 0.1150 memory: 28726 loss_ce: 0.0160 loss: 0.0160 2022/09/17 00:43:35 - mmengine - INFO - Epoch(train) [2][25600/42151] lr: 3.0000e-04 eta: 1 day, 12:54:42 time: 0.7302 data_time: 0.1965 memory: 28726 loss_ce: 0.0172 loss: 0.0172 2022/09/17 00:44:45 - mmengine - INFO - Epoch(train) [2][25700/42151] lr: 3.0000e-04 eta: 1 day, 12:53:24 time: 0.6801 data_time: 0.1486 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/17 00:45:54 - mmengine - INFO - Epoch(train) [2][25800/42151] lr: 3.0000e-04 eta: 1 day, 12:52:05 time: 0.5930 data_time: 0.0615 memory: 28726 loss_ce: 0.0146 loss: 0.0146 2022/09/17 00:46:28 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 00:47:02 - mmengine - INFO - Epoch(train) [2][25900/42151] lr: 3.0000e-04 eta: 1 day, 12:50:43 time: 0.5760 data_time: 0.0439 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 00:48:12 - mmengine - INFO - Epoch(train) [2][26000/42151] lr: 3.0000e-04 eta: 1 day, 12:49:25 time: 0.6954 data_time: 0.1618 memory: 28726 loss_ce: 0.0178 loss: 0.0178 2022/09/17 00:49:22 - mmengine - INFO - Epoch(train) [2][26100/42151] lr: 3.0000e-04 eta: 1 day, 12:48:09 time: 0.7315 data_time: 0.1859 memory: 28726 loss_ce: 0.0175 loss: 0.0175 2022/09/17 00:50:32 - mmengine - INFO - Epoch(train) [2][26200/42151] lr: 3.0000e-04 eta: 1 day, 12:46:52 time: 0.7631 data_time: 0.2081 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 00:51:41 - mmengine - INFO - Epoch(train) [2][26300/42151] lr: 3.0000e-04 eta: 1 day, 12:45:34 time: 0.7572 data_time: 0.2193 memory: 28726 loss_ce: 0.0175 loss: 0.0175 2022/09/17 00:52:50 - mmengine - INFO - Epoch(train) [2][26400/42151] lr: 3.0000e-04 eta: 1 day, 12:44:15 time: 0.5670 data_time: 0.0337 memory: 28726 loss_ce: 0.0168 loss: 0.0168 2022/09/17 00:54:23 - mmengine - INFO - Epoch(train) [2][26500/42151] lr: 3.0000e-04 eta: 1 day, 12:44:00 time: 0.5447 data_time: 0.0054 memory: 28726 loss_ce: 0.0168 loss: 0.0168 2022/09/17 00:55:28 - mmengine - INFO - Epoch(train) [2][26600/42151] lr: 3.0000e-04 eta: 1 day, 12:42:29 time: 0.5909 data_time: 0.0552 memory: 28726 loss_ce: 0.0171 loss: 0.0171 2022/09/17 00:56:37 - mmengine - INFO - Epoch(train) [2][26700/42151] lr: 3.0000e-04 eta: 1 day, 12:41:10 time: 0.8112 data_time: 0.2736 memory: 28726 loss_ce: 0.0161 loss: 0.0161 2022/09/17 00:57:44 - mmengine - INFO - Epoch(train) [2][26800/42151] lr: 3.0000e-04 eta: 1 day, 12:39:47 time: 0.5693 data_time: 0.0376 memory: 28726 loss_ce: 0.0157 loss: 0.0157 2022/09/17 00:58:17 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 00:58:51 - mmengine - INFO - Epoch(train) [2][26900/42151] lr: 3.0000e-04 eta: 1 day, 12:38:22 time: 0.5843 data_time: 0.0194 memory: 28726 loss_ce: 0.0148 loss: 0.0148 2022/09/17 00:59:59 - mmengine - INFO - Epoch(train) [2][27000/42151] lr: 3.0000e-04 eta: 1 day, 12:36:59 time: 0.5766 data_time: 0.0424 memory: 28726 loss_ce: 0.0176 loss: 0.0176 2022/09/17 01:01:31 - mmengine - INFO - Epoch(train) [2][27100/42151] lr: 3.0000e-04 eta: 1 day, 12:36:43 time: 0.5391 data_time: 0.0043 memory: 28726 loss_ce: 0.0160 loss: 0.0160 2022/09/17 01:02:52 - mmengine - INFO - Epoch(train) [2][27200/42151] lr: 3.0000e-04 eta: 1 day, 12:35:55 time: 0.5396 data_time: 0.0047 memory: 28726 loss_ce: 0.0137 loss: 0.0137 2022/09/17 01:03:56 - mmengine - INFO - Epoch(train) [2][27300/42151] lr: 3.0000e-04 eta: 1 day, 12:34:23 time: 0.6717 data_time: 0.1362 memory: 28726 loss_ce: 0.0157 loss: 0.0157 2022/09/17 01:05:03 - mmengine - INFO - Epoch(train) [2][27400/42151] lr: 3.0000e-04 eta: 1 day, 12:32:58 time: 0.8378 data_time: 0.2694 memory: 28726 loss_ce: 0.0175 loss: 0.0175 2022/09/17 01:06:11 - mmengine - INFO - Epoch(train) [2][27500/42151] lr: 3.0000e-04 eta: 1 day, 12:31:35 time: 0.6578 data_time: 0.1219 memory: 28726 loss_ce: 0.0157 loss: 0.0157 2022/09/17 01:07:20 - mmengine - INFO - Epoch(train) [2][27600/42151] lr: 3.0000e-04 eta: 1 day, 12:30:18 time: 0.5618 data_time: 0.0137 memory: 28726 loss_ce: 0.0167 loss: 0.0167 2022/09/17 01:08:30 - mmengine - INFO - Epoch(train) [2][27700/42151] lr: 3.0000e-04 eta: 1 day, 12:29:01 time: 0.5653 data_time: 0.0046 memory: 28726 loss_ce: 0.0156 loss: 0.0156 2022/09/17 01:09:36 - mmengine - INFO - Epoch(train) [2][27800/42151] lr: 3.0000e-04 eta: 1 day, 12:27:33 time: 0.5520 data_time: 0.0129 memory: 28726 loss_ce: 0.0167 loss: 0.0167 2022/09/17 01:10:09 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 01:10:46 - mmengine - INFO - Epoch(train) [2][27900/42151] lr: 3.0000e-04 eta: 1 day, 12:26:17 time: 0.8102 data_time: 0.2699 memory: 28726 loss_ce: 0.0159 loss: 0.0159 2022/09/17 01:11:52 - mmengine - INFO - Epoch(train) [2][28000/42151] lr: 3.0000e-04 eta: 1 day, 12:24:50 time: 0.6428 data_time: 0.1022 memory: 28726 loss_ce: 0.0166 loss: 0.0166 2022/09/17 01:12:59 - mmengine - INFO - Epoch(train) [2][28100/42151] lr: 3.0000e-04 eta: 1 day, 12:23:27 time: 0.6947 data_time: 0.1588 memory: 28726 loss_ce: 0.0156 loss: 0.0156 2022/09/17 01:14:07 - mmengine - INFO - Epoch(train) [2][28200/42151] lr: 3.0000e-04 eta: 1 day, 12:22:04 time: 0.5577 data_time: 0.0223 memory: 28726 loss_ce: 0.0157 loss: 0.0157 2022/09/17 01:15:15 - mmengine - INFO - Epoch(train) [2][28300/42151] lr: 3.0000e-04 eta: 1 day, 12:20:43 time: 0.5632 data_time: 0.0174 memory: 28726 loss_ce: 0.0164 loss: 0.0164 2022/09/17 01:16:23 - mmengine - INFO - Epoch(train) [2][28400/42151] lr: 3.0000e-04 eta: 1 day, 12:19:23 time: 0.7046 data_time: 0.1644 memory: 28726 loss_ce: 0.0185 loss: 0.0185 2022/09/17 01:17:32 - mmengine - INFO - Epoch(train) [2][28500/42151] lr: 3.0000e-04 eta: 1 day, 12:18:03 time: 0.7153 data_time: 0.1774 memory: 28726 loss_ce: 0.0154 loss: 0.0154 2022/09/17 01:18:39 - mmengine - INFO - Epoch(train) [2][28600/42151] lr: 3.0000e-04 eta: 1 day, 12:16:40 time: 0.6409 data_time: 0.1055 memory: 28726 loss_ce: 0.0153 loss: 0.0153 2022/09/17 01:19:47 - mmengine - INFO - Epoch(train) [2][28700/42151] lr: 3.0000e-04 eta: 1 day, 12:15:19 time: 0.6939 data_time: 0.1572 memory: 28726 loss_ce: 0.0156 loss: 0.0156 2022/09/17 01:20:54 - mmengine - INFO - Epoch(train) [2][28800/42151] lr: 3.0000e-04 eta: 1 day, 12:13:55 time: 0.6311 data_time: 0.0917 memory: 28726 loss_ce: 0.0170 loss: 0.0170 2022/09/17 01:21:28 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 01:22:02 - mmengine - INFO - Epoch(train) [2][28900/42151] lr: 3.0000e-04 eta: 1 day, 12:12:32 time: 0.5963 data_time: 0.0218 memory: 28726 loss_ce: 0.0141 loss: 0.0141 2022/09/17 01:23:09 - mmengine - INFO - Epoch(train) [2][29000/42151] lr: 3.0000e-04 eta: 1 day, 12:11:10 time: 0.6720 data_time: 0.1078 memory: 28726 loss_ce: 0.0166 loss: 0.0166 2022/09/17 01:24:17 - mmengine - INFO - Epoch(train) [2][29100/42151] lr: 3.0000e-04 eta: 1 day, 12:09:49 time: 0.6881 data_time: 0.1473 memory: 28726 loss_ce: 0.0155 loss: 0.0155 2022/09/17 01:25:25 - mmengine - INFO - Epoch(train) [2][29200/42151] lr: 3.0000e-04 eta: 1 day, 12:08:28 time: 0.6553 data_time: 0.1167 memory: 28726 loss_ce: 0.0166 loss: 0.0166 2022/09/17 01:26:34 - mmengine - INFO - Epoch(train) [2][29300/42151] lr: 3.0000e-04 eta: 1 day, 12:07:08 time: 0.7262 data_time: 0.1898 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 01:27:41 - mmengine - INFO - Epoch(train) [2][29400/42151] lr: 3.0000e-04 eta: 1 day, 12:05:46 time: 0.6199 data_time: 0.0850 memory: 28726 loss_ce: 0.0154 loss: 0.0154 2022/09/17 01:28:48 - mmengine - INFO - Epoch(train) [2][29500/42151] lr: 3.0000e-04 eta: 1 day, 12:04:22 time: 0.5900 data_time: 0.0545 memory: 28726 loss_ce: 0.0148 loss: 0.0148 2022/09/17 01:29:56 - mmengine - INFO - Epoch(train) [2][29600/42151] lr: 3.0000e-04 eta: 1 day, 12:03:01 time: 0.6546 data_time: 0.1149 memory: 28726 loss_ce: 0.0160 loss: 0.0160 2022/09/17 01:31:04 - mmengine - INFO - Epoch(train) [2][29700/42151] lr: 3.0000e-04 eta: 1 day, 12:01:39 time: 0.6924 data_time: 0.1496 memory: 28726 loss_ce: 0.0150 loss: 0.0150 2022/09/17 01:32:11 - mmengine - INFO - Epoch(train) [2][29800/42151] lr: 3.0000e-04 eta: 1 day, 12:00:18 time: 0.6501 data_time: 0.1056 memory: 28726 loss_ce: 0.0162 loss: 0.0162 2022/09/17 01:32:45 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 01:33:20 - mmengine - INFO - Epoch(train) [2][29900/42151] lr: 3.0000e-04 eta: 1 day, 11:58:59 time: 0.7204 data_time: 0.1860 memory: 28726 loss_ce: 0.0170 loss: 0.0170 2022/09/17 01:34:28 - mmengine - INFO - Epoch(train) [2][30000/42151] lr: 3.0000e-04 eta: 1 day, 11:57:38 time: 0.6408 data_time: 0.0974 memory: 28726 loss_ce: 0.0158 loss: 0.0158 2022/09/17 01:35:36 - mmengine - INFO - Epoch(train) [2][30100/42151] lr: 3.0000e-04 eta: 1 day, 11:56:18 time: 0.5885 data_time: 0.0532 memory: 28726 loss_ce: 0.0172 loss: 0.0172 2022/09/17 01:36:45 - mmengine - INFO - Epoch(train) [2][30200/42151] lr: 3.0000e-04 eta: 1 day, 11:54:59 time: 0.6622 data_time: 0.1263 memory: 28726 loss_ce: 0.0161 loss: 0.0161 2022/09/17 01:37:53 - mmengine - INFO - Epoch(train) [2][30300/42151] lr: 3.0000e-04 eta: 1 day, 11:53:38 time: 0.6798 data_time: 0.1414 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 01:39:01 - mmengine - INFO - Epoch(train) [2][30400/42151] lr: 3.0000e-04 eta: 1 day, 11:52:18 time: 0.6491 data_time: 0.1097 memory: 28726 loss_ce: 0.0151 loss: 0.0151 2022/09/17 01:40:09 - mmengine - INFO - Epoch(train) [2][30500/42151] lr: 3.0000e-04 eta: 1 day, 11:50:57 time: 0.7312 data_time: 0.1933 memory: 28726 loss_ce: 0.0172 loss: 0.0172 2022/09/17 01:41:16 - mmengine - INFO - Epoch(train) [2][30600/42151] lr: 3.0000e-04 eta: 1 day, 11:49:33 time: 0.6276 data_time: 0.0909 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 01:42:23 - mmengine - INFO - Epoch(train) [2][30700/42151] lr: 3.0000e-04 eta: 1 day, 11:48:10 time: 0.6010 data_time: 0.0593 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/17 01:43:30 - mmengine - INFO - Epoch(train) [2][30800/42151] lr: 3.0000e-04 eta: 1 day, 11:46:48 time: 0.6488 data_time: 0.1127 memory: 28726 loss_ce: 0.0164 loss: 0.0164 2022/09/17 01:44:03 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 01:44:38 - mmengine - INFO - Epoch(train) [2][30900/42151] lr: 3.0000e-04 eta: 1 day, 11:45:27 time: 0.6954 data_time: 0.1464 memory: 28726 loss_ce: 0.0149 loss: 0.0149 2022/09/17 01:45:45 - mmengine - INFO - Epoch(train) [2][31000/42151] lr: 3.0000e-04 eta: 1 day, 11:44:06 time: 0.6513 data_time: 0.1149 memory: 28726 loss_ce: 0.0169 loss: 0.0169 2022/09/17 01:46:54 - mmengine - INFO - Epoch(train) [2][31100/42151] lr: 3.0000e-04 eta: 1 day, 11:42:46 time: 0.7473 data_time: 0.2126 memory: 28726 loss_ce: 0.0156 loss: 0.0156 2022/09/17 01:48:01 - mmengine - INFO - Epoch(train) [2][31200/42151] lr: 3.0000e-04 eta: 1 day, 11:41:23 time: 0.6161 data_time: 0.0782 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 01:49:09 - mmengine - INFO - Epoch(train) [2][31300/42151] lr: 3.0000e-04 eta: 1 day, 11:40:03 time: 0.5898 data_time: 0.0541 memory: 28726 loss_ce: 0.0174 loss: 0.0174 2022/09/17 01:50:17 - mmengine - INFO - Epoch(train) [2][31400/42151] lr: 3.0000e-04 eta: 1 day, 11:38:43 time: 0.6598 data_time: 0.1206 memory: 28726 loss_ce: 0.0159 loss: 0.0159 2022/09/17 01:51:25 - mmengine - INFO - Epoch(train) [2][31500/42151] lr: 3.0000e-04 eta: 1 day, 11:37:23 time: 0.6944 data_time: 0.1581 memory: 28726 loss_ce: 0.0162 loss: 0.0162 2022/09/17 01:52:33 - mmengine - INFO - Epoch(train) [2][31600/42151] lr: 3.0000e-04 eta: 1 day, 11:36:03 time: 0.6553 data_time: 0.1176 memory: 28726 loss_ce: 0.0165 loss: 0.0165 2022/09/17 01:53:42 - mmengine - INFO - Epoch(train) [2][31700/42151] lr: 3.0000e-04 eta: 1 day, 11:34:45 time: 0.7512 data_time: 0.2175 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/17 01:54:49 - mmengine - INFO - Epoch(train) [2][31800/42151] lr: 3.0000e-04 eta: 1 day, 11:33:24 time: 0.6252 data_time: 0.0889 memory: 28726 loss_ce: 0.0150 loss: 0.0150 2022/09/17 01:55:23 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 01:55:57 - mmengine - INFO - Epoch(train) [2][31900/42151] lr: 3.0000e-04 eta: 1 day, 11:32:04 time: 0.5945 data_time: 0.0558 memory: 28726 loss_ce: 0.0156 loss: 0.0156 2022/09/17 01:57:06 - mmengine - INFO - Epoch(train) [2][32000/42151] lr: 3.0000e-04 eta: 1 day, 11:30:46 time: 0.6736 data_time: 0.1326 memory: 28726 loss_ce: 0.0155 loss: 0.0155 2022/09/17 01:58:14 - mmengine - INFO - Epoch(train) [2][32100/42151] lr: 3.0000e-04 eta: 1 day, 11:29:26 time: 0.6671 data_time: 0.1316 memory: 28726 loss_ce: 0.0174 loss: 0.0174 2022/09/17 01:59:23 - mmengine - INFO - Epoch(train) [2][32200/42151] lr: 3.0000e-04 eta: 1 day, 11:28:07 time: 0.6668 data_time: 0.1251 memory: 28726 loss_ce: 0.0159 loss: 0.0159 2022/09/17 02:00:35 - mmengine - INFO - Epoch(train) [2][32300/42151] lr: 3.0000e-04 eta: 1 day, 11:26:56 time: 0.7735 data_time: 0.2361 memory: 28726 loss_ce: 0.0146 loss: 0.0146 2022/09/17 02:01:43 - mmengine - INFO - Epoch(train) [2][32400/42151] lr: 3.0000e-04 eta: 1 day, 11:25:37 time: 0.6253 data_time: 0.0910 memory: 28726 loss_ce: 0.0141 loss: 0.0141 2022/09/17 02:02:52 - mmengine - INFO - Epoch(train) [2][32500/42151] lr: 3.0000e-04 eta: 1 day, 11:24:19 time: 0.5966 data_time: 0.0566 memory: 28726 loss_ce: 0.0172 loss: 0.0172 2022/09/17 02:04:00 - mmengine - INFO - Epoch(train) [2][32600/42151] lr: 3.0000e-04 eta: 1 day, 11:23:01 time: 0.6658 data_time: 0.1257 memory: 28726 loss_ce: 0.0148 loss: 0.0148 2022/09/17 02:05:09 - mmengine - INFO - Epoch(train) [2][32700/42151] lr: 3.0000e-04 eta: 1 day, 11:21:42 time: 0.6767 data_time: 0.1380 memory: 28726 loss_ce: 0.0161 loss: 0.0161 2022/09/17 02:06:17 - mmengine - INFO - Epoch(train) [2][32800/42151] lr: 3.0000e-04 eta: 1 day, 11:20:21 time: 0.6593 data_time: 0.1238 memory: 28726 loss_ce: 0.0173 loss: 0.0173 2022/09/17 02:06:50 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 02:07:26 - mmengine - INFO - Epoch(train) [2][32900/42151] lr: 3.0000e-04 eta: 1 day, 11:19:04 time: 0.7531 data_time: 0.2145 memory: 28726 loss_ce: 0.0174 loss: 0.0174 2022/09/17 02:08:33 - mmengine - INFO - Epoch(train) [2][33000/42151] lr: 3.0000e-04 eta: 1 day, 11:17:42 time: 0.6377 data_time: 0.1006 memory: 28726 loss_ce: 0.0172 loss: 0.0172 2022/09/17 02:09:40 - mmengine - INFO - Epoch(train) [2][33100/42151] lr: 3.0000e-04 eta: 1 day, 11:16:21 time: 0.5995 data_time: 0.0601 memory: 28726 loss_ce: 0.0160 loss: 0.0160 2022/09/17 02:10:49 - mmengine - INFO - Epoch(train) [2][33200/42151] lr: 3.0000e-04 eta: 1 day, 11:15:02 time: 0.6462 data_time: 0.1113 memory: 28726 loss_ce: 0.0168 loss: 0.0168 2022/09/17 02:11:56 - mmengine - INFO - Epoch(train) [2][33300/42151] lr: 3.0000e-04 eta: 1 day, 11:13:41 time: 0.6837 data_time: 0.1455 memory: 28726 loss_ce: 0.0158 loss: 0.0158 2022/09/17 02:13:05 - mmengine - INFO - Epoch(train) [2][33400/42151] lr: 3.0000e-04 eta: 1 day, 11:12:23 time: 0.6555 data_time: 0.1195 memory: 28726 loss_ce: 0.0146 loss: 0.0146 2022/09/17 02:14:13 - mmengine - INFO - Epoch(train) [2][33500/42151] lr: 3.0000e-04 eta: 1 day, 11:11:05 time: 0.7620 data_time: 0.2202 memory: 28726 loss_ce: 0.0161 loss: 0.0161 2022/09/17 02:15:21 - mmengine - INFO - Epoch(train) [2][33600/42151] lr: 3.0000e-04 eta: 1 day, 11:09:45 time: 0.6378 data_time: 0.0982 memory: 28726 loss_ce: 0.0155 loss: 0.0155 2022/09/17 02:16:30 - mmengine - INFO - Epoch(train) [2][33700/42151] lr: 3.0000e-04 eta: 1 day, 11:08:26 time: 0.5924 data_time: 0.0548 memory: 28726 loss_ce: 0.0156 loss: 0.0156 2022/09/17 02:17:38 - mmengine - INFO - Epoch(train) [2][33800/42151] lr: 3.0000e-04 eta: 1 day, 11:07:07 time: 0.6398 data_time: 0.1014 memory: 28726 loss_ce: 0.0175 loss: 0.0175 2022/09/17 02:18:11 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 02:18:46 - mmengine - INFO - Epoch(train) [2][33900/42151] lr: 3.0000e-04 eta: 1 day, 11:05:48 time: 0.6667 data_time: 0.1313 memory: 28726 loss_ce: 0.0151 loss: 0.0151 2022/09/17 02:19:55 - mmengine - INFO - Epoch(train) [2][34000/42151] lr: 3.0000e-04 eta: 1 day, 11:04:30 time: 0.6416 data_time: 0.1048 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 02:21:04 - mmengine - INFO - Epoch(train) [2][34100/42151] lr: 3.0000e-04 eta: 1 day, 11:03:13 time: 0.7487 data_time: 0.2094 memory: 28726 loss_ce: 0.0147 loss: 0.0147 2022/09/17 02:22:11 - mmengine - INFO - Epoch(train) [2][34200/42151] lr: 3.0000e-04 eta: 1 day, 11:01:53 time: 0.6377 data_time: 0.1015 memory: 28726 loss_ce: 0.0161 loss: 0.0161 2022/09/17 02:23:19 - mmengine - INFO - Epoch(train) [2][34300/42151] lr: 3.0000e-04 eta: 1 day, 11:00:32 time: 0.5967 data_time: 0.0546 memory: 28726 loss_ce: 0.0163 loss: 0.0163 2022/09/17 02:24:27 - mmengine - INFO - Epoch(train) [2][34400/42151] lr: 3.0000e-04 eta: 1 day, 10:59:14 time: 0.6794 data_time: 0.1394 memory: 28726 loss_ce: 0.0165 loss: 0.0165 2022/09/17 02:25:36 - mmengine - INFO - Epoch(train) [2][34500/42151] lr: 3.0000e-04 eta: 1 day, 10:57:56 time: 0.6686 data_time: 0.1309 memory: 28726 loss_ce: 0.0163 loss: 0.0163 2022/09/17 02:26:44 - mmengine - INFO - Epoch(train) [2][34600/42151] lr: 3.0000e-04 eta: 1 day, 10:56:36 time: 0.6597 data_time: 0.1252 memory: 28726 loss_ce: 0.0159 loss: 0.0159 2022/09/17 02:27:52 - mmengine - INFO - Epoch(train) [2][34700/42151] lr: 3.0000e-04 eta: 1 day, 10:55:18 time: 0.7406 data_time: 0.2055 memory: 28726 loss_ce: 0.0157 loss: 0.0157 2022/09/17 02:29:01 - mmengine - INFO - Epoch(train) [2][34800/42151] lr: 3.0000e-04 eta: 1 day, 10:54:00 time: 0.6353 data_time: 0.0992 memory: 28726 loss_ce: 0.0168 loss: 0.0168 2022/09/17 02:29:35 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 02:30:09 - mmengine - INFO - Epoch(train) [2][34900/42151] lr: 3.0000e-04 eta: 1 day, 10:52:42 time: 0.6046 data_time: 0.0657 memory: 28726 loss_ce: 0.0156 loss: 0.0156 2022/09/17 02:31:18 - mmengine - INFO - Epoch(train) [2][35000/42151] lr: 3.0000e-04 eta: 1 day, 10:51:24 time: 0.6812 data_time: 0.1389 memory: 28726 loss_ce: 0.0177 loss: 0.0177 2022/09/17 02:32:26 - mmengine - INFO - Epoch(train) [2][35100/42151] lr: 3.0000e-04 eta: 1 day, 10:50:05 time: 0.6664 data_time: 0.1254 memory: 28726 loss_ce: 0.0155 loss: 0.0155 2022/09/17 02:33:35 - mmengine - INFO - Epoch(train) [2][35200/42151] lr: 3.0000e-04 eta: 1 day, 10:48:48 time: 0.6626 data_time: 0.1251 memory: 28726 loss_ce: 0.0150 loss: 0.0150 2022/09/17 02:34:43 - mmengine - INFO - Epoch(train) [2][35300/42151] lr: 3.0000e-04 eta: 1 day, 10:47:29 time: 0.7428 data_time: 0.2054 memory: 28726 loss_ce: 0.0161 loss: 0.0161 2022/09/17 02:35:50 - mmengine - INFO - Epoch(train) [2][35400/42151] lr: 3.0000e-04 eta: 1 day, 10:46:08 time: 0.6280 data_time: 0.0921 memory: 28726 loss_ce: 0.0151 loss: 0.0151 2022/09/17 02:36:58 - mmengine - INFO - Epoch(train) [2][35500/42151] lr: 3.0000e-04 eta: 1 day, 10:44:49 time: 0.6106 data_time: 0.0739 memory: 28726 loss_ce: 0.0141 loss: 0.0141 2022/09/17 02:38:06 - mmengine - INFO - Epoch(train) [2][35600/42151] lr: 3.0000e-04 eta: 1 day, 10:43:29 time: 0.6755 data_time: 0.1329 memory: 28726 loss_ce: 0.0174 loss: 0.0174 2022/09/17 02:39:14 - mmengine - INFO - Epoch(train) [2][35700/42151] lr: 3.0000e-04 eta: 1 day, 10:42:10 time: 0.6839 data_time: 0.1458 memory: 28726 loss_ce: 0.0176 loss: 0.0176 2022/09/17 02:40:22 - mmengine - INFO - Epoch(train) [2][35800/42151] lr: 3.0000e-04 eta: 1 day, 10:40:52 time: 0.6574 data_time: 0.1173 memory: 28726 loss_ce: 0.0170 loss: 0.0170 2022/09/17 02:40:56 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 02:41:31 - mmengine - INFO - Epoch(train) [2][35900/42151] lr: 3.0000e-04 eta: 1 day, 10:39:34 time: 0.7293 data_time: 0.1940 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 02:42:38 - mmengine - INFO - Epoch(train) [2][36000/42151] lr: 3.0000e-04 eta: 1 day, 10:38:14 time: 0.6225 data_time: 0.0885 memory: 28726 loss_ce: 0.0160 loss: 0.0160 2022/09/17 02:43:46 - mmengine - INFO - Epoch(train) [2][36100/42151] lr: 3.0000e-04 eta: 1 day, 10:36:56 time: 0.5939 data_time: 0.0589 memory: 28726 loss_ce: 0.0147 loss: 0.0147 2022/09/17 02:44:54 - mmengine - INFO - Epoch(train) [2][36200/42151] lr: 3.0000e-04 eta: 1 day, 10:35:37 time: 0.6791 data_time: 0.1352 memory: 28726 loss_ce: 0.0165 loss: 0.0165 2022/09/17 02:46:02 - mmengine - INFO - Epoch(train) [2][36300/42151] lr: 3.0000e-04 eta: 1 day, 10:34:18 time: 0.6825 data_time: 0.1427 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 02:47:10 - mmengine - INFO - Epoch(train) [2][36400/42151] lr: 3.0000e-04 eta: 1 day, 10:32:59 time: 0.6454 data_time: 0.1101 memory: 28726 loss_ce: 0.0153 loss: 0.0153 2022/09/17 02:48:19 - mmengine - INFO - Epoch(train) [2][36500/42151] lr: 3.0000e-04 eta: 1 day, 10:31:41 time: 0.7543 data_time: 0.2147 memory: 28726 loss_ce: 0.0161 loss: 0.0161 2022/09/17 02:49:26 - mmengine - INFO - Epoch(train) [2][36600/42151] lr: 3.0000e-04 eta: 1 day, 10:30:20 time: 0.6219 data_time: 0.0840 memory: 28726 loss_ce: 0.0163 loss: 0.0163 2022/09/17 02:50:33 - mmengine - INFO - Epoch(train) [2][36700/42151] lr: 3.0000e-04 eta: 1 day, 10:29:01 time: 0.5977 data_time: 0.0586 memory: 28726 loss_ce: 0.0144 loss: 0.0144 2022/09/17 02:51:41 - mmengine - INFO - Epoch(train) [2][36800/42151] lr: 3.0000e-04 eta: 1 day, 10:27:42 time: 0.6779 data_time: 0.1330 memory: 28726 loss_ce: 0.0176 loss: 0.0176 2022/09/17 02:52:15 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 02:52:49 - mmengine - INFO - Epoch(train) [2][36900/42151] lr: 3.0000e-04 eta: 1 day, 10:26:23 time: 0.6815 data_time: 0.1405 memory: 28726 loss_ce: 0.0167 loss: 0.0167 2022/09/17 02:53:57 - mmengine - INFO - Epoch(train) [2][37000/42151] lr: 3.0000e-04 eta: 1 day, 10:25:05 time: 0.6543 data_time: 0.1094 memory: 28726 loss_ce: 0.0166 loss: 0.0166 2022/09/17 02:55:06 - mmengine - INFO - Epoch(train) [2][37100/42151] lr: 3.0000e-04 eta: 1 day, 10:23:48 time: 0.7502 data_time: 0.2061 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/17 02:56:13 - mmengine - INFO - Epoch(train) [2][37200/42151] lr: 3.0000e-04 eta: 1 day, 10:22:28 time: 0.6116 data_time: 0.0778 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 02:57:22 - mmengine - INFO - Epoch(train) [2][37300/42151] lr: 3.0000e-04 eta: 1 day, 10:21:10 time: 0.6270 data_time: 0.0886 memory: 28726 loss_ce: 0.0157 loss: 0.0157 2022/09/17 02:58:31 - mmengine - INFO - Epoch(train) [2][37400/42151] lr: 3.0000e-04 eta: 1 day, 10:19:53 time: 0.6729 data_time: 0.1352 memory: 28726 loss_ce: 0.0149 loss: 0.0149 2022/09/17 02:59:38 - mmengine - INFO - Epoch(train) [2][37500/42151] lr: 3.0000e-04 eta: 1 day, 10:18:33 time: 0.6760 data_time: 0.1404 memory: 28726 loss_ce: 0.0166 loss: 0.0166 2022/09/17 03:00:46 - mmengine - INFO - Epoch(train) [2][37600/42151] lr: 3.0000e-04 eta: 1 day, 10:17:15 time: 0.6573 data_time: 0.1221 memory: 28726 loss_ce: 0.0166 loss: 0.0166 2022/09/17 03:01:53 - mmengine - INFO - Epoch(train) [2][37700/42151] lr: 3.0000e-04 eta: 1 day, 10:15:55 time: 0.7286 data_time: 0.1918 memory: 28726 loss_ce: 0.0161 loss: 0.0161 2022/09/17 03:03:00 - mmengine - INFO - Epoch(train) [2][37800/42151] lr: 3.0000e-04 eta: 1 day, 10:14:34 time: 0.6099 data_time: 0.0750 memory: 28726 loss_ce: 0.0164 loss: 0.0164 2022/09/17 03:03:33 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 03:04:08 - mmengine - INFO - Epoch(train) [2][37900/42151] lr: 3.0000e-04 eta: 1 day, 10:13:14 time: 0.5877 data_time: 0.0527 memory: 28726 loss_ce: 0.0146 loss: 0.0146 2022/09/17 03:05:17 - mmengine - INFO - Epoch(train) [2][38000/42151] lr: 3.0000e-04 eta: 1 day, 10:11:58 time: 0.6688 data_time: 0.1338 memory: 28726 loss_ce: 0.0160 loss: 0.0160 2022/09/17 03:06:25 - mmengine - INFO - Epoch(train) [2][38100/42151] lr: 3.0000e-04 eta: 1 day, 10:10:40 time: 0.6841 data_time: 0.1431 memory: 28726 loss_ce: 0.0163 loss: 0.0163 2022/09/17 03:07:32 - mmengine - INFO - Epoch(train) [2][38200/42151] lr: 3.0000e-04 eta: 1 day, 10:09:21 time: 0.6674 data_time: 0.1172 memory: 28726 loss_ce: 0.0161 loss: 0.0161 2022/09/17 03:08:40 - mmengine - INFO - Epoch(train) [2][38300/42151] lr: 3.0000e-04 eta: 1 day, 10:08:01 time: 0.7128 data_time: 0.1775 memory: 28726 loss_ce: 0.0163 loss: 0.0163 2022/09/17 03:09:47 - mmengine - INFO - Epoch(train) [2][38400/42151] lr: 3.0000e-04 eta: 1 day, 10:06:41 time: 0.6286 data_time: 0.0898 memory: 28726 loss_ce: 0.0163 loss: 0.0163 2022/09/17 03:10:55 - mmengine - INFO - Epoch(train) [2][38500/42151] lr: 3.0000e-04 eta: 1 day, 10:05:23 time: 0.5923 data_time: 0.0540 memory: 28726 loss_ce: 0.0146 loss: 0.0146 2022/09/17 03:12:03 - mmengine - INFO - Epoch(train) [2][38600/42151] lr: 3.0000e-04 eta: 1 day, 10:04:05 time: 0.6684 data_time: 0.1351 memory: 28726 loss_ce: 0.0166 loss: 0.0166 2022/09/17 03:13:10 - mmengine - INFO - Epoch(train) [2][38700/42151] lr: 3.0000e-04 eta: 1 day, 10:02:44 time: 0.6729 data_time: 0.1362 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 03:14:17 - mmengine - INFO - Epoch(train) [2][38800/42151] lr: 3.0000e-04 eta: 1 day, 10:01:25 time: 0.6441 data_time: 0.1086 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 03:14:50 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 03:15:25 - mmengine - INFO - Epoch(train) [2][38900/42151] lr: 3.0000e-04 eta: 1 day, 10:00:06 time: 0.7397 data_time: 0.1969 memory: 28726 loss_ce: 0.0159 loss: 0.0159 2022/09/17 03:16:32 - mmengine - INFO - Epoch(train) [2][39000/42151] lr: 3.0000e-04 eta: 1 day, 9:58:47 time: 0.6225 data_time: 0.0829 memory: 28726 loss_ce: 0.0162 loss: 0.0162 2022/09/17 03:17:40 - mmengine - INFO - Epoch(train) [2][39100/42151] lr: 3.0000e-04 eta: 1 day, 9:57:28 time: 0.5984 data_time: 0.0617 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 03:18:49 - mmengine - INFO - Epoch(train) [2][39200/42151] lr: 3.0000e-04 eta: 1 day, 9:56:12 time: 0.6860 data_time: 0.1477 memory: 28726 loss_ce: 0.0134 loss: 0.0134 2022/09/17 03:19:57 - mmengine - INFO - Epoch(train) [2][39300/42151] lr: 3.0000e-04 eta: 1 day, 9:54:53 time: 0.6915 data_time: 0.1525 memory: 28726 loss_ce: 0.0166 loss: 0.0166 2022/09/17 03:21:05 - mmengine - INFO - Epoch(train) [2][39400/42151] lr: 3.0000e-04 eta: 1 day, 9:53:36 time: 0.6675 data_time: 0.1281 memory: 28726 loss_ce: 0.0158 loss: 0.0158 2022/09/17 03:22:14 - mmengine - INFO - Epoch(train) [2][39500/42151] lr: 3.0000e-04 eta: 1 day, 9:52:20 time: 0.7513 data_time: 0.2066 memory: 28726 loss_ce: 0.0166 loss: 0.0166 2022/09/17 03:23:22 - mmengine - INFO - Epoch(train) [2][39600/42151] lr: 3.0000e-04 eta: 1 day, 9:51:02 time: 0.6333 data_time: 0.0971 memory: 28726 loss_ce: 0.0157 loss: 0.0157 2022/09/17 03:24:30 - mmengine - INFO - Epoch(train) [2][39700/42151] lr: 3.0000e-04 eta: 1 day, 9:49:45 time: 0.5978 data_time: 0.0623 memory: 28726 loss_ce: 0.0162 loss: 0.0162 2022/09/17 03:25:39 - mmengine - INFO - Epoch(train) [2][39800/42151] lr: 3.0000e-04 eta: 1 day, 9:48:29 time: 0.6581 data_time: 0.1161 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 03:26:12 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 03:26:47 - mmengine - INFO - Epoch(train) [2][39900/42151] lr: 3.0000e-04 eta: 1 day, 9:47:10 time: 0.6741 data_time: 0.1393 memory: 28726 loss_ce: 0.0149 loss: 0.0149 2022/09/17 03:27:54 - mmengine - INFO - Epoch(train) [2][40000/42151] lr: 3.0000e-04 eta: 1 day, 9:45:51 time: 0.6680 data_time: 0.1243 memory: 28726 loss_ce: 0.0157 loss: 0.0157 2022/09/17 03:29:03 - mmengine - INFO - Epoch(train) [2][40100/42151] lr: 3.0000e-04 eta: 1 day, 9:44:34 time: 0.7167 data_time: 0.1801 memory: 28726 loss_ce: 0.0155 loss: 0.0155 2022/09/17 03:30:11 - mmengine - INFO - Epoch(train) [2][40200/42151] lr: 3.0000e-04 eta: 1 day, 9:43:17 time: 0.6439 data_time: 0.1075 memory: 28726 loss_ce: 0.0170 loss: 0.0170 2022/09/17 03:31:18 - mmengine - INFO - Epoch(train) [2][40300/42151] lr: 3.0000e-04 eta: 1 day, 9:41:57 time: 0.5935 data_time: 0.0513 memory: 28726 loss_ce: 0.0155 loss: 0.0155 2022/09/17 03:32:26 - mmengine - INFO - Epoch(train) [2][40400/42151] lr: 3.0000e-04 eta: 1 day, 9:40:40 time: 0.6505 data_time: 0.1145 memory: 28726 loss_ce: 0.0151 loss: 0.0151 2022/09/17 03:33:33 - mmengine - INFO - Epoch(train) [2][40500/42151] lr: 3.0000e-04 eta: 1 day, 9:39:20 time: 0.6799 data_time: 0.1390 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 03:34:41 - mmengine - INFO - Epoch(train) [2][40600/42151] lr: 3.0000e-04 eta: 1 day, 9:38:02 time: 0.6405 data_time: 0.1053 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/17 03:35:50 - mmengine - INFO - Epoch(train) [2][40700/42151] lr: 3.0000e-04 eta: 1 day, 9:36:46 time: 0.7724 data_time: 0.2326 memory: 28726 loss_ce: 0.0159 loss: 0.0159 2022/09/17 03:36:58 - mmengine - INFO - Epoch(train) [2][40800/42151] lr: 3.0000e-04 eta: 1 day, 9:35:28 time: 0.6382 data_time: 0.0958 memory: 28726 loss_ce: 0.0148 loss: 0.0148 2022/09/17 03:37:32 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 03:38:06 - mmengine - INFO - Epoch(train) [2][40900/42151] lr: 3.0000e-04 eta: 1 day, 9:34:11 time: 0.5995 data_time: 0.0633 memory: 28726 loss_ce: 0.0170 loss: 0.0170 2022/09/17 03:39:13 - mmengine - INFO - Epoch(train) [2][41000/42151] lr: 3.0000e-04 eta: 1 day, 9:32:52 time: 0.6504 data_time: 0.1162 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 03:40:21 - mmengine - INFO - Epoch(train) [2][41100/42151] lr: 3.0000e-04 eta: 1 day, 9:31:33 time: 0.6919 data_time: 0.1506 memory: 28726 loss_ce: 0.0163 loss: 0.0163 2022/09/17 03:41:29 - mmengine - INFO - Epoch(train) [2][41200/42151] lr: 3.0000e-04 eta: 1 day, 9:30:15 time: 0.6779 data_time: 0.1345 memory: 28726 loss_ce: 0.0146 loss: 0.0146 2022/09/17 03:42:37 - mmengine - INFO - Epoch(train) [2][41300/42151] lr: 3.0000e-04 eta: 1 day, 9:28:59 time: 0.7467 data_time: 0.2026 memory: 28726 loss_ce: 0.0147 loss: 0.0147 2022/09/17 03:43:45 - mmengine - INFO - Epoch(train) [2][41400/42151] lr: 3.0000e-04 eta: 1 day, 9:27:40 time: 0.6196 data_time: 0.0808 memory: 28726 loss_ce: 0.0159 loss: 0.0159 2022/09/17 03:44:52 - mmengine - INFO - Epoch(train) [2][41500/42151] lr: 3.0000e-04 eta: 1 day, 9:26:21 time: 0.5969 data_time: 0.0626 memory: 28726 loss_ce: 0.0148 loss: 0.0148 2022/09/17 03:46:00 - mmengine - INFO - Epoch(train) [2][41600/42151] lr: 3.0000e-04 eta: 1 day, 9:25:03 time: 0.6477 data_time: 0.1136 memory: 28726 loss_ce: 0.0157 loss: 0.0157 2022/09/17 03:47:07 - mmengine - INFO - Epoch(train) [2][41700/42151] lr: 3.0000e-04 eta: 1 day, 9:23:44 time: 0.6799 data_time: 0.1428 memory: 28726 loss_ce: 0.0147 loss: 0.0147 2022/09/17 03:48:14 - mmengine - INFO - Epoch(train) [2][41800/42151] lr: 3.0000e-04 eta: 1 day, 9:22:26 time: 0.6467 data_time: 0.1113 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/17 03:48:48 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 03:49:23 - mmengine - INFO - Epoch(train) [2][41900/42151] lr: 3.0000e-04 eta: 1 day, 9:21:09 time: 0.7299 data_time: 0.1952 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/17 03:50:30 - mmengine - INFO - Epoch(train) [2][42000/42151] lr: 3.0000e-04 eta: 1 day, 9:19:51 time: 0.6452 data_time: 0.1052 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 03:51:38 - mmengine - INFO - Epoch(train) [2][42100/42151] lr: 3.0000e-04 eta: 1 day, 9:18:32 time: 0.6270 data_time: 0.0609 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 03:52:11 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 03:52:11 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/09/17 03:53:47 - mmengine - INFO - Epoch(val) [2][100/7672] eta: 1:45:33 time: 0.8365 data_time: 0.0011 memory: 28726 2022/09/17 03:55:11 - mmengine - INFO - Epoch(val) [2][200/7672] eta: 1:44:42 time: 0.8407 data_time: 0.0024 memory: 1303 2022/09/17 03:56:28 - mmengine - INFO - Epoch(val) [2][300/7672] eta: 0:24:36 time: 0.2003 data_time: 0.0009 memory: 1303 2022/09/17 03:56:49 - mmengine - INFO - Epoch(val) [2][400/7672] eta: 0:24:57 time: 0.2059 data_time: 0.0010 memory: 1303 2022/09/17 03:57:10 - mmengine - INFO - Epoch(val) [2][500/7672] eta: 0:23:48 time: 0.1992 data_time: 0.0008 memory: 1303 2022/09/17 03:57:30 - mmengine - INFO - Epoch(val) [2][600/7672] eta: 0:23:26 time: 0.1988 data_time: 0.0012 memory: 1303 2022/09/17 03:57:51 - mmengine - INFO - Epoch(val) [2][700/7672] eta: 0:23:08 time: 0.1991 data_time: 0.0008 memory: 1303 2022/09/17 03:58:11 - mmengine - INFO - Epoch(val) [2][800/7672] eta: 0:22:56 time: 0.2003 data_time: 0.0008 memory: 1303 2022/09/17 03:58:32 - mmengine - INFO - Epoch(val) [2][900/7672] eta: 0:22:40 time: 0.2010 data_time: 0.0008 memory: 1303 2022/09/17 03:58:52 - mmengine - INFO - Epoch(val) [2][1000/7672] eta: 0:21:45 time: 0.1957 data_time: 0.0007 memory: 1303 2022/09/17 03:59:12 - mmengine - INFO - Epoch(val) [2][1100/7672] eta: 0:22:08 time: 0.2022 data_time: 0.0008 memory: 1303 2022/09/17 03:59:33 - mmengine - INFO - Epoch(val) [2][1200/7672] eta: 0:21:29 time: 0.1993 data_time: 0.0008 memory: 1303 2022/09/17 03:59:54 - mmengine - INFO - Epoch(val) [2][1300/7672] eta: 0:22:06 time: 0.2082 data_time: 0.0011 memory: 1303 2022/09/17 04:00:14 - mmengine - INFO - Epoch(val) [2][1400/7672] eta: 0:20:52 time: 0.1997 data_time: 0.0007 memory: 1303 2022/09/17 04:00:35 - mmengine - INFO - Epoch(val) [2][1500/7672] eta: 0:21:21 time: 0.2076 data_time: 0.0008 memory: 1303 2022/09/17 04:00:55 - mmengine - INFO - Epoch(val) [2][1600/7672] eta: 0:20:28 time: 0.2023 data_time: 0.0008 memory: 1303 2022/09/17 04:01:17 - mmengine - INFO - Epoch(val) [2][1700/7672] eta: 0:22:01 time: 0.2213 data_time: 0.0018 memory: 1303 2022/09/17 04:01:38 - mmengine - INFO - Epoch(val) [2][1800/7672] eta: 0:20:19 time: 0.2077 data_time: 0.0011 memory: 1303 2022/09/17 04:01:59 - mmengine - INFO - Epoch(val) [2][1900/7672] eta: 0:19:26 time: 0.2021 data_time: 0.0010 memory: 1303 2022/09/17 04:02:20 - mmengine - INFO - Epoch(val) [2][2000/7672] eta: 0:18:52 time: 0.1996 data_time: 0.0009 memory: 1303 2022/09/17 04:02:40 - mmengine - INFO - Epoch(val) [2][2100/7672] eta: 0:19:23 time: 0.2087 data_time: 0.0008 memory: 1303 2022/09/17 04:03:00 - mmengine - INFO - Epoch(val) [2][2200/7672] eta: 0:18:23 time: 0.2017 data_time: 0.0008 memory: 1303 2022/09/17 04:03:21 - mmengine - INFO - Epoch(val) [2][2300/7672] eta: 0:19:40 time: 0.2197 data_time: 0.0008 memory: 1303 2022/09/17 04:03:42 - mmengine - INFO - Epoch(val) [2][2400/7672] eta: 0:17:30 time: 0.1993 data_time: 0.0008 memory: 1303 2022/09/17 04:04:02 - mmengine - INFO - Epoch(val) [2][2500/7672] eta: 0:17:18 time: 0.2008 data_time: 0.0008 memory: 1303 2022/09/17 04:04:23 - mmengine - INFO - Epoch(val) [2][2600/7672] eta: 0:16:58 time: 0.2007 data_time: 0.0008 memory: 1303 2022/09/17 04:04:44 - mmengine - INFO - Epoch(val) [2][2700/7672] eta: 0:17:13 time: 0.2079 data_time: 0.0008 memory: 1303 2022/09/17 04:05:05 - mmengine - INFO - Epoch(val) [2][2800/7672] eta: 0:16:19 time: 0.2011 data_time: 0.0008 memory: 1303 2022/09/17 04:05:25 - mmengine - INFO - Epoch(val) [2][2900/7672] eta: 0:15:44 time: 0.1978 data_time: 0.0008 memory: 1303 2022/09/17 04:05:45 - mmengine - INFO - Epoch(val) [2][3000/7672] eta: 0:15:24 time: 0.1979 data_time: 0.0008 memory: 1303 2022/09/17 04:06:05 - mmengine - INFO - Epoch(val) [2][3100/7672] eta: 0:15:41 time: 0.2059 data_time: 0.0009 memory: 1303 2022/09/17 04:06:26 - mmengine - INFO - Epoch(val) [2][3200/7672] eta: 0:15:11 time: 0.2038 data_time: 0.0008 memory: 1303 2022/09/17 04:06:47 - mmengine - INFO - Epoch(val) [2][3300/7672] eta: 0:14:54 time: 0.2046 data_time: 0.0008 memory: 1303 2022/09/17 04:07:07 - mmengine - INFO - Epoch(val) [2][3400/7672] eta: 0:14:24 time: 0.2024 data_time: 0.0018 memory: 1303 2022/09/17 04:07:28 - mmengine - INFO - Epoch(val) [2][3500/7672] eta: 0:14:37 time: 0.2104 data_time: 0.0009 memory: 1303 2022/09/17 04:07:49 - mmengine - INFO - Epoch(val) [2][3600/7672] eta: 0:14:05 time: 0.2076 data_time: 0.0009 memory: 1303 2022/09/17 04:08:09 - mmengine - INFO - Epoch(val) [2][3700/7672] eta: 0:13:26 time: 0.2030 data_time: 0.0009 memory: 1303 2022/09/17 04:08:30 - mmengine - INFO - Epoch(val) [2][3800/7672] eta: 0:12:53 time: 0.1998 data_time: 0.0009 memory: 1303 2022/09/17 04:08:50 - mmengine - INFO - Epoch(val) [2][3900/7672] eta: 0:12:44 time: 0.2026 data_time: 0.0017 memory: 1303 2022/09/17 04:09:11 - mmengine - INFO - Epoch(val) [2][4000/7672] eta: 0:12:38 time: 0.2067 data_time: 0.0008 memory: 1303 2022/09/17 04:09:32 - mmengine - INFO - Epoch(val) [2][4100/7672] eta: 0:12:45 time: 0.2142 data_time: 0.0008 memory: 1303 2022/09/17 04:09:52 - mmengine - INFO - Epoch(val) [2][4200/7672] eta: 0:11:39 time: 0.2015 data_time: 0.0008 memory: 1303 2022/09/17 04:10:13 - mmengine - INFO - Epoch(val) [2][4300/7672] eta: 0:11:04 time: 0.1969 data_time: 0.0010 memory: 1303 2022/09/17 04:10:33 - mmengine - INFO - Epoch(val) [2][4400/7672] eta: 0:11:11 time: 0.2052 data_time: 0.0008 memory: 1303 2022/09/17 04:10:53 - mmengine - INFO - Epoch(val) [2][4500/7672] eta: 0:10:49 time: 0.2048 data_time: 0.0008 memory: 1303 2022/09/17 04:11:14 - mmengine - INFO - Epoch(val) [2][4600/7672] eta: 0:11:26 time: 0.2233 data_time: 0.0015 memory: 1303 2022/09/17 04:11:34 - mmengine - INFO - Epoch(val) [2][4700/7672] eta: 0:09:46 time: 0.1972 data_time: 0.0008 memory: 1303 2022/09/17 04:11:54 - mmengine - INFO - Epoch(val) [2][4800/7672] eta: 0:10:16 time: 0.2148 data_time: 0.0008 memory: 1303 2022/09/17 04:12:15 - mmengine - INFO - Epoch(val) [2][4900/7672] eta: 0:09:21 time: 0.2025 data_time: 0.0008 memory: 1303 2022/09/17 04:12:36 - mmengine - INFO - Epoch(val) [2][5000/7672] eta: 0:10:06 time: 0.2268 data_time: 0.0008 memory: 1303 2022/09/17 04:12:56 - mmengine - INFO - Epoch(val) [2][5100/7672] eta: 0:08:39 time: 0.2019 data_time: 0.0008 memory: 1303 2022/09/17 04:13:17 - mmengine - INFO - Epoch(val) [2][5200/7672] eta: 0:08:16 time: 0.2009 data_time: 0.0007 memory: 1303 2022/09/17 04:13:37 - mmengine - INFO - Epoch(val) [2][5300/7672] eta: 0:07:50 time: 0.1985 data_time: 0.0008 memory: 1303 2022/09/17 04:13:58 - mmengine - INFO - Epoch(val) [2][5400/7672] eta: 0:07:40 time: 0.2026 data_time: 0.0008 memory: 1303 2022/09/17 04:14:18 - mmengine - INFO - Epoch(val) [2][5500/7672] eta: 0:07:26 time: 0.2056 data_time: 0.0008 memory: 1303 2022/09/17 04:14:39 - mmengine - INFO - Epoch(val) [2][5600/7672] eta: 0:07:05 time: 0.2052 data_time: 0.0008 memory: 1303 2022/09/17 04:15:00 - mmengine - INFO - Epoch(val) [2][5700/7672] eta: 0:06:34 time: 0.2002 data_time: 0.0009 memory: 1303 2022/09/17 04:15:21 - mmengine - INFO - Epoch(val) [2][5800/7672] eta: 0:06:50 time: 0.2190 data_time: 0.0012 memory: 1303 2022/09/17 04:15:41 - mmengine - INFO - Epoch(val) [2][5900/7672] eta: 0:06:00 time: 0.2036 data_time: 0.0009 memory: 1303 2022/09/17 04:16:02 - mmengine - INFO - Epoch(val) [2][6000/7672] eta: 0:05:41 time: 0.2042 data_time: 0.0008 memory: 1303 2022/09/17 04:16:23 - mmengine - INFO - Epoch(val) [2][6100/7672] eta: 0:05:19 time: 0.2031 data_time: 0.0009 memory: 1303 2022/09/17 04:16:43 - mmengine - INFO - Epoch(val) [2][6200/7672] eta: 0:04:52 time: 0.1989 data_time: 0.0008 memory: 1303 2022/09/17 04:17:04 - mmengine - INFO - Epoch(val) [2][6300/7672] eta: 0:04:45 time: 0.2077 data_time: 0.0010 memory: 1303 2022/09/17 04:17:24 - mmengine - INFO - Epoch(val) [2][6400/7672] eta: 0:04:22 time: 0.2066 data_time: 0.0008 memory: 1303 2022/09/17 04:17:45 - mmengine - INFO - Epoch(val) [2][6500/7672] eta: 0:04:18 time: 0.2204 data_time: 0.0016 memory: 1303 2022/09/17 04:18:06 - mmengine - INFO - Epoch(val) [2][6600/7672] eta: 0:03:32 time: 0.1984 data_time: 0.0008 memory: 1303 2022/09/17 04:18:26 - mmengine - INFO - Epoch(val) [2][6700/7672] eta: 0:03:13 time: 0.1993 data_time: 0.0008 memory: 1303 2022/09/17 04:18:47 - mmengine - INFO - Epoch(val) [2][6800/7672] eta: 0:02:57 time: 0.2031 data_time: 0.0008 memory: 1303 2022/09/17 04:19:07 - mmengine - INFO - Epoch(val) [2][6900/7672] eta: 0:02:30 time: 0.1954 data_time: 0.0007 memory: 1303 2022/09/17 04:19:27 - mmengine - INFO - Epoch(val) [2][7000/7672] eta: 0:02:17 time: 0.2050 data_time: 0.0008 memory: 1303 2022/09/17 04:19:48 - mmengine - INFO - Epoch(val) [2][7100/7672] eta: 0:01:55 time: 0.2018 data_time: 0.0008 memory: 1303 2022/09/17 04:20:08 - mmengine - INFO - Epoch(val) [2][7200/7672] eta: 0:01:33 time: 0.1986 data_time: 0.0007 memory: 1303 2022/09/17 04:20:29 - mmengine - INFO - Epoch(val) [2][7300/7672] eta: 0:01:15 time: 0.2032 data_time: 0.0008 memory: 1303 2022/09/17 04:20:49 - mmengine - INFO - Epoch(val) [2][7400/7672] eta: 0:00:56 time: 0.2094 data_time: 0.0012 memory: 1303 2022/09/17 04:21:10 - mmengine - INFO - Epoch(val) [2][7500/7672] eta: 0:00:37 time: 0.2207 data_time: 0.0009 memory: 1303 2022/09/17 04:21:30 - mmengine - INFO - Epoch(val) [2][7600/7672] eta: 0:00:15 time: 0.2092 data_time: 0.0028 memory: 1303 2022/09/17 04:21:45 - mmengine - INFO - Epoch(val) [2][7672/7672] CUTE80/recog/word_acc_ignore_case_symbol: 0.8576 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9380 SVT/recog/word_acc_ignore_case_symbol: 0.8779 SVTP/recog/word_acc_ignore_case_symbol: 0.7767 IC13/recog/word_acc_ignore_case_symbol: 0.9409 IC15/recog/word_acc_ignore_case_symbol: 0.7140 2022/09/17 04:23:01 - mmengine - INFO - Epoch(train) [3][100/42151] lr: 3.0000e-04 eta: 1 day, 9:16:45 time: 0.8574 data_time: 0.2770 memory: 28726 loss_ce: 0.0158 loss: 0.0158 2022/09/17 04:24:08 - mmengine - INFO - Epoch(train) [3][200/42151] lr: 3.0000e-04 eta: 1 day, 9:15:27 time: 0.7894 data_time: 0.2524 memory: 28726 loss_ce: 0.0128 loss: 0.0128 2022/09/17 04:25:15 - mmengine - INFO - Epoch(train) [3][300/42151] lr: 3.0000e-04 eta: 1 day, 9:14:07 time: 0.5453 data_time: 0.0047 memory: 28726 loss_ce: 0.0136 loss: 0.0136 2022/09/17 04:26:22 - mmengine - INFO - Epoch(train) [3][400/42151] lr: 3.0000e-04 eta: 1 day, 9:12:48 time: 0.6044 data_time: 0.0047 memory: 28726 loss_ce: 0.0146 loss: 0.0146 2022/09/17 04:27:30 - mmengine - INFO - Epoch(train) [3][500/42151] lr: 3.0000e-04 eta: 1 day, 9:11:31 time: 0.5672 data_time: 0.0308 memory: 28726 loss_ce: 0.0141 loss: 0.0141 2022/09/17 04:28:38 - mmengine - INFO - Epoch(train) [3][600/42151] lr: 3.0000e-04 eta: 1 day, 9:10:13 time: 0.6405 data_time: 0.0734 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/17 04:29:47 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 04:29:48 - mmengine - INFO - Epoch(train) [3][700/42151] lr: 3.0000e-04 eta: 1 day, 9:09:00 time: 0.8749 data_time: 0.3069 memory: 28726 loss_ce: 0.0147 loss: 0.0147 2022/09/17 04:30:55 - mmengine - INFO - Epoch(train) [3][800/42151] lr: 3.0000e-04 eta: 1 day, 9:07:40 time: 0.7705 data_time: 0.2345 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 04:32:02 - mmengine - INFO - Epoch(train) [3][900/42151] lr: 3.0000e-04 eta: 1 day, 9:06:22 time: 0.5412 data_time: 0.0049 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 04:33:10 - mmengine - INFO - Epoch(train) [3][1000/42151] lr: 3.0000e-04 eta: 1 day, 9:05:05 time: 0.5727 data_time: 0.0047 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 04:34:18 - mmengine - INFO - Epoch(train) [3][1100/42151] lr: 3.0000e-04 eta: 1 day, 9:03:47 time: 0.5746 data_time: 0.0363 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 04:35:26 - mmengine - INFO - Epoch(train) [3][1200/42151] lr: 3.0000e-04 eta: 1 day, 9:02:29 time: 0.6410 data_time: 0.0758 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 04:36:35 - mmengine - INFO - Epoch(train) [3][1300/42151] lr: 3.0000e-04 eta: 1 day, 9:01:15 time: 0.8672 data_time: 0.2949 memory: 28726 loss_ce: 0.0151 loss: 0.0151 2022/09/17 04:37:42 - mmengine - INFO - Epoch(train) [3][1400/42151] lr: 3.0000e-04 eta: 1 day, 8:59:56 time: 0.7548 data_time: 0.2150 memory: 28726 loss_ce: 0.0141 loss: 0.0141 2022/09/17 04:38:49 - mmengine - INFO - Epoch(train) [3][1500/42151] lr: 3.0000e-04 eta: 1 day, 8:58:37 time: 0.5402 data_time: 0.0045 memory: 28726 loss_ce: 0.0150 loss: 0.0150 2022/09/17 04:39:57 - mmengine - INFO - Epoch(train) [3][1600/42151] lr: 3.0000e-04 eta: 1 day, 8:57:20 time: 0.5698 data_time: 0.0047 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 04:41:04 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 04:41:05 - mmengine - INFO - Epoch(train) [3][1700/42151] lr: 3.0000e-04 eta: 1 day, 8:56:04 time: 0.5702 data_time: 0.0340 memory: 28726 loss_ce: 0.0149 loss: 0.0149 2022/09/17 04:42:13 - mmengine - INFO - Epoch(train) [3][1800/42151] lr: 3.0000e-04 eta: 1 day, 8:54:47 time: 0.6517 data_time: 0.0759 memory: 28726 loss_ce: 0.0134 loss: 0.0134 2022/09/17 04:43:23 - mmengine - INFO - Epoch(train) [3][1900/42151] lr: 3.0000e-04 eta: 1 day, 8:53:33 time: 0.8581 data_time: 0.2884 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 04:44:30 - mmengine - INFO - Epoch(train) [3][2000/42151] lr: 3.0000e-04 eta: 1 day, 8:52:15 time: 0.7524 data_time: 0.2158 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 04:45:38 - mmengine - INFO - Epoch(train) [3][2100/42151] lr: 3.0000e-04 eta: 1 day, 8:50:57 time: 0.5401 data_time: 0.0046 memory: 28726 loss_ce: 0.0134 loss: 0.0134 2022/09/17 04:46:45 - mmengine - INFO - Epoch(train) [3][2200/42151] lr: 3.0000e-04 eta: 1 day, 8:49:38 time: 0.5627 data_time: 0.0049 memory: 28726 loss_ce: 0.0150 loss: 0.0150 2022/09/17 04:47:52 - mmengine - INFO - Epoch(train) [3][2300/42151] lr: 3.0000e-04 eta: 1 day, 8:48:19 time: 0.5740 data_time: 0.0334 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 04:49:00 - mmengine - INFO - Epoch(train) [3][2400/42151] lr: 3.0000e-04 eta: 1 day, 8:47:02 time: 0.6332 data_time: 0.0641 memory: 28726 loss_ce: 0.0150 loss: 0.0150 2022/09/17 04:50:09 - mmengine - INFO - Epoch(train) [3][2500/42151] lr: 3.0000e-04 eta: 1 day, 8:45:48 time: 0.8408 data_time: 0.2593 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 04:51:16 - mmengine - INFO - Epoch(train) [3][2600/42151] lr: 3.0000e-04 eta: 1 day, 8:44:29 time: 0.7493 data_time: 0.2143 memory: 28726 loss_ce: 0.0148 loss: 0.0148 2022/09/17 04:52:22 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 04:52:23 - mmengine - INFO - Epoch(train) [3][2700/42151] lr: 3.0000e-04 eta: 1 day, 8:43:11 time: 0.5656 data_time: 0.0148 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 04:53:30 - mmengine - INFO - Epoch(train) [3][2800/42151] lr: 3.0000e-04 eta: 1 day, 8:41:51 time: 0.5378 data_time: 0.0045 memory: 28726 loss_ce: 0.0155 loss: 0.0155 2022/09/17 04:54:37 - mmengine - INFO - Epoch(train) [3][2900/42151] lr: 3.0000e-04 eta: 1 day, 8:40:33 time: 0.6073 data_time: 0.0402 memory: 28726 loss_ce: 0.0144 loss: 0.0144 2022/09/17 04:55:45 - mmengine - INFO - Epoch(train) [3][3000/42151] lr: 3.0000e-04 eta: 1 day, 8:39:16 time: 0.6445 data_time: 0.1090 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 04:56:54 - mmengine - INFO - Epoch(train) [3][3100/42151] lr: 3.0000e-04 eta: 1 day, 8:38:02 time: 0.8651 data_time: 0.3041 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 04:58:01 - mmengine - INFO - Epoch(train) [3][3200/42151] lr: 3.0000e-04 eta: 1 day, 8:36:43 time: 0.7307 data_time: 0.1918 memory: 28726 loss_ce: 0.0123 loss: 0.0123 2022/09/17 04:59:08 - mmengine - INFO - Epoch(train) [3][3300/42151] lr: 3.0000e-04 eta: 1 day, 8:35:25 time: 0.5440 data_time: 0.0048 memory: 28726 loss_ce: 0.0144 loss: 0.0144 2022/09/17 05:00:16 - mmengine - INFO - Epoch(train) [3][3400/42151] lr: 3.0000e-04 eta: 1 day, 8:34:07 time: 0.5405 data_time: 0.0047 memory: 28726 loss_ce: 0.0146 loss: 0.0146 2022/09/17 05:01:24 - mmengine - INFO - Epoch(train) [3][3500/42151] lr: 3.0000e-04 eta: 1 day, 8:32:51 time: 0.6177 data_time: 0.0659 memory: 28726 loss_ce: 0.0153 loss: 0.0153 2022/09/17 05:02:32 - mmengine - INFO - Epoch(train) [3][3600/42151] lr: 3.0000e-04 eta: 1 day, 8:31:35 time: 0.6437 data_time: 0.1047 memory: 28726 loss_ce: 0.0137 loss: 0.0137 2022/09/17 05:03:41 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 05:03:42 - mmengine - INFO - Epoch(train) [3][3700/42151] lr: 3.0000e-04 eta: 1 day, 8:30:22 time: 0.8432 data_time: 0.3011 memory: 28726 loss_ce: 0.0147 loss: 0.0147 2022/09/17 05:04:50 - mmengine - INFO - Epoch(train) [3][3800/42151] lr: 3.0000e-04 eta: 1 day, 8:29:04 time: 0.7419 data_time: 0.1960 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 05:05:57 - mmengine - INFO - Epoch(train) [3][3900/42151] lr: 3.0000e-04 eta: 1 day, 8:27:47 time: 0.5419 data_time: 0.0046 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 05:07:04 - mmengine - INFO - Epoch(train) [3][4000/42151] lr: 3.0000e-04 eta: 1 day, 8:26:28 time: 0.5468 data_time: 0.0045 memory: 28726 loss_ce: 0.0141 loss: 0.0141 2022/09/17 05:08:10 - mmengine - INFO - Epoch(train) [3][4100/42151] lr: 3.0000e-04 eta: 1 day, 8:25:09 time: 0.5930 data_time: 0.0595 memory: 28726 loss_ce: 0.0141 loss: 0.0141 2022/09/17 05:09:18 - mmengine - INFO - Epoch(train) [3][4200/42151] lr: 3.0000e-04 eta: 1 day, 8:23:51 time: 0.6378 data_time: 0.1034 memory: 28726 loss_ce: 0.0136 loss: 0.0136 2022/09/17 05:10:27 - mmengine - INFO - Epoch(train) [3][4300/42151] lr: 3.0000e-04 eta: 1 day, 8:22:36 time: 0.8490 data_time: 0.3140 memory: 28726 loss_ce: 0.0127 loss: 0.0127 2022/09/17 05:11:33 - mmengine - INFO - Epoch(train) [3][4400/42151] lr: 3.0000e-04 eta: 1 day, 8:21:16 time: 0.7477 data_time: 0.2117 memory: 28726 loss_ce: 0.0177 loss: 0.0177 2022/09/17 05:12:39 - mmengine - INFO - Epoch(train) [3][4500/42151] lr: 3.0000e-04 eta: 1 day, 8:19:57 time: 0.5386 data_time: 0.0042 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/17 05:13:46 - mmengine - INFO - Epoch(train) [3][4600/42151] lr: 3.0000e-04 eta: 1 day, 8:18:39 time: 0.5459 data_time: 0.0048 memory: 28726 loss_ce: 0.0148 loss: 0.0148 2022/09/17 05:14:52 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 05:14:53 - mmengine - INFO - Epoch(train) [3][4700/42151] lr: 3.0000e-04 eta: 1 day, 8:17:21 time: 0.5944 data_time: 0.0612 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 05:16:01 - mmengine - INFO - Epoch(train) [3][4800/42151] lr: 3.0000e-04 eta: 1 day, 8:16:05 time: 0.6350 data_time: 0.1007 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 05:17:09 - mmengine - INFO - Epoch(train) [3][4900/42151] lr: 3.0000e-04 eta: 1 day, 8:14:49 time: 0.8144 data_time: 0.2783 memory: 28726 loss_ce: 0.0125 loss: 0.0125 2022/09/17 05:18:16 - mmengine - INFO - Epoch(train) [3][5000/42151] lr: 3.0000e-04 eta: 1 day, 8:13:29 time: 0.7146 data_time: 0.1781 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 05:19:23 - mmengine - INFO - Epoch(train) [3][5100/42151] lr: 3.0000e-04 eta: 1 day, 8:12:12 time: 0.5394 data_time: 0.0048 memory: 28726 loss_ce: 0.0121 loss: 0.0121 2022/09/17 05:20:30 - mmengine - INFO - Epoch(train) [3][5200/42151] lr: 3.0000e-04 eta: 1 day, 8:10:54 time: 0.5413 data_time: 0.0045 memory: 28726 loss_ce: 0.0116 loss: 0.0116 2022/09/17 05:21:38 - mmengine - INFO - Epoch(train) [3][5300/42151] lr: 3.0000e-04 eta: 1 day, 8:09:38 time: 0.6240 data_time: 0.0906 memory: 28726 loss_ce: 0.0144 loss: 0.0144 2022/09/17 05:22:46 - mmengine - INFO - Epoch(train) [3][5400/42151] lr: 3.0000e-04 eta: 1 day, 8:08:20 time: 0.6342 data_time: 0.0992 memory: 28726 loss_ce: 0.0129 loss: 0.0129 2022/09/17 05:23:54 - mmengine - INFO - Epoch(train) [3][5500/42151] lr: 3.0000e-04 eta: 1 day, 8:07:06 time: 0.8260 data_time: 0.2912 memory: 28726 loss_ce: 0.0130 loss: 0.0130 2022/09/17 05:25:01 - mmengine - INFO - Epoch(train) [3][5600/42151] lr: 3.0000e-04 eta: 1 day, 8:05:46 time: 0.7675 data_time: 0.2297 memory: 28726 loss_ce: 0.0145 loss: 0.0145 2022/09/17 05:26:05 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 05:26:06 - mmengine - INFO - Epoch(train) [3][5700/42151] lr: 3.0000e-04 eta: 1 day, 8:04:26 time: 0.5442 data_time: 0.0075 memory: 28726 loss_ce: 0.0155 loss: 0.0155 2022/09/17 05:27:13 - mmengine - INFO - Epoch(train) [3][5800/42151] lr: 3.0000e-04 eta: 1 day, 8:03:06 time: 0.5357 data_time: 0.0044 memory: 28726 loss_ce: 0.0117 loss: 0.0117 2022/09/17 05:28:19 - mmengine - INFO - Epoch(train) [3][5900/42151] lr: 3.0000e-04 eta: 1 day, 8:01:48 time: 0.6004 data_time: 0.0594 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 05:29:26 - mmengine - INFO - Epoch(train) [3][6000/42151] lr: 3.0000e-04 eta: 1 day, 8:00:30 time: 0.6339 data_time: 0.0987 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 05:30:34 - mmengine - INFO - Epoch(train) [3][6100/42151] lr: 3.0000e-04 eta: 1 day, 7:59:14 time: 0.8070 data_time: 0.2696 memory: 28726 loss_ce: 0.0144 loss: 0.0144 2022/09/17 05:31:41 - mmengine - INFO - Epoch(train) [3][6200/42151] lr: 3.0000e-04 eta: 1 day, 7:57:55 time: 0.7570 data_time: 0.2169 memory: 28726 loss_ce: 0.0144 loss: 0.0144 2022/09/17 05:32:47 - mmengine - INFO - Epoch(train) [3][6300/42151] lr: 3.0000e-04 eta: 1 day, 7:56:37 time: 0.5401 data_time: 0.0043 memory: 28726 loss_ce: 0.0132 loss: 0.0132 2022/09/17 05:33:54 - mmengine - INFO - Epoch(train) [3][6400/42151] lr: 3.0000e-04 eta: 1 day, 7:55:18 time: 0.5414 data_time: 0.0046 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 05:35:02 - mmengine - INFO - Epoch(train) [3][6500/42151] lr: 3.0000e-04 eta: 1 day, 7:54:02 time: 0.6012 data_time: 0.0682 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 05:36:09 - mmengine - INFO - Epoch(train) [3][6600/42151] lr: 3.0000e-04 eta: 1 day, 7:52:45 time: 0.6545 data_time: 0.1139 memory: 28726 loss_ce: 0.0127 loss: 0.0127 2022/09/17 05:37:18 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 05:37:19 - mmengine - INFO - Epoch(train) [3][6700/42151] lr: 3.0000e-04 eta: 1 day, 7:51:33 time: 0.8579 data_time: 0.2910 memory: 28726 loss_ce: 0.0146 loss: 0.0146 2022/09/17 05:38:27 - mmengine - INFO - Epoch(train) [3][6800/42151] lr: 3.0000e-04 eta: 1 day, 7:50:15 time: 0.7292 data_time: 0.1632 memory: 28726 loss_ce: 0.0147 loss: 0.0147 2022/09/17 05:39:34 - mmengine - INFO - Epoch(train) [3][6900/42151] lr: 3.0000e-04 eta: 1 day, 7:48:59 time: 0.5426 data_time: 0.0046 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 05:40:42 - mmengine - INFO - Epoch(train) [3][7000/42151] lr: 3.0000e-04 eta: 1 day, 7:47:43 time: 0.5534 data_time: 0.0130 memory: 28726 loss_ce: 0.0128 loss: 0.0128 2022/09/17 05:41:51 - mmengine - INFO - Epoch(train) [3][7100/42151] lr: 3.0000e-04 eta: 1 day, 7:46:28 time: 0.6297 data_time: 0.0576 memory: 28726 loss_ce: 0.0154 loss: 0.0154 2022/09/17 05:42:58 - mmengine - INFO - Epoch(train) [3][7200/42151] lr: 3.0000e-04 eta: 1 day, 7:45:11 time: 0.6634 data_time: 0.1167 memory: 28726 loss_ce: 0.0155 loss: 0.0155 2022/09/17 05:44:06 - mmengine - INFO - Epoch(train) [3][7300/42151] lr: 3.0000e-04 eta: 1 day, 7:43:55 time: 0.7680 data_time: 0.2173 memory: 28726 loss_ce: 0.0153 loss: 0.0153 2022/09/17 05:45:13 - mmengine - INFO - Epoch(train) [3][7400/42151] lr: 3.0000e-04 eta: 1 day, 7:42:38 time: 0.6947 data_time: 0.1582 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 05:46:20 - mmengine - INFO - Epoch(train) [3][7500/42151] lr: 3.0000e-04 eta: 1 day, 7:41:21 time: 0.5417 data_time: 0.0045 memory: 28726 loss_ce: 0.0147 loss: 0.0147 2022/09/17 05:47:28 - mmengine - INFO - Epoch(train) [3][7600/42151] lr: 3.0000e-04 eta: 1 day, 7:40:04 time: 0.5473 data_time: 0.0138 memory: 28726 loss_ce: 0.0129 loss: 0.0129 2022/09/17 05:48:34 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 05:48:36 - mmengine - INFO - Epoch(train) [3][7700/42151] lr: 3.0000e-04 eta: 1 day, 7:38:48 time: 0.6300 data_time: 0.0687 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 05:49:43 - mmengine - INFO - Epoch(train) [3][7800/42151] lr: 3.0000e-04 eta: 1 day, 7:37:31 time: 0.6632 data_time: 0.1170 memory: 28726 loss_ce: 0.0129 loss: 0.0129 2022/09/17 05:50:52 - mmengine - INFO - Epoch(train) [3][7900/42151] lr: 3.0000e-04 eta: 1 day, 7:36:18 time: 0.8108 data_time: 0.2167 memory: 28726 loss_ce: 0.0118 loss: 0.0118 2022/09/17 05:52:00 - mmengine - INFO - Epoch(train) [3][8000/42151] lr: 3.0000e-04 eta: 1 day, 7:35:01 time: 0.7256 data_time: 0.1568 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 05:53:07 - mmengine - INFO - Epoch(train) [3][8100/42151] lr: 3.0000e-04 eta: 1 day, 7:33:44 time: 0.5396 data_time: 0.0046 memory: 28726 loss_ce: 0.0151 loss: 0.0151 2022/09/17 05:54:15 - mmengine - INFO - Epoch(train) [3][8200/42151] lr: 3.0000e-04 eta: 1 day, 7:32:28 time: 0.5486 data_time: 0.0131 memory: 28726 loss_ce: 0.0124 loss: 0.0124 2022/09/17 05:55:23 - mmengine - INFO - Epoch(train) [3][8300/42151] lr: 3.0000e-04 eta: 1 day, 7:31:12 time: 0.6182 data_time: 0.0690 memory: 28726 loss_ce: 0.0147 loss: 0.0147 2022/09/17 05:56:30 - mmengine - INFO - Epoch(train) [3][8400/42151] lr: 3.0000e-04 eta: 1 day, 7:29:56 time: 0.6414 data_time: 0.1049 memory: 28726 loss_ce: 0.0130 loss: 0.0130 2022/09/17 05:57:40 - mmengine - INFO - Epoch(train) [3][8500/42151] lr: 3.0000e-04 eta: 1 day, 7:28:43 time: 0.8227 data_time: 0.2505 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 05:58:46 - mmengine - INFO - Epoch(train) [3][8600/42151] lr: 3.0000e-04 eta: 1 day, 7:27:24 time: 0.6983 data_time: 0.1645 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 05:59:53 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 05:59:54 - mmengine - INFO - Epoch(train) [3][8700/42151] lr: 3.0000e-04 eta: 1 day, 7:26:08 time: 0.5882 data_time: 0.0086 memory: 28726 loss_ce: 0.0158 loss: 0.0158 2022/09/17 06:01:05 - mmengine - INFO - Epoch(train) [3][8800/42151] lr: 3.0000e-04 eta: 1 day, 7:24:58 time: 0.5554 data_time: 0.0132 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/17 06:02:13 - mmengine - INFO - Epoch(train) [3][8900/42151] lr: 3.0000e-04 eta: 1 day, 7:23:42 time: 0.6284 data_time: 0.0889 memory: 28726 loss_ce: 0.0149 loss: 0.0149 2022/09/17 06:03:21 - mmengine - INFO - Epoch(train) [3][9000/42151] lr: 3.0000e-04 eta: 1 day, 7:22:26 time: 0.6446 data_time: 0.0820 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 06:04:30 - mmengine - INFO - Epoch(train) [3][9100/42151] lr: 3.0000e-04 eta: 1 day, 7:21:12 time: 0.7966 data_time: 0.2559 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 06:05:38 - mmengine - INFO - Epoch(train) [3][9200/42151] lr: 3.0000e-04 eta: 1 day, 7:19:57 time: 0.7447 data_time: 0.2025 memory: 28726 loss_ce: 0.0147 loss: 0.0147 2022/09/17 06:06:46 - mmengine - INFO - Epoch(train) [3][9300/42151] lr: 3.0000e-04 eta: 1 day, 7:18:41 time: 0.5427 data_time: 0.0045 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 06:07:54 - mmengine - INFO - Epoch(train) [3][9400/42151] lr: 3.0000e-04 eta: 1 day, 7:17:26 time: 0.5765 data_time: 0.0130 memory: 28726 loss_ce: 0.0156 loss: 0.0156 2022/09/17 06:09:02 - mmengine - INFO - Epoch(train) [3][9500/42151] lr: 3.0000e-04 eta: 1 day, 7:16:10 time: 0.5881 data_time: 0.0519 memory: 28726 loss_ce: 0.0120 loss: 0.0120 2022/09/17 06:10:10 - mmengine - INFO - Epoch(train) [3][9600/42151] lr: 3.0000e-04 eta: 1 day, 7:14:56 time: 0.6480 data_time: 0.1094 memory: 28726 loss_ce: 0.0148 loss: 0.0148 2022/09/17 06:11:18 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 06:11:20 - mmengine - INFO - Epoch(train) [3][9700/42151] lr: 3.0000e-04 eta: 1 day, 7:13:42 time: 0.8069 data_time: 0.2374 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 06:12:27 - mmengine - INFO - Epoch(train) [3][9800/42151] lr: 3.0000e-04 eta: 1 day, 7:12:26 time: 0.7388 data_time: 0.2008 memory: 28726 loss_ce: 0.0144 loss: 0.0144 2022/09/17 06:13:35 - mmengine - INFO - Epoch(train) [3][9900/42151] lr: 3.0000e-04 eta: 1 day, 7:11:11 time: 0.5457 data_time: 0.0047 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 06:14:42 - mmengine - INFO - Epoch(train) [3][10000/42151] lr: 3.0000e-04 eta: 1 day, 7:09:54 time: 0.5543 data_time: 0.0124 memory: 28726 loss_ce: 0.0130 loss: 0.0130 2022/09/17 06:15:51 - mmengine - INFO - Epoch(train) [3][10100/42151] lr: 3.0000e-04 eta: 1 day, 7:08:39 time: 0.5975 data_time: 0.0578 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 06:16:59 - mmengine - INFO - Epoch(train) [3][10200/42151] lr: 3.0000e-04 eta: 1 day, 7:07:24 time: 0.6350 data_time: 0.0829 memory: 28726 loss_ce: 0.0119 loss: 0.0119 2022/09/17 06:18:08 - mmengine - INFO - Epoch(train) [3][10300/42151] lr: 3.0000e-04 eta: 1 day, 7:06:11 time: 0.8197 data_time: 0.2859 memory: 28726 loss_ce: 0.0148 loss: 0.0148 2022/09/17 06:19:15 - mmengine - INFO - Epoch(train) [3][10400/42151] lr: 3.0000e-04 eta: 1 day, 7:04:54 time: 0.7049 data_time: 0.1674 memory: 28726 loss_ce: 0.0122 loss: 0.0122 2022/09/17 06:20:23 - mmengine - INFO - Epoch(train) [3][10500/42151] lr: 3.0000e-04 eta: 1 day, 7:03:39 time: 0.5406 data_time: 0.0046 memory: 28726 loss_ce: 0.0137 loss: 0.0137 2022/09/17 06:21:31 - mmengine - INFO - Epoch(train) [3][10600/42151] lr: 3.0000e-04 eta: 1 day, 7:02:22 time: 0.5533 data_time: 0.0132 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 06:22:36 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 06:22:38 - mmengine - INFO - Epoch(train) [3][10700/42151] lr: 3.0000e-04 eta: 1 day, 7:01:06 time: 0.5973 data_time: 0.0599 memory: 28726 loss_ce: 0.0122 loss: 0.0122 2022/09/17 06:23:46 - mmengine - INFO - Epoch(train) [3][10800/42151] lr: 3.0000e-04 eta: 1 day, 6:59:51 time: 0.6478 data_time: 0.0870 memory: 28726 loss_ce: 0.0156 loss: 0.0156 2022/09/17 06:24:56 - mmengine - INFO - Epoch(train) [3][10900/42151] lr: 3.0000e-04 eta: 1 day, 6:58:39 time: 0.8200 data_time: 0.2596 memory: 28726 loss_ce: 0.0147 loss: 0.0147 2022/09/17 06:26:03 - mmengine - INFO - Epoch(train) [3][11000/42151] lr: 3.0000e-04 eta: 1 day, 6:57:22 time: 0.7130 data_time: 0.1701 memory: 28726 loss_ce: 0.0167 loss: 0.0167 2022/09/17 06:27:10 - mmengine - INFO - Epoch(train) [3][11100/42151] lr: 3.0000e-04 eta: 1 day, 6:56:05 time: 0.5401 data_time: 0.0045 memory: 28726 loss_ce: 0.0146 loss: 0.0146 2022/09/17 06:28:18 - mmengine - INFO - Epoch(train) [3][11200/42151] lr: 3.0000e-04 eta: 1 day, 6:54:49 time: 0.5523 data_time: 0.0149 memory: 28726 loss_ce: 0.0132 loss: 0.0132 2022/09/17 06:29:25 - mmengine - INFO - Epoch(train) [3][11300/42151] lr: 3.0000e-04 eta: 1 day, 6:53:32 time: 0.6167 data_time: 0.0802 memory: 28726 loss_ce: 0.0157 loss: 0.0157 2022/09/17 06:30:33 - mmengine - INFO - Epoch(train) [3][11400/42151] lr: 3.0000e-04 eta: 1 day, 6:52:17 time: 0.6507 data_time: 0.0856 memory: 28726 loss_ce: 0.0136 loss: 0.0136 2022/09/17 06:31:42 - mmengine - INFO - Epoch(train) [3][11500/42151] lr: 3.0000e-04 eta: 1 day, 6:51:05 time: 0.8295 data_time: 0.2846 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 06:32:50 - mmengine - INFO - Epoch(train) [3][11600/42151] lr: 3.0000e-04 eta: 1 day, 6:49:49 time: 0.7501 data_time: 0.1898 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 06:33:56 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 06:33:58 - mmengine - INFO - Epoch(train) [3][11700/42151] lr: 3.0000e-04 eta: 1 day, 6:48:33 time: 0.5594 data_time: 0.0205 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 06:35:05 - mmengine - INFO - Epoch(train) [3][11800/42151] lr: 3.0000e-04 eta: 1 day, 6:47:18 time: 0.5853 data_time: 0.0515 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 06:36:13 - mmengine - INFO - Epoch(train) [3][11900/42151] lr: 3.0000e-04 eta: 1 day, 6:46:02 time: 0.6279 data_time: 0.0574 memory: 28726 loss_ce: 0.0141 loss: 0.0141 2022/09/17 06:37:20 - mmengine - INFO - Epoch(train) [3][12000/42151] lr: 3.0000e-04 eta: 1 day, 6:44:46 time: 0.6668 data_time: 0.1309 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 06:38:30 - mmengine - INFO - Epoch(train) [3][12100/42151] lr: 3.0000e-04 eta: 1 day, 6:43:34 time: 0.7956 data_time: 0.2576 memory: 28726 loss_ce: 0.0144 loss: 0.0144 2022/09/17 06:39:38 - mmengine - INFO - Epoch(train) [3][12200/42151] lr: 3.0000e-04 eta: 1 day, 6:42:18 time: 0.7597 data_time: 0.2177 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/17 06:40:45 - mmengine - INFO - Epoch(train) [3][12300/42151] lr: 3.0000e-04 eta: 1 day, 6:41:02 time: 0.5429 data_time: 0.0047 memory: 28726 loss_ce: 0.0136 loss: 0.0136 2022/09/17 06:41:52 - mmengine - INFO - Epoch(train) [3][12400/42151] lr: 3.0000e-04 eta: 1 day, 6:39:45 time: 0.5553 data_time: 0.0127 memory: 28726 loss_ce: 0.0132 loss: 0.0132 2022/09/17 06:42:59 - mmengine - INFO - Epoch(train) [3][12500/42151] lr: 3.0000e-04 eta: 1 day, 6:38:28 time: 0.5846 data_time: 0.0465 memory: 28726 loss_ce: 0.0145 loss: 0.0145 2022/09/17 06:44:07 - mmengine - INFO - Epoch(train) [3][12600/42151] lr: 3.0000e-04 eta: 1 day, 6:37:13 time: 0.6648 data_time: 0.1051 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 06:45:14 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 06:45:15 - mmengine - INFO - Epoch(train) [3][12700/42151] lr: 3.0000e-04 eta: 1 day, 6:35:59 time: 0.8331 data_time: 0.2892 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 06:46:21 - mmengine - INFO - Epoch(train) [3][12800/42151] lr: 3.0000e-04 eta: 1 day, 6:34:41 time: 0.7243 data_time: 0.1883 memory: 28726 loss_ce: 0.0126 loss: 0.0126 2022/09/17 06:47:28 - mmengine - INFO - Epoch(train) [3][12900/42151] lr: 3.0000e-04 eta: 1 day, 6:33:23 time: 0.5418 data_time: 0.0045 memory: 28726 loss_ce: 0.0129 loss: 0.0129 2022/09/17 06:48:34 - mmengine - INFO - Epoch(train) [3][13000/42151] lr: 3.0000e-04 eta: 1 day, 6:32:06 time: 0.5464 data_time: 0.0132 memory: 28726 loss_ce: 0.0149 loss: 0.0149 2022/09/17 06:49:41 - mmengine - INFO - Epoch(train) [3][13100/42151] lr: 3.0000e-04 eta: 1 day, 6:30:49 time: 0.6206 data_time: 0.0550 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/17 06:50:47 - mmengine - INFO - Epoch(train) [3][13200/42151] lr: 3.0000e-04 eta: 1 day, 6:29:32 time: 0.6693 data_time: 0.1150 memory: 28726 loss_ce: 0.0132 loss: 0.0132 2022/09/17 06:51:55 - mmengine - INFO - Epoch(train) [3][13300/42151] lr: 3.0000e-04 eta: 1 day, 6:28:17 time: 0.7901 data_time: 0.2539 memory: 28726 loss_ce: 0.0141 loss: 0.0141 2022/09/17 06:53:01 - mmengine - INFO - Epoch(train) [3][13400/42151] lr: 3.0000e-04 eta: 1 day, 6:26:58 time: 0.7102 data_time: 0.1732 memory: 28726 loss_ce: 0.0120 loss: 0.0120 2022/09/17 06:54:09 - mmengine - INFO - Epoch(train) [3][13500/42151] lr: 3.0000e-04 eta: 1 day, 6:25:43 time: 0.5381 data_time: 0.0045 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 06:55:15 - mmengine - INFO - Epoch(train) [3][13600/42151] lr: 3.0000e-04 eta: 1 day, 6:24:26 time: 0.5536 data_time: 0.0128 memory: 28726 loss_ce: 0.0157 loss: 0.0157 2022/09/17 06:56:21 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 06:56:23 - mmengine - INFO - Epoch(train) [3][13700/42151] lr: 3.0000e-04 eta: 1 day, 6:23:10 time: 0.6325 data_time: 0.0591 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 06:57:30 - mmengine - INFO - Epoch(train) [3][13800/42151] lr: 3.0000e-04 eta: 1 day, 6:21:54 time: 0.6562 data_time: 0.1161 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 06:58:39 - mmengine - INFO - Epoch(train) [3][13900/42151] lr: 3.0000e-04 eta: 1 day, 6:20:40 time: 0.7860 data_time: 0.2302 memory: 28726 loss_ce: 0.0125 loss: 0.0125 2022/09/17 06:59:46 - mmengine - INFO - Epoch(train) [3][14000/42151] lr: 3.0000e-04 eta: 1 day, 6:19:24 time: 0.7140 data_time: 0.1592 memory: 28726 loss_ce: 0.0150 loss: 0.0150 2022/09/17 07:00:52 - mmengine - INFO - Epoch(train) [3][14100/42151] lr: 3.0000e-04 eta: 1 day, 6:18:07 time: 0.5396 data_time: 0.0044 memory: 28726 loss_ce: 0.0149 loss: 0.0149 2022/09/17 07:02:00 - mmengine - INFO - Epoch(train) [3][14200/42151] lr: 3.0000e-04 eta: 1 day, 6:16:51 time: 0.5486 data_time: 0.0129 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 07:03:07 - mmengine - INFO - Epoch(train) [3][14300/42151] lr: 3.0000e-04 eta: 1 day, 6:15:36 time: 0.6231 data_time: 0.0468 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 07:04:15 - mmengine - INFO - Epoch(train) [3][14400/42151] lr: 3.0000e-04 eta: 1 day, 6:14:20 time: 0.6573 data_time: 0.1194 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 07:05:24 - mmengine - INFO - Epoch(train) [3][14500/42151] lr: 3.0000e-04 eta: 1 day, 6:13:07 time: 0.8125 data_time: 0.2147 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 07:06:31 - mmengine - INFO - Epoch(train) [3][14600/42151] lr: 3.0000e-04 eta: 1 day, 6:11:51 time: 0.7232 data_time: 0.1598 memory: 28726 loss_ce: 0.0141 loss: 0.0141 2022/09/17 07:07:37 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 07:07:38 - mmengine - INFO - Epoch(train) [3][14700/42151] lr: 3.0000e-04 eta: 1 day, 6:10:35 time: 0.5471 data_time: 0.0053 memory: 28726 loss_ce: 0.0118 loss: 0.0118 2022/09/17 07:08:45 - mmengine - INFO - Epoch(train) [3][14800/42151] lr: 3.0000e-04 eta: 1 day, 6:09:20 time: 0.5489 data_time: 0.0124 memory: 28726 loss_ce: 0.0144 loss: 0.0144 2022/09/17 07:09:53 - mmengine - INFO - Epoch(train) [3][14900/42151] lr: 3.0000e-04 eta: 1 day, 6:08:05 time: 0.6139 data_time: 0.0773 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 07:11:00 - mmengine - INFO - Epoch(train) [3][15000/42151] lr: 3.0000e-04 eta: 1 day, 6:06:49 time: 0.6515 data_time: 0.1140 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 07:12:10 - mmengine - INFO - Epoch(train) [3][15100/42151] lr: 3.0000e-04 eta: 1 day, 6:05:38 time: 0.8793 data_time: 0.2654 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 07:13:18 - mmengine - INFO - Epoch(train) [3][15200/42151] lr: 3.0000e-04 eta: 1 day, 6:04:22 time: 0.7379 data_time: 0.1965 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/17 07:14:25 - mmengine - INFO - Epoch(train) [3][15300/42151] lr: 3.0000e-04 eta: 1 day, 6:03:07 time: 0.5710 data_time: 0.0048 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 07:15:32 - mmengine - INFO - Epoch(train) [3][15400/42151] lr: 3.0000e-04 eta: 1 day, 6:01:51 time: 0.5470 data_time: 0.0126 memory: 28726 loss_ce: 0.0127 loss: 0.0127 2022/09/17 07:16:40 - mmengine - INFO - Epoch(train) [3][15500/42151] lr: 3.0000e-04 eta: 1 day, 6:00:35 time: 0.6161 data_time: 0.0768 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 07:17:47 - mmengine - INFO - Epoch(train) [3][15600/42151] lr: 3.0000e-04 eta: 1 day, 5:59:20 time: 0.6608 data_time: 0.0929 memory: 28726 loss_ce: 0.0123 loss: 0.0123 2022/09/17 07:18:55 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 07:18:56 - mmengine - INFO - Epoch(train) [3][15700/42151] lr: 3.0000e-04 eta: 1 day, 5:58:06 time: 0.8064 data_time: 0.2645 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 07:20:03 - mmengine - INFO - Epoch(train) [3][15800/42151] lr: 3.0000e-04 eta: 1 day, 5:56:50 time: 0.7187 data_time: 0.1795 memory: 28726 loss_ce: 0.0118 loss: 0.0118 2022/09/17 07:21:10 - mmengine - INFO - Epoch(train) [3][15900/42151] lr: 3.0000e-04 eta: 1 day, 5:55:34 time: 0.5523 data_time: 0.0043 memory: 28726 loss_ce: 0.0126 loss: 0.0126 2022/09/17 07:22:17 - mmengine - INFO - Epoch(train) [3][16000/42151] lr: 3.0000e-04 eta: 1 day, 5:54:18 time: 0.5796 data_time: 0.0129 memory: 28726 loss_ce: 0.0123 loss: 0.0123 2022/09/17 07:23:24 - mmengine - INFO - Epoch(train) [3][16100/42151] lr: 3.0000e-04 eta: 1 day, 5:53:02 time: 0.5810 data_time: 0.0465 memory: 28726 loss_ce: 0.0128 loss: 0.0128 2022/09/17 07:24:32 - mmengine - INFO - Epoch(train) [3][16200/42151] lr: 3.0000e-04 eta: 1 day, 5:51:47 time: 0.6619 data_time: 0.1241 memory: 28726 loss_ce: 0.0155 loss: 0.0155 2022/09/17 07:25:40 - mmengine - INFO - Epoch(train) [3][16300/42151] lr: 3.0000e-04 eta: 1 day, 5:50:34 time: 0.8234 data_time: 0.2546 memory: 28726 loss_ce: 0.0137 loss: 0.0137 2022/09/17 07:26:48 - mmengine - INFO - Epoch(train) [3][16400/42151] lr: 3.0000e-04 eta: 1 day, 5:49:19 time: 0.7382 data_time: 0.1978 memory: 28726 loss_ce: 0.0130 loss: 0.0130 2022/09/17 07:27:55 - mmengine - INFO - Epoch(train) [3][16500/42151] lr: 3.0000e-04 eta: 1 day, 5:48:03 time: 0.5440 data_time: 0.0044 memory: 28726 loss_ce: 0.0120 loss: 0.0120 2022/09/17 07:29:02 - mmengine - INFO - Epoch(train) [3][16600/42151] lr: 3.0000e-04 eta: 1 day, 5:46:47 time: 0.5483 data_time: 0.0132 memory: 28726 loss_ce: 0.0128 loss: 0.0128 2022/09/17 07:30:09 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 07:30:10 - mmengine - INFO - Epoch(train) [3][16700/42151] lr: 3.0000e-04 eta: 1 day, 5:45:33 time: 0.6208 data_time: 0.0848 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 07:31:18 - mmengine - INFO - Epoch(train) [3][16800/42151] lr: 3.0000e-04 eta: 1 day, 5:44:19 time: 0.6391 data_time: 0.0846 memory: 28726 loss_ce: 0.0146 loss: 0.0146 2022/09/17 07:32:28 - mmengine - INFO - Epoch(train) [3][16900/42151] lr: 3.0000e-04 eta: 1 day, 5:43:07 time: 0.8054 data_time: 0.2678 memory: 28726 loss_ce: 0.0157 loss: 0.0157 2022/09/17 07:33:37 - mmengine - INFO - Epoch(train) [3][17000/42151] lr: 3.0000e-04 eta: 1 day, 5:41:54 time: 0.7248 data_time: 0.1860 memory: 28726 loss_ce: 0.0136 loss: 0.0136 2022/09/17 07:34:44 - mmengine - INFO - Epoch(train) [3][17100/42151] lr: 3.0000e-04 eta: 1 day, 5:40:38 time: 0.5397 data_time: 0.0046 memory: 28726 loss_ce: 0.0150 loss: 0.0150 2022/09/17 07:35:52 - mmengine - INFO - Epoch(train) [3][17200/42151] lr: 3.0000e-04 eta: 1 day, 5:39:24 time: 0.5568 data_time: 0.0126 memory: 28726 loss_ce: 0.0141 loss: 0.0141 2022/09/17 07:37:00 - mmengine - INFO - Epoch(train) [3][17300/42151] lr: 3.0000e-04 eta: 1 day, 5:38:09 time: 0.6023 data_time: 0.0575 memory: 28726 loss_ce: 0.0136 loss: 0.0136 2022/09/17 07:38:09 - mmengine - INFO - Epoch(train) [3][17400/42151] lr: 3.0000e-04 eta: 1 day, 5:36:56 time: 0.6493 data_time: 0.0813 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 07:39:18 - mmengine - INFO - Epoch(train) [3][17500/42151] lr: 3.0000e-04 eta: 1 day, 5:35:45 time: 0.8493 data_time: 0.2772 memory: 28726 loss_ce: 0.0147 loss: 0.0147 2022/09/17 07:40:26 - mmengine - INFO - Epoch(train) [3][17600/42151] lr: 3.0000e-04 eta: 1 day, 5:34:30 time: 0.7354 data_time: 0.1983 memory: 28726 loss_ce: 0.0130 loss: 0.0130 2022/09/17 07:41:32 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 07:41:34 - mmengine - INFO - Epoch(train) [3][17700/42151] lr: 3.0000e-04 eta: 1 day, 5:33:15 time: 0.5563 data_time: 0.0103 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 07:42:41 - mmengine - INFO - Epoch(train) [3][17800/42151] lr: 3.0000e-04 eta: 1 day, 5:31:59 time: 0.5478 data_time: 0.0129 memory: 28726 loss_ce: 0.0122 loss: 0.0122 2022/09/17 07:43:50 - mmengine - INFO - Epoch(train) [3][17900/42151] lr: 3.0000e-04 eta: 1 day, 5:30:46 time: 0.6281 data_time: 0.0931 memory: 28726 loss_ce: 0.0134 loss: 0.0134 2022/09/17 07:44:57 - mmengine - INFO - Epoch(train) [3][18000/42151] lr: 3.0000e-04 eta: 1 day, 5:29:32 time: 0.6521 data_time: 0.0841 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 07:46:07 - mmengine - INFO - Epoch(train) [3][18100/42151] lr: 3.0000e-04 eta: 1 day, 5:28:20 time: 0.8066 data_time: 0.2682 memory: 28726 loss_ce: 0.0147 loss: 0.0147 2022/09/17 07:47:15 - mmengine - INFO - Epoch(train) [3][18200/42151] lr: 3.0000e-04 eta: 1 day, 5:27:05 time: 0.7616 data_time: 0.1747 memory: 28726 loss_ce: 0.0113 loss: 0.0113 2022/09/17 07:48:22 - mmengine - INFO - Epoch(train) [3][18300/42151] lr: 3.0000e-04 eta: 1 day, 5:25:51 time: 0.5408 data_time: 0.0046 memory: 28726 loss_ce: 0.0150 loss: 0.0150 2022/09/17 07:49:29 - mmengine - INFO - Epoch(train) [3][18400/42151] lr: 3.0000e-04 eta: 1 day, 5:24:35 time: 0.5719 data_time: 0.0365 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 07:50:37 - mmengine - INFO - Epoch(train) [3][18500/42151] lr: 3.0000e-04 eta: 1 day, 5:23:21 time: 0.6609 data_time: 0.0800 memory: 28726 loss_ce: 0.0132 loss: 0.0132 2022/09/17 07:51:45 - mmengine - INFO - Epoch(train) [3][18600/42151] lr: 3.0000e-04 eta: 1 day, 5:22:07 time: 0.6898 data_time: 0.1516 memory: 28726 loss_ce: 0.0146 loss: 0.0146 2022/09/17 07:52:54 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 07:52:55 - mmengine - INFO - Epoch(train) [3][18700/42151] lr: 3.0000e-04 eta: 1 day, 5:20:54 time: 0.8203 data_time: 0.2798 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/17 07:54:02 - mmengine - INFO - Epoch(train) [3][18800/42151] lr: 3.0000e-04 eta: 1 day, 5:19:40 time: 0.7209 data_time: 0.1817 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 07:55:09 - mmengine - INFO - Epoch(train) [3][18900/42151] lr: 3.0000e-04 eta: 1 day, 5:18:24 time: 0.5455 data_time: 0.0050 memory: 28726 loss_ce: 0.0121 loss: 0.0121 2022/09/17 07:56:18 - mmengine - INFO - Epoch(train) [3][19000/42151] lr: 3.0000e-04 eta: 1 day, 5:17:11 time: 0.5492 data_time: 0.0139 memory: 28726 loss_ce: 0.0157 loss: 0.0157 2022/09/17 07:57:26 - mmengine - INFO - Epoch(train) [3][19100/42151] lr: 3.0000e-04 eta: 1 day, 5:15:57 time: 0.5820 data_time: 0.0481 memory: 28726 loss_ce: 0.0129 loss: 0.0129 2022/09/17 07:58:35 - mmengine - INFO - Epoch(train) [3][19200/42151] lr: 3.0000e-04 eta: 1 day, 5:14:44 time: 0.6917 data_time: 0.1154 memory: 28726 loss_ce: 0.0118 loss: 0.0118 2022/09/17 07:59:44 - mmengine - INFO - Epoch(train) [3][19300/42151] lr: 3.0000e-04 eta: 1 day, 5:13:32 time: 0.8121 data_time: 0.2685 memory: 28726 loss_ce: 0.0129 loss: 0.0129 2022/09/17 08:00:51 - mmengine - INFO - Epoch(train) [3][19400/42151] lr: 3.0000e-04 eta: 1 day, 5:12:16 time: 0.7682 data_time: 0.2277 memory: 28726 loss_ce: 0.0115 loss: 0.0115 2022/09/17 08:01:58 - mmengine - INFO - Epoch(train) [3][19500/42151] lr: 3.0000e-04 eta: 1 day, 5:11:01 time: 0.5400 data_time: 0.0044 memory: 28726 loss_ce: 0.0119 loss: 0.0119 2022/09/17 08:03:05 - mmengine - INFO - Epoch(train) [3][19600/42151] lr: 3.0000e-04 eta: 1 day, 5:09:46 time: 0.5522 data_time: 0.0140 memory: 28726 loss_ce: 0.0114 loss: 0.0114 2022/09/17 08:04:12 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 08:04:13 - mmengine - INFO - Epoch(train) [3][19700/42151] lr: 3.0000e-04 eta: 1 day, 5:08:32 time: 0.6621 data_time: 0.0677 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 08:05:21 - mmengine - INFO - Epoch(train) [3][19800/42151] lr: 3.0000e-04 eta: 1 day, 5:07:17 time: 0.6603 data_time: 0.1051 memory: 28726 loss_ce: 0.0128 loss: 0.0128 2022/09/17 08:06:29 - mmengine - INFO - Epoch(train) [3][19900/42151] lr: 3.0000e-04 eta: 1 day, 5:06:03 time: 0.7946 data_time: 0.2554 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 08:07:36 - mmengine - INFO - Epoch(train) [3][20000/42151] lr: 3.0000e-04 eta: 1 day, 5:04:48 time: 0.7052 data_time: 0.1685 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 08:08:43 - mmengine - INFO - Epoch(train) [3][20100/42151] lr: 3.0000e-04 eta: 1 day, 5:03:33 time: 0.5402 data_time: 0.0044 memory: 28726 loss_ce: 0.0154 loss: 0.0154 2022/09/17 08:09:51 - mmengine - INFO - Epoch(train) [3][20200/42151] lr: 3.0000e-04 eta: 1 day, 5:02:19 time: 0.5557 data_time: 0.0148 memory: 28726 loss_ce: 0.0122 loss: 0.0122 2022/09/17 08:10:58 - mmengine - INFO - Epoch(train) [3][20300/42151] lr: 3.0000e-04 eta: 1 day, 5:01:03 time: 0.6201 data_time: 0.0582 memory: 28726 loss_ce: 0.0148 loss: 0.0148 2022/09/17 08:12:06 - mmengine - INFO - Epoch(train) [3][20400/42151] lr: 3.0000e-04 eta: 1 day, 4:59:49 time: 0.6596 data_time: 0.1237 memory: 28726 loss_ce: 0.0144 loss: 0.0144 2022/09/17 08:13:15 - mmengine - INFO - Epoch(train) [3][20500/42151] lr: 3.0000e-04 eta: 1 day, 4:58:36 time: 0.8023 data_time: 0.2406 memory: 28726 loss_ce: 0.0125 loss: 0.0125 2022/09/17 08:14:22 - mmengine - INFO - Epoch(train) [3][20600/42151] lr: 3.0000e-04 eta: 1 day, 4:57:21 time: 0.7091 data_time: 0.1720 memory: 28726 loss_ce: 0.0150 loss: 0.0150 2022/09/17 08:15:28 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 08:15:29 - mmengine - INFO - Epoch(train) [3][20700/42151] lr: 3.0000e-04 eta: 1 day, 4:56:06 time: 0.5426 data_time: 0.0072 memory: 28726 loss_ce: 0.0128 loss: 0.0128 2022/09/17 08:16:36 - mmengine - INFO - Epoch(train) [3][20800/42151] lr: 3.0000e-04 eta: 1 day, 4:54:51 time: 0.5478 data_time: 0.0128 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 08:17:44 - mmengine - INFO - Epoch(train) [3][20900/42151] lr: 3.0000e-04 eta: 1 day, 4:53:37 time: 0.6201 data_time: 0.0563 memory: 28726 loss_ce: 0.0137 loss: 0.0137 2022/09/17 08:18:51 - mmengine - INFO - Epoch(train) [3][21000/42151] lr: 3.0000e-04 eta: 1 day, 4:52:21 time: 0.6554 data_time: 0.1194 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 08:20:00 - mmengine - INFO - Epoch(train) [3][21100/42151] lr: 3.0000e-04 eta: 1 day, 4:51:09 time: 0.8112 data_time: 0.2238 memory: 28726 loss_ce: 0.0129 loss: 0.0129 2022/09/17 08:21:08 - mmengine - INFO - Epoch(train) [3][21200/42151] lr: 3.0000e-04 eta: 1 day, 4:49:55 time: 0.7218 data_time: 0.1616 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 08:22:15 - mmengine - INFO - Epoch(train) [3][21300/42151] lr: 3.0000e-04 eta: 1 day, 4:48:40 time: 0.5414 data_time: 0.0050 memory: 28726 loss_ce: 0.0128 loss: 0.0128 2022/09/17 08:23:23 - mmengine - INFO - Epoch(train) [3][21400/42151] lr: 3.0000e-04 eta: 1 day, 4:47:26 time: 0.5512 data_time: 0.0142 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 08:24:31 - mmengine - INFO - Epoch(train) [3][21500/42151] lr: 3.0000e-04 eta: 1 day, 4:46:12 time: 0.6255 data_time: 0.0905 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 08:25:39 - mmengine - INFO - Epoch(train) [3][21600/42151] lr: 3.0000e-04 eta: 1 day, 4:44:58 time: 0.6527 data_time: 0.1176 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 08:26:47 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 08:26:48 - mmengine - INFO - Epoch(train) [3][21700/42151] lr: 3.0000e-04 eta: 1 day, 4:43:47 time: 0.8202 data_time: 0.2479 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 08:27:56 - mmengine - INFO - Epoch(train) [3][21800/42151] lr: 3.0000e-04 eta: 1 day, 4:42:32 time: 0.6977 data_time: 0.1621 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 08:29:03 - mmengine - INFO - Epoch(train) [3][21900/42151] lr: 3.0000e-04 eta: 1 day, 4:41:18 time: 0.5811 data_time: 0.0044 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 08:30:10 - mmengine - INFO - Epoch(train) [3][22000/42151] lr: 3.0000e-04 eta: 1 day, 4:40:02 time: 0.5485 data_time: 0.0134 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 08:31:17 - mmengine - INFO - Epoch(train) [3][22100/42151] lr: 3.0000e-04 eta: 1 day, 4:38:47 time: 0.6330 data_time: 0.0872 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 08:32:25 - mmengine - INFO - Epoch(train) [3][22200/42151] lr: 3.0000e-04 eta: 1 day, 4:37:34 time: 0.6533 data_time: 0.0929 memory: 28726 loss_ce: 0.0128 loss: 0.0128 2022/09/17 08:33:35 - mmengine - INFO - Epoch(train) [3][22300/42151] lr: 3.0000e-04 eta: 1 day, 4:36:22 time: 0.7927 data_time: 0.2558 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 08:34:42 - mmengine - INFO - Epoch(train) [3][22400/42151] lr: 3.0000e-04 eta: 1 day, 4:35:08 time: 0.7295 data_time: 0.1917 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 08:35:50 - mmengine - INFO - Epoch(train) [3][22500/42151] lr: 3.0000e-04 eta: 1 day, 4:33:53 time: 0.5427 data_time: 0.0047 memory: 28726 loss_ce: 0.0154 loss: 0.0154 2022/09/17 08:36:58 - mmengine - INFO - Epoch(train) [3][22600/42151] lr: 3.0000e-04 eta: 1 day, 4:32:39 time: 0.5743 data_time: 0.0138 memory: 28726 loss_ce: 0.0129 loss: 0.0129 2022/09/17 08:38:04 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 08:38:05 - mmengine - INFO - Epoch(train) [3][22700/42151] lr: 3.0000e-04 eta: 1 day, 4:31:25 time: 0.6154 data_time: 0.0623 memory: 28726 loss_ce: 0.0126 loss: 0.0126 2022/09/17 08:39:13 - mmengine - INFO - Epoch(train) [3][22800/42151] lr: 3.0000e-04 eta: 1 day, 4:30:11 time: 0.6477 data_time: 0.1107 memory: 28726 loss_ce: 0.0124 loss: 0.0124 2022/09/17 08:40:22 - mmengine - INFO - Epoch(train) [3][22900/42151] lr: 3.0000e-04 eta: 1 day, 4:28:59 time: 0.7984 data_time: 0.2286 memory: 28726 loss_ce: 0.0121 loss: 0.0121 2022/09/17 08:41:30 - mmengine - INFO - Epoch(train) [3][23000/42151] lr: 3.0000e-04 eta: 1 day, 4:27:45 time: 0.7663 data_time: 0.2300 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 08:42:38 - mmengine - INFO - Epoch(train) [3][23100/42151] lr: 3.0000e-04 eta: 1 day, 4:26:31 time: 0.5421 data_time: 0.0045 memory: 28726 loss_ce: 0.0130 loss: 0.0130 2022/09/17 08:43:45 - mmengine - INFO - Epoch(train) [3][23200/42151] lr: 3.0000e-04 eta: 1 day, 4:25:16 time: 0.5492 data_time: 0.0136 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 08:44:52 - mmengine - INFO - Epoch(train) [3][23300/42151] lr: 3.0000e-04 eta: 1 day, 4:24:02 time: 0.5999 data_time: 0.0649 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 08:46:00 - mmengine - INFO - Epoch(train) [3][23400/42151] lr: 3.0000e-04 eta: 1 day, 4:22:48 time: 0.6481 data_time: 0.0893 memory: 28726 loss_ce: 0.0123 loss: 0.0123 2022/09/17 08:47:08 - mmengine - INFO - Epoch(train) [3][23500/42151] lr: 3.0000e-04 eta: 1 day, 4:21:35 time: 0.7682 data_time: 0.2323 memory: 28726 loss_ce: 0.0118 loss: 0.0118 2022/09/17 08:48:15 - mmengine - INFO - Epoch(train) [3][23600/42151] lr: 3.0000e-04 eta: 1 day, 4:20:20 time: 0.7107 data_time: 0.1751 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 08:49:22 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 08:49:23 - mmengine - INFO - Epoch(train) [3][23700/42151] lr: 3.0000e-04 eta: 1 day, 4:19:06 time: 0.5420 data_time: 0.0073 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 08:50:30 - mmengine - INFO - Epoch(train) [3][23800/42151] lr: 3.0000e-04 eta: 1 day, 4:17:52 time: 0.5509 data_time: 0.0126 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 08:51:38 - mmengine - INFO - Epoch(train) [3][23900/42151] lr: 3.0000e-04 eta: 1 day, 4:16:37 time: 0.5961 data_time: 0.0594 memory: 28726 loss_ce: 0.0132 loss: 0.0132 2022/09/17 08:52:45 - mmengine - INFO - Epoch(train) [3][24000/42151] lr: 3.0000e-04 eta: 1 day, 4:15:23 time: 0.6706 data_time: 0.1059 memory: 28726 loss_ce: 0.0153 loss: 0.0153 2022/09/17 08:53:54 - mmengine - INFO - Epoch(train) [3][24100/42151] lr: 3.0000e-04 eta: 1 day, 4:14:10 time: 0.8058 data_time: 0.2408 memory: 28726 loss_ce: 0.0125 loss: 0.0125 2022/09/17 08:55:01 - mmengine - INFO - Epoch(train) [3][24200/42151] lr: 3.0000e-04 eta: 1 day, 4:12:55 time: 0.7127 data_time: 0.1746 memory: 28726 loss_ce: 0.0115 loss: 0.0115 2022/09/17 08:56:08 - mmengine - INFO - Epoch(train) [3][24300/42151] lr: 3.0000e-04 eta: 1 day, 4:11:40 time: 0.5446 data_time: 0.0047 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/17 08:57:15 - mmengine - INFO - Epoch(train) [3][24400/42151] lr: 3.0000e-04 eta: 1 day, 4:10:26 time: 0.5563 data_time: 0.0130 memory: 28726 loss_ce: 0.0155 loss: 0.0155 2022/09/17 08:58:22 - mmengine - INFO - Epoch(train) [3][24500/42151] lr: 3.0000e-04 eta: 1 day, 4:09:12 time: 0.6554 data_time: 0.1192 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 08:59:30 - mmengine - INFO - Epoch(train) [3][24600/42151] lr: 3.0000e-04 eta: 1 day, 4:07:58 time: 0.6859 data_time: 0.0932 memory: 28726 loss_ce: 0.0137 loss: 0.0137 2022/09/17 09:00:39 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 09:00:40 - mmengine - INFO - Epoch(train) [3][24700/42151] lr: 3.0000e-04 eta: 1 day, 4:06:47 time: 0.7925 data_time: 0.2559 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 09:01:47 - mmengine - INFO - Epoch(train) [3][24800/42151] lr: 3.0000e-04 eta: 1 day, 4:05:32 time: 0.7200 data_time: 0.1528 memory: 28726 loss_ce: 0.0125 loss: 0.0125 2022/09/17 09:02:54 - mmengine - INFO - Epoch(train) [3][24900/42151] lr: 3.0000e-04 eta: 1 day, 4:04:17 time: 0.5393 data_time: 0.0045 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 09:04:00 - mmengine - INFO - Epoch(train) [3][25000/42151] lr: 3.0000e-04 eta: 1 day, 4:03:02 time: 0.5938 data_time: 0.0511 memory: 28726 loss_ce: 0.0120 loss: 0.0120 2022/09/17 09:05:08 - mmengine - INFO - Epoch(train) [3][25100/42151] lr: 3.0000e-04 eta: 1 day, 4:01:48 time: 0.6591 data_time: 0.0635 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 09:06:15 - mmengine - INFO - Epoch(train) [3][25200/42151] lr: 3.0000e-04 eta: 1 day, 4:00:33 time: 0.6689 data_time: 0.1308 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 09:07:24 - mmengine - INFO - Epoch(train) [3][25300/42151] lr: 3.0000e-04 eta: 1 day, 3:59:21 time: 0.7675 data_time: 0.2315 memory: 28726 loss_ce: 0.0126 loss: 0.0126 2022/09/17 09:08:32 - mmengine - INFO - Epoch(train) [3][25400/42151] lr: 3.0000e-04 eta: 1 day, 3:58:07 time: 0.7242 data_time: 0.1831 memory: 28726 loss_ce: 0.0128 loss: 0.0128 2022/09/17 09:09:38 - mmengine - INFO - Epoch(train) [3][25500/42151] lr: 3.0000e-04 eta: 1 day, 3:56:52 time: 0.5431 data_time: 0.0045 memory: 28726 loss_ce: 0.0134 loss: 0.0134 2022/09/17 09:10:46 - mmengine - INFO - Epoch(train) [3][25600/42151] lr: 3.0000e-04 eta: 1 day, 3:55:39 time: 0.5565 data_time: 0.0134 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 09:11:52 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 09:11:53 - mmengine - INFO - Epoch(train) [3][25700/42151] lr: 3.0000e-04 eta: 1 day, 3:54:24 time: 0.6037 data_time: 0.0673 memory: 28726 loss_ce: 0.0146 loss: 0.0146 2022/09/17 09:13:01 - mmengine - INFO - Epoch(train) [3][25800/42151] lr: 3.0000e-04 eta: 1 day, 3:53:10 time: 0.6905 data_time: 0.1235 memory: 28726 loss_ce: 0.0126 loss: 0.0126 2022/09/17 09:14:10 - mmengine - INFO - Epoch(train) [3][25900/42151] lr: 3.0000e-04 eta: 1 day, 3:51:58 time: 0.7846 data_time: 0.2455 memory: 28726 loss_ce: 0.0118 loss: 0.0118 2022/09/17 09:15:17 - mmengine - INFO - Epoch(train) [3][26000/42151] lr: 3.0000e-04 eta: 1 day, 3:50:44 time: 0.7179 data_time: 0.1814 memory: 28726 loss_ce: 0.0155 loss: 0.0155 2022/09/17 09:16:25 - mmengine - INFO - Epoch(train) [3][26100/42151] lr: 3.0000e-04 eta: 1 day, 3:49:31 time: 0.5427 data_time: 0.0047 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 09:17:33 - mmengine - INFO - Epoch(train) [3][26200/42151] lr: 3.0000e-04 eta: 1 day, 3:48:17 time: 0.5485 data_time: 0.0135 memory: 28726 loss_ce: 0.0123 loss: 0.0123 2022/09/17 09:18:41 - mmengine - INFO - Epoch(train) [3][26300/42151] lr: 3.0000e-04 eta: 1 day, 3:47:04 time: 0.6636 data_time: 0.0932 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 09:19:49 - mmengine - INFO - Epoch(train) [3][26400/42151] lr: 3.0000e-04 eta: 1 day, 3:45:51 time: 0.6736 data_time: 0.1340 memory: 28726 loss_ce: 0.0150 loss: 0.0150 2022/09/17 09:20:58 - mmengine - INFO - Epoch(train) [3][26500/42151] lr: 3.0000e-04 eta: 1 day, 3:44:39 time: 0.7943 data_time: 0.2522 memory: 28726 loss_ce: 0.0114 loss: 0.0114 2022/09/17 09:22:05 - mmengine - INFO - Epoch(train) [3][26600/42151] lr: 3.0000e-04 eta: 1 day, 3:43:24 time: 0.7012 data_time: 0.1661 memory: 28726 loss_ce: 0.0146 loss: 0.0146 2022/09/17 09:23:12 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 09:23:13 - mmengine - INFO - Epoch(train) [3][26700/42151] lr: 3.0000e-04 eta: 1 day, 3:42:11 time: 0.5515 data_time: 0.0104 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 09:24:21 - mmengine - INFO - Epoch(train) [3][26800/42151] lr: 3.0000e-04 eta: 1 day, 3:40:57 time: 0.5539 data_time: 0.0128 memory: 28726 loss_ce: 0.0166 loss: 0.0166 2022/09/17 09:25:28 - mmengine - INFO - Epoch(train) [3][26900/42151] lr: 3.0000e-04 eta: 1 day, 3:39:44 time: 0.6344 data_time: 0.0644 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 09:26:36 - mmengine - INFO - Epoch(train) [3][27000/42151] lr: 3.0000e-04 eta: 1 day, 3:38:30 time: 0.6648 data_time: 0.1231 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 09:27:44 - mmengine - INFO - Epoch(train) [3][27100/42151] lr: 3.0000e-04 eta: 1 day, 3:37:17 time: 0.7964 data_time: 0.2358 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 09:28:52 - mmengine - INFO - Epoch(train) [3][27200/42151] lr: 3.0000e-04 eta: 1 day, 3:36:03 time: 0.7144 data_time: 0.1732 memory: 28726 loss_ce: 0.0122 loss: 0.0122 2022/09/17 09:30:00 - mmengine - INFO - Epoch(train) [3][27300/42151] lr: 3.0000e-04 eta: 1 day, 3:34:50 time: 0.5450 data_time: 0.0049 memory: 28726 loss_ce: 0.0121 loss: 0.0121 2022/09/17 09:31:07 - mmengine - INFO - Epoch(train) [3][27400/42151] lr: 3.0000e-04 eta: 1 day, 3:33:36 time: 0.5483 data_time: 0.0131 memory: 28726 loss_ce: 0.0123 loss: 0.0123 2022/09/17 09:32:14 - mmengine - INFO - Epoch(train) [3][27500/42151] lr: 3.0000e-04 eta: 1 day, 3:32:22 time: 0.6194 data_time: 0.0609 memory: 28726 loss_ce: 0.0127 loss: 0.0127 2022/09/17 09:33:22 - mmengine - INFO - Epoch(train) [3][27600/42151] lr: 3.0000e-04 eta: 1 day, 3:31:08 time: 0.6551 data_time: 0.1183 memory: 28726 loss_ce: 0.0141 loss: 0.0141 2022/09/17 09:34:30 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 09:34:32 - mmengine - INFO - Epoch(train) [3][27700/42151] lr: 3.0000e-04 eta: 1 day, 3:29:57 time: 0.8575 data_time: 0.2580 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 09:35:40 - mmengine - INFO - Epoch(train) [3][27800/42151] lr: 3.0000e-04 eta: 1 day, 3:28:44 time: 0.7459 data_time: 0.1752 memory: 28726 loss_ce: 0.0127 loss: 0.0127 2022/09/17 09:36:47 - mmengine - INFO - Epoch(train) [3][27900/42151] lr: 3.0000e-04 eta: 1 day, 3:27:30 time: 0.5395 data_time: 0.0045 memory: 28726 loss_ce: 0.0121 loss: 0.0121 2022/09/17 09:37:54 - mmengine - INFO - Epoch(train) [3][28000/42151] lr: 3.0000e-04 eta: 1 day, 3:26:16 time: 0.5475 data_time: 0.0123 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 09:39:01 - mmengine - INFO - Epoch(train) [3][28100/42151] lr: 3.0000e-04 eta: 1 day, 3:25:01 time: 0.6209 data_time: 0.0841 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 09:40:08 - mmengine - INFO - Epoch(train) [3][28200/42151] lr: 3.0000e-04 eta: 1 day, 3:23:47 time: 0.6751 data_time: 0.1393 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 09:41:17 - mmengine - INFO - Epoch(train) [3][28300/42151] lr: 3.0000e-04 eta: 1 day, 3:22:35 time: 0.8199 data_time: 0.2463 memory: 28726 loss_ce: 0.0126 loss: 0.0126 2022/09/17 09:42:24 - mmengine - INFO - Epoch(train) [3][28400/42151] lr: 3.0000e-04 eta: 1 day, 3:21:20 time: 0.6955 data_time: 0.1615 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 09:43:32 - mmengine - INFO - Epoch(train) [3][28500/42151] lr: 3.0000e-04 eta: 1 day, 3:20:07 time: 0.5782 data_time: 0.0044 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 09:44:39 - mmengine - INFO - Epoch(train) [3][28600/42151] lr: 3.0000e-04 eta: 1 day, 3:18:54 time: 0.5464 data_time: 0.0122 memory: 28726 loss_ce: 0.0134 loss: 0.0134 2022/09/17 09:45:45 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 09:45:46 - mmengine - INFO - Epoch(train) [3][28700/42151] lr: 3.0000e-04 eta: 1 day, 3:17:40 time: 0.6056 data_time: 0.0719 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 09:46:55 - mmengine - INFO - Epoch(train) [3][28800/42151] lr: 3.0000e-04 eta: 1 day, 3:16:27 time: 0.6795 data_time: 0.0867 memory: 28726 loss_ce: 0.0144 loss: 0.0144 2022/09/17 09:48:04 - mmengine - INFO - Epoch(train) [3][28900/42151] lr: 3.0000e-04 eta: 1 day, 3:15:15 time: 0.8268 data_time: 0.2889 memory: 28726 loss_ce: 0.0155 loss: 0.0155 2022/09/17 09:49:11 - mmengine - INFO - Epoch(train) [3][29000/42151] lr: 3.0000e-04 eta: 1 day, 3:14:01 time: 0.7285 data_time: 0.1883 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 09:50:17 - mmengine - INFO - Epoch(train) [3][29100/42151] lr: 3.0000e-04 eta: 1 day, 3:12:46 time: 0.5365 data_time: 0.0043 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 09:51:24 - mmengine - INFO - Epoch(train) [3][29200/42151] lr: 3.0000e-04 eta: 1 day, 3:11:32 time: 0.6097 data_time: 0.0138 memory: 28726 loss_ce: 0.0128 loss: 0.0128 2022/09/17 09:52:32 - mmengine - INFO - Epoch(train) [3][29300/42151] lr: 3.0000e-04 eta: 1 day, 3:10:18 time: 0.5790 data_time: 0.0458 memory: 28726 loss_ce: 0.0120 loss: 0.0120 2022/09/17 09:53:39 - mmengine - INFO - Epoch(train) [3][29400/42151] lr: 3.0000e-04 eta: 1 day, 3:09:05 time: 0.6529 data_time: 0.1157 memory: 28726 loss_ce: 0.0122 loss: 0.0122 2022/09/17 09:54:47 - mmengine - INFO - Epoch(train) [3][29500/42151] lr: 3.0000e-04 eta: 1 day, 3:07:52 time: 0.7911 data_time: 0.2221 memory: 28726 loss_ce: 0.0128 loss: 0.0128 2022/09/17 09:55:54 - mmengine - INFO - Epoch(train) [3][29600/42151] lr: 3.0000e-04 eta: 1 day, 3:06:37 time: 0.7188 data_time: 0.1794 memory: 28726 loss_ce: 0.0134 loss: 0.0134 2022/09/17 09:57:00 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 09:57:01 - mmengine - INFO - Epoch(train) [3][29700/42151] lr: 3.0000e-04 eta: 1 day, 3:05:23 time: 0.5401 data_time: 0.0063 memory: 28726 loss_ce: 0.0148 loss: 0.0148 2022/09/17 09:58:07 - mmengine - INFO - Epoch(train) [3][29800/42151] lr: 3.0000e-04 eta: 1 day, 3:04:08 time: 0.5508 data_time: 0.0131 memory: 28726 loss_ce: 0.0132 loss: 0.0132 2022/09/17 09:59:14 - mmengine - INFO - Epoch(train) [3][29900/42151] lr: 3.0000e-04 eta: 1 day, 3:02:54 time: 0.5940 data_time: 0.0458 memory: 28726 loss_ce: 0.0151 loss: 0.0151 2022/09/17 10:00:22 - mmengine - INFO - Epoch(train) [3][30000/42151] lr: 3.0000e-04 eta: 1 day, 3:01:41 time: 0.6420 data_time: 0.0866 memory: 28726 loss_ce: 0.0128 loss: 0.0128 2022/09/17 10:01:31 - mmengine - INFO - Epoch(train) [3][30100/42151] lr: 3.0000e-04 eta: 1 day, 3:00:29 time: 0.7697 data_time: 0.2364 memory: 28726 loss_ce: 0.0148 loss: 0.0148 2022/09/17 10:02:38 - mmengine - INFO - Epoch(train) [3][30200/42151] lr: 3.0000e-04 eta: 1 day, 2:59:15 time: 0.7060 data_time: 0.1726 memory: 28726 loss_ce: 0.0145 loss: 0.0145 2022/09/17 10:03:45 - mmengine - INFO - Epoch(train) [3][30300/42151] lr: 3.0000e-04 eta: 1 day, 2:58:01 time: 0.5407 data_time: 0.0044 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 10:04:52 - mmengine - INFO - Epoch(train) [3][30400/42151] lr: 3.0000e-04 eta: 1 day, 2:56:47 time: 0.5516 data_time: 0.0129 memory: 28726 loss_ce: 0.0144 loss: 0.0144 2022/09/17 10:05:59 - mmengine - INFO - Epoch(train) [3][30500/42151] lr: 3.0000e-04 eta: 1 day, 2:55:33 time: 0.5844 data_time: 0.0480 memory: 28726 loss_ce: 0.0108 loss: 0.0108 2022/09/17 10:07:08 - mmengine - INFO - Epoch(train) [3][30600/42151] lr: 3.0000e-04 eta: 1 day, 2:54:21 time: 0.6758 data_time: 0.0903 memory: 28726 loss_ce: 0.0109 loss: 0.0109 2022/09/17 10:08:17 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 10:08:18 - mmengine - INFO - Epoch(train) [3][30700/42151] lr: 3.0000e-04 eta: 1 day, 2:53:10 time: 0.8440 data_time: 0.2724 memory: 28726 loss_ce: 0.0124 loss: 0.0124 2022/09/17 10:09:24 - mmengine - INFO - Epoch(train) [3][30800/42151] lr: 3.0000e-04 eta: 1 day, 2:51:55 time: 0.7161 data_time: 0.1646 memory: 28726 loss_ce: 0.0112 loss: 0.0112 2022/09/17 10:10:32 - mmengine - INFO - Epoch(train) [3][30900/42151] lr: 3.0000e-04 eta: 1 day, 2:50:43 time: 0.5432 data_time: 0.0048 memory: 28726 loss_ce: 0.0124 loss: 0.0124 2022/09/17 10:11:40 - mmengine - INFO - Epoch(train) [3][31000/42151] lr: 3.0000e-04 eta: 1 day, 2:49:30 time: 0.5491 data_time: 0.0148 memory: 28726 loss_ce: 0.0134 loss: 0.0134 2022/09/17 10:12:49 - mmengine - INFO - Epoch(train) [3][31100/42151] lr: 3.0000e-04 eta: 1 day, 2:48:18 time: 0.6252 data_time: 0.0851 memory: 28726 loss_ce: 0.0129 loss: 0.0129 2022/09/17 10:13:57 - mmengine - INFO - Epoch(train) [3][31200/42151] lr: 3.0000e-04 eta: 1 day, 2:47:05 time: 0.6712 data_time: 0.1028 memory: 28726 loss_ce: 0.0136 loss: 0.0136 2022/09/17 10:15:06 - mmengine - INFO - Epoch(train) [3][31300/42151] lr: 3.0000e-04 eta: 1 day, 2:45:53 time: 0.8169 data_time: 0.2786 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 10:16:13 - mmengine - INFO - Epoch(train) [3][31400/42151] lr: 3.0000e-04 eta: 1 day, 2:44:40 time: 0.7267 data_time: 0.1643 memory: 28726 loss_ce: 0.0147 loss: 0.0147 2022/09/17 10:17:20 - mmengine - INFO - Epoch(train) [3][31500/42151] lr: 3.0000e-04 eta: 1 day, 2:43:26 time: 0.5419 data_time: 0.0045 memory: 28726 loss_ce: 0.0126 loss: 0.0126 2022/09/17 10:18:29 - mmengine - INFO - Epoch(train) [3][31600/42151] lr: 3.0000e-04 eta: 1 day, 2:42:13 time: 0.5788 data_time: 0.0376 memory: 28726 loss_ce: 0.0123 loss: 0.0123 2022/09/17 10:19:35 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 10:19:37 - mmengine - INFO - Epoch(train) [3][31700/42151] lr: 3.0000e-04 eta: 1 day, 2:41:01 time: 0.6276 data_time: 0.0568 memory: 28726 loss_ce: 0.0122 loss: 0.0122 2022/09/17 10:20:45 - mmengine - INFO - Epoch(train) [3][31800/42151] lr: 3.0000e-04 eta: 1 day, 2:39:48 time: 0.6722 data_time: 0.1347 memory: 28726 loss_ce: 0.0126 loss: 0.0126 2022/09/17 10:21:54 - mmengine - INFO - Epoch(train) [3][31900/42151] lr: 3.0000e-04 eta: 1 day, 2:38:37 time: 0.7814 data_time: 0.2444 memory: 28726 loss_ce: 0.0128 loss: 0.0128 2022/09/17 10:23:02 - mmengine - INFO - Epoch(train) [3][32000/42151] lr: 3.0000e-04 eta: 1 day, 2:37:24 time: 0.7453 data_time: 0.2072 memory: 28726 loss_ce: 0.0118 loss: 0.0118 2022/09/17 10:24:08 - mmengine - INFO - Epoch(train) [3][32100/42151] lr: 3.0000e-04 eta: 1 day, 2:36:09 time: 0.5413 data_time: 0.0049 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 10:25:16 - mmengine - INFO - Epoch(train) [3][32200/42151] lr: 3.0000e-04 eta: 1 day, 2:34:56 time: 0.5472 data_time: 0.0125 memory: 28726 loss_ce: 0.0144 loss: 0.0144 2022/09/17 10:26:24 - mmengine - INFO - Epoch(train) [3][32300/42151] lr: 3.0000e-04 eta: 1 day, 2:33:44 time: 0.5952 data_time: 0.0593 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 10:27:32 - mmengine - INFO - Epoch(train) [3][32400/42151] lr: 3.0000e-04 eta: 1 day, 2:32:31 time: 0.6676 data_time: 0.1074 memory: 28726 loss_ce: 0.0123 loss: 0.0123 2022/09/17 10:28:41 - mmengine - INFO - Epoch(train) [3][32500/42151] lr: 3.0000e-04 eta: 1 day, 2:31:20 time: 0.7909 data_time: 0.2521 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 10:29:49 - mmengine - INFO - Epoch(train) [3][32600/42151] lr: 3.0000e-04 eta: 1 day, 2:30:06 time: 0.7136 data_time: 0.1764 memory: 28726 loss_ce: 0.0134 loss: 0.0134 2022/09/17 10:30:55 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 10:30:56 - mmengine - INFO - Epoch(train) [3][32700/42151] lr: 3.0000e-04 eta: 1 day, 2:28:53 time: 0.5598 data_time: 0.0233 memory: 28726 loss_ce: 0.0125 loss: 0.0125 2022/09/17 10:32:03 - mmengine - INFO - Epoch(train) [3][32800/42151] lr: 3.0000e-04 eta: 1 day, 2:27:39 time: 0.5502 data_time: 0.0132 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 10:33:11 - mmengine - INFO - Epoch(train) [3][32900/42151] lr: 3.0000e-04 eta: 1 day, 2:26:26 time: 0.6191 data_time: 0.0492 memory: 28726 loss_ce: 0.0137 loss: 0.0137 2022/09/17 10:34:19 - mmengine - INFO - Epoch(train) [3][33000/42151] lr: 3.0000e-04 eta: 1 day, 2:25:14 time: 0.6515 data_time: 0.1151 memory: 28726 loss_ce: 0.0121 loss: 0.0121 2022/09/17 10:35:29 - mmengine - INFO - Epoch(train) [3][33100/42151] lr: 3.0000e-04 eta: 1 day, 2:24:03 time: 0.8341 data_time: 0.2915 memory: 28726 loss_ce: 0.0127 loss: 0.0127 2022/09/17 10:36:36 - mmengine - INFO - Epoch(train) [3][33200/42151] lr: 3.0000e-04 eta: 1 day, 2:22:50 time: 0.6949 data_time: 0.1573 memory: 28726 loss_ce: 0.0136 loss: 0.0136 2022/09/17 10:37:44 - mmengine - INFO - Epoch(train) [3][33300/42151] lr: 3.0000e-04 eta: 1 day, 2:21:36 time: 0.5424 data_time: 0.0048 memory: 28726 loss_ce: 0.0144 loss: 0.0144 2022/09/17 10:38:51 - mmengine - INFO - Epoch(train) [3][33400/42151] lr: 3.0000e-04 eta: 1 day, 2:20:23 time: 0.5510 data_time: 0.0133 memory: 28726 loss_ce: 0.0121 loss: 0.0121 2022/09/17 10:39:59 - mmengine - INFO - Epoch(train) [3][33500/42151] lr: 3.0000e-04 eta: 1 day, 2:19:10 time: 0.6160 data_time: 0.0578 memory: 28726 loss_ce: 0.0134 loss: 0.0134 2022/09/17 10:41:07 - mmengine - INFO - Epoch(train) [3][33600/42151] lr: 3.0000e-04 eta: 1 day, 2:17:57 time: 0.6584 data_time: 0.1219 memory: 28726 loss_ce: 0.0139 loss: 0.0139 2022/09/17 10:42:15 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 10:42:16 - mmengine - INFO - Epoch(train) [3][33700/42151] lr: 3.0000e-04 eta: 1 day, 2:16:46 time: 0.8365 data_time: 0.2834 memory: 28726 loss_ce: 0.0136 loss: 0.0136 2022/09/17 10:43:23 - mmengine - INFO - Epoch(train) [3][33800/42151] lr: 3.0000e-04 eta: 1 day, 2:15:32 time: 0.7115 data_time: 0.1748 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 10:44:31 - mmengine - INFO - Epoch(train) [3][33900/42151] lr: 3.0000e-04 eta: 1 day, 2:14:20 time: 0.5412 data_time: 0.0065 memory: 28726 loss_ce: 0.0137 loss: 0.0137 2022/09/17 10:45:37 - mmengine - INFO - Epoch(train) [3][34000/42151] lr: 3.0000e-04 eta: 1 day, 2:13:06 time: 0.5482 data_time: 0.0131 memory: 28726 loss_ce: 0.0115 loss: 0.0115 2022/09/17 10:46:46 - mmengine - INFO - Epoch(train) [3][34100/42151] lr: 3.0000e-04 eta: 1 day, 2:11:54 time: 0.6357 data_time: 0.0674 memory: 28726 loss_ce: 0.0124 loss: 0.0124 2022/09/17 10:47:54 - mmengine - INFO - Epoch(train) [3][34200/42151] lr: 3.0000e-04 eta: 1 day, 2:10:41 time: 0.6540 data_time: 0.1170 memory: 28726 loss_ce: 0.0124 loss: 0.0124 2022/09/17 10:49:03 - mmengine - INFO - Epoch(train) [3][34300/42151] lr: 3.0000e-04 eta: 1 day, 2:09:30 time: 0.8286 data_time: 0.2228 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 10:50:11 - mmengine - INFO - Epoch(train) [3][34400/42151] lr: 3.0000e-04 eta: 1 day, 2:08:18 time: 0.7377 data_time: 0.1757 memory: 28726 loss_ce: 0.0122 loss: 0.0122 2022/09/17 10:51:18 - mmengine - INFO - Epoch(train) [3][34500/42151] lr: 3.0000e-04 eta: 1 day, 2:07:04 time: 0.5469 data_time: 0.0045 memory: 28726 loss_ce: 0.0140 loss: 0.0140 2022/09/17 10:52:25 - mmengine - INFO - Epoch(train) [3][34600/42151] lr: 3.0000e-04 eta: 1 day, 2:05:50 time: 0.5492 data_time: 0.0135 memory: 28726 loss_ce: 0.0126 loss: 0.0126 2022/09/17 10:53:32 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 10:53:33 - mmengine - INFO - Epoch(train) [3][34700/42151] lr: 3.0000e-04 eta: 1 day, 2:04:37 time: 0.6154 data_time: 0.0787 memory: 28726 loss_ce: 0.0129 loss: 0.0129 2022/09/17 10:54:42 - mmengine - INFO - Epoch(train) [3][34800/42151] lr: 3.0000e-04 eta: 1 day, 2:03:25 time: 0.6548 data_time: 0.1167 memory: 28726 loss_ce: 0.0129 loss: 0.0129 2022/09/17 10:55:51 - mmengine - INFO - Epoch(train) [3][34900/42151] lr: 3.0000e-04 eta: 1 day, 2:02:14 time: 0.8392 data_time: 0.2741 memory: 28726 loss_ce: 0.0150 loss: 0.0150 2022/09/17 10:56:58 - mmengine - INFO - Epoch(train) [3][35000/42151] lr: 3.0000e-04 eta: 1 day, 2:01:01 time: 0.7153 data_time: 0.1723 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 10:58:06 - mmengine - INFO - Epoch(train) [3][35100/42151] lr: 3.0000e-04 eta: 1 day, 1:59:49 time: 0.5677 data_time: 0.0048 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 10:59:13 - mmengine - INFO - Epoch(train) [3][35200/42151] lr: 3.0000e-04 eta: 1 day, 1:58:35 time: 0.5486 data_time: 0.0125 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 11:00:20 - mmengine - INFO - Epoch(train) [3][35300/42151] lr: 3.0000e-04 eta: 1 day, 1:57:21 time: 0.6466 data_time: 0.1064 memory: 28726 loss_ce: 0.0150 loss: 0.0150 2022/09/17 11:01:29 - mmengine - INFO - Epoch(train) [3][35400/42151] lr: 3.0000e-04 eta: 1 day, 1:56:09 time: 0.6467 data_time: 0.0839 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 11:02:39 - mmengine - INFO - Epoch(train) [3][35500/42151] lr: 3.0000e-04 eta: 1 day, 1:54:59 time: 0.7976 data_time: 0.2600 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 11:03:46 - mmengine - INFO - Epoch(train) [3][35600/42151] lr: 3.0000e-04 eta: 1 day, 1:53:46 time: 0.7165 data_time: 0.1806 memory: 28726 loss_ce: 0.0126 loss: 0.0126 2022/09/17 11:04:52 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 11:04:53 - mmengine - INFO - Epoch(train) [3][35700/42151] lr: 3.0000e-04 eta: 1 day, 1:52:32 time: 0.5514 data_time: 0.0157 memory: 28726 loss_ce: 0.0132 loss: 0.0132 2022/09/17 11:06:00 - mmengine - INFO - Epoch(train) [3][35800/42151] lr: 3.0000e-04 eta: 1 day, 1:51:19 time: 0.5755 data_time: 0.0132 memory: 28726 loss_ce: 0.0114 loss: 0.0114 2022/09/17 11:07:07 - mmengine - INFO - Epoch(train) [3][35900/42151] lr: 3.0000e-04 eta: 1 day, 1:50:05 time: 0.5857 data_time: 0.0495 memory: 28726 loss_ce: 0.0128 loss: 0.0128 2022/09/17 11:08:14 - mmengine - INFO - Epoch(train) [3][36000/42151] lr: 3.0000e-04 eta: 1 day, 1:48:51 time: 0.6415 data_time: 0.1059 memory: 28726 loss_ce: 0.0134 loss: 0.0134 2022/09/17 11:09:22 - mmengine - INFO - Epoch(train) [3][36100/42151] lr: 3.0000e-04 eta: 1 day, 1:47:39 time: 0.7874 data_time: 0.2246 memory: 28726 loss_ce: 0.0121 loss: 0.0121 2022/09/17 11:10:28 - mmengine - INFO - Epoch(train) [3][36200/42151] lr: 3.0000e-04 eta: 1 day, 1:46:25 time: 0.7141 data_time: 0.1765 memory: 28726 loss_ce: 0.0137 loss: 0.0137 2022/09/17 11:11:35 - mmengine - INFO - Epoch(train) [3][36300/42151] lr: 3.0000e-04 eta: 1 day, 1:45:11 time: 0.5395 data_time: 0.0046 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 11:12:43 - mmengine - INFO - Epoch(train) [3][36400/42151] lr: 3.0000e-04 eta: 1 day, 1:43:58 time: 0.5522 data_time: 0.0132 memory: 28726 loss_ce: 0.0129 loss: 0.0129 2022/09/17 11:13:51 - mmengine - INFO - Epoch(train) [3][36500/42151] lr: 3.0000e-04 eta: 1 day, 1:42:46 time: 0.5888 data_time: 0.0490 memory: 28726 loss_ce: 0.0124 loss: 0.0124 2022/09/17 11:14:58 - mmengine - INFO - Epoch(train) [3][36600/42151] lr: 3.0000e-04 eta: 1 day, 1:41:33 time: 0.6531 data_time: 0.0814 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 11:16:05 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 11:16:06 - mmengine - INFO - Epoch(train) [3][36700/42151] lr: 3.0000e-04 eta: 1 day, 1:40:21 time: 0.8277 data_time: 0.2881 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 11:17:14 - mmengine - INFO - Epoch(train) [3][36800/42151] lr: 3.0000e-04 eta: 1 day, 1:39:08 time: 0.7222 data_time: 0.1831 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 11:18:20 - mmengine - INFO - Epoch(train) [3][36900/42151] lr: 3.0000e-04 eta: 1 day, 1:37:54 time: 0.5384 data_time: 0.0045 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 11:19:27 - mmengine - INFO - Epoch(train) [3][37000/42151] lr: 3.0000e-04 eta: 1 day, 1:36:41 time: 0.5491 data_time: 0.0146 memory: 28726 loss_ce: 0.0113 loss: 0.0113 2022/09/17 11:20:35 - mmengine - INFO - Epoch(train) [3][37100/42151] lr: 3.0000e-04 eta: 1 day, 1:35:28 time: 0.5832 data_time: 0.0481 memory: 28726 loss_ce: 0.0150 loss: 0.0150 2022/09/17 11:21:43 - mmengine - INFO - Epoch(train) [3][37200/42151] lr: 3.0000e-04 eta: 1 day, 1:34:15 time: 0.6619 data_time: 0.0888 memory: 28726 loss_ce: 0.0132 loss: 0.0132 2022/09/17 11:22:51 - mmengine - INFO - Epoch(train) [3][37300/42151] lr: 3.0000e-04 eta: 1 day, 1:33:03 time: 0.8098 data_time: 0.2526 memory: 28726 loss_ce: 0.0126 loss: 0.0126 2022/09/17 11:23:58 - mmengine - INFO - Epoch(train) [3][37400/42151] lr: 3.0000e-04 eta: 1 day, 1:31:49 time: 0.6992 data_time: 0.1652 memory: 28726 loss_ce: 0.0125 loss: 0.0125 2022/09/17 11:25:04 - mmengine - INFO - Epoch(train) [3][37500/42151] lr: 3.0000e-04 eta: 1 day, 1:30:36 time: 0.5420 data_time: 0.0050 memory: 28726 loss_ce: 0.0123 loss: 0.0123 2022/09/17 11:26:12 - mmengine - INFO - Epoch(train) [3][37600/42151] lr: 3.0000e-04 eta: 1 day, 1:29:23 time: 0.5505 data_time: 0.0129 memory: 28726 loss_ce: 0.0148 loss: 0.0148 2022/09/17 11:27:19 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 11:27:20 - mmengine - INFO - Epoch(train) [3][37700/42151] lr: 3.0000e-04 eta: 1 day, 1:28:11 time: 0.6282 data_time: 0.0901 memory: 28726 loss_ce: 0.0138 loss: 0.0138 2022/09/17 11:28:27 - mmengine - INFO - Epoch(train) [3][37800/42151] lr: 3.0000e-04 eta: 1 day, 1:26:57 time: 0.6610 data_time: 0.0898 memory: 28726 loss_ce: 0.0146 loss: 0.0146 2022/09/17 11:29:36 - mmengine - INFO - Epoch(train) [3][37900/42151] lr: 3.0000e-04 eta: 1 day, 1:25:46 time: 0.7984 data_time: 0.2606 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 11:30:43 - mmengine - INFO - Epoch(train) [3][38000/42151] lr: 3.0000e-04 eta: 1 day, 1:24:33 time: 0.7573 data_time: 0.1827 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 11:31:50 - mmengine - INFO - Epoch(train) [3][38100/42151] lr: 3.0000e-04 eta: 1 day, 1:23:19 time: 0.5404 data_time: 0.0046 memory: 28726 loss_ce: 0.0117 loss: 0.0117 2022/09/17 11:32:57 - mmengine - INFO - Epoch(train) [3][38200/42151] lr: 3.0000e-04 eta: 1 day, 1:22:06 time: 0.5911 data_time: 0.0518 memory: 28726 loss_ce: 0.0128 loss: 0.0128 2022/09/17 11:34:03 - mmengine - INFO - Epoch(train) [3][38300/42151] lr: 3.0000e-04 eta: 1 day, 1:20:52 time: 0.6197 data_time: 0.0467 memory: 28726 loss_ce: 0.0118 loss: 0.0118 2022/09/17 11:35:12 - mmengine - INFO - Epoch(train) [3][38400/42151] lr: 3.0000e-04 eta: 1 day, 1:19:41 time: 0.6894 data_time: 0.1280 memory: 28726 loss_ce: 0.0127 loss: 0.0127 2022/09/17 11:36:21 - mmengine - INFO - Epoch(train) [3][38500/42151] lr: 3.0000e-04 eta: 1 day, 1:18:29 time: 0.7730 data_time: 0.2252 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 11:37:28 - mmengine - INFO - Epoch(train) [3][38600/42151] lr: 3.0000e-04 eta: 1 day, 1:17:16 time: 0.7208 data_time: 0.1777 memory: 28726 loss_ce: 0.0136 loss: 0.0136 2022/09/17 11:38:34 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 11:38:35 - mmengine - INFO - Epoch(train) [3][38700/42151] lr: 3.0000e-04 eta: 1 day, 1:16:03 time: 0.5494 data_time: 0.0111 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 11:39:43 - mmengine - INFO - Epoch(train) [3][38800/42151] lr: 3.0000e-04 eta: 1 day, 1:14:50 time: 0.5616 data_time: 0.0222 memory: 28726 loss_ce: 0.0123 loss: 0.0123 2022/09/17 11:40:50 - mmengine - INFO - Epoch(train) [3][38900/42151] lr: 3.0000e-04 eta: 1 day, 1:13:38 time: 0.5846 data_time: 0.0491 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 11:41:58 - mmengine - INFO - Epoch(train) [3][39000/42151] lr: 3.0000e-04 eta: 1 day, 1:12:25 time: 0.6807 data_time: 0.1143 memory: 28726 loss_ce: 0.0130 loss: 0.0130 2022/09/17 11:43:07 - mmengine - INFO - Epoch(train) [3][39100/42151] lr: 3.0000e-04 eta: 1 day, 1:11:14 time: 0.7850 data_time: 0.2447 memory: 28726 loss_ce: 0.0131 loss: 0.0131 2022/09/17 11:44:14 - mmengine - INFO - Epoch(train) [3][39200/42151] lr: 3.0000e-04 eta: 1 day, 1:10:01 time: 0.7565 data_time: 0.2071 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 11:45:21 - mmengine - INFO - Epoch(train) [3][39300/42151] lr: 3.0000e-04 eta: 1 day, 1:08:48 time: 0.5425 data_time: 0.0046 memory: 28726 loss_ce: 0.0119 loss: 0.0119 2022/09/17 11:46:28 - mmengine - INFO - Epoch(train) [3][39400/42151] lr: 3.0000e-04 eta: 1 day, 1:07:35 time: 0.5475 data_time: 0.0132 memory: 28726 loss_ce: 0.0122 loss: 0.0122 2022/09/17 11:47:35 - mmengine - INFO - Epoch(train) [3][39500/42151] lr: 3.0000e-04 eta: 1 day, 1:06:21 time: 0.6041 data_time: 0.0473 memory: 28726 loss_ce: 0.0120 loss: 0.0120 2022/09/17 11:48:42 - mmengine - INFO - Epoch(train) [3][39600/42151] lr: 3.0000e-04 eta: 1 day, 1:05:09 time: 0.6604 data_time: 0.1184 memory: 28726 loss_ce: 0.0130 loss: 0.0130 2022/09/17 11:49:50 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 11:49:51 - mmengine - INFO - Epoch(train) [3][39700/42151] lr: 3.0000e-04 eta: 1 day, 1:03:57 time: 0.7951 data_time: 0.2569 memory: 28726 loss_ce: 0.0124 loss: 0.0124 2022/09/17 11:50:59 - mmengine - INFO - Epoch(train) [3][39800/42151] lr: 3.0000e-04 eta: 1 day, 1:02:45 time: 0.7200 data_time: 0.1785 memory: 28726 loss_ce: 0.0114 loss: 0.0114 2022/09/17 11:52:06 - mmengine - INFO - Epoch(train) [3][39900/42151] lr: 3.0000e-04 eta: 1 day, 1:01:32 time: 0.5413 data_time: 0.0046 memory: 28726 loss_ce: 0.0125 loss: 0.0125 2022/09/17 11:53:13 - mmengine - INFO - Epoch(train) [3][40000/42151] lr: 3.0000e-04 eta: 1 day, 1:00:19 time: 0.5494 data_time: 0.0124 memory: 28726 loss_ce: 0.0122 loss: 0.0122 2022/09/17 11:54:21 - mmengine - INFO - Epoch(train) [3][40100/42151] lr: 3.0000e-04 eta: 1 day, 0:59:07 time: 0.6168 data_time: 0.0478 memory: 28726 loss_ce: 0.0130 loss: 0.0130 2022/09/17 11:55:29 - mmengine - INFO - Epoch(train) [3][40200/42151] lr: 3.0000e-04 eta: 1 day, 0:57:54 time: 0.6570 data_time: 0.1212 memory: 28726 loss_ce: 0.0101 loss: 0.0101 2022/09/17 11:56:38 - mmengine - INFO - Epoch(train) [3][40300/42151] lr: 3.0000e-04 eta: 1 day, 0:56:43 time: 0.7939 data_time: 0.2274 memory: 28726 loss_ce: 0.0117 loss: 0.0117 2022/09/17 11:57:45 - mmengine - INFO - Epoch(train) [3][40400/42151] lr: 3.0000e-04 eta: 1 day, 0:55:30 time: 0.7334 data_time: 0.1932 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 11:58:53 - mmengine - INFO - Epoch(train) [3][40500/42151] lr: 3.0000e-04 eta: 1 day, 0:54:18 time: 0.5433 data_time: 0.0051 memory: 28726 loss_ce: 0.0112 loss: 0.0112 2022/09/17 12:00:00 - mmengine - INFO - Epoch(train) [3][40600/42151] lr: 3.0000e-04 eta: 1 day, 0:53:06 time: 0.5477 data_time: 0.0129 memory: 28726 loss_ce: 0.0152 loss: 0.0152 2022/09/17 12:01:12 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 12:01:14 - mmengine - INFO - Epoch(train) [3][40700/42151] lr: 3.0000e-04 eta: 1 day, 0:51:59 time: 0.6244 data_time: 0.0580 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 12:02:22 - mmengine - INFO - Epoch(train) [3][40800/42151] lr: 3.0000e-04 eta: 1 day, 0:50:47 time: 0.6586 data_time: 0.1205 memory: 28726 loss_ce: 0.0125 loss: 0.0125 2022/09/17 12:03:31 - mmengine - INFO - Epoch(train) [3][40900/42151] lr: 3.0000e-04 eta: 1 day, 0:49:36 time: 0.8211 data_time: 0.2293 memory: 28726 loss_ce: 0.0129 loss: 0.0129 2022/09/17 12:04:39 - mmengine - INFO - Epoch(train) [3][41000/42151] lr: 3.0000e-04 eta: 1 day, 0:48:24 time: 0.7443 data_time: 0.1726 memory: 28726 loss_ce: 0.0114 loss: 0.0114 2022/09/17 12:05:46 - mmengine - INFO - Epoch(train) [3][41100/42151] lr: 3.0000e-04 eta: 1 day, 0:47:11 time: 0.5488 data_time: 0.0047 memory: 28726 loss_ce: 0.0134 loss: 0.0134 2022/09/17 12:06:54 - mmengine - INFO - Epoch(train) [3][41200/42151] lr: 3.0000e-04 eta: 1 day, 0:45:59 time: 0.5482 data_time: 0.0129 memory: 28726 loss_ce: 0.0129 loss: 0.0129 2022/09/17 12:08:02 - mmengine - INFO - Epoch(train) [3][41300/42151] lr: 3.0000e-04 eta: 1 day, 0:44:47 time: 0.6147 data_time: 0.0791 memory: 28726 loss_ce: 0.0135 loss: 0.0135 2022/09/17 12:09:09 - mmengine - INFO - Epoch(train) [3][41400/42151] lr: 3.0000e-04 eta: 1 day, 0:43:34 time: 0.6599 data_time: 0.1156 memory: 28726 loss_ce: 0.0143 loss: 0.0143 2022/09/17 12:10:19 - mmengine - INFO - Epoch(train) [3][41500/42151] lr: 3.0000e-04 eta: 1 day, 0:42:24 time: 0.8536 data_time: 0.2792 memory: 28726 loss_ce: 0.0119 loss: 0.0119 2022/09/17 12:11:26 - mmengine - INFO - Epoch(train) [3][41600/42151] lr: 3.0000e-04 eta: 1 day, 0:41:11 time: 0.7020 data_time: 0.1678 memory: 28726 loss_ce: 0.0119 loss: 0.0119 2022/09/17 12:12:32 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 12:12:33 - mmengine - INFO - Epoch(train) [3][41700/42151] lr: 3.0000e-04 eta: 1 day, 0:39:58 time: 0.5696 data_time: 0.0110 memory: 28726 loss_ce: 0.0123 loss: 0.0123 2022/09/17 12:13:40 - mmengine - INFO - Epoch(train) [3][41800/42151] lr: 3.0000e-04 eta: 1 day, 0:38:45 time: 0.5486 data_time: 0.0145 memory: 28726 loss_ce: 0.0142 loss: 0.0142 2022/09/17 12:14:48 - mmengine - INFO - Epoch(train) [3][41900/42151] lr: 3.0000e-04 eta: 1 day, 0:37:33 time: 0.6192 data_time: 0.0792 memory: 28726 loss_ce: 0.0114 loss: 0.0114 2022/09/17 12:15:56 - mmengine - INFO - Epoch(train) [3][42000/42151] lr: 3.0000e-04 eta: 1 day, 0:36:21 time: 0.6532 data_time: 0.0864 memory: 28726 loss_ce: 0.0133 loss: 0.0133 2022/09/17 12:17:05 - mmengine - INFO - Epoch(train) [3][42100/42151] lr: 3.0000e-04 eta: 1 day, 0:35:10 time: 0.8354 data_time: 0.2978 memory: 28726 loss_ce: 0.0136 loss: 0.0136 2022/09/17 12:17:36 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 12:17:36 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/09/17 12:19:09 - mmengine - INFO - Epoch(val) [3][100/7672] eta: 1:41:17 time: 0.8027 data_time: 0.0022 memory: 28726 2022/09/17 12:20:28 - mmengine - INFO - Epoch(val) [3][200/7672] eta: 1:18:38 time: 0.6314 data_time: 0.0010 memory: 1303 2022/09/17 12:21:08 - mmengine - INFO - Epoch(val) [3][300/7672] eta: 0:23:52 time: 0.1943 data_time: 0.0009 memory: 1303 2022/09/17 12:21:28 - mmengine - INFO - Epoch(val) [3][400/7672] eta: 0:25:44 time: 0.2123 data_time: 0.0008 memory: 1303 2022/09/17 12:21:49 - mmengine - INFO - Epoch(val) [3][500/7672] eta: 0:23:53 time: 0.1998 data_time: 0.0008 memory: 1303 2022/09/17 12:22:09 - mmengine - INFO - Epoch(val) [3][600/7672] eta: 0:23:14 time: 0.1972 data_time: 0.0007 memory: 1303 2022/09/17 12:22:30 - mmengine - INFO - Epoch(val) [3][700/7672] eta: 0:23:16 time: 0.2004 data_time: 0.0008 memory: 1303 2022/09/17 12:22:50 - mmengine - INFO - Epoch(val) [3][800/7672] eta: 0:22:34 time: 0.1971 data_time: 0.0008 memory: 1303 2022/09/17 12:23:11 - mmengine - INFO - Epoch(val) [3][900/7672] eta: 0:23:15 time: 0.2061 data_time: 0.0008 memory: 1303 2022/09/17 12:23:31 - mmengine - INFO - Epoch(val) [3][1000/7672] eta: 0:21:40 time: 0.1949 data_time: 0.0008 memory: 1303 2022/09/17 12:23:52 - mmengine - INFO - Epoch(val) [3][1100/7672] eta: 0:21:45 time: 0.1987 data_time: 0.0008 memory: 1303 2022/09/17 12:24:12 - mmengine - INFO - Epoch(val) [3][1200/7672] eta: 0:21:58 time: 0.2038 data_time: 0.0007 memory: 1303 2022/09/17 12:24:32 - mmengine - INFO - Epoch(val) [3][1300/7672] eta: 0:22:18 time: 0.2100 data_time: 0.0007 memory: 1303 2022/09/17 12:24:53 - mmengine - INFO - Epoch(val) [3][1400/7672] eta: 0:22:02 time: 0.2109 data_time: 0.0008 memory: 1303 2022/09/17 12:25:14 - mmengine - INFO - Epoch(val) [3][1500/7672] eta: 0:20:52 time: 0.2029 data_time: 0.0008 memory: 1303 2022/09/17 12:25:34 - mmengine - INFO - Epoch(val) [3][1600/7672] eta: 0:21:22 time: 0.2111 data_time: 0.0008 memory: 1303 2022/09/17 12:25:55 - mmengine - INFO - Epoch(val) [3][1700/7672] eta: 0:20:34 time: 0.2067 data_time: 0.0007 memory: 1303 2022/09/17 12:26:15 - mmengine - INFO - Epoch(val) [3][1800/7672] eta: 0:19:27 time: 0.1989 data_time: 0.0008 memory: 1303 2022/09/17 12:26:36 - mmengine - INFO - Epoch(val) [3][1900/7672] eta: 0:19:19 time: 0.2009 data_time: 0.0008 memory: 1303 2022/09/17 12:26:57 - mmengine - INFO - Epoch(val) [3][2000/7672] eta: 0:21:02 time: 0.2226 data_time: 0.0021 memory: 1303 2022/09/17 12:27:17 - mmengine - INFO - Epoch(val) [3][2100/7672] eta: 0:19:12 time: 0.2068 data_time: 0.0017 memory: 1303 2022/09/17 12:27:38 - mmengine - INFO - Epoch(val) [3][2200/7672] eta: 0:18:13 time: 0.1999 data_time: 0.0008 memory: 1303 2022/09/17 12:27:58 - mmengine - INFO - Epoch(val) [3][2300/7672] eta: 0:17:43 time: 0.1981 data_time: 0.0007 memory: 1303 2022/09/17 12:28:19 - mmengine - INFO - Epoch(val) [3][2400/7672] eta: 0:17:50 time: 0.2030 data_time: 0.0008 memory: 1303 2022/09/17 12:28:40 - mmengine - INFO - Epoch(val) [3][2500/7672] eta: 0:17:17 time: 0.2007 data_time: 0.0007 memory: 1303 2022/09/17 12:29:00 - mmengine - INFO - Epoch(val) [3][2600/7672] eta: 0:17:13 time: 0.2038 data_time: 0.0041 memory: 1303 2022/09/17 12:29:21 - mmengine - INFO - Epoch(val) [3][2700/7672] eta: 0:17:11 time: 0.2075 data_time: 0.0010 memory: 1303 2022/09/17 12:29:41 - mmengine - INFO - Epoch(val) [3][2800/7672] eta: 0:16:14 time: 0.1999 data_time: 0.0008 memory: 1303 2022/09/17 12:30:02 - mmengine - INFO - Epoch(val) [3][2900/7672] eta: 0:16:06 time: 0.2026 data_time: 0.0008 memory: 1303 2022/09/17 12:30:22 - mmengine - INFO - Epoch(val) [3][3000/7672] eta: 0:15:29 time: 0.1989 data_time: 0.0008 memory: 1303 2022/09/17 12:30:42 - mmengine - INFO - Epoch(val) [3][3100/7672] eta: 0:15:02 time: 0.1973 data_time: 0.0008 memory: 1303 2022/09/17 12:31:03 - mmengine - INFO - Epoch(val) [3][3200/7672] eta: 0:15:54 time: 0.2134 data_time: 0.0008 memory: 1303 2022/09/17 12:31:23 - mmengine - INFO - Epoch(val) [3][3300/7672] eta: 0:14:16 time: 0.1958 data_time: 0.0008 memory: 1303 2022/09/17 12:31:44 - mmengine - INFO - Epoch(val) [3][3400/7672] eta: 0:14:18 time: 0.2009 data_time: 0.0008 memory: 1303 2022/09/17 12:32:05 - mmengine - INFO - Epoch(val) [3][3500/7672] eta: 0:13:40 time: 0.1966 data_time: 0.0008 memory: 1303 2022/09/17 12:32:25 - mmengine - INFO - Epoch(val) [3][3600/7672] eta: 0:13:38 time: 0.2010 data_time: 0.0008 memory: 1303 2022/09/17 12:32:45 - mmengine - INFO - Epoch(val) [3][3700/7672] eta: 0:13:11 time: 0.1994 data_time: 0.0008 memory: 1303 2022/09/17 12:33:06 - mmengine - INFO - Epoch(val) [3][3800/7672] eta: 0:13:44 time: 0.2130 data_time: 0.0019 memory: 1303 2022/09/17 12:33:27 - mmengine - INFO - Epoch(val) [3][3900/7672] eta: 0:13:08 time: 0.2091 data_time: 0.0014 memory: 1303 2022/09/17 12:33:47 - mmengine - INFO - Epoch(val) [3][4000/7672] eta: 0:13:27 time: 0.2198 data_time: 0.0015 memory: 1303 2022/09/17 12:34:08 - mmengine - INFO - Epoch(val) [3][4100/7672] eta: 0:12:16 time: 0.2061 data_time: 0.0022 memory: 1303 2022/09/17 12:34:28 - mmengine - INFO - Epoch(val) [3][4200/7672] eta: 0:11:16 time: 0.1949 data_time: 0.0007 memory: 1303 2022/09/17 12:34:49 - mmengine - INFO - Epoch(val) [3][4300/7672] eta: 0:11:23 time: 0.2028 data_time: 0.0008 memory: 1303 2022/09/17 12:35:10 - mmengine - INFO - Epoch(val) [3][4400/7672] eta: 0:11:28 time: 0.2104 data_time: 0.0008 memory: 1303 2022/09/17 12:35:31 - mmengine - INFO - Epoch(val) [3][4500/7672] eta: 0:10:36 time: 0.2007 data_time: 0.0008 memory: 1303 2022/09/17 12:35:51 - mmengine - INFO - Epoch(val) [3][4600/7672] eta: 0:10:26 time: 0.2041 data_time: 0.0008 memory: 1303 2022/09/17 12:36:11 - mmengine - INFO - Epoch(val) [3][4700/7672] eta: 0:10:01 time: 0.2024 data_time: 0.0008 memory: 1303 2022/09/17 12:36:32 - mmengine - INFO - Epoch(val) [3][4800/7672] eta: 0:09:36 time: 0.2007 data_time: 0.0008 memory: 1303 2022/09/17 12:36:52 - mmengine - INFO - Epoch(val) [3][4900/7672] eta: 0:09:07 time: 0.1974 data_time: 0.0007 memory: 1303 2022/09/17 12:37:13 - mmengine - INFO - Epoch(val) [3][5000/7672] eta: 0:09:00 time: 0.2023 data_time: 0.0008 memory: 1303 2022/09/17 12:37:34 - mmengine - INFO - Epoch(val) [3][5100/7672] eta: 0:08:53 time: 0.2075 data_time: 0.0008 memory: 1303 2022/09/17 12:37:54 - mmengine - INFO - Epoch(val) [3][5200/7672] eta: 0:08:13 time: 0.1998 data_time: 0.0008 memory: 1303 2022/09/17 12:38:15 - mmengine - INFO - Epoch(val) [3][5300/7672] eta: 0:07:57 time: 0.2014 data_time: 0.0008 memory: 1303 2022/09/17 12:38:36 - mmengine - INFO - Epoch(val) [3][5400/7672] eta: 0:07:38 time: 0.2020 data_time: 0.0007 memory: 1303 2022/09/17 12:38:56 - mmengine - INFO - Epoch(val) [3][5500/7672] eta: 0:07:24 time: 0.2045 data_time: 0.0008 memory: 1303 2022/09/17 12:39:17 - mmengine - INFO - Epoch(val) [3][5600/7672] eta: 0:07:01 time: 0.2034 data_time: 0.0009 memory: 1303 2022/09/17 12:39:38 - mmengine - INFO - Epoch(val) [3][5700/7672] eta: 0:06:28 time: 0.1968 data_time: 0.0009 memory: 1303 2022/09/17 12:39:58 - mmengine - INFO - Epoch(val) [3][5800/7672] eta: 0:06:32 time: 0.2096 data_time: 0.0009 memory: 1303 2022/09/17 12:40:19 - mmengine - INFO - Epoch(val) [3][5900/7672] eta: 0:05:59 time: 0.2031 data_time: 0.0021 memory: 1303 2022/09/17 12:40:39 - mmengine - INFO - Epoch(val) [3][6000/7672] eta: 0:05:43 time: 0.2055 data_time: 0.0008 memory: 1303 2022/09/17 12:41:00 - mmengine - INFO - Epoch(val) [3][6100/7672] eta: 0:05:24 time: 0.2067 data_time: 0.0008 memory: 1303 2022/09/17 12:41:21 - mmengine - INFO - Epoch(val) [3][6200/7672] eta: 0:04:52 time: 0.1989 data_time: 0.0008 memory: 1303 2022/09/17 12:41:41 - mmengine - INFO - Epoch(val) [3][6300/7672] eta: 0:04:40 time: 0.2042 data_time: 0.0008 memory: 1303 2022/09/17 12:42:02 - mmengine - INFO - Epoch(val) [3][6400/7672] eta: 0:04:13 time: 0.1994 data_time: 0.0008 memory: 1303 2022/09/17 12:42:22 - mmengine - INFO - Epoch(val) [3][6500/7672] eta: 0:03:56 time: 0.2017 data_time: 0.0008 memory: 1303 2022/09/17 12:42:43 - mmengine - INFO - Epoch(val) [3][6600/7672] eta: 0:03:35 time: 0.2006 data_time: 0.0007 memory: 1303 2022/09/17 12:43:03 - mmengine - INFO - Epoch(val) [3][6700/7672] eta: 0:03:23 time: 0.2094 data_time: 0.0008 memory: 1303 2022/09/17 12:43:24 - mmengine - INFO - Epoch(val) [3][6800/7672] eta: 0:02:57 time: 0.2040 data_time: 0.0007 memory: 1303 2022/09/17 12:43:45 - mmengine - INFO - Epoch(val) [3][6900/7672] eta: 0:02:56 time: 0.2286 data_time: 0.0009 memory: 1303 2022/09/17 12:44:05 - mmengine - INFO - Epoch(val) [3][7000/7672] eta: 0:02:13 time: 0.1988 data_time: 0.0007 memory: 1303 2022/09/17 12:44:26 - mmengine - INFO - Epoch(val) [3][7100/7672] eta: 0:01:53 time: 0.1991 data_time: 0.0008 memory: 1303 2022/09/17 12:44:46 - mmengine - INFO - Epoch(val) [3][7200/7672] eta: 0:01:44 time: 0.2204 data_time: 0.0008 memory: 1303 2022/09/17 12:45:07 - mmengine - INFO - Epoch(val) [3][7300/7672] eta: 0:01:16 time: 0.2061 data_time: 0.0012 memory: 1303 2022/09/17 12:45:27 - mmengine - INFO - Epoch(val) [3][7400/7672] eta: 0:00:56 time: 0.2090 data_time: 0.0008 memory: 1303 2022/09/17 12:45:48 - mmengine - INFO - Epoch(val) [3][7500/7672] eta: 0:00:34 time: 0.2028 data_time: 0.0011 memory: 1303 2022/09/17 12:46:08 - mmengine - INFO - Epoch(val) [3][7600/7672] eta: 0:00:15 time: 0.2112 data_time: 0.0013 memory: 1303 2022/09/17 12:46:23 - mmengine - INFO - Epoch(val) [3][7672/7672] CUTE80/recog/word_acc_ignore_case_symbol: 0.8785 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9433 SVT/recog/word_acc_ignore_case_symbol: 0.8887 SVTP/recog/word_acc_ignore_case_symbol: 0.7922 IC13/recog/word_acc_ignore_case_symbol: 0.9458 IC15/recog/word_acc_ignore_case_symbol: 0.7342 2022/09/17 12:47:38 - mmengine - INFO - Epoch(train) [4][100/42151] lr: 3.0000e-05 eta: 1 day, 0:33:24 time: 0.7105 data_time: 0.1731 memory: 28726 loss_ce: 0.0109 loss: 0.0109 2022/09/17 12:48:44 - mmengine - INFO - Epoch(train) [4][200/42151] lr: 3.0000e-05 eta: 1 day, 0:32:10 time: 0.7077 data_time: 0.1715 memory: 28726 loss_ce: 0.0110 loss: 0.0110 2022/09/17 12:49:51 - mmengine - INFO - Epoch(train) [4][300/42151] lr: 3.0000e-05 eta: 1 day, 0:30:57 time: 0.5908 data_time: 0.0294 memory: 28726 loss_ce: 0.0102 loss: 0.0102 2022/09/17 12:50:58 - mmengine - INFO - Epoch(train) [4][400/42151] lr: 3.0000e-05 eta: 1 day, 0:29:44 time: 0.6551 data_time: 0.1148 memory: 28726 loss_ce: 0.0105 loss: 0.0105 2022/09/17 12:52:07 - mmengine - INFO - Epoch(train) [4][500/42151] lr: 3.0000e-05 eta: 1 day, 0:28:33 time: 0.6739 data_time: 0.1367 memory: 28726 loss_ce: 0.0110 loss: 0.0110 2022/09/17 12:52:40 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 12:53:14 - mmengine - INFO - Epoch(train) [4][600/42151] lr: 3.0000e-05 eta: 1 day, 0:27:20 time: 0.5656 data_time: 0.0049 memory: 28726 loss_ce: 0.0099 loss: 0.0099 2022/09/17 12:54:23 - mmengine - INFO - Epoch(train) [4][700/42151] lr: 3.0000e-05 eta: 1 day, 0:26:10 time: 0.7133 data_time: 0.1711 memory: 28726 loss_ce: 0.0100 loss: 0.0100 2022/09/17 12:55:31 - mmengine - INFO - Epoch(train) [4][800/42151] lr: 3.0000e-05 eta: 1 day, 0:24:57 time: 0.6922 data_time: 0.1553 memory: 28726 loss_ce: 0.0096 loss: 0.0096 2022/09/17 12:56:40 - mmengine - INFO - Epoch(train) [4][900/42151] lr: 3.0000e-05 eta: 1 day, 0:23:46 time: 0.5892 data_time: 0.0524 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 12:57:49 - mmengine - INFO - Epoch(train) [4][1000/42151] lr: 3.0000e-05 eta: 1 day, 0:22:35 time: 0.6614 data_time: 0.1207 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 12:58:56 - mmengine - INFO - Epoch(train) [4][1100/42151] lr: 3.0000e-05 eta: 1 day, 0:21:23 time: 0.6815 data_time: 0.1176 memory: 28726 loss_ce: 0.0097 loss: 0.0097 2022/09/17 13:00:04 - mmengine - INFO - Epoch(train) [4][1200/42151] lr: 3.0000e-05 eta: 1 day, 0:20:10 time: 0.5433 data_time: 0.0047 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 13:01:13 - mmengine - INFO - Epoch(train) [4][1300/42151] lr: 3.0000e-05 eta: 1 day, 0:19:00 time: 0.7456 data_time: 0.2065 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 13:02:22 - mmengine - INFO - Epoch(train) [4][1400/42151] lr: 3.0000e-05 eta: 1 day, 0:17:49 time: 0.7142 data_time: 0.1694 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 13:03:29 - mmengine - INFO - Epoch(train) [4][1500/42151] lr: 3.0000e-05 eta: 1 day, 0:16:36 time: 0.5886 data_time: 0.0338 memory: 28726 loss_ce: 0.0100 loss: 0.0100 2022/09/17 13:04:01 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 13:04:37 - mmengine - INFO - Epoch(train) [4][1600/42151] lr: 3.0000e-05 eta: 1 day, 0:15:24 time: 0.6439 data_time: 0.1058 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 13:05:45 - mmengine - INFO - Epoch(train) [4][1700/42151] lr: 3.0000e-05 eta: 1 day, 0:14:12 time: 0.6817 data_time: 0.1159 memory: 28726 loss_ce: 0.0115 loss: 0.0115 2022/09/17 13:06:51 - mmengine - INFO - Epoch(train) [4][1800/42151] lr: 3.0000e-05 eta: 1 day, 0:12:59 time: 0.5447 data_time: 0.0049 memory: 28726 loss_ce: 0.0112 loss: 0.0112 2022/09/17 13:08:00 - mmengine - INFO - Epoch(train) [4][1900/42151] lr: 3.0000e-05 eta: 1 day, 0:11:48 time: 0.7420 data_time: 0.1670 memory: 28726 loss_ce: 0.0099 loss: 0.0099 2022/09/17 13:09:08 - mmengine - INFO - Epoch(train) [4][2000/42151] lr: 3.0000e-05 eta: 1 day, 0:10:35 time: 0.7196 data_time: 0.1841 memory: 28726 loss_ce: 0.0109 loss: 0.0109 2022/09/17 13:10:15 - mmengine - INFO - Epoch(train) [4][2100/42151] lr: 3.0000e-05 eta: 1 day, 0:09:22 time: 0.5788 data_time: 0.0430 memory: 28726 loss_ce: 0.0099 loss: 0.0099 2022/09/17 13:11:24 - mmengine - INFO - Epoch(train) [4][2200/42151] lr: 3.0000e-05 eta: 1 day, 0:08:12 time: 0.6966 data_time: 0.1164 memory: 28726 loss_ce: 0.0095 loss: 0.0095 2022/09/17 13:12:32 - mmengine - INFO - Epoch(train) [4][2300/42151] lr: 3.0000e-05 eta: 1 day, 0:07:00 time: 0.6853 data_time: 0.1247 memory: 28726 loss_ce: 0.0106 loss: 0.0106 2022/09/17 13:13:39 - mmengine - INFO - Epoch(train) [4][2400/42151] lr: 3.0000e-05 eta: 1 day, 0:05:48 time: 0.5663 data_time: 0.0046 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 13:14:48 - mmengine - INFO - Epoch(train) [4][2500/42151] lr: 3.0000e-05 eta: 1 day, 0:04:37 time: 0.7106 data_time: 0.1715 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 13:15:21 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 13:15:56 - mmengine - INFO - Epoch(train) [4][2600/42151] lr: 3.0000e-05 eta: 1 day, 0:03:24 time: 0.7506 data_time: 0.2135 memory: 28726 loss_ce: 0.0097 loss: 0.0097 2022/09/17 13:17:02 - mmengine - INFO - Epoch(train) [4][2700/42151] lr: 3.0000e-05 eta: 1 day, 0:02:11 time: 0.5911 data_time: 0.0391 memory: 28726 loss_ce: 0.0103 loss: 0.0103 2022/09/17 13:18:11 - mmengine - INFO - Epoch(train) [4][2800/42151] lr: 3.0000e-05 eta: 1 day, 0:01:00 time: 0.6638 data_time: 0.0914 memory: 28726 loss_ce: 0.0108 loss: 0.0108 2022/09/17 13:19:18 - mmengine - INFO - Epoch(train) [4][2900/42151] lr: 3.0000e-05 eta: 23:59:48 time: 0.6494 data_time: 0.1126 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/17 13:20:26 - mmengine - INFO - Epoch(train) [4][3000/42151] lr: 3.0000e-05 eta: 23:58:35 time: 0.5677 data_time: 0.0050 memory: 28726 loss_ce: 0.0105 loss: 0.0105 2022/09/17 13:21:34 - mmengine - INFO - Epoch(train) [4][3100/42151] lr: 3.0000e-05 eta: 23:57:24 time: 0.7150 data_time: 0.1796 memory: 28726 loss_ce: 0.0113 loss: 0.0113 2022/09/17 13:22:42 - mmengine - INFO - Epoch(train) [4][3200/42151] lr: 3.0000e-05 eta: 23:56:11 time: 0.7463 data_time: 0.1776 memory: 28726 loss_ce: 0.0107 loss: 0.0107 2022/09/17 13:23:49 - mmengine - INFO - Epoch(train) [4][3300/42151] lr: 3.0000e-05 eta: 23:55:00 time: 0.5682 data_time: 0.0324 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 13:24:58 - mmengine - INFO - Epoch(train) [4][3400/42151] lr: 3.0000e-05 eta: 23:53:49 time: 0.6608 data_time: 0.1196 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 13:26:06 - mmengine - INFO - Epoch(train) [4][3500/42151] lr: 3.0000e-05 eta: 23:52:37 time: 0.6734 data_time: 0.1350 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/17 13:26:38 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 13:27:14 - mmengine - INFO - Epoch(train) [4][3600/42151] lr: 3.0000e-05 eta: 23:51:25 time: 0.5424 data_time: 0.0052 memory: 28726 loss_ce: 0.0095 loss: 0.0095 2022/09/17 13:28:24 - mmengine - INFO - Epoch(train) [4][3700/42151] lr: 3.0000e-05 eta: 23:50:15 time: 0.7661 data_time: 0.2028 memory: 28726 loss_ce: 0.0102 loss: 0.0102 2022/09/17 13:29:32 - mmengine - INFO - Epoch(train) [4][3800/42151] lr: 3.0000e-05 eta: 23:49:04 time: 0.7639 data_time: 0.1917 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 13:30:40 - mmengine - INFO - Epoch(train) [4][3900/42151] lr: 3.0000e-05 eta: 23:47:51 time: 0.5966 data_time: 0.0606 memory: 28726 loss_ce: 0.0100 loss: 0.0100 2022/09/17 13:31:49 - mmengine - INFO - Epoch(train) [4][4000/42151] lr: 3.0000e-05 eta: 23:46:41 time: 0.6642 data_time: 0.1294 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 13:32:58 - mmengine - INFO - Epoch(train) [4][4100/42151] lr: 3.0000e-05 eta: 23:45:30 time: 0.6865 data_time: 0.1118 memory: 28726 loss_ce: 0.0101 loss: 0.0101 2022/09/17 13:34:07 - mmengine - INFO - Epoch(train) [4][4200/42151] lr: 3.0000e-05 eta: 23:44:19 time: 0.5432 data_time: 0.0051 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/17 13:35:18 - mmengine - INFO - Epoch(train) [4][4300/42151] lr: 3.0000e-05 eta: 23:43:10 time: 0.7545 data_time: 0.1883 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/17 13:36:26 - mmengine - INFO - Epoch(train) [4][4400/42151] lr: 3.0000e-05 eta: 23:41:58 time: 0.7453 data_time: 0.1959 memory: 28726 loss_ce: 0.0097 loss: 0.0097 2022/09/17 13:37:34 - mmengine - INFO - Epoch(train) [4][4500/42151] lr: 3.0000e-05 eta: 23:40:47 time: 0.5730 data_time: 0.0324 memory: 28726 loss_ce: 0.0108 loss: 0.0108 2022/09/17 13:38:06 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 13:38:43 - mmengine - INFO - Epoch(train) [4][4600/42151] lr: 3.0000e-05 eta: 23:39:36 time: 0.6791 data_time: 0.1342 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 13:39:51 - mmengine - INFO - Epoch(train) [4][4700/42151] lr: 3.0000e-05 eta: 23:38:24 time: 0.6961 data_time: 0.1304 memory: 28726 loss_ce: 0.0113 loss: 0.0113 2022/09/17 13:40:59 - mmengine - INFO - Epoch(train) [4][4800/42151] lr: 3.0000e-05 eta: 23:37:13 time: 0.5451 data_time: 0.0054 memory: 28726 loss_ce: 0.0099 loss: 0.0099 2022/09/17 13:42:09 - mmengine - INFO - Epoch(train) [4][4900/42151] lr: 3.0000e-05 eta: 23:36:02 time: 0.7428 data_time: 0.2017 memory: 28726 loss_ce: 0.0097 loss: 0.0097 2022/09/17 13:43:16 - mmengine - INFO - Epoch(train) [4][5000/42151] lr: 3.0000e-05 eta: 23:34:50 time: 0.7255 data_time: 0.1843 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 13:44:25 - mmengine - INFO - Epoch(train) [4][5100/42151] lr: 3.0000e-05 eta: 23:33:39 time: 0.5894 data_time: 0.0321 memory: 28726 loss_ce: 0.0095 loss: 0.0095 2022/09/17 13:45:34 - mmengine - INFO - Epoch(train) [4][5200/42151] lr: 3.0000e-05 eta: 23:32:29 time: 0.6718 data_time: 0.1348 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/17 13:46:42 - mmengine - INFO - Epoch(train) [4][5300/42151] lr: 3.0000e-05 eta: 23:31:17 time: 0.6500 data_time: 0.1115 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/17 13:47:50 - mmengine - INFO - Epoch(train) [4][5400/42151] lr: 3.0000e-05 eta: 23:30:05 time: 0.5451 data_time: 0.0049 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 13:48:59 - mmengine - INFO - Epoch(train) [4][5500/42151] lr: 3.0000e-05 eta: 23:28:55 time: 0.7490 data_time: 0.1861 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 13:49:32 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 13:50:07 - mmengine - INFO - Epoch(train) [4][5600/42151] lr: 3.0000e-05 eta: 23:27:43 time: 0.7160 data_time: 0.1766 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 13:51:15 - mmengine - INFO - Epoch(train) [4][5700/42151] lr: 3.0000e-05 eta: 23:26:31 time: 0.5724 data_time: 0.0347 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 13:52:24 - mmengine - INFO - Epoch(train) [4][5800/42151] lr: 3.0000e-05 eta: 23:25:21 time: 0.6695 data_time: 0.1312 memory: 28726 loss_ce: 0.0107 loss: 0.0107 2022/09/17 13:53:32 - mmengine - INFO - Epoch(train) [4][5900/42151] lr: 3.0000e-05 eta: 23:24:09 time: 0.6543 data_time: 0.1135 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 13:54:41 - mmengine - INFO - Epoch(train) [4][6000/42151] lr: 3.0000e-05 eta: 23:22:58 time: 0.5648 data_time: 0.0048 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/17 13:55:50 - mmengine - INFO - Epoch(train) [4][6100/42151] lr: 3.0000e-05 eta: 23:21:47 time: 0.7357 data_time: 0.1946 memory: 28726 loss_ce: 0.0101 loss: 0.0101 2022/09/17 13:56:58 - mmengine - INFO - Epoch(train) [4][6200/42151] lr: 3.0000e-05 eta: 23:20:35 time: 0.7328 data_time: 0.1921 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/17 13:58:06 - mmengine - INFO - Epoch(train) [4][6300/42151] lr: 3.0000e-05 eta: 23:19:24 time: 0.5819 data_time: 0.0314 memory: 28726 loss_ce: 0.0103 loss: 0.0103 2022/09/17 13:59:17 - mmengine - INFO - Epoch(train) [4][6400/42151] lr: 3.0000e-05 eta: 23:18:15 time: 0.6973 data_time: 0.1217 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/17 14:00:25 - mmengine - INFO - Epoch(train) [4][6500/42151] lr: 3.0000e-05 eta: 23:17:03 time: 0.6751 data_time: 0.1348 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 14:00:57 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 14:01:33 - mmengine - INFO - Epoch(train) [4][6600/42151] lr: 3.0000e-05 eta: 23:15:52 time: 0.5664 data_time: 0.0048 memory: 28726 loss_ce: 0.0095 loss: 0.0095 2022/09/17 14:02:43 - mmengine - INFO - Epoch(train) [4][6700/42151] lr: 3.0000e-05 eta: 23:14:42 time: 0.7334 data_time: 0.1943 memory: 28726 loss_ce: 0.0099 loss: 0.0099 2022/09/17 14:03:51 - mmengine - INFO - Epoch(train) [4][6800/42151] lr: 3.0000e-05 eta: 23:13:30 time: 0.7398 data_time: 0.1734 memory: 28726 loss_ce: 0.0107 loss: 0.0107 2022/09/17 14:04:59 - mmengine - INFO - Epoch(train) [4][6900/42151] lr: 3.0000e-05 eta: 23:12:19 time: 0.5786 data_time: 0.0310 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/17 14:06:07 - mmengine - INFO - Epoch(train) [4][7000/42151] lr: 3.0000e-05 eta: 23:11:08 time: 0.6740 data_time: 0.1351 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 14:07:15 - mmengine - INFO - Epoch(train) [4][7100/42151] lr: 3.0000e-05 eta: 23:09:55 time: 0.6559 data_time: 0.1197 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 14:08:22 - mmengine - INFO - Epoch(train) [4][7200/42151] lr: 3.0000e-05 eta: 23:08:43 time: 0.5398 data_time: 0.0047 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/17 14:09:31 - mmengine - INFO - Epoch(train) [4][7300/42151] lr: 3.0000e-05 eta: 23:07:32 time: 0.7296 data_time: 0.1656 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 14:10:38 - mmengine - INFO - Epoch(train) [4][7400/42151] lr: 3.0000e-05 eta: 23:06:20 time: 0.7548 data_time: 0.1859 memory: 28726 loss_ce: 0.0097 loss: 0.0097 2022/09/17 14:11:45 - mmengine - INFO - Epoch(train) [4][7500/42151] lr: 3.0000e-05 eta: 23:05:08 time: 0.5959 data_time: 0.0564 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 14:12:17 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 14:12:54 - mmengine - INFO - Epoch(train) [4][7600/42151] lr: 3.0000e-05 eta: 23:03:57 time: 0.6585 data_time: 0.1232 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 14:14:01 - mmengine - INFO - Epoch(train) [4][7700/42151] lr: 3.0000e-05 eta: 23:02:45 time: 0.6737 data_time: 0.1043 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 14:15:08 - mmengine - INFO - Epoch(train) [4][7800/42151] lr: 3.0000e-05 eta: 23:01:32 time: 0.5410 data_time: 0.0047 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 14:16:17 - mmengine - INFO - Epoch(train) [4][7900/42151] lr: 3.0000e-05 eta: 23:00:21 time: 0.7348 data_time: 0.1692 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 14:17:24 - mmengine - INFO - Epoch(train) [4][8000/42151] lr: 3.0000e-05 eta: 22:59:09 time: 0.7327 data_time: 0.1959 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 14:18:32 - mmengine - INFO - Epoch(train) [4][8100/42151] lr: 3.0000e-05 eta: 22:57:57 time: 0.5667 data_time: 0.0315 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 14:19:40 - mmengine - INFO - Epoch(train) [4][8200/42151] lr: 3.0000e-05 eta: 22:56:46 time: 0.6440 data_time: 0.1068 memory: 28726 loss_ce: 0.0106 loss: 0.0106 2022/09/17 14:20:47 - mmengine - INFO - Epoch(train) [4][8300/42151] lr: 3.0000e-05 eta: 22:55:34 time: 0.6838 data_time: 0.1188 memory: 28726 loss_ce: 0.0099 loss: 0.0099 2022/09/17 14:21:54 - mmengine - INFO - Epoch(train) [4][8400/42151] lr: 3.0000e-05 eta: 22:54:21 time: 0.5418 data_time: 0.0047 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 14:23:03 - mmengine - INFO - Epoch(train) [4][8500/42151] lr: 3.0000e-05 eta: 22:53:11 time: 0.7430 data_time: 0.2005 memory: 28726 loss_ce: 0.0095 loss: 0.0095 2022/09/17 14:23:36 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 14:24:11 - mmengine - INFO - Epoch(train) [4][8600/42151] lr: 3.0000e-05 eta: 22:51:59 time: 0.7003 data_time: 0.1625 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 14:25:19 - mmengine - INFO - Epoch(train) [4][8700/42151] lr: 3.0000e-05 eta: 22:50:48 time: 0.5977 data_time: 0.0321 memory: 28726 loss_ce: 0.0099 loss: 0.0099 2022/09/17 14:26:28 - mmengine - INFO - Epoch(train) [4][8800/42151] lr: 3.0000e-05 eta: 22:49:37 time: 0.6540 data_time: 0.1177 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 14:27:36 - mmengine - INFO - Epoch(train) [4][8900/42151] lr: 3.0000e-05 eta: 22:48:25 time: 0.6585 data_time: 0.1214 memory: 28726 loss_ce: 0.0095 loss: 0.0095 2022/09/17 14:28:43 - mmengine - INFO - Epoch(train) [4][9000/42151] lr: 3.0000e-05 eta: 22:47:13 time: 0.5421 data_time: 0.0050 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 14:29:53 - mmengine - INFO - Epoch(train) [4][9100/42151] lr: 3.0000e-05 eta: 22:46:03 time: 0.7490 data_time: 0.1889 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 14:31:00 - mmengine - INFO - Epoch(train) [4][9200/42151] lr: 3.0000e-05 eta: 22:44:51 time: 0.7062 data_time: 0.1688 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 14:32:07 - mmengine - INFO - Epoch(train) [4][9300/42151] lr: 3.0000e-05 eta: 22:43:39 time: 0.5678 data_time: 0.0314 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 14:33:15 - mmengine - INFO - Epoch(train) [4][9400/42151] lr: 3.0000e-05 eta: 22:42:27 time: 0.6493 data_time: 0.1135 memory: 28726 loss_ce: 0.0097 loss: 0.0097 2022/09/17 14:34:23 - mmengine - INFO - Epoch(train) [4][9500/42151] lr: 3.0000e-05 eta: 22:41:15 time: 0.6617 data_time: 0.1241 memory: 28726 loss_ce: 0.0097 loss: 0.0097 2022/09/17 14:34:54 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 14:35:30 - mmengine - INFO - Epoch(train) [4][9600/42151] lr: 3.0000e-05 eta: 22:40:03 time: 0.6018 data_time: 0.0048 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 14:36:39 - mmengine - INFO - Epoch(train) [4][9700/42151] lr: 3.0000e-05 eta: 22:38:53 time: 0.7321 data_time: 0.1799 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 14:37:47 - mmengine - INFO - Epoch(train) [4][9800/42151] lr: 3.0000e-05 eta: 22:37:41 time: 0.7443 data_time: 0.2104 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 14:38:55 - mmengine - INFO - Epoch(train) [4][9900/42151] lr: 3.0000e-05 eta: 22:36:30 time: 0.5750 data_time: 0.0367 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 14:40:06 - mmengine - INFO - Epoch(train) [4][10000/42151] lr: 3.0000e-05 eta: 22:35:21 time: 0.6443 data_time: 0.0854 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 14:41:14 - mmengine - INFO - Epoch(train) [4][10100/42151] lr: 3.0000e-05 eta: 22:34:10 time: 0.6871 data_time: 0.1240 memory: 28726 loss_ce: 0.0096 loss: 0.0096 2022/09/17 14:42:22 - mmengine - INFO - Epoch(train) [4][10200/42151] lr: 3.0000e-05 eta: 22:32:58 time: 0.5673 data_time: 0.0047 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 14:43:33 - mmengine - INFO - Epoch(train) [4][10300/42151] lr: 3.0000e-05 eta: 22:31:49 time: 0.7789 data_time: 0.2213 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/17 14:44:42 - mmengine - INFO - Epoch(train) [4][10400/42151] lr: 3.0000e-05 eta: 22:30:39 time: 0.7537 data_time: 0.1907 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 14:45:50 - mmengine - INFO - Epoch(train) [4][10500/42151] lr: 3.0000e-05 eta: 22:29:27 time: 0.6028 data_time: 0.0305 memory: 28726 loss_ce: 0.0109 loss: 0.0109 2022/09/17 14:46:23 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 14:46:59 - mmengine - INFO - Epoch(train) [4][10600/42151] lr: 3.0000e-05 eta: 22:28:17 time: 0.6434 data_time: 0.1065 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/17 14:48:07 - mmengine - INFO - Epoch(train) [4][10700/42151] lr: 3.0000e-05 eta: 22:27:06 time: 0.6713 data_time: 0.1333 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 14:49:16 - mmengine - INFO - Epoch(train) [4][10800/42151] lr: 3.0000e-05 eta: 22:25:54 time: 0.5422 data_time: 0.0048 memory: 28726 loss_ce: 0.0103 loss: 0.0103 2022/09/17 14:50:26 - mmengine - INFO - Epoch(train) [4][10900/42151] lr: 3.0000e-05 eta: 22:24:45 time: 0.7763 data_time: 0.1879 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/17 14:51:35 - mmengine - INFO - Epoch(train) [4][11000/42151] lr: 3.0000e-05 eta: 22:23:35 time: 0.7795 data_time: 0.2102 memory: 28726 loss_ce: 0.0104 loss: 0.0104 2022/09/17 14:52:45 - mmengine - INFO - Epoch(train) [4][11100/42151] lr: 3.0000e-05 eta: 22:22:25 time: 0.6151 data_time: 0.0722 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 14:53:56 - mmengine - INFO - Epoch(train) [4][11200/42151] lr: 3.0000e-05 eta: 22:21:16 time: 0.6492 data_time: 0.1107 memory: 28726 loss_ce: 0.0102 loss: 0.0102 2022/09/17 14:55:06 - mmengine - INFO - Epoch(train) [4][11300/42151] lr: 3.0000e-05 eta: 22:20:06 time: 0.7186 data_time: 0.1105 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 14:56:16 - mmengine - INFO - Epoch(train) [4][11400/42151] lr: 3.0000e-05 eta: 22:18:56 time: 0.5396 data_time: 0.0048 memory: 28726 loss_ce: 0.0100 loss: 0.0100 2022/09/17 14:57:26 - mmengine - INFO - Epoch(train) [4][11500/42151] lr: 3.0000e-05 eta: 22:17:47 time: 0.7563 data_time: 0.1736 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 14:57:59 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 14:58:34 - mmengine - INFO - Epoch(train) [4][11600/42151] lr: 3.0000e-05 eta: 22:16:35 time: 0.7164 data_time: 0.1798 memory: 28726 loss_ce: 0.0109 loss: 0.0109 2022/09/17 14:59:43 - mmengine - INFO - Epoch(train) [4][11700/42151] lr: 3.0000e-05 eta: 22:15:25 time: 0.5844 data_time: 0.0326 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 15:00:55 - mmengine - INFO - Epoch(train) [4][11800/42151] lr: 3.0000e-05 eta: 22:14:16 time: 0.7047 data_time: 0.1482 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/17 15:02:03 - mmengine - INFO - Epoch(train) [4][11900/42151] lr: 3.0000e-05 eta: 22:13:05 time: 0.6973 data_time: 0.1272 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 15:03:12 - mmengine - INFO - Epoch(train) [4][12000/42151] lr: 3.0000e-05 eta: 22:11:54 time: 0.5507 data_time: 0.0050 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/17 15:04:22 - mmengine - INFO - Epoch(train) [4][12100/42151] lr: 3.0000e-05 eta: 22:10:45 time: 0.7695 data_time: 0.2127 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 15:05:30 - mmengine - INFO - Epoch(train) [4][12200/42151] lr: 3.0000e-05 eta: 22:09:34 time: 0.7230 data_time: 0.1860 memory: 28726 loss_ce: 0.0107 loss: 0.0107 2022/09/17 15:06:38 - mmengine - INFO - Epoch(train) [4][12300/42151] lr: 3.0000e-05 eta: 22:08:22 time: 0.5958 data_time: 0.0348 memory: 28726 loss_ce: 0.0102 loss: 0.0102 2022/09/17 15:07:47 - mmengine - INFO - Epoch(train) [4][12400/42151] lr: 3.0000e-05 eta: 22:07:12 time: 0.6708 data_time: 0.1320 memory: 28726 loss_ce: 0.0103 loss: 0.0103 2022/09/17 15:08:56 - mmengine - INFO - Epoch(train) [4][12500/42151] lr: 3.0000e-05 eta: 22:06:01 time: 0.6849 data_time: 0.1361 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/17 15:09:29 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 15:10:05 - mmengine - INFO - Epoch(train) [4][12600/42151] lr: 3.0000e-05 eta: 22:04:51 time: 0.5581 data_time: 0.0051 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/17 15:11:16 - mmengine - INFO - Epoch(train) [4][12700/42151] lr: 3.0000e-05 eta: 22:03:42 time: 0.7649 data_time: 0.1713 memory: 28726 loss_ce: 0.0101 loss: 0.0101 2022/09/17 15:12:24 - mmengine - INFO - Epoch(train) [4][12800/42151] lr: 3.0000e-05 eta: 22:02:31 time: 0.7211 data_time: 0.1631 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 15:13:33 - mmengine - INFO - Epoch(train) [4][12900/42151] lr: 3.0000e-05 eta: 22:01:20 time: 0.5995 data_time: 0.0413 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 15:14:43 - mmengine - INFO - Epoch(train) [4][13000/42151] lr: 3.0000e-05 eta: 22:00:10 time: 0.6786 data_time: 0.1185 memory: 28726 loss_ce: 0.0106 loss: 0.0106 2022/09/17 15:15:51 - mmengine - INFO - Epoch(train) [4][13100/42151] lr: 3.0000e-05 eta: 21:58:59 time: 0.6822 data_time: 0.1421 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 15:17:00 - mmengine - INFO - Epoch(train) [4][13200/42151] lr: 3.0000e-05 eta: 21:57:48 time: 0.5641 data_time: 0.0049 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 15:18:10 - mmengine - INFO - Epoch(train) [4][13300/42151] lr: 3.0000e-05 eta: 21:56:39 time: 0.7085 data_time: 0.1725 memory: 28726 loss_ce: 0.0103 loss: 0.0103 2022/09/17 15:19:19 - mmengine - INFO - Epoch(train) [4][13400/42151] lr: 3.0000e-05 eta: 21:55:28 time: 0.7777 data_time: 0.2119 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 15:20:27 - mmengine - INFO - Epoch(train) [4][13500/42151] lr: 3.0000e-05 eta: 21:54:17 time: 0.5821 data_time: 0.0411 memory: 28726 loss_ce: 0.0113 loss: 0.0113 2022/09/17 15:20:59 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 15:21:36 - mmengine - INFO - Epoch(train) [4][13600/42151] lr: 3.0000e-05 eta: 21:53:06 time: 0.6649 data_time: 0.0874 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 15:22:44 - mmengine - INFO - Epoch(train) [4][13700/42151] lr: 3.0000e-05 eta: 21:51:55 time: 0.6589 data_time: 0.1048 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 15:23:53 - mmengine - INFO - Epoch(train) [4][13800/42151] lr: 3.0000e-05 eta: 21:50:44 time: 0.5933 data_time: 0.0052 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/17 15:25:01 - mmengine - INFO - Epoch(train) [4][13900/42151] lr: 3.0000e-05 eta: 21:49:34 time: 0.7052 data_time: 0.1709 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/17 15:26:09 - mmengine - INFO - Epoch(train) [4][14000/42151] lr: 3.0000e-05 eta: 21:48:22 time: 0.7215 data_time: 0.1598 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/17 15:27:18 - mmengine - INFO - Epoch(train) [4][14100/42151] lr: 3.0000e-05 eta: 21:47:11 time: 0.6017 data_time: 0.0436 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 15:28:27 - mmengine - INFO - Epoch(train) [4][14200/42151] lr: 3.0000e-05 eta: 21:46:01 time: 0.6558 data_time: 0.1148 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 15:29:35 - mmengine - INFO - Epoch(train) [4][14300/42151] lr: 3.0000e-05 eta: 21:44:49 time: 0.6578 data_time: 0.1192 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/17 15:30:43 - mmengine - INFO - Epoch(train) [4][14400/42151] lr: 3.0000e-05 eta: 21:43:38 time: 0.5538 data_time: 0.0050 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 15:31:53 - mmengine - INFO - Epoch(train) [4][14500/42151] lr: 3.0000e-05 eta: 21:42:29 time: 0.7854 data_time: 0.2032 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 15:32:26 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 15:33:02 - mmengine - INFO - Epoch(train) [4][14600/42151] lr: 3.0000e-05 eta: 21:41:18 time: 0.7775 data_time: 0.2042 memory: 28726 loss_ce: 0.0100 loss: 0.0100 2022/09/17 15:34:10 - mmengine - INFO - Epoch(train) [4][14700/42151] lr: 3.0000e-05 eta: 21:40:07 time: 0.5912 data_time: 0.0546 memory: 28726 loss_ce: 0.0097 loss: 0.0097 2022/09/17 15:35:20 - mmengine - INFO - Epoch(train) [4][14800/42151] lr: 3.0000e-05 eta: 21:38:57 time: 0.7277 data_time: 0.1430 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 15:36:28 - mmengine - INFO - Epoch(train) [4][14900/42151] lr: 3.0000e-05 eta: 21:37:46 time: 0.7017 data_time: 0.0957 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 15:37:35 - mmengine - INFO - Epoch(train) [4][15000/42151] lr: 3.0000e-05 eta: 21:36:34 time: 0.5595 data_time: 0.0054 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 15:38:45 - mmengine - INFO - Epoch(train) [4][15100/42151] lr: 3.0000e-05 eta: 21:35:24 time: 0.7602 data_time: 0.1928 memory: 28726 loss_ce: 0.0104 loss: 0.0104 2022/09/17 15:39:53 - mmengine - INFO - Epoch(train) [4][15200/42151] lr: 3.0000e-05 eta: 21:34:13 time: 0.6952 data_time: 0.1599 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 15:41:01 - mmengine - INFO - Epoch(train) [4][15300/42151] lr: 3.0000e-05 eta: 21:33:02 time: 0.6083 data_time: 0.0740 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 15:42:10 - mmengine - INFO - Epoch(train) [4][15400/42151] lr: 3.0000e-05 eta: 21:31:51 time: 0.7231 data_time: 0.0872 memory: 28726 loss_ce: 0.0096 loss: 0.0096 2022/09/17 15:43:18 - mmengine - INFO - Epoch(train) [4][15500/42151] lr: 3.0000e-05 eta: 21:30:40 time: 0.7017 data_time: 0.1421 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 15:43:51 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 15:44:29 - mmengine - INFO - Epoch(train) [4][15600/42151] lr: 3.0000e-05 eta: 21:29:31 time: 0.5751 data_time: 0.0072 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/17 15:45:44 - mmengine - INFO - Epoch(train) [4][15700/42151] lr: 3.0000e-05 eta: 21:28:25 time: 0.7828 data_time: 0.2315 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 15:46:57 - mmengine - INFO - Epoch(train) [4][15800/42151] lr: 3.0000e-05 eta: 21:27:18 time: 0.8342 data_time: 0.2521 memory: 28726 loss_ce: 0.0097 loss: 0.0097 2022/09/17 15:48:11 - mmengine - INFO - Epoch(train) [4][15900/42151] lr: 3.0000e-05 eta: 21:26:11 time: 0.6518 data_time: 0.0526 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 15:49:23 - mmengine - INFO - Epoch(train) [4][16000/42151] lr: 3.0000e-05 eta: 21:25:03 time: 0.6786 data_time: 0.1249 memory: 28726 loss_ce: 0.0104 loss: 0.0104 2022/09/17 15:50:36 - mmengine - INFO - Epoch(train) [4][16100/42151] lr: 3.0000e-05 eta: 21:23:56 time: 0.6971 data_time: 0.1424 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 15:51:51 - mmengine - INFO - Epoch(train) [4][16200/42151] lr: 3.0000e-05 eta: 21:22:50 time: 0.6095 data_time: 0.0078 memory: 28726 loss_ce: 0.0100 loss: 0.0100 2022/09/17 15:53:05 - mmengine - INFO - Epoch(train) [4][16300/42151] lr: 3.0000e-05 eta: 21:21:44 time: 0.8100 data_time: 0.2201 memory: 28726 loss_ce: 0.0095 loss: 0.0095 2022/09/17 15:54:17 - mmengine - INFO - Epoch(train) [4][16400/42151] lr: 3.0000e-05 eta: 21:20:35 time: 0.7790 data_time: 0.2211 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 15:55:30 - mmengine - INFO - Epoch(train) [4][16500/42151] lr: 3.0000e-05 eta: 21:19:28 time: 0.6114 data_time: 0.0533 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/17 15:56:05 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 15:56:47 - mmengine - INFO - Epoch(train) [4][16600/42151] lr: 3.0000e-05 eta: 21:18:23 time: 0.7303 data_time: 0.1642 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 15:58:00 - mmengine - INFO - Epoch(train) [4][16700/42151] lr: 3.0000e-05 eta: 21:17:16 time: 0.7328 data_time: 0.1389 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 15:59:11 - mmengine - INFO - Epoch(train) [4][16800/42151] lr: 3.0000e-05 eta: 21:16:07 time: 0.5780 data_time: 0.0057 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 16:00:28 - mmengine - INFO - Epoch(train) [4][16900/42151] lr: 3.0000e-05 eta: 21:15:03 time: 0.7739 data_time: 0.2158 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 16:01:40 - mmengine - INFO - Epoch(train) [4][17000/42151] lr: 3.0000e-05 eta: 21:13:55 time: 0.7759 data_time: 0.2107 memory: 28726 loss_ce: 0.0097 loss: 0.0097 2022/09/17 16:02:52 - mmengine - INFO - Epoch(train) [4][17100/42151] lr: 3.0000e-05 eta: 21:12:47 time: 0.6100 data_time: 0.0536 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/17 16:04:05 - mmengine - INFO - Epoch(train) [4][17200/42151] lr: 3.0000e-05 eta: 21:11:39 time: 0.7222 data_time: 0.1484 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/17 16:05:17 - mmengine - INFO - Epoch(train) [4][17300/42151] lr: 3.0000e-05 eta: 21:10:31 time: 0.7010 data_time: 0.1399 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 16:06:31 - mmengine - INFO - Epoch(train) [4][17400/42151] lr: 3.0000e-05 eta: 21:09:24 time: 0.5663 data_time: 0.0066 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 16:07:45 - mmengine - INFO - Epoch(train) [4][17500/42151] lr: 3.0000e-05 eta: 21:08:17 time: 0.7610 data_time: 0.1998 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/17 16:08:20 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 16:08:59 - mmengine - INFO - Epoch(train) [4][17600/42151] lr: 3.0000e-05 eta: 21:07:10 time: 0.7684 data_time: 0.2222 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 16:10:14 - mmengine - INFO - Epoch(train) [4][17700/42151] lr: 3.0000e-05 eta: 21:06:04 time: 0.6654 data_time: 0.0800 memory: 28726 loss_ce: 0.0100 loss: 0.0100 2022/09/17 16:11:29 - mmengine - INFO - Epoch(train) [4][17800/42151] lr: 3.0000e-05 eta: 21:04:58 time: 0.6989 data_time: 0.1379 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/17 16:12:42 - mmengine - INFO - Epoch(train) [4][17900/42151] lr: 3.0000e-05 eta: 21:03:51 time: 0.7379 data_time: 0.1510 memory: 28726 loss_ce: 0.0096 loss: 0.0096 2022/09/17 16:13:53 - mmengine - INFO - Epoch(train) [4][18000/42151] lr: 3.0000e-05 eta: 21:02:42 time: 0.5596 data_time: 0.0055 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/17 16:15:09 - mmengine - INFO - Epoch(train) [4][18100/42151] lr: 3.0000e-05 eta: 21:01:37 time: 0.7894 data_time: 0.2103 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 16:16:22 - mmengine - INFO - Epoch(train) [4][18200/42151] lr: 3.0000e-05 eta: 21:00:29 time: 0.8110 data_time: 0.2447 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 16:17:35 - mmengine - INFO - Epoch(train) [4][18300/42151] lr: 3.0000e-05 eta: 20:59:21 time: 0.6636 data_time: 0.0820 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 16:18:50 - mmengine - INFO - Epoch(train) [4][18400/42151] lr: 3.0000e-05 eta: 20:58:15 time: 0.6974 data_time: 0.1434 memory: 28726 loss_ce: 0.0112 loss: 0.0112 2022/09/17 16:20:03 - mmengine - INFO - Epoch(train) [4][18500/42151] lr: 3.0000e-05 eta: 20:57:08 time: 0.7213 data_time: 0.1437 memory: 28726 loss_ce: 0.0100 loss: 0.0100 2022/09/17 16:20:37 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 16:21:15 - mmengine - INFO - Epoch(train) [4][18600/42151] lr: 3.0000e-05 eta: 20:55:59 time: 0.5717 data_time: 0.0065 memory: 28726 loss_ce: 0.0095 loss: 0.0095 2022/09/17 16:22:30 - mmengine - INFO - Epoch(train) [4][18700/42151] lr: 3.0000e-05 eta: 20:54:54 time: 0.8956 data_time: 0.2567 memory: 28726 loss_ce: 0.0101 loss: 0.0101 2022/09/17 16:23:43 - mmengine - INFO - Epoch(train) [4][18800/42151] lr: 3.0000e-05 eta: 20:53:46 time: 0.7680 data_time: 0.2161 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 16:24:56 - mmengine - INFO - Epoch(train) [4][18900/42151] lr: 3.0000e-05 eta: 20:52:38 time: 0.6231 data_time: 0.0754 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/17 16:26:09 - mmengine - INFO - Epoch(train) [4][19000/42151] lr: 3.0000e-05 eta: 20:51:30 time: 0.7111 data_time: 0.1462 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/17 16:27:21 - mmengine - INFO - Epoch(train) [4][19100/42151] lr: 3.0000e-05 eta: 20:50:22 time: 0.7278 data_time: 0.1105 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 16:28:34 - mmengine - INFO - Epoch(train) [4][19200/42151] lr: 3.0000e-05 eta: 20:49:14 time: 0.6327 data_time: 0.0063 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 16:29:49 - mmengine - INFO - Epoch(train) [4][19300/42151] lr: 3.0000e-05 eta: 20:48:08 time: 0.7927 data_time: 0.2422 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 16:31:00 - mmengine - INFO - Epoch(train) [4][19400/42151] lr: 3.0000e-05 eta: 20:46:59 time: 0.8081 data_time: 0.2365 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/17 16:32:13 - mmengine - INFO - Epoch(train) [4][19500/42151] lr: 3.0000e-05 eta: 20:45:51 time: 0.6260 data_time: 0.0711 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/17 16:32:48 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 16:33:26 - mmengine - INFO - Epoch(train) [4][19600/42151] lr: 3.0000e-05 eta: 20:44:44 time: 0.7184 data_time: 0.1090 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/17 16:34:38 - mmengine - INFO - Epoch(train) [4][19700/42151] lr: 3.0000e-05 eta: 20:43:35 time: 0.6929 data_time: 0.1493 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/17 16:35:50 - mmengine - INFO - Epoch(train) [4][19800/42151] lr: 3.0000e-05 eta: 20:42:27 time: 0.5689 data_time: 0.0056 memory: 28726 loss_ce: 0.0095 loss: 0.0095 2022/09/17 16:37:06 - mmengine - INFO - Epoch(train) [4][19900/42151] lr: 3.0000e-05 eta: 20:41:22 time: 0.7922 data_time: 0.2140 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 16:38:14 - mmengine - INFO - Epoch(train) [4][20000/42151] lr: 3.0000e-05 eta: 20:40:10 time: 0.7361 data_time: 0.2027 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/17 16:39:21 - mmengine - INFO - Epoch(train) [4][20100/42151] lr: 3.0000e-05 eta: 20:38:58 time: 0.6047 data_time: 0.0395 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 16:40:29 - mmengine - INFO - Epoch(train) [4][20200/42151] lr: 3.0000e-05 eta: 20:37:47 time: 0.6470 data_time: 0.1125 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 16:41:37 - mmengine - INFO - Epoch(train) [4][20300/42151] lr: 3.0000e-05 eta: 20:36:36 time: 0.6611 data_time: 0.1213 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/17 16:42:44 - mmengine - INFO - Epoch(train) [4][20400/42151] lr: 3.0000e-05 eta: 20:35:24 time: 0.5449 data_time: 0.0047 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 16:43:53 - mmengine - INFO - Epoch(train) [4][20500/42151] lr: 3.0000e-05 eta: 20:34:13 time: 0.7801 data_time: 0.2163 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/17 16:44:25 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 16:45:00 - mmengine - INFO - Epoch(train) [4][20600/42151] lr: 3.0000e-05 eta: 20:33:01 time: 0.7320 data_time: 0.1838 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 16:46:07 - mmengine - INFO - Epoch(train) [4][20700/42151] lr: 3.0000e-05 eta: 20:31:49 time: 0.5756 data_time: 0.0394 memory: 28726 loss_ce: 0.0106 loss: 0.0106 2022/09/17 16:47:15 - mmengine - INFO - Epoch(train) [4][20800/42151] lr: 3.0000e-05 eta: 20:30:38 time: 0.6615 data_time: 0.1070 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 16:48:22 - mmengine - INFO - Epoch(train) [4][20900/42151] lr: 3.0000e-05 eta: 20:29:26 time: 0.6578 data_time: 0.1126 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/17 16:49:29 - mmengine - INFO - Epoch(train) [4][21000/42151] lr: 3.0000e-05 eta: 20:28:14 time: 0.5426 data_time: 0.0060 memory: 28726 loss_ce: 0.0100 loss: 0.0100 2022/09/17 16:50:38 - mmengine - INFO - Epoch(train) [4][21100/42151] lr: 3.0000e-05 eta: 20:27:03 time: 0.7161 data_time: 0.1757 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/17 16:51:45 - mmengine - INFO - Epoch(train) [4][21200/42151] lr: 3.0000e-05 eta: 20:25:52 time: 0.7732 data_time: 0.2176 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 16:52:53 - mmengine - INFO - Epoch(train) [4][21300/42151] lr: 3.0000e-05 eta: 20:24:40 time: 0.6373 data_time: 0.0495 memory: 28726 loss_ce: 0.0102 loss: 0.0102 2022/09/17 16:54:06 - mmengine - INFO - Epoch(train) [4][21400/42151] lr: 3.0000e-05 eta: 20:23:33 time: 0.7200 data_time: 0.1385 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 16:55:15 - mmengine - INFO - Epoch(train) [4][21500/42151] lr: 3.0000e-05 eta: 20:22:22 time: 0.6911 data_time: 0.1202 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/17 16:55:46 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 16:56:22 - mmengine - INFO - Epoch(train) [4][21600/42151] lr: 3.0000e-05 eta: 20:21:10 time: 0.5527 data_time: 0.0045 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 16:57:31 - mmengine - INFO - Epoch(train) [4][21700/42151] lr: 3.0000e-05 eta: 20:19:59 time: 0.7203 data_time: 0.1834 memory: 28726 loss_ce: 0.0102 loss: 0.0102 2022/09/17 16:58:38 - mmengine - INFO - Epoch(train) [4][21800/42151] lr: 3.0000e-05 eta: 20:18:48 time: 0.7152 data_time: 0.1758 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/17 16:59:46 - mmengine - INFO - Epoch(train) [4][21900/42151] lr: 3.0000e-05 eta: 20:17:36 time: 0.6055 data_time: 0.0644 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 17:00:55 - mmengine - INFO - Epoch(train) [4][22000/42151] lr: 3.0000e-05 eta: 20:16:26 time: 0.6562 data_time: 0.1127 memory: 28726 loss_ce: 0.0102 loss: 0.0102 2022/09/17 17:02:04 - mmengine - INFO - Epoch(train) [4][22100/42151] lr: 3.0000e-05 eta: 20:15:15 time: 0.7236 data_time: 0.1289 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/17 17:03:11 - mmengine - INFO - Epoch(train) [4][22200/42151] lr: 3.0000e-05 eta: 20:14:03 time: 0.5410 data_time: 0.0048 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 17:04:20 - mmengine - INFO - Epoch(train) [4][22300/42151] lr: 3.0000e-05 eta: 20:12:53 time: 0.7185 data_time: 0.1836 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 17:05:28 - mmengine - INFO - Epoch(train) [4][22400/42151] lr: 3.0000e-05 eta: 20:11:42 time: 0.7792 data_time: 0.2241 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 17:06:36 - mmengine - INFO - Epoch(train) [4][22500/42151] lr: 3.0000e-05 eta: 20:10:30 time: 0.6267 data_time: 0.0633 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 17:07:08 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 17:07:45 - mmengine - INFO - Epoch(train) [4][22600/42151] lr: 3.0000e-05 eta: 20:09:20 time: 0.7243 data_time: 0.1313 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 17:08:52 - mmengine - INFO - Epoch(train) [4][22700/42151] lr: 3.0000e-05 eta: 20:08:08 time: 0.6618 data_time: 0.1246 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 17:10:00 - mmengine - INFO - Epoch(train) [4][22800/42151] lr: 3.0000e-05 eta: 20:06:56 time: 0.5416 data_time: 0.0050 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/17 17:11:08 - mmengine - INFO - Epoch(train) [4][22900/42151] lr: 3.0000e-05 eta: 20:05:46 time: 0.7586 data_time: 0.2119 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/17 17:12:15 - mmengine - INFO - Epoch(train) [4][23000/42151] lr: 3.0000e-05 eta: 20:04:33 time: 0.7411 data_time: 0.1885 memory: 28726 loss_ce: 0.0097 loss: 0.0097 2022/09/17 17:13:22 - mmengine - INFO - Epoch(train) [4][23100/42151] lr: 3.0000e-05 eta: 20:03:22 time: 0.6007 data_time: 0.0634 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 17:14:31 - mmengine - INFO - Epoch(train) [4][23200/42151] lr: 3.0000e-05 eta: 20:02:11 time: 0.6502 data_time: 0.1152 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/17 17:15:38 - mmengine - INFO - Epoch(train) [4][23300/42151] lr: 3.0000e-05 eta: 20:01:00 time: 0.6846 data_time: 0.0981 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/17 17:16:45 - mmengine - INFO - Epoch(train) [4][23400/42151] lr: 3.0000e-05 eta: 19:59:47 time: 0.5675 data_time: 0.0047 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 17:17:55 - mmengine - INFO - Epoch(train) [4][23500/42151] lr: 3.0000e-05 eta: 19:58:38 time: 0.7382 data_time: 0.2019 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 17:18:27 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 17:19:02 - mmengine - INFO - Epoch(train) [4][23600/42151] lr: 3.0000e-05 eta: 19:57:26 time: 0.7503 data_time: 0.1915 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 17:20:10 - mmengine - INFO - Epoch(train) [4][23700/42151] lr: 3.0000e-05 eta: 19:56:15 time: 0.6215 data_time: 0.0808 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 17:21:17 - mmengine - INFO - Epoch(train) [4][23800/42151] lr: 3.0000e-05 eta: 19:55:03 time: 0.6815 data_time: 0.0791 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 17:22:24 - mmengine - INFO - Epoch(train) [4][23900/42151] lr: 3.0000e-05 eta: 19:53:51 time: 0.6843 data_time: 0.1193 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 17:23:30 - mmengine - INFO - Epoch(train) [4][24000/42151] lr: 3.0000e-05 eta: 19:52:39 time: 0.5450 data_time: 0.0047 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/17 17:24:39 - mmengine - INFO - Epoch(train) [4][24100/42151] lr: 3.0000e-05 eta: 19:51:28 time: 0.7227 data_time: 0.1868 memory: 28726 loss_ce: 0.0100 loss: 0.0100 2022/09/17 17:25:47 - mmengine - INFO - Epoch(train) [4][24200/42151] lr: 3.0000e-05 eta: 19:50:17 time: 0.7639 data_time: 0.2101 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 17:26:55 - mmengine - INFO - Epoch(train) [4][24300/42151] lr: 3.0000e-05 eta: 19:49:06 time: 0.6126 data_time: 0.0391 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 17:28:03 - mmengine - INFO - Epoch(train) [4][24400/42151] lr: 3.0000e-05 eta: 19:47:55 time: 0.6638 data_time: 0.1273 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/17 17:29:10 - mmengine - INFO - Epoch(train) [4][24500/42151] lr: 3.0000e-05 eta: 19:46:43 time: 0.6584 data_time: 0.1221 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/17 17:29:42 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 17:30:18 - mmengine - INFO - Epoch(train) [4][24600/42151] lr: 3.0000e-05 eta: 19:45:32 time: 0.5577 data_time: 0.0046 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 17:31:28 - mmengine - INFO - Epoch(train) [4][24700/42151] lr: 3.0000e-05 eta: 19:44:22 time: 0.7550 data_time: 0.2100 memory: 28726 loss_ce: 0.0097 loss: 0.0097 2022/09/17 17:32:34 - mmengine - INFO - Epoch(train) [4][24800/42151] lr: 3.0000e-05 eta: 19:43:10 time: 0.7296 data_time: 0.1866 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 17:33:41 - mmengine - INFO - Epoch(train) [4][24900/42151] lr: 3.0000e-05 eta: 19:41:58 time: 0.5740 data_time: 0.0390 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 17:34:50 - mmengine - INFO - Epoch(train) [4][25000/42151] lr: 3.0000e-05 eta: 19:40:47 time: 0.6563 data_time: 0.1175 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/17 17:35:57 - mmengine - INFO - Epoch(train) [4][25100/42151] lr: 3.0000e-05 eta: 19:39:36 time: 0.6783 data_time: 0.1057 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 17:37:04 - mmengine - INFO - Epoch(train) [4][25200/42151] lr: 3.0000e-05 eta: 19:38:24 time: 0.5401 data_time: 0.0049 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/17 17:38:13 - mmengine - INFO - Epoch(train) [4][25300/42151] lr: 3.0000e-05 eta: 19:37:13 time: 0.7245 data_time: 0.1895 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 17:39:20 - mmengine - INFO - Epoch(train) [4][25400/42151] lr: 3.0000e-05 eta: 19:36:02 time: 0.7196 data_time: 0.1847 memory: 28726 loss_ce: 0.0095 loss: 0.0095 2022/09/17 17:40:27 - mmengine - INFO - Epoch(train) [4][25500/42151] lr: 3.0000e-05 eta: 19:34:50 time: 0.5955 data_time: 0.0366 memory: 28726 loss_ce: 0.0100 loss: 0.0100 2022/09/17 17:40:59 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 17:41:35 - mmengine - INFO - Epoch(train) [4][25600/42151] lr: 3.0000e-05 eta: 19:33:39 time: 0.6446 data_time: 0.1099 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/17 17:42:43 - mmengine - INFO - Epoch(train) [4][25700/42151] lr: 3.0000e-05 eta: 19:32:28 time: 0.6579 data_time: 0.1215 memory: 28726 loss_ce: 0.0102 loss: 0.0102 2022/09/17 17:43:51 - mmengine - INFO - Epoch(train) [4][25800/42151] lr: 3.0000e-05 eta: 19:31:17 time: 0.5396 data_time: 0.0044 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/17 17:45:00 - mmengine - INFO - Epoch(train) [4][25900/42151] lr: 3.0000e-05 eta: 19:30:06 time: 0.7103 data_time: 0.1649 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/17 17:46:08 - mmengine - INFO - Epoch(train) [4][26000/42151] lr: 3.0000e-05 eta: 19:28:55 time: 0.7173 data_time: 0.1817 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 17:47:15 - mmengine - INFO - Epoch(train) [4][26100/42151] lr: 3.0000e-05 eta: 19:27:44 time: 0.5943 data_time: 0.0589 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 17:48:23 - mmengine - INFO - Epoch(train) [4][26200/42151] lr: 3.0000e-05 eta: 19:26:33 time: 0.6364 data_time: 0.1010 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 17:49:31 - mmengine - INFO - Epoch(train) [4][26300/42151] lr: 3.0000e-05 eta: 19:25:22 time: 0.7072 data_time: 0.1275 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/17 17:50:39 - mmengine - INFO - Epoch(train) [4][26400/42151] lr: 3.0000e-05 eta: 19:24:11 time: 0.5940 data_time: 0.0052 memory: 28726 loss_ce: 0.0097 loss: 0.0097 2022/09/17 17:51:48 - mmengine - INFO - Epoch(train) [4][26500/42151] lr: 3.0000e-05 eta: 19:23:00 time: 0.7056 data_time: 0.1648 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 17:52:20 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 17:52:54 - mmengine - INFO - Epoch(train) [4][26600/42151] lr: 3.0000e-05 eta: 19:21:48 time: 0.7336 data_time: 0.1988 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/17 17:54:02 - mmengine - INFO - Epoch(train) [4][26700/42151] lr: 3.0000e-05 eta: 19:20:37 time: 0.6009 data_time: 0.0659 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 17:55:10 - mmengine - INFO - Epoch(train) [4][26800/42151] lr: 3.0000e-05 eta: 19:19:26 time: 0.6804 data_time: 0.1143 memory: 28726 loss_ce: 0.0096 loss: 0.0096 2022/09/17 17:56:18 - mmengine - INFO - Epoch(train) [4][26900/42151] lr: 3.0000e-05 eta: 19:18:15 time: 0.6367 data_time: 0.0988 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 17:57:26 - mmengine - INFO - Epoch(train) [4][27000/42151] lr: 3.0000e-05 eta: 19:17:04 time: 0.5412 data_time: 0.0049 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 17:58:34 - mmengine - INFO - Epoch(train) [4][27100/42151] lr: 3.0000e-05 eta: 19:15:53 time: 0.7398 data_time: 0.1971 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 17:59:41 - mmengine - INFO - Epoch(train) [4][27200/42151] lr: 3.0000e-05 eta: 19:14:41 time: 0.7557 data_time: 0.1810 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 18:00:55 - mmengine - INFO - Epoch(train) [4][27300/42151] lr: 3.0000e-05 eta: 19:13:34 time: 0.6007 data_time: 0.0651 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 18:02:03 - mmengine - INFO - Epoch(train) [4][27400/42151] lr: 3.0000e-05 eta: 19:12:23 time: 0.6560 data_time: 0.1069 memory: 28726 loss_ce: 0.0099 loss: 0.0099 2022/09/17 18:03:10 - mmengine - INFO - Epoch(train) [4][27500/42151] lr: 3.0000e-05 eta: 19:11:11 time: 0.7214 data_time: 0.0789 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 18:03:42 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 18:04:17 - mmengine - INFO - Epoch(train) [4][27600/42151] lr: 3.0000e-05 eta: 19:10:00 time: 0.5409 data_time: 0.0047 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 18:05:26 - mmengine - INFO - Epoch(train) [4][27700/42151] lr: 3.0000e-05 eta: 19:08:49 time: 0.7257 data_time: 0.1870 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/17 18:06:32 - mmengine - INFO - Epoch(train) [4][27800/42151] lr: 3.0000e-05 eta: 19:07:37 time: 0.7283 data_time: 0.1909 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 18:07:40 - mmengine - INFO - Epoch(train) [4][27900/42151] lr: 3.0000e-05 eta: 19:06:26 time: 0.6055 data_time: 0.0670 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 18:08:48 - mmengine - INFO - Epoch(train) [4][28000/42151] lr: 3.0000e-05 eta: 19:05:15 time: 0.7050 data_time: 0.0815 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 18:09:56 - mmengine - INFO - Epoch(train) [4][28100/42151] lr: 3.0000e-05 eta: 19:04:04 time: 0.6536 data_time: 0.1182 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 18:11:03 - mmengine - INFO - Epoch(train) [4][28200/42151] lr: 3.0000e-05 eta: 19:02:53 time: 0.5635 data_time: 0.0050 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 18:12:13 - mmengine - INFO - Epoch(train) [4][28300/42151] lr: 3.0000e-05 eta: 19:01:43 time: 0.7247 data_time: 0.1866 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/17 18:13:20 - mmengine - INFO - Epoch(train) [4][28400/42151] lr: 3.0000e-05 eta: 19:00:32 time: 0.7172 data_time: 0.1815 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 18:14:28 - mmengine - INFO - Epoch(train) [4][28500/42151] lr: 3.0000e-05 eta: 18:59:20 time: 0.6046 data_time: 0.0396 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/17 18:15:00 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 18:15:36 - mmengine - INFO - Epoch(train) [4][28600/42151] lr: 3.0000e-05 eta: 18:58:10 time: 0.6540 data_time: 0.1155 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 18:16:45 - mmengine - INFO - Epoch(train) [4][28700/42151] lr: 3.0000e-05 eta: 18:56:59 time: 0.6875 data_time: 0.1165 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 18:17:52 - mmengine - INFO - Epoch(train) [4][28800/42151] lr: 3.0000e-05 eta: 18:55:48 time: 0.5633 data_time: 0.0048 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 18:19:02 - mmengine - INFO - Epoch(train) [4][28900/42151] lr: 3.0000e-05 eta: 18:54:38 time: 0.7341 data_time: 0.1869 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 18:20:10 - mmengine - INFO - Epoch(train) [4][29000/42151] lr: 3.0000e-05 eta: 18:53:27 time: 0.7360 data_time: 0.1897 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 18:21:17 - mmengine - INFO - Epoch(train) [4][29100/42151] lr: 3.0000e-05 eta: 18:52:15 time: 0.5757 data_time: 0.0380 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 18:22:27 - mmengine - INFO - Epoch(train) [4][29200/42151] lr: 3.0000e-05 eta: 18:51:05 time: 0.6778 data_time: 0.1048 memory: 28726 loss_ce: 0.0104 loss: 0.0104 2022/09/17 18:23:34 - mmengine - INFO - Epoch(train) [4][29300/42151] lr: 3.0000e-05 eta: 18:49:54 time: 0.6947 data_time: 0.1086 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 18:24:41 - mmengine - INFO - Epoch(train) [4][29400/42151] lr: 3.0000e-05 eta: 18:48:43 time: 0.5458 data_time: 0.0048 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/17 18:25:49 - mmengine - INFO - Epoch(train) [4][29500/42151] lr: 3.0000e-05 eta: 18:47:32 time: 0.6826 data_time: 0.1484 memory: 28726 loss_ce: 0.0109 loss: 0.0109 2022/09/17 18:26:21 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 18:26:56 - mmengine - INFO - Epoch(train) [4][29600/42151] lr: 3.0000e-05 eta: 18:46:20 time: 0.7300 data_time: 0.1650 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/17 18:28:03 - mmengine - INFO - Epoch(train) [4][29700/42151] lr: 3.0000e-05 eta: 18:45:09 time: 0.5783 data_time: 0.0369 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 18:29:12 - mmengine - INFO - Epoch(train) [4][29800/42151] lr: 3.0000e-05 eta: 18:43:59 time: 0.6802 data_time: 0.1200 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/17 18:30:21 - mmengine - INFO - Epoch(train) [4][29900/42151] lr: 3.0000e-05 eta: 18:42:48 time: 0.6502 data_time: 0.1126 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/17 18:31:29 - mmengine - INFO - Epoch(train) [4][30000/42151] lr: 3.0000e-05 eta: 18:41:37 time: 0.5387 data_time: 0.0045 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 18:32:38 - mmengine - INFO - Epoch(train) [4][30100/42151] lr: 3.0000e-05 eta: 18:40:27 time: 0.7053 data_time: 0.1687 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 18:33:46 - mmengine - INFO - Epoch(train) [4][30200/42151] lr: 3.0000e-05 eta: 18:39:16 time: 0.7330 data_time: 0.1701 memory: 28726 loss_ce: 0.0108 loss: 0.0108 2022/09/17 18:34:54 - mmengine - INFO - Epoch(train) [4][30300/42151] lr: 3.0000e-05 eta: 18:38:05 time: 0.6100 data_time: 0.0582 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 18:36:02 - mmengine - INFO - Epoch(train) [4][30400/42151] lr: 3.0000e-05 eta: 18:36:54 time: 0.6683 data_time: 0.1045 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 18:37:08 - mmengine - INFO - Epoch(train) [4][30500/42151] lr: 3.0000e-05 eta: 18:35:42 time: 0.6357 data_time: 0.1022 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 18:37:40 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 18:38:15 - mmengine - INFO - Epoch(train) [4][30600/42151] lr: 3.0000e-05 eta: 18:34:30 time: 0.5398 data_time: 0.0046 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 18:39:23 - mmengine - INFO - Epoch(train) [4][30700/42151] lr: 3.0000e-05 eta: 18:33:20 time: 0.6915 data_time: 0.1543 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 18:40:31 - mmengine - INFO - Epoch(train) [4][30800/42151] lr: 3.0000e-05 eta: 18:32:09 time: 0.7383 data_time: 0.1803 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/17 18:41:39 - mmengine - INFO - Epoch(train) [4][30900/42151] lr: 3.0000e-05 eta: 18:30:58 time: 0.5942 data_time: 0.0588 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/17 18:42:47 - mmengine - INFO - Epoch(train) [4][31000/42151] lr: 3.0000e-05 eta: 18:29:47 time: 0.7023 data_time: 0.1219 memory: 28726 loss_ce: 0.0102 loss: 0.0102 2022/09/17 18:43:55 - mmengine - INFO - Epoch(train) [4][31100/42151] lr: 3.0000e-05 eta: 18:28:36 time: 0.6686 data_time: 0.1034 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/17 18:45:02 - mmengine - INFO - Epoch(train) [4][31200/42151] lr: 3.0000e-05 eta: 18:27:25 time: 0.5439 data_time: 0.0053 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 18:46:12 - mmengine - INFO - Epoch(train) [4][31300/42151] lr: 3.0000e-05 eta: 18:26:15 time: 0.7504 data_time: 0.1779 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/17 18:47:20 - mmengine - INFO - Epoch(train) [4][31400/42151] lr: 3.0000e-05 eta: 18:25:04 time: 0.7263 data_time: 0.1911 memory: 28726 loss_ce: 0.0103 loss: 0.0103 2022/09/17 18:48:27 - mmengine - INFO - Epoch(train) [4][31500/42151] lr: 3.0000e-05 eta: 18:23:53 time: 0.6000 data_time: 0.0591 memory: 28726 loss_ce: 0.0099 loss: 0.0099 2022/09/17 18:48:58 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 18:49:35 - mmengine - INFO - Epoch(train) [4][31600/42151] lr: 3.0000e-05 eta: 18:22:42 time: 0.6648 data_time: 0.1114 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 18:50:43 - mmengine - INFO - Epoch(train) [4][31700/42151] lr: 3.0000e-05 eta: 18:21:31 time: 0.7134 data_time: 0.0854 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/17 18:51:50 - mmengine - INFO - Epoch(train) [4][31800/42151] lr: 3.0000e-05 eta: 18:20:20 time: 0.5401 data_time: 0.0048 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 18:52:59 - mmengine - INFO - Epoch(train) [4][31900/42151] lr: 3.0000e-05 eta: 18:19:09 time: 0.7393 data_time: 0.1853 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 18:54:06 - mmengine - INFO - Epoch(train) [4][32000/42151] lr: 3.0000e-05 eta: 18:17:58 time: 0.7778 data_time: 0.2199 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 18:55:13 - mmengine - INFO - Epoch(train) [4][32100/42151] lr: 3.0000e-05 eta: 18:16:47 time: 0.6171 data_time: 0.0593 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 18:56:20 - mmengine - INFO - Epoch(train) [4][32200/42151] lr: 3.0000e-05 eta: 18:15:36 time: 0.6589 data_time: 0.0794 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 18:57:28 - mmengine - INFO - Epoch(train) [4][32300/42151] lr: 3.0000e-05 eta: 18:14:24 time: 0.6989 data_time: 0.1166 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 18:58:35 - mmengine - INFO - Epoch(train) [4][32400/42151] lr: 3.0000e-05 eta: 18:13:13 time: 0.5410 data_time: 0.0045 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 18:59:44 - mmengine - INFO - Epoch(train) [4][32500/42151] lr: 3.0000e-05 eta: 18:12:03 time: 0.7241 data_time: 0.1852 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 19:00:16 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 19:00:50 - mmengine - INFO - Epoch(train) [4][32600/42151] lr: 3.0000e-05 eta: 18:10:51 time: 0.7257 data_time: 0.1910 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 19:01:57 - mmengine - INFO - Epoch(train) [4][32700/42151] lr: 3.0000e-05 eta: 18:09:40 time: 0.6128 data_time: 0.0490 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/17 19:03:05 - mmengine - INFO - Epoch(train) [4][32800/42151] lr: 3.0000e-05 eta: 18:08:29 time: 0.6726 data_time: 0.1070 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 19:04:12 - mmengine - INFO - Epoch(train) [4][32900/42151] lr: 3.0000e-05 eta: 18:07:18 time: 0.6469 data_time: 0.1115 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/17 19:05:20 - mmengine - INFO - Epoch(train) [4][33000/42151] lr: 3.0000e-05 eta: 18:06:07 time: 0.5465 data_time: 0.0056 memory: 28726 loss_ce: 0.0101 loss: 0.0101 2022/09/17 19:06:29 - mmengine - INFO - Epoch(train) [4][33100/42151] lr: 3.0000e-05 eta: 18:04:57 time: 0.7185 data_time: 0.1794 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/17 19:07:36 - mmengine - INFO - Epoch(train) [4][33200/42151] lr: 3.0000e-05 eta: 18:03:45 time: 0.7349 data_time: 0.1718 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/17 19:08:44 - mmengine - INFO - Epoch(train) [4][33300/42151] lr: 3.0000e-05 eta: 18:02:34 time: 0.5749 data_time: 0.0392 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/17 19:09:51 - mmengine - INFO - Epoch(train) [4][33400/42151] lr: 3.0000e-05 eta: 18:01:23 time: 0.6390 data_time: 0.1041 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 19:11:00 - mmengine - INFO - Epoch(train) [4][33500/42151] lr: 3.0000e-05 eta: 18:00:13 time: 0.7388 data_time: 0.1566 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 19:11:32 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 19:12:07 - mmengine - INFO - Epoch(train) [4][33600/42151] lr: 3.0000e-05 eta: 17:59:02 time: 0.5917 data_time: 0.0055 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 19:13:16 - mmengine - INFO - Epoch(train) [4][33700/42151] lr: 3.0000e-05 eta: 17:57:51 time: 0.6896 data_time: 0.1452 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/17 19:14:23 - mmengine - INFO - Epoch(train) [4][33800/42151] lr: 3.0000e-05 eta: 17:56:40 time: 0.7056 data_time: 0.1703 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/17 19:15:30 - mmengine - INFO - Epoch(train) [4][33900/42151] lr: 3.0000e-05 eta: 17:55:29 time: 0.5787 data_time: 0.0406 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/17 19:16:39 - mmengine - INFO - Epoch(train) [4][34000/42151] lr: 3.0000e-05 eta: 17:54:18 time: 0.6811 data_time: 0.1174 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 19:17:45 - mmengine - INFO - Epoch(train) [4][34100/42151] lr: 3.0000e-05 eta: 17:53:07 time: 0.6920 data_time: 0.1184 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 19:18:52 - mmengine - INFO - Epoch(train) [4][34200/42151] lr: 3.0000e-05 eta: 17:51:55 time: 0.5411 data_time: 0.0048 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 19:20:00 - mmengine - INFO - Epoch(train) [4][34300/42151] lr: 3.0000e-05 eta: 17:50:45 time: 0.7119 data_time: 0.1533 memory: 28726 loss_ce: 0.0109 loss: 0.0109 2022/09/17 19:21:06 - mmengine - INFO - Epoch(train) [4][34400/42151] lr: 3.0000e-05 eta: 17:49:33 time: 0.6979 data_time: 0.1619 memory: 28726 loss_ce: 0.0096 loss: 0.0096 2022/09/17 19:22:14 - mmengine - INFO - Epoch(train) [4][34500/42151] lr: 3.0000e-05 eta: 17:48:22 time: 0.5962 data_time: 0.0626 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/17 19:22:46 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 19:23:22 - mmengine - INFO - Epoch(train) [4][34600/42151] lr: 3.0000e-05 eta: 17:47:11 time: 0.6513 data_time: 0.1061 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 19:24:30 - mmengine - INFO - Epoch(train) [4][34700/42151] lr: 3.0000e-05 eta: 17:46:00 time: 0.7131 data_time: 0.1272 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 19:25:37 - mmengine - INFO - Epoch(train) [4][34800/42151] lr: 3.0000e-05 eta: 17:44:49 time: 0.5410 data_time: 0.0047 memory: 28726 loss_ce: 0.0099 loss: 0.0099 2022/09/17 19:26:47 - mmengine - INFO - Epoch(train) [4][34900/42151] lr: 3.0000e-05 eta: 17:43:40 time: 0.7236 data_time: 0.1680 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 19:27:56 - mmengine - INFO - Epoch(train) [4][35000/42151] lr: 3.0000e-05 eta: 17:42:29 time: 0.7412 data_time: 0.2016 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 19:29:04 - mmengine - INFO - Epoch(train) [4][35100/42151] lr: 3.0000e-05 eta: 17:41:19 time: 0.5968 data_time: 0.0601 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 19:30:13 - mmengine - INFO - Epoch(train) [4][35200/42151] lr: 3.0000e-05 eta: 17:40:09 time: 0.6558 data_time: 0.1206 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/17 19:31:22 - mmengine - INFO - Epoch(train) [4][35300/42151] lr: 3.0000e-05 eta: 17:38:58 time: 0.6690 data_time: 0.1310 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 19:32:30 - mmengine - INFO - Epoch(train) [4][35400/42151] lr: 3.0000e-05 eta: 17:37:48 time: 0.5691 data_time: 0.0044 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 19:33:40 - mmengine - INFO - Epoch(train) [4][35500/42151] lr: 3.0000e-05 eta: 17:36:38 time: 0.7201 data_time: 0.1831 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/17 19:34:13 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 19:34:49 - mmengine - INFO - Epoch(train) [4][35600/42151] lr: 3.0000e-05 eta: 17:35:28 time: 0.7669 data_time: 0.2162 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 19:35:56 - mmengine - INFO - Epoch(train) [4][35700/42151] lr: 3.0000e-05 eta: 17:34:17 time: 0.6099 data_time: 0.0661 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 19:37:06 - mmengine - INFO - Epoch(train) [4][35800/42151] lr: 3.0000e-05 eta: 17:33:07 time: 0.6659 data_time: 0.1255 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 19:38:14 - mmengine - INFO - Epoch(train) [4][35900/42151] lr: 3.0000e-05 eta: 17:31:57 time: 0.6865 data_time: 0.0891 memory: 28726 loss_ce: 0.0099 loss: 0.0099 2022/09/17 19:39:21 - mmengine - INFO - Epoch(train) [4][36000/42151] lr: 3.0000e-05 eta: 17:30:46 time: 0.5544 data_time: 0.0050 memory: 28726 loss_ce: 0.0095 loss: 0.0095 2022/09/17 19:40:32 - mmengine - INFO - Epoch(train) [4][36100/42151] lr: 3.0000e-05 eta: 17:29:36 time: 0.7327 data_time: 0.1923 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/17 19:41:41 - mmengine - INFO - Epoch(train) [4][36200/42151] lr: 3.0000e-05 eta: 17:28:26 time: 0.7682 data_time: 0.2058 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/17 19:42:49 - mmengine - INFO - Epoch(train) [4][36300/42151] lr: 3.0000e-05 eta: 17:27:16 time: 0.6066 data_time: 0.0590 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 19:43:58 - mmengine - INFO - Epoch(train) [4][36400/42151] lr: 3.0000e-05 eta: 17:26:06 time: 0.7133 data_time: 0.0907 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 19:45:06 - mmengine - INFO - Epoch(train) [4][36500/42151] lr: 3.0000e-05 eta: 17:24:55 time: 0.6518 data_time: 0.1090 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 19:45:39 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 19:46:14 - mmengine - INFO - Epoch(train) [4][36600/42151] lr: 3.0000e-05 eta: 17:23:44 time: 0.5431 data_time: 0.0048 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 19:47:25 - mmengine - INFO - Epoch(train) [4][36700/42151] lr: 3.0000e-05 eta: 17:22:35 time: 0.7372 data_time: 0.1957 memory: 28726 loss_ce: 0.0095 loss: 0.0095 2022/09/17 19:48:34 - mmengine - INFO - Epoch(train) [4][36800/42151] lr: 3.0000e-05 eta: 17:21:25 time: 0.7421 data_time: 0.2051 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/17 19:49:42 - mmengine - INFO - Epoch(train) [4][36900/42151] lr: 3.0000e-05 eta: 17:20:14 time: 0.6555 data_time: 0.0422 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 19:50:50 - mmengine - INFO - Epoch(train) [4][37000/42151] lr: 3.0000e-05 eta: 17:19:04 time: 0.6534 data_time: 0.1162 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/17 19:51:58 - mmengine - INFO - Epoch(train) [4][37100/42151] lr: 3.0000e-05 eta: 17:17:53 time: 0.6819 data_time: 0.1184 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 19:53:06 - mmengine - INFO - Epoch(train) [4][37200/42151] lr: 3.0000e-05 eta: 17:16:43 time: 0.5406 data_time: 0.0046 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/17 19:54:16 - mmengine - INFO - Epoch(train) [4][37300/42151] lr: 3.0000e-05 eta: 17:15:33 time: 0.7585 data_time: 0.1886 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/17 19:55:24 - mmengine - INFO - Epoch(train) [4][37400/42151] lr: 3.0000e-05 eta: 17:14:23 time: 0.7197 data_time: 0.1771 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/17 19:56:33 - mmengine - INFO - Epoch(train) [4][37500/42151] lr: 3.0000e-05 eta: 17:13:12 time: 0.5975 data_time: 0.0618 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 19:57:06 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 19:57:43 - mmengine - INFO - Epoch(train) [4][37600/42151] lr: 3.0000e-05 eta: 17:12:02 time: 0.6582 data_time: 0.1172 memory: 28726 loss_ce: 0.0099 loss: 0.0099 2022/09/17 19:58:51 - mmengine - INFO - Epoch(train) [4][37700/42151] lr: 3.0000e-05 eta: 17:10:52 time: 0.6650 data_time: 0.1083 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/17 19:59:58 - mmengine - INFO - Epoch(train) [4][37800/42151] lr: 3.0000e-05 eta: 17:09:41 time: 0.5594 data_time: 0.0152 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/17 20:01:09 - mmengine - INFO - Epoch(train) [4][37900/42151] lr: 3.0000e-05 eta: 17:08:32 time: 0.7477 data_time: 0.2020 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/17 20:02:17 - mmengine - INFO - Epoch(train) [4][38000/42151] lr: 3.0000e-05 eta: 17:07:21 time: 0.7566 data_time: 0.1879 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/17 20:03:25 - mmengine - INFO - Epoch(train) [4][38100/42151] lr: 3.0000e-05 eta: 17:06:11 time: 0.6079 data_time: 0.0408 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 20:04:34 - mmengine - INFO - Epoch(train) [4][38200/42151] lr: 3.0000e-05 eta: 17:05:01 time: 0.6560 data_time: 0.0909 memory: 28726 loss_ce: 0.0097 loss: 0.0097 2022/09/17 20:05:41 - mmengine - INFO - Epoch(train) [4][38300/42151] lr: 3.0000e-05 eta: 17:03:49 time: 0.6271 data_time: 0.0781 memory: 28726 loss_ce: 0.0108 loss: 0.0108 2022/09/17 20:06:48 - mmengine - INFO - Epoch(train) [4][38400/42151] lr: 3.0000e-05 eta: 17:02:38 time: 0.6049 data_time: 0.0146 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 20:07:57 - mmengine - INFO - Epoch(train) [4][38500/42151] lr: 3.0000e-05 eta: 17:01:28 time: 0.7532 data_time: 0.1752 memory: 28726 loss_ce: 0.0095 loss: 0.0095 2022/09/17 20:08:29 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 20:09:04 - mmengine - INFO - Epoch(train) [4][38600/42151] lr: 3.0000e-05 eta: 17:00:17 time: 0.7299 data_time: 0.1967 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 20:10:11 - mmengine - INFO - Epoch(train) [4][38700/42151] lr: 3.0000e-05 eta: 16:59:06 time: 0.5764 data_time: 0.0375 memory: 28726 loss_ce: 0.0097 loss: 0.0097 2022/09/17 20:11:19 - mmengine - INFO - Epoch(train) [4][38800/42151] lr: 3.0000e-05 eta: 16:57:55 time: 0.6461 data_time: 0.1089 memory: 28726 loss_ce: 0.0102 loss: 0.0102 2022/09/17 20:12:26 - mmengine - INFO - Epoch(train) [4][38900/42151] lr: 3.0000e-05 eta: 16:56:44 time: 0.6493 data_time: 0.1099 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 20:13:34 - mmengine - INFO - Epoch(train) [4][39000/42151] lr: 3.0000e-05 eta: 16:55:33 time: 0.5508 data_time: 0.0138 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/17 20:14:44 - mmengine - INFO - Epoch(train) [4][39100/42151] lr: 3.0000e-05 eta: 16:54:24 time: 0.7827 data_time: 0.1989 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 20:15:52 - mmengine - INFO - Epoch(train) [4][39200/42151] lr: 3.0000e-05 eta: 16:53:13 time: 0.7474 data_time: 0.2093 memory: 28726 loss_ce: 0.0100 loss: 0.0100 2022/09/17 20:17:00 - mmengine - INFO - Epoch(train) [4][39300/42151] lr: 3.0000e-05 eta: 16:52:03 time: 0.6319 data_time: 0.0678 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/17 20:18:08 - mmengine - INFO - Epoch(train) [4][39400/42151] lr: 3.0000e-05 eta: 16:50:53 time: 0.6365 data_time: 0.0975 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/17 20:19:16 - mmengine - INFO - Epoch(train) [4][39500/42151] lr: 3.0000e-05 eta: 16:49:42 time: 0.6344 data_time: 0.0951 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 20:19:48 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 20:20:23 - mmengine - INFO - Epoch(train) [4][39600/42151] lr: 3.0000e-05 eta: 16:48:31 time: 0.5525 data_time: 0.0152 memory: 28726 loss_ce: 0.0096 loss: 0.0096 2022/09/17 20:21:32 - mmengine - INFO - Epoch(train) [4][39700/42151] lr: 3.0000e-05 eta: 16:47:21 time: 0.7436 data_time: 0.2041 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 20:22:40 - mmengine - INFO - Epoch(train) [4][39800/42151] lr: 3.0000e-05 eta: 16:46:10 time: 0.7259 data_time: 0.1880 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/17 20:23:47 - mmengine - INFO - Epoch(train) [4][39900/42151] lr: 3.0000e-05 eta: 16:44:59 time: 0.6041 data_time: 0.0698 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/17 20:24:55 - mmengine - INFO - Epoch(train) [4][40000/42151] lr: 3.0000e-05 eta: 16:43:48 time: 0.6929 data_time: 0.1050 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 20:26:03 - mmengine - INFO - Epoch(train) [4][40100/42151] lr: 3.0000e-05 eta: 16:42:38 time: 0.6426 data_time: 0.0967 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/17 20:27:11 - mmengine - INFO - Epoch(train) [4][40200/42151] lr: 3.0000e-05 eta: 16:41:27 time: 0.5493 data_time: 0.0158 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/17 20:28:21 - mmengine - INFO - Epoch(train) [4][40300/42151] lr: 3.0000e-05 eta: 16:40:18 time: 0.7443 data_time: 0.1979 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/17 20:29:29 - mmengine - INFO - Epoch(train) [4][40400/42151] lr: 3.0000e-05 eta: 16:39:07 time: 0.7394 data_time: 0.1743 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 20:30:37 - mmengine - INFO - Epoch(train) [4][40500/42151] lr: 3.0000e-05 eta: 16:37:57 time: 0.6112 data_time: 0.0421 memory: 28726 loss_ce: 0.0103 loss: 0.0103 2022/09/17 20:31:09 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 20:31:45 - mmengine - INFO - Epoch(train) [4][40600/42151] lr: 3.0000e-05 eta: 16:36:46 time: 0.6068 data_time: 0.0701 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/17 20:32:53 - mmengine - INFO - Epoch(train) [4][40700/42151] lr: 3.0000e-05 eta: 16:35:35 time: 0.6101 data_time: 0.0721 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/17 20:34:01 - mmengine - INFO - Epoch(train) [4][40800/42151] lr: 3.0000e-05 eta: 16:34:25 time: 0.5838 data_time: 0.0152 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 20:35:10 - mmengine - INFO - Epoch(train) [4][40900/42151] lr: 3.0000e-05 eta: 16:33:15 time: 0.7352 data_time: 0.1705 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 20:36:18 - mmengine - INFO - Epoch(train) [4][41000/42151] lr: 3.0000e-05 eta: 16:32:05 time: 0.7644 data_time: 0.1920 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 20:37:25 - mmengine - INFO - Epoch(train) [4][41100/42151] lr: 3.0000e-05 eta: 16:30:54 time: 0.5821 data_time: 0.0380 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 20:38:33 - mmengine - INFO - Epoch(train) [4][41200/42151] lr: 3.0000e-05 eta: 16:29:43 time: 0.6776 data_time: 0.1005 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 20:39:41 - mmengine - INFO - Epoch(train) [4][41300/42151] lr: 3.0000e-05 eta: 16:28:32 time: 0.6499 data_time: 0.1118 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 20:40:49 - mmengine - INFO - Epoch(train) [4][41400/42151] lr: 3.0000e-05 eta: 16:27:22 time: 0.5534 data_time: 0.0164 memory: 28726 loss_ce: 0.0095 loss: 0.0095 2022/09/17 20:41:59 - mmengine - INFO - Epoch(train) [4][41500/42151] lr: 3.0000e-05 eta: 16:26:13 time: 0.7391 data_time: 0.2026 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 20:42:32 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 20:43:07 - mmengine - INFO - Epoch(train) [4][41600/42151] lr: 3.0000e-05 eta: 16:25:02 time: 0.7233 data_time: 0.1866 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 20:44:14 - mmengine - INFO - Epoch(train) [4][41700/42151] lr: 3.0000e-05 eta: 16:23:51 time: 0.6255 data_time: 0.0606 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 20:45:23 - mmengine - INFO - Epoch(train) [4][41800/42151] lr: 3.0000e-05 eta: 16:22:41 time: 0.6740 data_time: 0.1209 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 20:46:30 - mmengine - INFO - Epoch(train) [4][41900/42151] lr: 3.0000e-05 eta: 16:21:30 time: 0.6424 data_time: 0.1019 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/17 20:47:38 - mmengine - INFO - Epoch(train) [4][42000/42151] lr: 3.0000e-05 eta: 16:20:19 time: 0.5584 data_time: 0.0142 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 20:48:46 - mmengine - INFO - Epoch(train) [4][42100/42151] lr: 3.0000e-05 eta: 16:19:09 time: 0.7276 data_time: 0.1927 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/17 20:49:18 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 20:49:18 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/09/17 20:50:20 - mmengine - INFO - Epoch(val) [4][100/7672] eta: 0:59:05 time: 0.4682 data_time: 0.0008 memory: 28726 2022/09/17 20:51:05 - mmengine - INFO - Epoch(val) [4][200/7672] eta: 0:51:03 time: 0.4100 data_time: 0.0010 memory: 1303 2022/09/17 20:51:50 - mmengine - INFO - Epoch(val) [4][300/7672] eta: 0:24:36 time: 0.2003 data_time: 0.0010 memory: 1303 2022/09/17 20:52:10 - mmengine - INFO - Epoch(val) [4][400/7672] eta: 0:24:18 time: 0.2006 data_time: 0.0008 memory: 1303 2022/09/17 20:52:30 - mmengine - INFO - Epoch(val) [4][500/7672] eta: 0:22:54 time: 0.1916 data_time: 0.0007 memory: 1303 2022/09/17 20:52:51 - mmengine - INFO - Epoch(val) [4][600/7672] eta: 0:22:53 time: 0.1942 data_time: 0.0008 memory: 1303 2022/09/17 20:53:12 - mmengine - INFO - Epoch(val) [4][700/7672] eta: 0:25:19 time: 0.2179 data_time: 0.0008 memory: 1303 2022/09/17 20:53:32 - mmengine - INFO - Epoch(val) [4][800/7672] eta: 0:22:44 time: 0.1985 data_time: 0.0007 memory: 1303 2022/09/17 20:53:52 - mmengine - INFO - Epoch(val) [4][900/7672] eta: 0:22:50 time: 0.2024 data_time: 0.0013 memory: 1303 2022/09/17 20:54:13 - mmengine - INFO - Epoch(val) [4][1000/7672] eta: 0:22:03 time: 0.1984 data_time: 0.0007 memory: 1303 2022/09/17 20:54:33 - mmengine - INFO - Epoch(val) [4][1100/7672] eta: 0:21:54 time: 0.2000 data_time: 0.0008 memory: 1303 2022/09/17 20:54:54 - mmengine - INFO - Epoch(val) [4][1200/7672] eta: 0:22:09 time: 0.2054 data_time: 0.0008 memory: 1303 2022/09/17 20:55:15 - mmengine - INFO - Epoch(val) [4][1300/7672] eta: 0:28:17 time: 0.2664 data_time: 0.0056 memory: 1303 2022/09/17 20:55:35 - mmengine - INFO - Epoch(val) [4][1400/7672] eta: 0:24:47 time: 0.2371 data_time: 0.0101 memory: 1303 2022/09/17 20:55:56 - mmengine - INFO - Epoch(val) [4][1500/7672] eta: 0:24:02 time: 0.2336 data_time: 0.0046 memory: 1303 2022/09/17 20:56:16 - mmengine - INFO - Epoch(val) [4][1600/7672] eta: 0:21:15 time: 0.2101 data_time: 0.0026 memory: 1303 2022/09/17 20:56:37 - mmengine - INFO - Epoch(val) [4][1700/7672] eta: 0:19:50 time: 0.1994 data_time: 0.0023 memory: 1303 2022/09/17 20:56:58 - mmengine - INFO - Epoch(val) [4][1800/7672] eta: 0:20:26 time: 0.2088 data_time: 0.0022 memory: 1303 2022/09/17 20:57:19 - mmengine - INFO - Epoch(val) [4][1900/7672] eta: 0:18:52 time: 0.1963 data_time: 0.0007 memory: 1303 2022/09/17 20:57:39 - mmengine - INFO - Epoch(val) [4][2000/7672] eta: 0:19:33 time: 0.2070 data_time: 0.0009 memory: 1303 2022/09/17 20:58:00 - mmengine - INFO - Epoch(val) [4][2100/7672] eta: 0:19:58 time: 0.2150 data_time: 0.0008 memory: 1303 2022/09/17 20:58:21 - mmengine - INFO - Epoch(val) [4][2200/7672] eta: 0:18:18 time: 0.2008 data_time: 0.0010 memory: 1303 2022/09/17 20:58:41 - mmengine - INFO - Epoch(val) [4][2300/7672] eta: 0:18:18 time: 0.2045 data_time: 0.0008 memory: 1303 2022/09/17 20:59:02 - mmengine - INFO - Epoch(val) [4][2400/7672] eta: 0:18:22 time: 0.2091 data_time: 0.0007 memory: 1303 2022/09/17 20:59:23 - mmengine - INFO - Epoch(val) [4][2500/7672] eta: 0:17:35 time: 0.2041 data_time: 0.0008 memory: 1303 2022/09/17 20:59:44 - mmengine - INFO - Epoch(val) [4][2600/7672] eta: 0:17:01 time: 0.2014 data_time: 0.0009 memory: 1303 2022/09/17 21:00:05 - mmengine - INFO - Epoch(val) [4][2700/7672] eta: 0:16:23 time: 0.1979 data_time: 0.0008 memory: 1303 2022/09/17 21:00:25 - mmengine - INFO - Epoch(val) [4][2800/7672] eta: 0:16:13 time: 0.1999 data_time: 0.0007 memory: 1303 2022/09/17 21:00:45 - mmengine - INFO - Epoch(val) [4][2900/7672] eta: 0:15:48 time: 0.1989 data_time: 0.0017 memory: 1303 2022/09/17 21:01:07 - mmengine - INFO - Epoch(val) [4][3000/7672] eta: 0:20:54 time: 0.2685 data_time: 0.0068 memory: 1303 2022/09/17 21:01:27 - mmengine - INFO - Epoch(val) [4][3100/7672] eta: 0:16:12 time: 0.2127 data_time: 0.0014 memory: 1303 2022/09/17 21:01:48 - mmengine - INFO - Epoch(val) [4][3200/7672] eta: 0:15:39 time: 0.2101 data_time: 0.0010 memory: 1303 2022/09/17 21:02:08 - mmengine - INFO - Epoch(val) [4][3300/7672] eta: 0:14:21 time: 0.1971 data_time: 0.0019 memory: 1303 2022/09/17 21:02:29 - mmengine - INFO - Epoch(val) [4][3400/7672] eta: 0:14:08 time: 0.1987 data_time: 0.0008 memory: 1303 2022/09/17 21:02:49 - mmengine - INFO - Epoch(val) [4][3500/7672] eta: 0:14:17 time: 0.2056 data_time: 0.0008 memory: 1303 2022/09/17 21:03:10 - mmengine - INFO - Epoch(val) [4][3600/7672] eta: 0:13:29 time: 0.1988 data_time: 0.0008 memory: 1303 2022/09/17 21:03:31 - mmengine - INFO - Epoch(val) [4][3700/7672] eta: 0:13:15 time: 0.2002 data_time: 0.0008 memory: 1303 2022/09/17 21:03:52 - mmengine - INFO - Epoch(val) [4][3800/7672] eta: 0:12:47 time: 0.1981 data_time: 0.0008 memory: 1303 2022/09/17 21:04:12 - mmengine - INFO - Epoch(val) [4][3900/7672] eta: 0:12:32 time: 0.1994 data_time: 0.0025 memory: 1303 2022/09/17 21:04:33 - mmengine - INFO - Epoch(val) [4][4000/7672] eta: 0:12:13 time: 0.1996 data_time: 0.0008 memory: 1303 2022/09/17 21:04:54 - mmengine - INFO - Epoch(val) [4][4100/7672] eta: 0:11:54 time: 0.2001 data_time: 0.0008 memory: 1303 2022/09/17 21:05:14 - mmengine - INFO - Epoch(val) [4][4200/7672] eta: 0:11:42 time: 0.2023 data_time: 0.0008 memory: 1303 2022/09/17 21:05:35 - mmengine - INFO - Epoch(val) [4][4300/7672] eta: 0:12:18 time: 0.2191 data_time: 0.0044 memory: 1303 2022/09/17 21:05:56 - mmengine - INFO - Epoch(val) [4][4400/7672] eta: 0:11:11 time: 0.2051 data_time: 0.0009 memory: 1303 2022/09/17 21:06:16 - mmengine - INFO - Epoch(val) [4][4500/7672] eta: 0:11:52 time: 0.2247 data_time: 0.0022 memory: 1303 2022/09/17 21:06:37 - mmengine - INFO - Epoch(val) [4][4600/7672] eta: 0:11:29 time: 0.2244 data_time: 0.0047 memory: 1303 2022/09/17 21:06:57 - mmengine - INFO - Epoch(val) [4][4700/7672] eta: 0:09:44 time: 0.1968 data_time: 0.0020 memory: 1303 2022/09/17 21:07:18 - mmengine - INFO - Epoch(val) [4][4800/7672] eta: 0:09:33 time: 0.1998 data_time: 0.0010 memory: 1303 2022/09/17 21:07:38 - mmengine - INFO - Epoch(val) [4][4900/7672] eta: 0:09:11 time: 0.1989 data_time: 0.0020 memory: 1303 2022/09/17 21:07:59 - mmengine - INFO - Epoch(val) [4][5000/7672] eta: 0:08:53 time: 0.1995 data_time: 0.0008 memory: 1303 2022/09/17 21:08:20 - mmengine - INFO - Epoch(val) [4][5100/7672] eta: 0:08:21 time: 0.1951 data_time: 0.0008 memory: 1303 2022/09/17 21:08:41 - mmengine - INFO - Epoch(val) [4][5200/7672] eta: 0:08:19 time: 0.2020 data_time: 0.0010 memory: 1303 2022/09/17 21:09:01 - mmengine - INFO - Epoch(val) [4][5300/7672] eta: 0:08:16 time: 0.2094 data_time: 0.0009 memory: 1303 2022/09/17 21:09:22 - mmengine - INFO - Epoch(val) [4][5400/7672] eta: 0:07:23 time: 0.1952 data_time: 0.0008 memory: 1303 2022/09/17 21:09:42 - mmengine - INFO - Epoch(val) [4][5500/7672] eta: 0:07:15 time: 0.2005 data_time: 0.0008 memory: 1303 2022/09/17 21:10:03 - mmengine - INFO - Epoch(val) [4][5600/7672] eta: 0:07:04 time: 0.2047 data_time: 0.0020 memory: 1303 2022/09/17 21:10:24 - mmengine - INFO - Epoch(val) [4][5700/7672] eta: 0:06:30 time: 0.1980 data_time: 0.0007 memory: 1303 2022/09/17 21:10:44 - mmengine - INFO - Epoch(val) [4][5800/7672] eta: 0:06:05 time: 0.1955 data_time: 0.0008 memory: 1303 2022/09/17 21:11:05 - mmengine - INFO - Epoch(val) [4][5900/7672] eta: 0:05:56 time: 0.2013 data_time: 0.0008 memory: 1303 2022/09/17 21:11:26 - mmengine - INFO - Epoch(val) [4][6000/7672] eta: 0:06:27 time: 0.2315 data_time: 0.0038 memory: 1303 2022/09/17 21:11:46 - mmengine - INFO - Epoch(val) [4][6100/7672] eta: 0:05:10 time: 0.1972 data_time: 0.0009 memory: 1303 2022/09/17 21:12:07 - mmengine - INFO - Epoch(val) [4][6200/7672] eta: 0:04:54 time: 0.2001 data_time: 0.0011 memory: 1303 2022/09/17 21:12:27 - mmengine - INFO - Epoch(val) [4][6300/7672] eta: 0:05:07 time: 0.2240 data_time: 0.0034 memory: 1303 2022/09/17 21:12:48 - mmengine - INFO - Epoch(val) [4][6400/7672] eta: 0:04:21 time: 0.2056 data_time: 0.0028 memory: 1303 2022/09/17 21:13:09 - mmengine - INFO - Epoch(val) [4][6500/7672] eta: 0:03:56 time: 0.2015 data_time: 0.0019 memory: 1303 2022/09/17 21:13:30 - mmengine - INFO - Epoch(val) [4][6600/7672] eta: 0:03:32 time: 0.1984 data_time: 0.0019 memory: 1303 2022/09/17 21:13:50 - mmengine - INFO - Epoch(val) [4][6700/7672] eta: 0:03:11 time: 0.1966 data_time: 0.0008 memory: 1303 2022/09/17 21:14:11 - mmengine - INFO - Epoch(val) [4][6800/7672] eta: 0:02:53 time: 0.1995 data_time: 0.0008 memory: 1303 2022/09/17 21:14:32 - mmengine - INFO - Epoch(val) [4][6900/7672] eta: 0:02:38 time: 0.2059 data_time: 0.0009 memory: 1303 2022/09/17 21:14:53 - mmengine - INFO - Epoch(val) [4][7000/7672] eta: 0:02:17 time: 0.2049 data_time: 0.0007 memory: 1303 2022/09/17 21:15:13 - mmengine - INFO - Epoch(val) [4][7100/7672] eta: 0:01:53 time: 0.1991 data_time: 0.0008 memory: 1303 2022/09/17 21:15:33 - mmengine - INFO - Epoch(val) [4][7200/7672] eta: 0:01:33 time: 0.1974 data_time: 0.0007 memory: 1303 2022/09/17 21:15:54 - mmengine - INFO - Epoch(val) [4][7300/7672] eta: 0:01:16 time: 0.2047 data_time: 0.0007 memory: 1303 2022/09/17 21:16:15 - mmengine - INFO - Epoch(val) [4][7400/7672] eta: 0:00:54 time: 0.2007 data_time: 0.0008 memory: 1303 2022/09/17 21:16:35 - mmengine - INFO - Epoch(val) [4][7500/7672] eta: 0:00:33 time: 0.1975 data_time: 0.0009 memory: 1303 2022/09/17 21:16:56 - mmengine - INFO - Epoch(val) [4][7600/7672] eta: 0:00:14 time: 0.2035 data_time: 0.0008 memory: 1303 2022/09/17 21:17:10 - mmengine - INFO - Epoch(val) [4][7672/7672] CUTE80/recog/word_acc_ignore_case_symbol: 0.8854 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9510 SVT/recog/word_acc_ignore_case_symbol: 0.8964 SVTP/recog/word_acc_ignore_case_symbol: 0.7969 IC13/recog/word_acc_ignore_case_symbol: 0.9507 IC15/recog/word_acc_ignore_case_symbol: 0.7458 2022/09/17 21:18:26 - mmengine - INFO - Epoch(train) [5][100/42151] lr: 3.0000e-06 eta: 16:17:25 time: 0.7926 data_time: 0.2474 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 21:19:32 - mmengine - INFO - Epoch(train) [5][200/42151] lr: 3.0000e-06 eta: 16:16:13 time: 0.7641 data_time: 0.1904 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/17 21:20:41 - mmengine - INFO - Epoch(train) [5][300/42151] lr: 3.0000e-06 eta: 16:15:03 time: 0.5887 data_time: 0.0048 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/17 21:21:46 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 21:21:48 - mmengine - INFO - Epoch(train) [5][400/42151] lr: 3.0000e-06 eta: 16:13:52 time: 0.5476 data_time: 0.0104 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/17 21:22:56 - mmengine - INFO - Epoch(train) [5][500/42151] lr: 3.0000e-06 eta: 16:12:42 time: 0.5644 data_time: 0.0292 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 21:24:04 - mmengine - INFO - Epoch(train) [5][600/42151] lr: 3.0000e-06 eta: 16:11:31 time: 0.6638 data_time: 0.0953 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/17 21:25:14 - mmengine - INFO - Epoch(train) [5][700/42151] lr: 3.0000e-06 eta: 16:10:22 time: 0.8352 data_time: 0.2532 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 21:26:21 - mmengine - INFO - Epoch(train) [5][800/42151] lr: 3.0000e-06 eta: 16:09:11 time: 0.7591 data_time: 0.2193 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/17 21:27:28 - mmengine - INFO - Epoch(train) [5][900/42151] lr: 3.0000e-06 eta: 16:08:00 time: 0.5829 data_time: 0.0065 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 21:28:37 - mmengine - INFO - Epoch(train) [5][1000/42151] lr: 3.0000e-06 eta: 16:06:50 time: 0.5803 data_time: 0.0135 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/17 21:29:45 - mmengine - INFO - Epoch(train) [5][1100/42151] lr: 3.0000e-06 eta: 16:05:39 time: 0.6104 data_time: 0.0408 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/17 21:30:54 - mmengine - INFO - Epoch(train) [5][1200/42151] lr: 3.0000e-06 eta: 16:04:29 time: 0.6579 data_time: 0.1191 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/17 21:32:02 - mmengine - INFO - Epoch(train) [5][1300/42151] lr: 3.0000e-06 eta: 16:03:19 time: 0.7879 data_time: 0.2456 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/17 21:33:08 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 21:33:10 - mmengine - INFO - Epoch(train) [5][1400/42151] lr: 3.0000e-06 eta: 16:02:08 time: 0.7511 data_time: 0.2131 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/17 21:34:18 - mmengine - INFO - Epoch(train) [5][1500/42151] lr: 3.0000e-06 eta: 16:00:58 time: 0.5409 data_time: 0.0046 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/17 21:35:27 - mmengine - INFO - Epoch(train) [5][1600/42151] lr: 3.0000e-06 eta: 15:59:48 time: 0.5593 data_time: 0.0147 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/17 21:36:34 - mmengine - INFO - Epoch(train) [5][1700/42151] lr: 3.0000e-06 eta: 15:58:37 time: 0.6305 data_time: 0.0418 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/17 21:37:43 - mmengine - INFO - Epoch(train) [5][1800/42151] lr: 3.0000e-06 eta: 15:57:27 time: 0.7117 data_time: 0.0862 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 21:38:53 - mmengine - INFO - Epoch(train) [5][1900/42151] lr: 3.0000e-06 eta: 15:56:18 time: 0.8058 data_time: 0.2456 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 21:40:02 - mmengine - INFO - Epoch(train) [5][2000/42151] lr: 3.0000e-06 eta: 15:55:07 time: 0.7596 data_time: 0.2157 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/17 21:41:09 - mmengine - INFO - Epoch(train) [5][2100/42151] lr: 3.0000e-06 eta: 15:53:57 time: 0.5487 data_time: 0.0054 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 21:42:16 - mmengine - INFO - Epoch(train) [5][2200/42151] lr: 3.0000e-06 eta: 15:52:46 time: 0.5577 data_time: 0.0136 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/17 21:43:24 - mmengine - INFO - Epoch(train) [5][2300/42151] lr: 3.0000e-06 eta: 15:51:36 time: 0.6188 data_time: 0.0683 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/17 21:44:31 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 21:44:33 - mmengine - INFO - Epoch(train) [5][2400/42151] lr: 3.0000e-06 eta: 15:50:26 time: 0.6436 data_time: 0.1083 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/17 21:45:42 - mmengine - INFO - Epoch(train) [5][2500/42151] lr: 3.0000e-06 eta: 15:49:16 time: 0.8549 data_time: 0.2515 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/17 21:46:50 - mmengine - INFO - Epoch(train) [5][2600/42151] lr: 3.0000e-06 eta: 15:48:05 time: 0.7392 data_time: 0.1765 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 21:48:01 - mmengine - INFO - Epoch(train) [5][2700/42151] lr: 3.0000e-06 eta: 15:46:56 time: 0.5743 data_time: 0.0057 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/17 21:49:10 - mmengine - INFO - Epoch(train) [5][2800/42151] lr: 3.0000e-06 eta: 15:45:46 time: 0.5549 data_time: 0.0153 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/17 21:50:18 - mmengine - INFO - Epoch(train) [5][2900/42151] lr: 3.0000e-06 eta: 15:44:36 time: 0.5754 data_time: 0.0397 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/17 21:51:26 - mmengine - INFO - Epoch(train) [5][3000/42151] lr: 3.0000e-06 eta: 15:43:26 time: 0.6466 data_time: 0.1104 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/17 21:52:38 - mmengine - INFO - Epoch(train) [5][3100/42151] lr: 3.0000e-06 eta: 15:42:17 time: 0.8356 data_time: 0.2296 memory: 28726 loss_ce: 0.0066 loss: 0.0066 2022/09/17 21:53:46 - mmengine - INFO - Epoch(train) [5][3200/42151] lr: 3.0000e-06 eta: 15:41:06 time: 0.7294 data_time: 0.1902 memory: 28726 loss_ce: 0.0065 loss: 0.0065 2022/09/17 21:54:54 - mmengine - INFO - Epoch(train) [5][3300/42151] lr: 3.0000e-06 eta: 15:39:56 time: 0.5679 data_time: 0.0062 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/17 21:56:04 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 21:56:06 - mmengine - INFO - Epoch(train) [5][3400/42151] lr: 3.0000e-06 eta: 15:38:48 time: 0.7371 data_time: 0.0497 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 21:57:15 - mmengine - INFO - Epoch(train) [5][3500/42151] lr: 3.0000e-06 eta: 15:37:38 time: 0.7309 data_time: 0.0909 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 21:58:24 - mmengine - INFO - Epoch(train) [5][3600/42151] lr: 3.0000e-06 eta: 15:36:27 time: 0.6470 data_time: 0.0817 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/17 21:59:33 - mmengine - INFO - Epoch(train) [5][3700/42151] lr: 3.0000e-06 eta: 15:35:18 time: 0.8287 data_time: 0.2499 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/17 22:00:41 - mmengine - INFO - Epoch(train) [5][3800/42151] lr: 3.0000e-06 eta: 15:34:07 time: 0.7248 data_time: 0.1600 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/17 22:01:49 - mmengine - INFO - Epoch(train) [5][3900/42151] lr: 3.0000e-06 eta: 15:32:57 time: 0.5590 data_time: 0.0244 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/17 22:02:56 - mmengine - INFO - Epoch(train) [5][4000/42151] lr: 3.0000e-06 eta: 15:31:46 time: 0.6353 data_time: 0.0732 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/17 22:04:03 - mmengine - INFO - Epoch(train) [5][4100/42151] lr: 3.0000e-06 eta: 15:30:35 time: 0.6071 data_time: 0.0655 memory: 28726 loss_ce: 0.0095 loss: 0.0095 2022/09/17 22:05:11 - mmengine - INFO - Epoch(train) [5][4200/42151] lr: 3.0000e-06 eta: 15:29:25 time: 0.6745 data_time: 0.1149 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/17 22:06:25 - mmengine - INFO - Epoch(train) [5][4300/42151] lr: 3.0000e-06 eta: 15:28:17 time: 0.9352 data_time: 0.3693 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/17 22:07:32 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 22:07:35 - mmengine - INFO - Epoch(train) [5][4400/42151] lr: 3.0000e-06 eta: 15:27:07 time: 0.7631 data_time: 0.1891 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/17 22:08:42 - mmengine - INFO - Epoch(train) [5][4500/42151] lr: 3.0000e-06 eta: 15:25:57 time: 0.5572 data_time: 0.0216 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 22:09:50 - mmengine - INFO - Epoch(train) [5][4600/42151] lr: 3.0000e-06 eta: 15:24:46 time: 0.6089 data_time: 0.0463 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/17 22:10:57 - mmengine - INFO - Epoch(train) [5][4700/42151] lr: 3.0000e-06 eta: 15:23:36 time: 0.6554 data_time: 0.1061 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/17 22:12:05 - mmengine - INFO - Epoch(train) [5][4800/42151] lr: 3.0000e-06 eta: 15:22:25 time: 0.6646 data_time: 0.1134 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 22:13:15 - mmengine - INFO - Epoch(train) [5][4900/42151] lr: 3.0000e-06 eta: 15:21:16 time: 0.7537 data_time: 0.1870 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/17 22:14:23 - mmengine - INFO - Epoch(train) [5][5000/42151] lr: 3.0000e-06 eta: 15:20:05 time: 0.7436 data_time: 0.2053 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 22:15:30 - mmengine - INFO - Epoch(train) [5][5100/42151] lr: 3.0000e-06 eta: 15:18:54 time: 0.5581 data_time: 0.0212 memory: 28726 loss_ce: 0.0106 loss: 0.0106 2022/09/17 22:16:40 - mmengine - INFO - Epoch(train) [5][5200/42151] lr: 3.0000e-06 eta: 15:17:45 time: 0.6083 data_time: 0.0719 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/17 22:17:48 - mmengine - INFO - Epoch(train) [5][5300/42151] lr: 3.0000e-06 eta: 15:16:35 time: 0.7068 data_time: 0.1108 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/17 22:18:54 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 22:18:57 - mmengine - INFO - Epoch(train) [5][5400/42151] lr: 3.0000e-06 eta: 15:15:25 time: 0.6697 data_time: 0.1301 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 22:20:06 - mmengine - INFO - Epoch(train) [5][5500/42151] lr: 3.0000e-06 eta: 15:14:15 time: 0.7656 data_time: 0.2279 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/17 22:21:15 - mmengine - INFO - Epoch(train) [5][5600/42151] lr: 3.0000e-06 eta: 15:13:05 time: 0.7578 data_time: 0.1717 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/17 22:22:24 - mmengine - INFO - Epoch(train) [5][5700/42151] lr: 3.0000e-06 eta: 15:11:55 time: 0.6036 data_time: 0.0595 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 22:23:33 - mmengine - INFO - Epoch(train) [5][5800/42151] lr: 3.0000e-06 eta: 15:10:45 time: 0.6046 data_time: 0.0405 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/17 22:24:41 - mmengine - INFO - Epoch(train) [5][5900/42151] lr: 3.0000e-06 eta: 15:09:34 time: 0.6402 data_time: 0.1036 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 22:25:49 - mmengine - INFO - Epoch(train) [5][6000/42151] lr: 3.0000e-06 eta: 15:08:24 time: 0.6488 data_time: 0.0855 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/17 22:26:59 - mmengine - INFO - Epoch(train) [5][6100/42151] lr: 3.0000e-06 eta: 15:07:15 time: 0.7993 data_time: 0.2197 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/17 22:28:07 - mmengine - INFO - Epoch(train) [5][6200/42151] lr: 3.0000e-06 eta: 15:06:04 time: 0.7000 data_time: 0.1605 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/17 22:29:16 - mmengine - INFO - Epoch(train) [5][6300/42151] lr: 3.0000e-06 eta: 15:04:54 time: 0.6164 data_time: 0.0327 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 22:30:21 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 22:30:23 - mmengine - INFO - Epoch(train) [5][6400/42151] lr: 3.0000e-06 eta: 15:03:44 time: 0.5800 data_time: 0.0423 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/17 22:31:31 - mmengine - INFO - Epoch(train) [5][6500/42151] lr: 3.0000e-06 eta: 15:02:33 time: 0.6296 data_time: 0.0951 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 22:32:40 - mmengine - INFO - Epoch(train) [5][6600/42151] lr: 3.0000e-06 eta: 15:01:23 time: 0.6676 data_time: 0.1212 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/17 22:33:49 - mmengine - INFO - Epoch(train) [5][6700/42151] lr: 3.0000e-06 eta: 15:00:14 time: 0.8172 data_time: 0.2526 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/17 22:34:57 - mmengine - INFO - Epoch(train) [5][6800/42151] lr: 3.0000e-06 eta: 14:59:03 time: 0.7261 data_time: 0.1804 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/17 22:36:05 - mmengine - INFO - Epoch(train) [5][6900/42151] lr: 3.0000e-06 eta: 14:57:53 time: 0.5877 data_time: 0.0289 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/17 22:37:14 - mmengine - INFO - Epoch(train) [5][7000/42151] lr: 3.0000e-06 eta: 14:56:43 time: 0.6255 data_time: 0.0589 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/17 22:38:23 - mmengine - INFO - Epoch(train) [5][7100/42151] lr: 3.0000e-06 eta: 14:55:33 time: 0.6320 data_time: 0.0666 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/17 22:39:31 - mmengine - INFO - Epoch(train) [5][7200/42151] lr: 3.0000e-06 eta: 14:54:23 time: 0.6447 data_time: 0.1087 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/17 22:40:42 - mmengine - INFO - Epoch(train) [5][7300/42151] lr: 3.0000e-06 eta: 14:53:14 time: 0.8205 data_time: 0.2751 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/17 22:41:49 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 22:41:51 - mmengine - INFO - Epoch(train) [5][7400/42151] lr: 3.0000e-06 eta: 14:52:04 time: 0.7260 data_time: 0.1650 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 22:42:59 - mmengine - INFO - Epoch(train) [5][7500/42151] lr: 3.0000e-06 eta: 14:50:54 time: 0.6161 data_time: 0.0258 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/17 22:44:08 - mmengine - INFO - Epoch(train) [5][7600/42151] lr: 3.0000e-06 eta: 14:49:44 time: 0.7027 data_time: 0.0543 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/17 22:45:17 - mmengine - INFO - Epoch(train) [5][7700/42151] lr: 3.0000e-06 eta: 14:48:34 time: 0.6608 data_time: 0.1083 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/17 22:46:27 - mmengine - INFO - Epoch(train) [5][7800/42151] lr: 3.0000e-06 eta: 14:47:24 time: 0.6714 data_time: 0.1024 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/17 22:47:38 - mmengine - INFO - Epoch(train) [5][7900/42151] lr: 3.0000e-06 eta: 14:46:16 time: 0.8646 data_time: 0.2620 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/17 22:48:48 - mmengine - INFO - Epoch(train) [5][8000/42151] lr: 3.0000e-06 eta: 14:45:06 time: 0.7955 data_time: 0.2156 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/17 22:49:56 - mmengine - INFO - Epoch(train) [5][8100/42151] lr: 3.0000e-06 eta: 14:43:56 time: 0.5709 data_time: 0.0260 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 22:51:06 - mmengine - INFO - Epoch(train) [5][8200/42151] lr: 3.0000e-06 eta: 14:42:46 time: 0.6212 data_time: 0.0796 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/17 22:52:15 - mmengine - INFO - Epoch(train) [5][8300/42151] lr: 3.0000e-06 eta: 14:41:36 time: 0.6152 data_time: 0.0724 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/17 22:53:22 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 22:53:25 - mmengine - INFO - Epoch(train) [5][8400/42151] lr: 3.0000e-06 eta: 14:40:27 time: 0.6697 data_time: 0.1300 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/17 22:54:35 - mmengine - INFO - Epoch(train) [5][8500/42151] lr: 3.0000e-06 eta: 14:39:17 time: 0.8019 data_time: 0.2257 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/17 22:55:45 - mmengine - INFO - Epoch(train) [5][8600/42151] lr: 3.0000e-06 eta: 14:38:08 time: 0.7190 data_time: 0.1783 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/17 22:56:55 - mmengine - INFO - Epoch(train) [5][8700/42151] lr: 3.0000e-06 eta: 14:36:59 time: 0.5897 data_time: 0.0230 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/17 22:58:04 - mmengine - INFO - Epoch(train) [5][8800/42151] lr: 3.0000e-06 eta: 14:35:49 time: 0.5933 data_time: 0.0504 memory: 28726 loss_ce: 0.0067 loss: 0.0067 2022/09/17 22:59:13 - mmengine - INFO - Epoch(train) [5][8900/42151] lr: 3.0000e-06 eta: 14:34:39 time: 0.6679 data_time: 0.1207 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/17 23:00:24 - mmengine - INFO - Epoch(train) [5][9000/42151] lr: 3.0000e-06 eta: 14:33:30 time: 0.6730 data_time: 0.1306 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/17 23:01:35 - mmengine - INFO - Epoch(train) [5][9100/42151] lr: 3.0000e-06 eta: 14:32:21 time: 0.8395 data_time: 0.2934 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/17 23:02:44 - mmengine - INFO - Epoch(train) [5][9200/42151] lr: 3.0000e-06 eta: 14:31:11 time: 0.7154 data_time: 0.1777 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/17 23:03:51 - mmengine - INFO - Epoch(train) [5][9300/42151] lr: 3.0000e-06 eta: 14:30:00 time: 0.5728 data_time: 0.0240 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/17 23:04:57 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 23:04:59 - mmengine - INFO - Epoch(train) [5][9400/42151] lr: 3.0000e-06 eta: 14:28:50 time: 0.5858 data_time: 0.0506 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 23:06:07 - mmengine - INFO - Epoch(train) [5][9500/42151] lr: 3.0000e-06 eta: 14:27:40 time: 0.6410 data_time: 0.1028 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/17 23:07:14 - mmengine - INFO - Epoch(train) [5][9600/42151] lr: 3.0000e-06 eta: 14:26:29 time: 0.6683 data_time: 0.1119 memory: 28726 loss_ce: 0.0098 loss: 0.0098 2022/09/17 23:08:23 - mmengine - INFO - Epoch(train) [5][9700/42151] lr: 3.0000e-06 eta: 14:25:19 time: 0.7633 data_time: 0.2237 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/17 23:09:31 - mmengine - INFO - Epoch(train) [5][9800/42151] lr: 3.0000e-06 eta: 14:24:09 time: 0.7299 data_time: 0.1671 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/17 23:10:40 - mmengine - INFO - Epoch(train) [5][9900/42151] lr: 3.0000e-06 eta: 14:22:59 time: 0.5906 data_time: 0.0225 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/17 23:11:48 - mmengine - INFO - Epoch(train) [5][10000/42151] lr: 3.0000e-06 eta: 14:21:48 time: 0.5836 data_time: 0.0478 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/17 23:12:55 - mmengine - INFO - Epoch(train) [5][10100/42151] lr: 3.0000e-06 eta: 14:20:38 time: 0.6398 data_time: 0.0858 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/17 23:14:02 - mmengine - INFO - Epoch(train) [5][10200/42151] lr: 3.0000e-06 eta: 14:19:27 time: 0.6652 data_time: 0.0903 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/17 23:15:11 - mmengine - INFO - Epoch(train) [5][10300/42151] lr: 3.0000e-06 eta: 14:18:17 time: 0.7620 data_time: 0.2025 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/17 23:16:15 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 23:16:18 - mmengine - INFO - Epoch(train) [5][10400/42151] lr: 3.0000e-06 eta: 14:17:07 time: 0.7331 data_time: 0.1608 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/17 23:17:25 - mmengine - INFO - Epoch(train) [5][10500/42151] lr: 3.0000e-06 eta: 14:15:56 time: 0.5563 data_time: 0.0221 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/17 23:18:32 - mmengine - INFO - Epoch(train) [5][10600/42151] lr: 3.0000e-06 eta: 14:14:45 time: 0.6057 data_time: 0.0685 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 23:19:39 - mmengine - INFO - Epoch(train) [5][10700/42151] lr: 3.0000e-06 eta: 14:13:35 time: 0.6335 data_time: 0.0966 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/17 23:20:48 - mmengine - INFO - Epoch(train) [5][10800/42151] lr: 3.0000e-06 eta: 14:12:25 time: 0.6506 data_time: 0.0785 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/17 23:21:57 - mmengine - INFO - Epoch(train) [5][10900/42151] lr: 3.0000e-06 eta: 14:11:15 time: 0.8066 data_time: 0.2541 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/17 23:23:05 - mmengine - INFO - Epoch(train) [5][11000/42151] lr: 3.0000e-06 eta: 14:10:05 time: 0.7173 data_time: 0.1539 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/17 23:24:13 - mmengine - INFO - Epoch(train) [5][11100/42151] lr: 3.0000e-06 eta: 14:08:54 time: 0.5560 data_time: 0.0200 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/17 23:25:20 - mmengine - INFO - Epoch(train) [5][11200/42151] lr: 3.0000e-06 eta: 14:07:44 time: 0.6068 data_time: 0.0394 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/17 23:26:28 - mmengine - INFO - Epoch(train) [5][11300/42151] lr: 3.0000e-06 eta: 14:06:33 time: 0.5986 data_time: 0.0658 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/17 23:27:32 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 23:27:35 - mmengine - INFO - Epoch(train) [5][11400/42151] lr: 3.0000e-06 eta: 14:05:23 time: 0.6467 data_time: 0.1132 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/17 23:28:43 - mmengine - INFO - Epoch(train) [5][11500/42151] lr: 3.0000e-06 eta: 14:04:13 time: 0.8015 data_time: 0.2508 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/17 23:29:50 - mmengine - INFO - Epoch(train) [5][11600/42151] lr: 3.0000e-06 eta: 14:03:02 time: 0.7003 data_time: 0.1554 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/17 23:30:57 - mmengine - INFO - Epoch(train) [5][11700/42151] lr: 3.0000e-06 eta: 14:01:51 time: 0.5746 data_time: 0.0422 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/17 23:32:04 - mmengine - INFO - Epoch(train) [5][11800/42151] lr: 3.0000e-06 eta: 14:00:41 time: 0.5770 data_time: 0.0393 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/17 23:33:11 - mmengine - INFO - Epoch(train) [5][11900/42151] lr: 3.0000e-06 eta: 13:59:30 time: 0.6668 data_time: 0.0717 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/17 23:34:18 - mmengine - INFO - Epoch(train) [5][12000/42151] lr: 3.0000e-06 eta: 13:58:19 time: 0.6328 data_time: 0.0999 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/17 23:35:27 - mmengine - INFO - Epoch(train) [5][12100/42151] lr: 3.0000e-06 eta: 13:57:10 time: 0.7923 data_time: 0.2360 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/17 23:36:33 - mmengine - INFO - Epoch(train) [5][12200/42151] lr: 3.0000e-06 eta: 13:55:59 time: 0.7237 data_time: 0.1564 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/17 23:37:40 - mmengine - INFO - Epoch(train) [5][12300/42151] lr: 3.0000e-06 eta: 13:54:48 time: 0.5941 data_time: 0.0581 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/17 23:38:45 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 23:38:47 - mmengine - INFO - Epoch(train) [5][12400/42151] lr: 3.0000e-06 eta: 13:53:38 time: 0.5779 data_time: 0.0457 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/17 23:39:54 - mmengine - INFO - Epoch(train) [5][12500/42151] lr: 3.0000e-06 eta: 13:52:27 time: 0.6423 data_time: 0.0713 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/17 23:41:01 - mmengine - INFO - Epoch(train) [5][12600/42151] lr: 3.0000e-06 eta: 13:51:16 time: 0.6489 data_time: 0.1157 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/17 23:42:09 - mmengine - INFO - Epoch(train) [5][12700/42151] lr: 3.0000e-06 eta: 13:50:06 time: 0.7619 data_time: 0.1953 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 23:43:15 - mmengine - INFO - Epoch(train) [5][12800/42151] lr: 3.0000e-06 eta: 13:48:55 time: 0.7257 data_time: 0.1661 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/17 23:44:22 - mmengine - INFO - Epoch(train) [5][12900/42151] lr: 3.0000e-06 eta: 13:47:45 time: 0.5753 data_time: 0.0221 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/17 23:45:29 - mmengine - INFO - Epoch(train) [5][13000/42151] lr: 3.0000e-06 eta: 13:46:34 time: 0.5982 data_time: 0.0647 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/17 23:46:35 - mmengine - INFO - Epoch(train) [5][13100/42151] lr: 3.0000e-06 eta: 13:45:23 time: 0.6294 data_time: 0.0888 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/17 23:47:42 - mmengine - INFO - Epoch(train) [5][13200/42151] lr: 3.0000e-06 eta: 13:44:12 time: 0.6407 data_time: 0.1072 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/17 23:48:50 - mmengine - INFO - Epoch(train) [5][13300/42151] lr: 3.0000e-06 eta: 13:43:02 time: 0.7784 data_time: 0.2154 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/17 23:49:53 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/17 23:49:55 - mmengine - INFO - Epoch(train) [5][13400/42151] lr: 3.0000e-06 eta: 13:41:51 time: 0.6996 data_time: 0.1481 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/17 23:51:02 - mmengine - INFO - Epoch(train) [5][13500/42151] lr: 3.0000e-06 eta: 13:40:40 time: 0.5822 data_time: 0.0215 memory: 28726 loss_ce: 0.0063 loss: 0.0063 2022/09/17 23:52:09 - mmengine - INFO - Epoch(train) [5][13600/42151] lr: 3.0000e-06 eta: 13:39:30 time: 0.5978 data_time: 0.0649 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/17 23:53:16 - mmengine - INFO - Epoch(train) [5][13700/42151] lr: 3.0000e-06 eta: 13:38:19 time: 0.6402 data_time: 0.0600 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/17 23:54:22 - mmengine - INFO - Epoch(train) [5][13800/42151] lr: 3.0000e-06 eta: 13:37:08 time: 0.6366 data_time: 0.0839 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/17 23:55:31 - mmengine - INFO - Epoch(train) [5][13900/42151] lr: 3.0000e-06 eta: 13:35:58 time: 0.7997 data_time: 0.2386 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/17 23:56:38 - mmengine - INFO - Epoch(train) [5][14000/42151] lr: 3.0000e-06 eta: 13:34:48 time: 0.7496 data_time: 0.1966 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/17 23:57:45 - mmengine - INFO - Epoch(train) [5][14100/42151] lr: 3.0000e-06 eta: 13:33:37 time: 0.5851 data_time: 0.0206 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/17 23:58:52 - mmengine - INFO - Epoch(train) [5][14200/42151] lr: 3.0000e-06 eta: 13:32:27 time: 0.6258 data_time: 0.0726 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/17 23:59:59 - mmengine - INFO - Epoch(train) [5][14300/42151] lr: 3.0000e-06 eta: 13:31:16 time: 0.6144 data_time: 0.0824 memory: 28726 loss_ce: 0.0067 loss: 0.0067 2022/09/18 00:01:08 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 00:01:11 - mmengine - INFO - Epoch(train) [5][14400/42151] lr: 3.0000e-06 eta: 13:30:08 time: 0.6544 data_time: 0.0895 memory: 28726 loss_ce: 0.0064 loss: 0.0064 2022/09/18 00:02:19 - mmengine - INFO - Epoch(train) [5][14500/42151] lr: 3.0000e-06 eta: 13:28:57 time: 0.7732 data_time: 0.2345 memory: 28726 loss_ce: 0.0095 loss: 0.0095 2022/09/18 00:03:26 - mmengine - INFO - Epoch(train) [5][14600/42151] lr: 3.0000e-06 eta: 13:27:47 time: 0.7823 data_time: 0.1873 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 00:04:33 - mmengine - INFO - Epoch(train) [5][14700/42151] lr: 3.0000e-06 eta: 13:26:36 time: 0.5788 data_time: 0.0217 memory: 28726 loss_ce: 0.0056 loss: 0.0056 2022/09/18 00:05:40 - mmengine - INFO - Epoch(train) [5][14800/42151] lr: 3.0000e-06 eta: 13:25:26 time: 0.5933 data_time: 0.0600 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 00:06:46 - mmengine - INFO - Epoch(train) [5][14900/42151] lr: 3.0000e-06 eta: 13:24:15 time: 0.6305 data_time: 0.0916 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 00:07:54 - mmengine - INFO - Epoch(train) [5][15000/42151] lr: 3.0000e-06 eta: 13:23:05 time: 0.6779 data_time: 0.1005 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 00:09:02 - mmengine - INFO - Epoch(train) [5][15100/42151] lr: 3.0000e-06 eta: 13:21:55 time: 0.7653 data_time: 0.2314 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 00:10:09 - mmengine - INFO - Epoch(train) [5][15200/42151] lr: 3.0000e-06 eta: 13:20:44 time: 0.7194 data_time: 0.1582 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 00:11:15 - mmengine - INFO - Epoch(train) [5][15300/42151] lr: 3.0000e-06 eta: 13:19:34 time: 0.5536 data_time: 0.0203 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 00:12:20 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 00:12:23 - mmengine - INFO - Epoch(train) [5][15400/42151] lr: 3.0000e-06 eta: 13:18:23 time: 0.5821 data_time: 0.0504 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 00:13:29 - mmengine - INFO - Epoch(train) [5][15500/42151] lr: 3.0000e-06 eta: 13:17:13 time: 0.6386 data_time: 0.1036 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 00:14:36 - mmengine - INFO - Epoch(train) [5][15600/42151] lr: 3.0000e-06 eta: 13:16:02 time: 0.6661 data_time: 0.1286 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 00:15:43 - mmengine - INFO - Epoch(train) [5][15700/42151] lr: 3.0000e-06 eta: 13:14:52 time: 0.7499 data_time: 0.2179 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 00:16:50 - mmengine - INFO - Epoch(train) [5][15800/42151] lr: 3.0000e-06 eta: 13:13:41 time: 0.7079 data_time: 0.1713 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 00:17:58 - mmengine - INFO - Epoch(train) [5][15900/42151] lr: 3.0000e-06 eta: 13:12:31 time: 0.5799 data_time: 0.0463 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 00:19:05 - mmengine - INFO - Epoch(train) [5][16000/42151] lr: 3.0000e-06 eta: 13:11:21 time: 0.6476 data_time: 0.0390 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 00:20:13 - mmengine - INFO - Epoch(train) [5][16100/42151] lr: 3.0000e-06 eta: 13:10:11 time: 0.6459 data_time: 0.1122 memory: 28726 loss_ce: 0.0064 loss: 0.0064 2022/09/18 00:21:20 - mmengine - INFO - Epoch(train) [5][16200/42151] lr: 3.0000e-06 eta: 13:09:00 time: 0.6945 data_time: 0.1407 memory: 28726 loss_ce: 0.0099 loss: 0.0099 2022/09/18 00:22:29 - mmengine - INFO - Epoch(train) [5][16300/42151] lr: 3.0000e-06 eta: 13:07:50 time: 0.7812 data_time: 0.2002 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/18 00:23:33 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 00:23:35 - mmengine - INFO - Epoch(train) [5][16400/42151] lr: 3.0000e-06 eta: 13:06:40 time: 0.7425 data_time: 0.1559 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 00:24:42 - mmengine - INFO - Epoch(train) [5][16500/42151] lr: 3.0000e-06 eta: 13:05:29 time: 0.5562 data_time: 0.0227 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 00:25:49 - mmengine - INFO - Epoch(train) [5][16600/42151] lr: 3.0000e-06 eta: 13:04:19 time: 0.6195 data_time: 0.0474 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 00:26:56 - mmengine - INFO - Epoch(train) [5][16700/42151] lr: 3.0000e-06 eta: 13:03:08 time: 0.6477 data_time: 0.1070 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 00:28:03 - mmengine - INFO - Epoch(train) [5][16800/42151] lr: 3.0000e-06 eta: 13:01:58 time: 0.6532 data_time: 0.1200 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 00:29:11 - mmengine - INFO - Epoch(train) [5][16900/42151] lr: 3.0000e-06 eta: 13:00:48 time: 0.8133 data_time: 0.1996 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 00:30:18 - mmengine - INFO - Epoch(train) [5][17000/42151] lr: 3.0000e-06 eta: 12:59:37 time: 0.7213 data_time: 0.1787 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 00:31:25 - mmengine - INFO - Epoch(train) [5][17100/42151] lr: 3.0000e-06 eta: 12:58:27 time: 0.5631 data_time: 0.0197 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 00:32:31 - mmengine - INFO - Epoch(train) [5][17200/42151] lr: 3.0000e-06 eta: 12:57:16 time: 0.5918 data_time: 0.0354 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 00:33:38 - mmengine - INFO - Epoch(train) [5][17300/42151] lr: 3.0000e-06 eta: 12:56:06 time: 0.6364 data_time: 0.0919 memory: 28726 loss_ce: 0.0065 loss: 0.0065 2022/09/18 00:34:43 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 00:34:45 - mmengine - INFO - Epoch(train) [5][17400/42151] lr: 3.0000e-06 eta: 12:54:55 time: 0.6802 data_time: 0.1362 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 00:35:53 - mmengine - INFO - Epoch(train) [5][17500/42151] lr: 3.0000e-06 eta: 12:53:45 time: 0.8229 data_time: 0.2321 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 00:36:59 - mmengine - INFO - Epoch(train) [5][17600/42151] lr: 3.0000e-06 eta: 12:52:34 time: 0.7311 data_time: 0.1687 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 00:38:06 - mmengine - INFO - Epoch(train) [5][17700/42151] lr: 3.0000e-06 eta: 12:51:24 time: 0.5532 data_time: 0.0209 memory: 28726 loss_ce: 0.0066 loss: 0.0066 2022/09/18 00:39:12 - mmengine - INFO - Epoch(train) [5][17800/42151] lr: 3.0000e-06 eta: 12:50:13 time: 0.5740 data_time: 0.0386 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 00:40:19 - mmengine - INFO - Epoch(train) [5][17900/42151] lr: 3.0000e-06 eta: 12:49:03 time: 0.6110 data_time: 0.0746 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 00:41:26 - mmengine - INFO - Epoch(train) [5][18000/42151] lr: 3.0000e-06 eta: 12:47:52 time: 0.6911 data_time: 0.1217 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 00:42:33 - mmengine - INFO - Epoch(train) [5][18100/42151] lr: 3.0000e-06 eta: 12:46:42 time: 0.7592 data_time: 0.2240 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 00:43:40 - mmengine - INFO - Epoch(train) [5][18200/42151] lr: 3.0000e-06 eta: 12:45:32 time: 0.7081 data_time: 0.1742 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/18 00:44:48 - mmengine - INFO - Epoch(train) [5][18300/42151] lr: 3.0000e-06 eta: 12:44:21 time: 0.6178 data_time: 0.0677 memory: 28726 loss_ce: 0.0061 loss: 0.0061 2022/09/18 00:45:52 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 00:45:54 - mmengine - INFO - Epoch(train) [5][18400/42151] lr: 3.0000e-06 eta: 12:43:11 time: 0.6049 data_time: 0.0719 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 00:47:03 - mmengine - INFO - Epoch(train) [5][18500/42151] lr: 3.0000e-06 eta: 12:42:01 time: 0.6209 data_time: 0.0737 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 00:48:14 - mmengine - INFO - Epoch(train) [5][18600/42151] lr: 3.0000e-06 eta: 12:40:52 time: 0.7130 data_time: 0.1695 memory: 28726 loss_ce: 0.0096 loss: 0.0096 2022/09/18 00:49:26 - mmengine - INFO - Epoch(train) [5][18700/42151] lr: 3.0000e-06 eta: 12:39:44 time: 0.8049 data_time: 0.2567 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 00:50:35 - mmengine - INFO - Epoch(train) [5][18800/42151] lr: 3.0000e-06 eta: 12:38:34 time: 0.7198 data_time: 0.1814 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 00:51:44 - mmengine - INFO - Epoch(train) [5][18900/42151] lr: 3.0000e-06 eta: 12:37:24 time: 0.5670 data_time: 0.0269 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 00:52:52 - mmengine - INFO - Epoch(train) [5][19000/42151] lr: 3.0000e-06 eta: 12:36:14 time: 0.5851 data_time: 0.0413 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/18 00:54:03 - mmengine - INFO - Epoch(train) [5][19100/42151] lr: 3.0000e-06 eta: 12:35:05 time: 0.6559 data_time: 0.0723 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/18 00:55:14 - mmengine - INFO - Epoch(train) [5][19200/42151] lr: 3.0000e-06 eta: 12:33:56 time: 0.6697 data_time: 0.1005 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 00:56:23 - mmengine - INFO - Epoch(train) [5][19300/42151] lr: 3.0000e-06 eta: 12:32:47 time: 0.8156 data_time: 0.2689 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 00:57:30 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 00:57:32 - mmengine - INFO - Epoch(train) [5][19400/42151] lr: 3.0000e-06 eta: 12:31:37 time: 0.8100 data_time: 0.2187 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 00:58:42 - mmengine - INFO - Epoch(train) [5][19500/42151] lr: 3.0000e-06 eta: 12:30:27 time: 0.5807 data_time: 0.0232 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 00:59:51 - mmengine - INFO - Epoch(train) [5][19600/42151] lr: 3.0000e-06 eta: 12:29:18 time: 0.6132 data_time: 0.0726 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 01:00:58 - mmengine - INFO - Epoch(train) [5][19700/42151] lr: 3.0000e-06 eta: 12:28:07 time: 0.6277 data_time: 0.0680 memory: 28726 loss_ce: 0.0059 loss: 0.0059 2022/09/18 01:02:06 - mmengine - INFO - Epoch(train) [5][19800/42151] lr: 3.0000e-06 eta: 12:26:57 time: 0.6440 data_time: 0.1145 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 01:03:15 - mmengine - INFO - Epoch(train) [5][19900/42151] lr: 3.0000e-06 eta: 12:25:48 time: 0.8079 data_time: 0.2514 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 01:04:22 - mmengine - INFO - Epoch(train) [5][20000/42151] lr: 3.0000e-06 eta: 12:24:38 time: 0.7474 data_time: 0.2157 memory: 28726 loss_ce: 0.0064 loss: 0.0064 2022/09/18 01:05:29 - mmengine - INFO - Epoch(train) [5][20100/42151] lr: 3.0000e-06 eta: 12:23:27 time: 0.5558 data_time: 0.0235 memory: 28726 loss_ce: 0.0065 loss: 0.0065 2022/09/18 01:06:37 - mmengine - INFO - Epoch(train) [5][20200/42151] lr: 3.0000e-06 eta: 12:22:17 time: 0.6205 data_time: 0.0632 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 01:07:47 - mmengine - INFO - Epoch(train) [5][20300/42151] lr: 3.0000e-06 eta: 12:21:08 time: 0.7067 data_time: 0.1738 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/18 01:08:56 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 01:08:59 - mmengine - INFO - Epoch(train) [5][20400/42151] lr: 3.0000e-06 eta: 12:19:59 time: 0.7117 data_time: 0.1599 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 01:10:12 - mmengine - INFO - Epoch(train) [5][20500/42151] lr: 3.0000e-06 eta: 12:18:51 time: 0.7866 data_time: 0.2035 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 01:11:20 - mmengine - INFO - Epoch(train) [5][20600/42151] lr: 3.0000e-06 eta: 12:17:41 time: 0.7404 data_time: 0.1983 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 01:12:29 - mmengine - INFO - Epoch(train) [5][20700/42151] lr: 3.0000e-06 eta: 12:16:31 time: 0.5945 data_time: 0.0280 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/18 01:13:38 - mmengine - INFO - Epoch(train) [5][20800/42151] lr: 3.0000e-06 eta: 12:15:22 time: 0.6189 data_time: 0.0848 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 01:14:48 - mmengine - INFO - Epoch(train) [5][20900/42151] lr: 3.0000e-06 eta: 12:14:12 time: 0.6433 data_time: 0.1100 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 01:16:00 - mmengine - INFO - Epoch(train) [5][21000/42151] lr: 3.0000e-06 eta: 12:13:03 time: 0.6858 data_time: 0.1240 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 01:17:12 - mmengine - INFO - Epoch(train) [5][21100/42151] lr: 3.0000e-06 eta: 12:11:55 time: 0.8420 data_time: 0.2799 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 01:18:22 - mmengine - INFO - Epoch(train) [5][21200/42151] lr: 3.0000e-06 eta: 12:10:46 time: 0.7655 data_time: 0.2279 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/18 01:19:32 - mmengine - INFO - Epoch(train) [5][21300/42151] lr: 3.0000e-06 eta: 12:09:36 time: 0.6409 data_time: 0.0659 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 01:20:40 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 01:20:42 - mmengine - INFO - Epoch(train) [5][21400/42151] lr: 3.0000e-06 eta: 12:08:27 time: 0.6036 data_time: 0.0472 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 01:21:52 - mmengine - INFO - Epoch(train) [5][21500/42151] lr: 3.0000e-06 eta: 12:07:17 time: 0.6813 data_time: 0.1323 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 01:23:01 - mmengine - INFO - Epoch(train) [5][21600/42151] lr: 3.0000e-06 eta: 12:06:08 time: 0.6842 data_time: 0.1165 memory: 28726 loss_ce: 0.0067 loss: 0.0067 2022/09/18 01:24:11 - mmengine - INFO - Epoch(train) [5][21700/42151] lr: 3.0000e-06 eta: 12:04:59 time: 0.8262 data_time: 0.2275 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 01:25:20 - mmengine - INFO - Epoch(train) [5][21800/42151] lr: 3.0000e-06 eta: 12:03:49 time: 0.8112 data_time: 0.2308 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 01:26:31 - mmengine - INFO - Epoch(train) [5][21900/42151] lr: 3.0000e-06 eta: 12:02:40 time: 0.6284 data_time: 0.0329 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 01:27:41 - mmengine - INFO - Epoch(train) [5][22000/42151] lr: 3.0000e-06 eta: 12:01:30 time: 0.5894 data_time: 0.0484 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 01:28:52 - mmengine - INFO - Epoch(train) [5][22100/42151] lr: 3.0000e-06 eta: 12:00:21 time: 0.6931 data_time: 0.1272 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 01:30:01 - mmengine - INFO - Epoch(train) [5][22200/42151] lr: 3.0000e-06 eta: 11:59:12 time: 0.6972 data_time: 0.1367 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 01:31:12 - mmengine - INFO - Epoch(train) [5][22300/42151] lr: 3.0000e-06 eta: 11:58:03 time: 0.8290 data_time: 0.2777 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/18 01:32:18 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 01:32:20 - mmengine - INFO - Epoch(train) [5][22400/42151] lr: 3.0000e-06 eta: 11:56:53 time: 0.8033 data_time: 0.2584 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 01:33:29 - mmengine - INFO - Epoch(train) [5][22500/42151] lr: 3.0000e-06 eta: 11:55:43 time: 0.5699 data_time: 0.0308 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 01:34:38 - mmengine - INFO - Epoch(train) [5][22600/42151] lr: 3.0000e-06 eta: 11:54:33 time: 0.6125 data_time: 0.0787 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 01:35:47 - mmengine - INFO - Epoch(train) [5][22700/42151] lr: 3.0000e-06 eta: 11:53:24 time: 0.6698 data_time: 0.0849 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 01:37:00 - mmengine - INFO - Epoch(train) [5][22800/42151] lr: 3.0000e-06 eta: 11:52:15 time: 0.6583 data_time: 0.1206 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 01:38:12 - mmengine - INFO - Epoch(train) [5][22900/42151] lr: 3.0000e-06 eta: 11:51:07 time: 0.8428 data_time: 0.2999 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 01:39:21 - mmengine - INFO - Epoch(train) [5][23000/42151] lr: 3.0000e-06 eta: 11:49:57 time: 0.7679 data_time: 0.1959 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 01:40:29 - mmengine - INFO - Epoch(train) [5][23100/42151] lr: 3.0000e-06 eta: 11:48:47 time: 0.5545 data_time: 0.0218 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 01:41:39 - mmengine - INFO - Epoch(train) [5][23200/42151] lr: 3.0000e-06 eta: 11:47:38 time: 0.6191 data_time: 0.0412 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 01:42:47 - mmengine - INFO - Epoch(train) [5][23300/42151] lr: 3.0000e-06 eta: 11:46:28 time: 0.6476 data_time: 0.1105 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 01:43:54 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 01:43:57 - mmengine - INFO - Epoch(train) [5][23400/42151] lr: 3.0000e-06 eta: 11:45:18 time: 0.7342 data_time: 0.0908 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 01:45:09 - mmengine - INFO - Epoch(train) [5][23500/42151] lr: 3.0000e-06 eta: 11:44:10 time: 0.8842 data_time: 0.2641 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 01:46:18 - mmengine - INFO - Epoch(train) [5][23600/42151] lr: 3.0000e-06 eta: 11:43:00 time: 0.7985 data_time: 0.2168 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 01:47:29 - mmengine - INFO - Epoch(train) [5][23700/42151] lr: 3.0000e-06 eta: 11:41:51 time: 0.5656 data_time: 0.0310 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 01:48:40 - mmengine - INFO - Epoch(train) [5][23800/42151] lr: 3.0000e-06 eta: 11:40:42 time: 0.6647 data_time: 0.1113 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 01:49:51 - mmengine - INFO - Epoch(train) [5][23900/42151] lr: 3.0000e-06 eta: 11:39:33 time: 0.6450 data_time: 0.1099 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 01:51:00 - mmengine - INFO - Epoch(train) [5][24000/42151] lr: 3.0000e-06 eta: 11:38:23 time: 0.6552 data_time: 0.1231 memory: 28726 loss_ce: 0.0066 loss: 0.0066 2022/09/18 01:52:10 - mmengine - INFO - Epoch(train) [5][24100/42151] lr: 3.0000e-06 eta: 11:37:14 time: 0.8659 data_time: 0.2827 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 01:53:19 - mmengine - INFO - Epoch(train) [5][24200/42151] lr: 3.0000e-06 eta: 11:36:04 time: 0.7414 data_time: 0.1743 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 01:54:29 - mmengine - INFO - Epoch(train) [5][24300/42151] lr: 3.0000e-06 eta: 11:34:55 time: 0.6177 data_time: 0.0468 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 01:55:37 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 01:55:39 - mmengine - INFO - Epoch(train) [5][24400/42151] lr: 3.0000e-06 eta: 11:33:46 time: 0.6113 data_time: 0.0807 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 01:56:50 - mmengine - INFO - Epoch(train) [5][24500/42151] lr: 3.0000e-06 eta: 11:32:36 time: 0.6621 data_time: 0.1250 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 01:57:57 - mmengine - INFO - Epoch(train) [5][24600/42151] lr: 3.0000e-06 eta: 11:31:26 time: 0.6304 data_time: 0.0970 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 01:59:05 - mmengine - INFO - Epoch(train) [5][24700/42151] lr: 3.0000e-06 eta: 11:30:16 time: 0.7679 data_time: 0.2168 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 02:00:15 - mmengine - INFO - Epoch(train) [5][24800/42151] lr: 3.0000e-06 eta: 11:29:07 time: 0.9441 data_time: 0.1906 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 02:01:24 - mmengine - INFO - Epoch(train) [5][24900/42151] lr: 3.0000e-06 eta: 11:27:57 time: 0.5688 data_time: 0.0385 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 02:02:32 - mmengine - INFO - Epoch(train) [5][25000/42151] lr: 3.0000e-06 eta: 11:26:47 time: 0.5708 data_time: 0.0400 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 02:03:39 - mmengine - INFO - Epoch(train) [5][25100/42151] lr: 3.0000e-06 eta: 11:25:37 time: 0.6521 data_time: 0.1004 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 02:04:46 - mmengine - INFO - Epoch(train) [5][25200/42151] lr: 3.0000e-06 eta: 11:24:27 time: 0.6845 data_time: 0.1138 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 02:05:55 - mmengine - INFO - Epoch(train) [5][25300/42151] lr: 3.0000e-06 eta: 11:23:17 time: 0.7514 data_time: 0.1866 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 02:07:02 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 02:07:04 - mmengine - INFO - Epoch(train) [5][25400/42151] lr: 3.0000e-06 eta: 11:22:07 time: 0.6995 data_time: 0.1709 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 02:08:12 - mmengine - INFO - Epoch(train) [5][25500/42151] lr: 3.0000e-06 eta: 11:20:58 time: 0.5505 data_time: 0.0209 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 02:09:20 - mmengine - INFO - Epoch(train) [5][25600/42151] lr: 3.0000e-06 eta: 11:19:47 time: 0.6100 data_time: 0.0663 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 02:10:27 - mmengine - INFO - Epoch(train) [5][25700/42151] lr: 3.0000e-06 eta: 11:18:37 time: 0.6388 data_time: 0.0637 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 02:11:35 - mmengine - INFO - Epoch(train) [5][25800/42151] lr: 3.0000e-06 eta: 11:17:27 time: 0.6966 data_time: 0.0975 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 02:12:43 - mmengine - INFO - Epoch(train) [5][25900/42151] lr: 3.0000e-06 eta: 11:16:17 time: 0.7343 data_time: 0.1776 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 02:13:50 - mmengine - INFO - Epoch(train) [5][26000/42151] lr: 3.0000e-06 eta: 11:15:07 time: 0.7073 data_time: 0.1516 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/18 02:14:57 - mmengine - INFO - Epoch(train) [5][26100/42151] lr: 3.0000e-06 eta: 11:13:57 time: 0.5619 data_time: 0.0228 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 02:16:05 - mmengine - INFO - Epoch(train) [5][26200/42151] lr: 3.0000e-06 eta: 11:12:47 time: 0.6311 data_time: 0.0993 memory: 28726 loss_ce: 0.0062 loss: 0.0062 2022/09/18 02:17:12 - mmengine - INFO - Epoch(train) [5][26300/42151] lr: 3.0000e-06 eta: 11:11:37 time: 0.6185 data_time: 0.0898 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 02:18:17 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 02:18:20 - mmengine - INFO - Epoch(train) [5][26400/42151] lr: 3.0000e-06 eta: 11:10:27 time: 0.7059 data_time: 0.1363 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 02:19:28 - mmengine - INFO - Epoch(train) [5][26500/42151] lr: 3.0000e-06 eta: 11:09:17 time: 0.7933 data_time: 0.2531 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 02:20:37 - mmengine - INFO - Epoch(train) [5][26600/42151] lr: 3.0000e-06 eta: 11:08:07 time: 0.7325 data_time: 0.1682 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 02:21:45 - mmengine - INFO - Epoch(train) [5][26700/42151] lr: 3.0000e-06 eta: 11:06:57 time: 0.5561 data_time: 0.0221 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 02:22:53 - mmengine - INFO - Epoch(train) [5][26800/42151] lr: 3.0000e-06 eta: 11:05:47 time: 0.5917 data_time: 0.0393 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 02:23:59 - mmengine - INFO - Epoch(train) [5][26900/42151] lr: 3.0000e-06 eta: 11:04:37 time: 0.5988 data_time: 0.0628 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 02:25:06 - mmengine - INFO - Epoch(train) [5][27000/42151] lr: 3.0000e-06 eta: 11:03:27 time: 0.6827 data_time: 0.1504 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 02:26:14 - mmengine - INFO - Epoch(train) [5][27100/42151] lr: 3.0000e-06 eta: 11:02:17 time: 0.7852 data_time: 0.2533 memory: 28726 loss_ce: 0.0097 loss: 0.0097 2022/09/18 02:27:23 - mmengine - INFO - Epoch(train) [5][27200/42151] lr: 3.0000e-06 eta: 11:01:07 time: 0.7718 data_time: 0.2046 memory: 28726 loss_ce: 0.0066 loss: 0.0066 2022/09/18 02:28:31 - mmengine - INFO - Epoch(train) [5][27300/42151] lr: 3.0000e-06 eta: 10:59:57 time: 0.5889 data_time: 0.0535 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 02:29:36 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 02:29:38 - mmengine - INFO - Epoch(train) [5][27400/42151] lr: 3.0000e-06 eta: 10:58:47 time: 0.5871 data_time: 0.0558 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 02:30:46 - mmengine - INFO - Epoch(train) [5][27500/42151] lr: 3.0000e-06 eta: 10:57:37 time: 0.6286 data_time: 0.0627 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 02:31:52 - mmengine - INFO - Epoch(train) [5][27600/42151] lr: 3.0000e-06 eta: 10:56:27 time: 0.6747 data_time: 0.1442 memory: 28726 loss_ce: 0.0096 loss: 0.0096 2022/09/18 02:33:01 - mmengine - INFO - Epoch(train) [5][27700/42151] lr: 3.0000e-06 eta: 10:55:17 time: 0.7642 data_time: 0.2123 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 02:34:08 - mmengine - INFO - Epoch(train) [5][27800/42151] lr: 3.0000e-06 eta: 10:54:07 time: 0.6803 data_time: 0.1500 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 02:35:15 - mmengine - INFO - Epoch(train) [5][27900/42151] lr: 3.0000e-06 eta: 10:52:57 time: 0.5941 data_time: 0.0602 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 02:36:25 - mmengine - INFO - Epoch(train) [5][28000/42151] lr: 3.0000e-06 eta: 10:51:47 time: 0.6216 data_time: 0.0792 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 02:37:33 - mmengine - INFO - Epoch(train) [5][28100/42151] lr: 3.0000e-06 eta: 10:50:37 time: 0.6424 data_time: 0.0648 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 02:38:40 - mmengine - INFO - Epoch(train) [5][28200/42151] lr: 3.0000e-06 eta: 10:49:27 time: 0.6914 data_time: 0.1478 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 02:39:47 - mmengine - INFO - Epoch(train) [5][28300/42151] lr: 3.0000e-06 eta: 10:48:17 time: 0.7517 data_time: 0.1877 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 02:40:52 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 02:40:54 - mmengine - INFO - Epoch(train) [5][28400/42151] lr: 3.0000e-06 eta: 10:47:07 time: 0.7187 data_time: 0.1598 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/18 02:42:00 - mmengine - INFO - Epoch(train) [5][28500/42151] lr: 3.0000e-06 eta: 10:45:56 time: 0.5947 data_time: 0.0210 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 02:43:07 - mmengine - INFO - Epoch(train) [5][28600/42151] lr: 3.0000e-06 eta: 10:44:46 time: 0.6030 data_time: 0.0613 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 02:44:13 - mmengine - INFO - Epoch(train) [5][28700/42151] lr: 3.0000e-06 eta: 10:43:36 time: 0.6275 data_time: 0.0927 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 02:45:21 - mmengine - INFO - Epoch(train) [5][28800/42151] lr: 3.0000e-06 eta: 10:42:26 time: 0.6768 data_time: 0.1264 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/18 02:46:29 - mmengine - INFO - Epoch(train) [5][28900/42151] lr: 3.0000e-06 eta: 10:41:16 time: 0.7658 data_time: 0.1889 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 02:47:36 - mmengine - INFO - Epoch(train) [5][29000/42151] lr: 3.0000e-06 eta: 10:40:06 time: 0.6987 data_time: 0.1643 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 02:48:42 - mmengine - INFO - Epoch(train) [5][29100/42151] lr: 3.0000e-06 eta: 10:38:55 time: 0.6001 data_time: 0.0215 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 02:49:48 - mmengine - INFO - Epoch(train) [5][29200/42151] lr: 3.0000e-06 eta: 10:37:45 time: 0.6091 data_time: 0.0648 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 02:50:55 - mmengine - INFO - Epoch(train) [5][29300/42151] lr: 3.0000e-06 eta: 10:36:35 time: 0.6496 data_time: 0.0745 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/18 02:51:59 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 02:52:02 - mmengine - INFO - Epoch(train) [5][29400/42151] lr: 3.0000e-06 eta: 10:35:25 time: 0.6591 data_time: 0.0803 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 02:53:10 - mmengine - INFO - Epoch(train) [5][29500/42151] lr: 3.0000e-06 eta: 10:34:15 time: 0.7983 data_time: 0.2348 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 02:54:17 - mmengine - INFO - Epoch(train) [5][29600/42151] lr: 3.0000e-06 eta: 10:33:05 time: 0.7395 data_time: 0.1892 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/18 02:55:24 - mmengine - INFO - Epoch(train) [5][29700/42151] lr: 3.0000e-06 eta: 10:31:55 time: 0.6002 data_time: 0.0299 memory: 28726 loss_ce: 0.0067 loss: 0.0067 2022/09/18 02:56:31 - mmengine - INFO - Epoch(train) [5][29800/42151] lr: 3.0000e-06 eta: 10:30:45 time: 0.6391 data_time: 0.0746 memory: 28726 loss_ce: 0.0059 loss: 0.0059 2022/09/18 02:57:38 - mmengine - INFO - Epoch(train) [5][29900/42151] lr: 3.0000e-06 eta: 10:29:34 time: 0.6478 data_time: 0.0832 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 02:58:45 - mmengine - INFO - Epoch(train) [5][30000/42151] lr: 3.0000e-06 eta: 10:28:24 time: 0.7028 data_time: 0.1001 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 02:59:53 - mmengine - INFO - Epoch(train) [5][30100/42151] lr: 3.0000e-06 eta: 10:27:15 time: 0.7763 data_time: 0.2230 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 03:01:00 - mmengine - INFO - Epoch(train) [5][30200/42151] lr: 3.0000e-06 eta: 10:26:04 time: 0.7060 data_time: 0.1730 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 03:02:07 - mmengine - INFO - Epoch(train) [5][30300/42151] lr: 3.0000e-06 eta: 10:24:54 time: 0.5989 data_time: 0.0305 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 03:03:11 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 03:03:14 - mmengine - INFO - Epoch(train) [5][30400/42151] lr: 3.0000e-06 eta: 10:23:44 time: 0.6011 data_time: 0.0690 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 03:04:20 - mmengine - INFO - Epoch(train) [5][30500/42151] lr: 3.0000e-06 eta: 10:22:34 time: 0.6522 data_time: 0.0836 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 03:05:27 - mmengine - INFO - Epoch(train) [5][30600/42151] lr: 3.0000e-06 eta: 10:21:24 time: 0.6518 data_time: 0.0931 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 03:06:34 - mmengine - INFO - Epoch(train) [5][30700/42151] lr: 3.0000e-06 eta: 10:20:14 time: 0.7454 data_time: 0.2134 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 03:07:41 - mmengine - INFO - Epoch(train) [5][30800/42151] lr: 3.0000e-06 eta: 10:19:03 time: 0.7345 data_time: 0.1528 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 03:08:47 - mmengine - INFO - Epoch(train) [5][30900/42151] lr: 3.0000e-06 eta: 10:17:53 time: 0.5621 data_time: 0.0305 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/18 03:09:54 - mmengine - INFO - Epoch(train) [5][31000/42151] lr: 3.0000e-06 eta: 10:16:43 time: 0.5841 data_time: 0.0379 memory: 28726 loss_ce: 0.0067 loss: 0.0067 2022/09/18 03:11:01 - mmengine - INFO - Epoch(train) [5][31100/42151] lr: 3.0000e-06 eta: 10:15:33 time: 0.6311 data_time: 0.0820 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 03:12:07 - mmengine - INFO - Epoch(train) [5][31200/42151] lr: 3.0000e-06 eta: 10:14:23 time: 0.6440 data_time: 0.1119 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 03:13:15 - mmengine - INFO - Epoch(train) [5][31300/42151] lr: 3.0000e-06 eta: 10:13:13 time: 0.7810 data_time: 0.2365 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 03:14:19 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 03:14:22 - mmengine - INFO - Epoch(train) [5][31400/42151] lr: 3.0000e-06 eta: 10:12:03 time: 0.7158 data_time: 0.1692 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 03:15:28 - mmengine - INFO - Epoch(train) [5][31500/42151] lr: 3.0000e-06 eta: 10:10:52 time: 0.5918 data_time: 0.0595 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 03:16:43 - mmengine - INFO - Epoch(train) [5][31600/42151] lr: 3.0000e-06 eta: 10:09:45 time: 0.5616 data_time: 0.0134 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 03:17:50 - mmengine - INFO - Epoch(train) [5][31700/42151] lr: 3.0000e-06 eta: 10:08:34 time: 0.6340 data_time: 0.0893 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 03:18:57 - mmengine - INFO - Epoch(train) [5][31800/42151] lr: 3.0000e-06 eta: 10:07:24 time: 0.6796 data_time: 0.1224 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 03:20:05 - mmengine - INFO - Epoch(train) [5][31900/42151] lr: 3.0000e-06 eta: 10:06:15 time: 0.7728 data_time: 0.2117 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 03:21:12 - mmengine - INFO - Epoch(train) [5][32000/42151] lr: 3.0000e-06 eta: 10:05:04 time: 0.7573 data_time: 0.2030 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 03:22:19 - mmengine - INFO - Epoch(train) [5][32100/42151] lr: 3.0000e-06 eta: 10:03:54 time: 0.5637 data_time: 0.0047 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 03:23:24 - mmengine - INFO - Epoch(train) [5][32200/42151] lr: 3.0000e-06 eta: 10:02:44 time: 0.5718 data_time: 0.0126 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 03:24:31 - mmengine - INFO - Epoch(train) [5][32300/42151] lr: 3.0000e-06 eta: 10:01:34 time: 0.6100 data_time: 0.0528 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 03:25:35 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 03:25:38 - mmengine - INFO - Epoch(train) [5][32400/42151] lr: 3.0000e-06 eta: 10:00:24 time: 0.6480 data_time: 0.1100 memory: 28726 loss_ce: 0.0094 loss: 0.0094 2022/09/18 03:26:45 - mmengine - INFO - Epoch(train) [5][32500/42151] lr: 3.0000e-06 eta: 9:59:14 time: 0.8184 data_time: 0.2582 memory: 28726 loss_ce: 0.0096 loss: 0.0096 2022/09/18 03:27:51 - mmengine - INFO - Epoch(train) [5][32600/42151] lr: 3.0000e-06 eta: 9:58:03 time: 0.7433 data_time: 0.1711 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 03:28:58 - mmengine - INFO - Epoch(train) [5][32700/42151] lr: 3.0000e-06 eta: 9:56:53 time: 0.5588 data_time: 0.0045 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 03:30:04 - mmengine - INFO - Epoch(train) [5][32800/42151] lr: 3.0000e-06 eta: 9:55:43 time: 0.5674 data_time: 0.0146 memory: 28726 loss_ce: 0.0066 loss: 0.0066 2022/09/18 03:31:11 - mmengine - INFO - Epoch(train) [5][32900/42151] lr: 3.0000e-06 eta: 9:54:33 time: 0.6042 data_time: 0.0736 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 03:32:18 - mmengine - INFO - Epoch(train) [5][33000/42151] lr: 3.0000e-06 eta: 9:53:23 time: 0.6883 data_time: 0.1161 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 03:33:25 - mmengine - INFO - Epoch(train) [5][33100/42151] lr: 3.0000e-06 eta: 9:52:13 time: 0.7618 data_time: 0.1988 memory: 28726 loss_ce: 0.0067 loss: 0.0067 2022/09/18 03:34:31 - mmengine - INFO - Epoch(train) [5][33200/42151] lr: 3.0000e-06 eta: 9:51:03 time: 0.7271 data_time: 0.1896 memory: 28726 loss_ce: 0.0060 loss: 0.0060 2022/09/18 03:35:38 - mmengine - INFO - Epoch(train) [5][33300/42151] lr: 3.0000e-06 eta: 9:49:53 time: 0.5605 data_time: 0.0044 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/18 03:36:42 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 03:36:44 - mmengine - INFO - Epoch(train) [5][33400/42151] lr: 3.0000e-06 eta: 9:48:43 time: 0.5666 data_time: 0.0201 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 03:37:52 - mmengine - INFO - Epoch(train) [5][33500/42151] lr: 3.0000e-06 eta: 9:47:33 time: 0.6341 data_time: 0.0529 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 03:38:58 - mmengine - INFO - Epoch(train) [5][33600/42151] lr: 3.0000e-06 eta: 9:46:22 time: 0.6379 data_time: 0.0994 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 03:40:06 - mmengine - INFO - Epoch(train) [5][33700/42151] lr: 3.0000e-06 eta: 9:45:13 time: 0.7702 data_time: 0.2302 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 03:41:12 - mmengine - INFO - Epoch(train) [5][33800/42151] lr: 3.0000e-06 eta: 9:44:02 time: 0.7346 data_time: 0.1703 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/18 03:42:18 - mmengine - INFO - Epoch(train) [5][33900/42151] lr: 3.0000e-06 eta: 9:42:52 time: 0.5653 data_time: 0.0045 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 03:43:24 - mmengine - INFO - Epoch(train) [5][34000/42151] lr: 3.0000e-06 eta: 9:41:42 time: 0.5487 data_time: 0.0149 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 03:44:31 - mmengine - INFO - Epoch(train) [5][34100/42151] lr: 3.0000e-06 eta: 9:40:32 time: 0.6107 data_time: 0.0784 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 03:45:38 - mmengine - INFO - Epoch(train) [5][34200/42151] lr: 3.0000e-06 eta: 9:39:22 time: 0.6489 data_time: 0.1162 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 03:46:46 - mmengine - INFO - Epoch(train) [5][34300/42151] lr: 3.0000e-06 eta: 9:38:12 time: 0.7584 data_time: 0.2049 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 03:47:49 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 03:47:51 - mmengine - INFO - Epoch(train) [5][34400/42151] lr: 3.0000e-06 eta: 9:37:02 time: 0.7268 data_time: 0.1936 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 03:48:58 - mmengine - INFO - Epoch(train) [5][34500/42151] lr: 3.0000e-06 eta: 9:35:52 time: 0.5708 data_time: 0.0048 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/18 03:50:03 - mmengine - INFO - Epoch(train) [5][34600/42151] lr: 3.0000e-06 eta: 9:34:41 time: 0.5497 data_time: 0.0126 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 03:51:10 - mmengine - INFO - Epoch(train) [5][34700/42151] lr: 3.0000e-06 eta: 9:33:31 time: 0.6231 data_time: 0.0626 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 03:52:17 - mmengine - INFO - Epoch(train) [5][34800/42151] lr: 3.0000e-06 eta: 9:32:21 time: 0.6553 data_time: 0.1016 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 03:53:25 - mmengine - INFO - Epoch(train) [5][34900/42151] lr: 3.0000e-06 eta: 9:31:12 time: 0.7610 data_time: 0.2297 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 03:54:31 - mmengine - INFO - Epoch(train) [5][35000/42151] lr: 3.0000e-06 eta: 9:30:01 time: 0.7740 data_time: 0.1985 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 03:55:37 - mmengine - INFO - Epoch(train) [5][35100/42151] lr: 3.0000e-06 eta: 9:28:51 time: 0.5855 data_time: 0.0068 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 03:56:43 - mmengine - INFO - Epoch(train) [5][35200/42151] lr: 3.0000e-06 eta: 9:27:41 time: 0.5455 data_time: 0.0124 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 03:57:50 - mmengine - INFO - Epoch(train) [5][35300/42151] lr: 3.0000e-06 eta: 9:26:31 time: 0.6247 data_time: 0.0887 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 03:58:55 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 03:58:57 - mmengine - INFO - Epoch(train) [5][35400/42151] lr: 3.0000e-06 eta: 9:25:21 time: 0.6719 data_time: 0.1094 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 04:00:06 - mmengine - INFO - Epoch(train) [5][35500/42151] lr: 3.0000e-06 eta: 9:24:12 time: 0.7809 data_time: 0.2142 memory: 28726 loss_ce: 0.0067 loss: 0.0067 2022/09/18 04:01:12 - mmengine - INFO - Epoch(train) [5][35600/42151] lr: 3.0000e-06 eta: 9:23:01 time: 0.7461 data_time: 0.2124 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 04:02:18 - mmengine - INFO - Epoch(train) [5][35700/42151] lr: 3.0000e-06 eta: 9:21:51 time: 0.6006 data_time: 0.0048 memory: 28726 loss_ce: 0.0067 loss: 0.0067 2022/09/18 04:03:25 - mmengine - INFO - Epoch(train) [5][35800/42151] lr: 3.0000e-06 eta: 9:20:41 time: 0.5471 data_time: 0.0138 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 04:04:32 - mmengine - INFO - Epoch(train) [5][35900/42151] lr: 3.0000e-06 eta: 9:19:31 time: 0.6194 data_time: 0.0613 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 04:05:38 - mmengine - INFO - Epoch(train) [5][36000/42151] lr: 3.0000e-06 eta: 9:18:21 time: 0.6508 data_time: 0.1087 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 04:06:46 - mmengine - INFO - Epoch(train) [5][36100/42151] lr: 3.0000e-06 eta: 9:17:12 time: 0.7795 data_time: 0.2426 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 04:07:53 - mmengine - INFO - Epoch(train) [5][36200/42151] lr: 3.0000e-06 eta: 9:16:02 time: 0.7637 data_time: 0.1783 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 04:08:59 - mmengine - INFO - Epoch(train) [5][36300/42151] lr: 3.0000e-06 eta: 9:14:51 time: 0.5922 data_time: 0.0055 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 04:10:03 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 04:10:05 - mmengine - INFO - Epoch(train) [5][36400/42151] lr: 3.0000e-06 eta: 9:13:41 time: 0.5451 data_time: 0.0128 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 04:11:12 - mmengine - INFO - Epoch(train) [5][36500/42151] lr: 3.0000e-06 eta: 9:12:31 time: 0.6185 data_time: 0.0766 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/18 04:12:19 - mmengine - INFO - Epoch(train) [5][36600/42151] lr: 3.0000e-06 eta: 9:11:21 time: 0.6476 data_time: 0.1070 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 04:13:27 - mmengine - INFO - Epoch(train) [5][36700/42151] lr: 3.0000e-06 eta: 9:10:12 time: 0.7982 data_time: 0.2025 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 04:14:33 - mmengine - INFO - Epoch(train) [5][36800/42151] lr: 3.0000e-06 eta: 9:09:02 time: 0.7784 data_time: 0.2156 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 04:15:39 - mmengine - INFO - Epoch(train) [5][36900/42151] lr: 3.0000e-06 eta: 9:07:52 time: 0.5892 data_time: 0.0063 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 04:16:45 - mmengine - INFO - Epoch(train) [5][37000/42151] lr: 3.0000e-06 eta: 9:06:41 time: 0.5445 data_time: 0.0123 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 04:17:52 - mmengine - INFO - Epoch(train) [5][37100/42151] lr: 3.0000e-06 eta: 9:05:31 time: 0.6313 data_time: 0.0529 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 04:18:57 - mmengine - INFO - Epoch(train) [5][37200/42151] lr: 3.0000e-06 eta: 9:04:21 time: 0.6451 data_time: 0.1027 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 04:20:06 - mmengine - INFO - Epoch(train) [5][37300/42151] lr: 3.0000e-06 eta: 9:03:12 time: 0.7725 data_time: 0.2380 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 04:21:10 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 04:21:12 - mmengine - INFO - Epoch(train) [5][37400/42151] lr: 3.0000e-06 eta: 9:02:02 time: 0.7240 data_time: 0.1648 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 04:22:18 - mmengine - INFO - Epoch(train) [5][37500/42151] lr: 3.0000e-06 eta: 9:00:52 time: 0.5703 data_time: 0.0044 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 04:23:24 - mmengine - INFO - Epoch(train) [5][37600/42151] lr: 3.0000e-06 eta: 8:59:41 time: 0.5476 data_time: 0.0145 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 04:24:30 - mmengine - INFO - Epoch(train) [5][37700/42151] lr: 3.0000e-06 eta: 8:58:31 time: 0.6162 data_time: 0.0834 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 04:25:36 - mmengine - INFO - Epoch(train) [5][37800/42151] lr: 3.0000e-06 eta: 8:57:21 time: 0.6517 data_time: 0.0984 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 04:26:44 - mmengine - INFO - Epoch(train) [5][37900/42151] lr: 3.0000e-06 eta: 8:56:12 time: 0.7684 data_time: 0.2042 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 04:27:50 - mmengine - INFO - Epoch(train) [5][38000/42151] lr: 3.0000e-06 eta: 8:55:01 time: 0.7321 data_time: 0.2001 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 04:28:57 - mmengine - INFO - Epoch(train) [5][38100/42151] lr: 3.0000e-06 eta: 8:53:52 time: 0.5613 data_time: 0.0046 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 04:30:04 - mmengine - INFO - Epoch(train) [5][38200/42151] lr: 3.0000e-06 eta: 8:52:42 time: 0.5503 data_time: 0.0142 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 04:31:11 - mmengine - INFO - Epoch(train) [5][38300/42151] lr: 3.0000e-06 eta: 8:51:32 time: 0.6359 data_time: 0.0753 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 04:32:15 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 04:32:18 - mmengine - INFO - Epoch(train) [5][38400/42151] lr: 3.0000e-06 eta: 8:50:22 time: 0.6645 data_time: 0.1122 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 04:33:26 - mmengine - INFO - Epoch(train) [5][38500/42151] lr: 3.0000e-06 eta: 8:49:12 time: 0.7808 data_time: 0.2349 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 04:34:33 - mmengine - INFO - Epoch(train) [5][38600/42151] lr: 3.0000e-06 eta: 8:48:03 time: 0.7585 data_time: 0.1937 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 04:35:39 - mmengine - INFO - Epoch(train) [5][38700/42151] lr: 3.0000e-06 eta: 8:46:53 time: 0.5745 data_time: 0.0051 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 04:36:46 - mmengine - INFO - Epoch(train) [5][38800/42151] lr: 3.0000e-06 eta: 8:45:43 time: 0.5472 data_time: 0.0135 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/18 04:37:53 - mmengine - INFO - Epoch(train) [5][38900/42151] lr: 3.0000e-06 eta: 8:44:33 time: 0.6569 data_time: 0.0959 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/18 04:38:59 - mmengine - INFO - Epoch(train) [5][39000/42151] lr: 3.0000e-06 eta: 8:43:23 time: 0.6427 data_time: 0.1090 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 04:40:07 - mmengine - INFO - Epoch(train) [5][39100/42151] lr: 3.0000e-06 eta: 8:42:13 time: 0.7642 data_time: 0.1943 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/18 04:41:14 - mmengine - INFO - Epoch(train) [5][39200/42151] lr: 3.0000e-06 eta: 8:41:03 time: 0.7364 data_time: 0.2040 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 04:42:20 - mmengine - INFO - Epoch(train) [5][39300/42151] lr: 3.0000e-06 eta: 8:39:53 time: 0.5590 data_time: 0.0056 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 04:43:24 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 04:43:26 - mmengine - INFO - Epoch(train) [5][39400/42151] lr: 3.0000e-06 eta: 8:38:43 time: 0.5477 data_time: 0.0154 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 04:44:33 - mmengine - INFO - Epoch(train) [5][39500/42151] lr: 3.0000e-06 eta: 8:37:33 time: 0.6878 data_time: 0.0755 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 04:45:40 - mmengine - INFO - Epoch(train) [5][39600/42151] lr: 3.0000e-06 eta: 8:36:24 time: 0.6781 data_time: 0.1236 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 04:46:48 - mmengine - INFO - Epoch(train) [5][39700/42151] lr: 3.0000e-06 eta: 8:35:14 time: 0.7580 data_time: 0.2221 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 04:47:55 - mmengine - INFO - Epoch(train) [5][39800/42151] lr: 3.0000e-06 eta: 8:34:04 time: 0.7620 data_time: 0.1858 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 04:49:02 - mmengine - INFO - Epoch(train) [5][39900/42151] lr: 3.0000e-06 eta: 8:32:54 time: 0.5625 data_time: 0.0049 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 04:50:08 - mmengine - INFO - Epoch(train) [5][40000/42151] lr: 3.0000e-06 eta: 8:31:44 time: 0.5446 data_time: 0.0124 memory: 28726 loss_ce: 0.0065 loss: 0.0065 2022/09/18 04:51:15 - mmengine - INFO - Epoch(train) [5][40100/42151] lr: 3.0000e-06 eta: 8:30:34 time: 0.6741 data_time: 0.0909 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 04:52:21 - mmengine - INFO - Epoch(train) [5][40200/42151] lr: 3.0000e-06 eta: 8:29:24 time: 0.7013 data_time: 0.1128 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 04:53:28 - mmengine - INFO - Epoch(train) [5][40300/42151] lr: 3.0000e-06 eta: 8:28:15 time: 0.7482 data_time: 0.1934 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 04:54:31 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 04:54:34 - mmengine - INFO - Epoch(train) [5][40400/42151] lr: 3.0000e-06 eta: 8:27:05 time: 0.7533 data_time: 0.2121 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 04:55:40 - mmengine - INFO - Epoch(train) [5][40500/42151] lr: 3.0000e-06 eta: 8:25:55 time: 0.5776 data_time: 0.0048 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 04:56:46 - mmengine - INFO - Epoch(train) [5][40600/42151] lr: 3.0000e-06 eta: 8:24:45 time: 0.5462 data_time: 0.0135 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 04:57:53 - mmengine - INFO - Epoch(train) [5][40700/42151] lr: 3.0000e-06 eta: 8:23:35 time: 0.6307 data_time: 0.0528 memory: 28726 loss_ce: 0.0096 loss: 0.0096 2022/09/18 04:58:59 - mmengine - INFO - Epoch(train) [5][40800/42151] lr: 3.0000e-06 eta: 8:22:25 time: 0.6694 data_time: 0.1253 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 05:00:07 - mmengine - INFO - Epoch(train) [5][40900/42151] lr: 3.0000e-06 eta: 8:21:15 time: 0.7645 data_time: 0.2272 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/18 05:01:14 - mmengine - INFO - Epoch(train) [5][41000/42151] lr: 3.0000e-06 eta: 8:20:05 time: 0.7526 data_time: 0.1883 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 05:02:19 - mmengine - INFO - Epoch(train) [5][41100/42151] lr: 3.0000e-06 eta: 8:18:55 time: 0.5584 data_time: 0.0048 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 05:03:26 - mmengine - INFO - Epoch(train) [5][41200/42151] lr: 3.0000e-06 eta: 8:17:46 time: 0.5604 data_time: 0.0138 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/18 05:04:34 - mmengine - INFO - Epoch(train) [5][41300/42151] lr: 3.0000e-06 eta: 8:16:36 time: 0.6321 data_time: 0.0993 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 05:05:39 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 05:05:42 - mmengine - INFO - Epoch(train) [5][41400/42151] lr: 3.0000e-06 eta: 8:15:26 time: 0.6623 data_time: 0.1072 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 05:06:51 - mmengine - INFO - Epoch(train) [5][41500/42151] lr: 3.0000e-06 eta: 8:14:17 time: 0.7605 data_time: 0.1919 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/18 05:07:58 - mmengine - INFO - Epoch(train) [5][41600/42151] lr: 3.0000e-06 eta: 8:13:07 time: 0.7693 data_time: 0.2279 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 05:09:06 - mmengine - INFO - Epoch(train) [5][41700/42151] lr: 3.0000e-06 eta: 8:11:58 time: 0.6326 data_time: 0.0054 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 05:10:13 - mmengine - INFO - Epoch(train) [5][41800/42151] lr: 3.0000e-06 eta: 8:10:48 time: 0.5464 data_time: 0.0127 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 05:11:20 - mmengine - INFO - Epoch(train) [5][41900/42151] lr: 3.0000e-06 eta: 8:09:38 time: 0.6380 data_time: 0.0633 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 05:12:26 - mmengine - INFO - Epoch(train) [5][42000/42151] lr: 3.0000e-06 eta: 8:08:28 time: 0.6564 data_time: 0.1155 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/18 05:13:35 - mmengine - INFO - Epoch(train) [5][42100/42151] lr: 3.0000e-06 eta: 8:07:19 time: 0.7693 data_time: 0.2345 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 05:14:06 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 05:14:06 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/09/18 05:15:00 - mmengine - INFO - Epoch(val) [5][100/7672] eta: 0:49:40 time: 0.3937 data_time: 0.0021 memory: 28726 2022/09/18 05:15:39 - mmengine - INFO - Epoch(val) [5][200/7672] eta: 0:47:53 time: 0.3846 data_time: 0.0044 memory: 1303 2022/09/18 05:16:15 - mmengine - INFO - Epoch(val) [5][300/7672] eta: 0:24:01 time: 0.1956 data_time: 0.0019 memory: 1303 2022/09/18 05:16:35 - mmengine - INFO - Epoch(val) [5][400/7672] eta: 0:23:30 time: 0.1940 data_time: 0.0019 memory: 1303 2022/09/18 05:16:55 - mmengine - INFO - Epoch(val) [5][500/7672] eta: 0:23:37 time: 0.1977 data_time: 0.0025 memory: 1303 2022/09/18 05:17:15 - mmengine - INFO - Epoch(val) [5][600/7672] eta: 0:24:25 time: 0.2072 data_time: 0.0019 memory: 1303 2022/09/18 05:17:35 - mmengine - INFO - Epoch(val) [5][700/7672] eta: 0:22:58 time: 0.1977 data_time: 0.0019 memory: 1303 2022/09/18 05:17:54 - mmengine - INFO - Epoch(val) [5][800/7672] eta: 0:22:29 time: 0.1964 data_time: 0.0019 memory: 1303 2022/09/18 05:18:14 - mmengine - INFO - Epoch(val) [5][900/7672] eta: 0:21:59 time: 0.1949 data_time: 0.0019 memory: 1303 2022/09/18 05:18:35 - mmengine - INFO - Epoch(val) [5][1000/7672] eta: 0:21:28 time: 0.1931 data_time: 0.0018 memory: 1303 2022/09/18 05:18:55 - mmengine - INFO - Epoch(val) [5][1100/7672] eta: 0:21:31 time: 0.1966 data_time: 0.0019 memory: 1303 2022/09/18 05:19:15 - mmengine - INFO - Epoch(val) [5][1200/7672] eta: 0:21:03 time: 0.1952 data_time: 0.0008 memory: 1303 2022/09/18 05:19:34 - mmengine - INFO - Epoch(val) [5][1300/7672] eta: 0:20:59 time: 0.1977 data_time: 0.0018 memory: 1303 2022/09/18 05:19:55 - mmengine - INFO - Epoch(val) [5][1400/7672] eta: 0:20:18 time: 0.1943 data_time: 0.0019 memory: 1303 2022/09/18 05:20:15 - mmengine - INFO - Epoch(val) [5][1500/7672] eta: 0:19:51 time: 0.1931 data_time: 0.0018 memory: 1303 2022/09/18 05:20:34 - mmengine - INFO - Epoch(val) [5][1600/7672] eta: 0:20:42 time: 0.2047 data_time: 0.0047 memory: 1303 2022/09/18 05:20:54 - mmengine - INFO - Epoch(val) [5][1700/7672] eta: 0:20:21 time: 0.2045 data_time: 0.0024 memory: 1303 2022/09/18 05:21:14 - mmengine - INFO - Epoch(val) [5][1800/7672] eta: 0:21:07 time: 0.2159 data_time: 0.0029 memory: 1303 2022/09/18 05:21:34 - mmengine - INFO - Epoch(val) [5][1900/7672] eta: 0:21:52 time: 0.2274 data_time: 0.0052 memory: 1303 2022/09/18 05:21:54 - mmengine - INFO - Epoch(val) [5][2000/7672] eta: 0:22:09 time: 0.2345 data_time: 0.0027 memory: 1303 2022/09/18 05:22:14 - mmengine - INFO - Epoch(val) [5][2100/7672] eta: 0:21:34 time: 0.2324 data_time: 0.0028 memory: 1303 2022/09/18 05:22:33 - mmengine - INFO - Epoch(val) [5][2200/7672] eta: 0:18:58 time: 0.2081 data_time: 0.0034 memory: 1303 2022/09/18 05:22:53 - mmengine - INFO - Epoch(val) [5][2300/7672] eta: 0:20:22 time: 0.2275 data_time: 0.0098 memory: 1303 2022/09/18 05:23:13 - mmengine - INFO - Epoch(val) [5][2400/7672] eta: 0:19:14 time: 0.2190 data_time: 0.0051 memory: 1303 2022/09/18 05:23:33 - mmengine - INFO - Epoch(val) [5][2500/7672] eta: 0:17:38 time: 0.2047 data_time: 0.0023 memory: 1303 2022/09/18 05:23:52 - mmengine - INFO - Epoch(val) [5][2600/7672] eta: 0:16:35 time: 0.1963 data_time: 0.0007 memory: 1303 2022/09/18 05:24:12 - mmengine - INFO - Epoch(val) [5][2700/7672] eta: 0:16:04 time: 0.1939 data_time: 0.0007 memory: 1303 2022/09/18 05:24:32 - mmengine - INFO - Epoch(val) [5][2800/7672] eta: 0:15:41 time: 0.1931 data_time: 0.0007 memory: 1303 2022/09/18 05:24:52 - mmengine - INFO - Epoch(val) [5][2900/7672] eta: 0:15:23 time: 0.1935 data_time: 0.0007 memory: 1303 2022/09/18 05:25:12 - mmengine - INFO - Epoch(val) [5][3000/7672] eta: 0:15:11 time: 0.1951 data_time: 0.0008 memory: 1303 2022/09/18 05:25:32 - mmengine - INFO - Epoch(val) [5][3100/7672] eta: 0:15:34 time: 0.2044 data_time: 0.0008 memory: 1303 2022/09/18 05:25:51 - mmengine - INFO - Epoch(val) [5][3200/7672] eta: 0:14:22 time: 0.1929 data_time: 0.0007 memory: 1303 2022/09/18 05:26:11 - mmengine - INFO - Epoch(val) [5][3300/7672] eta: 0:14:03 time: 0.1930 data_time: 0.0007 memory: 1303 2022/09/18 05:26:31 - mmengine - INFO - Epoch(val) [5][3400/7672] eta: 0:13:35 time: 0.1909 data_time: 0.0007 memory: 1303 2022/09/18 05:26:51 - mmengine - INFO - Epoch(val) [5][3500/7672] eta: 0:13:33 time: 0.1949 data_time: 0.0007 memory: 1303 2022/09/18 05:27:11 - mmengine - INFO - Epoch(val) [5][3600/7672] eta: 0:13:02 time: 0.1922 data_time: 0.0007 memory: 1303 2022/09/18 05:27:30 - mmengine - INFO - Epoch(val) [5][3700/7672] eta: 0:12:42 time: 0.1919 data_time: 0.0007 memory: 1303 2022/09/18 05:27:50 - mmengine - INFO - Epoch(val) [5][3800/7672] eta: 0:12:22 time: 0.1918 data_time: 0.0006 memory: 1303 2022/09/18 05:28:09 - mmengine - INFO - Epoch(val) [5][3900/7672] eta: 0:11:53 time: 0.1891 data_time: 0.0005 memory: 1303 2022/09/18 05:28:29 - mmengine - INFO - Epoch(val) [5][4000/7672] eta: 0:11:51 time: 0.1939 data_time: 0.0007 memory: 1303 2022/09/18 05:28:49 - mmengine - INFO - Epoch(val) [5][4100/7672] eta: 0:11:31 time: 0.1935 data_time: 0.0007 memory: 1303 2022/09/18 05:29:09 - mmengine - INFO - Epoch(val) [5][4200/7672] eta: 0:11:10 time: 0.1930 data_time: 0.0012 memory: 1303 2022/09/18 05:29:28 - mmengine - INFO - Epoch(val) [5][4300/7672] eta: 0:10:43 time: 0.1909 data_time: 0.0006 memory: 1303 2022/09/18 05:29:48 - mmengine - INFO - Epoch(val) [5][4400/7672] eta: 0:10:43 time: 0.1966 data_time: 0.0007 memory: 1303 2022/09/18 05:30:08 - mmengine - INFO - Epoch(val) [5][4500/7672] eta: 0:10:21 time: 0.1959 data_time: 0.0007 memory: 1303 2022/09/18 05:30:28 - mmengine - INFO - Epoch(val) [5][4600/7672] eta: 0:10:04 time: 0.1968 data_time: 0.0013 memory: 1303 2022/09/18 05:30:48 - mmengine - INFO - Epoch(val) [5][4700/7672] eta: 0:09:42 time: 0.1960 data_time: 0.0008 memory: 1303 2022/09/18 05:31:08 - mmengine - INFO - Epoch(val) [5][4800/7672] eta: 0:09:13 time: 0.1929 data_time: 0.0007 memory: 1303 2022/09/18 05:31:28 - mmengine - INFO - Epoch(val) [5][4900/7672] eta: 0:08:57 time: 0.1938 data_time: 0.0007 memory: 1303 2022/09/18 05:31:48 - mmengine - INFO - Epoch(val) [5][5000/7672] eta: 0:08:47 time: 0.1975 data_time: 0.0009 memory: 1303 2022/09/18 05:32:08 - mmengine - INFO - Epoch(val) [5][5100/7672] eta: 0:08:15 time: 0.1927 data_time: 0.0007 memory: 1303 2022/09/18 05:32:28 - mmengine - INFO - Epoch(val) [5][5200/7672] eta: 0:08:05 time: 0.1963 data_time: 0.0007 memory: 1303 2022/09/18 05:32:48 - mmengine - INFO - Epoch(val) [5][5300/7672] eta: 0:07:41 time: 0.1947 data_time: 0.0007 memory: 1303 2022/09/18 05:33:08 - mmengine - INFO - Epoch(val) [5][5400/7672] eta: 0:07:17 time: 0.1924 data_time: 0.0007 memory: 1303 2022/09/18 05:33:27 - mmengine - INFO - Epoch(val) [5][5500/7672] eta: 0:06:57 time: 0.1923 data_time: 0.0007 memory: 1303 2022/09/18 05:33:48 - mmengine - INFO - Epoch(val) [5][5600/7672] eta: 0:07:05 time: 0.2054 data_time: 0.0014 memory: 1303 2022/09/18 05:34:07 - mmengine - INFO - Epoch(val) [5][5700/7672] eta: 0:06:22 time: 0.1939 data_time: 0.0020 memory: 1303 2022/09/18 05:34:27 - mmengine - INFO - Epoch(val) [5][5800/7672] eta: 0:06:09 time: 0.1973 data_time: 0.0021 memory: 1303 2022/09/18 05:34:47 - mmengine - INFO - Epoch(val) [5][5900/7672] eta: 0:05:59 time: 0.2027 data_time: 0.0020 memory: 1303 2022/09/18 05:35:07 - mmengine - INFO - Epoch(val) [5][6000/7672] eta: 0:05:21 time: 0.1926 data_time: 0.0019 memory: 1303 2022/09/18 05:35:27 - mmengine - INFO - Epoch(val) [5][6100/7672] eta: 0:04:59 time: 0.1906 data_time: 0.0020 memory: 1303 2022/09/18 05:35:47 - mmengine - INFO - Epoch(val) [5][6200/7672] eta: 0:04:44 time: 0.1936 data_time: 0.0020 memory: 1303 2022/09/18 05:36:07 - mmengine - INFO - Epoch(val) [5][6300/7672] eta: 0:04:26 time: 0.1944 data_time: 0.0018 memory: 1303 2022/09/18 05:36:27 - mmengine - INFO - Epoch(val) [5][6400/7672] eta: 0:04:04 time: 0.1923 data_time: 0.0020 memory: 1303 2022/09/18 05:36:47 - mmengine - INFO - Epoch(val) [5][6500/7672] eta: 0:03:51 time: 0.1976 data_time: 0.0010 memory: 1303 2022/09/18 05:37:07 - mmengine - INFO - Epoch(val) [5][6600/7672] eta: 0:03:44 time: 0.2099 data_time: 0.0026 memory: 1303 2022/09/18 05:37:26 - mmengine - INFO - Epoch(val) [5][6700/7672] eta: 0:03:09 time: 0.1954 data_time: 0.0019 memory: 1303 2022/09/18 05:37:46 - mmengine - INFO - Epoch(val) [5][6800/7672] eta: 0:02:48 time: 0.1936 data_time: 0.0020 memory: 1303 2022/09/18 05:38:06 - mmengine - INFO - Epoch(val) [5][6900/7672] eta: 0:02:44 time: 0.2130 data_time: 0.0059 memory: 1303 2022/09/18 05:38:27 - mmengine - INFO - Epoch(val) [5][7000/7672] eta: 0:02:13 time: 0.1991 data_time: 0.0021 memory: 1303 2022/09/18 05:38:47 - mmengine - INFO - Epoch(val) [5][7100/7672] eta: 0:01:53 time: 0.1983 data_time: 0.0022 memory: 1303 2022/09/18 05:39:07 - mmengine - INFO - Epoch(val) [5][7200/7672] eta: 0:01:31 time: 0.1943 data_time: 0.0020 memory: 1303 2022/09/18 05:39:27 - mmengine - INFO - Epoch(val) [5][7300/7672] eta: 0:01:13 time: 0.1967 data_time: 0.0019 memory: 1303 2022/09/18 05:39:46 - mmengine - INFO - Epoch(val) [5][7400/7672] eta: 0:00:53 time: 0.1984 data_time: 0.0029 memory: 1303 2022/09/18 05:40:06 - mmengine - INFO - Epoch(val) [5][7500/7672] eta: 0:00:35 time: 0.2076 data_time: 0.0040 memory: 1303 2022/09/18 05:40:26 - mmengine - INFO - Epoch(val) [5][7600/7672] eta: 0:00:15 time: 0.2144 data_time: 0.0050 memory: 1303 2022/09/18 05:40:40 - mmengine - INFO - Epoch(val) [5][7672/7672] CUTE80/recog/word_acc_ignore_case_symbol: 0.8854 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9493 SVT/recog/word_acc_ignore_case_symbol: 0.8841 SVTP/recog/word_acc_ignore_case_symbol: 0.8016 IC13/recog/word_acc_ignore_case_symbol: 0.9498 IC15/recog/word_acc_ignore_case_symbol: 0.7545 2022/09/18 05:41:56 - mmengine - INFO - Epoch(train) [6][100/42151] lr: 3.0000e-06 eta: 8:05:34 time: 0.7449 data_time: 0.1763 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 05:43:03 - mmengine - INFO - Epoch(train) [6][200/42151] lr: 3.0000e-06 eta: 8:04:25 time: 0.7833 data_time: 0.2460 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 05:43:33 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 05:44:10 - mmengine - INFO - Epoch(train) [6][300/42151] lr: 3.0000e-06 eta: 8:03:15 time: 0.7224 data_time: 0.1600 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/18 05:45:17 - mmengine - INFO - Epoch(train) [6][400/42151] lr: 3.0000e-06 eta: 8:02:05 time: 0.6735 data_time: 0.1166 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/18 05:46:23 - mmengine - INFO - Epoch(train) [6][500/42151] lr: 3.0000e-06 eta: 8:00:55 time: 0.5993 data_time: 0.0661 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 05:47:29 - mmengine - INFO - Epoch(train) [6][600/42151] lr: 3.0000e-06 eta: 7:59:45 time: 0.5496 data_time: 0.0050 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 05:48:37 - mmengine - INFO - Epoch(train) [6][700/42151] lr: 3.0000e-06 eta: 7:58:36 time: 0.6860 data_time: 0.1456 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 05:49:43 - mmengine - INFO - Epoch(train) [6][800/42151] lr: 3.0000e-06 eta: 7:57:26 time: 0.7502 data_time: 0.1910 memory: 28726 loss_ce: 0.0065 loss: 0.0065 2022/09/18 05:50:49 - mmengine - INFO - Epoch(train) [6][900/42151] lr: 3.0000e-06 eta: 7:56:16 time: 0.7201 data_time: 0.1867 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/18 05:51:55 - mmengine - INFO - Epoch(train) [6][1000/42151] lr: 3.0000e-06 eta: 7:55:06 time: 0.6501 data_time: 0.1193 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 05:53:00 - mmengine - INFO - Epoch(train) [6][1100/42151] lr: 3.0000e-06 eta: 7:53:56 time: 0.5797 data_time: 0.0428 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 05:54:06 - mmengine - INFO - Epoch(train) [6][1200/42151] lr: 3.0000e-06 eta: 7:52:46 time: 0.5380 data_time: 0.0063 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 05:54:36 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 05:55:14 - mmengine - INFO - Epoch(train) [6][1300/42151] lr: 3.0000e-06 eta: 7:51:37 time: 0.7001 data_time: 0.1444 memory: 28726 loss_ce: 0.0062 loss: 0.0062 2022/09/18 05:56:20 - mmengine - INFO - Epoch(train) [6][1400/42151] lr: 3.0000e-06 eta: 7:50:27 time: 0.7723 data_time: 0.2227 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 05:57:26 - mmengine - INFO - Epoch(train) [6][1500/42151] lr: 3.0000e-06 eta: 7:49:17 time: 0.7113 data_time: 0.1649 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 05:58:31 - mmengine - INFO - Epoch(train) [6][1600/42151] lr: 3.0000e-06 eta: 7:48:07 time: 0.6494 data_time: 0.1201 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 05:59:36 - mmengine - INFO - Epoch(train) [6][1700/42151] lr: 3.0000e-06 eta: 7:46:57 time: 0.5673 data_time: 0.0366 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 06:00:44 - mmengine - INFO - Epoch(train) [6][1800/42151] lr: 3.0000e-06 eta: 7:45:47 time: 0.5374 data_time: 0.0047 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 06:01:51 - mmengine - INFO - Epoch(train) [6][1900/42151] lr: 3.0000e-06 eta: 7:44:37 time: 0.7120 data_time: 0.1601 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/18 06:02:58 - mmengine - INFO - Epoch(train) [6][2000/42151] lr: 3.0000e-06 eta: 7:43:28 time: 0.7534 data_time: 0.2158 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 06:04:04 - mmengine - INFO - Epoch(train) [6][2100/42151] lr: 3.0000e-06 eta: 7:42:18 time: 0.7243 data_time: 0.1929 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 06:05:09 - mmengine - INFO - Epoch(train) [6][2200/42151] lr: 3.0000e-06 eta: 7:41:08 time: 0.6724 data_time: 0.1124 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 06:05:39 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 06:06:14 - mmengine - INFO - Epoch(train) [6][2300/42151] lr: 3.0000e-06 eta: 7:39:58 time: 0.5806 data_time: 0.0479 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 06:07:19 - mmengine - INFO - Epoch(train) [6][2400/42151] lr: 3.0000e-06 eta: 7:38:48 time: 0.5395 data_time: 0.0046 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/18 06:08:25 - mmengine - INFO - Epoch(train) [6][2500/42151] lr: 3.0000e-06 eta: 7:37:38 time: 0.6578 data_time: 0.1280 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 06:09:31 - mmengine - INFO - Epoch(train) [6][2600/42151] lr: 3.0000e-06 eta: 7:36:28 time: 0.7690 data_time: 0.2381 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 06:10:37 - mmengine - INFO - Epoch(train) [6][2700/42151] lr: 3.0000e-06 eta: 7:35:18 time: 0.6843 data_time: 0.1521 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 06:11:42 - mmengine - INFO - Epoch(train) [6][2800/42151] lr: 3.0000e-06 eta: 7:34:08 time: 0.6516 data_time: 0.1164 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 06:12:47 - mmengine - INFO - Epoch(train) [6][2900/42151] lr: 3.0000e-06 eta: 7:32:58 time: 0.5823 data_time: 0.0276 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 06:13:53 - mmengine - INFO - Epoch(train) [6][3000/42151] lr: 3.0000e-06 eta: 7:31:48 time: 0.5754 data_time: 0.0048 memory: 28726 loss_ce: 0.0067 loss: 0.0067 2022/09/18 06:15:00 - mmengine - INFO - Epoch(train) [6][3100/42151] lr: 3.0000e-06 eta: 7:30:38 time: 0.6962 data_time: 0.1400 memory: 28726 loss_ce: 0.0065 loss: 0.0065 2022/09/18 06:16:07 - mmengine - INFO - Epoch(train) [6][3200/42151] lr: 3.0000e-06 eta: 7:29:29 time: 0.7899 data_time: 0.2363 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 06:16:36 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 06:17:12 - mmengine - INFO - Epoch(train) [6][3300/42151] lr: 3.0000e-06 eta: 7:28:19 time: 0.7404 data_time: 0.1822 memory: 28726 loss_ce: 0.0064 loss: 0.0064 2022/09/18 06:18:18 - mmengine - INFO - Epoch(train) [6][3400/42151] lr: 3.0000e-06 eta: 7:27:09 time: 0.6686 data_time: 0.1327 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 06:19:24 - mmengine - INFO - Epoch(train) [6][3500/42151] lr: 3.0000e-06 eta: 7:25:59 time: 0.5584 data_time: 0.0283 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 06:20:29 - mmengine - INFO - Epoch(train) [6][3600/42151] lr: 3.0000e-06 eta: 7:24:49 time: 0.5507 data_time: 0.0047 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 06:21:36 - mmengine - INFO - Epoch(train) [6][3700/42151] lr: 3.0000e-06 eta: 7:23:39 time: 0.6780 data_time: 0.1210 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 06:22:42 - mmengine - INFO - Epoch(train) [6][3800/42151] lr: 3.0000e-06 eta: 7:22:30 time: 0.7609 data_time: 0.2292 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 06:23:47 - mmengine - INFO - Epoch(train) [6][3900/42151] lr: 3.0000e-06 eta: 7:21:20 time: 0.6821 data_time: 0.1509 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 06:24:53 - mmengine - INFO - Epoch(train) [6][4000/42151] lr: 3.0000e-06 eta: 7:20:10 time: 0.6767 data_time: 0.1345 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 06:25:59 - mmengine - INFO - Epoch(train) [6][4100/42151] lr: 3.0000e-06 eta: 7:19:00 time: 0.5670 data_time: 0.0278 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 06:27:04 - mmengine - INFO - Epoch(train) [6][4200/42151] lr: 3.0000e-06 eta: 7:17:50 time: 0.5402 data_time: 0.0052 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/18 06:27:35 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 06:28:12 - mmengine - INFO - Epoch(train) [6][4300/42151] lr: 3.0000e-06 eta: 7:16:41 time: 0.6959 data_time: 0.1626 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 06:29:18 - mmengine - INFO - Epoch(train) [6][4400/42151] lr: 3.0000e-06 eta: 7:15:31 time: 0.7619 data_time: 0.1861 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 06:30:23 - mmengine - INFO - Epoch(train) [6][4500/42151] lr: 3.0000e-06 eta: 7:14:21 time: 0.7159 data_time: 0.1668 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 06:31:29 - mmengine - INFO - Epoch(train) [6][4600/42151] lr: 3.0000e-06 eta: 7:13:11 time: 0.7017 data_time: 0.1428 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 06:32:34 - mmengine - INFO - Epoch(train) [6][4700/42151] lr: 3.0000e-06 eta: 7:12:01 time: 0.5953 data_time: 0.0325 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/18 06:33:40 - mmengine - INFO - Epoch(train) [6][4800/42151] lr: 3.0000e-06 eta: 7:10:51 time: 0.5364 data_time: 0.0048 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 06:34:47 - mmengine - INFO - Epoch(train) [6][4900/42151] lr: 3.0000e-06 eta: 7:09:42 time: 0.7016 data_time: 0.1346 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 06:35:53 - mmengine - INFO - Epoch(train) [6][5000/42151] lr: 3.0000e-06 eta: 7:08:32 time: 0.7927 data_time: 0.2361 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 06:36:59 - mmengine - INFO - Epoch(train) [6][5100/42151] lr: 3.0000e-06 eta: 7:07:22 time: 0.7134 data_time: 0.1816 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 06:38:05 - mmengine - INFO - Epoch(train) [6][5200/42151] lr: 3.0000e-06 eta: 7:06:12 time: 0.7328 data_time: 0.1640 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 06:38:35 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 06:39:10 - mmengine - INFO - Epoch(train) [6][5300/42151] lr: 3.0000e-06 eta: 7:05:02 time: 0.5844 data_time: 0.0357 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 06:40:16 - mmengine - INFO - Epoch(train) [6][5400/42151] lr: 3.0000e-06 eta: 7:03:53 time: 0.5378 data_time: 0.0046 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/18 06:41:23 - mmengine - INFO - Epoch(train) [6][5500/42151] lr: 3.0000e-06 eta: 7:02:43 time: 0.6968 data_time: 0.1380 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 06:42:29 - mmengine - INFO - Epoch(train) [6][5600/42151] lr: 3.0000e-06 eta: 7:01:33 time: 0.7569 data_time: 0.2267 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 06:43:35 - mmengine - INFO - Epoch(train) [6][5700/42151] lr: 3.0000e-06 eta: 7:00:24 time: 0.7518 data_time: 0.1972 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 06:44:40 - mmengine - INFO - Epoch(train) [6][5800/42151] lr: 3.0000e-06 eta: 6:59:14 time: 0.6498 data_time: 0.0962 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/18 06:45:45 - mmengine - INFO - Epoch(train) [6][5900/42151] lr: 3.0000e-06 eta: 6:58:04 time: 0.6191 data_time: 0.0399 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 06:46:51 - mmengine - INFO - Epoch(train) [6][6000/42151] lr: 3.0000e-06 eta: 6:56:54 time: 0.5474 data_time: 0.0046 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 06:47:58 - mmengine - INFO - Epoch(train) [6][6100/42151] lr: 3.0000e-06 eta: 6:55:44 time: 0.6557 data_time: 0.1257 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/18 06:49:05 - mmengine - INFO - Epoch(train) [6][6200/42151] lr: 3.0000e-06 eta: 6:54:35 time: 0.7642 data_time: 0.2305 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 06:49:35 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 06:50:11 - mmengine - INFO - Epoch(train) [6][6300/42151] lr: 3.0000e-06 eta: 6:53:25 time: 0.7569 data_time: 0.2038 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 06:51:17 - mmengine - INFO - Epoch(train) [6][6400/42151] lr: 3.0000e-06 eta: 6:52:15 time: 0.6813 data_time: 0.1227 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 06:52:24 - mmengine - INFO - Epoch(train) [6][6500/42151] lr: 3.0000e-06 eta: 6:51:06 time: 0.6027 data_time: 0.0445 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 06:53:31 - mmengine - INFO - Epoch(train) [6][6600/42151] lr: 3.0000e-06 eta: 6:49:56 time: 0.5403 data_time: 0.0050 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 06:54:39 - mmengine - INFO - Epoch(train) [6][6700/42151] lr: 3.0000e-06 eta: 6:48:47 time: 0.7127 data_time: 0.1466 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 06:55:47 - mmengine - INFO - Epoch(train) [6][6800/42151] lr: 3.0000e-06 eta: 6:47:37 time: 0.7795 data_time: 0.2052 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 06:56:55 - mmengine - INFO - Epoch(train) [6][6900/42151] lr: 3.0000e-06 eta: 6:46:28 time: 0.7677 data_time: 0.1749 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 06:58:01 - mmengine - INFO - Epoch(train) [6][7000/42151] lr: 3.0000e-06 eta: 6:45:18 time: 0.6559 data_time: 0.1014 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 06:59:08 - mmengine - INFO - Epoch(train) [6][7100/42151] lr: 3.0000e-06 eta: 6:44:09 time: 0.5947 data_time: 0.0425 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 07:00:15 - mmengine - INFO - Epoch(train) [6][7200/42151] lr: 3.0000e-06 eta: 6:42:59 time: 0.5488 data_time: 0.0052 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/18 07:00:46 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 07:01:24 - mmengine - INFO - Epoch(train) [6][7300/42151] lr: 3.0000e-06 eta: 6:41:50 time: 0.6989 data_time: 0.1296 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 07:02:31 - mmengine - INFO - Epoch(train) [6][7400/42151] lr: 3.0000e-06 eta: 6:40:41 time: 0.7759 data_time: 0.2392 memory: 28726 loss_ce: 0.0066 loss: 0.0066 2022/09/18 07:03:39 - mmengine - INFO - Epoch(train) [6][7500/42151] lr: 3.0000e-06 eta: 6:39:31 time: 0.7558 data_time: 0.2024 memory: 28726 loss_ce: 0.0063 loss: 0.0063 2022/09/18 07:04:45 - mmengine - INFO - Epoch(train) [6][7600/42151] lr: 3.0000e-06 eta: 6:38:22 time: 0.6758 data_time: 0.1401 memory: 28726 loss_ce: 0.0066 loss: 0.0066 2022/09/18 07:05:52 - mmengine - INFO - Epoch(train) [6][7700/42151] lr: 3.0000e-06 eta: 6:37:12 time: 0.6072 data_time: 0.0646 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 07:06:58 - mmengine - INFO - Epoch(train) [6][7800/42151] lr: 3.0000e-06 eta: 6:36:02 time: 0.5614 data_time: 0.0051 memory: 28726 loss_ce: 0.0061 loss: 0.0061 2022/09/18 07:08:07 - mmengine - INFO - Epoch(train) [6][7900/42151] lr: 3.0000e-06 eta: 6:34:53 time: 0.6965 data_time: 0.1589 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 07:09:14 - mmengine - INFO - Epoch(train) [6][8000/42151] lr: 3.0000e-06 eta: 6:33:44 time: 0.7867 data_time: 0.2330 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/18 07:10:21 - mmengine - INFO - Epoch(train) [6][8100/42151] lr: 3.0000e-06 eta: 6:32:34 time: 0.7015 data_time: 0.1418 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 07:11:28 - mmengine - INFO - Epoch(train) [6][8200/42151] lr: 3.0000e-06 eta: 6:31:25 time: 0.6816 data_time: 0.1175 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 07:11:59 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 07:12:35 - mmengine - INFO - Epoch(train) [6][8300/42151] lr: 3.0000e-06 eta: 6:30:15 time: 0.6140 data_time: 0.0626 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 07:13:42 - mmengine - INFO - Epoch(train) [6][8400/42151] lr: 3.0000e-06 eta: 6:29:05 time: 0.5373 data_time: 0.0047 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 07:14:50 - mmengine - INFO - Epoch(train) [6][8500/42151] lr: 3.0000e-06 eta: 6:27:56 time: 0.7353 data_time: 0.1469 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 07:15:58 - mmengine - INFO - Epoch(train) [6][8600/42151] lr: 3.0000e-06 eta: 6:26:47 time: 0.7362 data_time: 0.1995 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 07:17:05 - mmengine - INFO - Epoch(train) [6][8700/42151] lr: 3.0000e-06 eta: 6:25:37 time: 0.7527 data_time: 0.1929 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 07:18:12 - mmengine - INFO - Epoch(train) [6][8800/42151] lr: 3.0000e-06 eta: 6:24:28 time: 0.6812 data_time: 0.1486 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 07:19:19 - mmengine - INFO - Epoch(train) [6][8900/42151] lr: 3.0000e-06 eta: 6:23:18 time: 0.5737 data_time: 0.0292 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 07:20:25 - mmengine - INFO - Epoch(train) [6][9000/42151] lr: 3.0000e-06 eta: 6:22:09 time: 0.5437 data_time: 0.0060 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 07:21:33 - mmengine - INFO - Epoch(train) [6][9100/42151] lr: 3.0000e-06 eta: 6:20:59 time: 0.6900 data_time: 0.1344 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 07:22:40 - mmengine - INFO - Epoch(train) [6][9200/42151] lr: 3.0000e-06 eta: 6:19:50 time: 0.7670 data_time: 0.2034 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/18 07:23:10 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 07:23:48 - mmengine - INFO - Epoch(train) [6][9300/42151] lr: 3.0000e-06 eta: 6:18:40 time: 0.7467 data_time: 0.1772 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 07:24:53 - mmengine - INFO - Epoch(train) [6][9400/42151] lr: 3.0000e-06 eta: 6:17:31 time: 0.6552 data_time: 0.1000 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 07:25:59 - mmengine - INFO - Epoch(train) [6][9500/42151] lr: 3.0000e-06 eta: 6:16:21 time: 0.5847 data_time: 0.0292 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 07:27:06 - mmengine - INFO - Epoch(train) [6][9600/42151] lr: 3.0000e-06 eta: 6:15:11 time: 0.5529 data_time: 0.0049 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 07:28:14 - mmengine - INFO - Epoch(train) [6][9700/42151] lr: 3.0000e-06 eta: 6:14:02 time: 0.6647 data_time: 0.1323 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 07:29:21 - mmengine - INFO - Epoch(train) [6][9800/42151] lr: 3.0000e-06 eta: 6:12:53 time: 0.7761 data_time: 0.2314 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 07:30:28 - mmengine - INFO - Epoch(train) [6][9900/42151] lr: 3.0000e-06 eta: 6:11:43 time: 0.7410 data_time: 0.1872 memory: 28726 loss_ce: 0.0061 loss: 0.0061 2022/09/18 07:31:35 - mmengine - INFO - Epoch(train) [6][10000/42151] lr: 3.0000e-06 eta: 6:10:34 time: 0.6862 data_time: 0.1477 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 07:32:42 - mmengine - INFO - Epoch(train) [6][10100/42151] lr: 3.0000e-06 eta: 6:09:24 time: 0.5634 data_time: 0.0284 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/18 07:33:50 - mmengine - INFO - Epoch(train) [6][10200/42151] lr: 3.0000e-06 eta: 6:08:15 time: 0.5564 data_time: 0.0051 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/18 07:34:20 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 07:34:58 - mmengine - INFO - Epoch(train) [6][10300/42151] lr: 3.0000e-06 eta: 6:07:06 time: 0.7072 data_time: 0.1456 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 07:36:07 - mmengine - INFO - Epoch(train) [6][10400/42151] lr: 3.0000e-06 eta: 6:05:56 time: 0.8059 data_time: 0.2210 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 07:37:15 - mmengine - INFO - Epoch(train) [6][10500/42151] lr: 3.0000e-06 eta: 6:04:47 time: 0.7485 data_time: 0.1706 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 07:38:22 - mmengine - INFO - Epoch(train) [6][10600/42151] lr: 3.0000e-06 eta: 6:03:38 time: 0.6818 data_time: 0.1491 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 07:39:29 - mmengine - INFO - Epoch(train) [6][10700/42151] lr: 3.0000e-06 eta: 6:02:28 time: 0.5886 data_time: 0.0499 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 07:40:36 - mmengine - INFO - Epoch(train) [6][10800/42151] lr: 3.0000e-06 eta: 6:01:19 time: 0.5973 data_time: 0.0051 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 07:41:44 - mmengine - INFO - Epoch(train) [6][10900/42151] lr: 3.0000e-06 eta: 6:00:09 time: 0.6662 data_time: 0.1330 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 07:42:51 - mmengine - INFO - Epoch(train) [6][11000/42151] lr: 3.0000e-06 eta: 5:59:00 time: 0.7798 data_time: 0.2435 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 07:43:58 - mmengine - INFO - Epoch(train) [6][11100/42151] lr: 3.0000e-06 eta: 5:57:50 time: 0.7193 data_time: 0.1857 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 07:45:05 - mmengine - INFO - Epoch(train) [6][11200/42151] lr: 3.0000e-06 eta: 5:56:41 time: 0.6622 data_time: 0.1273 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 07:45:36 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 07:46:12 - mmengine - INFO - Epoch(train) [6][11300/42151] lr: 3.0000e-06 eta: 5:55:32 time: 0.5724 data_time: 0.0311 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 07:47:20 - mmengine - INFO - Epoch(train) [6][11400/42151] lr: 3.0000e-06 eta: 5:54:22 time: 0.5495 data_time: 0.0050 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 07:48:28 - mmengine - INFO - Epoch(train) [6][11500/42151] lr: 3.0000e-06 eta: 5:53:13 time: 0.6851 data_time: 0.1511 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 07:49:35 - mmengine - INFO - Epoch(train) [6][11600/42151] lr: 3.0000e-06 eta: 5:52:03 time: 0.7690 data_time: 0.2340 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/18 07:50:42 - mmengine - INFO - Epoch(train) [6][11700/42151] lr: 3.0000e-06 eta: 5:50:54 time: 0.7738 data_time: 0.1618 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 07:51:48 - mmengine - INFO - Epoch(train) [6][11800/42151] lr: 3.0000e-06 eta: 5:49:44 time: 0.6950 data_time: 0.1075 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 07:52:55 - mmengine - INFO - Epoch(train) [6][11900/42151] lr: 3.0000e-06 eta: 5:48:35 time: 0.5667 data_time: 0.0299 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 07:54:02 - mmengine - INFO - Epoch(train) [6][12000/42151] lr: 3.0000e-06 eta: 5:47:25 time: 0.5397 data_time: 0.0050 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 07:55:10 - mmengine - INFO - Epoch(train) [6][12100/42151] lr: 3.0000e-06 eta: 5:46:16 time: 0.7271 data_time: 0.1584 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 07:56:17 - mmengine - INFO - Epoch(train) [6][12200/42151] lr: 3.0000e-06 eta: 5:45:07 time: 0.7493 data_time: 0.1979 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 07:56:47 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 07:57:24 - mmengine - INFO - Epoch(train) [6][12300/42151] lr: 3.0000e-06 eta: 5:43:57 time: 0.7525 data_time: 0.2128 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 07:58:30 - mmengine - INFO - Epoch(train) [6][12400/42151] lr: 3.0000e-06 eta: 5:42:48 time: 0.6696 data_time: 0.1330 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 07:59:37 - mmengine - INFO - Epoch(train) [6][12500/42151] lr: 3.0000e-06 eta: 5:41:38 time: 0.5877 data_time: 0.0533 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 08:00:43 - mmengine - INFO - Epoch(train) [6][12600/42151] lr: 3.0000e-06 eta: 5:40:29 time: 0.5393 data_time: 0.0050 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/18 08:01:52 - mmengine - INFO - Epoch(train) [6][12700/42151] lr: 3.0000e-06 eta: 5:39:20 time: 0.7289 data_time: 0.1549 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 08:02:59 - mmengine - INFO - Epoch(train) [6][12800/42151] lr: 3.0000e-06 eta: 5:38:10 time: 0.7609 data_time: 0.2013 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 08:04:07 - mmengine - INFO - Epoch(train) [6][12900/42151] lr: 3.0000e-06 eta: 5:37:01 time: 0.7374 data_time: 0.2035 memory: 28726 loss_ce: 0.0062 loss: 0.0062 2022/09/18 08:05:14 - mmengine - INFO - Epoch(train) [6][13000/42151] lr: 3.0000e-06 eta: 5:35:51 time: 0.7452 data_time: 0.1746 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 08:06:20 - mmengine - INFO - Epoch(train) [6][13100/42151] lr: 3.0000e-06 eta: 5:34:42 time: 0.5713 data_time: 0.0382 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/18 08:07:26 - mmengine - INFO - Epoch(train) [6][13200/42151] lr: 3.0000e-06 eta: 5:33:32 time: 0.5382 data_time: 0.0059 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 08:07:57 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 08:08:34 - mmengine - INFO - Epoch(train) [6][13300/42151] lr: 3.0000e-06 eta: 5:32:23 time: 0.7001 data_time: 0.1404 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 08:09:42 - mmengine - INFO - Epoch(train) [6][13400/42151] lr: 3.0000e-06 eta: 5:31:14 time: 0.8056 data_time: 0.2451 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 08:10:48 - mmengine - INFO - Epoch(train) [6][13500/42151] lr: 3.0000e-06 eta: 5:30:04 time: 0.7154 data_time: 0.1824 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 08:11:54 - mmengine - INFO - Epoch(train) [6][13600/42151] lr: 3.0000e-06 eta: 5:28:55 time: 0.7080 data_time: 0.1487 memory: 28726 loss_ce: 0.0066 loss: 0.0066 2022/09/18 08:13:00 - mmengine - INFO - Epoch(train) [6][13700/42151] lr: 3.0000e-06 eta: 5:27:45 time: 0.5675 data_time: 0.0305 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 08:14:06 - mmengine - INFO - Epoch(train) [6][13800/42151] lr: 3.0000e-06 eta: 5:26:36 time: 0.5384 data_time: 0.0049 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 08:15:15 - mmengine - INFO - Epoch(train) [6][13900/42151] lr: 3.0000e-06 eta: 5:25:27 time: 0.7088 data_time: 0.1660 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 08:16:22 - mmengine - INFO - Epoch(train) [6][14000/42151] lr: 3.0000e-06 eta: 5:24:17 time: 0.8048 data_time: 0.2265 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 08:17:30 - mmengine - INFO - Epoch(train) [6][14100/42151] lr: 3.0000e-06 eta: 5:23:08 time: 0.7515 data_time: 0.1698 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 08:18:36 - mmengine - INFO - Epoch(train) [6][14200/42151] lr: 3.0000e-06 eta: 5:21:58 time: 0.7181 data_time: 0.1647 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 08:19:06 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 08:19:42 - mmengine - INFO - Epoch(train) [6][14300/42151] lr: 3.0000e-06 eta: 5:20:49 time: 0.5996 data_time: 0.0290 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 08:20:50 - mmengine - INFO - Epoch(train) [6][14400/42151] lr: 3.0000e-06 eta: 5:19:40 time: 0.5384 data_time: 0.0051 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/18 08:21:58 - mmengine - INFO - Epoch(train) [6][14500/42151] lr: 3.0000e-06 eta: 5:18:30 time: 0.6871 data_time: 0.1351 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 08:23:05 - mmengine - INFO - Epoch(train) [6][14600/42151] lr: 3.0000e-06 eta: 5:17:21 time: 0.7761 data_time: 0.2329 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 08:24:13 - mmengine - INFO - Epoch(train) [6][14700/42151] lr: 3.0000e-06 eta: 5:16:12 time: 0.7285 data_time: 0.1949 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 08:25:19 - mmengine - INFO - Epoch(train) [6][14800/42151] lr: 3.0000e-06 eta: 5:15:02 time: 0.7095 data_time: 0.1765 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 08:26:26 - mmengine - INFO - Epoch(train) [6][14900/42151] lr: 3.0000e-06 eta: 5:13:53 time: 0.5648 data_time: 0.0316 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 08:27:33 - mmengine - INFO - Epoch(train) [6][15000/42151] lr: 3.0000e-06 eta: 5:12:44 time: 0.5450 data_time: 0.0049 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 08:28:43 - mmengine - INFO - Epoch(train) [6][15100/42151] lr: 3.0000e-06 eta: 5:11:34 time: 0.7527 data_time: 0.1642 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 08:29:50 - mmengine - INFO - Epoch(train) [6][15200/42151] lr: 3.0000e-06 eta: 5:10:25 time: 0.7763 data_time: 0.2249 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 08:30:20 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 08:30:58 - mmengine - INFO - Epoch(train) [6][15300/42151] lr: 3.0000e-06 eta: 5:09:16 time: 0.7551 data_time: 0.1978 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 08:32:05 - mmengine - INFO - Epoch(train) [6][15400/42151] lr: 3.0000e-06 eta: 5:08:06 time: 0.7191 data_time: 0.1465 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 08:33:12 - mmengine - INFO - Epoch(train) [6][15500/42151] lr: 3.0000e-06 eta: 5:06:57 time: 0.6496 data_time: 0.0300 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 08:34:18 - mmengine - INFO - Epoch(train) [6][15600/42151] lr: 3.0000e-06 eta: 5:05:48 time: 0.5399 data_time: 0.0047 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 08:35:26 - mmengine - INFO - Epoch(train) [6][15700/42151] lr: 3.0000e-06 eta: 5:04:38 time: 0.6867 data_time: 0.1533 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 08:36:34 - mmengine - INFO - Epoch(train) [6][15800/42151] lr: 3.0000e-06 eta: 5:03:29 time: 0.7773 data_time: 0.2193 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 08:37:40 - mmengine - INFO - Epoch(train) [6][15900/42151] lr: 3.0000e-06 eta: 5:02:20 time: 0.7426 data_time: 0.2066 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 08:38:47 - mmengine - INFO - Epoch(train) [6][16000/42151] lr: 3.0000e-06 eta: 5:01:10 time: 0.6962 data_time: 0.1543 memory: 28726 loss_ce: 0.0093 loss: 0.0093 2022/09/18 08:39:53 - mmengine - INFO - Epoch(train) [6][16100/42151] lr: 3.0000e-06 eta: 5:00:01 time: 0.5663 data_time: 0.0306 memory: 28726 loss_ce: 0.0067 loss: 0.0067 2022/09/18 08:41:00 - mmengine - INFO - Epoch(train) [6][16200/42151] lr: 3.0000e-06 eta: 4:58:52 time: 0.5395 data_time: 0.0047 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 08:41:30 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 08:42:07 - mmengine - INFO - Epoch(train) [6][16300/42151] lr: 3.0000e-06 eta: 4:57:42 time: 0.7011 data_time: 0.1450 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 08:43:14 - mmengine - INFO - Epoch(train) [6][16400/42151] lr: 3.0000e-06 eta: 4:56:33 time: 0.8251 data_time: 0.2078 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 08:44:21 - mmengine - INFO - Epoch(train) [6][16500/42151] lr: 3.0000e-06 eta: 4:55:24 time: 0.7236 data_time: 0.1685 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 08:45:28 - mmengine - INFO - Epoch(train) [6][16600/42151] lr: 3.0000e-06 eta: 4:54:14 time: 0.6714 data_time: 0.1120 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 08:46:34 - mmengine - INFO - Epoch(train) [6][16700/42151] lr: 3.0000e-06 eta: 4:53:05 time: 0.5838 data_time: 0.0294 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 08:47:40 - mmengine - INFO - Epoch(train) [6][16800/42151] lr: 3.0000e-06 eta: 4:51:55 time: 0.5425 data_time: 0.0051 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 08:48:49 - mmengine - INFO - Epoch(train) [6][16900/42151] lr: 3.0000e-06 eta: 4:50:46 time: 0.7128 data_time: 0.1625 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/18 08:49:57 - mmengine - INFO - Epoch(train) [6][17000/42151] lr: 3.0000e-06 eta: 4:49:37 time: 0.8204 data_time: 0.2402 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 08:51:04 - mmengine - INFO - Epoch(train) [6][17100/42151] lr: 3.0000e-06 eta: 4:48:28 time: 0.7276 data_time: 0.1930 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 08:52:11 - mmengine - INFO - Epoch(train) [6][17200/42151] lr: 3.0000e-06 eta: 4:47:18 time: 0.6695 data_time: 0.1380 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 08:52:42 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 08:53:18 - mmengine - INFO - Epoch(train) [6][17300/42151] lr: 3.0000e-06 eta: 4:46:09 time: 0.5960 data_time: 0.0554 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 08:54:25 - mmengine - INFO - Epoch(train) [6][17400/42151] lr: 3.0000e-06 eta: 4:45:00 time: 0.5477 data_time: 0.0049 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 08:55:34 - mmengine - INFO - Epoch(train) [6][17500/42151] lr: 3.0000e-06 eta: 4:43:51 time: 0.7194 data_time: 0.1650 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 08:56:41 - mmengine - INFO - Epoch(train) [6][17600/42151] lr: 3.0000e-06 eta: 4:42:41 time: 0.7700 data_time: 0.2336 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 08:57:49 - mmengine - INFO - Epoch(train) [6][17700/42151] lr: 3.0000e-06 eta: 4:41:32 time: 0.7115 data_time: 0.1562 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/18 08:58:56 - mmengine - INFO - Epoch(train) [6][17800/42151] lr: 3.0000e-06 eta: 4:40:23 time: 0.7304 data_time: 0.1117 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 09:00:03 - mmengine - INFO - Epoch(train) [6][17900/42151] lr: 3.0000e-06 eta: 4:39:13 time: 0.5844 data_time: 0.0503 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 09:01:10 - mmengine - INFO - Epoch(train) [6][18000/42151] lr: 3.0000e-06 eta: 4:38:04 time: 0.5552 data_time: 0.0047 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 09:02:18 - mmengine - INFO - Epoch(train) [6][18100/42151] lr: 3.0000e-06 eta: 4:36:55 time: 0.7507 data_time: 0.1583 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 09:03:26 - mmengine - INFO - Epoch(train) [6][18200/42151] lr: 3.0000e-06 eta: 4:35:46 time: 0.7463 data_time: 0.1962 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/18 09:03:56 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 09:04:33 - mmengine - INFO - Epoch(train) [6][18300/42151] lr: 3.0000e-06 eta: 4:34:36 time: 0.7410 data_time: 0.2088 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 09:05:40 - mmengine - INFO - Epoch(train) [6][18400/42151] lr: 3.0000e-06 eta: 4:33:27 time: 0.6967 data_time: 0.1359 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 09:06:47 - mmengine - INFO - Epoch(train) [6][18500/42151] lr: 3.0000e-06 eta: 4:32:18 time: 0.5620 data_time: 0.0294 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 09:07:54 - mmengine - INFO - Epoch(train) [6][18600/42151] lr: 3.0000e-06 eta: 4:31:08 time: 0.5451 data_time: 0.0050 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 09:09:03 - mmengine - INFO - Epoch(train) [6][18700/42151] lr: 3.0000e-06 eta: 4:29:59 time: 0.7097 data_time: 0.1523 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 09:10:10 - mmengine - INFO - Epoch(train) [6][18800/42151] lr: 3.0000e-06 eta: 4:28:50 time: 0.7648 data_time: 0.1958 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 09:11:17 - mmengine - INFO - Epoch(train) [6][18900/42151] lr: 3.0000e-06 eta: 4:27:41 time: 0.8035 data_time: 0.2137 memory: 28726 loss_ce: 0.0061 loss: 0.0061 2022/09/18 09:12:24 - mmengine - INFO - Epoch(train) [6][19000/42151] lr: 3.0000e-06 eta: 4:26:31 time: 0.6813 data_time: 0.1231 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/18 09:13:30 - mmengine - INFO - Epoch(train) [6][19100/42151] lr: 3.0000e-06 eta: 4:25:22 time: 0.5687 data_time: 0.0284 memory: 28726 loss_ce: 0.0097 loss: 0.0097 2022/09/18 09:14:36 - mmengine - INFO - Epoch(train) [6][19200/42151] lr: 3.0000e-06 eta: 4:24:13 time: 0.5606 data_time: 0.0051 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 09:15:07 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 09:15:45 - mmengine - INFO - Epoch(train) [6][19300/42151] lr: 3.0000e-06 eta: 4:23:04 time: 0.7012 data_time: 0.1524 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 09:16:52 - mmengine - INFO - Epoch(train) [6][19400/42151] lr: 3.0000e-06 eta: 4:21:54 time: 0.7928 data_time: 0.2478 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/18 09:17:59 - mmengine - INFO - Epoch(train) [6][19500/42151] lr: 3.0000e-06 eta: 4:20:45 time: 0.7458 data_time: 0.1731 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 09:19:05 - mmengine - INFO - Epoch(train) [6][19600/42151] lr: 3.0000e-06 eta: 4:19:36 time: 0.6930 data_time: 0.1574 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 09:20:12 - mmengine - INFO - Epoch(train) [6][19700/42151] lr: 3.0000e-06 eta: 4:18:26 time: 0.5751 data_time: 0.0376 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/18 09:21:18 - mmengine - INFO - Epoch(train) [6][19800/42151] lr: 3.0000e-06 eta: 4:17:17 time: 0.5458 data_time: 0.0050 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 09:22:26 - mmengine - INFO - Epoch(train) [6][19900/42151] lr: 3.0000e-06 eta: 4:16:08 time: 0.6974 data_time: 0.1502 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 09:23:33 - mmengine - INFO - Epoch(train) [6][20000/42151] lr: 3.0000e-06 eta: 4:14:59 time: 0.7662 data_time: 0.1900 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 09:24:40 - mmengine - INFO - Epoch(train) [6][20100/42151] lr: 3.0000e-06 eta: 4:13:49 time: 0.7502 data_time: 0.1673 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/18 09:25:48 - mmengine - INFO - Epoch(train) [6][20200/42151] lr: 3.0000e-06 eta: 4:12:40 time: 0.7728 data_time: 0.1683 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 09:26:18 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 09:26:54 - mmengine - INFO - Epoch(train) [6][20300/42151] lr: 3.0000e-06 eta: 4:11:31 time: 0.5921 data_time: 0.0579 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 09:28:01 - mmengine - INFO - Epoch(train) [6][20400/42151] lr: 3.0000e-06 eta: 4:10:21 time: 0.5680 data_time: 0.0050 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/18 09:29:09 - mmengine - INFO - Epoch(train) [6][20500/42151] lr: 3.0000e-06 eta: 4:09:12 time: 0.7144 data_time: 0.1624 memory: 28726 loss_ce: 0.0066 loss: 0.0066 2022/09/18 09:30:17 - mmengine - INFO - Epoch(train) [6][20600/42151] lr: 3.0000e-06 eta: 4:08:03 time: 0.7954 data_time: 0.2377 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 09:31:24 - mmengine - INFO - Epoch(train) [6][20700/42151] lr: 3.0000e-06 eta: 4:06:54 time: 0.7125 data_time: 0.1795 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 09:32:31 - mmengine - INFO - Epoch(train) [6][20800/42151] lr: 3.0000e-06 eta: 4:05:45 time: 0.6957 data_time: 0.1643 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 09:33:37 - mmengine - INFO - Epoch(train) [6][20900/42151] lr: 3.0000e-06 eta: 4:04:35 time: 0.5897 data_time: 0.0378 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 09:34:44 - mmengine - INFO - Epoch(train) [6][21000/42151] lr: 3.0000e-06 eta: 4:03:26 time: 0.5367 data_time: 0.0049 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/18 09:35:52 - mmengine - INFO - Epoch(train) [6][21100/42151] lr: 3.0000e-06 eta: 4:02:17 time: 0.6702 data_time: 0.1370 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 09:37:00 - mmengine - INFO - Epoch(train) [6][21200/42151] lr: 3.0000e-06 eta: 4:01:08 time: 0.7963 data_time: 0.2336 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 09:37:30 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 09:38:08 - mmengine - INFO - Epoch(train) [6][21300/42151] lr: 3.0000e-06 eta: 3:59:59 time: 0.7461 data_time: 0.1549 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 09:39:14 - mmengine - INFO - Epoch(train) [6][21400/42151] lr: 3.0000e-06 eta: 3:58:49 time: 0.6824 data_time: 0.1272 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 09:40:22 - mmengine - INFO - Epoch(train) [6][21500/42151] lr: 3.0000e-06 eta: 3:57:40 time: 0.5844 data_time: 0.0431 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 09:41:28 - mmengine - INFO - Epoch(train) [6][21600/42151] lr: 3.0000e-06 eta: 3:56:31 time: 0.5379 data_time: 0.0047 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 09:42:37 - mmengine - INFO - Epoch(train) [6][21700/42151] lr: 3.0000e-06 eta: 3:55:22 time: 0.6965 data_time: 0.1555 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 09:43:45 - mmengine - INFO - Epoch(train) [6][21800/42151] lr: 3.0000e-06 eta: 3:54:13 time: 0.7640 data_time: 0.2020 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 09:44:52 - mmengine - INFO - Epoch(train) [6][21900/42151] lr: 3.0000e-06 eta: 3:53:03 time: 0.7499 data_time: 0.2121 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 09:45:59 - mmengine - INFO - Epoch(train) [6][22000/42151] lr: 3.0000e-06 eta: 3:51:54 time: 0.6910 data_time: 0.1463 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 09:47:05 - mmengine - INFO - Epoch(train) [6][22100/42151] lr: 3.0000e-06 eta: 3:50:45 time: 0.5962 data_time: 0.0601 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 09:48:12 - mmengine - INFO - Epoch(train) [6][22200/42151] lr: 3.0000e-06 eta: 3:49:35 time: 0.5541 data_time: 0.0050 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 09:48:42 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 09:49:20 - mmengine - INFO - Epoch(train) [6][22300/42151] lr: 3.0000e-06 eta: 3:48:26 time: 0.7110 data_time: 0.1573 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 09:50:27 - mmengine - INFO - Epoch(train) [6][22400/42151] lr: 3.0000e-06 eta: 3:47:17 time: 0.7556 data_time: 0.1991 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 09:51:34 - mmengine - INFO - Epoch(train) [6][22500/42151] lr: 3.0000e-06 eta: 3:46:08 time: 0.7751 data_time: 0.2113 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 09:52:41 - mmengine - INFO - Epoch(train) [6][22600/42151] lr: 3.0000e-06 eta: 3:44:59 time: 0.7131 data_time: 0.1796 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 09:53:48 - mmengine - INFO - Epoch(train) [6][22700/42151] lr: 3.0000e-06 eta: 3:43:49 time: 0.5631 data_time: 0.0291 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 09:54:55 - mmengine - INFO - Epoch(train) [6][22800/42151] lr: 3.0000e-06 eta: 3:42:40 time: 0.5407 data_time: 0.0050 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 09:56:03 - mmengine - INFO - Epoch(train) [6][22900/42151] lr: 3.0000e-06 eta: 3:41:31 time: 0.7026 data_time: 0.1585 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/18 09:57:10 - mmengine - INFO - Epoch(train) [6][23000/42151] lr: 3.0000e-06 eta: 3:40:22 time: 0.7769 data_time: 0.2163 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 09:58:17 - mmengine - INFO - Epoch(train) [6][23100/42151] lr: 3.0000e-06 eta: 3:39:13 time: 0.7285 data_time: 0.1956 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 09:59:23 - mmengine - INFO - Epoch(train) [6][23200/42151] lr: 3.0000e-06 eta: 3:38:03 time: 0.6750 data_time: 0.1311 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/18 09:59:54 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 10:00:31 - mmengine - INFO - Epoch(train) [6][23300/42151] lr: 3.0000e-06 eta: 3:36:54 time: 0.5642 data_time: 0.0300 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 10:01:37 - mmengine - INFO - Epoch(train) [6][23400/42151] lr: 3.0000e-06 eta: 3:35:45 time: 0.5395 data_time: 0.0048 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 10:02:45 - mmengine - INFO - Epoch(train) [6][23500/42151] lr: 3.0000e-06 eta: 3:34:36 time: 0.7303 data_time: 0.1762 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 10:03:51 - mmengine - INFO - Epoch(train) [6][23600/42151] lr: 3.0000e-06 eta: 3:33:27 time: 0.7525 data_time: 0.1901 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/18 10:04:58 - mmengine - INFO - Epoch(train) [6][23700/42151] lr: 3.0000e-06 eta: 3:32:17 time: 0.7334 data_time: 0.1849 memory: 28726 loss_ce: 0.0095 loss: 0.0095 2022/09/18 10:06:05 - mmengine - INFO - Epoch(train) [6][23800/42151] lr: 3.0000e-06 eta: 3:31:08 time: 0.6855 data_time: 0.1349 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 10:07:12 - mmengine - INFO - Epoch(train) [6][23900/42151] lr: 3.0000e-06 eta: 3:29:59 time: 0.5879 data_time: 0.0278 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 10:08:18 - mmengine - INFO - Epoch(train) [6][24000/42151] lr: 3.0000e-06 eta: 3:28:50 time: 0.5433 data_time: 0.0052 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/18 10:09:27 - mmengine - INFO - Epoch(train) [6][24100/42151] lr: 3.0000e-06 eta: 3:27:41 time: 0.7221 data_time: 0.1595 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 10:10:35 - mmengine - INFO - Epoch(train) [6][24200/42151] lr: 3.0000e-06 eta: 3:26:31 time: 0.7713 data_time: 0.2255 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 10:11:05 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 10:11:43 - mmengine - INFO - Epoch(train) [6][24300/42151] lr: 3.0000e-06 eta: 3:25:22 time: 0.7680 data_time: 0.2287 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 10:12:50 - mmengine - INFO - Epoch(train) [6][24400/42151] lr: 3.0000e-06 eta: 3:24:13 time: 0.6818 data_time: 0.1485 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 10:13:56 - mmengine - INFO - Epoch(train) [6][24500/42151] lr: 3.0000e-06 eta: 3:23:04 time: 0.5747 data_time: 0.0380 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 10:15:03 - mmengine - INFO - Epoch(train) [6][24600/42151] lr: 3.0000e-06 eta: 3:21:55 time: 0.5566 data_time: 0.0060 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 10:16:11 - mmengine - INFO - Epoch(train) [6][24700/42151] lr: 3.0000e-06 eta: 3:20:46 time: 0.7149 data_time: 0.1515 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 10:17:18 - mmengine - INFO - Epoch(train) [6][24800/42151] lr: 3.0000e-06 eta: 3:19:36 time: 0.7451 data_time: 0.2128 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 10:18:26 - mmengine - INFO - Epoch(train) [6][24900/42151] lr: 3.0000e-06 eta: 3:18:27 time: 0.7670 data_time: 0.2268 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 10:19:32 - mmengine - INFO - Epoch(train) [6][25000/42151] lr: 3.0000e-06 eta: 3:17:18 time: 0.6660 data_time: 0.1050 memory: 28726 loss_ce: 0.0065 loss: 0.0065 2022/09/18 10:20:39 - mmengine - INFO - Epoch(train) [6][25100/42151] lr: 3.0000e-06 eta: 3:16:09 time: 0.6012 data_time: 0.0388 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 10:21:46 - mmengine - INFO - Epoch(train) [6][25200/42151] lr: 3.0000e-06 eta: 3:15:00 time: 0.5405 data_time: 0.0048 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 10:22:17 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 10:22:55 - mmengine - INFO - Epoch(train) [6][25300/42151] lr: 3.0000e-06 eta: 3:13:51 time: 0.7190 data_time: 0.1633 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 10:24:04 - mmengine - INFO - Epoch(train) [6][25400/42151] lr: 3.0000e-06 eta: 3:12:42 time: 0.7669 data_time: 0.2313 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/18 10:25:11 - mmengine - INFO - Epoch(train) [6][25500/42151] lr: 3.0000e-06 eta: 3:11:33 time: 0.7708 data_time: 0.2371 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/18 10:26:18 - mmengine - INFO - Epoch(train) [6][25600/42151] lr: 3.0000e-06 eta: 3:10:23 time: 0.6987 data_time: 0.1431 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/18 10:27:24 - mmengine - INFO - Epoch(train) [6][25700/42151] lr: 3.0000e-06 eta: 3:09:14 time: 0.5604 data_time: 0.0285 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 10:28:31 - mmengine - INFO - Epoch(train) [6][25800/42151] lr: 3.0000e-06 eta: 3:08:05 time: 0.5424 data_time: 0.0050 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 10:29:39 - mmengine - INFO - Epoch(train) [6][25900/42151] lr: 3.0000e-06 eta: 3:06:56 time: 0.7432 data_time: 0.1481 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 10:30:47 - mmengine - INFO - Epoch(train) [6][26000/42151] lr: 3.0000e-06 eta: 3:05:47 time: 0.8277 data_time: 0.2049 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 10:31:54 - mmengine - INFO - Epoch(train) [6][26100/42151] lr: 3.0000e-06 eta: 3:04:38 time: 0.7634 data_time: 0.2039 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 10:33:00 - mmengine - INFO - Epoch(train) [6][26200/42151] lr: 3.0000e-06 eta: 3:03:28 time: 0.6817 data_time: 0.1235 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 10:33:31 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 10:34:08 - mmengine - INFO - Epoch(train) [6][26300/42151] lr: 3.0000e-06 eta: 3:02:19 time: 0.5656 data_time: 0.0292 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 10:35:14 - mmengine - INFO - Epoch(train) [6][26400/42151] lr: 3.0000e-06 eta: 3:01:10 time: 0.5442 data_time: 0.0050 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 10:36:22 - mmengine - INFO - Epoch(train) [6][26500/42151] lr: 3.0000e-06 eta: 3:00:01 time: 0.6760 data_time: 0.1413 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 10:37:29 - mmengine - INFO - Epoch(train) [6][26600/42151] lr: 3.0000e-06 eta: 2:58:52 time: 0.7526 data_time: 0.2125 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 10:38:36 - mmengine - INFO - Epoch(train) [6][26700/42151] lr: 3.0000e-06 eta: 2:57:43 time: 0.7685 data_time: 0.2130 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 10:39:42 - mmengine - INFO - Epoch(train) [6][26800/42151] lr: 3.0000e-06 eta: 2:56:33 time: 0.6557 data_time: 0.1223 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 10:40:49 - mmengine - INFO - Epoch(train) [6][26900/42151] lr: 3.0000e-06 eta: 2:55:24 time: 0.6019 data_time: 0.0538 memory: 28726 loss_ce: 0.0064 loss: 0.0064 2022/09/18 10:41:56 - mmengine - INFO - Epoch(train) [6][27000/42151] lr: 3.0000e-06 eta: 2:54:15 time: 0.5522 data_time: 0.0051 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 10:43:04 - mmengine - INFO - Epoch(train) [6][27100/42151] lr: 3.0000e-06 eta: 2:53:06 time: 0.6787 data_time: 0.1441 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/18 10:44:11 - mmengine - INFO - Epoch(train) [6][27200/42151] lr: 3.0000e-06 eta: 2:51:57 time: 0.7518 data_time: 0.2190 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 10:44:40 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 10:45:18 - mmengine - INFO - Epoch(train) [6][27300/42151] lr: 3.0000e-06 eta: 2:50:48 time: 0.7251 data_time: 0.1663 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 10:46:24 - mmengine - INFO - Epoch(train) [6][27400/42151] lr: 3.0000e-06 eta: 2:49:39 time: 0.6773 data_time: 0.1003 memory: 28726 loss_ce: 0.0066 loss: 0.0066 2022/09/18 10:47:31 - mmengine - INFO - Epoch(train) [6][27500/42151] lr: 3.0000e-06 eta: 2:48:29 time: 0.6017 data_time: 0.0652 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 10:48:37 - mmengine - INFO - Epoch(train) [6][27600/42151] lr: 3.0000e-06 eta: 2:47:20 time: 0.5397 data_time: 0.0047 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 10:49:45 - mmengine - INFO - Epoch(train) [6][27700/42151] lr: 3.0000e-06 eta: 2:46:11 time: 0.7327 data_time: 0.1548 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 10:50:53 - mmengine - INFO - Epoch(train) [6][27800/42151] lr: 3.0000e-06 eta: 2:45:02 time: 0.7455 data_time: 0.2116 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 10:52:00 - mmengine - INFO - Epoch(train) [6][27900/42151] lr: 3.0000e-06 eta: 2:43:53 time: 0.7396 data_time: 0.2056 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 10:53:07 - mmengine - INFO - Epoch(train) [6][28000/42151] lr: 3.0000e-06 eta: 2:42:44 time: 0.7102 data_time: 0.1546 memory: 28726 loss_ce: 0.0104 loss: 0.0104 2022/09/18 10:54:14 - mmengine - INFO - Epoch(train) [6][28100/42151] lr: 3.0000e-06 eta: 2:41:35 time: 0.5898 data_time: 0.0400 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 10:55:21 - mmengine - INFO - Epoch(train) [6][28200/42151] lr: 3.0000e-06 eta: 2:40:26 time: 0.5462 data_time: 0.0051 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/18 10:55:52 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 10:56:29 - mmengine - INFO - Epoch(train) [6][28300/42151] lr: 3.0000e-06 eta: 2:39:17 time: 0.7023 data_time: 0.1589 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/18 10:57:37 - mmengine - INFO - Epoch(train) [6][28400/42151] lr: 3.0000e-06 eta: 2:38:08 time: 0.7456 data_time: 0.2128 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 10:58:44 - mmengine - INFO - Epoch(train) [6][28500/42151] lr: 3.0000e-06 eta: 2:36:58 time: 0.7577 data_time: 0.2245 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 10:59:51 - mmengine - INFO - Epoch(train) [6][28600/42151] lr: 3.0000e-06 eta: 2:35:49 time: 0.7178 data_time: 0.1364 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 11:00:58 - mmengine - INFO - Epoch(train) [6][28700/42151] lr: 3.0000e-06 eta: 2:34:40 time: 0.6559 data_time: 0.0369 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 11:02:06 - mmengine - INFO - Epoch(train) [6][28800/42151] lr: 3.0000e-06 eta: 2:33:31 time: 0.5961 data_time: 0.0059 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 11:03:14 - mmengine - INFO - Epoch(train) [6][28900/42151] lr: 3.0000e-06 eta: 2:32:22 time: 0.7131 data_time: 0.1767 memory: 28726 loss_ce: 0.0065 loss: 0.0065 2022/09/18 11:04:22 - mmengine - INFO - Epoch(train) [6][29000/42151] lr: 3.0000e-06 eta: 2:31:13 time: 0.7598 data_time: 0.2226 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 11:05:29 - mmengine - INFO - Epoch(train) [6][29100/42151] lr: 3.0000e-06 eta: 2:30:04 time: 0.7492 data_time: 0.2120 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 11:06:36 - mmengine - INFO - Epoch(train) [6][29200/42151] lr: 3.0000e-06 eta: 2:28:55 time: 0.6833 data_time: 0.1291 memory: 28726 loss_ce: 0.0064 loss: 0.0064 2022/09/18 11:07:07 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 11:07:44 - mmengine - INFO - Epoch(train) [6][29300/42151] lr: 3.0000e-06 eta: 2:27:46 time: 0.5623 data_time: 0.0285 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 11:08:51 - mmengine - INFO - Epoch(train) [6][29400/42151] lr: 3.0000e-06 eta: 2:26:37 time: 0.5640 data_time: 0.0050 memory: 28726 loss_ce: 0.0060 loss: 0.0060 2022/09/18 11:09:59 - mmengine - INFO - Epoch(train) [6][29500/42151] lr: 3.0000e-06 eta: 2:25:28 time: 0.6871 data_time: 0.1386 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 11:11:07 - mmengine - INFO - Epoch(train) [6][29600/42151] lr: 3.0000e-06 eta: 2:24:19 time: 0.7719 data_time: 0.2269 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/18 11:12:14 - mmengine - INFO - Epoch(train) [6][29700/42151] lr: 3.0000e-06 eta: 2:23:09 time: 0.7515 data_time: 0.2159 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 11:13:21 - mmengine - INFO - Epoch(train) [6][29800/42151] lr: 3.0000e-06 eta: 2:22:00 time: 0.7364 data_time: 0.1754 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 11:14:28 - mmengine - INFO - Epoch(train) [6][29900/42151] lr: 3.0000e-06 eta: 2:20:51 time: 0.5761 data_time: 0.0299 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 11:15:34 - mmengine - INFO - Epoch(train) [6][30000/42151] lr: 3.0000e-06 eta: 2:19:42 time: 0.5657 data_time: 0.0049 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 11:16:42 - mmengine - INFO - Epoch(train) [6][30100/42151] lr: 3.0000e-06 eta: 2:18:33 time: 0.6966 data_time: 0.1336 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 11:17:50 - mmengine - INFO - Epoch(train) [6][30200/42151] lr: 3.0000e-06 eta: 2:17:24 time: 0.7968 data_time: 0.2188 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 11:18:20 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 11:18:57 - mmengine - INFO - Epoch(train) [6][30300/42151] lr: 3.0000e-06 eta: 2:16:15 time: 0.7114 data_time: 0.1773 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 11:20:04 - mmengine - INFO - Epoch(train) [6][30400/42151] lr: 3.0000e-06 eta: 2:15:06 time: 0.7000 data_time: 0.1631 memory: 28726 loss_ce: 0.0060 loss: 0.0060 2022/09/18 11:21:11 - mmengine - INFO - Epoch(train) [6][30500/42151] lr: 3.0000e-06 eta: 2:13:57 time: 0.5638 data_time: 0.0299 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 11:22:19 - mmengine - INFO - Epoch(train) [6][30600/42151] lr: 3.0000e-06 eta: 2:12:48 time: 0.5709 data_time: 0.0048 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/18 11:23:28 - mmengine - INFO - Epoch(train) [6][30700/42151] lr: 3.0000e-06 eta: 2:11:39 time: 0.7244 data_time: 0.1883 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 11:24:35 - mmengine - INFO - Epoch(train) [6][30800/42151] lr: 3.0000e-06 eta: 2:10:30 time: 0.7724 data_time: 0.2360 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 11:25:42 - mmengine - INFO - Epoch(train) [6][30900/42151] lr: 3.0000e-06 eta: 2:09:21 time: 0.7785 data_time: 0.1851 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 11:26:49 - mmengine - INFO - Epoch(train) [6][31000/42151] lr: 3.0000e-06 eta: 2:08:12 time: 0.6856 data_time: 0.1515 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/18 11:27:57 - mmengine - INFO - Epoch(train) [6][31100/42151] lr: 3.0000e-06 eta: 2:07:03 time: 0.5708 data_time: 0.0304 memory: 28726 loss_ce: 0.0083 loss: 0.0083 2022/09/18 11:29:04 - mmengine - INFO - Epoch(train) [6][31200/42151] lr: 3.0000e-06 eta: 2:05:53 time: 0.5385 data_time: 0.0048 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 11:29:35 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 11:30:13 - mmengine - INFO - Epoch(train) [6][31300/42151] lr: 3.0000e-06 eta: 2:04:44 time: 0.7068 data_time: 0.1573 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 11:31:21 - mmengine - INFO - Epoch(train) [6][31400/42151] lr: 3.0000e-06 eta: 2:03:35 time: 0.7614 data_time: 0.2227 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 11:32:29 - mmengine - INFO - Epoch(train) [6][31500/42151] lr: 3.0000e-06 eta: 2:02:26 time: 0.7572 data_time: 0.1619 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 11:33:37 - mmengine - INFO - Epoch(train) [6][31600/42151] lr: 3.0000e-06 eta: 2:01:17 time: 0.7035 data_time: 0.1470 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 11:34:44 - mmengine - INFO - Epoch(train) [6][31700/42151] lr: 3.0000e-06 eta: 2:00:08 time: 0.5848 data_time: 0.0504 memory: 28726 loss_ce: 0.0065 loss: 0.0065 2022/09/18 11:35:52 - mmengine - INFO - Epoch(train) [6][31800/42151] lr: 3.0000e-06 eta: 1:58:59 time: 0.5637 data_time: 0.0054 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 11:37:02 - mmengine - INFO - Epoch(train) [6][31900/42151] lr: 3.0000e-06 eta: 1:57:50 time: 0.7110 data_time: 0.1554 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 11:38:11 - mmengine - INFO - Epoch(train) [6][32000/42151] lr: 3.0000e-06 eta: 1:56:41 time: 0.8247 data_time: 0.2385 memory: 28726 loss_ce: 0.0066 loss: 0.0066 2022/09/18 11:39:19 - mmengine - INFO - Epoch(train) [6][32100/42151] lr: 3.0000e-06 eta: 1:55:32 time: 0.7735 data_time: 0.2190 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 11:40:27 - mmengine - INFO - Epoch(train) [6][32200/42151] lr: 3.0000e-06 eta: 1:54:23 time: 0.7022 data_time: 0.1634 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 11:40:58 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 11:41:34 - mmengine - INFO - Epoch(train) [6][32300/42151] lr: 3.0000e-06 eta: 1:53:14 time: 0.5645 data_time: 0.0305 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 11:42:42 - mmengine - INFO - Epoch(train) [6][32400/42151] lr: 3.0000e-06 eta: 1:52:05 time: 0.5822 data_time: 0.0056 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 11:43:51 - mmengine - INFO - Epoch(train) [6][32500/42151] lr: 3.0000e-06 eta: 1:50:56 time: 0.7463 data_time: 0.1640 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 11:44:58 - mmengine - INFO - Epoch(train) [6][32600/42151] lr: 3.0000e-06 eta: 1:49:47 time: 0.7529 data_time: 0.2075 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 11:46:06 - mmengine - INFO - Epoch(train) [6][32700/42151] lr: 3.0000e-06 eta: 1:48:38 time: 0.7257 data_time: 0.1896 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 11:47:13 - mmengine - INFO - Epoch(train) [6][32800/42151] lr: 3.0000e-06 eta: 1:47:29 time: 0.7120 data_time: 0.1627 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 11:48:21 - mmengine - INFO - Epoch(train) [6][32900/42151] lr: 3.0000e-06 eta: 1:46:20 time: 0.6081 data_time: 0.0306 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 11:49:28 - mmengine - INFO - Epoch(train) [6][33000/42151] lr: 3.0000e-06 eta: 1:45:11 time: 0.5410 data_time: 0.0049 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/18 11:50:36 - mmengine - INFO - Epoch(train) [6][33100/42151] lr: 3.0000e-06 eta: 1:44:02 time: 0.7117 data_time: 0.1692 memory: 28726 loss_ce: 0.0063 loss: 0.0063 2022/09/18 11:51:43 - mmengine - INFO - Epoch(train) [6][33200/42151] lr: 3.0000e-06 eta: 1:42:53 time: 0.7840 data_time: 0.2230 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 11:52:13 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 11:52:51 - mmengine - INFO - Epoch(train) [6][33300/42151] lr: 3.0000e-06 eta: 1:41:44 time: 0.7504 data_time: 0.1983 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 11:53:58 - mmengine - INFO - Epoch(train) [6][33400/42151] lr: 3.0000e-06 eta: 1:40:35 time: 0.6670 data_time: 0.1326 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 11:55:04 - mmengine - INFO - Epoch(train) [6][33500/42151] lr: 3.0000e-06 eta: 1:39:26 time: 0.5872 data_time: 0.0306 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 11:56:11 - mmengine - INFO - Epoch(train) [6][33600/42151] lr: 3.0000e-06 eta: 1:38:17 time: 0.5871 data_time: 0.0056 memory: 28726 loss_ce: 0.0061 loss: 0.0061 2022/09/18 11:57:19 - mmengine - INFO - Epoch(train) [6][33700/42151] lr: 3.0000e-06 eta: 1:37:08 time: 0.6791 data_time: 0.1369 memory: 28726 loss_ce: 0.0059 loss: 0.0059 2022/09/18 11:58:27 - mmengine - INFO - Epoch(train) [6][33800/42151] lr: 3.0000e-06 eta: 1:35:59 time: 0.7815 data_time: 0.2114 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 11:59:34 - mmengine - INFO - Epoch(train) [6][33900/42151] lr: 3.0000e-06 eta: 1:34:50 time: 0.7296 data_time: 0.1634 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 12:00:44 - mmengine - INFO - Epoch(train) [6][34000/42151] lr: 3.0000e-06 eta: 1:33:41 time: 0.7261 data_time: 0.1625 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 12:01:50 - mmengine - INFO - Epoch(train) [6][34100/42151] lr: 3.0000e-06 eta: 1:32:32 time: 0.5851 data_time: 0.0294 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/18 12:02:57 - mmengine - INFO - Epoch(train) [6][34200/42151] lr: 3.0000e-06 eta: 1:31:23 time: 0.5642 data_time: 0.0049 memory: 28726 loss_ce: 0.0062 loss: 0.0062 2022/09/18 12:03:27 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 12:04:05 - mmengine - INFO - Epoch(train) [6][34300/42151] lr: 3.0000e-06 eta: 1:30:14 time: 0.7009 data_time: 0.1391 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 12:05:12 - mmengine - INFO - Epoch(train) [6][34400/42151] lr: 3.0000e-06 eta: 1:29:05 time: 0.7897 data_time: 0.2322 memory: 28726 loss_ce: 0.0067 loss: 0.0067 2022/09/18 12:06:19 - mmengine - INFO - Epoch(train) [6][34500/42151] lr: 3.0000e-06 eta: 1:27:56 time: 0.7451 data_time: 0.2090 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 12:07:25 - mmengine - INFO - Epoch(train) [6][34600/42151] lr: 3.0000e-06 eta: 1:26:47 time: 0.6630 data_time: 0.1253 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 12:08:32 - mmengine - INFO - Epoch(train) [6][34700/42151] lr: 3.0000e-06 eta: 1:25:38 time: 0.5772 data_time: 0.0294 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 12:09:38 - mmengine - INFO - Epoch(train) [6][34800/42151] lr: 3.0000e-06 eta: 1:24:29 time: 0.5388 data_time: 0.0048 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 12:10:46 - mmengine - INFO - Epoch(train) [6][34900/42151] lr: 3.0000e-06 eta: 1:23:20 time: 0.6883 data_time: 0.1364 memory: 28726 loss_ce: 0.0091 loss: 0.0091 2022/09/18 12:11:53 - mmengine - INFO - Epoch(train) [6][35000/42151] lr: 3.0000e-06 eta: 1:22:11 time: 0.7774 data_time: 0.1915 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 12:13:00 - mmengine - INFO - Epoch(train) [6][35100/42151] lr: 3.0000e-06 eta: 1:21:02 time: 0.7333 data_time: 0.1772 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 12:14:07 - mmengine - INFO - Epoch(train) [6][35200/42151] lr: 3.0000e-06 eta: 1:19:53 time: 0.6793 data_time: 0.1440 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 12:14:38 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 12:15:14 - mmengine - INFO - Epoch(train) [6][35300/42151] lr: 3.0000e-06 eta: 1:18:44 time: 0.5718 data_time: 0.0314 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 12:16:20 - mmengine - INFO - Epoch(train) [6][35400/42151] lr: 3.0000e-06 eta: 1:17:35 time: 0.5404 data_time: 0.0050 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 12:17:29 - mmengine - INFO - Epoch(train) [6][35500/42151] lr: 3.0000e-06 eta: 1:16:26 time: 0.7093 data_time: 0.1710 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 12:18:37 - mmengine - INFO - Epoch(train) [6][35600/42151] lr: 3.0000e-06 eta: 1:15:17 time: 0.8077 data_time: 0.2139 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 12:19:47 - mmengine - INFO - Epoch(train) [6][35700/42151] lr: 3.0000e-06 eta: 1:14:08 time: 0.7800 data_time: 0.1815 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 12:20:55 - mmengine - INFO - Epoch(train) [6][35800/42151] lr: 3.0000e-06 eta: 1:12:59 time: 0.7237 data_time: 0.1514 memory: 28726 loss_ce: 0.0077 loss: 0.0077 2022/09/18 12:22:03 - mmengine - INFO - Epoch(train) [6][35900/42151] lr: 3.0000e-06 eta: 1:11:50 time: 0.6242 data_time: 0.0620 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/18 12:23:11 - mmengine - INFO - Epoch(train) [6][36000/42151] lr: 3.0000e-06 eta: 1:10:41 time: 0.5412 data_time: 0.0051 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 12:24:21 - mmengine - INFO - Epoch(train) [6][36100/42151] lr: 3.0000e-06 eta: 1:09:32 time: 0.6843 data_time: 0.1494 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 12:25:29 - mmengine - INFO - Epoch(train) [6][36200/42151] lr: 3.0000e-06 eta: 1:08:23 time: 0.7999 data_time: 0.2142 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 12:26:00 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 12:26:38 - mmengine - INFO - Epoch(train) [6][36300/42151] lr: 3.0000e-06 eta: 1:07:14 time: 0.7398 data_time: 0.1991 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/18 12:27:46 - mmengine - INFO - Epoch(train) [6][36400/42151] lr: 3.0000e-06 eta: 1:06:05 time: 0.7408 data_time: 0.1481 memory: 28726 loss_ce: 0.0092 loss: 0.0092 2022/09/18 12:28:53 - mmengine - INFO - Epoch(train) [6][36500/42151] lr: 3.0000e-06 eta: 1:04:56 time: 0.5886 data_time: 0.0304 memory: 28726 loss_ce: 0.0073 loss: 0.0073 2022/09/18 12:30:00 - mmengine - INFO - Epoch(train) [6][36600/42151] lr: 3.0000e-06 eta: 1:03:47 time: 0.5401 data_time: 0.0053 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 12:31:09 - mmengine - INFO - Epoch(train) [6][36700/42151] lr: 3.0000e-06 eta: 1:02:38 time: 0.6968 data_time: 0.1355 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 12:32:17 - mmengine - INFO - Epoch(train) [6][36800/42151] lr: 3.0000e-06 eta: 1:01:29 time: 0.7688 data_time: 0.2145 memory: 28726 loss_ce: 0.0064 loss: 0.0064 2022/09/18 12:33:26 - mmengine - INFO - Epoch(train) [6][36900/42151] lr: 3.0000e-06 eta: 1:00:20 time: 0.7472 data_time: 0.1975 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/18 12:34:35 - mmengine - INFO - Epoch(train) [6][37000/42151] lr: 3.0000e-06 eta: 0:59:11 time: 0.7504 data_time: 0.1467 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 12:35:42 - mmengine - INFO - Epoch(train) [6][37100/42151] lr: 3.0000e-06 eta: 0:58:02 time: 0.5630 data_time: 0.0286 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 12:36:50 - mmengine - INFO - Epoch(train) [6][37200/42151] lr: 3.0000e-06 eta: 0:56:53 time: 0.5430 data_time: 0.0053 memory: 28726 loss_ce: 0.0064 loss: 0.0064 2022/09/18 12:37:21 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 12:38:00 - mmengine - INFO - Epoch(train) [6][37300/42151] lr: 3.0000e-06 eta: 0:55:44 time: 0.7257 data_time: 0.1133 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/18 12:39:09 - mmengine - INFO - Epoch(train) [6][37400/42151] lr: 3.0000e-06 eta: 0:54:35 time: 0.7989 data_time: 0.2056 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 12:40:18 - mmengine - INFO - Epoch(train) [6][37500/42151] lr: 3.0000e-06 eta: 0:53:26 time: 0.7779 data_time: 0.2287 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 12:41:25 - mmengine - INFO - Epoch(train) [6][37600/42151] lr: 3.0000e-06 eta: 0:52:17 time: 0.6940 data_time: 0.1599 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 12:42:33 - mmengine - INFO - Epoch(train) [6][37700/42151] lr: 3.0000e-06 eta: 0:51:08 time: 0.5998 data_time: 0.0316 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 12:43:40 - mmengine - INFO - Epoch(train) [6][37800/42151] lr: 3.0000e-06 eta: 0:49:59 time: 0.5447 data_time: 0.0051 memory: 28726 loss_ce: 0.0087 loss: 0.0087 2022/09/18 12:44:50 - mmengine - INFO - Epoch(train) [6][37900/42151] lr: 3.0000e-06 eta: 0:48:50 time: 0.6772 data_time: 0.1067 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/18 12:45:58 - mmengine - INFO - Epoch(train) [6][38000/42151] lr: 3.0000e-06 eta: 0:47:41 time: 0.7747 data_time: 0.1888 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 12:47:06 - mmengine - INFO - Epoch(train) [6][38100/42151] lr: 3.0000e-06 eta: 0:46:33 time: 0.7366 data_time: 0.1857 memory: 28726 loss_ce: 0.0081 loss: 0.0081 2022/09/18 12:48:14 - mmengine - INFO - Epoch(train) [6][38200/42151] lr: 3.0000e-06 eta: 0:45:24 time: 0.7011 data_time: 0.1655 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 12:48:44 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 12:49:21 - mmengine - INFO - Epoch(train) [6][38300/42151] lr: 3.0000e-06 eta: 0:44:15 time: 0.6372 data_time: 0.0721 memory: 28726 loss_ce: 0.0065 loss: 0.0065 2022/09/18 12:50:28 - mmengine - INFO - Epoch(train) [6][38400/42151] lr: 3.0000e-06 eta: 0:43:06 time: 0.5562 data_time: 0.0049 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 12:51:36 - mmengine - INFO - Epoch(train) [6][38500/42151] lr: 3.0000e-06 eta: 0:41:57 time: 0.6897 data_time: 0.1295 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 12:52:45 - mmengine - INFO - Epoch(train) [6][38600/42151] lr: 3.0000e-06 eta: 0:40:48 time: 0.7614 data_time: 0.2239 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 12:53:54 - mmengine - INFO - Epoch(train) [6][38700/42151] lr: 3.0000e-06 eta: 0:39:39 time: 0.7553 data_time: 0.1863 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 12:55:02 - mmengine - INFO - Epoch(train) [6][38800/42151] lr: 3.0000e-06 eta: 0:38:30 time: 0.7457 data_time: 0.1644 memory: 28726 loss_ce: 0.0067 loss: 0.0067 2022/09/18 12:56:09 - mmengine - INFO - Epoch(train) [6][38900/42151] lr: 3.0000e-06 eta: 0:37:21 time: 0.5963 data_time: 0.0625 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 12:57:15 - mmengine - INFO - Epoch(train) [6][39000/42151] lr: 3.0000e-06 eta: 0:36:12 time: 0.5392 data_time: 0.0048 memory: 28726 loss_ce: 0.0089 loss: 0.0089 2022/09/18 12:58:25 - mmengine - INFO - Epoch(train) [6][39100/42151] lr: 3.0000e-06 eta: 0:35:03 time: 0.7048 data_time: 0.1702 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 12:59:33 - mmengine - INFO - Epoch(train) [6][39200/42151] lr: 3.0000e-06 eta: 0:33:54 time: 0.7889 data_time: 0.2202 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 13:00:03 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 13:00:41 - mmengine - INFO - Epoch(train) [6][39300/42151] lr: 3.0000e-06 eta: 0:32:45 time: 0.8290 data_time: 0.2430 memory: 28726 loss_ce: 0.0086 loss: 0.0086 2022/09/18 13:01:49 - mmengine - INFO - Epoch(train) [6][39400/42151] lr: 3.0000e-06 eta: 0:31:36 time: 0.7217 data_time: 0.1468 memory: 28726 loss_ce: 0.0063 loss: 0.0063 2022/09/18 13:02:57 - mmengine - INFO - Epoch(train) [6][39500/42151] lr: 3.0000e-06 eta: 0:30:27 time: 0.6204 data_time: 0.0295 memory: 28726 loss_ce: 0.0072 loss: 0.0072 2022/09/18 13:04:05 - mmengine - INFO - Epoch(train) [6][39600/42151] lr: 3.0000e-06 eta: 0:29:18 time: 0.5431 data_time: 0.0053 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 13:05:14 - mmengine - INFO - Epoch(train) [6][39700/42151] lr: 3.0000e-06 eta: 0:28:09 time: 0.7348 data_time: 0.1613 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 13:06:23 - mmengine - INFO - Epoch(train) [6][39800/42151] lr: 3.0000e-06 eta: 0:27:00 time: 0.7431 data_time: 0.2060 memory: 28726 loss_ce: 0.0074 loss: 0.0074 2022/09/18 13:07:31 - mmengine - INFO - Epoch(train) [6][39900/42151] lr: 3.0000e-06 eta: 0:25:51 time: 0.7907 data_time: 0.2323 memory: 28726 loss_ce: 0.0071 loss: 0.0071 2022/09/18 13:08:39 - mmengine - INFO - Epoch(train) [6][40000/42151] lr: 3.0000e-06 eta: 0:24:42 time: 0.7204 data_time: 0.1555 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 13:09:47 - mmengine - INFO - Epoch(train) [6][40100/42151] lr: 3.0000e-06 eta: 0:23:33 time: 0.5675 data_time: 0.0302 memory: 28726 loss_ce: 0.0090 loss: 0.0090 2022/09/18 13:10:55 - mmengine - INFO - Epoch(train) [6][40200/42151] lr: 3.0000e-06 eta: 0:22:24 time: 0.5601 data_time: 0.0049 memory: 28726 loss_ce: 0.0070 loss: 0.0070 2022/09/18 13:11:26 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 13:12:04 - mmengine - INFO - Epoch(train) [6][40300/42151] lr: 3.0000e-06 eta: 0:21:16 time: 0.7199 data_time: 0.1560 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 13:13:13 - mmengine - INFO - Epoch(train) [6][40400/42151] lr: 3.0000e-06 eta: 0:20:07 time: 0.7998 data_time: 0.2563 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 13:14:22 - mmengine - INFO - Epoch(train) [6][40500/42151] lr: 3.0000e-06 eta: 0:18:58 time: 0.7957 data_time: 0.2171 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 13:15:30 - mmengine - INFO - Epoch(train) [6][40600/42151] lr: 3.0000e-06 eta: 0:17:49 time: 0.6979 data_time: 0.1616 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 13:16:38 - mmengine - INFO - Epoch(train) [6][40700/42151] lr: 3.0000e-06 eta: 0:16:40 time: 0.5687 data_time: 0.0312 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 13:17:46 - mmengine - INFO - Epoch(train) [6][40800/42151] lr: 3.0000e-06 eta: 0:15:31 time: 0.5726 data_time: 0.0051 memory: 28726 loss_ce: 0.0080 loss: 0.0080 2022/09/18 13:18:55 - mmengine - INFO - Epoch(train) [6][40900/42151] lr: 3.0000e-06 eta: 0:14:22 time: 0.6919 data_time: 0.1526 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 13:20:04 - mmengine - INFO - Epoch(train) [6][41000/42151] lr: 3.0000e-06 eta: 0:13:13 time: 0.7890 data_time: 0.2365 memory: 28726 loss_ce: 0.0084 loss: 0.0084 2022/09/18 13:21:13 - mmengine - INFO - Epoch(train) [6][41100/42151] lr: 3.0000e-06 eta: 0:12:04 time: 0.7783 data_time: 0.2138 memory: 28726 loss_ce: 0.0069 loss: 0.0069 2022/09/18 13:22:21 - mmengine - INFO - Epoch(train) [6][41200/42151] lr: 3.0000e-06 eta: 0:10:55 time: 0.7202 data_time: 0.1685 memory: 28726 loss_ce: 0.0076 loss: 0.0076 2022/09/18 13:22:52 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 13:23:29 - mmengine - INFO - Epoch(train) [6][41300/42151] lr: 3.0000e-06 eta: 0:09:46 time: 0.6038 data_time: 0.0296 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 13:24:37 - mmengine - INFO - Epoch(train) [6][41400/42151] lr: 3.0000e-06 eta: 0:08:37 time: 0.5412 data_time: 0.0052 memory: 28726 loss_ce: 0.0082 loss: 0.0082 2022/09/18 13:25:47 - mmengine - INFO - Epoch(train) [6][41500/42151] lr: 3.0000e-06 eta: 0:07:28 time: 0.6637 data_time: 0.1281 memory: 28726 loss_ce: 0.0079 loss: 0.0079 2022/09/18 13:26:56 - mmengine - INFO - Epoch(train) [6][41600/42151] lr: 3.0000e-06 eta: 0:06:19 time: 0.7662 data_time: 0.2046 memory: 28726 loss_ce: 0.0068 loss: 0.0068 2022/09/18 13:28:04 - mmengine - INFO - Epoch(train) [6][41700/42151] lr: 3.0000e-06 eta: 0:05:10 time: 0.8055 data_time: 0.2296 memory: 28726 loss_ce: 0.0075 loss: 0.0075 2022/09/18 13:29:11 - mmengine - INFO - Epoch(train) [6][41800/42151] lr: 3.0000e-06 eta: 0:04:01 time: 0.7036 data_time: 0.1305 memory: 28726 loss_ce: 0.0078 loss: 0.0078 2022/09/18 13:30:18 - mmengine - INFO - Epoch(train) [6][41900/42151] lr: 3.0000e-06 eta: 0:02:53 time: 0.5929 data_time: 0.0546 memory: 28726 loss_ce: 0.0085 loss: 0.0085 2022/09/18 13:31:26 - mmengine - INFO - Epoch(train) [6][42000/42151] lr: 3.0000e-06 eta: 0:01:44 time: 0.5445 data_time: 0.0054 memory: 28726 loss_ce: 0.0088 loss: 0.0088 2022/09/18 13:32:36 - mmengine - INFO - Epoch(train) [6][42100/42151] lr: 3.0000e-06 eta: 0:00:35 time: 0.7549 data_time: 0.1545 memory: 28726 loss_ce: 0.0067 loss: 0.0067 2022/09/18 13:33:08 - mmengine - INFO - Exp name: nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322 2022/09/18 13:33:08 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/09/18 13:34:04 - mmengine - INFO - Epoch(val) [6][100/7672] eta: 0:49:06 time: 0.3891 data_time: 0.0022 memory: 28726 2022/09/18 13:34:43 - mmengine - INFO - Epoch(val) [6][200/7672] eta: 0:44:05 time: 0.3540 data_time: 0.0025 memory: 1303 2022/09/18 13:35:21 - mmengine - INFO - Epoch(val) [6][300/7672] eta: 0:24:48 time: 0.2020 data_time: 0.0017 memory: 1303 2022/09/18 13:35:41 - mmengine - INFO - Epoch(val) [6][400/7672] eta: 0:24:40 time: 0.2036 data_time: 0.0020 memory: 1303 2022/09/18 13:36:01 - mmengine - INFO - Epoch(val) [6][500/7672] eta: 0:30:25 time: 0.2546 data_time: 0.0057 memory: 1303 2022/09/18 13:36:22 - mmengine - INFO - Epoch(val) [6][600/7672] eta: 0:25:57 time: 0.2202 data_time: 0.0070 memory: 1303 2022/09/18 13:36:43 - mmengine - INFO - Epoch(val) [6][700/7672] eta: 0:23:08 time: 0.1991 data_time: 0.0008 memory: 1303 2022/09/18 13:37:03 - mmengine - INFO - Epoch(val) [6][800/7672] eta: 0:23:39 time: 0.2065 data_time: 0.0008 memory: 1303 2022/09/18 13:37:23 - mmengine - INFO - Epoch(val) [6][900/7672] eta: 0:22:06 time: 0.1959 data_time: 0.0023 memory: 1303 2022/09/18 13:37:43 - mmengine - INFO - Epoch(val) [6][1000/7672] eta: 0:21:28 time: 0.1931 data_time: 0.0007 memory: 1303 2022/09/18 13:38:04 - mmengine - INFO - Epoch(val) [6][1100/7672] eta: 0:22:43 time: 0.2075 data_time: 0.0009 memory: 1303 2022/09/18 13:38:24 - mmengine - INFO - Epoch(val) [6][1200/7672] eta: 0:21:17 time: 0.1974 data_time: 0.0009 memory: 1303 2022/09/18 13:38:44 - mmengine - INFO - Epoch(val) [6][1300/7672] eta: 0:20:57 time: 0.1973 data_time: 0.0009 memory: 1303 2022/09/18 13:39:04 - mmengine - INFO - Epoch(val) [6][1400/7672] eta: 0:19:25 time: 0.1859 data_time: 0.0021 memory: 1303 2022/09/18 13:39:23 - mmengine - INFO - Epoch(val) [6][1500/7672] eta: 0:19:23 time: 0.1886 data_time: 0.0022 memory: 1303 2022/09/18 13:39:43 - mmengine - INFO - Epoch(val) [6][1600/7672] eta: 0:19:18 time: 0.1908 data_time: 0.0020 memory: 1303 2022/09/18 13:40:02 - mmengine - INFO - Epoch(val) [6][1700/7672] eta: 0:19:21 time: 0.1946 data_time: 0.0021 memory: 1303 2022/09/18 13:40:22 - mmengine - INFO - Epoch(val) [6][1800/7672] eta: 0:19:53 time: 0.2032 data_time: 0.0014 memory: 1303 2022/09/18 13:40:41 - mmengine - INFO - Epoch(val) [6][1900/7672] eta: 0:20:42 time: 0.2153 data_time: 0.0035 memory: 1303 2022/09/18 13:41:01 - mmengine - INFO - Epoch(val) [6][2000/7672] eta: 0:21:30 time: 0.2276 data_time: 0.0030 memory: 1303 2022/09/18 13:41:20 - mmengine - INFO - Epoch(val) [6][2100/7672] eta: 0:17:43 time: 0.1908 data_time: 0.0008 memory: 1303 2022/09/18 13:41:39 - mmengine - INFO - Epoch(val) [6][2200/7672] eta: 0:17:06 time: 0.1875 data_time: 0.0009 memory: 1303 2022/09/18 13:41:59 - mmengine - INFO - Epoch(val) [6][2300/7672] eta: 0:16:52 time: 0.1886 data_time: 0.0008 memory: 1303 2022/09/18 13:42:18 - mmengine - INFO - Epoch(val) [6][2400/7672] eta: 0:16:22 time: 0.1864 data_time: 0.0009 memory: 1303 2022/09/18 13:42:38 - mmengine - INFO - Epoch(val) [6][2500/7672] eta: 0:17:11 time: 0.1995 data_time: 0.0009 memory: 1303 2022/09/18 13:42:58 - mmengine - INFO - Epoch(val) [6][2600/7672] eta: 0:16:38 time: 0.1968 data_time: 0.0009 memory: 1303 2022/09/18 13:43:19 - mmengine - INFO - Epoch(val) [6][2700/7672] eta: 0:16:32 time: 0.1997 data_time: 0.0009 memory: 1303 2022/09/18 13:43:39 - mmengine - INFO - Epoch(val) [6][2800/7672] eta: 0:15:40 time: 0.1931 data_time: 0.0009 memory: 1303 2022/09/18 13:44:00 - mmengine - INFO - Epoch(val) [6][2900/7672] eta: 0:16:21 time: 0.2057 data_time: 0.0009 memory: 1303 2022/09/18 13:44:20 - mmengine - INFO - Epoch(val) [6][3000/7672] eta: 0:15:08 time: 0.1945 data_time: 0.0009 memory: 1303 2022/09/18 13:44:41 - mmengine - INFO - Epoch(val) [6][3100/7672] eta: 0:15:38 time: 0.2052 data_time: 0.0009 memory: 1303 2022/09/18 13:45:01 - mmengine - INFO - Epoch(val) [6][3200/7672] eta: 0:14:56 time: 0.2005 data_time: 0.0009 memory: 1303 2022/09/18 13:45:21 - mmengine - INFO - Epoch(val) [6][3300/7672] eta: 0:13:34 time: 0.1863 data_time: 0.0009 memory: 1303 2022/09/18 13:45:40 - mmengine - INFO - Epoch(val) [6][3400/7672] eta: 0:13:19 time: 0.1873 data_time: 0.0008 memory: 1303 2022/09/18 13:46:00 - mmengine - INFO - Epoch(val) [6][3500/7672] eta: 0:13:31 time: 0.1946 data_time: 0.0009 memory: 1303 2022/09/18 13:46:20 - mmengine - INFO - Epoch(val) [6][3600/7672] eta: 0:12:43 time: 0.1874 data_time: 0.0008 memory: 1303 2022/09/18 13:46:40 - mmengine - INFO - Epoch(val) [6][3700/7672] eta: 0:12:26 time: 0.1879 data_time: 0.0008 memory: 1303 2022/09/18 13:46:59 - mmengine - INFO - Epoch(val) [6][3800/7672] eta: 0:12:01 time: 0.1863 data_time: 0.0008 memory: 1303 2022/09/18 13:47:18 - mmengine - INFO - Epoch(val) [6][3900/7672] eta: 0:12:14 time: 0.1948 data_time: 0.0008 memory: 1303 2022/09/18 13:47:38 - mmengine - INFO - Epoch(val) [6][4000/7672] eta: 0:11:33 time: 0.1889 data_time: 0.0008 memory: 1303 2022/09/18 13:47:57 - mmengine - INFO - Epoch(val) [6][4100/7672] eta: 0:11:30 time: 0.1934 data_time: 0.0009 memory: 1303 2022/09/18 13:48:17 - mmengine - INFO - Epoch(val) [6][4200/7672] eta: 0:10:57 time: 0.1895 data_time: 0.0008 memory: 1303 2022/09/18 13:48:36 - mmengine - INFO - Epoch(val) [6][4300/7672] eta: 0:11:09 time: 0.1986 data_time: 0.0009 memory: 1303 2022/09/18 13:48:55 - mmengine - INFO - Epoch(val) [6][4400/7672] eta: 0:10:55 time: 0.2003 data_time: 0.0009 memory: 1303 2022/09/18 13:49:15 - mmengine - INFO - Epoch(val) [6][4500/7672] eta: 0:09:46 time: 0.1848 data_time: 0.0008 memory: 1303 2022/09/18 13:49:34 - mmengine - INFO - Epoch(val) [6][4600/7672] eta: 0:09:41 time: 0.1891 data_time: 0.0028 memory: 1303 2022/09/18 13:49:54 - mmengine - INFO - Epoch(val) [6][4700/7672] eta: 0:09:27 time: 0.1909 data_time: 0.0020 memory: 1303 2022/09/18 13:50:14 - mmengine - INFO - Epoch(val) [6][4800/7672] eta: 0:09:17 time: 0.1940 data_time: 0.0021 memory: 1303 2022/09/18 13:50:33 - mmengine - INFO - Epoch(val) [6][4900/7672] eta: 0:08:57 time: 0.1941 data_time: 0.0042 memory: 1303 2022/09/18 13:50:53 - mmengine - INFO - Epoch(val) [6][5000/7672] eta: 0:10:24 time: 0.2336 data_time: 0.0048 memory: 1303 2022/09/18 13:51:12 - mmengine - INFO - Epoch(val) [6][5100/7672] eta: 0:08:55 time: 0.2082 data_time: 0.0035 memory: 1303 2022/09/18 13:51:32 - mmengine - INFO - Epoch(val) [6][5200/7672] eta: 0:08:55 time: 0.2168 data_time: 0.0075 memory: 1303 2022/09/18 13:51:51 - mmengine - INFO - Epoch(val) [6][5300/7672] eta: 0:08:22 time: 0.2119 data_time: 0.0028 memory: 1303 2022/09/18 13:52:11 - mmengine - INFO - Epoch(val) [6][5400/7672] eta: 0:07:05 time: 0.1872 data_time: 0.0008 memory: 1303 2022/09/18 13:52:30 - mmengine - INFO - Epoch(val) [6][5500/7672] eta: 0:06:49 time: 0.1883 data_time: 0.0008 memory: 1303 2022/09/18 13:52:49 - mmengine - INFO - Epoch(val) [6][5600/7672] eta: 0:06:29 time: 0.1877 data_time: 0.0008 memory: 1303 2022/09/18 13:53:09 - mmengine - INFO - Epoch(val) [6][5700/7672] eta: 0:06:14 time: 0.1897 data_time: 0.0009 memory: 1303 2022/09/18 13:53:29 - mmengine - INFO - Epoch(val) [6][5800/7672] eta: 0:06:10 time: 0.1981 data_time: 0.0009 memory: 1303 2022/09/18 13:53:49 - mmengine - INFO - Epoch(val) [6][5900/7672] eta: 0:06:07 time: 0.2074 data_time: 0.0030 memory: 1303 2022/09/18 13:54:09 - mmengine - INFO - Epoch(val) [6][6000/7672] eta: 0:05:22 time: 0.1928 data_time: 0.0009 memory: 1303 2022/09/18 13:54:30 - mmengine - INFO - Epoch(val) [6][6100/7672] eta: 0:05:05 time: 0.1942 data_time: 0.0009 memory: 1303 2022/09/18 13:54:50 - mmengine - INFO - Epoch(val) [6][6200/7672] eta: 0:04:52 time: 0.1985 data_time: 0.0009 memory: 1303 2022/09/18 13:55:10 - mmengine - INFO - Epoch(val) [6][6300/7672] eta: 0:04:32 time: 0.1989 data_time: 0.0009 memory: 1303 2022/09/18 13:55:31 - mmengine - INFO - Epoch(val) [6][6400/7672] eta: 0:04:09 time: 0.1965 data_time: 0.0009 memory: 1303 2022/09/18 13:55:51 - mmengine - INFO - Epoch(val) [6][6500/7672] eta: 0:03:50 time: 0.1970 data_time: 0.0009 memory: 1303 2022/09/18 13:56:11 - mmengine - INFO - Epoch(val) [6][6600/7672] eta: 0:03:21 time: 0.1882 data_time: 0.0008 memory: 1303 2022/09/18 13:56:31 - mmengine - INFO - Epoch(val) [6][6700/7672] eta: 0:02:59 time: 0.1851 data_time: 0.0008 memory: 1303 2022/09/18 13:56:50 - mmengine - INFO - Epoch(val) [6][6800/7672] eta: 0:02:42 time: 0.1868 data_time: 0.0009 memory: 1303 2022/09/18 13:57:10 - mmengine - INFO - Epoch(val) [6][6900/7672] eta: 0:02:28 time: 0.1929 data_time: 0.0008 memory: 1303 2022/09/18 13:57:30 - mmengine - INFO - Epoch(val) [6][7000/7672] eta: 0:02:10 time: 0.1940 data_time: 0.0009 memory: 1303 2022/09/18 13:57:49 - mmengine - INFO - Epoch(val) [6][7100/7672] eta: 0:01:45 time: 0.1845 data_time: 0.0008 memory: 1303 2022/09/18 13:58:09 - mmengine - INFO - Epoch(val) [6][7200/7672] eta: 0:01:28 time: 0.1884 data_time: 0.0008 memory: 1303 2022/09/18 13:58:28 - mmengine - INFO - Epoch(val) [6][7300/7672] eta: 0:01:10 time: 0.1895 data_time: 0.0009 memory: 1303 2022/09/18 13:58:48 - mmengine - INFO - Epoch(val) [6][7400/7672] eta: 0:00:51 time: 0.1887 data_time: 0.0009 memory: 1303 2022/09/18 13:59:07 - mmengine - INFO - Epoch(val) [6][7500/7672] eta: 0:00:32 time: 0.1916 data_time: 0.0010 memory: 1303 2022/09/18 13:59:27 - mmengine - INFO - Epoch(val) [6][7600/7672] eta: 0:00:13 time: 0.1925 data_time: 0.0010 memory: 1303 2022/09/18 13:59:41 - mmengine - INFO - Epoch(val) [6][7672/7672] CUTE80/recog/word_acc_ignore_case_symbol: 0.8889 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9483 SVT/recog/word_acc_ignore_case_symbol: 0.8825 SVTP/recog/word_acc_ignore_case_symbol: 0.8016 IC13/recog/word_acc_ignore_case_symbol: 0.9507 IC15/recog/word_acc_ignore_case_symbol: 0.7559