2022/09/20 14:33:58 - 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: 1547934377 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 ------------------------------------------------------------ 2022/09/20 14:33:59 - 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=True), 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_resnet31-1by16-1by8_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 2022/09/20 14:37:28 - mmengine - INFO - Checkpoints will be saved to sproject:s3://1.0.0rc0_nrtr_retest/nrtr_resnet31-1by16-1by8_6e_st_mj by PetrelBackend. 2022/09/20 14:43:44 - mmengine - INFO - Epoch(train) [1][100/42151] lr: 3.0000e-04 eta: 10 days, 23:37:21 time: 0.5287 data_time: 0.1451 memory: 23393 loss_ce: 0.6055 loss: 0.6055 2022/09/20 14:44:39 - mmengine - INFO - Epoch(train) [1][200/42151] lr: 3.0000e-04 eta: 6 days, 7:06:00 time: 0.6642 data_time: 0.2841 memory: 14682 loss_ce: 0.5717 loss: 0.5717 2022/09/20 14:45:38 - mmengine - INFO - Epoch(train) [1][300/42151] lr: 3.0000e-04 eta: 4 days, 18:32:53 time: 0.5353 data_time: 0.1539 memory: 14682 loss_ce: 0.5436 loss: 0.5436 2022/09/20 14:46:41 - mmengine - INFO - Epoch(train) [1][400/42151] lr: 3.0000e-04 eta: 4 days, 0:52:34 time: 0.4755 data_time: 0.0725 memory: 14682 loss_ce: 0.5055 loss: 0.5055 2022/09/20 14:47:48 - mmengine - INFO - Epoch(train) [1][500/42151] lr: 3.0000e-04 eta: 3 days, 14:49:45 time: 0.3864 data_time: 0.0044 memory: 14682 loss_ce: 0.4710 loss: 0.4710 2022/09/20 14:48:36 - mmengine - INFO - Epoch(train) [1][600/42151] lr: 3.0000e-04 eta: 3 days, 6:01:18 time: 0.4326 data_time: 0.0465 memory: 14682 loss_ce: 0.3592 loss: 0.3592 2022/09/20 14:49:51 - mmengine - INFO - Epoch(train) [1][700/42151] lr: 3.0000e-04 eta: 3 days, 2:20:53 time: 0.3860 data_time: 0.0044 memory: 14682 loss_ce: 0.2163 loss: 0.2163 2022/09/20 14:50:44 - mmengine - INFO - Epoch(train) [1][800/42151] lr: 3.0000e-04 eta: 2 days, 21:39:23 time: 0.4678 data_time: 0.0488 memory: 14682 loss_ce: 0.1526 loss: 0.1526 2022/09/20 14:51:38 - mmengine - INFO - Epoch(train) [1][900/42151] lr: 3.0000e-04 eta: 2 days, 18:03:33 time: 0.5909 data_time: 0.2067 memory: 14682 loss_ce: 0.1333 loss: 0.1333 2022/09/20 14:52:31 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 14:52:31 - mmengine - INFO - Epoch(train) [1][1000/42151] lr: 3.0000e-04 eta: 2 days, 15:08:44 time: 0.6613 data_time: 0.1533 memory: 14682 loss_ce: 0.1150 loss: 0.1150 2022/09/20 14:53:24 - mmengine - INFO - Epoch(train) [1][1100/42151] lr: 3.0000e-04 eta: 2 days, 12:45:58 time: 0.4444 data_time: 0.0321 memory: 14682 loss_ce: 0.1046 loss: 0.1046 2022/09/20 14:54:18 - mmengine - INFO - Epoch(train) [1][1200/42151] lr: 3.0000e-04 eta: 2 days, 10:49:41 time: 0.4143 data_time: 0.0044 memory: 14682 loss_ce: 0.0954 loss: 0.0954 2022/09/20 14:55:14 - mmengine - INFO - Epoch(train) [1][1300/42151] lr: 3.0000e-04 eta: 2 days, 9:18:02 time: 0.4690 data_time: 0.0857 memory: 14682 loss_ce: 0.0917 loss: 0.0917 2022/09/20 14:56:09 - mmengine - INFO - Epoch(train) [1][1400/42151] lr: 3.0000e-04 eta: 2 days, 7:55:29 time: 0.7479 data_time: 0.2023 memory: 14682 loss_ce: 0.0897 loss: 0.0897 2022/09/20 14:57:03 - mmengine - INFO - Epoch(train) [1][1500/42151] lr: 3.0000e-04 eta: 2 days, 6:40:33 time: 0.5694 data_time: 0.1412 memory: 14682 loss_ce: 0.0811 loss: 0.0811 2022/09/20 14:57:59 - mmengine - INFO - Epoch(train) [1][1600/42151] lr: 3.0000e-04 eta: 2 days, 5:41:38 time: 0.5697 data_time: 0.1809 memory: 14682 loss_ce: 0.0826 loss: 0.0826 2022/09/20 14:58:52 - mmengine - INFO - Epoch(train) [1][1700/42151] lr: 3.0000e-04 eta: 2 days, 4:40:30 time: 0.4046 data_time: 0.0270 memory: 14682 loss_ce: 0.0743 loss: 0.0743 2022/09/20 14:59:52 - mmengine - INFO - Epoch(train) [1][1800/42151] lr: 3.0000e-04 eta: 2 days, 4:04:07 time: 0.3823 data_time: 0.0046 memory: 14682 loss_ce: 0.0722 loss: 0.0722 2022/09/20 15:00:41 - mmengine - INFO - Epoch(train) [1][1900/42151] lr: 3.0000e-04 eta: 2 days, 3:07:19 time: 0.4879 data_time: 0.1060 memory: 14682 loss_ce: 0.0728 loss: 0.0728 2022/09/20 15:01:54 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 15:01:54 - mmengine - INFO - Epoch(train) [1][2000/42151] lr: 3.0000e-04 eta: 2 days, 3:04:15 time: 0.4970 data_time: 0.0970 memory: 14682 loss_ce: 0.0698 loss: 0.0698 2022/09/20 15:02:49 - mmengine - INFO - Epoch(train) [1][2100/42151] lr: 3.0000e-04 eta: 2 days, 2:26:33 time: 0.5160 data_time: 0.1297 memory: 14682 loss_ce: 0.0705 loss: 0.0705 2022/09/20 15:03:44 - mmengine - INFO - Epoch(train) [1][2200/42151] lr: 3.0000e-04 eta: 2 days, 1:52:37 time: 0.5435 data_time: 0.1652 memory: 14682 loss_ce: 0.0726 loss: 0.0726 2022/09/20 15:04:39 - mmengine - INFO - Epoch(train) [1][2300/42151] lr: 3.0000e-04 eta: 2 days, 1:20:54 time: 0.3853 data_time: 0.0043 memory: 14682 loss_ce: 0.0624 loss: 0.0624 2022/09/20 15:05:33 - mmengine - INFO - Epoch(train) [1][2400/42151] lr: 3.0000e-04 eta: 2 days, 0:50:24 time: 0.4039 data_time: 0.0238 memory: 14682 loss_ce: 0.0650 loss: 0.0650 2022/09/20 15:06:29 - mmengine - INFO - Epoch(train) [1][2500/42151] lr: 3.0000e-04 eta: 2 days, 0:25:25 time: 0.5875 data_time: 0.1357 memory: 14682 loss_ce: 0.0629 loss: 0.0629 2022/09/20 15:07:22 - mmengine - INFO - Epoch(train) [1][2600/42151] lr: 3.0000e-04 eta: 1 day, 23:57:35 time: 0.5232 data_time: 0.1386 memory: 14682 loss_ce: 0.0596 loss: 0.0596 2022/09/20 15:08:18 - mmengine - INFO - Epoch(train) [1][2700/42151] lr: 3.0000e-04 eta: 1 day, 23:36:21 time: 0.6326 data_time: 0.2359 memory: 14682 loss_ce: 0.0631 loss: 0.0631 2022/09/20 15:09:12 - mmengine - INFO - Epoch(train) [1][2800/42151] lr: 3.0000e-04 eta: 1 day, 23:14:33 time: 0.7227 data_time: 0.3393 memory: 14682 loss_ce: 0.0600 loss: 0.0600 2022/09/20 15:10:05 - mmengine - INFO - Epoch(train) [1][2900/42151] lr: 3.0000e-04 eta: 1 day, 22:51:17 time: 0.5049 data_time: 0.1219 memory: 14682 loss_ce: 0.0600 loss: 0.0600 2022/09/20 15:10:59 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 15:10:59 - mmengine - INFO - Epoch(train) [1][3000/42151] lr: 3.0000e-04 eta: 1 day, 22:31:10 time: 0.5105 data_time: 0.0764 memory: 14682 loss_ce: 0.0574 loss: 0.0574 2022/09/20 15:11:54 - mmengine - INFO - Epoch(train) [1][3100/42151] lr: 3.0000e-04 eta: 1 day, 22:14:24 time: 0.4178 data_time: 0.0356 memory: 14682 loss_ce: 0.0593 loss: 0.0593 2022/09/20 15:12:50 - mmengine - INFO - Epoch(train) [1][3200/42151] lr: 3.0000e-04 eta: 1 day, 21:58:46 time: 0.4771 data_time: 0.0988 memory: 14682 loss_ce: 0.0563 loss: 0.0563 2022/09/20 15:13:45 - mmengine - INFO - Epoch(train) [1][3300/42151] lr: 3.0000e-04 eta: 1 day, 21:44:17 time: 0.5944 data_time: 0.1570 memory: 14682 loss_ce: 0.0531 loss: 0.0531 2022/09/20 15:14:42 - mmengine - INFO - Epoch(train) [1][3400/42151] lr: 3.0000e-04 eta: 1 day, 21:31:12 time: 0.7328 data_time: 0.2027 memory: 14682 loss_ce: 0.0588 loss: 0.0588 2022/09/20 15:15:36 - mmengine - INFO - Epoch(train) [1][3500/42151] lr: 3.0000e-04 eta: 1 day, 21:17:09 time: 0.4548 data_time: 0.0771 memory: 14682 loss_ce: 0.0574 loss: 0.0574 2022/09/20 15:16:32 - mmengine - INFO - Epoch(train) [1][3600/42151] lr: 3.0000e-04 eta: 1 day, 21:04:56 time: 0.5789 data_time: 0.0813 memory: 14682 loss_ce: 0.0564 loss: 0.0564 2022/09/20 15:17:27 - mmengine - INFO - Epoch(train) [1][3700/42151] lr: 3.0000e-04 eta: 1 day, 20:52:22 time: 0.4808 data_time: 0.0696 memory: 14682 loss_ce: 0.0546 loss: 0.0546 2022/09/20 15:18:23 - mmengine - INFO - Epoch(train) [1][3800/42151] lr: 3.0000e-04 eta: 1 day, 20:41:41 time: 0.5173 data_time: 0.1418 memory: 14682 loss_ce: 0.0516 loss: 0.0516 2022/09/20 15:19:19 - mmengine - INFO - Epoch(train) [1][3900/42151] lr: 3.0000e-04 eta: 1 day, 20:31:32 time: 0.5958 data_time: 0.2172 memory: 14682 loss_ce: 0.0507 loss: 0.0507 2022/09/20 15:20:15 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 15:20:15 - mmengine - INFO - Epoch(train) [1][4000/42151] lr: 3.0000e-04 eta: 1 day, 20:21:30 time: 0.6101 data_time: 0.2236 memory: 14682 loss_ce: 0.0522 loss: 0.0522 2022/09/20 15:21:12 - mmengine - INFO - Epoch(train) [1][4100/42151] lr: 3.0000e-04 eta: 1 day, 20:13:00 time: 0.6345 data_time: 0.1027 memory: 14682 loss_ce: 0.0535 loss: 0.0535 2022/09/20 15:22:07 - mmengine - INFO - Epoch(train) [1][4200/42151] lr: 3.0000e-04 eta: 1 day, 20:03:37 time: 0.4416 data_time: 0.0550 memory: 14682 loss_ce: 0.0529 loss: 0.0529 2022/09/20 15:23:03 - mmengine - INFO - Epoch(train) [1][4300/42151] lr: 3.0000e-04 eta: 1 day, 19:55:21 time: 0.5170 data_time: 0.1401 memory: 14682 loss_ce: 0.0530 loss: 0.0530 2022/09/20 15:23:59 - mmengine - INFO - Epoch(train) [1][4400/42151] lr: 3.0000e-04 eta: 1 day, 19:46:48 time: 0.6080 data_time: 0.1873 memory: 14682 loss_ce: 0.0484 loss: 0.0484 2022/09/20 15:24:55 - mmengine - INFO - Epoch(train) [1][4500/42151] lr: 3.0000e-04 eta: 1 day, 19:38:46 time: 0.7060 data_time: 0.1710 memory: 14682 loss_ce: 0.0504 loss: 0.0504 2022/09/20 15:25:52 - mmengine - INFO - Epoch(train) [1][4600/42151] lr: 3.0000e-04 eta: 1 day, 19:32:16 time: 0.7167 data_time: 0.2086 memory: 14682 loss_ce: 0.0486 loss: 0.0486 2022/09/20 15:26:47 - mmengine - INFO - Epoch(train) [1][4700/42151] lr: 3.0000e-04 eta: 1 day, 19:24:25 time: 0.4770 data_time: 0.1007 memory: 14682 loss_ce: 0.0513 loss: 0.0513 2022/09/20 15:27:43 - mmengine - INFO - Epoch(train) [1][4800/42151] lr: 3.0000e-04 eta: 1 day, 19:17:20 time: 0.5113 data_time: 0.1331 memory: 14682 loss_ce: 0.0464 loss: 0.0464 2022/09/20 15:28:41 - mmengine - INFO - Epoch(train) [1][4900/42151] lr: 3.0000e-04 eta: 1 day, 19:11:30 time: 0.5678 data_time: 0.1635 memory: 14682 loss_ce: 0.0480 loss: 0.0480 2022/09/20 15:29:34 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 15:29:34 - mmengine - INFO - Epoch(train) [1][5000/42151] lr: 3.0000e-04 eta: 1 day, 19:02:57 time: 0.4572 data_time: 0.0524 memory: 14682 loss_ce: 0.0463 loss: 0.0463 2022/09/20 15:30:29 - mmengine - INFO - Epoch(train) [1][5100/42151] lr: 3.0000e-04 eta: 1 day, 18:55:48 time: 0.5064 data_time: 0.0714 memory: 14682 loss_ce: 0.0444 loss: 0.0444 2022/09/20 15:31:23 - mmengine - INFO - Epoch(train) [1][5200/42151] lr: 3.0000e-04 eta: 1 day, 18:48:11 time: 0.5128 data_time: 0.1062 memory: 14682 loss_ce: 0.0464 loss: 0.0464 2022/09/20 15:32:18 - mmengine - INFO - Epoch(train) [1][5300/42151] lr: 3.0000e-04 eta: 1 day, 18:41:35 time: 0.4486 data_time: 0.0666 memory: 14682 loss_ce: 0.0474 loss: 0.0474 2022/09/20 15:33:15 - mmengine - INFO - Epoch(train) [1][5400/42151] lr: 3.0000e-04 eta: 1 day, 18:36:39 time: 0.6726 data_time: 0.1604 memory: 14682 loss_ce: 0.0479 loss: 0.0479 2022/09/20 15:34:11 - mmengine - INFO - Epoch(train) [1][5500/42151] lr: 3.0000e-04 eta: 1 day, 18:31:14 time: 0.6084 data_time: 0.1213 memory: 14682 loss_ce: 0.0476 loss: 0.0476 2022/09/20 15:35:06 - mmengine - INFO - Epoch(train) [1][5600/42151] lr: 3.0000e-04 eta: 1 day, 18:25:11 time: 0.5403 data_time: 0.1417 memory: 14682 loss_ce: 0.0450 loss: 0.0450 2022/09/20 15:36:04 - mmengine - INFO - Epoch(train) [1][5700/42151] lr: 3.0000e-04 eta: 1 day, 18:20:48 time: 0.6091 data_time: 0.1765 memory: 14682 loss_ce: 0.0493 loss: 0.0493 2022/09/20 15:37:00 - mmengine - INFO - Epoch(train) [1][5800/42151] lr: 3.0000e-04 eta: 1 day, 18:15:41 time: 0.5328 data_time: 0.1314 memory: 14682 loss_ce: 0.0441 loss: 0.0441 2022/09/20 15:37:55 - mmengine - INFO - Epoch(train) [1][5900/42151] lr: 3.0000e-04 eta: 1 day, 18:10:10 time: 0.4325 data_time: 0.0469 memory: 14682 loss_ce: 0.0447 loss: 0.0447 2022/09/20 15:38:49 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 15:38:49 - mmengine - INFO - Epoch(train) [1][6000/42151] lr: 3.0000e-04 eta: 1 day, 18:04:33 time: 0.5067 data_time: 0.1265 memory: 14682 loss_ce: 0.0442 loss: 0.0442 2022/09/20 15:39:44 - mmengine - INFO - Epoch(train) [1][6100/42151] lr: 3.0000e-04 eta: 1 day, 17:59:12 time: 0.6185 data_time: 0.1577 memory: 14682 loss_ce: 0.0449 loss: 0.0449 2022/09/20 15:40:39 - mmengine - INFO - Epoch(train) [1][6200/42151] lr: 3.0000e-04 eta: 1 day, 17:54:05 time: 0.5390 data_time: 0.1602 memory: 14682 loss_ce: 0.0463 loss: 0.0463 2022/09/20 15:41:35 - mmengine - INFO - Epoch(train) [1][6300/42151] lr: 3.0000e-04 eta: 1 day, 17:49:44 time: 0.5379 data_time: 0.1427 memory: 14682 loss_ce: 0.0437 loss: 0.0437 2022/09/20 15:42:30 - mmengine - INFO - Epoch(train) [1][6400/42151] lr: 3.0000e-04 eta: 1 day, 17:44:20 time: 0.5457 data_time: 0.1348 memory: 14682 loss_ce: 0.0421 loss: 0.0421 2022/09/20 15:43:26 - mmengine - INFO - Epoch(train) [1][6500/42151] lr: 3.0000e-04 eta: 1 day, 17:40:08 time: 0.4222 data_time: 0.0463 memory: 14682 loss_ce: 0.0416 loss: 0.0416 2022/09/20 15:44:21 - mmengine - INFO - Epoch(train) [1][6600/42151] lr: 3.0000e-04 eta: 1 day, 17:35:28 time: 0.5537 data_time: 0.1038 memory: 14682 loss_ce: 0.0409 loss: 0.0409 2022/09/20 15:45:17 - mmengine - INFO - Epoch(train) [1][6700/42151] lr: 3.0000e-04 eta: 1 day, 17:31:27 time: 0.6522 data_time: 0.1683 memory: 14682 loss_ce: 0.0437 loss: 0.0437 2022/09/20 15:46:15 - mmengine - INFO - Epoch(train) [1][6800/42151] lr: 3.0000e-04 eta: 1 day, 17:28:51 time: 0.6498 data_time: 0.1592 memory: 14682 loss_ce: 0.0404 loss: 0.0404 2022/09/20 15:47:10 - mmengine - INFO - Epoch(train) [1][6900/42151] lr: 3.0000e-04 eta: 1 day, 17:24:56 time: 0.5447 data_time: 0.1609 memory: 14682 loss_ce: 0.0410 loss: 0.0410 2022/09/20 15:48:05 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 15:48:05 - mmengine - INFO - Epoch(train) [1][7000/42151] lr: 3.0000e-04 eta: 1 day, 17:20:30 time: 0.4886 data_time: 0.0919 memory: 14682 loss_ce: 0.0394 loss: 0.0394 2022/09/20 15:49:02 - mmengine - INFO - Epoch(train) [1][7100/42151] lr: 3.0000e-04 eta: 1 day, 17:17:21 time: 0.4817 data_time: 0.0702 memory: 14682 loss_ce: 0.0398 loss: 0.0398 2022/09/20 15:49:57 - mmengine - INFO - Epoch(train) [1][7200/42151] lr: 3.0000e-04 eta: 1 day, 17:13:08 time: 0.5841 data_time: 0.1853 memory: 14682 loss_ce: 0.0415 loss: 0.0415 2022/09/20 15:50:53 - mmengine - INFO - Epoch(train) [1][7300/42151] lr: 3.0000e-04 eta: 1 day, 17:09:47 time: 0.3846 data_time: 0.0043 memory: 14682 loss_ce: 0.0416 loss: 0.0416 2022/09/20 15:51:48 - mmengine - INFO - Epoch(train) [1][7400/42151] lr: 3.0000e-04 eta: 1 day, 17:05:34 time: 0.4489 data_time: 0.0661 memory: 14682 loss_ce: 0.0428 loss: 0.0428 2022/09/20 15:52:43 - mmengine - INFO - Epoch(train) [1][7500/42151] lr: 3.0000e-04 eta: 1 day, 17:02:04 time: 0.6287 data_time: 0.1625 memory: 14682 loss_ce: 0.0401 loss: 0.0401 2022/09/20 15:53:37 - mmengine - INFO - Epoch(train) [1][7600/42151] lr: 3.0000e-04 eta: 1 day, 16:57:46 time: 0.5446 data_time: 0.1614 memory: 14682 loss_ce: 0.0445 loss: 0.0445 2022/09/20 15:54:34 - mmengine - INFO - Epoch(train) [1][7700/42151] lr: 3.0000e-04 eta: 1 day, 16:54:43 time: 0.5554 data_time: 0.1414 memory: 14682 loss_ce: 0.0406 loss: 0.0406 2022/09/20 15:55:28 - mmengine - INFO - Epoch(train) [1][7800/42151] lr: 3.0000e-04 eta: 1 day, 16:50:59 time: 0.5626 data_time: 0.1837 memory: 14682 loss_ce: 0.0444 loss: 0.0444 2022/09/20 15:56:21 - mmengine - INFO - Epoch(train) [1][7900/42151] lr: 3.0000e-04 eta: 1 day, 16:46:06 time: 0.4215 data_time: 0.0406 memory: 14682 loss_ce: 0.0413 loss: 0.0413 2022/09/20 15:57:16 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 15:57:16 - mmengine - INFO - Epoch(train) [1][8000/42151] lr: 3.0000e-04 eta: 1 day, 16:42:33 time: 0.4135 data_time: 0.0046 memory: 14682 loss_ce: 0.0455 loss: 0.0455 2022/09/20 15:58:10 - mmengine - INFO - Epoch(train) [1][8100/42151] lr: 3.0000e-04 eta: 1 day, 16:38:53 time: 0.5159 data_time: 0.1371 memory: 14682 loss_ce: 0.0380 loss: 0.0380 2022/09/20 15:59:07 - mmengine - INFO - Epoch(train) [1][8200/42151] lr: 3.0000e-04 eta: 1 day, 16:36:31 time: 0.7524 data_time: 0.2476 memory: 14682 loss_ce: 0.0403 loss: 0.0403 2022/09/20 15:59:58 - mmengine - INFO - Epoch(train) [1][8300/42151] lr: 3.0000e-04 eta: 1 day, 16:30:56 time: 0.5236 data_time: 0.1504 memory: 14682 loss_ce: 0.0378 loss: 0.0378 2022/09/20 16:00:49 - mmengine - INFO - Epoch(train) [1][8400/42151] lr: 3.0000e-04 eta: 1 day, 16:25:56 time: 0.3919 data_time: 0.0202 memory: 14682 loss_ce: 0.0361 loss: 0.0361 2022/09/20 16:01:39 - mmengine - INFO - Epoch(train) [1][8500/42151] lr: 3.0000e-04 eta: 1 day, 16:20:28 time: 0.3906 data_time: 0.0209 memory: 14682 loss_ce: 0.0424 loss: 0.0424 2022/09/20 16:02:33 - mmengine - INFO - Epoch(train) [1][8600/42151] lr: 3.0000e-04 eta: 1 day, 16:16:35 time: 0.5187 data_time: 0.1017 memory: 14682 loss_ce: 0.0353 loss: 0.0353 2022/09/20 16:03:26 - mmengine - INFO - Epoch(train) [1][8700/42151] lr: 3.0000e-04 eta: 1 day, 16:12:59 time: 0.5753 data_time: 0.1232 memory: 14682 loss_ce: 0.0362 loss: 0.0362 2022/09/20 16:04:22 - mmengine - INFO - Epoch(train) [1][8800/42151] lr: 3.0000e-04 eta: 1 day, 16:10:09 time: 0.5453 data_time: 0.1706 memory: 14682 loss_ce: 0.0365 loss: 0.0365 2022/09/20 16:05:17 - mmengine - INFO - Epoch(train) [1][8900/42151] lr: 3.0000e-04 eta: 1 day, 16:07:33 time: 0.7739 data_time: 0.3403 memory: 14682 loss_ce: 0.0392 loss: 0.0392 2022/09/20 16:06:14 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 16:06:15 - mmengine - INFO - Epoch(train) [1][9000/42151] lr: 3.0000e-04 eta: 1 day, 16:05:35 time: 0.5994 data_time: 0.2010 memory: 14682 loss_ce: 0.0374 loss: 0.0374 2022/09/20 16:07:05 - mmengine - INFO - Epoch(train) [1][9100/42151] lr: 3.0000e-04 eta: 1 day, 16:00:50 time: 0.4581 data_time: 0.0514 memory: 14682 loss_ce: 0.0340 loss: 0.0340 2022/09/20 16:07:55 - mmengine - INFO - Epoch(train) [1][9200/42151] lr: 3.0000e-04 eta: 1 day, 15:55:40 time: 0.4443 data_time: 0.0687 memory: 14682 loss_ce: 0.0406 loss: 0.0406 2022/09/20 16:08:45 - mmengine - INFO - Epoch(train) [1][9300/42151] lr: 3.0000e-04 eta: 1 day, 15:50:57 time: 0.4821 data_time: 0.0758 memory: 14682 loss_ce: 0.0379 loss: 0.0379 2022/09/20 16:09:35 - mmengine - INFO - Epoch(train) [1][9400/42151] lr: 3.0000e-04 eta: 1 day, 15:46:12 time: 0.4713 data_time: 0.0992 memory: 14682 loss_ce: 0.0379 loss: 0.0379 2022/09/20 16:10:30 - mmengine - INFO - Epoch(train) [1][9500/42151] lr: 3.0000e-04 eta: 1 day, 15:43:21 time: 0.5824 data_time: 0.2049 memory: 14682 loss_ce: 0.0358 loss: 0.0358 2022/09/20 16:11:23 - mmengine - INFO - Epoch(train) [1][9600/42151] lr: 3.0000e-04 eta: 1 day, 15:40:08 time: 0.5083 data_time: 0.1259 memory: 14682 loss_ce: 0.0353 loss: 0.0353 2022/09/20 16:12:14 - mmengine - INFO - Epoch(train) [1][9700/42151] lr: 3.0000e-04 eta: 1 day, 15:35:54 time: 0.4058 data_time: 0.0042 memory: 14682 loss_ce: 0.0344 loss: 0.0344 2022/09/20 16:13:06 - mmengine - INFO - Epoch(train) [1][9800/42151] lr: 3.0000e-04 eta: 1 day, 15:32:04 time: 0.4237 data_time: 0.0490 memory: 14682 loss_ce: 0.0342 loss: 0.0342 2022/09/20 16:13:57 - mmengine - INFO - Epoch(train) [1][9900/42151] lr: 3.0000e-04 eta: 1 day, 15:28:11 time: 0.5080 data_time: 0.1002 memory: 14682 loss_ce: 0.0355 loss: 0.0355 2022/09/20 16:14:49 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 16:14:49 - mmengine - INFO - Epoch(train) [1][10000/42151] lr: 3.0000e-04 eta: 1 day, 15:24:24 time: 0.5300 data_time: 0.1567 memory: 14682 loss_ce: 0.0363 loss: 0.0363 2022/09/20 16:15:40 - mmengine - INFO - Epoch(train) [1][10100/42151] lr: 3.0000e-04 eta: 1 day, 15:20:24 time: 0.5399 data_time: 0.1693 memory: 14682 loss_ce: 0.0417 loss: 0.0417 2022/09/20 16:16:31 - mmengine - INFO - Epoch(train) [1][10200/42151] lr: 3.0000e-04 eta: 1 day, 15:16:32 time: 0.5891 data_time: 0.2141 memory: 14682 loss_ce: 0.0367 loss: 0.0367 2022/09/20 16:17:22 - mmengine - INFO - Epoch(train) [1][10300/42151] lr: 3.0000e-04 eta: 1 day, 15:12:43 time: 0.4983 data_time: 0.1202 memory: 14682 loss_ce: 0.0393 loss: 0.0393 2022/09/20 16:18:18 - mmengine - INFO - Epoch(train) [1][10400/42151] lr: 3.0000e-04 eta: 1 day, 15:10:46 time: 0.4754 data_time: 0.0901 memory: 14682 loss_ce: 0.0355 loss: 0.0355 2022/09/20 16:19:12 - mmengine - INFO - Epoch(train) [1][10500/42151] lr: 3.0000e-04 eta: 1 day, 15:08:23 time: 0.5079 data_time: 0.1266 memory: 14682 loss_ce: 0.0360 loss: 0.0360 2022/09/20 16:20:04 - mmengine - INFO - Epoch(train) [1][10600/42151] lr: 3.0000e-04 eta: 1 day, 15:05:16 time: 0.5246 data_time: 0.1099 memory: 14682 loss_ce: 0.0363 loss: 0.0363 2022/09/20 16:20:56 - mmengine - INFO - Epoch(train) [1][10700/42151] lr: 3.0000e-04 eta: 1 day, 15:01:47 time: 0.5473 data_time: 0.1483 memory: 14682 loss_ce: 0.0348 loss: 0.0348 2022/09/20 16:21:47 - mmengine - INFO - Epoch(train) [1][10800/42151] lr: 3.0000e-04 eta: 1 day, 14:58:25 time: 0.5836 data_time: 0.2113 memory: 14682 loss_ce: 0.0364 loss: 0.0364 2022/09/20 16:22:39 - mmengine - INFO - Epoch(train) [1][10900/42151] lr: 3.0000e-04 eta: 1 day, 14:54:59 time: 0.5277 data_time: 0.1119 memory: 14682 loss_ce: 0.0363 loss: 0.0363 2022/09/20 16:23:30 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 16:23:30 - mmengine - INFO - Epoch(train) [1][11000/42151] lr: 3.0000e-04 eta: 1 day, 14:51:30 time: 0.4704 data_time: 0.0651 memory: 14682 loss_ce: 0.0332 loss: 0.0332 2022/09/20 16:24:20 - mmengine - INFO - Epoch(train) [1][11100/42151] lr: 3.0000e-04 eta: 1 day, 14:47:55 time: 0.4855 data_time: 0.0920 memory: 14682 loss_ce: 0.0333 loss: 0.0333 2022/09/20 16:25:12 - mmengine - INFO - Epoch(train) [1][11200/42151] lr: 3.0000e-04 eta: 1 day, 14:44:39 time: 0.5315 data_time: 0.1311 memory: 14682 loss_ce: 0.0340 loss: 0.0340 2022/09/20 16:26:02 - mmengine - INFO - Epoch(train) [1][11300/42151] lr: 3.0000e-04 eta: 1 day, 14:41:10 time: 0.5022 data_time: 0.1289 memory: 14682 loss_ce: 0.0367 loss: 0.0367 2022/09/20 16:26:54 - mmengine - INFO - Epoch(train) [1][11400/42151] lr: 3.0000e-04 eta: 1 day, 14:37:58 time: 0.5364 data_time: 0.1426 memory: 14682 loss_ce: 0.0344 loss: 0.0344 2022/09/20 16:27:44 - mmengine - INFO - Epoch(train) [1][11500/42151] lr: 3.0000e-04 eta: 1 day, 14:34:29 time: 0.4741 data_time: 0.0997 memory: 14682 loss_ce: 0.0350 loss: 0.0350 2022/09/20 16:28:35 - mmengine - INFO - Epoch(train) [1][11600/42151] lr: 3.0000e-04 eta: 1 day, 14:31:11 time: 0.4590 data_time: 0.0560 memory: 14682 loss_ce: 0.0331 loss: 0.0331 2022/09/20 16:29:26 - mmengine - INFO - Epoch(train) [1][11700/42151] lr: 3.0000e-04 eta: 1 day, 14:28:06 time: 0.5198 data_time: 0.1083 memory: 14682 loss_ce: 0.0330 loss: 0.0330 2022/09/20 16:30:17 - mmengine - INFO - Epoch(train) [1][11800/42151] lr: 3.0000e-04 eta: 1 day, 14:24:51 time: 0.5682 data_time: 0.1399 memory: 14682 loss_ce: 0.0343 loss: 0.0343 2022/09/20 16:31:11 - mmengine - INFO - Epoch(train) [1][11900/42151] lr: 3.0000e-04 eta: 1 day, 14:22:43 time: 0.4998 data_time: 0.1202 memory: 14682 loss_ce: 0.0351 loss: 0.0351 2022/09/20 16:32:05 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 16:32:05 - mmengine - INFO - Epoch(train) [1][12000/42151] lr: 3.0000e-04 eta: 1 day, 14:20:54 time: 0.6384 data_time: 0.1957 memory: 14682 loss_ce: 0.0335 loss: 0.0335 2022/09/20 16:32:58 - mmengine - INFO - Epoch(train) [1][12100/42151] lr: 3.0000e-04 eta: 1 day, 14:18:11 time: 0.5003 data_time: 0.1202 memory: 14682 loss_ce: 0.0364 loss: 0.0364 2022/09/20 16:33:49 - mmengine - INFO - Epoch(train) [1][12200/42151] lr: 3.0000e-04 eta: 1 day, 14:15:22 time: 0.4630 data_time: 0.0545 memory: 14682 loss_ce: 0.0330 loss: 0.0330 2022/09/20 16:34:41 - mmengine - INFO - Epoch(train) [1][12300/42151] lr: 3.0000e-04 eta: 1 day, 14:12:47 time: 0.4818 data_time: 0.1093 memory: 14682 loss_ce: 0.0331 loss: 0.0331 2022/09/20 16:35:34 - mmengine - INFO - Epoch(train) [1][12400/42151] lr: 3.0000e-04 eta: 1 day, 14:10:15 time: 0.5316 data_time: 0.1539 memory: 14682 loss_ce: 0.0323 loss: 0.0323 2022/09/20 16:36:25 - mmengine - INFO - Epoch(train) [1][12500/42151] lr: 3.0000e-04 eta: 1 day, 14:07:26 time: 0.4786 data_time: 0.1070 memory: 14682 loss_ce: 0.0325 loss: 0.0325 2022/09/20 16:37:17 - mmengine - INFO - Epoch(train) [1][12600/42151] lr: 3.0000e-04 eta: 1 day, 14:04:54 time: 0.4910 data_time: 0.1181 memory: 14682 loss_ce: 0.0293 loss: 0.0293 2022/09/20 16:38:08 - mmengine - INFO - Epoch(train) [1][12700/42151] lr: 3.0000e-04 eta: 1 day, 14:02:00 time: 0.5812 data_time: 0.1474 memory: 14682 loss_ce: 0.0341 loss: 0.0341 2022/09/20 16:38:58 - mmengine - INFO - Epoch(train) [1][12800/42151] lr: 3.0000e-04 eta: 1 day, 13:58:53 time: 0.4350 data_time: 0.0616 memory: 14682 loss_ce: 0.0353 loss: 0.0353 2022/09/20 16:39:49 - mmengine - INFO - Epoch(train) [1][12900/42151] lr: 3.0000e-04 eta: 1 day, 13:56:12 time: 0.5603 data_time: 0.1276 memory: 14682 loss_ce: 0.0326 loss: 0.0326 2022/09/20 16:40:41 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 16:40:41 - mmengine - INFO - Epoch(train) [1][13000/42151] lr: 3.0000e-04 eta: 1 day, 13:53:29 time: 0.5028 data_time: 0.1325 memory: 14682 loss_ce: 0.0315 loss: 0.0315 2022/09/20 16:41:31 - mmengine - INFO - Epoch(train) [1][13100/42151] lr: 3.0000e-04 eta: 1 day, 13:50:31 time: 0.4796 data_time: 0.0986 memory: 14682 loss_ce: 0.0318 loss: 0.0318 2022/09/20 16:42:24 - mmengine - INFO - Epoch(train) [1][13200/42151] lr: 3.0000e-04 eta: 1 day, 13:48:36 time: 0.5199 data_time: 0.1443 memory: 14682 loss_ce: 0.0323 loss: 0.0323 2022/09/20 16:43:21 - mmengine - INFO - Epoch(train) [1][13300/42151] lr: 3.0000e-04 eta: 1 day, 13:47:28 time: 0.6108 data_time: 0.2043 memory: 14682 loss_ce: 0.0329 loss: 0.0329 2022/09/20 16:44:14 - mmengine - INFO - Epoch(train) [1][13400/42151] lr: 3.0000e-04 eta: 1 day, 13:45:35 time: 0.4802 data_time: 0.0785 memory: 14682 loss_ce: 0.0324 loss: 0.0324 2022/09/20 16:45:06 - mmengine - INFO - Epoch(train) [1][13500/42151] lr: 3.0000e-04 eta: 1 day, 13:43:10 time: 0.4804 data_time: 0.1057 memory: 14682 loss_ce: 0.0351 loss: 0.0351 2022/09/20 16:45:58 - mmengine - INFO - Epoch(train) [1][13600/42151] lr: 3.0000e-04 eta: 1 day, 13:40:47 time: 0.5707 data_time: 0.0804 memory: 14682 loss_ce: 0.0328 loss: 0.0328 2022/09/20 16:46:50 - mmengine - INFO - Epoch(train) [1][13700/42151] lr: 3.0000e-04 eta: 1 day, 13:38:28 time: 0.5712 data_time: 0.1717 memory: 14682 loss_ce: 0.0338 loss: 0.0338 2022/09/20 16:47:40 - mmengine - INFO - Epoch(train) [1][13800/42151] lr: 3.0000e-04 eta: 1 day, 13:35:39 time: 0.5801 data_time: 0.1292 memory: 14682 loss_ce: 0.0311 loss: 0.0311 2022/09/20 16:48:34 - mmengine - INFO - Epoch(train) [1][13900/42151] lr: 3.0000e-04 eta: 1 day, 13:34:04 time: 0.4825 data_time: 0.0633 memory: 14682 loss_ce: 0.0325 loss: 0.0325 2022/09/20 16:49:30 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 16:49:30 - mmengine - INFO - Epoch(train) [1][14000/42151] lr: 3.0000e-04 eta: 1 day, 13:32:55 time: 0.4868 data_time: 0.0885 memory: 14682 loss_ce: 0.0310 loss: 0.0310 2022/09/20 16:50:22 - mmengine - INFO - Epoch(train) [1][14100/42151] lr: 3.0000e-04 eta: 1 day, 13:30:33 time: 0.5046 data_time: 0.1211 memory: 14682 loss_ce: 0.0303 loss: 0.0303 2022/09/20 16:51:14 - mmengine - INFO - Epoch(train) [1][14200/42151] lr: 3.0000e-04 eta: 1 day, 13:28:26 time: 0.5407 data_time: 0.1273 memory: 14682 loss_ce: 0.0323 loss: 0.0323 2022/09/20 16:52:09 - mmengine - INFO - Epoch(train) [1][14300/42151] lr: 3.0000e-04 eta: 1 day, 13:27:06 time: 0.5765 data_time: 0.1646 memory: 14682 loss_ce: 0.0309 loss: 0.0309 2022/09/20 16:53:07 - mmengine - INFO - Epoch(train) [1][14400/42151] lr: 3.0000e-04 eta: 1 day, 13:26:27 time: 0.6320 data_time: 0.1924 memory: 14682 loss_ce: 0.0315 loss: 0.0315 2022/09/20 16:54:09 - mmengine - INFO - Epoch(train) [1][14500/42151] lr: 3.0000e-04 eta: 1 day, 13:27:05 time: 0.6423 data_time: 0.1551 memory: 14682 loss_ce: 0.0290 loss: 0.0290 2022/09/20 16:55:09 - mmengine - INFO - Epoch(train) [1][14600/42151] lr: 3.0000e-04 eta: 1 day, 13:27:12 time: 0.4974 data_time: 0.0645 memory: 14682 loss_ce: 0.0325 loss: 0.0325 2022/09/20 16:56:11 - mmengine - INFO - Epoch(train) [1][14700/42151] lr: 3.0000e-04 eta: 1 day, 13:27:31 time: 0.5896 data_time: 0.1667 memory: 14682 loss_ce: 0.0320 loss: 0.0320 2022/09/20 16:57:09 - mmengine - INFO - Epoch(train) [1][14800/42151] lr: 3.0000e-04 eta: 1 day, 13:26:57 time: 0.5780 data_time: 0.1932 memory: 14682 loss_ce: 0.0310 loss: 0.0310 2022/09/20 16:58:04 - mmengine - INFO - Epoch(train) [1][14900/42151] lr: 3.0000e-04 eta: 1 day, 13:25:42 time: 0.5350 data_time: 0.1184 memory: 14682 loss_ce: 0.0299 loss: 0.0299 2022/09/20 16:59:00 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 16:59:00 - mmengine - INFO - Epoch(train) [1][15000/42151] lr: 3.0000e-04 eta: 1 day, 13:24:33 time: 0.6248 data_time: 0.1673 memory: 14682 loss_ce: 0.0301 loss: 0.0301 2022/09/20 16:59:56 - mmengine - INFO - Epoch(train) [1][15100/42151] lr: 3.0000e-04 eta: 1 day, 13:23:21 time: 0.4855 data_time: 0.1047 memory: 14682 loss_ce: 0.0304 loss: 0.0304 2022/09/20 17:00:51 - mmengine - INFO - Epoch(train) [1][15200/42151] lr: 3.0000e-04 eta: 1 day, 13:22:07 time: 0.6159 data_time: 0.1259 memory: 14682 loss_ce: 0.0299 loss: 0.0299 2022/09/20 17:01:47 - mmengine - INFO - Epoch(train) [1][15300/42151] lr: 3.0000e-04 eta: 1 day, 13:20:55 time: 0.5325 data_time: 0.1229 memory: 14682 loss_ce: 0.0295 loss: 0.0295 2022/09/20 17:02:44 - mmengine - INFO - Epoch(train) [1][15400/42151] lr: 3.0000e-04 eta: 1 day, 13:20:06 time: 0.5722 data_time: 0.1747 memory: 14682 loss_ce: 0.0296 loss: 0.0296 2022/09/20 17:03:41 - mmengine - INFO - Epoch(train) [1][15500/42151] lr: 3.0000e-04 eta: 1 day, 13:19:11 time: 0.6187 data_time: 0.1734 memory: 14682 loss_ce: 0.0311 loss: 0.0311 2022/09/20 17:04:36 - mmengine - INFO - Epoch(train) [1][15600/42151] lr: 3.0000e-04 eta: 1 day, 13:17:51 time: 0.5893 data_time: 0.1275 memory: 14682 loss_ce: 0.0295 loss: 0.0295 2022/09/20 17:05:28 - mmengine - INFO - Epoch(train) [1][15700/42151] lr: 3.0000e-04 eta: 1 day, 13:15:54 time: 0.4933 data_time: 0.0595 memory: 14682 loss_ce: 0.0280 loss: 0.0280 2022/09/20 17:06:22 - mmengine - INFO - Epoch(train) [1][15800/42151] lr: 3.0000e-04 eta: 1 day, 13:14:21 time: 0.4962 data_time: 0.1159 memory: 14682 loss_ce: 0.0270 loss: 0.0270 2022/09/20 17:07:17 - mmengine - INFO - Epoch(train) [1][15900/42151] lr: 3.0000e-04 eta: 1 day, 13:13:01 time: 0.5821 data_time: 0.1912 memory: 14682 loss_ce: 0.0295 loss: 0.0295 2022/09/20 17:08:18 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 17:08:18 - mmengine - INFO - Epoch(train) [1][16000/42151] lr: 3.0000e-04 eta: 1 day, 13:13:01 time: 0.4207 data_time: 0.0044 memory: 14682 loss_ce: 0.0303 loss: 0.0303 2022/09/20 17:09:13 - mmengine - INFO - Epoch(train) [1][16100/42151] lr: 3.0000e-04 eta: 1 day, 13:11:47 time: 0.6120 data_time: 0.1954 memory: 14682 loss_ce: 0.0319 loss: 0.0319 2022/09/20 17:10:07 - mmengine - INFO - Epoch(train) [1][16200/42151] lr: 3.0000e-04 eta: 1 day, 13:10:15 time: 0.6047 data_time: 0.2172 memory: 14682 loss_ce: 0.0324 loss: 0.0324 2022/09/20 17:11:03 - mmengine - INFO - Epoch(train) [1][16300/42151] lr: 3.0000e-04 eta: 1 day, 13:09:11 time: 0.6312 data_time: 0.2106 memory: 14682 loss_ce: 0.0307 loss: 0.0307 2022/09/20 17:12:00 - mmengine - INFO - Epoch(train) [1][16400/42151] lr: 3.0000e-04 eta: 1 day, 13:08:13 time: 0.4633 data_time: 0.0046 memory: 14682 loss_ce: 0.0295 loss: 0.0295 2022/09/20 17:12:55 - mmengine - INFO - Epoch(train) [1][16500/42151] lr: 3.0000e-04 eta: 1 day, 13:07:06 time: 0.4988 data_time: 0.1149 memory: 14682 loss_ce: 0.0331 loss: 0.0331 2022/09/20 17:13:51 - mmengine - INFO - Epoch(train) [1][16600/42151] lr: 3.0000e-04 eta: 1 day, 13:06:01 time: 0.5881 data_time: 0.1672 memory: 14682 loss_ce: 0.0307 loss: 0.0307 2022/09/20 17:14:46 - mmengine - INFO - Epoch(train) [1][16700/42151] lr: 3.0000e-04 eta: 1 day, 13:04:36 time: 0.4786 data_time: 0.1014 memory: 14682 loss_ce: 0.0286 loss: 0.0286 2022/09/20 17:15:42 - mmengine - INFO - Epoch(train) [1][16800/42151] lr: 3.0000e-04 eta: 1 day, 13:03:40 time: 0.5367 data_time: 0.1555 memory: 14682 loss_ce: 0.0305 loss: 0.0305 2022/09/20 17:16:39 - mmengine - INFO - Epoch(train) [1][16900/42151] lr: 3.0000e-04 eta: 1 day, 13:02:48 time: 0.6056 data_time: 0.2131 memory: 14682 loss_ce: 0.0303 loss: 0.0303 2022/09/20 17:17:36 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 17:17:36 - mmengine - INFO - Epoch(train) [1][17000/42151] lr: 3.0000e-04 eta: 1 day, 13:01:48 time: 0.4098 data_time: 0.0236 memory: 14682 loss_ce: 0.0287 loss: 0.0287 2022/09/20 17:18:32 - mmengine - INFO - Epoch(train) [1][17100/42151] lr: 3.0000e-04 eta: 1 day, 13:00:47 time: 0.5174 data_time: 0.1342 memory: 14682 loss_ce: 0.0284 loss: 0.0284 2022/09/20 17:19:28 - mmengine - INFO - Epoch(train) [1][17200/42151] lr: 3.0000e-04 eta: 1 day, 12:59:46 time: 0.6869 data_time: 0.2863 memory: 14682 loss_ce: 0.0289 loss: 0.0289 2022/09/20 17:20:26 - mmengine - INFO - Epoch(train) [1][17300/42151] lr: 3.0000e-04 eta: 1 day, 12:59:05 time: 0.7003 data_time: 0.2119 memory: 14682 loss_ce: 0.0294 loss: 0.0294 2022/09/20 17:21:22 - mmengine - INFO - Epoch(train) [1][17400/42151] lr: 3.0000e-04 eta: 1 day, 12:58:07 time: 0.4528 data_time: 0.0045 memory: 14682 loss_ce: 0.0280 loss: 0.0280 2022/09/20 17:22:16 - mmengine - INFO - Epoch(train) [1][17500/42151] lr: 3.0000e-04 eta: 1 day, 12:56:33 time: 0.5084 data_time: 0.1261 memory: 14682 loss_ce: 0.0300 loss: 0.0300 2022/09/20 17:23:11 - mmengine - INFO - Epoch(train) [1][17600/42151] lr: 3.0000e-04 eta: 1 day, 12:55:22 time: 0.4817 data_time: 0.0410 memory: 14682 loss_ce: 0.0310 loss: 0.0310 2022/09/20 17:24:08 - mmengine - INFO - Epoch(train) [1][17700/42151] lr: 3.0000e-04 eta: 1 day, 12:54:26 time: 0.5738 data_time: 0.1684 memory: 14682 loss_ce: 0.0290 loss: 0.0290 2022/09/20 17:25:03 - mmengine - INFO - Epoch(train) [1][17800/42151] lr: 3.0000e-04 eta: 1 day, 12:53:17 time: 0.6393 data_time: 0.1752 memory: 14682 loss_ce: 0.0303 loss: 0.0303 2022/09/20 17:25:59 - mmengine - INFO - Epoch(train) [1][17900/42151] lr: 3.0000e-04 eta: 1 day, 12:52:11 time: 0.5444 data_time: 0.1636 memory: 14682 loss_ce: 0.0284 loss: 0.0284 2022/09/20 17:26:54 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 17:26:54 - mmengine - INFO - Epoch(train) [1][18000/42151] lr: 3.0000e-04 eta: 1 day, 12:50:55 time: 0.4666 data_time: 0.0811 memory: 14682 loss_ce: 0.0269 loss: 0.0269 2022/09/20 17:27:49 - mmengine - INFO - Epoch(train) [1][18100/42151] lr: 3.0000e-04 eta: 1 day, 12:49:34 time: 0.4853 data_time: 0.0773 memory: 14682 loss_ce: 0.0269 loss: 0.0269 2022/09/20 17:28:43 - mmengine - INFO - Epoch(train) [1][18200/42151] lr: 3.0000e-04 eta: 1 day, 12:48:10 time: 0.5199 data_time: 0.1203 memory: 14682 loss_ce: 0.0288 loss: 0.0288 2022/09/20 17:29:36 - mmengine - INFO - Epoch(train) [1][18300/42151] lr: 3.0000e-04 eta: 1 day, 12:46:35 time: 0.4330 data_time: 0.0582 memory: 14682 loss_ce: 0.0294 loss: 0.0294 2022/09/20 17:30:32 - mmengine - INFO - Epoch(train) [1][18400/42151] lr: 3.0000e-04 eta: 1 day, 12:45:22 time: 0.6124 data_time: 0.1109 memory: 14682 loss_ce: 0.0306 loss: 0.0306 2022/09/20 17:31:28 - mmengine - INFO - Epoch(train) [1][18500/42151] lr: 3.0000e-04 eta: 1 day, 12:44:25 time: 0.5636 data_time: 0.1598 memory: 14682 loss_ce: 0.0274 loss: 0.0274 2022/09/20 17:32:23 - mmengine - INFO - Epoch(train) [1][18600/42151] lr: 3.0000e-04 eta: 1 day, 12:43:14 time: 0.5264 data_time: 0.1448 memory: 14682 loss_ce: 0.0248 loss: 0.0248 2022/09/20 17:33:19 - mmengine - INFO - Epoch(train) [1][18700/42151] lr: 3.0000e-04 eta: 1 day, 12:42:04 time: 0.5211 data_time: 0.1415 memory: 14682 loss_ce: 0.0289 loss: 0.0289 2022/09/20 17:34:15 - mmengine - INFO - Epoch(train) [1][18800/42151] lr: 3.0000e-04 eta: 1 day, 12:41:08 time: 0.6880 data_time: 0.1770 memory: 14682 loss_ce: 0.0294 loss: 0.0294 2022/09/20 17:35:11 - mmengine - INFO - Epoch(train) [1][18900/42151] lr: 3.0000e-04 eta: 1 day, 12:40:03 time: 0.5972 data_time: 0.2073 memory: 14682 loss_ce: 0.0284 loss: 0.0284 2022/09/20 17:36:09 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 17:36:09 - mmengine - INFO - Epoch(train) [1][19000/42151] lr: 3.0000e-04 eta: 1 day, 12:39:34 time: 0.4446 data_time: 0.0540 memory: 14682 loss_ce: 0.0279 loss: 0.0279 2022/09/20 17:37:07 - mmengine - INFO - Epoch(train) [1][19100/42151] lr: 3.0000e-04 eta: 1 day, 12:38:53 time: 0.5368 data_time: 0.1500 memory: 14682 loss_ce: 0.0301 loss: 0.0301 2022/09/20 17:38:02 - mmengine - INFO - Epoch(train) [1][19200/42151] lr: 3.0000e-04 eta: 1 day, 12:37:44 time: 0.5847 data_time: 0.1259 memory: 14682 loss_ce: 0.0292 loss: 0.0292 2022/09/20 17:38:58 - mmengine - INFO - Epoch(train) [1][19300/42151] lr: 3.0000e-04 eta: 1 day, 12:36:34 time: 0.4180 data_time: 0.0138 memory: 14682 loss_ce: 0.0292 loss: 0.0292 2022/09/20 17:39:54 - mmengine - INFO - Epoch(train) [1][19400/42151] lr: 3.0000e-04 eta: 1 day, 12:35:37 time: 0.5736 data_time: 0.1943 memory: 14682 loss_ce: 0.0278 loss: 0.0278 2022/09/20 17:40:47 - mmengine - INFO - Epoch(train) [1][19500/42151] lr: 3.0000e-04 eta: 1 day, 12:33:59 time: 0.5463 data_time: 0.1385 memory: 14682 loss_ce: 0.0284 loss: 0.0284 2022/09/20 17:41:43 - mmengine - INFO - Epoch(train) [1][19600/42151] lr: 3.0000e-04 eta: 1 day, 12:32:55 time: 0.6192 data_time: 0.1635 memory: 14682 loss_ce: 0.0285 loss: 0.0285 2022/09/20 17:42:37 - mmengine - INFO - Epoch(train) [1][19700/42151] lr: 3.0000e-04 eta: 1 day, 12:31:36 time: 0.6148 data_time: 0.2051 memory: 14682 loss_ce: 0.0296 loss: 0.0296 2022/09/20 17:43:31 - mmengine - INFO - Epoch(train) [1][19800/42151] lr: 3.0000e-04 eta: 1 day, 12:30:10 time: 0.5293 data_time: 0.1167 memory: 14682 loss_ce: 0.0310 loss: 0.0310 2022/09/20 17:44:26 - mmengine - INFO - Epoch(train) [1][19900/42151] lr: 3.0000e-04 eta: 1 day, 12:28:55 time: 0.5659 data_time: 0.1053 memory: 14682 loss_ce: 0.0244 loss: 0.0244 2022/09/20 17:45:20 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 17:45:20 - mmengine - INFO - Epoch(train) [1][20000/42151] lr: 3.0000e-04 eta: 1 day, 12:27:36 time: 0.5640 data_time: 0.1361 memory: 14682 loss_ce: 0.0284 loss: 0.0284 2022/09/20 17:46:14 - mmengine - INFO - Epoch(train) [1][20100/42151] lr: 3.0000e-04 eta: 1 day, 12:26:13 time: 0.5069 data_time: 0.0965 memory: 14682 loss_ce: 0.0265 loss: 0.0265 2022/09/20 17:47:10 - mmengine - INFO - Epoch(train) [1][20200/42151] lr: 3.0000e-04 eta: 1 day, 12:25:10 time: 0.6636 data_time: 0.2384 memory: 14682 loss_ce: 0.0283 loss: 0.0283 2022/09/20 17:48:03 - mmengine - INFO - Epoch(train) [1][20300/42151] lr: 3.0000e-04 eta: 1 day, 12:23:36 time: 0.5574 data_time: 0.1485 memory: 14682 loss_ce: 0.0241 loss: 0.0241 2022/09/20 17:48:58 - mmengine - INFO - Epoch(train) [1][20400/42151] lr: 3.0000e-04 eta: 1 day, 12:22:15 time: 0.6379 data_time: 0.1708 memory: 14682 loss_ce: 0.0258 loss: 0.0258 2022/09/20 17:49:51 - mmengine - INFO - Epoch(train) [1][20500/42151] lr: 3.0000e-04 eta: 1 day, 12:20:44 time: 0.4180 data_time: 0.0170 memory: 14682 loss_ce: 0.0270 loss: 0.0270 2022/09/20 17:50:46 - mmengine - INFO - Epoch(train) [1][20600/42151] lr: 3.0000e-04 eta: 1 day, 12:19:32 time: 0.5546 data_time: 0.1449 memory: 14682 loss_ce: 0.0239 loss: 0.0239 2022/09/20 17:51:40 - mmengine - INFO - Epoch(train) [1][20700/42151] lr: 3.0000e-04 eta: 1 day, 12:18:08 time: 0.5269 data_time: 0.1410 memory: 14682 loss_ce: 0.0282 loss: 0.0282 2022/09/20 17:52:37 - mmengine - INFO - Epoch(train) [1][20800/42151] lr: 3.0000e-04 eta: 1 day, 12:17:19 time: 0.7306 data_time: 0.2818 memory: 14682 loss_ce: 0.0258 loss: 0.0258 2022/09/20 17:53:32 - mmengine - INFO - Epoch(train) [1][20900/42151] lr: 3.0000e-04 eta: 1 day, 12:16:09 time: 0.5519 data_time: 0.1557 memory: 14682 loss_ce: 0.0262 loss: 0.0262 2022/09/20 17:54:25 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 17:54:25 - mmengine - INFO - Epoch(train) [1][21000/42151] lr: 3.0000e-04 eta: 1 day, 12:14:45 time: 0.6314 data_time: 0.1864 memory: 14682 loss_ce: 0.0258 loss: 0.0258 2022/09/20 17:55:19 - mmengine - INFO - Epoch(train) [1][21100/42151] lr: 3.0000e-04 eta: 1 day, 12:13:21 time: 0.4850 data_time: 0.0491 memory: 14682 loss_ce: 0.0296 loss: 0.0296 2022/09/20 17:56:14 - mmengine - INFO - Epoch(train) [1][21200/42151] lr: 3.0000e-04 eta: 1 day, 12:12:05 time: 0.5690 data_time: 0.1739 memory: 14682 loss_ce: 0.0283 loss: 0.0283 2022/09/20 17:57:07 - mmengine - INFO - Epoch(train) [1][21300/42151] lr: 3.0000e-04 eta: 1 day, 12:10:35 time: 0.4269 data_time: 0.0218 memory: 14682 loss_ce: 0.0248 loss: 0.0248 2022/09/20 17:58:01 - mmengine - INFO - Epoch(train) [1][21400/42151] lr: 3.0000e-04 eta: 1 day, 12:09:15 time: 0.5446 data_time: 0.1636 memory: 14682 loss_ce: 0.0268 loss: 0.0268 2022/09/20 17:58:56 - mmengine - INFO - Epoch(train) [1][21500/42151] lr: 3.0000e-04 eta: 1 day, 12:08:03 time: 0.6327 data_time: 0.2051 memory: 14682 loss_ce: 0.0292 loss: 0.0292 2022/09/20 17:59:47 - mmengine - INFO - Epoch(train) [1][21600/42151] lr: 3.0000e-04 eta: 1 day, 12:06:20 time: 0.6438 data_time: 0.2280 memory: 14682 loss_ce: 0.0290 loss: 0.0290 2022/09/20 18:00:46 - mmengine - INFO - Epoch(train) [1][21700/42151] lr: 3.0000e-04 eta: 1 day, 12:05:47 time: 0.4437 data_time: 0.0046 memory: 14682 loss_ce: 0.0262 loss: 0.0262 2022/09/20 18:01:38 - mmengine - INFO - Epoch(train) [1][21800/42151] lr: 3.0000e-04 eta: 1 day, 12:04:11 time: 0.4532 data_time: 0.0748 memory: 14682 loss_ce: 0.0282 loss: 0.0282 2022/09/20 18:02:33 - mmengine - INFO - Epoch(train) [1][21900/42151] lr: 3.0000e-04 eta: 1 day, 12:02:59 time: 0.5902 data_time: 0.1887 memory: 14682 loss_ce: 0.0253 loss: 0.0253 2022/09/20 18:03:26 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 18:03:26 - mmengine - INFO - Epoch(train) [1][22000/42151] lr: 3.0000e-04 eta: 1 day, 12:01:32 time: 0.5487 data_time: 0.1471 memory: 14682 loss_ce: 0.0274 loss: 0.0274 2022/09/20 18:04:21 - mmengine - INFO - Epoch(train) [1][22100/42151] lr: 3.0000e-04 eta: 1 day, 12:00:20 time: 0.5242 data_time: 0.1431 memory: 14682 loss_ce: 0.0274 loss: 0.0274 2022/09/20 18:05:13 - mmengine - INFO - Epoch(train) [1][22200/42151] lr: 3.0000e-04 eta: 1 day, 11:58:47 time: 0.5298 data_time: 0.1475 memory: 14682 loss_ce: 0.0256 loss: 0.0256 2022/09/20 18:06:07 - mmengine - INFO - Epoch(train) [1][22300/42151] lr: 3.0000e-04 eta: 1 day, 11:57:28 time: 0.4746 data_time: 0.0429 memory: 14682 loss_ce: 0.0298 loss: 0.0298 2022/09/20 18:07:00 - mmengine - INFO - Epoch(train) [1][22400/42151] lr: 3.0000e-04 eta: 1 day, 11:56:00 time: 0.4599 data_time: 0.0504 memory: 14682 loss_ce: 0.0260 loss: 0.0260 2022/09/20 18:07:55 - mmengine - INFO - Epoch(train) [1][22500/42151] lr: 3.0000e-04 eta: 1 day, 11:54:53 time: 0.5213 data_time: 0.1397 memory: 14682 loss_ce: 0.0257 loss: 0.0257 2022/09/20 18:08:51 - mmengine - INFO - Epoch(train) [1][22600/42151] lr: 3.0000e-04 eta: 1 day, 11:53:48 time: 0.5663 data_time: 0.1803 memory: 14682 loss_ce: 0.0282 loss: 0.0282 2022/09/20 18:09:47 - mmengine - INFO - Epoch(train) [1][22700/42151] lr: 3.0000e-04 eta: 1 day, 11:52:57 time: 0.6552 data_time: 0.1831 memory: 14682 loss_ce: 0.0264 loss: 0.0264 2022/09/20 18:10:41 - mmengine - INFO - Epoch(train) [1][22800/42151] lr: 3.0000e-04 eta: 1 day, 11:51:39 time: 0.4935 data_time: 0.1151 memory: 14682 loss_ce: 0.0284 loss: 0.0284 2022/09/20 18:11:35 - mmengine - INFO - Epoch(train) [1][22900/42151] lr: 3.0000e-04 eta: 1 day, 11:50:18 time: 0.4179 data_time: 0.0233 memory: 14682 loss_ce: 0.0272 loss: 0.0272 2022/09/20 18:12:29 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 18:12:29 - mmengine - INFO - Epoch(train) [1][23000/42151] lr: 3.0000e-04 eta: 1 day, 11:49:01 time: 0.5045 data_time: 0.0658 memory: 14682 loss_ce: 0.0255 loss: 0.0255 2022/09/20 18:13:23 - mmengine - INFO - Epoch(train) [1][23100/42151] lr: 3.0000e-04 eta: 1 day, 11:47:42 time: 0.5021 data_time: 0.1217 memory: 14682 loss_ce: 0.0269 loss: 0.0269 2022/09/20 18:14:16 - mmengine - INFO - Epoch(train) [1][23200/42151] lr: 3.0000e-04 eta: 1 day, 11:46:22 time: 0.4798 data_time: 0.0922 memory: 14682 loss_ce: 0.0261 loss: 0.0261 2022/09/20 18:15:12 - mmengine - INFO - Epoch(train) [1][23300/42151] lr: 3.0000e-04 eta: 1 day, 11:45:19 time: 0.6260 data_time: 0.2198 memory: 14682 loss_ce: 0.0259 loss: 0.0259 2022/09/20 18:16:04 - mmengine - INFO - Epoch(train) [1][23400/42151] lr: 3.0000e-04 eta: 1 day, 11:43:40 time: 0.4002 data_time: 0.0138 memory: 14682 loss_ce: 0.0277 loss: 0.0277 2022/09/20 18:16:56 - mmengine - INFO - Epoch(train) [1][23500/42151] lr: 3.0000e-04 eta: 1 day, 11:42:13 time: 0.4633 data_time: 0.0835 memory: 14682 loss_ce: 0.0258 loss: 0.0258 2022/09/20 18:17:50 - mmengine - INFO - Epoch(train) [1][23600/42151] lr: 3.0000e-04 eta: 1 day, 11:40:57 time: 0.5673 data_time: 0.1745 memory: 14682 loss_ce: 0.0279 loss: 0.0279 2022/09/20 18:18:52 - mmengine - INFO - Epoch(train) [1][23700/42151] lr: 3.0000e-04 eta: 1 day, 11:40:53 time: 1.0772 data_time: 0.1582 memory: 14682 loss_ce: 0.0272 loss: 0.0272 2022/09/20 18:19:49 - mmengine - INFO - Epoch(train) [1][23800/42151] lr: 3.0000e-04 eta: 1 day, 11:40:13 time: 0.5557 data_time: 0.0670 memory: 14682 loss_ce: 0.0256 loss: 0.0256 2022/09/20 18:20:43 - mmengine - INFO - Epoch(train) [1][23900/42151] lr: 3.0000e-04 eta: 1 day, 11:38:50 time: 0.5041 data_time: 0.1250 memory: 14682 loss_ce: 0.0252 loss: 0.0252 2022/09/20 18:21:36 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 18:21:36 - mmengine - INFO - Epoch(train) [1][24000/42151] lr: 3.0000e-04 eta: 1 day, 11:37:29 time: 0.5570 data_time: 0.1314 memory: 14682 loss_ce: 0.0254 loss: 0.0254 2022/09/20 18:22:30 - mmengine - INFO - Epoch(train) [1][24100/42151] lr: 3.0000e-04 eta: 1 day, 11:36:10 time: 0.5599 data_time: 0.1729 memory: 14682 loss_ce: 0.0259 loss: 0.0259 2022/09/20 18:23:22 - mmengine - INFO - Epoch(train) [1][24200/42151] lr: 3.0000e-04 eta: 1 day, 11:34:41 time: 0.5202 data_time: 0.1374 memory: 14682 loss_ce: 0.0241 loss: 0.0241 2022/09/20 18:24:17 - mmengine - INFO - Epoch(train) [1][24300/42151] lr: 3.0000e-04 eta: 1 day, 11:33:30 time: 0.6384 data_time: 0.2499 memory: 14682 loss_ce: 0.0257 loss: 0.0257 2022/09/20 18:25:11 - mmengine - INFO - Epoch(train) [1][24400/42151] lr: 3.0000e-04 eta: 1 day, 11:32:13 time: 0.6217 data_time: 0.1956 memory: 14682 loss_ce: 0.0268 loss: 0.0268 2022/09/20 18:26:05 - mmengine - INFO - Epoch(train) [1][24500/42151] lr: 3.0000e-04 eta: 1 day, 11:31:00 time: 0.5367 data_time: 0.1256 memory: 14682 loss_ce: 0.0257 loss: 0.0257 2022/09/20 18:27:00 - mmengine - INFO - Epoch(train) [1][24600/42151] lr: 3.0000e-04 eta: 1 day, 11:29:57 time: 0.5265 data_time: 0.0805 memory: 14682 loss_ce: 0.0256 loss: 0.0256 2022/09/20 18:27:55 - mmengine - INFO - Epoch(train) [1][24700/42151] lr: 3.0000e-04 eta: 1 day, 11:28:50 time: 0.5325 data_time: 0.1284 memory: 14682 loss_ce: 0.0254 loss: 0.0254 2022/09/20 18:28:49 - mmengine - INFO - Epoch(train) [1][24800/42151] lr: 3.0000e-04 eta: 1 day, 11:27:40 time: 0.6493 data_time: 0.2082 memory: 14682 loss_ce: 0.0248 loss: 0.0248 2022/09/20 18:29:43 - mmengine - INFO - Epoch(train) [1][24900/42151] lr: 3.0000e-04 eta: 1 day, 11:26:25 time: 0.5416 data_time: 0.1608 memory: 14682 loss_ce: 0.0239 loss: 0.0239 2022/09/20 18:30:36 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 18:30:36 - mmengine - INFO - Epoch(train) [1][25000/42151] lr: 3.0000e-04 eta: 1 day, 11:25:05 time: 0.4996 data_time: 0.0966 memory: 14682 loss_ce: 0.0238 loss: 0.0238 2022/09/20 18:31:29 - mmengine - INFO - Epoch(train) [1][25100/42151] lr: 3.0000e-04 eta: 1 day, 11:23:39 time: 0.4920 data_time: 0.0868 memory: 14682 loss_ce: 0.0229 loss: 0.0229 2022/09/20 18:32:23 - mmengine - INFO - Epoch(train) [1][25200/42151] lr: 3.0000e-04 eta: 1 day, 11:22:29 time: 0.6393 data_time: 0.2574 memory: 14682 loss_ce: 0.0248 loss: 0.0248 2022/09/20 18:33:16 - mmengine - INFO - Epoch(train) [1][25300/42151] lr: 3.0000e-04 eta: 1 day, 11:21:01 time: 0.4990 data_time: 0.1166 memory: 14682 loss_ce: 0.0257 loss: 0.0257 2022/09/20 18:34:09 - mmengine - INFO - Epoch(train) [1][25400/42151] lr: 3.0000e-04 eta: 1 day, 11:19:43 time: 0.4452 data_time: 0.0240 memory: 14682 loss_ce: 0.0250 loss: 0.0250 2022/09/20 18:35:04 - mmengine - INFO - Epoch(train) [1][25500/42151] lr: 3.0000e-04 eta: 1 day, 11:18:36 time: 0.5213 data_time: 0.1382 memory: 14682 loss_ce: 0.0249 loss: 0.0249 2022/09/20 18:35:58 - mmengine - INFO - Epoch(train) [1][25600/42151] lr: 3.0000e-04 eta: 1 day, 11:17:24 time: 0.3984 data_time: 0.0059 memory: 14682 loss_ce: 0.0225 loss: 0.0225 2022/09/20 18:36:51 - mmengine - INFO - Epoch(train) [1][25700/42151] lr: 3.0000e-04 eta: 1 day, 11:16:03 time: 0.5697 data_time: 0.1408 memory: 14682 loss_ce: 0.0258 loss: 0.0258 2022/09/20 18:37:44 - mmengine - INFO - Epoch(train) [1][25800/42151] lr: 3.0000e-04 eta: 1 day, 11:14:39 time: 0.5974 data_time: 0.2065 memory: 14682 loss_ce: 0.0288 loss: 0.0288 2022/09/20 18:38:37 - mmengine - INFO - Epoch(train) [1][25900/42151] lr: 3.0000e-04 eta: 1 day, 11:13:17 time: 0.4161 data_time: 0.0317 memory: 14682 loss_ce: 0.0245 loss: 0.0245 2022/09/20 18:39:30 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 18:39:30 - mmengine - INFO - Epoch(train) [1][26000/42151] lr: 3.0000e-04 eta: 1 day, 11:12:00 time: 0.4461 data_time: 0.0333 memory: 14682 loss_ce: 0.0238 loss: 0.0238 2022/09/20 18:40:23 - mmengine - INFO - Epoch(train) [1][26100/42151] lr: 3.0000e-04 eta: 1 day, 11:10:40 time: 0.4279 data_time: 0.0418 memory: 14682 loss_ce: 0.0241 loss: 0.0241 2022/09/20 18:41:17 - mmengine - INFO - Epoch(train) [1][26200/42151] lr: 3.0000e-04 eta: 1 day, 11:09:23 time: 0.4826 data_time: 0.1060 memory: 14682 loss_ce: 0.0267 loss: 0.0267 2022/09/20 18:42:11 - mmengine - INFO - Epoch(train) [1][26300/42151] lr: 3.0000e-04 eta: 1 day, 11:08:19 time: 0.6504 data_time: 0.2399 memory: 14682 loss_ce: 0.0225 loss: 0.0225 2022/09/20 18:43:03 - mmengine - INFO - Epoch(train) [1][26400/42151] lr: 3.0000e-04 eta: 1 day, 11:06:46 time: 0.6601 data_time: 0.2587 memory: 14682 loss_ce: 0.0232 loss: 0.0232 2022/09/20 18:43:56 - mmengine - INFO - Epoch(train) [1][26500/42151] lr: 3.0000e-04 eta: 1 day, 11:05:25 time: 0.3915 data_time: 0.0046 memory: 14682 loss_ce: 0.0245 loss: 0.0245 2022/09/20 18:44:51 - mmengine - INFO - Epoch(train) [1][26600/42151] lr: 3.0000e-04 eta: 1 day, 11:04:24 time: 0.5433 data_time: 0.1289 memory: 14682 loss_ce: 0.0246 loss: 0.0246 2022/09/20 18:45:47 - mmengine - INFO - Epoch(train) [1][26700/42151] lr: 3.0000e-04 eta: 1 day, 11:03:29 time: 0.5850 data_time: 0.2053 memory: 14682 loss_ce: 0.0231 loss: 0.0231 2022/09/20 18:46:41 - mmengine - INFO - Epoch(train) [1][26800/42151] lr: 3.0000e-04 eta: 1 day, 11:02:15 time: 0.4341 data_time: 0.0331 memory: 14682 loss_ce: 0.0251 loss: 0.0251 2022/09/20 18:47:36 - mmengine - INFO - Epoch(train) [1][26900/42151] lr: 3.0000e-04 eta: 1 day, 11:01:17 time: 0.6531 data_time: 0.2651 memory: 14682 loss_ce: 0.0236 loss: 0.0236 2022/09/20 18:48:35 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 18:48:35 - mmengine - INFO - Epoch(train) [1][27000/42151] lr: 3.0000e-04 eta: 1 day, 11:00:51 time: 1.2755 data_time: 0.8621 memory: 14682 loss_ce: 0.0239 loss: 0.0239 2022/09/20 18:49:37 - mmengine - INFO - Epoch(train) [1][27100/42151] lr: 3.0000e-04 eta: 1 day, 11:00:45 time: 0.3938 data_time: 0.0136 memory: 14682 loss_ce: 0.0269 loss: 0.0269 2022/09/20 18:50:28 - mmengine - INFO - Epoch(train) [1][27200/42151] lr: 3.0000e-04 eta: 1 day, 10:59:10 time: 0.4356 data_time: 0.0238 memory: 14682 loss_ce: 0.0245 loss: 0.0245 2022/09/20 18:51:22 - mmengine - INFO - Epoch(train) [1][27300/42151] lr: 3.0000e-04 eta: 1 day, 10:57:54 time: 0.4098 data_time: 0.0053 memory: 14682 loss_ce: 0.0247 loss: 0.0247 2022/09/20 18:52:15 - mmengine - INFO - Epoch(train) [1][27400/42151] lr: 3.0000e-04 eta: 1 day, 10:56:37 time: 0.5584 data_time: 0.1396 memory: 14682 loss_ce: 0.0217 loss: 0.0217 2022/09/20 18:53:10 - mmengine - INFO - Epoch(train) [1][27500/42151] lr: 3.0000e-04 eta: 1 day, 10:55:33 time: 0.6281 data_time: 0.2112 memory: 14682 loss_ce: 0.0241 loss: 0.0241 2022/09/20 18:54:03 - mmengine - INFO - Epoch(train) [1][27600/42151] lr: 3.0000e-04 eta: 1 day, 10:54:17 time: 0.6802 data_time: 0.2909 memory: 14682 loss_ce: 0.0250 loss: 0.0250 2022/09/20 18:55:01 - mmengine - INFO - Epoch(train) [1][27700/42151] lr: 3.0000e-04 eta: 1 day, 10:53:38 time: 0.3869 data_time: 0.0043 memory: 14682 loss_ce: 0.0257 loss: 0.0257 2022/09/20 18:55:53 - mmengine - INFO - Epoch(train) [1][27800/42151] lr: 3.0000e-04 eta: 1 day, 10:52:14 time: 0.4485 data_time: 0.0685 memory: 14682 loss_ce: 0.0232 loss: 0.0232 2022/09/20 18:56:46 - mmengine - INFO - Epoch(train) [1][27900/42151] lr: 3.0000e-04 eta: 1 day, 10:50:55 time: 0.4846 data_time: 0.1040 memory: 14682 loss_ce: 0.0227 loss: 0.0227 2022/09/20 18:57:38 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 18:57:38 - mmengine - INFO - Epoch(train) [1][28000/42151] lr: 3.0000e-04 eta: 1 day, 10:49:31 time: 0.5189 data_time: 0.1123 memory: 14682 loss_ce: 0.0233 loss: 0.0233 2022/09/20 18:58:32 - mmengine - INFO - Epoch(train) [1][28100/42151] lr: 3.0000e-04 eta: 1 day, 10:48:21 time: 0.6562 data_time: 0.2314 memory: 14682 loss_ce: 0.0223 loss: 0.0223 2022/09/20 18:59:25 - mmengine - INFO - Epoch(train) [1][28200/42151] lr: 3.0000e-04 eta: 1 day, 10:46:59 time: 0.5943 data_time: 0.1844 memory: 14682 loss_ce: 0.0251 loss: 0.0251 2022/09/20 19:00:18 - mmengine - INFO - Epoch(train) [1][28300/42151] lr: 3.0000e-04 eta: 1 day, 10:45:41 time: 0.4316 data_time: 0.0535 memory: 14682 loss_ce: 0.0260 loss: 0.0260 2022/09/20 19:01:13 - mmengine - INFO - Epoch(train) [1][28400/42151] lr: 3.0000e-04 eta: 1 day, 10:44:40 time: 0.5491 data_time: 0.1431 memory: 14682 loss_ce: 0.0255 loss: 0.0255 2022/09/20 19:02:07 - mmengine - INFO - Epoch(train) [1][28500/42151] lr: 3.0000e-04 eta: 1 day, 10:43:35 time: 0.5261 data_time: 0.1123 memory: 14682 loss_ce: 0.0210 loss: 0.0210 2022/09/20 19:03:00 - mmengine - INFO - Epoch(train) [1][28600/42151] lr: 3.0000e-04 eta: 1 day, 10:42:17 time: 0.4431 data_time: 0.0650 memory: 14682 loss_ce: 0.0235 loss: 0.0235 2022/09/20 19:03:55 - mmengine - INFO - Epoch(train) [1][28700/42151] lr: 3.0000e-04 eta: 1 day, 10:41:11 time: 0.6581 data_time: 0.2289 memory: 14682 loss_ce: 0.0246 loss: 0.0246 2022/09/20 19:04:47 - mmengine - INFO - Epoch(train) [1][28800/42151] lr: 3.0000e-04 eta: 1 day, 10:39:46 time: 0.5544 data_time: 0.1736 memory: 14682 loss_ce: 0.0223 loss: 0.0223 2022/09/20 19:05:41 - mmengine - INFO - Epoch(train) [1][28900/42151] lr: 3.0000e-04 eta: 1 day, 10:38:41 time: 0.4310 data_time: 0.0143 memory: 14682 loss_ce: 0.0213 loss: 0.0213 2022/09/20 19:06:36 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 19:06:36 - mmengine - INFO - Epoch(train) [1][29000/42151] lr: 3.0000e-04 eta: 1 day, 10:37:42 time: 0.3922 data_time: 0.0051 memory: 14682 loss_ce: 0.0242 loss: 0.0242 2022/09/20 19:07:31 - mmengine - INFO - Epoch(train) [1][29100/42151] lr: 3.0000e-04 eta: 1 day, 10:36:37 time: 0.4375 data_time: 0.0383 memory: 14682 loss_ce: 0.0246 loss: 0.0246 2022/09/20 19:08:26 - mmengine - INFO - Epoch(train) [1][29200/42151] lr: 3.0000e-04 eta: 1 day, 10:35:41 time: 0.4729 data_time: 0.0697 memory: 14682 loss_ce: 0.0244 loss: 0.0244 2022/09/20 19:09:23 - mmengine - INFO - Epoch(train) [1][29300/42151] lr: 3.0000e-04 eta: 1 day, 10:34:53 time: 0.6903 data_time: 0.2637 memory: 14682 loss_ce: 0.0236 loss: 0.0236 2022/09/20 19:10:19 - mmengine - INFO - Epoch(train) [1][29400/42151] lr: 3.0000e-04 eta: 1 day, 10:33:57 time: 0.6634 data_time: 0.2651 memory: 14682 loss_ce: 0.0245 loss: 0.0245 2022/09/20 19:11:13 - mmengine - INFO - Epoch(train) [1][29500/42151] lr: 3.0000e-04 eta: 1 day, 10:32:54 time: 0.4437 data_time: 0.0483 memory: 14682 loss_ce: 0.0276 loss: 0.0276 2022/09/20 19:12:10 - mmengine - INFO - Epoch(train) [1][29600/42151] lr: 3.0000e-04 eta: 1 day, 10:32:02 time: 0.4847 data_time: 0.0980 memory: 14682 loss_ce: 0.0234 loss: 0.0234 2022/09/20 19:13:02 - mmengine - INFO - Epoch(train) [1][29700/42151] lr: 3.0000e-04 eta: 1 day, 10:30:41 time: 0.4623 data_time: 0.0806 memory: 14682 loss_ce: 0.0222 loss: 0.0222 2022/09/20 19:13:55 - mmengine - INFO - Epoch(train) [1][29800/42151] lr: 3.0000e-04 eta: 1 day, 10:29:25 time: 0.4239 data_time: 0.0047 memory: 14682 loss_ce: 0.0263 loss: 0.0263 2022/09/20 19:14:47 - mmengine - INFO - Epoch(train) [1][29900/42151] lr: 3.0000e-04 eta: 1 day, 10:28:04 time: 0.5399 data_time: 0.1606 memory: 14682 loss_ce: 0.0239 loss: 0.0239 2022/09/20 19:15:40 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 19:15:40 - mmengine - INFO - Epoch(train) [1][30000/42151] lr: 3.0000e-04 eta: 1 day, 10:26:49 time: 0.4594 data_time: 0.0790 memory: 14682 loss_ce: 0.0260 loss: 0.0260 2022/09/20 19:16:34 - mmengine - INFO - Epoch(train) [1][30100/42151] lr: 3.0000e-04 eta: 1 day, 10:25:43 time: 0.4044 data_time: 0.0057 memory: 14682 loss_ce: 0.0229 loss: 0.0229 2022/09/20 19:17:29 - mmengine - INFO - Epoch(train) [1][30200/42151] lr: 3.0000e-04 eta: 1 day, 10:24:43 time: 0.5333 data_time: 0.1399 memory: 14682 loss_ce: 0.0243 loss: 0.0243 2022/09/20 19:18:23 - mmengine - INFO - Epoch(train) [1][30300/42151] lr: 3.0000e-04 eta: 1 day, 10:23:28 time: 0.5447 data_time: 0.1449 memory: 14682 loss_ce: 0.0257 loss: 0.0257 2022/09/20 19:19:18 - mmengine - INFO - Epoch(train) [1][30400/42151] lr: 3.0000e-04 eta: 1 day, 10:22:29 time: 0.4831 data_time: 0.0994 memory: 14682 loss_ce: 0.0251 loss: 0.0251 2022/09/20 19:20:14 - mmengine - INFO - Epoch(train) [1][30500/42151] lr: 3.0000e-04 eta: 1 day, 10:21:42 time: 0.5913 data_time: 0.2077 memory: 14682 loss_ce: 0.0231 loss: 0.0231 2022/09/20 19:21:09 - mmengine - INFO - Epoch(train) [1][30600/42151] lr: 3.0000e-04 eta: 1 day, 10:20:38 time: 0.5412 data_time: 0.1562 memory: 14682 loss_ce: 0.0240 loss: 0.0240 2022/09/20 19:22:04 - mmengine - INFO - Epoch(train) [1][30700/42151] lr: 3.0000e-04 eta: 1 day, 10:19:41 time: 0.4799 data_time: 0.0769 memory: 14682 loss_ce: 0.0247 loss: 0.0247 2022/09/20 19:23:00 - mmengine - INFO - Epoch(train) [1][30800/42151] lr: 3.0000e-04 eta: 1 day, 10:18:45 time: 0.6045 data_time: 0.2115 memory: 14682 loss_ce: 0.0216 loss: 0.0216 2022/09/20 19:23:54 - mmengine - INFO - Epoch(train) [1][30900/42151] lr: 3.0000e-04 eta: 1 day, 10:17:38 time: 0.4873 data_time: 0.1009 memory: 14682 loss_ce: 0.0214 loss: 0.0214 2022/09/20 19:24:50 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 19:24:50 - mmengine - INFO - Epoch(train) [1][31000/42151] lr: 3.0000e-04 eta: 1 day, 10:16:45 time: 0.5551 data_time: 0.1713 memory: 14682 loss_ce: 0.0224 loss: 0.0224 2022/09/20 19:25:44 - mmengine - INFO - Epoch(train) [1][31100/42151] lr: 3.0000e-04 eta: 1 day, 10:15:35 time: 0.5076 data_time: 0.1161 memory: 14682 loss_ce: 0.0220 loss: 0.0220 2022/09/20 19:26:38 - mmengine - INFO - Epoch(train) [1][31200/42151] lr: 3.0000e-04 eta: 1 day, 10:14:29 time: 0.4292 data_time: 0.0457 memory: 14682 loss_ce: 0.0248 loss: 0.0248 2022/09/20 19:27:34 - mmengine - INFO - Epoch(train) [1][31300/42151] lr: 3.0000e-04 eta: 1 day, 10:13:35 time: 0.5100 data_time: 0.1057 memory: 14682 loss_ce: 0.0231 loss: 0.0231 2022/09/20 19:28:30 - mmengine - INFO - Epoch(train) [1][31400/42151] lr: 3.0000e-04 eta: 1 day, 10:12:43 time: 0.5379 data_time: 0.1513 memory: 14682 loss_ce: 0.0231 loss: 0.0231 2022/09/20 19:29:25 - mmengine - INFO - Epoch(train) [1][31500/42151] lr: 3.0000e-04 eta: 1 day, 10:11:48 time: 0.5287 data_time: 0.0816 memory: 14682 loss_ce: 0.0223 loss: 0.0223 2022/09/20 19:30:22 - mmengine - INFO - Epoch(train) [1][31600/42151] lr: 3.0000e-04 eta: 1 day, 10:10:56 time: 0.5542 data_time: 0.1175 memory: 14682 loss_ce: 0.0242 loss: 0.0242 2022/09/20 19:31:17 - mmengine - INFO - Epoch(train) [1][31700/42151] lr: 3.0000e-04 eta: 1 day, 10:10:00 time: 0.6169 data_time: 0.2295 memory: 14682 loss_ce: 0.0232 loss: 0.0232 2022/09/20 19:32:13 - mmengine - INFO - Epoch(train) [1][31800/42151] lr: 3.0000e-04 eta: 1 day, 10:09:04 time: 0.6142 data_time: 0.2103 memory: 14682 loss_ce: 0.0219 loss: 0.0219 2022/09/20 19:33:08 - mmengine - INFO - Epoch(train) [1][31900/42151] lr: 3.0000e-04 eta: 1 day, 10:08:05 time: 0.4800 data_time: 0.0980 memory: 14682 loss_ce: 0.0209 loss: 0.0209 2022/09/20 19:34:01 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 19:34:01 - mmengine - INFO - Epoch(train) [1][32000/42151] lr: 3.0000e-04 eta: 1 day, 10:06:55 time: 0.4369 data_time: 0.0551 memory: 14682 loss_ce: 0.0240 loss: 0.0240 2022/09/20 19:34:57 - mmengine - INFO - Epoch(train) [1][32100/42151] lr: 3.0000e-04 eta: 1 day, 10:06:01 time: 0.5349 data_time: 0.1082 memory: 14682 loss_ce: 0.0217 loss: 0.0217 2022/09/20 19:35:51 - mmengine - INFO - Epoch(train) [1][32200/42151] lr: 3.0000e-04 eta: 1 day, 10:04:52 time: 0.4721 data_time: 0.0898 memory: 14682 loss_ce: 0.0214 loss: 0.0214 2022/09/20 19:36:48 - mmengine - INFO - Epoch(train) [1][32300/42151] lr: 3.0000e-04 eta: 1 day, 10:04:06 time: 0.6937 data_time: 0.2609 memory: 14682 loss_ce: 0.0241 loss: 0.0241 2022/09/20 19:37:41 - mmengine - INFO - Epoch(train) [1][32400/42151] lr: 3.0000e-04 eta: 1 day, 10:02:53 time: 0.5539 data_time: 0.1639 memory: 14682 loss_ce: 0.0217 loss: 0.0217 2022/09/20 19:38:36 - mmengine - INFO - Epoch(train) [1][32500/42151] lr: 3.0000e-04 eta: 1 day, 10:01:53 time: 0.5167 data_time: 0.0766 memory: 14682 loss_ce: 0.0214 loss: 0.0214 2022/09/20 19:39:32 - mmengine - INFO - Epoch(train) [1][32600/42151] lr: 3.0000e-04 eta: 1 day, 10:01:02 time: 0.5292 data_time: 0.1443 memory: 14682 loss_ce: 0.0219 loss: 0.0219 2022/09/20 19:40:26 - mmengine - INFO - Epoch(train) [1][32700/42151] lr: 3.0000e-04 eta: 1 day, 9:59:55 time: 0.5518 data_time: 0.1327 memory: 14682 loss_ce: 0.0218 loss: 0.0218 2022/09/20 19:41:20 - mmengine - INFO - Epoch(train) [1][32800/42151] lr: 3.0000e-04 eta: 1 day, 9:58:52 time: 0.4085 data_time: 0.0237 memory: 14682 loss_ce: 0.0203 loss: 0.0203 2022/09/20 19:42:17 - mmengine - INFO - Epoch(train) [1][32900/42151] lr: 3.0000e-04 eta: 1 day, 9:58:01 time: 0.5885 data_time: 0.1936 memory: 14682 loss_ce: 0.0212 loss: 0.0212 2022/09/20 19:43:11 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 19:43:11 - mmengine - INFO - Epoch(train) [1][33000/42151] lr: 3.0000e-04 eta: 1 day, 9:56:55 time: 0.5580 data_time: 0.1786 memory: 14682 loss_ce: 0.0223 loss: 0.0223 2022/09/20 19:44:05 - mmengine - INFO - Epoch(train) [1][33100/42151] lr: 3.0000e-04 eta: 1 day, 9:55:52 time: 0.5085 data_time: 0.0358 memory: 14682 loss_ce: 0.0226 loss: 0.0226 2022/09/20 19:45:01 - mmengine - INFO - Epoch(train) [1][33200/42151] lr: 3.0000e-04 eta: 1 day, 9:54:55 time: 0.5385 data_time: 0.1445 memory: 14682 loss_ce: 0.0245 loss: 0.0245 2022/09/20 19:45:55 - mmengine - INFO - Epoch(train) [1][33300/42151] lr: 3.0000e-04 eta: 1 day, 9:53:53 time: 0.5394 data_time: 0.1099 memory: 14682 loss_ce: 0.0228 loss: 0.0228 2022/09/20 19:46:49 - mmengine - INFO - Epoch(train) [1][33400/42151] lr: 3.0000e-04 eta: 1 day, 9:52:46 time: 0.4009 data_time: 0.0155 memory: 14682 loss_ce: 0.0219 loss: 0.0219 2022/09/20 19:47:45 - mmengine - INFO - Epoch(train) [1][33500/42151] lr: 3.0000e-04 eta: 1 day, 9:51:54 time: 0.6678 data_time: 0.2251 memory: 14682 loss_ce: 0.0217 loss: 0.0217 2022/09/20 19:48:39 - mmengine - INFO - Epoch(train) [1][33600/42151] lr: 3.0000e-04 eta: 1 day, 9:50:49 time: 0.5941 data_time: 0.2051 memory: 14682 loss_ce: 0.0233 loss: 0.0233 2022/09/20 19:49:34 - mmengine - INFO - Epoch(train) [1][33700/42151] lr: 3.0000e-04 eta: 1 day, 9:49:45 time: 0.3889 data_time: 0.0059 memory: 14682 loss_ce: 0.0251 loss: 0.0251 2022/09/20 19:50:28 - mmengine - INFO - Epoch(train) [1][33800/42151] lr: 3.0000e-04 eta: 1 day, 9:48:39 time: 0.4671 data_time: 0.0758 memory: 14682 loss_ce: 0.0218 loss: 0.0218 2022/09/20 19:51:24 - mmengine - INFO - Epoch(train) [1][33900/42151] lr: 3.0000e-04 eta: 1 day, 9:47:47 time: 0.4792 data_time: 0.0947 memory: 14682 loss_ce: 0.0216 loss: 0.0216 2022/09/20 19:52:18 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 19:52:18 - mmengine - INFO - Epoch(train) [1][34000/42151] lr: 3.0000e-04 eta: 1 day, 9:46:46 time: 0.5098 data_time: 0.0771 memory: 14682 loss_ce: 0.0223 loss: 0.0223 2022/09/20 19:53:14 - mmengine - INFO - Epoch(train) [1][34100/42151] lr: 3.0000e-04 eta: 1 day, 9:45:54 time: 0.6196 data_time: 0.2270 memory: 14682 loss_ce: 0.0229 loss: 0.0229 2022/09/20 19:54:11 - mmengine - INFO - Epoch(train) [1][34200/42151] lr: 3.0000e-04 eta: 1 day, 9:45:01 time: 0.6805 data_time: 0.2951 memory: 14682 loss_ce: 0.0226 loss: 0.0226 2022/09/20 19:55:04 - mmengine - INFO - Epoch(train) [1][34300/42151] lr: 3.0000e-04 eta: 1 day, 9:43:54 time: 0.4827 data_time: 0.0997 memory: 14682 loss_ce: 0.0214 loss: 0.0214 2022/09/20 19:55:59 - mmengine - INFO - Epoch(train) [1][34400/42151] lr: 3.0000e-04 eta: 1 day, 9:42:52 time: 0.5503 data_time: 0.1656 memory: 14682 loss_ce: 0.0237 loss: 0.0237 2022/09/20 19:56:54 - mmengine - INFO - Epoch(train) [1][34500/42151] lr: 3.0000e-04 eta: 1 day, 9:41:53 time: 0.4261 data_time: 0.0428 memory: 14682 loss_ce: 0.0238 loss: 0.0238 2022/09/20 19:57:50 - mmengine - INFO - Epoch(train) [1][34600/42151] lr: 3.0000e-04 eta: 1 day, 9:40:59 time: 0.4511 data_time: 0.0561 memory: 14682 loss_ce: 0.0227 loss: 0.0227 2022/09/20 19:58:45 - mmengine - INFO - Epoch(train) [1][34700/42151] lr: 3.0000e-04 eta: 1 day, 9:40:05 time: 0.6408 data_time: 0.2459 memory: 14682 loss_ce: 0.0237 loss: 0.0237 2022/09/20 19:59:39 - mmengine - INFO - Epoch(train) [1][34800/42151] lr: 3.0000e-04 eta: 1 day, 9:39:00 time: 0.5975 data_time: 0.1759 memory: 14682 loss_ce: 0.0209 loss: 0.0209 2022/09/20 20:00:34 - mmengine - INFO - Epoch(train) [1][34900/42151] lr: 3.0000e-04 eta: 1 day, 9:37:58 time: 0.4250 data_time: 0.0053 memory: 14682 loss_ce: 0.0204 loss: 0.0204 2022/09/20 20:01:29 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 20:01:29 - mmengine - INFO - Epoch(train) [1][35000/42151] lr: 3.0000e-04 eta: 1 day, 9:36:56 time: 0.4756 data_time: 0.0471 memory: 14682 loss_ce: 0.0221 loss: 0.0221 2022/09/20 20:02:25 - mmengine - INFO - Epoch(train) [1][35100/42151] lr: 3.0000e-04 eta: 1 day, 9:36:03 time: 0.5687 data_time: 0.1895 memory: 14682 loss_ce: 0.0207 loss: 0.0207 2022/09/20 20:03:19 - mmengine - INFO - Epoch(train) [1][35200/42151] lr: 3.0000e-04 eta: 1 day, 9:35:03 time: 0.4328 data_time: 0.0532 memory: 14682 loss_ce: 0.0221 loss: 0.0221 2022/09/20 20:04:15 - mmengine - INFO - Epoch(train) [1][35300/42151] lr: 3.0000e-04 eta: 1 day, 9:34:10 time: 0.6089 data_time: 0.1708 memory: 14682 loss_ce: 0.0199 loss: 0.0199 2022/09/20 20:05:09 - mmengine - INFO - Epoch(train) [1][35400/42151] lr: 3.0000e-04 eta: 1 day, 9:33:04 time: 0.6172 data_time: 0.1780 memory: 14682 loss_ce: 0.0217 loss: 0.0217 2022/09/20 20:06:03 - mmengine - INFO - Epoch(train) [1][35500/42151] lr: 3.0000e-04 eta: 1 day, 9:31:58 time: 0.5570 data_time: 0.1711 memory: 14682 loss_ce: 0.0232 loss: 0.0232 2022/09/20 20:06:56 - mmengine - INFO - Epoch(train) [1][35600/42151] lr: 3.0000e-04 eta: 1 day, 9:30:50 time: 0.4066 data_time: 0.0049 memory: 14682 loss_ce: 0.0210 loss: 0.0210 2022/09/20 20:07:51 - mmengine - INFO - Epoch(train) [1][35700/42151] lr: 3.0000e-04 eta: 1 day, 9:29:51 time: 0.5227 data_time: 0.1347 memory: 14682 loss_ce: 0.0234 loss: 0.0234 2022/09/20 20:08:45 - mmengine - INFO - Epoch(train) [1][35800/42151] lr: 3.0000e-04 eta: 1 day, 9:28:45 time: 0.5014 data_time: 0.0944 memory: 14682 loss_ce: 0.0209 loss: 0.0209 2022/09/20 20:09:42 - mmengine - INFO - Epoch(train) [1][35900/42151] lr: 3.0000e-04 eta: 1 day, 9:28:00 time: 0.6485 data_time: 0.2343 memory: 14682 loss_ce: 0.0209 loss: 0.0209 2022/09/20 20:10:39 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 20:10:39 - mmengine - INFO - Epoch(train) [1][36000/42151] lr: 3.0000e-04 eta: 1 day, 9:27:13 time: 0.6399 data_time: 0.2233 memory: 14682 loss_ce: 0.0207 loss: 0.0207 2022/09/20 20:11:34 - mmengine - INFO - Epoch(train) [1][36100/42151] lr: 3.0000e-04 eta: 1 day, 9:26:09 time: 0.5454 data_time: 0.1608 memory: 14682 loss_ce: 0.0225 loss: 0.0225 2022/09/20 20:12:28 - mmengine - INFO - Epoch(train) [1][36200/42151] lr: 3.0000e-04 eta: 1 day, 9:25:08 time: 0.4950 data_time: 0.1131 memory: 14682 loss_ce: 0.0210 loss: 0.0210 2022/09/20 20:13:24 - mmengine - INFO - Epoch(train) [1][36300/42151] lr: 3.0000e-04 eta: 1 day, 9:24:12 time: 0.4936 data_time: 0.0736 memory: 14682 loss_ce: 0.0207 loss: 0.0207 2022/09/20 20:14:18 - mmengine - INFO - Epoch(train) [1][36400/42151] lr: 3.0000e-04 eta: 1 day, 9:23:08 time: 0.5159 data_time: 0.1005 memory: 14682 loss_ce: 0.0221 loss: 0.0221 2022/09/20 20:15:14 - mmengine - INFO - Epoch(train) [1][36500/42151] lr: 3.0000e-04 eta: 1 day, 9:22:15 time: 0.6217 data_time: 0.2430 memory: 14682 loss_ce: 0.0224 loss: 0.0224 2022/09/20 20:16:07 - mmengine - INFO - Epoch(train) [1][36600/42151] lr: 3.0000e-04 eta: 1 day, 9:21:05 time: 0.5700 data_time: 0.1712 memory: 14682 loss_ce: 0.0218 loss: 0.0218 2022/09/20 20:17:02 - mmengine - INFO - Epoch(train) [1][36700/42151] lr: 3.0000e-04 eta: 1 day, 9:20:08 time: 0.5078 data_time: 0.1291 memory: 14682 loss_ce: 0.0203 loss: 0.0203 2022/09/20 20:17:56 - mmengine - INFO - Epoch(train) [1][36800/42151] lr: 3.0000e-04 eta: 1 day, 9:19:02 time: 0.5442 data_time: 0.1352 memory: 14682 loss_ce: 0.0230 loss: 0.0230 2022/09/20 20:18:50 - mmengine - INFO - Epoch(train) [1][36900/42151] lr: 3.0000e-04 eta: 1 day, 9:18:01 time: 0.5121 data_time: 0.0995 memory: 14682 loss_ce: 0.0212 loss: 0.0212 2022/09/20 20:19:45 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 20:19:45 - mmengine - INFO - Epoch(train) [1][37000/42151] lr: 3.0000e-04 eta: 1 day, 9:17:01 time: 0.5049 data_time: 0.1243 memory: 14682 loss_ce: 0.0221 loss: 0.0221 2022/09/20 20:20:42 - mmengine - INFO - Epoch(train) [1][37100/42151] lr: 3.0000e-04 eta: 1 day, 9:16:14 time: 0.7608 data_time: 0.3333 memory: 14682 loss_ce: 0.0220 loss: 0.0220 2022/09/20 20:21:36 - mmengine - INFO - Epoch(train) [1][37200/42151] lr: 3.0000e-04 eta: 1 day, 9:15:08 time: 0.6009 data_time: 0.2139 memory: 14682 loss_ce: 0.0186 loss: 0.0186 2022/09/20 20:22:29 - mmengine - INFO - Epoch(train) [1][37300/42151] lr: 3.0000e-04 eta: 1 day, 9:14:01 time: 0.4247 data_time: 0.0155 memory: 14682 loss_ce: 0.0225 loss: 0.0225 2022/09/20 20:23:23 - mmengine - INFO - Epoch(train) [1][37400/42151] lr: 3.0000e-04 eta: 1 day, 9:12:55 time: 0.4361 data_time: 0.0524 memory: 14682 loss_ce: 0.0224 loss: 0.0224 2022/09/20 20:24:18 - mmengine - INFO - Epoch(train) [1][37500/42151] lr: 3.0000e-04 eta: 1 day, 9:11:59 time: 0.5139 data_time: 0.1328 memory: 14682 loss_ce: 0.0210 loss: 0.0210 2022/09/20 20:25:13 - mmengine - INFO - Epoch(train) [1][37600/42151] lr: 3.0000e-04 eta: 1 day, 9:10:58 time: 0.6245 data_time: 0.1746 memory: 14682 loss_ce: 0.0202 loss: 0.0202 2022/09/20 20:26:10 - mmengine - INFO - Epoch(train) [1][37700/42151] lr: 3.0000e-04 eta: 1 day, 9:10:13 time: 0.6104 data_time: 0.1617 memory: 14682 loss_ce: 0.0220 loss: 0.0220 2022/09/20 20:27:05 - mmengine - INFO - Epoch(train) [1][37800/42151] lr: 3.0000e-04 eta: 1 day, 9:09:16 time: 0.5663 data_time: 0.1831 memory: 14682 loss_ce: 0.0211 loss: 0.0211 2022/09/20 20:28:01 - mmengine - INFO - Epoch(train) [1][37900/42151] lr: 3.0000e-04 eta: 1 day, 9:08:22 time: 0.4740 data_time: 0.0705 memory: 14682 loss_ce: 0.0236 loss: 0.0236 2022/09/20 20:28:59 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 20:28:59 - mmengine - INFO - Epoch(train) [1][38000/42151] lr: 3.0000e-04 eta: 1 day, 9:07:39 time: 0.6054 data_time: 0.2098 memory: 14682 loss_ce: 0.0220 loss: 0.0220 2022/09/20 20:29:55 - mmengine - INFO - Epoch(train) [1][38100/42151] lr: 3.0000e-04 eta: 1 day, 9:06:44 time: 0.5518 data_time: 0.1644 memory: 14682 loss_ce: 0.0233 loss: 0.0233 2022/09/20 20:30:50 - mmengine - INFO - Epoch(train) [1][38200/42151] lr: 3.0000e-04 eta: 1 day, 9:05:49 time: 0.5850 data_time: 0.1875 memory: 14682 loss_ce: 0.0214 loss: 0.0214 2022/09/20 20:31:47 - mmengine - INFO - Epoch(train) [1][38300/42151] lr: 3.0000e-04 eta: 1 day, 9:05:01 time: 0.5477 data_time: 0.1617 memory: 14682 loss_ce: 0.0217 loss: 0.0217 2022/09/20 20:32:42 - mmengine - INFO - Epoch(train) [1][38400/42151] lr: 3.0000e-04 eta: 1 day, 9:04:02 time: 0.5750 data_time: 0.1653 memory: 14682 loss_ce: 0.0223 loss: 0.0223 2022/09/20 20:33:37 - mmengine - INFO - Epoch(train) [1][38500/42151] lr: 3.0000e-04 eta: 1 day, 9:03:04 time: 0.4327 data_time: 0.0546 memory: 14682 loss_ce: 0.0200 loss: 0.0200 2022/09/20 20:34:33 - mmengine - INFO - Epoch(train) [1][38600/42151] lr: 3.0000e-04 eta: 1 day, 9:02:11 time: 0.5534 data_time: 0.1505 memory: 14682 loss_ce: 0.0225 loss: 0.0225 2022/09/20 20:35:29 - mmengine - INFO - Epoch(train) [1][38700/42151] lr: 3.0000e-04 eta: 1 day, 9:01:17 time: 0.5550 data_time: 0.1512 memory: 14682 loss_ce: 0.0216 loss: 0.0216 2022/09/20 20:36:24 - mmengine - INFO - Epoch(train) [1][38800/42151] lr: 3.0000e-04 eta: 1 day, 9:00:21 time: 0.6732 data_time: 0.2366 memory: 14682 loss_ce: 0.0218 loss: 0.0218 2022/09/20 20:37:19 - mmengine - INFO - Epoch(train) [1][38900/42151] lr: 3.0000e-04 eta: 1 day, 8:59:24 time: 0.5804 data_time: 0.1355 memory: 14682 loss_ce: 0.0196 loss: 0.0196 2022/09/20 20:38:15 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 20:38:15 - mmengine - INFO - Epoch(train) [1][39000/42151] lr: 3.0000e-04 eta: 1 day, 8:58:28 time: 0.5415 data_time: 0.1263 memory: 14682 loss_ce: 0.0221 loss: 0.0221 2022/09/20 20:39:13 - mmengine - INFO - Epoch(train) [1][39100/42151] lr: 3.0000e-04 eta: 1 day, 8:57:45 time: 0.5680 data_time: 0.1212 memory: 14682 loss_ce: 0.0210 loss: 0.0210 2022/09/20 20:40:09 - mmengine - INFO - Epoch(train) [1][39200/42151] lr: 3.0000e-04 eta: 1 day, 8:56:53 time: 0.4924 data_time: 0.0892 memory: 14682 loss_ce: 0.0221 loss: 0.0221 2022/09/20 20:41:05 - mmengine - INFO - Epoch(train) [1][39300/42151] lr: 3.0000e-04 eta: 1 day, 8:56:03 time: 0.5283 data_time: 0.1443 memory: 14682 loss_ce: 0.0206 loss: 0.0206 2022/09/20 20:42:01 - mmengine - INFO - Epoch(train) [1][39400/42151] lr: 3.0000e-04 eta: 1 day, 8:55:09 time: 0.5627 data_time: 0.1789 memory: 14682 loss_ce: 0.0201 loss: 0.0201 2022/09/20 20:42:57 - mmengine - INFO - Epoch(train) [1][39500/42151] lr: 3.0000e-04 eta: 1 day, 8:54:15 time: 0.5395 data_time: 0.1569 memory: 14682 loss_ce: 0.0212 loss: 0.0212 2022/09/20 20:43:51 - mmengine - INFO - Epoch(train) [1][39600/42151] lr: 3.0000e-04 eta: 1 day, 8:53:10 time: 0.5544 data_time: 0.1446 memory: 14682 loss_ce: 0.0218 loss: 0.0218 2022/09/20 20:44:44 - mmengine - INFO - Epoch(train) [1][39700/42151] lr: 3.0000e-04 eta: 1 day, 8:52:05 time: 0.4366 data_time: 0.0494 memory: 14682 loss_ce: 0.0205 loss: 0.0205 2022/09/20 20:45:41 - mmengine - INFO - Epoch(train) [1][39800/42151] lr: 3.0000e-04 eta: 1 day, 8:51:13 time: 0.5466 data_time: 0.1532 memory: 14682 loss_ce: 0.0208 loss: 0.0208 2022/09/20 20:46:53 - mmengine - INFO - Epoch(train) [1][39900/42151] lr: 3.0000e-04 eta: 1 day, 8:51:49 time: 0.3872 data_time: 0.0053 memory: 14682 loss_ce: 0.0229 loss: 0.0229 2022/09/20 20:48:18 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 20:48:18 - mmengine - INFO - Epoch(train) [1][40000/42151] lr: 3.0000e-04 eta: 1 day, 8:53:31 time: 0.4016 data_time: 0.0052 memory: 14682 loss_ce: 0.0231 loss: 0.0231 2022/09/20 20:49:25 - mmengine - INFO - Epoch(train) [1][40100/42151] lr: 3.0000e-04 eta: 1 day, 8:53:35 time: 0.5917 data_time: 0.1957 memory: 14682 loss_ce: 0.0197 loss: 0.0197 2022/09/20 20:50:17 - mmengine - INFO - Epoch(train) [1][40200/42151] lr: 3.0000e-04 eta: 1 day, 8:52:19 time: 0.5214 data_time: 0.1290 memory: 14682 loss_ce: 0.0193 loss: 0.0193 2022/09/20 20:51:12 - mmengine - INFO - Epoch(train) [1][40300/42151] lr: 3.0000e-04 eta: 1 day, 8:51:21 time: 0.6080 data_time: 0.1777 memory: 14682 loss_ce: 0.0213 loss: 0.0213 2022/09/20 20:52:28 - mmengine - INFO - Epoch(train) [1][40400/42151] lr: 3.0000e-04 eta: 1 day, 8:52:12 time: 0.3922 data_time: 0.0053 memory: 14682 loss_ce: 0.0199 loss: 0.0199 2022/09/20 20:53:47 - mmengine - INFO - Epoch(train) [1][40500/42151] lr: 3.0000e-04 eta: 1 day, 8:53:19 time: 0.3972 data_time: 0.0054 memory: 14682 loss_ce: 0.0202 loss: 0.0202 2022/09/20 20:54:41 - mmengine - INFO - Epoch(train) [1][40600/42151] lr: 3.0000e-04 eta: 1 day, 8:52:15 time: 0.4612 data_time: 0.0249 memory: 14682 loss_ce: 0.0223 loss: 0.0223 2022/09/20 20:55:41 - mmengine - INFO - Epoch(train) [1][40700/42151] lr: 3.0000e-04 eta: 1 day, 8:51:38 time: 0.8308 data_time: 0.4084 memory: 14682 loss_ce: 0.0203 loss: 0.0203 2022/09/20 20:56:52 - mmengine - INFO - Epoch(train) [1][40800/42151] lr: 3.0000e-04 eta: 1 day, 8:52:01 time: 2.6667 data_time: 2.2240 memory: 14682 loss_ce: 0.0201 loss: 0.0201 2022/09/20 20:58:04 - mmengine - INFO - Epoch(train) [1][40900/42151] lr: 3.0000e-04 eta: 1 day, 8:52:30 time: 0.4045 data_time: 0.0051 memory: 14682 loss_ce: 0.0204 loss: 0.0204 2022/09/20 20:59:03 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 20:59:03 - mmengine - INFO - Epoch(train) [1][41000/42151] lr: 3.0000e-04 eta: 1 day, 8:51:51 time: 0.9745 data_time: 0.5803 memory: 14682 loss_ce: 0.0209 loss: 0.0209 2022/09/20 20:59:53 - mmengine - INFO - Epoch(train) [1][41100/42151] lr: 3.0000e-04 eta: 1 day, 8:50:24 time: 0.5043 data_time: 0.1207 memory: 14682 loss_ce: 0.0211 loss: 0.0211 2022/09/20 21:01:07 - mmengine - INFO - Epoch(train) [1][41200/42151] lr: 3.0000e-04 eta: 1 day, 8:51:05 time: 0.3856 data_time: 0.0048 memory: 14682 loss_ce: 0.0210 loss: 0.0210 2022/09/20 21:01:59 - mmengine - INFO - Epoch(train) [1][41300/42151] lr: 3.0000e-04 eta: 1 day, 8:49:49 time: 0.5314 data_time: 0.1412 memory: 14682 loss_ce: 0.0183 loss: 0.0183 2022/09/20 21:02:57 - mmengine - INFO - Epoch(train) [1][41400/42151] lr: 3.0000e-04 eta: 1 day, 8:49:03 time: 0.7203 data_time: 0.3047 memory: 14682 loss_ce: 0.0191 loss: 0.0191 2022/09/20 21:03:51 - mmengine - INFO - Epoch(train) [1][41500/42151] lr: 3.0000e-04 eta: 1 day, 8:47:59 time: 0.4513 data_time: 0.0053 memory: 14682 loss_ce: 0.0214 loss: 0.0214 2022/09/20 21:04:45 - mmengine - INFO - Epoch(train) [1][41600/42151] lr: 3.0000e-04 eta: 1 day, 8:46:52 time: 0.4259 data_time: 0.0372 memory: 14682 loss_ce: 0.0201 loss: 0.0201 2022/09/20 21:05:39 - mmengine - INFO - Epoch(train) [1][41700/42151] lr: 3.0000e-04 eta: 1 day, 8:45:46 time: 0.4345 data_time: 0.0523 memory: 14682 loss_ce: 0.0187 loss: 0.0187 2022/09/20 21:06:36 - mmengine - INFO - Epoch(train) [1][41800/42151] lr: 3.0000e-04 eta: 1 day, 8:44:57 time: 0.6548 data_time: 0.2048 memory: 14682 loss_ce: 0.0203 loss: 0.0203 2022/09/20 21:07:32 - mmengine - INFO - Epoch(train) [1][41900/42151] lr: 3.0000e-04 eta: 1 day, 8:44:01 time: 0.6300 data_time: 0.2388 memory: 14682 loss_ce: 0.0200 loss: 0.0200 2022/09/20 21:08:27 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 21:08:27 - mmengine - INFO - Epoch(train) [1][42000/42151] lr: 3.0000e-04 eta: 1 day, 8:43:00 time: 0.5727 data_time: 0.1135 memory: 14682 loss_ce: 0.0195 loss: 0.0195 2022/09/20 21:09:21 - mmengine - INFO - Epoch(train) [1][42100/42151] lr: 3.0000e-04 eta: 1 day, 8:41:56 time: 0.5010 data_time: 0.1115 memory: 14682 loss_ce: 0.0223 loss: 0.0223 2022/09/20 21:09:52 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 21:09:52 - mmengine - INFO - Saving checkpoint at 1 epochs 2022/09/20 21:12:21 - mmengine - INFO - Epoch(val) [1][100/7672] eta: 0:55:40 time: 0.4411 data_time: 0.0080 memory: 23083 2022/09/20 21:12:58 - mmengine - INFO - Epoch(val) [1][200/7672] eta: 0:45:23 time: 0.3645 data_time: 0.0010 memory: 1304 2022/09/20 21:13:36 - mmengine - INFO - Epoch(val) [1][300/7672] eta: 0:27:46 time: 0.2261 data_time: 0.0024 memory: 1304 2022/09/20 21:14:02 - mmengine - INFO - Epoch(val) [1][400/7672] eta: 0:27:41 time: 0.2285 data_time: 0.0034 memory: 1304 2022/09/20 21:14:25 - mmengine - INFO - Epoch(val) [1][500/7672] eta: 0:26:57 time: 0.2255 data_time: 0.0022 memory: 1304 2022/09/20 21:14:48 - mmengine - INFO - Epoch(val) [1][600/7672] eta: 0:25:00 time: 0.2121 data_time: 0.0008 memory: 1304 2022/09/20 21:15:09 - mmengine - INFO - Epoch(val) [1][700/7672] eta: 0:24:14 time: 0.2087 data_time: 0.0008 memory: 1304 2022/09/20 21:15:31 - mmengine - INFO - Epoch(val) [1][800/7672] eta: 0:24:38 time: 0.2151 data_time: 0.0008 memory: 1304 2022/09/20 21:15:54 - mmengine - INFO - Epoch(val) [1][900/7672] eta: 0:24:42 time: 0.2189 data_time: 0.0012 memory: 1304 2022/09/20 21:16:16 - mmengine - INFO - Epoch(val) [1][1000/7672] eta: 0:24:09 time: 0.2173 data_time: 0.0025 memory: 1304 2022/09/20 21:16:37 - mmengine - INFO - Epoch(val) [1][1100/7672] eta: 0:24:23 time: 0.2227 data_time: 0.0008 memory: 1304 2022/09/20 21:17:00 - mmengine - INFO - Epoch(val) [1][1200/7672] eta: 0:23:53 time: 0.2214 data_time: 0.0009 memory: 1304 2022/09/20 21:17:22 - mmengine - INFO - Epoch(val) [1][1300/7672] eta: 0:22:38 time: 0.2132 data_time: 0.0008 memory: 1304 2022/09/20 21:17:44 - mmengine - INFO - Epoch(val) [1][1400/7672] eta: 0:22:47 time: 0.2180 data_time: 0.0029 memory: 1304 2022/09/20 21:18:07 - mmengine - INFO - Epoch(val) [1][1500/7672] eta: 0:22:38 time: 0.2202 data_time: 0.0024 memory: 1304 2022/09/20 21:18:29 - mmengine - INFO - Epoch(val) [1][1600/7672] eta: 0:21:36 time: 0.2136 data_time: 0.0008 memory: 1304 2022/09/20 21:18:51 - mmengine - INFO - Epoch(val) [1][1700/7672] eta: 0:21:19 time: 0.2143 data_time: 0.0008 memory: 1304 2022/09/20 21:19:13 - mmengine - INFO - Epoch(val) [1][1800/7672] eta: 0:21:30 time: 0.2199 data_time: 0.0012 memory: 1304 2022/09/20 21:19:36 - mmengine - INFO - Epoch(val) [1][1900/7672] eta: 0:20:50 time: 0.2166 data_time: 0.0023 memory: 1304 2022/09/20 21:19:58 - mmengine - INFO - Epoch(val) [1][2000/7672] eta: 0:21:02 time: 0.2225 data_time: 0.0008 memory: 1304 2022/09/20 21:20:20 - mmengine - INFO - Epoch(val) [1][2100/7672] eta: 0:20:23 time: 0.2195 data_time: 0.0017 memory: 1304 2022/09/20 21:20:43 - mmengine - INFO - Epoch(val) [1][2200/7672] eta: 0:20:18 time: 0.2228 data_time: 0.0015 memory: 1304 2022/09/20 21:21:05 - mmengine - INFO - Epoch(val) [1][2300/7672] eta: 0:19:57 time: 0.2229 data_time: 0.0034 memory: 1304 2022/09/20 21:21:28 - mmengine - INFO - Epoch(val) [1][2400/7672] eta: 0:18:36 time: 0.2118 data_time: 0.0011 memory: 1304 2022/09/20 21:21:51 - mmengine - INFO - Epoch(val) [1][2500/7672] eta: 0:19:08 time: 0.2220 data_time: 0.0008 memory: 1304 2022/09/20 21:22:13 - mmengine - INFO - Epoch(val) [1][2600/7672] eta: 0:18:07 time: 0.2143 data_time: 0.0048 memory: 1304 2022/09/20 21:22:35 - mmengine - INFO - Epoch(val) [1][2700/7672] eta: 0:18:06 time: 0.2184 data_time: 0.0025 memory: 1304 2022/09/20 21:22:58 - mmengine - INFO - Epoch(val) [1][2800/7672] eta: 0:17:18 time: 0.2132 data_time: 0.0008 memory: 1304 2022/09/20 21:23:20 - mmengine - INFO - Epoch(val) [1][2900/7672] eta: 0:17:09 time: 0.2157 data_time: 0.0008 memory: 1304 2022/09/20 21:23:42 - mmengine - INFO - Epoch(val) [1][3000/7672] eta: 0:17:09 time: 0.2203 data_time: 0.0009 memory: 1304 2022/09/20 21:24:05 - mmengine - INFO - Epoch(val) [1][3100/7672] eta: 0:16:55 time: 0.2221 data_time: 0.0010 memory: 1304 2022/09/20 21:24:27 - mmengine - INFO - Epoch(val) [1][3200/7672] eta: 0:16:41 time: 0.2239 data_time: 0.0024 memory: 1304 2022/09/20 21:24:50 - mmengine - INFO - Epoch(val) [1][3300/7672] eta: 0:16:11 time: 0.2223 data_time: 0.0008 memory: 1304 2022/09/20 21:25:13 - mmengine - INFO - Epoch(val) [1][3400/7672] eta: 0:15:42 time: 0.2205 data_time: 0.0009 memory: 1304 2022/09/20 21:25:34 - mmengine - INFO - Epoch(val) [1][3500/7672] eta: 0:15:39 time: 0.2251 data_time: 0.0025 memory: 1304 2022/09/20 21:25:56 - mmengine - INFO - Epoch(val) [1][3600/7672] eta: 0:14:59 time: 0.2209 data_time: 0.0025 memory: 1304 2022/09/20 21:26:18 - mmengine - INFO - Epoch(val) [1][3700/7672] eta: 0:13:58 time: 0.2112 data_time: 0.0008 memory: 1304 2022/09/20 21:26:40 - mmengine - INFO - Epoch(val) [1][3800/7672] eta: 0:13:13 time: 0.2050 data_time: 0.0008 memory: 1304 2022/09/20 21:27:02 - mmengine - INFO - Epoch(val) [1][3900/7672] eta: 0:13:19 time: 0.2119 data_time: 0.0008 memory: 1304 2022/09/20 21:27:24 - mmengine - INFO - Epoch(val) [1][4000/7672] eta: 0:13:11 time: 0.2154 data_time: 0.0008 memory: 1304 2022/09/20 21:27:45 - mmengine - INFO - Epoch(val) [1][4100/7672] eta: 0:12:44 time: 0.2141 data_time: 0.0028 memory: 1304 2022/09/20 21:28:07 - mmengine - INFO - Epoch(val) [1][4200/7672] eta: 0:12:13 time: 0.2114 data_time: 0.0026 memory: 1304 2022/09/20 21:28:29 - mmengine - INFO - Epoch(val) [1][4300/7672] eta: 0:12:16 time: 0.2185 data_time: 0.0008 memory: 1304 2022/09/20 21:28:51 - mmengine - INFO - Epoch(val) [1][4400/7672] eta: 0:11:53 time: 0.2180 data_time: 0.0009 memory: 1304 2022/09/20 21:29:13 - mmengine - INFO - Epoch(val) [1][4500/7672] eta: 0:11:47 time: 0.2231 data_time: 0.0043 memory: 1304 2022/09/20 21:29:35 - mmengine - INFO - Epoch(val) [1][4600/7672] eta: 0:11:10 time: 0.2184 data_time: 0.0031 memory: 1304 2022/09/20 21:29:58 - mmengine - INFO - Epoch(val) [1][4700/7672] eta: 0:11:29 time: 0.2320 data_time: 0.0009 memory: 1304 2022/09/20 21:30:20 - mmengine - INFO - Epoch(val) [1][4800/7672] eta: 0:10:10 time: 0.2126 data_time: 0.0008 memory: 1304 2022/09/20 21:30:42 - mmengine - INFO - Epoch(val) [1][4900/7672] eta: 0:09:33 time: 0.2068 data_time: 0.0008 memory: 1304 2022/09/20 21:31:05 - mmengine - INFO - Epoch(val) [1][5000/7672] eta: 0:11:32 time: 0.2591 data_time: 0.0030 memory: 1304 2022/09/20 21:31:28 - mmengine - INFO - Epoch(val) [1][5100/7672] eta: 0:09:30 time: 0.2217 data_time: 0.0024 memory: 1304 2022/09/20 21:31:50 - mmengine - INFO - Epoch(val) [1][5200/7672] eta: 0:08:42 time: 0.2114 data_time: 0.0008 memory: 1304 2022/09/20 21:32:12 - mmengine - INFO - Epoch(val) [1][5300/7672] eta: 0:08:12 time: 0.2077 data_time: 0.0008 memory: 1304 2022/09/20 21:32:34 - mmengine - INFO - Epoch(val) [1][5400/7672] eta: 0:08:15 time: 0.2181 data_time: 0.0009 memory: 1304 2022/09/20 21:32:55 - mmengine - INFO - Epoch(val) [1][5500/7672] eta: 0:07:47 time: 0.2154 data_time: 0.0023 memory: 1304 2022/09/20 21:33:17 - mmengine - INFO - Epoch(val) [1][5600/7672] eta: 0:07:14 time: 0.2096 data_time: 0.0027 memory: 1304 2022/09/20 21:33:38 - mmengine - INFO - Epoch(val) [1][5700/7672] eta: 0:07:03 time: 0.2146 data_time: 0.0009 memory: 1304 2022/09/20 21:34:00 - mmengine - INFO - Epoch(val) [1][5800/7672] eta: 0:06:39 time: 0.2135 data_time: 0.0008 memory: 1304 2022/09/20 21:34:22 - mmengine - INFO - Epoch(val) [1][5900/7672] eta: 0:05:59 time: 0.2028 data_time: 0.0008 memory: 1304 2022/09/20 21:34:43 - mmengine - INFO - Epoch(val) [1][6000/7672] eta: 0:05:46 time: 0.2073 data_time: 0.0008 memory: 1304 2022/09/20 21:35:06 - mmengine - INFO - Epoch(val) [1][6100/7672] eta: 0:06:24 time: 0.2446 data_time: 0.0025 memory: 1304 2022/09/20 21:35:28 - mmengine - INFO - Epoch(val) [1][6200/7672] eta: 0:05:19 time: 0.2169 data_time: 0.0020 memory: 1304 2022/09/20 21:35:50 - mmengine - INFO - Epoch(val) [1][6300/7672] eta: 0:04:54 time: 0.2144 data_time: 0.0008 memory: 1304 2022/09/20 21:36:12 - mmengine - INFO - Epoch(val) [1][6400/7672] eta: 0:04:24 time: 0.2078 data_time: 0.0008 memory: 1304 2022/09/20 21:36:33 - mmengine - INFO - Epoch(val) [1][6500/7672] eta: 0:04:01 time: 0.2065 data_time: 0.0008 memory: 1304 2022/09/20 21:36:55 - mmengine - INFO - Epoch(val) [1][6600/7672] eta: 0:03:45 time: 0.2099 data_time: 0.0014 memory: 1304 2022/09/20 21:37:17 - mmengine - INFO - Epoch(val) [1][6700/7672] eta: 0:03:21 time: 0.2075 data_time: 0.0023 memory: 1304 2022/09/20 21:37:38 - mmengine - INFO - Epoch(val) [1][6800/7672] eta: 0:03:00 time: 0.2075 data_time: 0.0021 memory: 1304 2022/09/20 21:38:00 - mmengine - INFO - Epoch(val) [1][6900/7672] eta: 0:02:41 time: 0.2087 data_time: 0.0009 memory: 1304 2022/09/20 21:38:21 - mmengine - INFO - Epoch(val) [1][7000/7672] eta: 0:02:19 time: 0.2082 data_time: 0.0010 memory: 1304 2022/09/20 21:38:43 - mmengine - INFO - Epoch(val) [1][7100/7672] eta: 0:01:59 time: 0.2094 data_time: 0.0008 memory: 1304 2022/09/20 21:39:05 - mmengine - INFO - Epoch(val) [1][7200/7672] eta: 0:01:41 time: 0.2146 data_time: 0.0014 memory: 1304 2022/09/20 21:39:27 - mmengine - INFO - Epoch(val) [1][7300/7672] eta: 0:01:49 time: 0.2936 data_time: 0.0045 memory: 1304 2022/09/20 21:39:48 - mmengine - INFO - Epoch(val) [1][7400/7672] eta: 0:00:56 time: 0.2075 data_time: 0.0024 memory: 1304 2022/09/20 21:40:10 - mmengine - INFO - Epoch(val) [1][7500/7672] eta: 0:00:35 time: 0.2083 data_time: 0.0008 memory: 1304 2022/09/20 21:40:31 - mmengine - INFO - Epoch(val) [1][7600/7672] eta: 0:00:15 time: 0.2116 data_time: 0.0008 memory: 1304 2022/09/20 21:40:47 - mmengine - INFO - Epoch(val) [1][7672/7672] CUTE80/recog/word_acc_ignore_case_symbol: 0.8160 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9247 SVT/recog/word_acc_ignore_case_symbol: 0.8563 SVTP/recog/word_acc_ignore_case_symbol: 0.7256 IC13/recog/word_acc_ignore_case_symbol: 0.9202 IC15/recog/word_acc_ignore_case_symbol: 0.6774 2022/09/20 21:41:56 - mmengine - INFO - Epoch(train) [2][100/42151] lr: 3.0000e-04 eta: 1 day, 8:41:28 time: 1.2853 data_time: 0.8106 memory: 14682 loss_ce: 0.0221 loss: 0.0221 2022/09/20 21:43:03 - mmengine - INFO - Epoch(train) [2][200/42151] lr: 3.0000e-04 eta: 1 day, 8:41:27 time: 0.3873 data_time: 0.0057 memory: 14682 loss_ce: 0.0198 loss: 0.0198 2022/09/20 21:44:29 - mmengine - INFO - Epoch(train) [2][300/42151] lr: 3.0000e-04 eta: 1 day, 8:43:01 time: 0.3849 data_time: 0.0049 memory: 14682 loss_ce: 0.0205 loss: 0.0205 2022/09/20 21:45:56 - mmengine - INFO - Epoch(train) [2][400/42151] lr: 3.0000e-04 eta: 1 day, 8:44:38 time: 4.1382 data_time: 3.6327 memory: 14682 loss_ce: 0.0203 loss: 0.0203 2022/09/20 21:46:46 - mmengine - INFO - Epoch(train) [2][500/42151] lr: 3.0000e-04 eta: 1 day, 8:43:14 time: 0.7026 data_time: 0.2879 memory: 14682 loss_ce: 0.0175 loss: 0.0175 2022/09/20 21:47:41 - mmengine - INFO - Epoch(train) [2][600/42151] lr: 3.0000e-04 eta: 1 day, 8:42:14 time: 0.6067 data_time: 0.2207 memory: 14682 loss_ce: 0.0206 loss: 0.0206 2022/09/20 21:48:38 - mmengine - INFO - Epoch(train) [2][700/42151] lr: 3.0000e-04 eta: 1 day, 8:41:20 time: 0.4336 data_time: 0.0062 memory: 14682 loss_ce: 0.0222 loss: 0.0222 2022/09/20 21:49:31 - mmengine - INFO - Epoch(train) [2][800/42151] lr: 3.0000e-04 eta: 1 day, 8:40:11 time: 0.4674 data_time: 0.0827 memory: 14682 loss_ce: 0.0202 loss: 0.0202 2022/09/20 21:49:59 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 21:50:27 - mmengine - INFO - Epoch(train) [2][900/42151] lr: 3.0000e-04 eta: 1 day, 8:39:13 time: 0.5161 data_time: 0.1258 memory: 14682 loss_ce: 0.0222 loss: 0.0222 2022/09/20 21:51:24 - mmengine - INFO - Epoch(train) [2][1000/42151] lr: 3.0000e-04 eta: 1 day, 8:38:22 time: 0.6778 data_time: 0.2924 memory: 14682 loss_ce: 0.0202 loss: 0.0202 2022/09/20 21:52:20 - mmengine - INFO - Epoch(train) [2][1100/42151] lr: 3.0000e-04 eta: 1 day, 8:37:27 time: 0.6335 data_time: 0.1963 memory: 14682 loss_ce: 0.0208 loss: 0.0208 2022/09/20 21:53:17 - mmengine - INFO - Epoch(train) [2][1200/42151] lr: 3.0000e-04 eta: 1 day, 8:36:33 time: 0.6722 data_time: 0.2145 memory: 14682 loss_ce: 0.0211 loss: 0.0211 2022/09/20 21:54:11 - mmengine - INFO - Epoch(train) [2][1300/42151] lr: 3.0000e-04 eta: 1 day, 8:35:30 time: 0.5168 data_time: 0.1337 memory: 14682 loss_ce: 0.0194 loss: 0.0194 2022/09/20 21:55:05 - mmengine - INFO - Epoch(train) [2][1400/42151] lr: 3.0000e-04 eta: 1 day, 8:34:25 time: 0.4300 data_time: 0.0464 memory: 14682 loss_ce: 0.0216 loss: 0.0216 2022/09/20 21:55:59 - mmengine - INFO - Epoch(train) [2][1500/42151] lr: 3.0000e-04 eta: 1 day, 8:33:18 time: 0.4639 data_time: 0.0675 memory: 14682 loss_ce: 0.0185 loss: 0.0185 2022/09/20 21:56:55 - mmengine - INFO - Epoch(train) [2][1600/42151] lr: 3.0000e-04 eta: 1 day, 8:32:23 time: 0.5951 data_time: 0.1762 memory: 14682 loss_ce: 0.0182 loss: 0.0182 2022/09/20 21:57:51 - mmengine - INFO - Epoch(train) [2][1700/42151] lr: 3.0000e-04 eta: 1 day, 8:31:23 time: 0.6627 data_time: 0.2243 memory: 14682 loss_ce: 0.0194 loss: 0.0194 2022/09/20 21:58:45 - mmengine - INFO - Epoch(train) [2][1800/42151] lr: 3.0000e-04 eta: 1 day, 8:30:21 time: 0.4171 data_time: 0.0350 memory: 14682 loss_ce: 0.0195 loss: 0.0195 2022/09/20 21:59:13 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 21:59:41 - mmengine - INFO - Epoch(train) [2][1900/42151] lr: 3.0000e-04 eta: 1 day, 8:29:22 time: 0.4946 data_time: 0.0503 memory: 14682 loss_ce: 0.0171 loss: 0.0171 2022/09/20 22:00:35 - mmengine - INFO - Epoch(train) [2][2000/42151] lr: 3.0000e-04 eta: 1 day, 8:28:17 time: 0.4677 data_time: 0.0843 memory: 14682 loss_ce: 0.0199 loss: 0.0199 2022/09/20 22:01:31 - mmengine - INFO - Epoch(train) [2][2100/42151] lr: 3.0000e-04 eta: 1 day, 8:27:20 time: 0.5124 data_time: 0.1252 memory: 14682 loss_ce: 0.0210 loss: 0.0210 2022/09/20 22:02:26 - mmengine - INFO - Epoch(train) [2][2200/42151] lr: 3.0000e-04 eta: 1 day, 8:26:21 time: 0.5811 data_time: 0.1931 memory: 14682 loss_ce: 0.0179 loss: 0.0179 2022/09/20 22:03:22 - mmengine - INFO - Epoch(train) [2][2300/42151] lr: 3.0000e-04 eta: 1 day, 8:25:24 time: 0.6044 data_time: 0.1695 memory: 14682 loss_ce: 0.0195 loss: 0.0195 2022/09/20 22:04:18 - mmengine - INFO - Epoch(train) [2][2400/42151] lr: 3.0000e-04 eta: 1 day, 8:24:27 time: 0.5455 data_time: 0.1447 memory: 14682 loss_ce: 0.0181 loss: 0.0181 2022/09/20 22:05:13 - mmengine - INFO - Epoch(train) [2][2500/42151] lr: 3.0000e-04 eta: 1 day, 8:23:28 time: 0.5135 data_time: 0.0814 memory: 14682 loss_ce: 0.0177 loss: 0.0177 2022/09/20 22:06:08 - mmengine - INFO - Epoch(train) [2][2600/42151] lr: 3.0000e-04 eta: 1 day, 8:22:29 time: 0.4571 data_time: 0.0466 memory: 14682 loss_ce: 0.0167 loss: 0.0167 2022/09/20 22:07:04 - mmengine - INFO - Epoch(train) [2][2700/42151] lr: 3.0000e-04 eta: 1 day, 8:21:30 time: 0.5194 data_time: 0.1367 memory: 14682 loss_ce: 0.0194 loss: 0.0194 2022/09/20 22:08:00 - mmengine - INFO - Epoch(train) [2][2800/42151] lr: 3.0000e-04 eta: 1 day, 8:20:34 time: 0.5176 data_time: 0.0874 memory: 14682 loss_ce: 0.0193 loss: 0.0193 2022/09/20 22:08:27 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 22:08:56 - mmengine - INFO - Epoch(train) [2][2900/42151] lr: 3.0000e-04 eta: 1 day, 8:19:38 time: 0.5201 data_time: 0.1414 memory: 14682 loss_ce: 0.0190 loss: 0.0190 2022/09/20 22:09:52 - mmengine - INFO - Epoch(train) [2][3000/42151] lr: 3.0000e-04 eta: 1 day, 8:18:42 time: 0.6817 data_time: 0.2782 memory: 14682 loss_ce: 0.0205 loss: 0.0205 2022/09/20 22:10:47 - mmengine - INFO - Epoch(train) [2][3100/42151] lr: 3.0000e-04 eta: 1 day, 8:17:42 time: 0.5888 data_time: 0.1814 memory: 14682 loss_ce: 0.0199 loss: 0.0199 2022/09/20 22:11:42 - mmengine - INFO - Epoch(train) [2][3200/42151] lr: 3.0000e-04 eta: 1 day, 8:16:42 time: 0.4937 data_time: 0.0862 memory: 14682 loss_ce: 0.0187 loss: 0.0187 2022/09/20 22:12:37 - mmengine - INFO - Epoch(train) [2][3300/42151] lr: 3.0000e-04 eta: 1 day, 8:15:40 time: 0.4529 data_time: 0.0749 memory: 14682 loss_ce: 0.0187 loss: 0.0187 2022/09/20 22:13:32 - mmengine - INFO - Epoch(train) [2][3400/42151] lr: 3.0000e-04 eta: 1 day, 8:14:41 time: 0.6195 data_time: 0.1514 memory: 14682 loss_ce: 0.0190 loss: 0.0190 2022/09/20 22:14:26 - mmengine - INFO - Epoch(train) [2][3500/42151] lr: 3.0000e-04 eta: 1 day, 8:13:37 time: 0.4676 data_time: 0.0854 memory: 14682 loss_ce: 0.0208 loss: 0.0208 2022/09/20 22:15:22 - mmengine - INFO - Epoch(train) [2][3600/42151] lr: 3.0000e-04 eta: 1 day, 8:12:41 time: 0.5998 data_time: 0.2146 memory: 14682 loss_ce: 0.0180 loss: 0.0180 2022/09/20 22:16:18 - mmengine - INFO - Epoch(train) [2][3700/42151] lr: 3.0000e-04 eta: 1 day, 8:11:46 time: 0.5471 data_time: 0.1668 memory: 14682 loss_ce: 0.0184 loss: 0.0184 2022/09/20 22:17:13 - mmengine - INFO - Epoch(train) [2][3800/42151] lr: 3.0000e-04 eta: 1 day, 8:10:44 time: 0.5690 data_time: 0.1028 memory: 14682 loss_ce: 0.0179 loss: 0.0179 2022/09/20 22:17:41 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 22:18:11 - mmengine - INFO - Epoch(train) [2][3900/42151] lr: 3.0000e-04 eta: 1 day, 8:09:55 time: 0.5983 data_time: 0.0813 memory: 14682 loss_ce: 0.0188 loss: 0.0188 2022/09/20 22:19:06 - mmengine - INFO - Epoch(train) [2][4000/42151] lr: 3.0000e-04 eta: 1 day, 8:08:57 time: 0.5125 data_time: 0.1292 memory: 14682 loss_ce: 0.0196 loss: 0.0196 2022/09/20 22:20:04 - mmengine - INFO - Epoch(train) [2][4100/42151] lr: 3.0000e-04 eta: 1 day, 8:08:09 time: 0.6189 data_time: 0.2302 memory: 14682 loss_ce: 0.0178 loss: 0.0178 2022/09/20 22:21:01 - mmengine - INFO - Epoch(train) [2][4200/42151] lr: 3.0000e-04 eta: 1 day, 8:07:15 time: 0.6651 data_time: 0.2320 memory: 14682 loss_ce: 0.0192 loss: 0.0192 2022/09/20 22:21:56 - mmengine - INFO - Epoch(train) [2][4300/42151] lr: 3.0000e-04 eta: 1 day, 8:06:17 time: 0.5826 data_time: 0.1972 memory: 14682 loss_ce: 0.0206 loss: 0.0206 2022/09/20 22:22:52 - mmengine - INFO - Epoch(train) [2][4400/42151] lr: 3.0000e-04 eta: 1 day, 8:05:20 time: 0.5200 data_time: 0.1274 memory: 14682 loss_ce: 0.0193 loss: 0.0193 2022/09/20 22:23:47 - mmengine - INFO - Epoch(train) [2][4500/42151] lr: 3.0000e-04 eta: 1 day, 8:04:20 time: 0.4464 data_time: 0.0628 memory: 14682 loss_ce: 0.0181 loss: 0.0181 2022/09/20 22:24:42 - mmengine - INFO - Epoch(train) [2][4600/42151] lr: 3.0000e-04 eta: 1 day, 8:03:18 time: 0.5245 data_time: 0.1318 memory: 14682 loss_ce: 0.0190 loss: 0.0190 2022/09/20 22:25:39 - mmengine - INFO - Epoch(train) [2][4700/42151] lr: 3.0000e-04 eta: 1 day, 8:02:27 time: 0.4925 data_time: 0.0887 memory: 14682 loss_ce: 0.0193 loss: 0.0193 2022/09/20 22:26:35 - mmengine - INFO - Epoch(train) [2][4800/42151] lr: 3.0000e-04 eta: 1 day, 8:01:32 time: 0.6613 data_time: 0.2103 memory: 14682 loss_ce: 0.0198 loss: 0.0198 2022/09/20 22:27:01 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 22:27:30 - mmengine - INFO - Epoch(train) [2][4900/42151] lr: 3.0000e-04 eta: 1 day, 8:00:33 time: 0.6213 data_time: 0.1875 memory: 14682 loss_ce: 0.0193 loss: 0.0193 2022/09/20 22:28:25 - mmengine - INFO - Epoch(train) [2][5000/42151] lr: 3.0000e-04 eta: 1 day, 7:59:32 time: 0.5605 data_time: 0.1461 memory: 14682 loss_ce: 0.0185 loss: 0.0185 2022/09/20 22:29:21 - mmengine - INFO - Epoch(train) [2][5100/42151] lr: 3.0000e-04 eta: 1 day, 7:58:36 time: 0.5681 data_time: 0.1299 memory: 14682 loss_ce: 0.0180 loss: 0.0180 2022/09/20 22:30:16 - mmengine - INFO - Epoch(train) [2][5200/42151] lr: 3.0000e-04 eta: 1 day, 7:57:37 time: 0.5547 data_time: 0.1675 memory: 14682 loss_ce: 0.0186 loss: 0.0186 2022/09/20 22:31:13 - mmengine - INFO - Epoch(train) [2][5300/42151] lr: 3.0000e-04 eta: 1 day, 7:56:42 time: 0.5149 data_time: 0.1199 memory: 14682 loss_ce: 0.0191 loss: 0.0191 2022/09/20 22:32:08 - mmengine - INFO - Epoch(train) [2][5400/42151] lr: 3.0000e-04 eta: 1 day, 7:55:44 time: 0.5601 data_time: 0.1743 memory: 14682 loss_ce: 0.0187 loss: 0.0187 2022/09/20 22:33:04 - mmengine - INFO - Epoch(train) [2][5500/42151] lr: 3.0000e-04 eta: 1 day, 7:54:46 time: 0.6691 data_time: 0.2099 memory: 14682 loss_ce: 0.0181 loss: 0.0181 2022/09/20 22:34:00 - mmengine - INFO - Epoch(train) [2][5600/42151] lr: 3.0000e-04 eta: 1 day, 7:53:52 time: 0.5930 data_time: 0.1625 memory: 14682 loss_ce: 0.0183 loss: 0.0183 2022/09/20 22:34:55 - mmengine - INFO - Epoch(train) [2][5700/42151] lr: 3.0000e-04 eta: 1 day, 7:52:50 time: 0.5046 data_time: 0.1060 memory: 14682 loss_ce: 0.0191 loss: 0.0191 2022/09/20 22:35:51 - mmengine - INFO - Epoch(train) [2][5800/42151] lr: 3.0000e-04 eta: 1 day, 7:51:55 time: 0.5433 data_time: 0.1503 memory: 14682 loss_ce: 0.0212 loss: 0.0212 2022/09/20 22:36:18 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 22:36:47 - mmengine - INFO - Epoch(train) [2][5900/42151] lr: 3.0000e-04 eta: 1 day, 7:50:58 time: 0.5050 data_time: 0.1150 memory: 14682 loss_ce: 0.0210 loss: 0.0210 2022/09/20 22:37:42 - mmengine - INFO - Epoch(train) [2][6000/42151] lr: 3.0000e-04 eta: 1 day, 7:50:00 time: 0.6029 data_time: 0.1915 memory: 14682 loss_ce: 0.0180 loss: 0.0180 2022/09/20 22:38:37 - mmengine - INFO - Epoch(train) [2][6100/42151] lr: 3.0000e-04 eta: 1 day, 7:48:59 time: 0.5409 data_time: 0.1576 memory: 14682 loss_ce: 0.0181 loss: 0.0181 2022/09/20 22:39:32 - mmengine - INFO - Epoch(train) [2][6200/42151] lr: 3.0000e-04 eta: 1 day, 7:47:58 time: 0.5673 data_time: 0.1781 memory: 14682 loss_ce: 0.0204 loss: 0.0204 2022/09/20 22:40:29 - mmengine - INFO - Epoch(train) [2][6300/42151] lr: 3.0000e-04 eta: 1 day, 7:47:06 time: 0.5918 data_time: 0.2074 memory: 14682 loss_ce: 0.0179 loss: 0.0179 2022/09/20 22:41:24 - mmengine - INFO - Epoch(train) [2][6400/42151] lr: 3.0000e-04 eta: 1 day, 7:46:09 time: 0.5395 data_time: 0.1517 memory: 14682 loss_ce: 0.0180 loss: 0.0180 2022/09/20 22:42:21 - mmengine - INFO - Epoch(train) [2][6500/42151] lr: 3.0000e-04 eta: 1 day, 7:45:15 time: 0.4865 data_time: 0.1024 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/20 22:43:18 - mmengine - INFO - Epoch(train) [2][6600/42151] lr: 3.0000e-04 eta: 1 day, 7:44:25 time: 0.5675 data_time: 0.1821 memory: 14682 loss_ce: 0.0217 loss: 0.0217 2022/09/20 22:44:13 - mmengine - INFO - Epoch(train) [2][6700/42151] lr: 3.0000e-04 eta: 1 day, 7:43:23 time: 0.5527 data_time: 0.1683 memory: 14682 loss_ce: 0.0209 loss: 0.0209 2022/09/20 22:45:09 - mmengine - INFO - Epoch(train) [2][6800/42151] lr: 3.0000e-04 eta: 1 day, 7:42:27 time: 0.5634 data_time: 0.1777 memory: 14682 loss_ce: 0.0178 loss: 0.0178 2022/09/20 22:45:36 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 22:46:03 - mmengine - INFO - Epoch(train) [2][6900/42151] lr: 3.0000e-04 eta: 1 day, 7:41:26 time: 0.5404 data_time: 0.1348 memory: 14682 loss_ce: 0.0188 loss: 0.0188 2022/09/20 22:46:59 - mmengine - INFO - Epoch(train) [2][7000/42151] lr: 3.0000e-04 eta: 1 day, 7:40:28 time: 0.5816 data_time: 0.1903 memory: 14682 loss_ce: 0.0185 loss: 0.0185 2022/09/20 22:47:54 - mmengine - INFO - Epoch(train) [2][7100/42151] lr: 3.0000e-04 eta: 1 day, 7:39:27 time: 0.5258 data_time: 0.1293 memory: 14682 loss_ce: 0.0203 loss: 0.0203 2022/09/20 22:48:48 - mmengine - INFO - Epoch(train) [2][7200/42151] lr: 3.0000e-04 eta: 1 day, 7:38:25 time: 0.5565 data_time: 0.1732 memory: 14682 loss_ce: 0.0185 loss: 0.0185 2022/09/20 22:49:43 - mmengine - INFO - Epoch(train) [2][7300/42151] lr: 3.0000e-04 eta: 1 day, 7:37:25 time: 0.5656 data_time: 0.1686 memory: 14682 loss_ce: 0.0207 loss: 0.0207 2022/09/20 22:50:38 - mmengine - INFO - Epoch(train) [2][7400/42151] lr: 3.0000e-04 eta: 1 day, 7:36:25 time: 0.5832 data_time: 0.1993 memory: 14682 loss_ce: 0.0192 loss: 0.0192 2022/09/20 22:51:32 - mmengine - INFO - Epoch(train) [2][7500/42151] lr: 3.0000e-04 eta: 1 day, 7:35:19 time: 0.5120 data_time: 0.1199 memory: 14682 loss_ce: 0.0199 loss: 0.0199 2022/09/20 22:52:26 - mmengine - INFO - Epoch(train) [2][7600/42151] lr: 3.0000e-04 eta: 1 day, 7:34:16 time: 0.5332 data_time: 0.1514 memory: 14682 loss_ce: 0.0206 loss: 0.0206 2022/09/20 22:53:20 - mmengine - INFO - Epoch(train) [2][7700/42151] lr: 3.0000e-04 eta: 1 day, 7:33:13 time: 0.4851 data_time: 0.1045 memory: 14682 loss_ce: 0.0184 loss: 0.0184 2022/09/20 22:54:16 - mmengine - INFO - Epoch(train) [2][7800/42151] lr: 3.0000e-04 eta: 1 day, 7:32:16 time: 0.6561 data_time: 0.2159 memory: 14682 loss_ce: 0.0205 loss: 0.0205 2022/09/20 22:54:42 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 22:55:09 - mmengine - INFO - Epoch(train) [2][7900/42151] lr: 3.0000e-04 eta: 1 day, 7:31:09 time: 0.5412 data_time: 0.1590 memory: 14682 loss_ce: 0.0179 loss: 0.0179 2022/09/20 22:56:03 - mmengine - INFO - Epoch(train) [2][8000/42151] lr: 3.0000e-04 eta: 1 day, 7:30:05 time: 0.6047 data_time: 0.2110 memory: 14682 loss_ce: 0.0180 loss: 0.0180 2022/09/20 22:56:58 - mmengine - INFO - Epoch(train) [2][8100/42151] lr: 3.0000e-04 eta: 1 day, 7:29:04 time: 0.5000 data_time: 0.1180 memory: 14682 loss_ce: 0.0157 loss: 0.0157 2022/09/20 22:57:53 - mmengine - INFO - Epoch(train) [2][8200/42151] lr: 3.0000e-04 eta: 1 day, 7:28:06 time: 0.5794 data_time: 0.1548 memory: 14682 loss_ce: 0.0186 loss: 0.0186 2022/09/20 22:58:47 - mmengine - INFO - Epoch(train) [2][8300/42151] lr: 3.0000e-04 eta: 1 day, 7:27:02 time: 0.4855 data_time: 0.1052 memory: 14682 loss_ce: 0.0166 loss: 0.0166 2022/09/20 22:59:43 - mmengine - INFO - Epoch(train) [2][8400/42151] lr: 3.0000e-04 eta: 1 day, 7:26:05 time: 0.5560 data_time: 0.1709 memory: 14682 loss_ce: 0.0198 loss: 0.0198 2022/09/20 23:00:38 - mmengine - INFO - Epoch(train) [2][8500/42151] lr: 3.0000e-04 eta: 1 day, 7:25:05 time: 0.5884 data_time: 0.1530 memory: 14682 loss_ce: 0.0191 loss: 0.0191 2022/09/20 23:01:34 - mmengine - INFO - Epoch(train) [2][8600/42151] lr: 3.0000e-04 eta: 1 day, 7:24:08 time: 0.6367 data_time: 0.2053 memory: 14682 loss_ce: 0.0168 loss: 0.0168 2022/09/20 23:02:30 - mmengine - INFO - Epoch(train) [2][8700/42151] lr: 3.0000e-04 eta: 1 day, 7:23:12 time: 0.5528 data_time: 0.1658 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/20 23:03:25 - mmengine - INFO - Epoch(train) [2][8800/42151] lr: 3.0000e-04 eta: 1 day, 7:22:14 time: 0.5780 data_time: 0.1901 memory: 14682 loss_ce: 0.0209 loss: 0.0209 2022/09/20 23:03:52 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 23:04:20 - mmengine - INFO - Epoch(train) [2][8900/42151] lr: 3.0000e-04 eta: 1 day, 7:21:15 time: 0.5554 data_time: 0.1706 memory: 14682 loss_ce: 0.0187 loss: 0.0187 2022/09/20 23:05:16 - mmengine - INFO - Epoch(train) [2][9000/42151] lr: 3.0000e-04 eta: 1 day, 7:20:17 time: 0.5488 data_time: 0.1625 memory: 14682 loss_ce: 0.0205 loss: 0.0205 2022/09/20 23:06:12 - mmengine - INFO - Epoch(train) [2][9100/42151] lr: 3.0000e-04 eta: 1 day, 7:19:24 time: 0.5427 data_time: 0.1574 memory: 14682 loss_ce: 0.0190 loss: 0.0190 2022/09/20 23:07:07 - mmengine - INFO - Epoch(train) [2][9200/42151] lr: 3.0000e-04 eta: 1 day, 7:18:25 time: 0.5905 data_time: 0.2010 memory: 14682 loss_ce: 0.0184 loss: 0.0184 2022/09/20 23:08:02 - mmengine - INFO - Epoch(train) [2][9300/42151] lr: 3.0000e-04 eta: 1 day, 7:17:25 time: 0.5234 data_time: 0.1365 memory: 14682 loss_ce: 0.0197 loss: 0.0197 2022/09/20 23:08:57 - mmengine - INFO - Epoch(train) [2][9400/42151] lr: 3.0000e-04 eta: 1 day, 7:16:26 time: 0.5398 data_time: 0.1553 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/20 23:09:51 - mmengine - INFO - Epoch(train) [2][9500/42151] lr: 3.0000e-04 eta: 1 day, 7:15:23 time: 0.5344 data_time: 0.1524 memory: 14682 loss_ce: 0.0201 loss: 0.0201 2022/09/20 23:10:48 - mmengine - INFO - Epoch(train) [2][9600/42151] lr: 3.0000e-04 eta: 1 day, 7:14:29 time: 0.5815 data_time: 0.1790 memory: 14682 loss_ce: 0.0186 loss: 0.0186 2022/09/20 23:11:43 - mmengine - INFO - Epoch(train) [2][9700/42151] lr: 3.0000e-04 eta: 1 day, 7:13:30 time: 0.5376 data_time: 0.1485 memory: 14682 loss_ce: 0.0194 loss: 0.0194 2022/09/20 23:12:38 - mmengine - INFO - Epoch(train) [2][9800/42151] lr: 3.0000e-04 eta: 1 day, 7:12:31 time: 0.6068 data_time: 0.1934 memory: 14682 loss_ce: 0.0195 loss: 0.0195 2022/09/20 23:13:05 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 23:13:31 - mmengine - INFO - Epoch(train) [2][9900/42151] lr: 3.0000e-04 eta: 1 day, 7:11:23 time: 0.4847 data_time: 0.1053 memory: 14682 loss_ce: 0.0183 loss: 0.0183 2022/09/20 23:14:26 - mmengine - INFO - Epoch(train) [2][10000/42151] lr: 3.0000e-04 eta: 1 day, 7:10:25 time: 0.5556 data_time: 0.1739 memory: 14682 loss_ce: 0.0186 loss: 0.0186 2022/09/20 23:15:22 - mmengine - INFO - Epoch(train) [2][10100/42151] lr: 3.0000e-04 eta: 1 day, 7:09:28 time: 0.5063 data_time: 0.1274 memory: 14682 loss_ce: 0.0189 loss: 0.0189 2022/09/20 23:16:19 - mmengine - INFO - Epoch(train) [2][10200/42151] lr: 3.0000e-04 eta: 1 day, 7:08:38 time: 0.5565 data_time: 0.1763 memory: 14682 loss_ce: 0.0166 loss: 0.0166 2022/09/20 23:17:14 - mmengine - INFO - Epoch(train) [2][10300/42151] lr: 3.0000e-04 eta: 1 day, 7:07:38 time: 0.5547 data_time: 0.1499 memory: 14682 loss_ce: 0.0187 loss: 0.0187 2022/09/20 23:18:10 - mmengine - INFO - Epoch(train) [2][10400/42151] lr: 3.0000e-04 eta: 1 day, 7:06:39 time: 0.5878 data_time: 0.2014 memory: 14682 loss_ce: 0.0175 loss: 0.0175 2022/09/20 23:19:05 - mmengine - INFO - Epoch(train) [2][10500/42151] lr: 3.0000e-04 eta: 1 day, 7:05:40 time: 0.5414 data_time: 0.1317 memory: 14682 loss_ce: 0.0153 loss: 0.0153 2022/09/20 23:20:00 - mmengine - INFO - Epoch(train) [2][10600/42151] lr: 3.0000e-04 eta: 1 day, 7:04:43 time: 0.6265 data_time: 0.2176 memory: 14682 loss_ce: 0.0201 loss: 0.0201 2022/09/20 23:20:55 - mmengine - INFO - Epoch(train) [2][10700/42151] lr: 3.0000e-04 eta: 1 day, 7:03:43 time: 0.5644 data_time: 0.1312 memory: 14682 loss_ce: 0.0163 loss: 0.0163 2022/09/20 23:21:52 - mmengine - INFO - Epoch(train) [2][10800/42151] lr: 3.0000e-04 eta: 1 day, 7:02:51 time: 0.5598 data_time: 0.1736 memory: 14682 loss_ce: 0.0207 loss: 0.0207 2022/09/20 23:22:19 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 23:22:48 - mmengine - INFO - Epoch(train) [2][10900/42151] lr: 3.0000e-04 eta: 1 day, 7:01:55 time: 0.5515 data_time: 0.1651 memory: 14682 loss_ce: 0.0200 loss: 0.0200 2022/09/20 23:23:45 - mmengine - INFO - Epoch(train) [2][11000/42151] lr: 3.0000e-04 eta: 1 day, 7:01:04 time: 0.6093 data_time: 0.1877 memory: 14682 loss_ce: 0.0170 loss: 0.0170 2022/09/20 23:24:39 - mmengine - INFO - Epoch(train) [2][11100/42151] lr: 3.0000e-04 eta: 1 day, 7:00:01 time: 0.5207 data_time: 0.1355 memory: 14682 loss_ce: 0.0202 loss: 0.0202 2022/09/20 23:25:36 - mmengine - INFO - Epoch(train) [2][11200/42151] lr: 3.0000e-04 eta: 1 day, 6:59:08 time: 0.5530 data_time: 0.1708 memory: 14682 loss_ce: 0.0187 loss: 0.0187 2022/09/20 23:26:31 - mmengine - INFO - Epoch(train) [2][11300/42151] lr: 3.0000e-04 eta: 1 day, 6:58:11 time: 0.5268 data_time: 0.1440 memory: 14682 loss_ce: 0.0224 loss: 0.0224 2022/09/20 23:27:28 - mmengine - INFO - Epoch(train) [2][11400/42151] lr: 3.0000e-04 eta: 1 day, 6:57:16 time: 0.5600 data_time: 0.1675 memory: 14682 loss_ce: 0.0197 loss: 0.0197 2022/09/20 23:28:22 - mmengine - INFO - Epoch(train) [2][11500/42151] lr: 3.0000e-04 eta: 1 day, 6:56:15 time: 0.5554 data_time: 0.1713 memory: 14682 loss_ce: 0.0177 loss: 0.0177 2022/09/20 23:29:17 - mmengine - INFO - Epoch(train) [2][11600/42151] lr: 3.0000e-04 eta: 1 day, 6:55:16 time: 0.6431 data_time: 0.2081 memory: 14682 loss_ce: 0.0208 loss: 0.0208 2022/09/20 23:30:12 - mmengine - INFO - Epoch(train) [2][11700/42151] lr: 3.0000e-04 eta: 1 day, 6:54:16 time: 0.5075 data_time: 0.1254 memory: 14682 loss_ce: 0.0172 loss: 0.0172 2022/09/20 23:31:07 - mmengine - INFO - Epoch(train) [2][11800/42151] lr: 3.0000e-04 eta: 1 day, 6:53:17 time: 0.5766 data_time: 0.1891 memory: 14682 loss_ce: 0.0182 loss: 0.0182 2022/09/20 23:31:34 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 23:32:03 - mmengine - INFO - Epoch(train) [2][11900/42151] lr: 3.0000e-04 eta: 1 day, 6:52:20 time: 0.4967 data_time: 0.1152 memory: 14682 loss_ce: 0.0204 loss: 0.0204 2022/09/20 23:33:00 - mmengine - INFO - Epoch(train) [2][12000/42151] lr: 3.0000e-04 eta: 1 day, 6:51:29 time: 0.6444 data_time: 0.2167 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/20 23:33:57 - mmengine - INFO - Epoch(train) [2][12100/42151] lr: 3.0000e-04 eta: 1 day, 6:50:38 time: 0.7003 data_time: 0.2235 memory: 14682 loss_ce: 0.0185 loss: 0.0185 2022/09/20 23:34:53 - mmengine - INFO - Epoch(train) [2][12200/42151] lr: 3.0000e-04 eta: 1 day, 6:49:43 time: 0.6002 data_time: 0.2138 memory: 14682 loss_ce: 0.0195 loss: 0.0195 2022/09/20 23:35:49 - mmengine - INFO - Epoch(train) [2][12300/42151] lr: 3.0000e-04 eta: 1 day, 6:48:47 time: 0.4986 data_time: 0.1164 memory: 14682 loss_ce: 0.0166 loss: 0.0166 2022/09/20 23:36:47 - mmengine - INFO - Epoch(train) [2][12400/42151] lr: 3.0000e-04 eta: 1 day, 6:47:56 time: 0.6043 data_time: 0.1966 memory: 14682 loss_ce: 0.0188 loss: 0.0188 2022/09/20 23:37:42 - mmengine - INFO - Epoch(train) [2][12500/42151] lr: 3.0000e-04 eta: 1 day, 6:46:57 time: 0.5023 data_time: 0.1203 memory: 14682 loss_ce: 0.0194 loss: 0.0194 2022/09/20 23:38:38 - mmengine - INFO - Epoch(train) [2][12600/42151] lr: 3.0000e-04 eta: 1 day, 6:46:01 time: 0.5763 data_time: 0.1910 memory: 14682 loss_ce: 0.0177 loss: 0.0177 2022/09/20 23:39:32 - mmengine - INFO - Epoch(train) [2][12700/42151] lr: 3.0000e-04 eta: 1 day, 6:45:01 time: 0.5641 data_time: 0.1844 memory: 14682 loss_ce: 0.0200 loss: 0.0200 2022/09/20 23:40:29 - mmengine - INFO - Epoch(train) [2][12800/42151] lr: 3.0000e-04 eta: 1 day, 6:44:06 time: 0.5858 data_time: 0.1963 memory: 14682 loss_ce: 0.0169 loss: 0.0169 2022/09/20 23:40:56 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 23:41:24 - mmengine - INFO - Epoch(train) [2][12900/42151] lr: 3.0000e-04 eta: 1 day, 6:43:08 time: 0.4879 data_time: 0.1067 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/20 23:42:19 - mmengine - INFO - Epoch(train) [2][13000/42151] lr: 3.0000e-04 eta: 1 day, 6:42:10 time: 0.5263 data_time: 0.1452 memory: 14682 loss_ce: 0.0199 loss: 0.0199 2022/09/20 23:43:14 - mmengine - INFO - Epoch(train) [2][13100/42151] lr: 3.0000e-04 eta: 1 day, 6:41:09 time: 0.5121 data_time: 0.1270 memory: 14682 loss_ce: 0.0201 loss: 0.0201 2022/09/20 23:44:10 - mmengine - INFO - Epoch(train) [2][13200/42151] lr: 3.0000e-04 eta: 1 day, 6:40:16 time: 0.6076 data_time: 0.1865 memory: 14682 loss_ce: 0.0153 loss: 0.0153 2022/09/20 23:45:06 - mmengine - INFO - Epoch(train) [2][13300/42151] lr: 3.0000e-04 eta: 1 day, 6:39:19 time: 0.5345 data_time: 0.1524 memory: 14682 loss_ce: 0.0192 loss: 0.0192 2022/09/20 23:46:02 - mmengine - INFO - Epoch(train) [2][13400/42151] lr: 3.0000e-04 eta: 1 day, 6:38:23 time: 0.5788 data_time: 0.1881 memory: 14682 loss_ce: 0.0179 loss: 0.0179 2022/09/20 23:46:57 - mmengine - INFO - Epoch(train) [2][13500/42151] lr: 3.0000e-04 eta: 1 day, 6:37:26 time: 0.5289 data_time: 0.0966 memory: 14682 loss_ce: 0.0192 loss: 0.0192 2022/09/20 23:47:53 - mmengine - INFO - Epoch(train) [2][13600/42151] lr: 3.0000e-04 eta: 1 day, 6:36:31 time: 0.5822 data_time: 0.1948 memory: 14682 loss_ce: 0.0165 loss: 0.0165 2022/09/20 23:48:48 - mmengine - INFO - Epoch(train) [2][13700/42151] lr: 3.0000e-04 eta: 1 day, 6:35:31 time: 0.4963 data_time: 0.1080 memory: 14682 loss_ce: 0.0171 loss: 0.0171 2022/09/20 23:49:46 - mmengine - INFO - Epoch(train) [2][13800/42151] lr: 3.0000e-04 eta: 1 day, 6:34:40 time: 0.5528 data_time: 0.1729 memory: 14682 loss_ce: 0.0195 loss: 0.0195 2022/09/20 23:50:12 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 23:50:40 - mmengine - INFO - Epoch(train) [2][13900/42151] lr: 3.0000e-04 eta: 1 day, 6:33:37 time: 0.5944 data_time: 0.1700 memory: 14682 loss_ce: 0.0202 loss: 0.0202 2022/09/20 23:51:34 - mmengine - INFO - Epoch(train) [2][14000/42151] lr: 3.0000e-04 eta: 1 day, 6:32:35 time: 0.5598 data_time: 0.1735 memory: 14682 loss_ce: 0.0153 loss: 0.0153 2022/09/20 23:52:30 - mmengine - INFO - Epoch(train) [2][14100/42151] lr: 3.0000e-04 eta: 1 day, 6:31:40 time: 0.5049 data_time: 0.1062 memory: 14682 loss_ce: 0.0184 loss: 0.0184 2022/09/20 23:53:26 - mmengine - INFO - Epoch(train) [2][14200/42151] lr: 3.0000e-04 eta: 1 day, 6:30:45 time: 0.5400 data_time: 0.1496 memory: 14682 loss_ce: 0.0178 loss: 0.0178 2022/09/20 23:54:21 - mmengine - INFO - Epoch(train) [2][14300/42151] lr: 3.0000e-04 eta: 1 day, 6:29:46 time: 0.5360 data_time: 0.1187 memory: 14682 loss_ce: 0.0177 loss: 0.0177 2022/09/20 23:55:17 - mmengine - INFO - Epoch(train) [2][14400/42151] lr: 3.0000e-04 eta: 1 day, 6:28:50 time: 0.5491 data_time: 0.1676 memory: 14682 loss_ce: 0.0181 loss: 0.0181 2022/09/20 23:56:12 - mmengine - INFO - Epoch(train) [2][14500/42151] lr: 3.0000e-04 eta: 1 day, 6:27:50 time: 0.5889 data_time: 0.1844 memory: 14682 loss_ce: 0.0194 loss: 0.0194 2022/09/20 23:57:07 - mmengine - INFO - Epoch(train) [2][14600/42151] lr: 3.0000e-04 eta: 1 day, 6:26:51 time: 0.5732 data_time: 0.1851 memory: 14682 loss_ce: 0.0164 loss: 0.0164 2022/09/20 23:58:05 - mmengine - INFO - Epoch(train) [2][14700/42151] lr: 3.0000e-04 eta: 1 day, 6:26:02 time: 0.4811 data_time: 0.0990 memory: 14682 loss_ce: 0.0181 loss: 0.0181 2022/09/20 23:59:01 - mmengine - INFO - Epoch(train) [2][14800/42151] lr: 3.0000e-04 eta: 1 day, 6:25:10 time: 0.5638 data_time: 0.1374 memory: 14682 loss_ce: 0.0189 loss: 0.0189 2022/09/20 23:59:28 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/20 23:59:56 - mmengine - INFO - Epoch(train) [2][14900/42151] lr: 3.0000e-04 eta: 1 day, 6:24:08 time: 0.5044 data_time: 0.1111 memory: 14682 loss_ce: 0.0162 loss: 0.0162 2022/09/21 00:00:56 - mmengine - INFO - Epoch(train) [2][15000/42151] lr: 3.0000e-04 eta: 1 day, 6:23:26 time: 0.5299 data_time: 0.1525 memory: 14682 loss_ce: 0.0161 loss: 0.0161 2022/09/21 00:01:51 - mmengine - INFO - Epoch(train) [2][15100/42151] lr: 3.0000e-04 eta: 1 day, 6:22:27 time: 0.6014 data_time: 0.1577 memory: 14682 loss_ce: 0.0163 loss: 0.0163 2022/09/21 00:02:46 - mmengine - INFO - Epoch(train) [2][15200/42151] lr: 3.0000e-04 eta: 1 day, 6:21:29 time: 0.5464 data_time: 0.1616 memory: 14682 loss_ce: 0.0180 loss: 0.0180 2022/09/21 00:03:41 - mmengine - INFO - Epoch(train) [2][15300/42151] lr: 3.0000e-04 eta: 1 day, 6:20:30 time: 0.4875 data_time: 0.1054 memory: 14682 loss_ce: 0.0189 loss: 0.0189 2022/09/21 00:04:37 - mmengine - INFO - Epoch(train) [2][15400/42151] lr: 3.0000e-04 eta: 1 day, 6:19:35 time: 0.5951 data_time: 0.2058 memory: 14682 loss_ce: 0.0186 loss: 0.0186 2022/09/21 00:05:34 - mmengine - INFO - Epoch(train) [2][15500/42151] lr: 3.0000e-04 eta: 1 day, 6:18:41 time: 0.5706 data_time: 0.1283 memory: 14682 loss_ce: 0.0170 loss: 0.0170 2022/09/21 00:06:30 - mmengine - INFO - Epoch(train) [2][15600/42151] lr: 3.0000e-04 eta: 1 day, 6:17:47 time: 0.5923 data_time: 0.1726 memory: 14682 loss_ce: 0.0180 loss: 0.0180 2022/09/21 00:07:25 - mmengine - INFO - Epoch(train) [2][15700/42151] lr: 3.0000e-04 eta: 1 day, 6:16:46 time: 0.5460 data_time: 0.1555 memory: 14682 loss_ce: 0.0187 loss: 0.0187 2022/09/21 00:08:21 - mmengine - INFO - Epoch(train) [2][15800/42151] lr: 3.0000e-04 eta: 1 day, 6:15:53 time: 0.5508 data_time: 0.1676 memory: 14682 loss_ce: 0.0172 loss: 0.0172 2022/09/21 00:08:49 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 00:09:17 - mmengine - INFO - Epoch(train) [2][15900/42151] lr: 3.0000e-04 eta: 1 day, 6:14:58 time: 0.5000 data_time: 0.1156 memory: 14682 loss_ce: 0.0198 loss: 0.0198 2022/09/21 00:10:13 - mmengine - INFO - Epoch(train) [2][16000/42151] lr: 3.0000e-04 eta: 1 day, 6:13:59 time: 0.5632 data_time: 0.1768 memory: 14682 loss_ce: 0.0177 loss: 0.0177 2022/09/21 00:11:08 - mmengine - INFO - Epoch(train) [2][16100/42151] lr: 3.0000e-04 eta: 1 day, 6:13:01 time: 0.4883 data_time: 0.1054 memory: 14682 loss_ce: 0.0186 loss: 0.0186 2022/09/21 00:12:04 - mmengine - INFO - Epoch(train) [2][16200/42151] lr: 3.0000e-04 eta: 1 day, 6:12:05 time: 0.6333 data_time: 0.1971 memory: 14682 loss_ce: 0.0191 loss: 0.0191 2022/09/21 00:12:57 - mmengine - INFO - Epoch(train) [2][16300/42151] lr: 3.0000e-04 eta: 1 day, 6:11:02 time: 0.5384 data_time: 0.1587 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/21 00:13:54 - mmengine - INFO - Epoch(train) [2][16400/42151] lr: 3.0000e-04 eta: 1 day, 6:10:08 time: 0.5687 data_time: 0.1716 memory: 14682 loss_ce: 0.0179 loss: 0.0179 2022/09/21 00:14:50 - mmengine - INFO - Epoch(train) [2][16500/42151] lr: 3.0000e-04 eta: 1 day, 6:09:13 time: 0.5111 data_time: 0.1286 memory: 14682 loss_ce: 0.0154 loss: 0.0154 2022/09/21 00:15:46 - mmengine - INFO - Epoch(train) [2][16600/42151] lr: 3.0000e-04 eta: 1 day, 6:08:17 time: 0.5535 data_time: 0.1705 memory: 14682 loss_ce: 0.0184 loss: 0.0184 2022/09/21 00:16:42 - mmengine - INFO - Epoch(train) [2][16700/42151] lr: 3.0000e-04 eta: 1 day, 6:07:20 time: 0.5880 data_time: 0.1220 memory: 14682 loss_ce: 0.0170 loss: 0.0170 2022/09/21 00:17:37 - mmengine - INFO - Epoch(train) [2][16800/42151] lr: 3.0000e-04 eta: 1 day, 6:06:24 time: 0.5443 data_time: 0.1597 memory: 14682 loss_ce: 0.0191 loss: 0.0191 2022/09/21 00:18:05 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 00:18:32 - mmengine - INFO - Epoch(train) [2][16900/42151] lr: 3.0000e-04 eta: 1 day, 6:05:25 time: 0.5549 data_time: 0.1576 memory: 14682 loss_ce: 0.0203 loss: 0.0203 2022/09/21 00:19:28 - mmengine - INFO - Epoch(train) [2][17000/42151] lr: 3.0000e-04 eta: 1 day, 6:04:27 time: 0.5501 data_time: 0.1574 memory: 14682 loss_ce: 0.0181 loss: 0.0181 2022/09/21 00:20:22 - mmengine - INFO - Epoch(train) [2][17100/42151] lr: 3.0000e-04 eta: 1 day, 6:03:27 time: 0.4923 data_time: 0.0949 memory: 14682 loss_ce: 0.0199 loss: 0.0199 2022/09/21 00:21:18 - mmengine - INFO - Epoch(train) [2][17200/42151] lr: 3.0000e-04 eta: 1 day, 6:02:29 time: 0.5494 data_time: 0.1710 memory: 14682 loss_ce: 0.0175 loss: 0.0175 2022/09/21 00:22:12 - mmengine - INFO - Epoch(train) [2][17300/42151] lr: 3.0000e-04 eta: 1 day, 6:01:29 time: 0.4903 data_time: 0.1081 memory: 14682 loss_ce: 0.0186 loss: 0.0186 2022/09/21 00:23:07 - mmengine - INFO - Epoch(train) [2][17400/42151] lr: 3.0000e-04 eta: 1 day, 6:00:30 time: 0.5456 data_time: 0.1619 memory: 14682 loss_ce: 0.0188 loss: 0.0188 2022/09/21 00:24:03 - mmengine - INFO - Epoch(train) [2][17500/42151] lr: 3.0000e-04 eta: 1 day, 5:59:34 time: 0.5715 data_time: 0.1839 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/21 00:24:59 - mmengine - INFO - Epoch(train) [2][17600/42151] lr: 3.0000e-04 eta: 1 day, 5:58:39 time: 0.5417 data_time: 0.1603 memory: 14682 loss_ce: 0.0156 loss: 0.0156 2022/09/21 00:25:55 - mmengine - INFO - Epoch(train) [2][17700/42151] lr: 3.0000e-04 eta: 1 day, 5:57:42 time: 0.5539 data_time: 0.1144 memory: 14682 loss_ce: 0.0177 loss: 0.0177 2022/09/21 00:26:50 - mmengine - INFO - Epoch(train) [2][17800/42151] lr: 3.0000e-04 eta: 1 day, 5:56:44 time: 0.5570 data_time: 0.1749 memory: 14682 loss_ce: 0.0179 loss: 0.0179 2022/09/21 00:27:17 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 00:27:45 - mmengine - INFO - Epoch(train) [2][17900/42151] lr: 3.0000e-04 eta: 1 day, 5:55:45 time: 0.5347 data_time: 0.1460 memory: 14682 loss_ce: 0.0181 loss: 0.0181 2022/09/21 00:28:41 - mmengine - INFO - Epoch(train) [2][18000/42151] lr: 3.0000e-04 eta: 1 day, 5:54:52 time: 0.5938 data_time: 0.2023 memory: 14682 loss_ce: 0.0168 loss: 0.0168 2022/09/21 00:29:37 - mmengine - INFO - Epoch(train) [2][18100/42151] lr: 3.0000e-04 eta: 1 day, 5:53:54 time: 0.5340 data_time: 0.1497 memory: 14682 loss_ce: 0.0182 loss: 0.0182 2022/09/21 00:30:32 - mmengine - INFO - Epoch(train) [2][18200/42151] lr: 3.0000e-04 eta: 1 day, 5:52:56 time: 0.5623 data_time: 0.1756 memory: 14682 loss_ce: 0.0167 loss: 0.0167 2022/09/21 00:31:28 - mmengine - INFO - Epoch(train) [2][18300/42151] lr: 3.0000e-04 eta: 1 day, 5:52:00 time: 0.5205 data_time: 0.1353 memory: 14682 loss_ce: 0.0158 loss: 0.0158 2022/09/21 00:32:24 - mmengine - INFO - Epoch(train) [2][18400/42151] lr: 3.0000e-04 eta: 1 day, 5:51:05 time: 0.6317 data_time: 0.1978 memory: 14682 loss_ce: 0.0165 loss: 0.0165 2022/09/21 00:33:19 - mmengine - INFO - Epoch(train) [2][18500/42151] lr: 3.0000e-04 eta: 1 day, 5:50:05 time: 0.4865 data_time: 0.1030 memory: 14682 loss_ce: 0.0180 loss: 0.0180 2022/09/21 00:34:15 - mmengine - INFO - Epoch(train) [2][18600/42151] lr: 3.0000e-04 eta: 1 day, 5:49:11 time: 0.5707 data_time: 0.1798 memory: 14682 loss_ce: 0.0198 loss: 0.0198 2022/09/21 00:35:10 - mmengine - INFO - Epoch(train) [2][18700/42151] lr: 3.0000e-04 eta: 1 day, 5:48:11 time: 0.5332 data_time: 0.1478 memory: 14682 loss_ce: 0.0176 loss: 0.0176 2022/09/21 00:36:04 - mmengine - INFO - Epoch(train) [2][18800/42151] lr: 3.0000e-04 eta: 1 day, 5:47:10 time: 0.5632 data_time: 0.1733 memory: 14682 loss_ce: 0.0166 loss: 0.0166 2022/09/21 00:36:31 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 00:36:58 - mmengine - INFO - Epoch(train) [2][18900/42151] lr: 3.0000e-04 eta: 1 day, 5:46:09 time: 0.5053 data_time: 0.1126 memory: 14682 loss_ce: 0.0180 loss: 0.0180 2022/09/21 00:37:54 - mmengine - INFO - Epoch(train) [2][19000/42151] lr: 3.0000e-04 eta: 1 day, 5:45:13 time: 0.5642 data_time: 0.1619 memory: 14682 loss_ce: 0.0191 loss: 0.0191 2022/09/21 00:38:49 - mmengine - INFO - Epoch(train) [2][19100/42151] lr: 3.0000e-04 eta: 1 day, 5:44:14 time: 0.5023 data_time: 0.1202 memory: 14682 loss_ce: 0.0193 loss: 0.0193 2022/09/21 00:39:44 - mmengine - INFO - Epoch(train) [2][19200/42151] lr: 3.0000e-04 eta: 1 day, 5:43:16 time: 0.5802 data_time: 0.1775 memory: 14682 loss_ce: 0.0170 loss: 0.0170 2022/09/21 00:40:39 - mmengine - INFO - Epoch(train) [2][19300/42151] lr: 3.0000e-04 eta: 1 day, 5:42:18 time: 0.6450 data_time: 0.2360 memory: 14682 loss_ce: 0.0152 loss: 0.0152 2022/09/21 00:41:35 - mmengine - INFO - Epoch(train) [2][19400/42151] lr: 3.0000e-04 eta: 1 day, 5:41:22 time: 0.5818 data_time: 0.1649 memory: 14682 loss_ce: 0.0178 loss: 0.0178 2022/09/21 00:42:30 - mmengine - INFO - Epoch(train) [2][19500/42151] lr: 3.0000e-04 eta: 1 day, 5:40:24 time: 0.4845 data_time: 0.1035 memory: 14682 loss_ce: 0.0172 loss: 0.0172 2022/09/21 00:43:25 - mmengine - INFO - Epoch(train) [2][19600/42151] lr: 3.0000e-04 eta: 1 day, 5:39:25 time: 0.5599 data_time: 0.1784 memory: 14682 loss_ce: 0.0195 loss: 0.0195 2022/09/21 00:44:20 - mmengine - INFO - Epoch(train) [2][19700/42151] lr: 3.0000e-04 eta: 1 day, 5:38:27 time: 0.4989 data_time: 0.1144 memory: 14682 loss_ce: 0.0171 loss: 0.0171 2022/09/21 00:45:16 - mmengine - INFO - Epoch(train) [2][19800/42151] lr: 3.0000e-04 eta: 1 day, 5:37:31 time: 0.5797 data_time: 0.1920 memory: 14682 loss_ce: 0.0169 loss: 0.0169 2022/09/21 00:45:44 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 00:46:11 - mmengine - INFO - Epoch(train) [2][19900/42151] lr: 3.0000e-04 eta: 1 day, 5:36:33 time: 0.5490 data_time: 0.1637 memory: 14682 loss_ce: 0.0170 loss: 0.0170 2022/09/21 00:47:07 - mmengine - INFO - Epoch(train) [2][20000/42151] lr: 3.0000e-04 eta: 1 day, 5:35:35 time: 0.5699 data_time: 0.1703 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/21 00:48:01 - mmengine - INFO - Epoch(train) [2][20100/42151] lr: 3.0000e-04 eta: 1 day, 5:34:36 time: 0.4887 data_time: 0.1082 memory: 14682 loss_ce: 0.0171 loss: 0.0171 2022/09/21 00:48:58 - mmengine - INFO - Epoch(train) [2][20200/42151] lr: 3.0000e-04 eta: 1 day, 5:33:42 time: 0.5823 data_time: 0.1994 memory: 14682 loss_ce: 0.0188 loss: 0.0188 2022/09/21 00:49:54 - mmengine - INFO - Epoch(train) [2][20300/42151] lr: 3.0000e-04 eta: 1 day, 5:32:47 time: 0.5207 data_time: 0.1195 memory: 14682 loss_ce: 0.0192 loss: 0.0192 2022/09/21 00:50:50 - mmengine - INFO - Epoch(train) [2][20400/42151] lr: 3.0000e-04 eta: 1 day, 5:31:50 time: 0.5396 data_time: 0.1587 memory: 14682 loss_ce: 0.0155 loss: 0.0155 2022/09/21 00:51:45 - mmengine - INFO - Epoch(train) [2][20500/42151] lr: 3.0000e-04 eta: 1 day, 5:30:53 time: 0.6726 data_time: 0.2108 memory: 14682 loss_ce: 0.0166 loss: 0.0166 2022/09/21 00:52:40 - mmengine - INFO - Epoch(train) [2][20600/42151] lr: 3.0000e-04 eta: 1 day, 5:29:53 time: 0.5293 data_time: 0.1507 memory: 14682 loss_ce: 0.0156 loss: 0.0156 2022/09/21 00:53:34 - mmengine - INFO - Epoch(train) [2][20700/42151] lr: 3.0000e-04 eta: 1 day, 5:28:53 time: 0.4738 data_time: 0.0939 memory: 14682 loss_ce: 0.0161 loss: 0.0161 2022/09/21 00:54:30 - mmengine - INFO - Epoch(train) [2][20800/42151] lr: 3.0000e-04 eta: 1 day, 5:27:58 time: 0.5626 data_time: 0.1814 memory: 14682 loss_ce: 0.0176 loss: 0.0176 2022/09/21 00:54:56 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 00:55:25 - mmengine - INFO - Epoch(train) [2][20900/42151] lr: 3.0000e-04 eta: 1 day, 5:26:58 time: 0.5023 data_time: 0.0996 memory: 14682 loss_ce: 0.0187 loss: 0.0187 2022/09/21 00:56:19 - mmengine - INFO - Epoch(train) [2][21000/42151] lr: 3.0000e-04 eta: 1 day, 5:25:59 time: 0.5711 data_time: 0.1872 memory: 14682 loss_ce: 0.0178 loss: 0.0178 2022/09/21 00:57:13 - mmengine - INFO - Epoch(train) [2][21100/42151] lr: 3.0000e-04 eta: 1 day, 5:24:56 time: 0.5897 data_time: 0.2104 memory: 14682 loss_ce: 0.0194 loss: 0.0194 2022/09/21 00:58:08 - mmengine - INFO - Epoch(train) [2][21200/42151] lr: 3.0000e-04 eta: 1 day, 5:23:57 time: 0.5587 data_time: 0.1740 memory: 14682 loss_ce: 0.0169 loss: 0.0169 2022/09/21 00:59:02 - mmengine - INFO - Epoch(train) [2][21300/42151] lr: 3.0000e-04 eta: 1 day, 5:22:57 time: 0.4951 data_time: 0.1120 memory: 14682 loss_ce: 0.0159 loss: 0.0159 2022/09/21 00:59:58 - mmengine - INFO - Epoch(train) [2][21400/42151] lr: 3.0000e-04 eta: 1 day, 5:22:01 time: 0.5583 data_time: 0.1542 memory: 14682 loss_ce: 0.0153 loss: 0.0153 2022/09/21 01:00:53 - mmengine - INFO - Epoch(train) [2][21500/42151] lr: 3.0000e-04 eta: 1 day, 5:21:03 time: 0.4974 data_time: 0.1133 memory: 14682 loss_ce: 0.0179 loss: 0.0179 2022/09/21 01:01:50 - mmengine - INFO - Epoch(train) [2][21600/42151] lr: 3.0000e-04 eta: 1 day, 5:20:10 time: 0.5486 data_time: 0.1669 memory: 14682 loss_ce: 0.0154 loss: 0.0154 2022/09/21 01:02:45 - mmengine - INFO - Epoch(train) [2][21700/42151] lr: 3.0000e-04 eta: 1 day, 5:19:12 time: 0.5806 data_time: 0.1986 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/21 01:03:39 - mmengine - INFO - Epoch(train) [2][21800/42151] lr: 3.0000e-04 eta: 1 day, 5:18:12 time: 0.5899 data_time: 0.1639 memory: 14682 loss_ce: 0.0184 loss: 0.0184 2022/09/21 01:04:06 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 01:04:34 - mmengine - INFO - Epoch(train) [2][21900/42151] lr: 3.0000e-04 eta: 1 day, 5:17:12 time: 0.4924 data_time: 0.1082 memory: 14682 loss_ce: 0.0175 loss: 0.0175 2022/09/21 01:05:29 - mmengine - INFO - Epoch(train) [2][22000/42151] lr: 3.0000e-04 eta: 1 day, 5:16:14 time: 0.5723 data_time: 0.1652 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/21 01:06:24 - mmengine - INFO - Epoch(train) [2][22100/42151] lr: 3.0000e-04 eta: 1 day, 5:15:15 time: 0.5031 data_time: 0.1203 memory: 14682 loss_ce: 0.0172 loss: 0.0172 2022/09/21 01:07:20 - mmengine - INFO - Epoch(train) [2][22200/42151] lr: 3.0000e-04 eta: 1 day, 5:14:19 time: 0.5543 data_time: 0.1697 memory: 14682 loss_ce: 0.0162 loss: 0.0162 2022/09/21 01:08:15 - mmengine - INFO - Epoch(train) [2][22300/42151] lr: 3.0000e-04 eta: 1 day, 5:13:22 time: 0.5570 data_time: 0.1727 memory: 14682 loss_ce: 0.0166 loss: 0.0166 2022/09/21 01:09:12 - mmengine - INFO - Epoch(train) [2][22400/42151] lr: 3.0000e-04 eta: 1 day, 5:12:29 time: 0.5566 data_time: 0.1731 memory: 14682 loss_ce: 0.0174 loss: 0.0174 2022/09/21 01:10:06 - mmengine - INFO - Epoch(train) [2][22500/42151] lr: 3.0000e-04 eta: 1 day, 5:11:28 time: 0.5318 data_time: 0.1231 memory: 14682 loss_ce: 0.0174 loss: 0.0174 2022/09/21 01:11:00 - mmengine - INFO - Epoch(train) [2][22600/42151] lr: 3.0000e-04 eta: 1 day, 5:10:26 time: 0.5366 data_time: 0.1562 memory: 14682 loss_ce: 0.0177 loss: 0.0177 2022/09/21 01:11:55 - mmengine - INFO - Epoch(train) [2][22700/42151] lr: 3.0000e-04 eta: 1 day, 5:09:28 time: 0.5164 data_time: 0.1289 memory: 14682 loss_ce: 0.0185 loss: 0.0185 2022/09/21 01:12:51 - mmengine - INFO - Epoch(train) [2][22800/42151] lr: 3.0000e-04 eta: 1 day, 5:08:33 time: 0.5612 data_time: 0.1755 memory: 14682 loss_ce: 0.0188 loss: 0.0188 2022/09/21 01:13:18 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 01:13:46 - mmengine - INFO - Epoch(train) [2][22900/42151] lr: 3.0000e-04 eta: 1 day, 5:07:35 time: 0.5466 data_time: 0.1562 memory: 14682 loss_ce: 0.0160 loss: 0.0160 2022/09/21 01:14:40 - mmengine - INFO - Epoch(train) [2][23000/42151] lr: 3.0000e-04 eta: 1 day, 5:06:35 time: 0.5436 data_time: 0.1641 memory: 14682 loss_ce: 0.0167 loss: 0.0167 2022/09/21 01:15:34 - mmengine - INFO - Epoch(train) [2][23100/42151] lr: 3.0000e-04 eta: 1 day, 5:05:33 time: 0.5027 data_time: 0.1201 memory: 14682 loss_ce: 0.0177 loss: 0.0177 2022/09/21 01:16:29 - mmengine - INFO - Epoch(train) [2][23200/42151] lr: 3.0000e-04 eta: 1 day, 5:04:34 time: 0.5520 data_time: 0.1517 memory: 14682 loss_ce: 0.0154 loss: 0.0154 2022/09/21 01:17:24 - mmengine - INFO - Epoch(train) [2][23300/42151] lr: 3.0000e-04 eta: 1 day, 5:03:35 time: 0.5368 data_time: 0.1495 memory: 14682 loss_ce: 0.0161 loss: 0.0161 2022/09/21 01:18:20 - mmengine - INFO - Epoch(train) [2][23400/42151] lr: 3.0000e-04 eta: 1 day, 5:02:41 time: 0.6712 data_time: 0.2135 memory: 14682 loss_ce: 0.0184 loss: 0.0184 2022/09/21 01:19:13 - mmengine - INFO - Epoch(train) [2][23500/42151] lr: 3.0000e-04 eta: 1 day, 5:01:38 time: 0.5329 data_time: 0.1500 memory: 14682 loss_ce: 0.0166 loss: 0.0166 2022/09/21 01:20:08 - mmengine - INFO - Epoch(train) [2][23600/42151] lr: 3.0000e-04 eta: 1 day, 5:00:39 time: 0.5369 data_time: 0.1138 memory: 14682 loss_ce: 0.0177 loss: 0.0177 2022/09/21 01:21:02 - mmengine - INFO - Epoch(train) [2][23700/42151] lr: 3.0000e-04 eta: 1 day, 4:59:39 time: 0.5081 data_time: 0.1288 memory: 14682 loss_ce: 0.0165 loss: 0.0165 2022/09/21 01:21:56 - mmengine - INFO - Epoch(train) [2][23800/42151] lr: 3.0000e-04 eta: 1 day, 4:58:37 time: 0.5354 data_time: 0.1258 memory: 14682 loss_ce: 0.0171 loss: 0.0171 2022/09/21 01:22:23 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 01:22:51 - mmengine - INFO - Epoch(train) [2][23900/42151] lr: 3.0000e-04 eta: 1 day, 4:57:39 time: 0.5142 data_time: 0.1291 memory: 14682 loss_ce: 0.0157 loss: 0.0157 2022/09/21 01:23:48 - mmengine - INFO - Epoch(train) [2][24000/42151] lr: 3.0000e-04 eta: 1 day, 4:56:48 time: 0.5613 data_time: 0.1798 memory: 14682 loss_ce: 0.0178 loss: 0.0178 2022/09/21 01:24:43 - mmengine - INFO - Epoch(train) [2][24100/42151] lr: 3.0000e-04 eta: 1 day, 4:55:48 time: 0.5367 data_time: 0.1247 memory: 14682 loss_ce: 0.0178 loss: 0.0178 2022/09/21 01:25:38 - mmengine - INFO - Epoch(train) [2][24200/42151] lr: 3.0000e-04 eta: 1 day, 4:54:49 time: 0.6336 data_time: 0.1878 memory: 14682 loss_ce: 0.0184 loss: 0.0184 2022/09/21 01:26:33 - mmengine - INFO - Epoch(train) [2][24300/42151] lr: 3.0000e-04 eta: 1 day, 4:53:52 time: 0.5541 data_time: 0.1536 memory: 14682 loss_ce: 0.0182 loss: 0.0182 2022/09/21 01:27:29 - mmengine - INFO - Epoch(train) [2][24400/42151] lr: 3.0000e-04 eta: 1 day, 4:52:56 time: 0.5749 data_time: 0.1542 memory: 14682 loss_ce: 0.0178 loss: 0.0178 2022/09/21 01:28:24 - mmengine - INFO - Epoch(train) [2][24500/42151] lr: 3.0000e-04 eta: 1 day, 4:51:58 time: 0.4784 data_time: 0.1000 memory: 14682 loss_ce: 0.0172 loss: 0.0172 2022/09/21 01:29:20 - mmengine - INFO - Epoch(train) [2][24600/42151] lr: 3.0000e-04 eta: 1 day, 4:51:03 time: 0.6262 data_time: 0.2428 memory: 14682 loss_ce: 0.0179 loss: 0.0179 2022/09/21 01:30:14 - mmengine - INFO - Epoch(train) [2][24700/42151] lr: 3.0000e-04 eta: 1 day, 4:50:02 time: 0.5690 data_time: 0.1588 memory: 14682 loss_ce: 0.0183 loss: 0.0183 2022/09/21 01:31:09 - mmengine - INFO - Epoch(train) [2][24800/42151] lr: 3.0000e-04 eta: 1 day, 4:49:05 time: 0.5592 data_time: 0.1441 memory: 14682 loss_ce: 0.0174 loss: 0.0174 2022/09/21 01:31:36 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 01:32:04 - mmengine - INFO - Epoch(train) [2][24900/42151] lr: 3.0000e-04 eta: 1 day, 4:48:07 time: 0.5349 data_time: 0.1444 memory: 14682 loss_ce: 0.0179 loss: 0.0179 2022/09/21 01:32:59 - mmengine - INFO - Epoch(train) [2][25000/42151] lr: 3.0000e-04 eta: 1 day, 4:47:08 time: 0.5034 data_time: 0.1215 memory: 14682 loss_ce: 0.0179 loss: 0.0179 2022/09/21 01:33:53 - mmengine - INFO - Epoch(train) [2][25100/42151] lr: 3.0000e-04 eta: 1 day, 4:46:08 time: 0.4970 data_time: 0.1177 memory: 14682 loss_ce: 0.0160 loss: 0.0160 2022/09/21 01:34:49 - mmengine - INFO - Epoch(train) [2][25200/42151] lr: 3.0000e-04 eta: 1 day, 4:45:12 time: 0.6300 data_time: 0.2289 memory: 14682 loss_ce: 0.0164 loss: 0.0164 2022/09/21 01:35:42 - mmengine - INFO - Epoch(train) [2][25300/42151] lr: 3.0000e-04 eta: 1 day, 4:44:08 time: 0.5717 data_time: 0.1772 memory: 14682 loss_ce: 0.0181 loss: 0.0181 2022/09/21 01:36:36 - mmengine - INFO - Epoch(train) [2][25400/42151] lr: 3.0000e-04 eta: 1 day, 4:43:09 time: 0.5494 data_time: 0.1656 memory: 14682 loss_ce: 0.0176 loss: 0.0176 2022/09/21 01:37:31 - mmengine - INFO - Epoch(train) [2][25500/42151] lr: 3.0000e-04 eta: 1 day, 4:42:11 time: 0.5306 data_time: 0.1001 memory: 14682 loss_ce: 0.0160 loss: 0.0160 2022/09/21 01:38:25 - mmengine - INFO - Epoch(train) [2][25600/42151] lr: 3.0000e-04 eta: 1 day, 4:41:10 time: 0.5475 data_time: 0.1633 memory: 14682 loss_ce: 0.0170 loss: 0.0170 2022/09/21 01:39:19 - mmengine - INFO - Epoch(train) [2][25700/42151] lr: 3.0000e-04 eta: 1 day, 4:40:10 time: 0.5034 data_time: 0.0854 memory: 14682 loss_ce: 0.0183 loss: 0.0183 2022/09/21 01:40:14 - mmengine - INFO - Epoch(train) [2][25800/42151] lr: 3.0000e-04 eta: 1 day, 4:39:11 time: 0.5834 data_time: 0.2032 memory: 14682 loss_ce: 0.0157 loss: 0.0157 2022/09/21 01:40:41 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 01:41:08 - mmengine - INFO - Epoch(train) [2][25900/42151] lr: 3.0000e-04 eta: 1 day, 4:38:10 time: 0.5227 data_time: 0.1445 memory: 14682 loss_ce: 0.0186 loss: 0.0186 2022/09/21 01:42:03 - mmengine - INFO - Epoch(train) [2][26000/42151] lr: 3.0000e-04 eta: 1 day, 4:37:13 time: 0.5271 data_time: 0.1142 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/21 01:42:58 - mmengine - INFO - Epoch(train) [2][26100/42151] lr: 3.0000e-04 eta: 1 day, 4:36:14 time: 0.4847 data_time: 0.0931 memory: 14682 loss_ce: 0.0169 loss: 0.0169 2022/09/21 01:43:53 - mmengine - INFO - Epoch(train) [2][26200/42151] lr: 3.0000e-04 eta: 1 day, 4:35:17 time: 0.5746 data_time: 0.1833 memory: 14682 loss_ce: 0.0179 loss: 0.0179 2022/09/21 01:44:48 - mmengine - INFO - Epoch(train) [2][26300/42151] lr: 3.0000e-04 eta: 1 day, 4:34:17 time: 0.4876 data_time: 0.1068 memory: 14682 loss_ce: 0.0161 loss: 0.0161 2022/09/21 01:45:43 - mmengine - INFO - Epoch(train) [2][26400/42151] lr: 3.0000e-04 eta: 1 day, 4:33:22 time: 0.6046 data_time: 0.2108 memory: 14682 loss_ce: 0.0153 loss: 0.0153 2022/09/21 01:46:38 - mmengine - INFO - Epoch(train) [2][26500/42151] lr: 3.0000e-04 eta: 1 day, 4:32:24 time: 0.5310 data_time: 0.1270 memory: 14682 loss_ce: 0.0191 loss: 0.0191 2022/09/21 01:47:34 - mmengine - INFO - Epoch(train) [2][26600/42151] lr: 3.0000e-04 eta: 1 day, 4:31:29 time: 0.6487 data_time: 0.2097 memory: 14682 loss_ce: 0.0161 loss: 0.0161 2022/09/21 01:48:29 - mmengine - INFO - Epoch(train) [2][26700/42151] lr: 3.0000e-04 eta: 1 day, 4:30:31 time: 0.5398 data_time: 0.1133 memory: 14682 loss_ce: 0.0171 loss: 0.0171 2022/09/21 01:49:25 - mmengine - INFO - Epoch(train) [2][26800/42151] lr: 3.0000e-04 eta: 1 day, 4:29:33 time: 0.5736 data_time: 0.1850 memory: 14682 loss_ce: 0.0165 loss: 0.0165 2022/09/21 01:49:52 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 01:50:20 - mmengine - INFO - Epoch(train) [2][26900/42151] lr: 3.0000e-04 eta: 1 day, 4:28:37 time: 0.5293 data_time: 0.1250 memory: 14682 loss_ce: 0.0184 loss: 0.0184 2022/09/21 01:51:17 - mmengine - INFO - Epoch(train) [2][27000/42151] lr: 3.0000e-04 eta: 1 day, 4:27:45 time: 0.5855 data_time: 0.1953 memory: 14682 loss_ce: 0.0166 loss: 0.0166 2022/09/21 01:52:12 - mmengine - INFO - Epoch(train) [2][27100/42151] lr: 3.0000e-04 eta: 1 day, 4:26:46 time: 0.5515 data_time: 0.1381 memory: 14682 loss_ce: 0.0168 loss: 0.0168 2022/09/21 01:53:07 - mmengine - INFO - Epoch(train) [2][27200/42151] lr: 3.0000e-04 eta: 1 day, 4:25:49 time: 0.5596 data_time: 0.1766 memory: 14682 loss_ce: 0.0170 loss: 0.0170 2022/09/21 01:54:02 - mmengine - INFO - Epoch(train) [2][27300/42151] lr: 3.0000e-04 eta: 1 day, 4:24:52 time: 0.4771 data_time: 0.0972 memory: 14682 loss_ce: 0.0171 loss: 0.0171 2022/09/21 01:54:57 - mmengine - INFO - Epoch(train) [2][27400/42151] lr: 3.0000e-04 eta: 1 day, 4:23:54 time: 0.5549 data_time: 0.1705 memory: 14682 loss_ce: 0.0160 loss: 0.0160 2022/09/21 01:55:52 - mmengine - INFO - Epoch(train) [2][27500/42151] lr: 3.0000e-04 eta: 1 day, 4:22:55 time: 0.5145 data_time: 0.1183 memory: 14682 loss_ce: 0.0180 loss: 0.0180 2022/09/21 01:56:48 - mmengine - INFO - Epoch(train) [2][27600/42151] lr: 3.0000e-04 eta: 1 day, 4:22:00 time: 0.5892 data_time: 0.2036 memory: 14682 loss_ce: 0.0161 loss: 0.0161 2022/09/21 01:57:44 - mmengine - INFO - Epoch(train) [2][27700/42151] lr: 3.0000e-04 eta: 1 day, 4:21:06 time: 0.7061 data_time: 0.2283 memory: 14682 loss_ce: 0.0186 loss: 0.0186 2022/09/21 01:58:41 - mmengine - INFO - Epoch(train) [2][27800/42151] lr: 3.0000e-04 eta: 1 day, 4:20:12 time: 0.6024 data_time: 0.1917 memory: 14682 loss_ce: 0.0166 loss: 0.0166 2022/09/21 01:59:07 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 01:59:36 - mmengine - INFO - Epoch(train) [2][27900/42151] lr: 3.0000e-04 eta: 1 day, 4:19:14 time: 0.4811 data_time: 0.0728 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/21 02:00:35 - mmengine - INFO - Epoch(train) [2][28000/42151] lr: 3.0000e-04 eta: 1 day, 4:18:26 time: 0.7881 data_time: 0.1733 memory: 14682 loss_ce: 0.0171 loss: 0.0171 2022/09/21 02:01:29 - mmengine - INFO - Epoch(train) [2][28100/42151] lr: 3.0000e-04 eta: 1 day, 4:17:27 time: 0.4969 data_time: 0.0802 memory: 14682 loss_ce: 0.0169 loss: 0.0169 2022/09/21 02:02:26 - mmengine - INFO - Epoch(train) [2][28200/42151] lr: 3.0000e-04 eta: 1 day, 4:16:35 time: 0.5562 data_time: 0.1688 memory: 14682 loss_ce: 0.0182 loss: 0.0182 2022/09/21 02:03:22 - mmengine - INFO - Epoch(train) [2][28300/42151] lr: 3.0000e-04 eta: 1 day, 4:15:38 time: 0.5270 data_time: 0.1458 memory: 14682 loss_ce: 0.0176 loss: 0.0176 2022/09/21 02:04:18 - mmengine - INFO - Epoch(train) [2][28400/42151] lr: 3.0000e-04 eta: 1 day, 4:14:44 time: 0.5702 data_time: 0.1495 memory: 14682 loss_ce: 0.0175 loss: 0.0175 2022/09/21 02:05:12 - mmengine - INFO - Epoch(train) [2][28500/42151] lr: 3.0000e-04 eta: 1 day, 4:13:43 time: 0.4975 data_time: 0.0985 memory: 14682 loss_ce: 0.0169 loss: 0.0169 2022/09/21 02:06:07 - mmengine - INFO - Epoch(train) [2][28600/42151] lr: 3.0000e-04 eta: 1 day, 4:12:46 time: 0.5519 data_time: 0.1702 memory: 14682 loss_ce: 0.0170 loss: 0.0170 2022/09/21 02:07:01 - mmengine - INFO - Epoch(train) [2][28700/42151] lr: 3.0000e-04 eta: 1 day, 4:11:45 time: 0.5187 data_time: 0.1096 memory: 14682 loss_ce: 0.0169 loss: 0.0169 2022/09/21 02:07:58 - mmengine - INFO - Epoch(train) [2][28800/42151] lr: 3.0000e-04 eta: 1 day, 4:10:51 time: 0.5595 data_time: 0.1760 memory: 14682 loss_ce: 0.0168 loss: 0.0168 2022/09/21 02:08:25 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 02:08:51 - mmengine - INFO - Epoch(train) [2][28900/42151] lr: 3.0000e-04 eta: 1 day, 4:09:51 time: 0.5303 data_time: 0.1152 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/21 02:09:46 - mmengine - INFO - Epoch(train) [2][29000/42151] lr: 3.0000e-04 eta: 1 day, 4:08:52 time: 0.5973 data_time: 0.1934 memory: 14682 loss_ce: 0.0179 loss: 0.0179 2022/09/21 02:10:41 - mmengine - INFO - Epoch(train) [2][29100/42151] lr: 3.0000e-04 eta: 1 day, 4:07:53 time: 0.4686 data_time: 0.0891 memory: 14682 loss_ce: 0.0149 loss: 0.0149 2022/09/21 02:11:36 - mmengine - INFO - Epoch(train) [2][29200/42151] lr: 3.0000e-04 eta: 1 day, 4:06:56 time: 0.5746 data_time: 0.1459 memory: 14682 loss_ce: 0.0159 loss: 0.0159 2022/09/21 02:12:30 - mmengine - INFO - Epoch(train) [2][29300/42151] lr: 3.0000e-04 eta: 1 day, 4:05:57 time: 0.4924 data_time: 0.1064 memory: 14682 loss_ce: 0.0186 loss: 0.0186 2022/09/21 02:13:26 - mmengine - INFO - Epoch(train) [2][29400/42151] lr: 3.0000e-04 eta: 1 day, 4:05:02 time: 0.6617 data_time: 0.2323 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/21 02:14:20 - mmengine - INFO - Epoch(train) [2][29500/42151] lr: 3.0000e-04 eta: 1 day, 4:04:01 time: 0.5315 data_time: 0.1267 memory: 14682 loss_ce: 0.0169 loss: 0.0169 2022/09/21 02:15:15 - mmengine - INFO - Epoch(train) [2][29600/42151] lr: 3.0000e-04 eta: 1 day, 4:03:03 time: 0.5537 data_time: 0.1659 memory: 14682 loss_ce: 0.0158 loss: 0.0158 2022/09/21 02:16:09 - mmengine - INFO - Epoch(train) [2][29700/42151] lr: 3.0000e-04 eta: 1 day, 4:02:04 time: 0.4874 data_time: 0.1072 memory: 14682 loss_ce: 0.0156 loss: 0.0156 2022/09/21 02:17:03 - mmengine - INFO - Epoch(train) [2][29800/42151] lr: 3.0000e-04 eta: 1 day, 4:01:04 time: 0.5493 data_time: 0.1423 memory: 14682 loss_ce: 0.0167 loss: 0.0167 2022/09/21 02:17:30 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 02:17:57 - mmengine - INFO - Epoch(train) [2][29900/42151] lr: 3.0000e-04 eta: 1 day, 4:00:03 time: 0.5059 data_time: 0.1219 memory: 14682 loss_ce: 0.0158 loss: 0.0158 2022/09/21 02:18:53 - mmengine - INFO - Epoch(train) [2][30000/42151] lr: 3.0000e-04 eta: 1 day, 3:59:07 time: 0.6243 data_time: 0.2421 memory: 14682 loss_ce: 0.0156 loss: 0.0156 2022/09/21 02:19:47 - mmengine - INFO - Epoch(train) [2][30100/42151] lr: 3.0000e-04 eta: 1 day, 3:58:07 time: 0.6169 data_time: 0.1988 memory: 14682 loss_ce: 0.0160 loss: 0.0160 2022/09/21 02:20:42 - mmengine - INFO - Epoch(train) [2][30200/42151] lr: 3.0000e-04 eta: 1 day, 3:57:09 time: 0.5692 data_time: 0.1830 memory: 14682 loss_ce: 0.0170 loss: 0.0170 2022/09/21 02:21:36 - mmengine - INFO - Epoch(train) [2][30300/42151] lr: 3.0000e-04 eta: 1 day, 3:56:10 time: 0.4838 data_time: 0.0727 memory: 14682 loss_ce: 0.0180 loss: 0.0180 2022/09/21 02:22:30 - mmengine - INFO - Epoch(train) [2][30400/42151] lr: 3.0000e-04 eta: 1 day, 3:55:10 time: 0.5005 data_time: 0.1207 memory: 14682 loss_ce: 0.0162 loss: 0.0162 2022/09/21 02:23:26 - mmengine - INFO - Epoch(train) [2][30500/42151] lr: 3.0000e-04 eta: 1 day, 3:54:14 time: 0.5560 data_time: 0.0971 memory: 14682 loss_ce: 0.0161 loss: 0.0161 2022/09/21 02:24:21 - mmengine - INFO - Epoch(train) [2][30600/42151] lr: 3.0000e-04 eta: 1 day, 3:53:17 time: 0.5744 data_time: 0.1953 memory: 14682 loss_ce: 0.0161 loss: 0.0161 2022/09/21 02:25:15 - mmengine - INFO - Epoch(train) [2][30700/42151] lr: 3.0000e-04 eta: 1 day, 3:52:17 time: 0.5399 data_time: 0.1588 memory: 14682 loss_ce: 0.0166 loss: 0.0166 2022/09/21 02:26:10 - mmengine - INFO - Epoch(train) [2][30800/42151] lr: 3.0000e-04 eta: 1 day, 3:51:20 time: 0.5592 data_time: 0.1454 memory: 14682 loss_ce: 0.0177 loss: 0.0177 2022/09/21 02:26:38 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 02:27:07 - mmengine - INFO - Epoch(train) [2][30900/42151] lr: 3.0000e-04 eta: 1 day, 3:50:25 time: 0.5420 data_time: 0.1076 memory: 14682 loss_ce: 0.0172 loss: 0.0172 2022/09/21 02:28:02 - mmengine - INFO - Epoch(train) [2][31000/42151] lr: 3.0000e-04 eta: 1 day, 3:49:28 time: 0.5122 data_time: 0.1288 memory: 14682 loss_ce: 0.0163 loss: 0.0163 2022/09/21 02:28:57 - mmengine - INFO - Epoch(train) [2][31100/42151] lr: 3.0000e-04 eta: 1 day, 3:48:31 time: 0.4899 data_time: 0.1085 memory: 14682 loss_ce: 0.0184 loss: 0.0184 2022/09/21 02:29:53 - mmengine - INFO - Epoch(train) [2][31200/42151] lr: 3.0000e-04 eta: 1 day, 3:47:36 time: 0.5816 data_time: 0.1984 memory: 14682 loss_ce: 0.0157 loss: 0.0157 2022/09/21 02:30:47 - mmengine - INFO - Epoch(train) [2][31300/42151] lr: 3.0000e-04 eta: 1 day, 3:46:37 time: 0.6239 data_time: 0.1645 memory: 14682 loss_ce: 0.0165 loss: 0.0165 2022/09/21 02:31:41 - mmengine - INFO - Epoch(train) [2][31400/42151] lr: 3.0000e-04 eta: 1 day, 3:45:37 time: 0.5370 data_time: 0.1579 memory: 14682 loss_ce: 0.0179 loss: 0.0179 2022/09/21 02:32:35 - mmengine - INFO - Epoch(train) [2][31500/42151] lr: 3.0000e-04 eta: 1 day, 3:44:36 time: 0.5195 data_time: 0.1254 memory: 14682 loss_ce: 0.0143 loss: 0.0143 2022/09/21 02:33:30 - mmengine - INFO - Epoch(train) [2][31600/42151] lr: 3.0000e-04 eta: 1 day, 3:43:38 time: 0.5277 data_time: 0.1257 memory: 14682 loss_ce: 0.0164 loss: 0.0164 2022/09/21 02:34:24 - mmengine - INFO - Epoch(train) [2][31700/42151] lr: 3.0000e-04 eta: 1 day, 3:42:39 time: 0.5355 data_time: 0.1144 memory: 14682 loss_ce: 0.0172 loss: 0.0172 2022/09/21 02:35:20 - mmengine - INFO - Epoch(train) [2][31800/42151] lr: 3.0000e-04 eta: 1 day, 3:41:43 time: 0.5659 data_time: 0.1858 memory: 14682 loss_ce: 0.0159 loss: 0.0159 2022/09/21 02:35:46 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 02:36:14 - mmengine - INFO - Epoch(train) [2][31900/42151] lr: 3.0000e-04 eta: 1 day, 3:40:44 time: 0.5605 data_time: 0.1548 memory: 14682 loss_ce: 0.0201 loss: 0.0201 2022/09/21 02:37:08 - mmengine - INFO - Epoch(train) [2][32000/42151] lr: 3.0000e-04 eta: 1 day, 3:39:44 time: 0.5195 data_time: 0.1344 memory: 14682 loss_ce: 0.0177 loss: 0.0177 2022/09/21 02:38:03 - mmengine - INFO - Epoch(train) [2][32100/42151] lr: 3.0000e-04 eta: 1 day, 3:38:45 time: 0.5116 data_time: 0.1279 memory: 14682 loss_ce: 0.0168 loss: 0.0168 2022/09/21 02:38:58 - mmengine - INFO - Epoch(train) [2][32200/42151] lr: 3.0000e-04 eta: 1 day, 3:37:48 time: 0.5218 data_time: 0.1396 memory: 14682 loss_ce: 0.0181 loss: 0.0181 2022/09/21 02:39:52 - mmengine - INFO - Epoch(train) [2][32300/42151] lr: 3.0000e-04 eta: 1 day, 3:36:48 time: 0.5030 data_time: 0.1239 memory: 14682 loss_ce: 0.0169 loss: 0.0169 2022/09/21 02:40:47 - mmengine - INFO - Epoch(train) [2][32400/42151] lr: 3.0000e-04 eta: 1 day, 3:35:51 time: 0.6136 data_time: 0.2015 memory: 14682 loss_ce: 0.0152 loss: 0.0152 2022/09/21 02:41:40 - mmengine - INFO - Epoch(train) [2][32500/42151] lr: 3.0000e-04 eta: 1 day, 3:34:50 time: 0.5737 data_time: 0.1945 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 02:42:34 - mmengine - INFO - Epoch(train) [2][32600/42151] lr: 3.0000e-04 eta: 1 day, 3:33:49 time: 0.5665 data_time: 0.1855 memory: 14682 loss_ce: 0.0190 loss: 0.0190 2022/09/21 02:43:28 - mmengine - INFO - Epoch(train) [2][32700/42151] lr: 3.0000e-04 eta: 1 day, 3:32:51 time: 0.5318 data_time: 0.0890 memory: 14682 loss_ce: 0.0176 loss: 0.0176 2022/09/21 02:44:22 - mmengine - INFO - Epoch(train) [2][32800/42151] lr: 3.0000e-04 eta: 1 day, 3:31:51 time: 0.5173 data_time: 0.1323 memory: 14682 loss_ce: 0.0179 loss: 0.0179 2022/09/21 02:44:50 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 02:45:17 - mmengine - INFO - Epoch(train) [2][32900/42151] lr: 3.0000e-04 eta: 1 day, 3:30:52 time: 0.5089 data_time: 0.1039 memory: 14682 loss_ce: 0.0162 loss: 0.0162 2022/09/21 02:46:10 - mmengine - INFO - Epoch(train) [2][33000/42151] lr: 3.0000e-04 eta: 1 day, 3:29:51 time: 0.5522 data_time: 0.1743 memory: 14682 loss_ce: 0.0168 loss: 0.0168 2022/09/21 02:47:03 - mmengine - INFO - Epoch(train) [2][33100/42151] lr: 3.0000e-04 eta: 1 day, 3:28:49 time: 0.5229 data_time: 0.1452 memory: 14682 loss_ce: 0.0156 loss: 0.0156 2022/09/21 02:47:57 - mmengine - INFO - Epoch(train) [2][33200/42151] lr: 3.0000e-04 eta: 1 day, 3:27:49 time: 0.5359 data_time: 0.1285 memory: 14682 loss_ce: 0.0171 loss: 0.0171 2022/09/21 02:48:50 - mmengine - INFO - Epoch(train) [2][33300/42151] lr: 3.0000e-04 eta: 1 day, 3:26:46 time: 0.4918 data_time: 0.1144 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/21 02:49:44 - mmengine - INFO - Epoch(train) [2][33400/42151] lr: 3.0000e-04 eta: 1 day, 3:25:46 time: 0.5354 data_time: 0.1548 memory: 14682 loss_ce: 0.0155 loss: 0.0155 2022/09/21 02:50:38 - mmengine - INFO - Epoch(train) [2][33500/42151] lr: 3.0000e-04 eta: 1 day, 3:24:48 time: 0.5115 data_time: 0.1318 memory: 14682 loss_ce: 0.0166 loss: 0.0166 2022/09/21 02:51:33 - mmengine - INFO - Epoch(train) [2][33600/42151] lr: 3.0000e-04 eta: 1 day, 3:23:50 time: 0.5245 data_time: 0.1447 memory: 14682 loss_ce: 0.0157 loss: 0.0157 2022/09/21 02:52:27 - mmengine - INFO - Epoch(train) [2][33700/42151] lr: 3.0000e-04 eta: 1 day, 3:22:50 time: 0.5542 data_time: 0.1235 memory: 14682 loss_ce: 0.0167 loss: 0.0167 2022/09/21 02:53:20 - mmengine - INFO - Epoch(train) [2][33800/42151] lr: 3.0000e-04 eta: 1 day, 3:21:49 time: 0.5857 data_time: 0.1751 memory: 14682 loss_ce: 0.0170 loss: 0.0170 2022/09/21 02:53:46 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 02:54:13 - mmengine - INFO - Epoch(train) [2][33900/42151] lr: 3.0000e-04 eta: 1 day, 3:20:46 time: 0.4823 data_time: 0.1045 memory: 14682 loss_ce: 0.0179 loss: 0.0179 2022/09/21 02:55:07 - mmengine - INFO - Epoch(train) [2][34000/42151] lr: 3.0000e-04 eta: 1 day, 3:19:47 time: 0.5225 data_time: 0.1447 memory: 14682 loss_ce: 0.0157 loss: 0.0157 2022/09/21 02:56:01 - mmengine - INFO - Epoch(train) [2][34100/42151] lr: 3.0000e-04 eta: 1 day, 3:18:47 time: 0.5007 data_time: 0.1194 memory: 14682 loss_ce: 0.0170 loss: 0.0170 2022/09/21 02:56:55 - mmengine - INFO - Epoch(train) [2][34200/42151] lr: 3.0000e-04 eta: 1 day, 3:17:49 time: 0.6320 data_time: 0.2043 memory: 14682 loss_ce: 0.0154 loss: 0.0154 2022/09/21 02:57:48 - mmengine - INFO - Epoch(train) [2][34300/42151] lr: 3.0000e-04 eta: 1 day, 3:16:46 time: 0.5329 data_time: 0.1337 memory: 14682 loss_ce: 0.0150 loss: 0.0150 2022/09/21 02:58:41 - mmengine - INFO - Epoch(train) [2][34400/42151] lr: 3.0000e-04 eta: 1 day, 3:15:45 time: 0.5101 data_time: 0.1300 memory: 14682 loss_ce: 0.0151 loss: 0.0151 2022/09/21 02:59:35 - mmengine - INFO - Epoch(train) [2][34500/42151] lr: 3.0000e-04 eta: 1 day, 3:14:45 time: 0.5013 data_time: 0.1014 memory: 14682 loss_ce: 0.0170 loss: 0.0170 2022/09/21 03:00:29 - mmengine - INFO - Epoch(train) [2][34600/42151] lr: 3.0000e-04 eta: 1 day, 3:13:44 time: 0.5195 data_time: 0.1415 memory: 14682 loss_ce: 0.0176 loss: 0.0176 2022/09/21 03:01:22 - mmengine - INFO - Epoch(train) [2][34700/42151] lr: 3.0000e-04 eta: 1 day, 3:12:42 time: 0.5001 data_time: 0.1201 memory: 14682 loss_ce: 0.0192 loss: 0.0192 2022/09/21 03:02:16 - mmengine - INFO - Epoch(train) [2][34800/42151] lr: 3.0000e-04 eta: 1 day, 3:11:44 time: 0.6243 data_time: 0.2042 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/21 03:02:42 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 03:03:09 - mmengine - INFO - Epoch(train) [2][34900/42151] lr: 3.0000e-04 eta: 1 day, 3:10:42 time: 0.5810 data_time: 0.1968 memory: 14682 loss_ce: 0.0175 loss: 0.0175 2022/09/21 03:04:03 - mmengine - INFO - Epoch(train) [2][35000/42151] lr: 3.0000e-04 eta: 1 day, 3:09:42 time: 0.5642 data_time: 0.1873 memory: 14682 loss_ce: 0.0179 loss: 0.0179 2022/09/21 03:04:57 - mmengine - INFO - Epoch(train) [2][35100/42151] lr: 3.0000e-04 eta: 1 day, 3:08:43 time: 0.5023 data_time: 0.0761 memory: 14682 loss_ce: 0.0145 loss: 0.0145 2022/09/21 03:05:50 - mmengine - INFO - Epoch(train) [2][35200/42151] lr: 3.0000e-04 eta: 1 day, 3:07:42 time: 0.5419 data_time: 0.1578 memory: 14682 loss_ce: 0.0162 loss: 0.0162 2022/09/21 03:06:43 - mmengine - INFO - Epoch(train) [2][35300/42151] lr: 3.0000e-04 eta: 1 day, 3:06:40 time: 0.4974 data_time: 0.0963 memory: 14682 loss_ce: 0.0176 loss: 0.0176 2022/09/21 03:07:36 - mmengine - INFO - Epoch(train) [2][35400/42151] lr: 3.0000e-04 eta: 1 day, 3:05:39 time: 0.5229 data_time: 0.1470 memory: 14682 loss_ce: 0.0175 loss: 0.0175 2022/09/21 03:08:30 - mmengine - INFO - Epoch(train) [2][35500/42151] lr: 3.0000e-04 eta: 1 day, 3:04:39 time: 0.6098 data_time: 0.1912 memory: 14682 loss_ce: 0.0171 loss: 0.0171 2022/09/21 03:09:23 - mmengine - INFO - Epoch(train) [2][35600/42151] lr: 3.0000e-04 eta: 1 day, 3:03:38 time: 0.5297 data_time: 0.1243 memory: 14682 loss_ce: 0.0181 loss: 0.0181 2022/09/21 03:10:16 - mmengine - INFO - Epoch(train) [2][35700/42151] lr: 3.0000e-04 eta: 1 day, 3:02:36 time: 0.4752 data_time: 0.0974 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/21 03:11:09 - mmengine - INFO - Epoch(train) [2][35800/42151] lr: 3.0000e-04 eta: 1 day, 3:01:35 time: 0.5197 data_time: 0.1365 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 03:11:35 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 03:12:03 - mmengine - INFO - Epoch(train) [2][35900/42151] lr: 3.0000e-04 eta: 1 day, 3:00:35 time: 0.5134 data_time: 0.1324 memory: 14682 loss_ce: 0.0166 loss: 0.0166 2022/09/21 03:12:57 - mmengine - INFO - Epoch(train) [2][36000/42151] lr: 3.0000e-04 eta: 1 day, 2:59:35 time: 0.5430 data_time: 0.1618 memory: 14682 loss_ce: 0.0152 loss: 0.0152 2022/09/21 03:13:51 - mmengine - INFO - Epoch(train) [2][36100/42151] lr: 3.0000e-04 eta: 1 day, 2:58:36 time: 0.6134 data_time: 0.1676 memory: 14682 loss_ce: 0.0171 loss: 0.0171 2022/09/21 03:14:43 - mmengine - INFO - Epoch(train) [2][36200/42151] lr: 3.0000e-04 eta: 1 day, 2:57:32 time: 0.5304 data_time: 0.1531 memory: 14682 loss_ce: 0.0160 loss: 0.0160 2022/09/21 03:15:36 - mmengine - INFO - Epoch(train) [2][36300/42151] lr: 3.0000e-04 eta: 1 day, 2:56:32 time: 0.4701 data_time: 0.0918 memory: 14682 loss_ce: 0.0164 loss: 0.0164 2022/09/21 03:16:31 - mmengine - INFO - Epoch(train) [2][36400/42151] lr: 3.0000e-04 eta: 1 day, 2:55:34 time: 0.5433 data_time: 0.1464 memory: 14682 loss_ce: 0.0183 loss: 0.0183 2022/09/21 03:17:24 - mmengine - INFO - Epoch(train) [2][36500/42151] lr: 3.0000e-04 eta: 1 day, 2:54:32 time: 0.5091 data_time: 0.1316 memory: 14682 loss_ce: 0.0164 loss: 0.0164 2022/09/21 03:18:17 - mmengine - INFO - Epoch(train) [2][36600/42151] lr: 3.0000e-04 eta: 1 day, 2:53:32 time: 0.5749 data_time: 0.1913 memory: 14682 loss_ce: 0.0172 loss: 0.0172 2022/09/21 03:19:10 - mmengine - INFO - Epoch(train) [2][36700/42151] lr: 3.0000e-04 eta: 1 day, 2:52:30 time: 0.5856 data_time: 0.1172 memory: 14682 loss_ce: 0.0163 loss: 0.0163 2022/09/21 03:20:03 - mmengine - INFO - Epoch(train) [2][36800/42151] lr: 3.0000e-04 eta: 1 day, 2:51:29 time: 0.5189 data_time: 0.1422 memory: 14682 loss_ce: 0.0156 loss: 0.0156 2022/09/21 03:20:29 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 03:20:56 - mmengine - INFO - Epoch(train) [2][36900/42151] lr: 3.0000e-04 eta: 1 day, 2:50:28 time: 0.4859 data_time: 0.1057 memory: 14682 loss_ce: 0.0156 loss: 0.0156 2022/09/21 03:21:50 - mmengine - INFO - Epoch(train) [2][37000/42151] lr: 3.0000e-04 eta: 1 day, 2:49:29 time: 0.5629 data_time: 0.1407 memory: 14682 loss_ce: 0.0168 loss: 0.0168 2022/09/21 03:22:43 - mmengine - INFO - Epoch(train) [2][37100/42151] lr: 3.0000e-04 eta: 1 day, 2:48:27 time: 0.5132 data_time: 0.1336 memory: 14682 loss_ce: 0.0167 loss: 0.0167 2022/09/21 03:23:36 - mmengine - INFO - Epoch(train) [2][37200/42151] lr: 3.0000e-04 eta: 1 day, 2:47:26 time: 0.5838 data_time: 0.2039 memory: 14682 loss_ce: 0.0156 loss: 0.0156 2022/09/21 03:24:30 - mmengine - INFO - Epoch(train) [2][37300/42151] lr: 3.0000e-04 eta: 1 day, 2:46:26 time: 0.6190 data_time: 0.2088 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 03:25:23 - mmengine - INFO - Epoch(train) [2][37400/42151] lr: 3.0000e-04 eta: 1 day, 2:45:25 time: 0.5674 data_time: 0.1849 memory: 14682 loss_ce: 0.0172 loss: 0.0172 2022/09/21 03:26:15 - mmengine - INFO - Epoch(train) [2][37500/42151] lr: 3.0000e-04 eta: 1 day, 2:44:23 time: 0.4812 data_time: 0.0704 memory: 14682 loss_ce: 0.0148 loss: 0.0148 2022/09/21 03:27:08 - mmengine - INFO - Epoch(train) [2][37600/42151] lr: 3.0000e-04 eta: 1 day, 2:43:22 time: 0.5324 data_time: 0.1195 memory: 14682 loss_ce: 0.0158 loss: 0.0158 2022/09/21 03:28:01 - mmengine - INFO - Epoch(train) [2][37700/42151] lr: 3.0000e-04 eta: 1 day, 2:42:20 time: 0.5033 data_time: 0.0927 memory: 14682 loss_ce: 0.0168 loss: 0.0168 2022/09/21 03:28:55 - mmengine - INFO - Epoch(train) [2][37800/42151] lr: 3.0000e-04 eta: 1 day, 2:41:20 time: 0.5596 data_time: 0.1828 memory: 14682 loss_ce: 0.0174 loss: 0.0174 2022/09/21 03:29:20 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 03:29:47 - mmengine - INFO - Epoch(train) [2][37900/42151] lr: 3.0000e-04 eta: 1 day, 2:40:19 time: 0.5296 data_time: 0.1521 memory: 14682 loss_ce: 0.0181 loss: 0.0181 2022/09/21 03:30:41 - mmengine - INFO - Epoch(train) [2][38000/42151] lr: 3.0000e-04 eta: 1 day, 2:39:18 time: 0.5128 data_time: 0.1054 memory: 14682 loss_ce: 0.0167 loss: 0.0167 2022/09/21 03:31:34 - mmengine - INFO - Epoch(train) [2][38100/42151] lr: 3.0000e-04 eta: 1 day, 2:38:17 time: 0.4857 data_time: 0.1059 memory: 14682 loss_ce: 0.0161 loss: 0.0161 2022/09/21 03:32:27 - mmengine - INFO - Epoch(train) [2][38200/42151] lr: 3.0000e-04 eta: 1 day, 2:37:16 time: 0.4961 data_time: 0.1187 memory: 14682 loss_ce: 0.0180 loss: 0.0180 2022/09/21 03:33:19 - mmengine - INFO - Epoch(train) [2][38300/42151] lr: 3.0000e-04 eta: 1 day, 2:36:14 time: 0.4945 data_time: 0.1042 memory: 14682 loss_ce: 0.0152 loss: 0.0152 2022/09/21 03:34:14 - mmengine - INFO - Epoch(train) [2][38400/42151] lr: 3.0000e-04 eta: 1 day, 2:35:16 time: 0.5924 data_time: 0.1877 memory: 14682 loss_ce: 0.0163 loss: 0.0163 2022/09/21 03:35:07 - mmengine - INFO - Epoch(train) [2][38500/42151] lr: 3.0000e-04 eta: 1 day, 2:34:15 time: 0.5309 data_time: 0.1282 memory: 14682 loss_ce: 0.0173 loss: 0.0173 2022/09/21 03:36:00 - mmengine - INFO - Epoch(train) [2][38600/42151] lr: 3.0000e-04 eta: 1 day, 2:33:14 time: 0.5446 data_time: 0.1637 memory: 14682 loss_ce: 0.0156 loss: 0.0156 2022/09/21 03:36:52 - mmengine - INFO - Epoch(train) [2][38700/42151] lr: 3.0000e-04 eta: 1 day, 2:32:13 time: 0.5416 data_time: 0.1166 memory: 14682 loss_ce: 0.0160 loss: 0.0160 2022/09/21 03:37:46 - mmengine - INFO - Epoch(train) [2][38800/42151] lr: 3.0000e-04 eta: 1 day, 2:31:12 time: 0.5137 data_time: 0.1355 memory: 14682 loss_ce: 0.0154 loss: 0.0154 2022/09/21 03:38:12 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 03:38:39 - mmengine - INFO - Epoch(train) [2][38900/42151] lr: 3.0000e-04 eta: 1 day, 2:30:12 time: 0.4809 data_time: 0.1012 memory: 14682 loss_ce: 0.0166 loss: 0.0166 2022/09/21 03:39:32 - mmengine - INFO - Epoch(train) [2][39000/42151] lr: 3.0000e-04 eta: 1 day, 2:29:11 time: 0.6012 data_time: 0.2045 memory: 14682 loss_ce: 0.0162 loss: 0.0162 2022/09/21 03:40:25 - mmengine - INFO - Epoch(train) [2][39100/42151] lr: 3.0000e-04 eta: 1 day, 2:28:10 time: 0.5797 data_time: 0.1673 memory: 14682 loss_ce: 0.0157 loss: 0.0157 2022/09/21 03:41:20 - mmengine - INFO - Epoch(train) [2][39200/42151] lr: 3.0000e-04 eta: 1 day, 2:27:13 time: 0.5440 data_time: 0.1632 memory: 14682 loss_ce: 0.0150 loss: 0.0150 2022/09/21 03:42:13 - mmengine - INFO - Epoch(train) [2][39300/42151] lr: 3.0000e-04 eta: 1 day, 2:26:12 time: 0.5078 data_time: 0.1112 memory: 14682 loss_ce: 0.0180 loss: 0.0180 2022/09/21 03:43:05 - mmengine - INFO - Epoch(train) [2][39400/42151] lr: 3.0000e-04 eta: 1 day, 2:25:11 time: 0.5123 data_time: 0.1338 memory: 14682 loss_ce: 0.0180 loss: 0.0180 2022/09/21 03:43:58 - mmengine - INFO - Epoch(train) [2][39500/42151] lr: 3.0000e-04 eta: 1 day, 2:24:09 time: 0.4992 data_time: 0.1194 memory: 14682 loss_ce: 0.0171 loss: 0.0171 2022/09/21 03:44:51 - mmengine - INFO - Epoch(train) [2][39600/42151] lr: 3.0000e-04 eta: 1 day, 2:23:09 time: 0.5819 data_time: 0.1956 memory: 14682 loss_ce: 0.0162 loss: 0.0162 2022/09/21 03:45:43 - mmengine - INFO - Epoch(train) [2][39700/42151] lr: 3.0000e-04 eta: 1 day, 2:22:05 time: 0.5560 data_time: 0.1782 memory: 14682 loss_ce: 0.0149 loss: 0.0149 2022/09/21 03:46:36 - mmengine - INFO - Epoch(train) [2][39800/42151] lr: 3.0000e-04 eta: 1 day, 2:21:05 time: 0.5923 data_time: 0.2129 memory: 14682 loss_ce: 0.0146 loss: 0.0146 2022/09/21 03:47:02 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 03:47:29 - mmengine - INFO - Epoch(train) [2][39900/42151] lr: 3.0000e-04 eta: 1 day, 2:20:04 time: 0.4824 data_time: 0.0732 memory: 14682 loss_ce: 0.0155 loss: 0.0155 2022/09/21 03:48:23 - mmengine - INFO - Epoch(train) [2][40000/42151] lr: 3.0000e-04 eta: 1 day, 2:19:05 time: 0.5204 data_time: 0.1414 memory: 14682 loss_ce: 0.0167 loss: 0.0167 2022/09/21 03:49:17 - mmengine - INFO - Epoch(train) [2][40100/42151] lr: 3.0000e-04 eta: 1 day, 2:18:07 time: 0.4803 data_time: 0.0787 memory: 14682 loss_ce: 0.0151 loss: 0.0151 2022/09/21 03:50:12 - mmengine - INFO - Epoch(train) [2][40200/42151] lr: 3.0000e-04 eta: 1 day, 2:17:09 time: 0.5551 data_time: 0.1628 memory: 14682 loss_ce: 0.0155 loss: 0.0155 2022/09/21 03:51:05 - mmengine - INFO - Epoch(train) [2][40300/42151] lr: 3.0000e-04 eta: 1 day, 2:16:08 time: 0.5266 data_time: 0.1465 memory: 14682 loss_ce: 0.0151 loss: 0.0151 2022/09/21 03:51:57 - mmengine - INFO - Epoch(train) [2][40400/42151] lr: 3.0000e-04 eta: 1 day, 2:15:07 time: 0.5104 data_time: 0.1134 memory: 14682 loss_ce: 0.0166 loss: 0.0166 2022/09/21 03:52:50 - mmengine - INFO - Epoch(train) [2][40500/42151] lr: 3.0000e-04 eta: 1 day, 2:14:05 time: 0.4681 data_time: 0.0904 memory: 14682 loss_ce: 0.0165 loss: 0.0165 2022/09/21 03:53:43 - mmengine - INFO - Epoch(train) [2][40600/42151] lr: 3.0000e-04 eta: 1 day, 2:13:05 time: 0.5430 data_time: 0.1388 memory: 14682 loss_ce: 0.0161 loss: 0.0161 2022/09/21 03:54:35 - mmengine - INFO - Epoch(train) [2][40700/42151] lr: 3.0000e-04 eta: 1 day, 2:12:03 time: 0.4810 data_time: 0.1021 memory: 14682 loss_ce: 0.0159 loss: 0.0159 2022/09/21 03:55:29 - mmengine - INFO - Epoch(train) [2][40800/42151] lr: 3.0000e-04 eta: 1 day, 2:11:05 time: 0.5356 data_time: 0.1574 memory: 14682 loss_ce: 0.0167 loss: 0.0167 2022/09/21 03:55:55 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 03:56:22 - mmengine - INFO - Epoch(train) [2][40900/42151] lr: 3.0000e-04 eta: 1 day, 2:10:04 time: 0.5617 data_time: 0.1307 memory: 14682 loss_ce: 0.0157 loss: 0.0157 2022/09/21 03:57:15 - mmengine - INFO - Epoch(train) [2][41000/42151] lr: 3.0000e-04 eta: 1 day, 2:09:04 time: 0.5365 data_time: 0.1606 memory: 14682 loss_ce: 0.0153 loss: 0.0153 2022/09/21 03:58:09 - mmengine - INFO - Epoch(train) [2][41100/42151] lr: 3.0000e-04 eta: 1 day, 2:08:04 time: 0.4678 data_time: 0.0909 memory: 14682 loss_ce: 0.0147 loss: 0.0147 2022/09/21 03:59:02 - mmengine - INFO - Epoch(train) [2][41200/42151] lr: 3.0000e-04 eta: 1 day, 2:07:03 time: 0.5423 data_time: 0.1554 memory: 14682 loss_ce: 0.0157 loss: 0.0157 2022/09/21 03:59:56 - mmengine - INFO - Epoch(train) [2][41300/42151] lr: 3.0000e-04 eta: 1 day, 2:06:05 time: 0.5245 data_time: 0.1442 memory: 14682 loss_ce: 0.0158 loss: 0.0158 2022/09/21 04:00:49 - mmengine - INFO - Epoch(train) [2][41400/42151] lr: 3.0000e-04 eta: 1 day, 2:05:04 time: 0.5738 data_time: 0.1973 memory: 14682 loss_ce: 0.0157 loss: 0.0157 2022/09/21 04:01:42 - mmengine - INFO - Epoch(train) [2][41500/42151] lr: 3.0000e-04 eta: 1 day, 2:04:05 time: 0.5575 data_time: 0.1350 memory: 14682 loss_ce: 0.0175 loss: 0.0175 2022/09/21 04:02:36 - mmengine - INFO - Epoch(train) [2][41600/42151] lr: 3.0000e-04 eta: 1 day, 2:03:05 time: 0.5332 data_time: 0.1544 memory: 14682 loss_ce: 0.0148 loss: 0.0148 2022/09/21 04:03:29 - mmengine - INFO - Epoch(train) [2][41700/42151] lr: 3.0000e-04 eta: 1 day, 2:02:06 time: 0.4644 data_time: 0.0885 memory: 14682 loss_ce: 0.0160 loss: 0.0160 2022/09/21 04:04:23 - mmengine - INFO - Epoch(train) [2][41800/42151] lr: 3.0000e-04 eta: 1 day, 2:01:08 time: 0.5797 data_time: 0.1648 memory: 14682 loss_ce: 0.0167 loss: 0.0167 2022/09/21 04:04:49 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 04:05:17 - mmengine - INFO - Epoch(train) [2][41900/42151] lr: 3.0000e-04 eta: 1 day, 2:00:09 time: 0.5108 data_time: 0.1296 memory: 14682 loss_ce: 0.0158 loss: 0.0158 2022/09/21 04:06:12 - mmengine - INFO - Epoch(train) [2][42000/42151] lr: 3.0000e-04 eta: 1 day, 1:59:14 time: 0.5809 data_time: 0.2030 memory: 14682 loss_ce: 0.0139 loss: 0.0139 2022/09/21 04:07:07 - mmengine - INFO - Epoch(train) [2][42100/42151] lr: 3.0000e-04 eta: 1 day, 1:58:16 time: 0.6059 data_time: 0.2088 memory: 14682 loss_ce: 0.0174 loss: 0.0174 2022/09/21 04:07:32 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 04:07:32 - mmengine - INFO - Saving checkpoint at 2 epochs 2022/09/21 04:08:19 - mmengine - INFO - Epoch(val) [2][100/7672] eta: 0:39:54 time: 0.3162 data_time: 0.0005 memory: 14682 2022/09/21 04:08:52 - mmengine - INFO - Epoch(val) [2][200/7672] eta: 0:44:23 time: 0.3564 data_time: 0.0029 memory: 1304 2022/09/21 04:09:24 - mmengine - INFO - Epoch(val) [2][300/7672] eta: 0:25:39 time: 0.2088 data_time: 0.0008 memory: 1304 2022/09/21 04:09:45 - mmengine - INFO - Epoch(val) [2][400/7672] eta: 0:24:43 time: 0.2040 data_time: 0.0019 memory: 1304 2022/09/21 04:10:06 - mmengine - INFO - Epoch(val) [2][500/7672] eta: 0:24:28 time: 0.2048 data_time: 0.0026 memory: 1304 2022/09/21 04:10:27 - mmengine - INFO - Epoch(val) [2][600/7672] eta: 0:24:40 time: 0.2094 data_time: 0.0008 memory: 1304 2022/09/21 04:10:48 - mmengine - INFO - Epoch(val) [2][700/7672] eta: 0:23:55 time: 0.2059 data_time: 0.0007 memory: 1304 2022/09/21 04:11:10 - mmengine - INFO - Epoch(val) [2][800/7672] eta: 0:22:55 time: 0.2002 data_time: 0.0008 memory: 1304 2022/09/21 04:11:31 - mmengine - INFO - Epoch(val) [2][900/7672] eta: 0:22:52 time: 0.2027 data_time: 0.0008 memory: 1304 2022/09/21 04:11:51 - mmengine - INFO - Epoch(val) [2][1000/7672] eta: 0:22:20 time: 0.2009 data_time: 0.0008 memory: 1304 2022/09/21 04:12:12 - mmengine - INFO - Epoch(val) [2][1100/7672] eta: 0:22:56 time: 0.2095 data_time: 0.0007 memory: 1304 2022/09/21 04:12:33 - mmengine - INFO - Epoch(val) [2][1200/7672] eta: 0:22:01 time: 0.2041 data_time: 0.0017 memory: 1304 2022/09/21 04:12:53 - mmengine - INFO - Epoch(val) [2][1300/7672] eta: 0:22:02 time: 0.2076 data_time: 0.0012 memory: 1304 2022/09/21 04:13:15 - mmengine - INFO - Epoch(val) [2][1400/7672] eta: 0:21:27 time: 0.2053 data_time: 0.0021 memory: 1304 2022/09/21 04:13:35 - mmengine - INFO - Epoch(val) [2][1500/7672] eta: 0:20:49 time: 0.2025 data_time: 0.0019 memory: 1304 2022/09/21 04:13:56 - mmengine - INFO - Epoch(val) [2][1600/7672] eta: 0:20:34 time: 0.2033 data_time: 0.0019 memory: 1304 2022/09/21 04:14:16 - mmengine - INFO - Epoch(val) [2][1700/7672] eta: 0:19:58 time: 0.2007 data_time: 0.0008 memory: 1304 2022/09/21 04:14:37 - mmengine - INFO - Epoch(val) [2][1800/7672] eta: 0:19:43 time: 0.2015 data_time: 0.0008 memory: 1304 2022/09/21 04:14:57 - mmengine - INFO - Epoch(val) [2][1900/7672] eta: 0:19:12 time: 0.1997 data_time: 0.0008 memory: 1304 2022/09/21 04:15:18 - mmengine - INFO - Epoch(val) [2][2000/7672] eta: 0:19:10 time: 0.2028 data_time: 0.0008 memory: 1304 2022/09/21 04:15:38 - mmengine - INFO - Epoch(val) [2][2100/7672] eta: 0:18:43 time: 0.2016 data_time: 0.0007 memory: 1304 2022/09/21 04:15:59 - mmengine - INFO - Epoch(val) [2][2200/7672] eta: 0:18:18 time: 0.2007 data_time: 0.0007 memory: 1304 2022/09/21 04:16:20 - mmengine - INFO - Epoch(val) [2][2300/7672] eta: 0:18:08 time: 0.2025 data_time: 0.0008 memory: 1304 2022/09/21 04:16:40 - mmengine - INFO - Epoch(val) [2][2400/7672] eta: 0:17:38 time: 0.2007 data_time: 0.0007 memory: 1304 2022/09/21 04:17:01 - mmengine - INFO - Epoch(val) [2][2500/7672] eta: 0:17:23 time: 0.2017 data_time: 0.0008 memory: 1304 2022/09/21 04:17:21 - mmengine - INFO - Epoch(val) [2][2600/7672] eta: 0:17:02 time: 0.2016 data_time: 0.0008 memory: 1304 2022/09/21 04:17:42 - mmengine - INFO - Epoch(val) [2][2700/7672] eta: 0:17:27 time: 0.2106 data_time: 0.0009 memory: 1304 2022/09/21 04:18:03 - mmengine - INFO - Epoch(val) [2][2800/7672] eta: 0:16:37 time: 0.2048 data_time: 0.0008 memory: 1304 2022/09/21 04:18:24 - mmengine - INFO - Epoch(val) [2][2900/7672] eta: 0:17:37 time: 0.2216 data_time: 0.0026 memory: 1304 2022/09/21 04:18:44 - mmengine - INFO - Epoch(val) [2][3000/7672] eta: 0:18:11 time: 0.2335 data_time: 0.0023 memory: 1304 2022/09/21 04:19:05 - mmengine - INFO - Epoch(val) [2][3100/7672] eta: 0:17:02 time: 0.2236 data_time: 0.0031 memory: 1304 2022/09/21 04:19:25 - mmengine - INFO - Epoch(val) [2][3200/7672] eta: 0:15:04 time: 0.2022 data_time: 0.0021 memory: 1304 2022/09/21 04:19:46 - mmengine - INFO - Epoch(val) [2][3300/7672] eta: 0:14:42 time: 0.2018 data_time: 0.0022 memory: 1304 2022/09/21 04:20:06 - mmengine - INFO - Epoch(val) [2][3400/7672] eta: 0:14:15 time: 0.2003 data_time: 0.0018 memory: 1304 2022/09/21 04:20:26 - mmengine - INFO - Epoch(val) [2][3500/7672] eta: 0:14:02 time: 0.2021 data_time: 0.0018 memory: 1304 2022/09/21 04:20:47 - mmengine - INFO - Epoch(val) [2][3600/7672] eta: 0:13:36 time: 0.2006 data_time: 0.0008 memory: 1304 2022/09/21 04:21:08 - mmengine - INFO - Epoch(val) [2][3700/7672] eta: 0:13:15 time: 0.2003 data_time: 0.0008 memory: 1304 2022/09/21 04:21:28 - mmengine - INFO - Epoch(val) [2][3800/7672] eta: 0:12:58 time: 0.2009 data_time: 0.0008 memory: 1304 2022/09/21 04:21:49 - mmengine - INFO - Epoch(val) [2][3900/7672] eta: 0:12:35 time: 0.2003 data_time: 0.0008 memory: 1304 2022/09/21 04:22:09 - mmengine - INFO - Epoch(val) [2][4000/7672] eta: 0:12:20 time: 0.2017 data_time: 0.0008 memory: 1304 2022/09/21 04:22:30 - mmengine - INFO - Epoch(val) [2][4100/7672] eta: 0:12:04 time: 0.2029 data_time: 0.0009 memory: 1304 2022/09/21 04:22:51 - mmengine - INFO - Epoch(val) [2][4200/7672] eta: 0:11:38 time: 0.2011 data_time: 0.0008 memory: 1304 2022/09/21 04:23:11 - mmengine - INFO - Epoch(val) [2][4300/7672] eta: 0:11:26 time: 0.2036 data_time: 0.0007 memory: 1304 2022/09/21 04:23:32 - mmengine - INFO - Epoch(val) [2][4400/7672] eta: 0:11:01 time: 0.2021 data_time: 0.0007 memory: 1304 2022/09/21 04:23:53 - mmengine - INFO - Epoch(val) [2][4500/7672] eta: 0:10:45 time: 0.2035 data_time: 0.0008 memory: 1304 2022/09/21 04:24:13 - mmengine - INFO - Epoch(val) [2][4600/7672] eta: 0:10:16 time: 0.2006 data_time: 0.0008 memory: 1304 2022/09/21 04:24:34 - mmengine - INFO - Epoch(val) [2][4700/7672] eta: 0:10:26 time: 0.2108 data_time: 0.0012 memory: 1304 2022/09/21 04:24:54 - mmengine - INFO - Epoch(val) [2][4800/7672] eta: 0:11:56 time: 0.2495 data_time: 0.0021 memory: 1304 2022/09/21 04:25:15 - mmengine - INFO - Epoch(val) [2][4900/7672] eta: 0:11:33 time: 0.2503 data_time: 0.0077 memory: 1304 2022/09/21 04:25:36 - mmengine - INFO - Epoch(val) [2][5000/7672] eta: 0:09:13 time: 0.2073 data_time: 0.0019 memory: 1304 2022/09/21 04:25:57 - mmengine - INFO - Epoch(val) [2][5100/7672] eta: 0:09:00 time: 0.2101 data_time: 0.0019 memory: 1304 2022/09/21 04:26:18 - mmengine - INFO - Epoch(val) [2][5200/7672] eta: 0:08:21 time: 0.2030 data_time: 0.0008 memory: 1304 2022/09/21 04:26:38 - mmengine - INFO - Epoch(val) [2][5300/7672] eta: 0:07:58 time: 0.2019 data_time: 0.0008 memory: 1304 2022/09/21 04:26:59 - mmengine - INFO - Epoch(val) [2][5400/7672] eta: 0:07:38 time: 0.2018 data_time: 0.0008 memory: 1304 2022/09/21 04:27:20 - mmengine - INFO - Epoch(val) [2][5500/7672] eta: 0:07:47 time: 0.2152 data_time: 0.0008 memory: 1304 2022/09/21 04:27:41 - mmengine - INFO - Epoch(val) [2][5600/7672] eta: 0:07:11 time: 0.2081 data_time: 0.0008 memory: 1304 2022/09/21 04:28:02 - mmengine - INFO - Epoch(val) [2][5700/7672] eta: 0:06:46 time: 0.2059 data_time: 0.0008 memory: 1304 2022/09/21 04:28:22 - mmengine - INFO - Epoch(val) [2][5800/7672] eta: 0:06:23 time: 0.2047 data_time: 0.0008 memory: 1304 2022/09/21 04:28:43 - mmengine - INFO - Epoch(val) [2][5900/7672] eta: 0:05:58 time: 0.2025 data_time: 0.0008 memory: 1304 2022/09/21 04:29:04 - mmengine - INFO - Epoch(val) [2][6000/7672] eta: 0:06:01 time: 0.2164 data_time: 0.0020 memory: 1304 2022/09/21 04:29:24 - mmengine - INFO - Epoch(val) [2][6100/7672] eta: 0:05:35 time: 0.2137 data_time: 0.0018 memory: 1304 2022/09/21 04:29:45 - mmengine - INFO - Epoch(val) [2][6200/7672] eta: 0:05:33 time: 0.2266 data_time: 0.0040 memory: 1304 2022/09/21 04:30:06 - mmengine - INFO - Epoch(val) [2][6300/7672] eta: 0:05:51 time: 0.2565 data_time: 0.0046 memory: 1304 2022/09/21 04:30:26 - mmengine - INFO - Epoch(val) [2][6400/7672] eta: 0:04:18 time: 0.2033 data_time: 0.0021 memory: 1304 2022/09/21 04:30:47 - mmengine - INFO - Epoch(val) [2][6500/7672] eta: 0:04:00 time: 0.2052 data_time: 0.0022 memory: 1304 2022/09/21 04:31:08 - mmengine - INFO - Epoch(val) [2][6600/7672] eta: 0:03:36 time: 0.2018 data_time: 0.0019 memory: 1304 2022/09/21 04:31:28 - mmengine - INFO - Epoch(val) [2][6700/7672] eta: 0:03:17 time: 0.2030 data_time: 0.0008 memory: 1304 2022/09/21 04:31:49 - mmengine - INFO - Epoch(val) [2][6800/7672] eta: 0:02:57 time: 0.2040 data_time: 0.0008 memory: 1304 2022/09/21 04:32:10 - mmengine - INFO - Epoch(val) [2][6900/7672] eta: 0:02:34 time: 0.2007 data_time: 0.0008 memory: 1304 2022/09/21 04:32:31 - mmengine - INFO - Epoch(val) [2][7000/7672] eta: 0:02:14 time: 0.2001 data_time: 0.0008 memory: 1304 2022/09/21 04:32:51 - mmengine - INFO - Epoch(val) [2][7100/7672] eta: 0:01:56 time: 0.2043 data_time: 0.0010 memory: 1304 2022/09/21 04:33:12 - mmengine - INFO - Epoch(val) [2][7200/7672] eta: 0:01:35 time: 0.2018 data_time: 0.0008 memory: 1304 2022/09/21 04:33:33 - mmengine - INFO - Epoch(val) [2][7300/7672] eta: 0:01:15 time: 0.2034 data_time: 0.0007 memory: 1304 2022/09/21 04:33:53 - mmengine - INFO - Epoch(val) [2][7400/7672] eta: 0:00:54 time: 0.2017 data_time: 0.0009 memory: 1304 2022/09/21 04:34:14 - mmengine - INFO - Epoch(val) [2][7500/7672] eta: 0:00:34 time: 0.2030 data_time: 0.0012 memory: 1304 2022/09/21 04:34:35 - mmengine - INFO - Epoch(val) [2][7600/7672] eta: 0:00:15 time: 0.2101 data_time: 0.0012 memory: 1304 2022/09/21 04:34:49 - mmengine - INFO - Epoch(val) [2][7672/7672] CUTE80/recog/word_acc_ignore_case_symbol: 0.8368 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9367 SVT/recog/word_acc_ignore_case_symbol: 0.8779 SVTP/recog/word_acc_ignore_case_symbol: 0.7550 IC13/recog/word_acc_ignore_case_symbol: 0.9350 IC15/recog/word_acc_ignore_case_symbol: 0.6972 2022/09/21 04:35:49 - mmengine - INFO - Epoch(train) [3][100/42151] lr: 3.0000e-04 eta: 1 day, 1:56:54 time: 0.5825 data_time: 0.1988 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 04:36:41 - mmengine - INFO - Epoch(train) [3][200/42151] lr: 3.0000e-04 eta: 1 day, 1:55:51 time: 0.5733 data_time: 0.1958 memory: 14682 loss_ce: 0.0147 loss: 0.0147 2022/09/21 04:37:33 - mmengine - INFO - Epoch(train) [3][300/42151] lr: 3.0000e-04 eta: 1 day, 1:54:48 time: 0.5347 data_time: 0.1337 memory: 14682 loss_ce: 0.0131 loss: 0.0131 2022/09/21 04:38:26 - mmengine - INFO - Epoch(train) [3][400/42151] lr: 3.0000e-04 eta: 1 day, 1:53:48 time: 0.5469 data_time: 0.1393 memory: 14682 loss_ce: 0.0150 loss: 0.0150 2022/09/21 04:39:19 - mmengine - INFO - Epoch(train) [3][500/42151] lr: 3.0000e-04 eta: 1 day, 1:52:47 time: 0.5173 data_time: 0.1345 memory: 14682 loss_ce: 0.0143 loss: 0.0143 2022/09/21 04:40:12 - mmengine - INFO - Epoch(train) [3][600/42151] lr: 3.0000e-04 eta: 1 day, 1:51:47 time: 0.5160 data_time: 0.1399 memory: 14682 loss_ce: 0.0145 loss: 0.0145 2022/09/21 04:41:03 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 04:41:06 - mmengine - INFO - Epoch(train) [3][700/42151] lr: 3.0000e-04 eta: 1 day, 1:50:49 time: 0.6155 data_time: 0.2080 memory: 14682 loss_ce: 0.0126 loss: 0.0126 2022/09/21 04:41:58 - mmengine - INFO - Epoch(train) [3][800/42151] lr: 3.0000e-04 eta: 1 day, 1:49:47 time: 0.5950 data_time: 0.2007 memory: 14682 loss_ce: 0.0166 loss: 0.0166 2022/09/21 04:42:50 - mmengine - INFO - Epoch(train) [3][900/42151] lr: 3.0000e-04 eta: 1 day, 1:48:45 time: 0.5070 data_time: 0.1272 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 04:43:44 - mmengine - INFO - Epoch(train) [3][1000/42151] lr: 3.0000e-04 eta: 1 day, 1:47:46 time: 0.5233 data_time: 0.1226 memory: 14682 loss_ce: 0.0143 loss: 0.0143 2022/09/21 04:44:36 - mmengine - INFO - Epoch(train) [3][1100/42151] lr: 3.0000e-04 eta: 1 day, 1:46:45 time: 0.5482 data_time: 0.1480 memory: 14682 loss_ce: 0.0166 loss: 0.0166 2022/09/21 04:45:29 - mmengine - INFO - Epoch(train) [3][1200/42151] lr: 3.0000e-04 eta: 1 day, 1:45:44 time: 0.5522 data_time: 0.1436 memory: 14682 loss_ce: 0.0144 loss: 0.0144 2022/09/21 04:46:22 - mmengine - INFO - Epoch(train) [3][1300/42151] lr: 3.0000e-04 eta: 1 day, 1:44:44 time: 0.5332 data_time: 0.1557 memory: 14682 loss_ce: 0.0153 loss: 0.0153 2022/09/21 04:47:15 - mmengine - INFO - Epoch(train) [3][1400/42151] lr: 3.0000e-04 eta: 1 day, 1:43:44 time: 0.5619 data_time: 0.1841 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 04:48:08 - mmengine - INFO - Epoch(train) [3][1500/42151] lr: 3.0000e-04 eta: 1 day, 1:42:43 time: 0.5430 data_time: 0.1370 memory: 14682 loss_ce: 0.0142 loss: 0.0142 2022/09/21 04:49:02 - mmengine - INFO - Epoch(train) [3][1600/42151] lr: 3.0000e-04 eta: 1 day, 1:41:45 time: 0.5369 data_time: 0.1287 memory: 14682 loss_ce: 0.0155 loss: 0.0155 2022/09/21 04:49:54 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 04:49:55 - mmengine - INFO - Epoch(train) [3][1700/42151] lr: 3.0000e-04 eta: 1 day, 1:40:45 time: 0.5048 data_time: 0.1290 memory: 14682 loss_ce: 0.0150 loss: 0.0150 2022/09/21 04:50:48 - mmengine - INFO - Epoch(train) [3][1800/42151] lr: 3.0000e-04 eta: 1 day, 1:39:45 time: 0.5722 data_time: 0.1411 memory: 14682 loss_ce: 0.0146 loss: 0.0146 2022/09/21 04:51:42 - mmengine - INFO - Epoch(train) [3][1900/42151] lr: 3.0000e-04 eta: 1 day, 1:38:48 time: 0.5985 data_time: 0.2207 memory: 14682 loss_ce: 0.0143 loss: 0.0143 2022/09/21 04:52:35 - mmengine - INFO - Epoch(train) [3][2000/42151] lr: 3.0000e-04 eta: 1 day, 1:37:47 time: 0.5715 data_time: 0.1900 memory: 14682 loss_ce: 0.0141 loss: 0.0141 2022/09/21 04:53:28 - mmengine - INFO - Epoch(train) [3][2100/42151] lr: 3.0000e-04 eta: 1 day, 1:36:47 time: 0.5262 data_time: 0.1166 memory: 14682 loss_ce: 0.0162 loss: 0.0162 2022/09/21 04:54:22 - mmengine - INFO - Epoch(train) [3][2200/42151] lr: 3.0000e-04 eta: 1 day, 1:35:48 time: 0.4968 data_time: 0.1187 memory: 14682 loss_ce: 0.0147 loss: 0.0147 2022/09/21 04:55:15 - mmengine - INFO - Epoch(train) [3][2300/42151] lr: 3.0000e-04 eta: 1 day, 1:34:49 time: 0.5215 data_time: 0.1397 memory: 14682 loss_ce: 0.0153 loss: 0.0153 2022/09/21 04:56:09 - mmengine - INFO - Epoch(train) [3][2400/42151] lr: 3.0000e-04 eta: 1 day, 1:33:50 time: 0.5735 data_time: 0.1783 memory: 14682 loss_ce: 0.0144 loss: 0.0144 2022/09/21 04:57:03 - mmengine - INFO - Epoch(train) [3][2500/42151] lr: 3.0000e-04 eta: 1 day, 1:32:53 time: 0.5947 data_time: 0.2172 memory: 14682 loss_ce: 0.0170 loss: 0.0170 2022/09/21 04:57:57 - mmengine - INFO - Epoch(train) [3][2600/42151] lr: 3.0000e-04 eta: 1 day, 1:31:54 time: 0.6300 data_time: 0.2517 memory: 14682 loss_ce: 0.0152 loss: 0.0152 2022/09/21 04:58:49 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 04:58:50 - mmengine - INFO - Epoch(train) [3][2700/42151] lr: 3.0000e-04 eta: 1 day, 1:30:54 time: 0.5364 data_time: 0.1330 memory: 14682 loss_ce: 0.0117 loss: 0.0117 2022/09/21 04:59:44 - mmengine - INFO - Epoch(train) [3][2800/42151] lr: 3.0000e-04 eta: 1 day, 1:29:57 time: 0.5082 data_time: 0.1300 memory: 14682 loss_ce: 0.0125 loss: 0.0125 2022/09/21 05:00:38 - mmengine - INFO - Epoch(train) [3][2900/42151] lr: 3.0000e-04 eta: 1 day, 1:28:59 time: 0.5251 data_time: 0.1433 memory: 14682 loss_ce: 0.0155 loss: 0.0155 2022/09/21 05:01:31 - mmengine - INFO - Epoch(train) [3][3000/42151] lr: 3.0000e-04 eta: 1 day, 1:27:59 time: 0.5738 data_time: 0.1647 memory: 14682 loss_ce: 0.0149 loss: 0.0149 2022/09/21 05:02:25 - mmengine - INFO - Epoch(train) [3][3100/42151] lr: 3.0000e-04 eta: 1 day, 1:27:00 time: 0.5771 data_time: 0.1953 memory: 14682 loss_ce: 0.0155 loss: 0.0155 2022/09/21 05:03:17 - mmengine - INFO - Epoch(train) [3][3200/42151] lr: 3.0000e-04 eta: 1 day, 1:26:00 time: 0.5809 data_time: 0.2040 memory: 14682 loss_ce: 0.0146 loss: 0.0146 2022/09/21 05:04:12 - mmengine - INFO - Epoch(train) [3][3300/42151] lr: 3.0000e-04 eta: 1 day, 1:25:02 time: 0.5825 data_time: 0.1335 memory: 14682 loss_ce: 0.0156 loss: 0.0156 2022/09/21 05:05:05 - mmengine - INFO - Epoch(train) [3][3400/42151] lr: 3.0000e-04 eta: 1 day, 1:24:04 time: 0.5105 data_time: 0.1319 memory: 14682 loss_ce: 0.0150 loss: 0.0150 2022/09/21 05:05:58 - mmengine - INFO - Epoch(train) [3][3500/42151] lr: 3.0000e-04 eta: 1 day, 1:23:04 time: 0.5518 data_time: 0.1248 memory: 14682 loss_ce: 0.0146 loss: 0.0146 2022/09/21 05:06:51 - mmengine - INFO - Epoch(train) [3][3600/42151] lr: 3.0000e-04 eta: 1 day, 1:22:03 time: 0.5573 data_time: 0.1581 memory: 14682 loss_ce: 0.0136 loss: 0.0136 2022/09/21 05:07:43 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 05:07:45 - mmengine - INFO - Epoch(train) [3][3700/42151] lr: 3.0000e-04 eta: 1 day, 1:21:06 time: 0.5496 data_time: 0.1708 memory: 14682 loss_ce: 0.0169 loss: 0.0169 2022/09/21 05:08:39 - mmengine - INFO - Epoch(train) [3][3800/42151] lr: 3.0000e-04 eta: 1 day, 1:20:07 time: 0.6068 data_time: 0.2038 memory: 14682 loss_ce: 0.0170 loss: 0.0170 2022/09/21 05:09:32 - mmengine - INFO - Epoch(train) [3][3900/42151] lr: 3.0000e-04 eta: 1 day, 1:19:08 time: 0.5250 data_time: 0.1461 memory: 14682 loss_ce: 0.0133 loss: 0.0133 2022/09/21 05:10:27 - mmengine - INFO - Epoch(train) [3][4000/42151] lr: 3.0000e-04 eta: 1 day, 1:18:11 time: 0.5372 data_time: 0.1589 memory: 14682 loss_ce: 0.0128 loss: 0.0128 2022/09/21 05:11:20 - mmengine - INFO - Epoch(train) [3][4100/42151] lr: 3.0000e-04 eta: 1 day, 1:17:13 time: 0.5581 data_time: 0.1250 memory: 14682 loss_ce: 0.0128 loss: 0.0128 2022/09/21 05:12:13 - mmengine - INFO - Epoch(train) [3][4200/42151] lr: 3.0000e-04 eta: 1 day, 1:16:12 time: 0.5448 data_time: 0.1617 memory: 14682 loss_ce: 0.0129 loss: 0.0129 2022/09/21 05:13:07 - mmengine - INFO - Epoch(train) [3][4300/42151] lr: 3.0000e-04 eta: 1 day, 1:15:15 time: 0.5419 data_time: 0.1634 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 05:14:01 - mmengine - INFO - Epoch(train) [3][4400/42151] lr: 3.0000e-04 eta: 1 day, 1:14:16 time: 0.6117 data_time: 0.2032 memory: 14682 loss_ce: 0.0143 loss: 0.0143 2022/09/21 05:14:52 - mmengine - INFO - Epoch(train) [3][4500/42151] lr: 3.0000e-04 eta: 1 day, 1:13:14 time: 0.5224 data_time: 0.1330 memory: 14682 loss_ce: 0.0139 loss: 0.0139 2022/09/21 05:15:45 - mmengine - INFO - Epoch(train) [3][4600/42151] lr: 3.0000e-04 eta: 1 day, 1:12:15 time: 0.5219 data_time: 0.1436 memory: 14682 loss_ce: 0.0126 loss: 0.0126 2022/09/21 05:16:37 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 05:16:38 - mmengine - INFO - Epoch(train) [3][4700/42151] lr: 3.0000e-04 eta: 1 day, 1:11:14 time: 0.5231 data_time: 0.1441 memory: 14682 loss_ce: 0.0148 loss: 0.0148 2022/09/21 05:17:31 - mmengine - INFO - Epoch(train) [3][4800/42151] lr: 3.0000e-04 eta: 1 day, 1:10:14 time: 0.5393 data_time: 0.1471 memory: 14682 loss_ce: 0.0128 loss: 0.0128 2022/09/21 05:18:25 - mmengine - INFO - Epoch(train) [3][4900/42151] lr: 3.0000e-04 eta: 1 day, 1:09:17 time: 0.5613 data_time: 0.1832 memory: 14682 loss_ce: 0.0136 loss: 0.0136 2022/09/21 05:19:18 - mmengine - INFO - Epoch(train) [3][5000/42151] lr: 3.0000e-04 eta: 1 day, 1:08:17 time: 0.6152 data_time: 0.2379 memory: 14682 loss_ce: 0.0158 loss: 0.0158 2022/09/21 05:20:10 - mmengine - INFO - Epoch(train) [3][5100/42151] lr: 3.0000e-04 eta: 1 day, 1:07:16 time: 0.5203 data_time: 0.1405 memory: 14682 loss_ce: 0.0165 loss: 0.0165 2022/09/21 05:21:05 - mmengine - INFO - Epoch(train) [3][5200/42151] lr: 3.0000e-04 eta: 1 day, 1:06:19 time: 0.5279 data_time: 0.1502 memory: 14682 loss_ce: 0.0134 loss: 0.0134 2022/09/21 05:21:58 - mmengine - INFO - Epoch(train) [3][5300/42151] lr: 3.0000e-04 eta: 1 day, 1:05:19 time: 0.5240 data_time: 0.1467 memory: 14682 loss_ce: 0.0156 loss: 0.0156 2022/09/21 05:22:50 - mmengine - INFO - Epoch(train) [3][5400/42151] lr: 3.0000e-04 eta: 1 day, 1:04:19 time: 0.5506 data_time: 0.1709 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 05:23:46 - mmengine - INFO - Epoch(train) [3][5500/42151] lr: 3.0000e-04 eta: 1 day, 1:03:25 time: 0.5428 data_time: 0.1643 memory: 14682 loss_ce: 0.0137 loss: 0.0137 2022/09/21 05:24:39 - mmengine - INFO - Epoch(train) [3][5600/42151] lr: 3.0000e-04 eta: 1 day, 1:02:24 time: 0.5786 data_time: 0.2020 memory: 14682 loss_ce: 0.0146 loss: 0.0146 2022/09/21 05:25:30 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 05:25:31 - mmengine - INFO - Epoch(train) [3][5700/42151] lr: 3.0000e-04 eta: 1 day, 1:01:24 time: 0.5412 data_time: 0.1565 memory: 14682 loss_ce: 0.0144 loss: 0.0144 2022/09/21 05:26:26 - mmengine - INFO - Epoch(train) [3][5800/42151] lr: 3.0000e-04 eta: 1 day, 1:00:27 time: 0.5651 data_time: 0.1546 memory: 14682 loss_ce: 0.0152 loss: 0.0152 2022/09/21 05:27:18 - mmengine - INFO - Epoch(train) [3][5900/42151] lr: 3.0000e-04 eta: 1 day, 0:59:27 time: 0.5316 data_time: 0.1341 memory: 14682 loss_ce: 0.0166 loss: 0.0166 2022/09/21 05:28:12 - mmengine - INFO - Epoch(train) [3][6000/42151] lr: 3.0000e-04 eta: 1 day, 0:58:29 time: 0.5283 data_time: 0.1460 memory: 14682 loss_ce: 0.0141 loss: 0.0141 2022/09/21 05:29:06 - mmengine - INFO - Epoch(train) [3][6100/42151] lr: 3.0000e-04 eta: 1 day, 0:57:32 time: 0.5448 data_time: 0.1662 memory: 14682 loss_ce: 0.0131 loss: 0.0131 2022/09/21 05:29:59 - mmengine - INFO - Epoch(train) [3][6200/42151] lr: 3.0000e-04 eta: 1 day, 0:56:32 time: 0.5918 data_time: 0.2076 memory: 14682 loss_ce: 0.0137 loss: 0.0137 2022/09/21 05:30:52 - mmengine - INFO - Epoch(train) [3][6300/42151] lr: 3.0000e-04 eta: 1 day, 0:55:31 time: 0.5121 data_time: 0.1316 memory: 14682 loss_ce: 0.0146 loss: 0.0146 2022/09/21 05:31:46 - mmengine - INFO - Epoch(train) [3][6400/42151] lr: 3.0000e-04 eta: 1 day, 0:54:34 time: 0.5380 data_time: 0.1384 memory: 14682 loss_ce: 0.0142 loss: 0.0142 2022/09/21 05:32:37 - mmengine - INFO - Epoch(train) [3][6500/42151] lr: 3.0000e-04 eta: 1 day, 0:53:32 time: 0.5160 data_time: 0.1396 memory: 14682 loss_ce: 0.0135 loss: 0.0135 2022/09/21 05:33:30 - mmengine - INFO - Epoch(train) [3][6600/42151] lr: 3.0000e-04 eta: 1 day, 0:52:32 time: 0.5126 data_time: 0.1350 memory: 14682 loss_ce: 0.0154 loss: 0.0154 2022/09/21 05:34:22 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 05:34:24 - mmengine - INFO - Epoch(train) [3][6700/42151] lr: 3.0000e-04 eta: 1 day, 0:51:34 time: 0.5795 data_time: 0.1897 memory: 14682 loss_ce: 0.0141 loss: 0.0141 2022/09/21 05:35:17 - mmengine - INFO - Epoch(train) [3][6800/42151] lr: 3.0000e-04 eta: 1 day, 0:50:35 time: 0.5854 data_time: 0.2061 memory: 14682 loss_ce: 0.0154 loss: 0.0154 2022/09/21 05:36:08 - mmengine - INFO - Epoch(train) [3][6900/42151] lr: 3.0000e-04 eta: 1 day, 0:49:33 time: 0.4973 data_time: 0.1208 memory: 14682 loss_ce: 0.0142 loss: 0.0142 2022/09/21 05:37:03 - mmengine - INFO - Epoch(train) [3][7000/42151] lr: 3.0000e-04 eta: 1 day, 0:48:37 time: 0.5677 data_time: 0.1607 memory: 14682 loss_ce: 0.0123 loss: 0.0123 2022/09/21 05:37:56 - mmengine - INFO - Epoch(train) [3][7100/42151] lr: 3.0000e-04 eta: 1 day, 0:47:38 time: 0.5468 data_time: 0.1685 memory: 14682 loss_ce: 0.0139 loss: 0.0139 2022/09/21 05:38:49 - mmengine - INFO - Epoch(train) [3][7200/42151] lr: 3.0000e-04 eta: 1 day, 0:46:38 time: 0.5203 data_time: 0.1444 memory: 14682 loss_ce: 0.0142 loss: 0.0142 2022/09/21 05:39:44 - mmengine - INFO - Epoch(train) [3][7300/42151] lr: 3.0000e-04 eta: 1 day, 0:45:43 time: 0.6371 data_time: 0.2378 memory: 14682 loss_ce: 0.0161 loss: 0.0161 2022/09/21 05:40:37 - mmengine - INFO - Epoch(train) [3][7400/42151] lr: 3.0000e-04 eta: 1 day, 0:44:43 time: 0.6099 data_time: 0.2311 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 05:41:30 - mmengine - INFO - Epoch(train) [3][7500/42151] lr: 3.0000e-04 eta: 1 day, 0:43:44 time: 0.5021 data_time: 0.1252 memory: 14682 loss_ce: 0.0149 loss: 0.0149 2022/09/21 05:42:24 - mmengine - INFO - Epoch(train) [3][7600/42151] lr: 3.0000e-04 eta: 1 day, 0:42:45 time: 0.5366 data_time: 0.1328 memory: 14682 loss_ce: 0.0156 loss: 0.0156 2022/09/21 05:43:16 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 05:43:17 - mmengine - INFO - Epoch(train) [3][7700/42151] lr: 3.0000e-04 eta: 1 day, 0:41:47 time: 0.5482 data_time: 0.1669 memory: 14682 loss_ce: 0.0134 loss: 0.0134 2022/09/21 05:44:10 - mmengine - INFO - Epoch(train) [3][7800/42151] lr: 3.0000e-04 eta: 1 day, 0:40:48 time: 0.5465 data_time: 0.1702 memory: 14682 loss_ce: 0.0147 loss: 0.0147 2022/09/21 05:45:05 - mmengine - INFO - Epoch(train) [3][7900/42151] lr: 3.0000e-04 eta: 1 day, 0:39:51 time: 0.6073 data_time: 0.2003 memory: 14682 loss_ce: 0.0153 loss: 0.0153 2022/09/21 05:45:57 - mmengine - INFO - Epoch(train) [3][8000/42151] lr: 3.0000e-04 eta: 1 day, 0:38:52 time: 0.5629 data_time: 0.1853 memory: 14682 loss_ce: 0.0156 loss: 0.0156 2022/09/21 05:46:50 - mmengine - INFO - Epoch(train) [3][8100/42151] lr: 3.0000e-04 eta: 1 day, 0:37:52 time: 0.5398 data_time: 0.1321 memory: 14682 loss_ce: 0.0131 loss: 0.0131 2022/09/21 05:47:44 - mmengine - INFO - Epoch(train) [3][8200/42151] lr: 3.0000e-04 eta: 1 day, 0:36:54 time: 0.5259 data_time: 0.1417 memory: 14682 loss_ce: 0.0147 loss: 0.0147 2022/09/21 05:48:37 - mmengine - INFO - Epoch(train) [3][8300/42151] lr: 3.0000e-04 eta: 1 day, 0:35:55 time: 0.5594 data_time: 0.1523 memory: 14682 loss_ce: 0.0169 loss: 0.0169 2022/09/21 05:49:30 - mmengine - INFO - Epoch(train) [3][8400/42151] lr: 3.0000e-04 eta: 1 day, 0:34:56 time: 0.5464 data_time: 0.1461 memory: 14682 loss_ce: 0.0165 loss: 0.0165 2022/09/21 05:50:24 - mmengine - INFO - Epoch(train) [3][8500/42151] lr: 3.0000e-04 eta: 1 day, 0:33:59 time: 0.5485 data_time: 0.1684 memory: 14682 loss_ce: 0.0152 loss: 0.0152 2022/09/21 05:51:17 - mmengine - INFO - Epoch(train) [3][8600/42151] lr: 3.0000e-04 eta: 1 day, 0:33:00 time: 0.6265 data_time: 0.2233 memory: 14682 loss_ce: 0.0144 loss: 0.0144 2022/09/21 05:52:09 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 05:52:09 - mmengine - INFO - Epoch(train) [3][8700/42151] lr: 3.0000e-04 eta: 1 day, 0:31:59 time: 0.5133 data_time: 0.1365 memory: 14682 loss_ce: 0.0153 loss: 0.0153 2022/09/21 05:53:05 - mmengine - INFO - Epoch(train) [3][8800/42151] lr: 3.0000e-04 eta: 1 day, 0:31:04 time: 0.5451 data_time: 0.1534 memory: 14682 loss_ce: 0.0145 loss: 0.0145 2022/09/21 05:53:57 - mmengine - INFO - Epoch(train) [3][8900/42151] lr: 3.0000e-04 eta: 1 day, 0:30:04 time: 0.5795 data_time: 0.1504 memory: 14682 loss_ce: 0.0162 loss: 0.0162 2022/09/21 05:54:50 - mmengine - INFO - Epoch(train) [3][9000/42151] lr: 3.0000e-04 eta: 1 day, 0:29:05 time: 0.5576 data_time: 0.1783 memory: 14682 loss_ce: 0.0132 loss: 0.0132 2022/09/21 05:55:45 - mmengine - INFO - Epoch(train) [3][9100/42151] lr: 3.0000e-04 eta: 1 day, 0:28:09 time: 0.5488 data_time: 0.1635 memory: 14682 loss_ce: 0.0142 loss: 0.0142 2022/09/21 05:56:38 - mmengine - INFO - Epoch(train) [3][9200/42151] lr: 3.0000e-04 eta: 1 day, 0:27:10 time: 0.6204 data_time: 0.2365 memory: 14682 loss_ce: 0.0144 loss: 0.0144 2022/09/21 05:57:32 - mmengine - INFO - Epoch(train) [3][9300/42151] lr: 3.0000e-04 eta: 1 day, 0:26:12 time: 0.5416 data_time: 0.1631 memory: 14682 loss_ce: 0.0133 loss: 0.0133 2022/09/21 05:58:26 - mmengine - INFO - Epoch(train) [3][9400/42151] lr: 3.0000e-04 eta: 1 day, 0:25:15 time: 0.5427 data_time: 0.1519 memory: 14682 loss_ce: 0.0150 loss: 0.0150 2022/09/21 05:59:18 - mmengine - INFO - Epoch(train) [3][9500/42151] lr: 3.0000e-04 eta: 1 day, 0:24:15 time: 0.5433 data_time: 0.1632 memory: 14682 loss_ce: 0.0135 loss: 0.0135 2022/09/21 06:00:13 - mmengine - INFO - Epoch(train) [3][9600/42151] lr: 3.0000e-04 eta: 1 day, 0:23:19 time: 0.5207 data_time: 0.1417 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 06:01:06 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 06:01:08 - mmengine - INFO - Epoch(train) [3][9700/42151] lr: 3.0000e-04 eta: 1 day, 0:22:23 time: 0.6089 data_time: 0.2039 memory: 14682 loss_ce: 0.0154 loss: 0.0154 2022/09/21 06:02:01 - mmengine - INFO - Epoch(train) [3][9800/42151] lr: 3.0000e-04 eta: 1 day, 0:21:24 time: 0.6020 data_time: 0.2238 memory: 14682 loss_ce: 0.0156 loss: 0.0156 2022/09/21 06:02:55 - mmengine - INFO - Epoch(train) [3][9900/42151] lr: 3.0000e-04 eta: 1 day, 0:20:27 time: 0.5602 data_time: 0.1667 memory: 14682 loss_ce: 0.0156 loss: 0.0156 2022/09/21 06:03:50 - mmengine - INFO - Epoch(train) [3][10000/42151] lr: 3.0000e-04 eta: 1 day, 0:19:32 time: 0.5120 data_time: 0.1333 memory: 14682 loss_ce: 0.0143 loss: 0.0143 2022/09/21 06:04:43 - mmengine - INFO - Epoch(train) [3][10100/42151] lr: 3.0000e-04 eta: 1 day, 0:18:32 time: 0.5235 data_time: 0.1418 memory: 14682 loss_ce: 0.0157 loss: 0.0157 2022/09/21 06:05:35 - mmengine - INFO - Epoch(train) [3][10200/42151] lr: 3.0000e-04 eta: 1 day, 0:17:32 time: 0.5134 data_time: 0.1245 memory: 14682 loss_ce: 0.0130 loss: 0.0130 2022/09/21 06:06:30 - mmengine - INFO - Epoch(train) [3][10300/42151] lr: 3.0000e-04 eta: 1 day, 0:16:37 time: 0.5596 data_time: 0.1811 memory: 14682 loss_ce: 0.0148 loss: 0.0148 2022/09/21 06:07:22 - mmengine - INFO - Epoch(train) [3][10400/42151] lr: 3.0000e-04 eta: 1 day, 0:15:36 time: 0.5780 data_time: 0.2005 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 06:08:15 - mmengine - INFO - Epoch(train) [3][10500/42151] lr: 3.0000e-04 eta: 1 day, 0:14:36 time: 0.5146 data_time: 0.1295 memory: 14682 loss_ce: 0.0152 loss: 0.0152 2022/09/21 06:09:11 - mmengine - INFO - Epoch(train) [3][10600/42151] lr: 3.0000e-04 eta: 1 day, 0:13:42 time: 0.5559 data_time: 0.1395 memory: 14682 loss_ce: 0.0137 loss: 0.0137 2022/09/21 06:10:03 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 06:10:04 - mmengine - INFO - Epoch(train) [3][10700/42151] lr: 3.0000e-04 eta: 1 day, 0:12:43 time: 0.5582 data_time: 0.1469 memory: 14682 loss_ce: 0.0150 loss: 0.0150 2022/09/21 06:10:56 - mmengine - INFO - Epoch(train) [3][10800/42151] lr: 3.0000e-04 eta: 1 day, 0:11:43 time: 0.4919 data_time: 0.1141 memory: 14682 loss_ce: 0.0141 loss: 0.0141 2022/09/21 06:11:51 - mmengine - INFO - Epoch(train) [3][10900/42151] lr: 3.0000e-04 eta: 1 day, 0:10:47 time: 0.5773 data_time: 0.1959 memory: 14682 loss_ce: 0.0141 loss: 0.0141 2022/09/21 06:12:43 - mmengine - INFO - Epoch(train) [3][11000/42151] lr: 3.0000e-04 eta: 1 day, 0:09:48 time: 0.5951 data_time: 0.2148 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 06:13:36 - mmengine - INFO - Epoch(train) [3][11100/42151] lr: 3.0000e-04 eta: 1 day, 0:08:48 time: 0.5208 data_time: 0.1435 memory: 14682 loss_ce: 0.0137 loss: 0.0137 2022/09/21 06:14:30 - mmengine - INFO - Epoch(train) [3][11200/42151] lr: 3.0000e-04 eta: 1 day, 0:07:51 time: 0.5366 data_time: 0.1309 memory: 14682 loss_ce: 0.0153 loss: 0.0153 2022/09/21 06:15:22 - mmengine - INFO - Epoch(train) [3][11300/42151] lr: 3.0000e-04 eta: 1 day, 0:06:50 time: 0.5090 data_time: 0.1323 memory: 14682 loss_ce: 0.0143 loss: 0.0143 2022/09/21 06:16:15 - mmengine - INFO - Epoch(train) [3][11400/42151] lr: 3.0000e-04 eta: 1 day, 0:05:52 time: 0.5253 data_time: 0.1455 memory: 14682 loss_ce: 0.0130 loss: 0.0130 2022/09/21 06:17:11 - mmengine - INFO - Epoch(train) [3][11500/42151] lr: 3.0000e-04 eta: 1 day, 0:04:57 time: 0.5992 data_time: 0.2198 memory: 14682 loss_ce: 0.0145 loss: 0.0145 2022/09/21 06:18:02 - mmengine - INFO - Epoch(train) [3][11600/42151] lr: 3.0000e-04 eta: 1 day, 0:03:56 time: 0.5554 data_time: 0.1786 memory: 14682 loss_ce: 0.0152 loss: 0.0152 2022/09/21 06:18:55 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 06:18:55 - mmengine - INFO - Epoch(train) [3][11700/42151] lr: 3.0000e-04 eta: 1 day, 0:02:57 time: 0.5267 data_time: 0.1456 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 06:19:49 - mmengine - INFO - Epoch(train) [3][11800/42151] lr: 3.0000e-04 eta: 1 day, 0:02:00 time: 0.5187 data_time: 0.1292 memory: 14682 loss_ce: 0.0146 loss: 0.0146 2022/09/21 06:20:41 - mmengine - INFO - Epoch(train) [3][11900/42151] lr: 3.0000e-04 eta: 1 day, 0:00:59 time: 0.5295 data_time: 0.1528 memory: 14682 loss_ce: 0.0160 loss: 0.0160 2022/09/21 06:21:33 - mmengine - INFO - Epoch(train) [3][12000/42151] lr: 3.0000e-04 eta: 23:59:59 time: 0.5036 data_time: 0.1267 memory: 14682 loss_ce: 0.0148 loss: 0.0148 2022/09/21 06:22:28 - mmengine - INFO - Epoch(train) [3][12100/42151] lr: 3.0000e-04 eta: 23:59:04 time: 0.6109 data_time: 0.2205 memory: 14682 loss_ce: 0.0145 loss: 0.0145 2022/09/21 06:23:21 - mmengine - INFO - Epoch(train) [3][12200/42151] lr: 3.0000e-04 eta: 23:58:04 time: 0.5906 data_time: 0.2139 memory: 14682 loss_ce: 0.0155 loss: 0.0155 2022/09/21 06:24:14 - mmengine - INFO - Epoch(train) [3][12300/42151] lr: 3.0000e-04 eta: 23:57:05 time: 0.5084 data_time: 0.1310 memory: 14682 loss_ce: 0.0152 loss: 0.0152 2022/09/21 06:25:08 - mmengine - INFO - Epoch(train) [3][12400/42151] lr: 3.0000e-04 eta: 23:56:09 time: 0.5246 data_time: 0.1352 memory: 14682 loss_ce: 0.0143 loss: 0.0143 2022/09/21 06:26:01 - mmengine - INFO - Epoch(train) [3][12500/42151] lr: 3.0000e-04 eta: 23:55:10 time: 0.5416 data_time: 0.1635 memory: 14682 loss_ce: 0.0143 loss: 0.0143 2022/09/21 06:26:53 - mmengine - INFO - Epoch(train) [3][12600/42151] lr: 3.0000e-04 eta: 23:54:10 time: 0.5110 data_time: 0.1352 memory: 14682 loss_ce: 0.0149 loss: 0.0149 2022/09/21 06:27:45 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 06:27:47 - mmengine - INFO - Epoch(train) [3][12700/42151] lr: 3.0000e-04 eta: 23:53:13 time: 0.6141 data_time: 0.2074 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 06:28:40 - mmengine - INFO - Epoch(train) [3][12800/42151] lr: 3.0000e-04 eta: 23:52:14 time: 0.5897 data_time: 0.2059 memory: 14682 loss_ce: 0.0139 loss: 0.0139 2022/09/21 06:29:33 - mmengine - INFO - Epoch(train) [3][12900/42151] lr: 3.0000e-04 eta: 23:51:16 time: 0.5369 data_time: 0.1287 memory: 14682 loss_ce: 0.0136 loss: 0.0136 2022/09/21 06:30:28 - mmengine - INFO - Epoch(train) [3][13000/42151] lr: 3.0000e-04 eta: 23:50:19 time: 0.5449 data_time: 0.1362 memory: 14682 loss_ce: 0.0141 loss: 0.0141 2022/09/21 06:31:20 - mmengine - INFO - Epoch(train) [3][13100/42151] lr: 3.0000e-04 eta: 23:49:20 time: 0.5522 data_time: 0.1405 memory: 14682 loss_ce: 0.0149 loss: 0.0149 2022/09/21 06:32:13 - mmengine - INFO - Epoch(train) [3][13200/42151] lr: 3.0000e-04 eta: 23:48:22 time: 0.5569 data_time: 0.1580 memory: 14682 loss_ce: 0.0147 loss: 0.0147 2022/09/21 06:33:08 - mmengine - INFO - Epoch(train) [3][13300/42151] lr: 3.0000e-04 eta: 23:47:26 time: 0.5950 data_time: 0.1911 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 06:34:01 - mmengine - INFO - Epoch(train) [3][13400/42151] lr: 3.0000e-04 eta: 23:46:27 time: 0.5642 data_time: 0.1846 memory: 14682 loss_ce: 0.0132 loss: 0.0132 2022/09/21 06:34:53 - mmengine - INFO - Epoch(train) [3][13500/42151] lr: 3.0000e-04 eta: 23:45:27 time: 0.5131 data_time: 0.1346 memory: 14682 loss_ce: 0.0137 loss: 0.0137 2022/09/21 06:35:47 - mmengine - INFO - Epoch(train) [3][13600/42151] lr: 3.0000e-04 eta: 23:44:30 time: 0.5240 data_time: 0.1198 memory: 14682 loss_ce: 0.0147 loss: 0.0147 2022/09/21 06:36:39 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 06:36:39 - mmengine - INFO - Epoch(train) [3][13700/42151] lr: 3.0000e-04 eta: 23:43:30 time: 0.5681 data_time: 0.1581 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 06:37:32 - mmengine - INFO - Epoch(train) [3][13800/42151] lr: 3.0000e-04 eta: 23:42:32 time: 0.5134 data_time: 0.1367 memory: 14682 loss_ce: 0.0139 loss: 0.0139 2022/09/21 06:38:28 - mmengine - INFO - Epoch(train) [3][13900/42151] lr: 3.0000e-04 eta: 23:41:38 time: 0.6184 data_time: 0.2104 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 06:39:22 - mmengine - INFO - Epoch(train) [3][14000/42151] lr: 3.0000e-04 eta: 23:40:40 time: 0.6040 data_time: 0.2211 memory: 14682 loss_ce: 0.0146 loss: 0.0146 2022/09/21 06:40:14 - mmengine - INFO - Epoch(train) [3][14100/42151] lr: 3.0000e-04 eta: 23:39:40 time: 0.5096 data_time: 0.1333 memory: 14682 loss_ce: 0.0146 loss: 0.0146 2022/09/21 06:41:09 - mmengine - INFO - Epoch(train) [3][14200/42151] lr: 3.0000e-04 eta: 23:38:45 time: 0.5563 data_time: 0.1483 memory: 14682 loss_ce: 0.0146 loss: 0.0146 2022/09/21 06:42:01 - mmengine - INFO - Epoch(train) [3][14300/42151] lr: 3.0000e-04 eta: 23:37:45 time: 0.5231 data_time: 0.1451 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 06:42:54 - mmengine - INFO - Epoch(train) [3][14400/42151] lr: 3.0000e-04 eta: 23:36:47 time: 0.5355 data_time: 0.1546 memory: 14682 loss_ce: 0.0133 loss: 0.0133 2022/09/21 06:43:51 - mmengine - INFO - Epoch(train) [3][14500/42151] lr: 3.0000e-04 eta: 23:35:53 time: 0.6509 data_time: 0.2365 memory: 14682 loss_ce: 0.0159 loss: 0.0159 2022/09/21 06:44:43 - mmengine - INFO - Epoch(train) [3][14600/42151] lr: 3.0000e-04 eta: 23:34:55 time: 0.5824 data_time: 0.2059 memory: 14682 loss_ce: 0.0143 loss: 0.0143 2022/09/21 06:45:36 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 06:45:37 - mmengine - INFO - Epoch(train) [3][14700/42151] lr: 3.0000e-04 eta: 23:33:56 time: 0.4964 data_time: 0.1186 memory: 14682 loss_ce: 0.0130 loss: 0.0130 2022/09/21 06:46:31 - mmengine - INFO - Epoch(train) [3][14800/42151] lr: 3.0000e-04 eta: 23:33:00 time: 0.5320 data_time: 0.1266 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 06:47:23 - mmengine - INFO - Epoch(train) [3][14900/42151] lr: 3.0000e-04 eta: 23:32:00 time: 0.5231 data_time: 0.1433 memory: 14682 loss_ce: 0.0135 loss: 0.0135 2022/09/21 06:48:16 - mmengine - INFO - Epoch(train) [3][15000/42151] lr: 3.0000e-04 eta: 23:31:01 time: 0.5092 data_time: 0.1307 memory: 14682 loss_ce: 0.0135 loss: 0.0135 2022/09/21 06:49:10 - mmengine - INFO - Epoch(train) [3][15100/42151] lr: 3.0000e-04 eta: 23:30:05 time: 0.6175 data_time: 0.2147 memory: 14682 loss_ce: 0.0152 loss: 0.0152 2022/09/21 06:50:02 - mmengine - INFO - Epoch(train) [3][15200/42151] lr: 3.0000e-04 eta: 23:29:05 time: 0.5959 data_time: 0.2142 memory: 14682 loss_ce: 0.0149 loss: 0.0149 2022/09/21 06:50:55 - mmengine - INFO - Epoch(train) [3][15300/42151] lr: 3.0000e-04 eta: 23:28:06 time: 0.5092 data_time: 0.1334 memory: 14682 loss_ce: 0.0152 loss: 0.0152 2022/09/21 06:51:50 - mmengine - INFO - Epoch(train) [3][15400/42151] lr: 3.0000e-04 eta: 23:27:11 time: 0.5264 data_time: 0.1254 memory: 14682 loss_ce: 0.0130 loss: 0.0130 2022/09/21 06:52:42 - mmengine - INFO - Epoch(train) [3][15500/42151] lr: 3.0000e-04 eta: 23:26:11 time: 0.5783 data_time: 0.1617 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 06:53:35 - mmengine - INFO - Epoch(train) [3][15600/42151] lr: 3.0000e-04 eta: 23:25:12 time: 0.5173 data_time: 0.1388 memory: 14682 loss_ce: 0.0133 loss: 0.0133 2022/09/21 06:54:26 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 06:54:28 - mmengine - INFO - Epoch(train) [3][15700/42151] lr: 3.0000e-04 eta: 23:24:14 time: 0.5891 data_time: 0.2097 memory: 14682 loss_ce: 0.0132 loss: 0.0132 2022/09/21 06:55:21 - mmengine - INFO - Epoch(train) [3][15800/42151] lr: 3.0000e-04 eta: 23:23:16 time: 0.5887 data_time: 0.1754 memory: 14682 loss_ce: 0.0157 loss: 0.0157 2022/09/21 06:56:14 - mmengine - INFO - Epoch(train) [3][15900/42151] lr: 3.0000e-04 eta: 23:22:18 time: 0.4984 data_time: 0.1218 memory: 14682 loss_ce: 0.0133 loss: 0.0133 2022/09/21 06:57:08 - mmengine - INFO - Epoch(train) [3][16000/42151] lr: 3.0000e-04 eta: 23:21:21 time: 0.5526 data_time: 0.1403 memory: 14682 loss_ce: 0.0139 loss: 0.0139 2022/09/21 06:58:00 - mmengine - INFO - Epoch(train) [3][16100/42151] lr: 3.0000e-04 eta: 23:20:21 time: 0.5390 data_time: 0.1569 memory: 14682 loss_ce: 0.0129 loss: 0.0129 2022/09/21 06:58:53 - mmengine - INFO - Epoch(train) [3][16200/42151] lr: 3.0000e-04 eta: 23:19:22 time: 0.5613 data_time: 0.1391 memory: 14682 loss_ce: 0.0151 loss: 0.0151 2022/09/21 06:59:47 - mmengine - INFO - Epoch(train) [3][16300/42151] lr: 3.0000e-04 eta: 23:18:26 time: 0.6173 data_time: 0.2385 memory: 14682 loss_ce: 0.0145 loss: 0.0145 2022/09/21 07:00:39 - mmengine - INFO - Epoch(train) [3][16400/42151] lr: 3.0000e-04 eta: 23:17:26 time: 0.5605 data_time: 0.1706 memory: 14682 loss_ce: 0.0136 loss: 0.0136 2022/09/21 07:01:32 - mmengine - INFO - Epoch(train) [3][16500/42151] lr: 3.0000e-04 eta: 23:16:27 time: 0.5309 data_time: 0.1221 memory: 14682 loss_ce: 0.0159 loss: 0.0159 2022/09/21 07:02:26 - mmengine - INFO - Epoch(train) [3][16600/42151] lr: 3.0000e-04 eta: 23:15:31 time: 0.5095 data_time: 0.1315 memory: 14682 loss_ce: 0.0129 loss: 0.0129 2022/09/21 07:03:17 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 07:03:18 - mmengine - INFO - Epoch(train) [3][16700/42151] lr: 3.0000e-04 eta: 23:14:31 time: 0.5221 data_time: 0.1451 memory: 14682 loss_ce: 0.0115 loss: 0.0115 2022/09/21 07:04:08 - mmengine - INFO - Epoch(train) [3][16800/42151] lr: 3.0000e-04 eta: 23:13:29 time: 0.4962 data_time: 0.1204 memory: 14682 loss_ce: 0.0171 loss: 0.0171 2022/09/21 07:05:01 - mmengine - INFO - Epoch(train) [3][16900/42151] lr: 3.0000e-04 eta: 23:12:30 time: 0.5685 data_time: 0.1846 memory: 14682 loss_ce: 0.0146 loss: 0.0146 2022/09/21 07:05:52 - mmengine - INFO - Epoch(train) [3][17000/42151] lr: 3.0000e-04 eta: 23:11:29 time: 0.5826 data_time: 0.1973 memory: 14682 loss_ce: 0.0136 loss: 0.0136 2022/09/21 07:06:44 - mmengine - INFO - Epoch(train) [3][17100/42151] lr: 3.0000e-04 eta: 23:10:29 time: 0.5109 data_time: 0.1325 memory: 14682 loss_ce: 0.0129 loss: 0.0129 2022/09/21 07:07:38 - mmengine - INFO - Epoch(train) [3][17200/42151] lr: 3.0000e-04 eta: 23:09:33 time: 0.5016 data_time: 0.1233 memory: 14682 loss_ce: 0.0135 loss: 0.0135 2022/09/21 07:08:30 - mmengine - INFO - Epoch(train) [3][17300/42151] lr: 3.0000e-04 eta: 23:08:33 time: 0.5246 data_time: 0.1343 memory: 14682 loss_ce: 0.0143 loss: 0.0143 2022/09/21 07:09:22 - mmengine - INFO - Epoch(train) [3][17400/42151] lr: 3.0000e-04 eta: 23:07:34 time: 0.5473 data_time: 0.1581 memory: 14682 loss_ce: 0.0152 loss: 0.0152 2022/09/21 07:10:17 - mmengine - INFO - Epoch(train) [3][17500/42151] lr: 3.0000e-04 eta: 23:06:38 time: 0.5356 data_time: 0.1587 memory: 14682 loss_ce: 0.0141 loss: 0.0141 2022/09/21 07:11:09 - mmengine - INFO - Epoch(train) [3][17600/42151] lr: 3.0000e-04 eta: 23:05:39 time: 0.5832 data_time: 0.2018 memory: 14682 loss_ce: 0.0147 loss: 0.0147 2022/09/21 07:12:01 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 07:12:02 - mmengine - INFO - Epoch(train) [3][17700/42151] lr: 3.0000e-04 eta: 23:04:40 time: 0.5654 data_time: 0.1402 memory: 14682 loss_ce: 0.0136 loss: 0.0136 2022/09/21 07:12:56 - mmengine - INFO - Epoch(train) [3][17800/42151] lr: 3.0000e-04 eta: 23:03:44 time: 0.5271 data_time: 0.1470 memory: 14682 loss_ce: 0.0155 loss: 0.0155 2022/09/21 07:13:52 - mmengine - INFO - Epoch(train) [3][17900/42151] lr: 3.0000e-04 eta: 23:02:49 time: 0.5586 data_time: 0.1409 memory: 14682 loss_ce: 0.0149 loss: 0.0149 2022/09/21 07:14:46 - mmengine - INFO - Epoch(train) [3][18000/42151] lr: 3.0000e-04 eta: 23:01:54 time: 0.5605 data_time: 0.1540 memory: 14682 loss_ce: 0.0143 loss: 0.0143 2022/09/21 07:15:43 - mmengine - INFO - Epoch(train) [3][18100/42151] lr: 3.0000e-04 eta: 23:01:01 time: 0.5854 data_time: 0.1793 memory: 14682 loss_ce: 0.0142 loss: 0.0142 2022/09/21 07:16:36 - mmengine - INFO - Epoch(train) [3][18200/42151] lr: 3.0000e-04 eta: 23:00:03 time: 0.5809 data_time: 0.1995 memory: 14682 loss_ce: 0.0131 loss: 0.0131 2022/09/21 07:17:29 - mmengine - INFO - Epoch(train) [3][18300/42151] lr: 3.0000e-04 eta: 22:59:05 time: 0.5057 data_time: 0.1278 memory: 14682 loss_ce: 0.0139 loss: 0.0139 2022/09/21 07:18:25 - mmengine - INFO - Epoch(train) [3][18400/42151] lr: 3.0000e-04 eta: 22:58:10 time: 0.5698 data_time: 0.1425 memory: 14682 loss_ce: 0.0149 loss: 0.0149 2022/09/21 07:19:18 - mmengine - INFO - Epoch(train) [3][18500/42151] lr: 3.0000e-04 eta: 22:57:13 time: 0.5543 data_time: 0.1465 memory: 14682 loss_ce: 0.0141 loss: 0.0141 2022/09/21 07:20:11 - mmengine - INFO - Epoch(train) [3][18600/42151] lr: 3.0000e-04 eta: 22:56:15 time: 0.5351 data_time: 0.1347 memory: 14682 loss_ce: 0.0122 loss: 0.0122 2022/09/21 07:21:05 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 07:21:07 - mmengine - INFO - Epoch(train) [3][18700/42151] lr: 3.0000e-04 eta: 22:55:22 time: 0.5607 data_time: 0.1802 memory: 14682 loss_ce: 0.0157 loss: 0.0157 2022/09/21 07:22:01 - mmengine - INFO - Epoch(train) [3][18800/42151] lr: 3.0000e-04 eta: 22:54:25 time: 0.6229 data_time: 0.2115 memory: 14682 loss_ce: 0.0163 loss: 0.0163 2022/09/21 07:22:54 - mmengine - INFO - Epoch(train) [3][18900/42151] lr: 3.0000e-04 eta: 22:53:27 time: 0.5065 data_time: 0.1260 memory: 14682 loss_ce: 0.0153 loss: 0.0153 2022/09/21 07:23:49 - mmengine - INFO - Epoch(train) [3][19000/42151] lr: 3.0000e-04 eta: 22:52:31 time: 0.5234 data_time: 0.1415 memory: 14682 loss_ce: 0.0162 loss: 0.0162 2022/09/21 07:24:43 - mmengine - INFO - Epoch(train) [3][19100/42151] lr: 3.0000e-04 eta: 22:51:35 time: 0.5753 data_time: 0.1665 memory: 14682 loss_ce: 0.0145 loss: 0.0145 2022/09/21 07:25:37 - mmengine - INFO - Epoch(train) [3][19200/42151] lr: 3.0000e-04 eta: 22:50:38 time: 0.5408 data_time: 0.1617 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 07:26:32 - mmengine - INFO - Epoch(train) [3][19300/42151] lr: 3.0000e-04 eta: 22:49:43 time: 0.6034 data_time: 0.2218 memory: 14682 loss_ce: 0.0127 loss: 0.0127 2022/09/21 07:27:26 - mmengine - INFO - Epoch(train) [3][19400/42151] lr: 3.0000e-04 eta: 22:48:46 time: 0.6185 data_time: 0.2137 memory: 14682 loss_ce: 0.0128 loss: 0.0128 2022/09/21 07:28:20 - mmengine - INFO - Epoch(train) [3][19500/42151] lr: 3.0000e-04 eta: 22:47:50 time: 0.5327 data_time: 0.1470 memory: 14682 loss_ce: 0.0139 loss: 0.0139 2022/09/21 07:29:17 - mmengine - INFO - Epoch(train) [3][19600/42151] lr: 3.0000e-04 eta: 22:46:58 time: 0.5367 data_time: 0.1563 memory: 14682 loss_ce: 0.0144 loss: 0.0144 2022/09/21 07:30:10 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 07:30:10 - mmengine - INFO - Epoch(train) [3][19700/42151] lr: 3.0000e-04 eta: 22:46:00 time: 0.5300 data_time: 0.1499 memory: 14682 loss_ce: 0.0129 loss: 0.0129 2022/09/21 07:31:04 - mmengine - INFO - Epoch(train) [3][19800/42151] lr: 3.0000e-04 eta: 22:45:02 time: 0.5691 data_time: 0.1653 memory: 14682 loss_ce: 0.0153 loss: 0.0153 2022/09/21 07:31:58 - mmengine - INFO - Epoch(train) [3][19900/42151] lr: 3.0000e-04 eta: 22:44:06 time: 0.5776 data_time: 0.1990 memory: 14682 loss_ce: 0.0142 loss: 0.0142 2022/09/21 07:32:51 - mmengine - INFO - Epoch(train) [3][20000/42151] lr: 3.0000e-04 eta: 22:43:08 time: 0.5489 data_time: 0.1696 memory: 14682 loss_ce: 0.0131 loss: 0.0131 2022/09/21 07:33:44 - mmengine - INFO - Epoch(train) [3][20100/42151] lr: 3.0000e-04 eta: 22:42:11 time: 0.5593 data_time: 0.1597 memory: 14682 loss_ce: 0.0151 loss: 0.0151 2022/09/21 07:34:39 - mmengine - INFO - Epoch(train) [3][20200/42151] lr: 3.0000e-04 eta: 22:41:15 time: 0.5505 data_time: 0.1420 memory: 14682 loss_ce: 0.0139 loss: 0.0139 2022/09/21 07:35:34 - mmengine - INFO - Epoch(train) [3][20300/42151] lr: 3.0000e-04 eta: 22:40:19 time: 0.5858 data_time: 0.1513 memory: 14682 loss_ce: 0.0143 loss: 0.0143 2022/09/21 07:36:28 - mmengine - INFO - Epoch(train) [3][20400/42151] lr: 3.0000e-04 eta: 22:39:23 time: 0.5321 data_time: 0.1530 memory: 14682 loss_ce: 0.0157 loss: 0.0157 2022/09/21 07:37:24 - mmengine - INFO - Epoch(train) [3][20500/42151] lr: 3.0000e-04 eta: 22:38:30 time: 0.6342 data_time: 0.2162 memory: 14682 loss_ce: 0.0143 loss: 0.0143 2022/09/21 07:38:17 - mmengine - INFO - Epoch(train) [3][20600/42151] lr: 3.0000e-04 eta: 22:37:32 time: 0.5722 data_time: 0.1863 memory: 14682 loss_ce: 0.0149 loss: 0.0149 2022/09/21 07:39:10 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 07:39:10 - mmengine - INFO - Epoch(train) [3][20700/42151] lr: 3.0000e-04 eta: 22:36:34 time: 0.5205 data_time: 0.1376 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 07:40:05 - mmengine - INFO - Epoch(train) [3][20800/42151] lr: 3.0000e-04 eta: 22:35:39 time: 0.5923 data_time: 0.1429 memory: 14682 loss_ce: 0.0151 loss: 0.0151 2022/09/21 07:40:58 - mmengine - INFO - Epoch(train) [3][20900/42151] lr: 3.0000e-04 eta: 22:34:40 time: 0.5146 data_time: 0.1339 memory: 14682 loss_ce: 0.0135 loss: 0.0135 2022/09/21 07:41:51 - mmengine - INFO - Epoch(train) [3][21000/42151] lr: 3.0000e-04 eta: 22:33:42 time: 0.5135 data_time: 0.1354 memory: 14682 loss_ce: 0.0142 loss: 0.0142 2022/09/21 07:42:46 - mmengine - INFO - Epoch(train) [3][21100/42151] lr: 3.0000e-04 eta: 22:32:47 time: 0.6003 data_time: 0.2173 memory: 14682 loss_ce: 0.0149 loss: 0.0149 2022/09/21 07:43:38 - mmengine - INFO - Epoch(train) [3][21200/42151] lr: 3.0000e-04 eta: 22:31:48 time: 0.6240 data_time: 0.1918 memory: 14682 loss_ce: 0.0132 loss: 0.0132 2022/09/21 07:44:31 - mmengine - INFO - Epoch(train) [3][21300/42151] lr: 3.0000e-04 eta: 22:30:50 time: 0.5107 data_time: 0.1314 memory: 14682 loss_ce: 0.0143 loss: 0.0143 2022/09/21 07:45:25 - mmengine - INFO - Epoch(train) [3][21400/42151] lr: 3.0000e-04 eta: 22:29:54 time: 0.5088 data_time: 0.1284 memory: 14682 loss_ce: 0.0144 loss: 0.0144 2022/09/21 07:46:17 - mmengine - INFO - Epoch(train) [3][21500/42151] lr: 3.0000e-04 eta: 22:28:55 time: 0.5445 data_time: 0.1415 memory: 14682 loss_ce: 0.0125 loss: 0.0125 2022/09/21 07:47:10 - mmengine - INFO - Epoch(train) [3][21600/42151] lr: 3.0000e-04 eta: 22:27:57 time: 0.5301 data_time: 0.1504 memory: 14682 loss_ce: 0.0150 loss: 0.0150 2022/09/21 07:48:04 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 07:48:06 - mmengine - INFO - Epoch(train) [3][21700/42151] lr: 3.0000e-04 eta: 22:27:03 time: 0.6009 data_time: 0.2204 memory: 14682 loss_ce: 0.0131 loss: 0.0131 2022/09/21 07:48:59 - mmengine - INFO - Epoch(train) [3][21800/42151] lr: 3.0000e-04 eta: 22:26:05 time: 0.6513 data_time: 0.2323 memory: 14682 loss_ce: 0.0154 loss: 0.0154 2022/09/21 07:49:52 - mmengine - INFO - Epoch(train) [3][21900/42151] lr: 3.0000e-04 eta: 22:25:08 time: 0.5259 data_time: 0.1434 memory: 14682 loss_ce: 0.0135 loss: 0.0135 2022/09/21 07:50:48 - mmengine - INFO - Epoch(train) [3][22000/42151] lr: 3.0000e-04 eta: 22:24:13 time: 0.5327 data_time: 0.1342 memory: 14682 loss_ce: 0.0141 loss: 0.0141 2022/09/21 07:51:42 - mmengine - INFO - Epoch(train) [3][22100/42151] lr: 3.0000e-04 eta: 22:23:17 time: 0.5226 data_time: 0.1435 memory: 14682 loss_ce: 0.0156 loss: 0.0156 2022/09/21 07:52:35 - mmengine - INFO - Epoch(train) [3][22200/42151] lr: 3.0000e-04 eta: 22:22:19 time: 0.5690 data_time: 0.1612 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 07:53:30 - mmengine - INFO - Epoch(train) [3][22300/42151] lr: 3.0000e-04 eta: 22:21:25 time: 0.5617 data_time: 0.1789 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 07:54:23 - mmengine - INFO - Epoch(train) [3][22400/42151] lr: 3.0000e-04 eta: 22:20:27 time: 0.5596 data_time: 0.1787 memory: 14682 loss_ce: 0.0159 loss: 0.0159 2022/09/21 07:55:17 - mmengine - INFO - Epoch(train) [3][22500/42151] lr: 3.0000e-04 eta: 22:19:30 time: 0.5699 data_time: 0.1462 memory: 14682 loss_ce: 0.0158 loss: 0.0158 2022/09/21 07:56:11 - mmengine - INFO - Epoch(train) [3][22600/42151] lr: 3.0000e-04 eta: 22:18:34 time: 0.5231 data_time: 0.1415 memory: 14682 loss_ce: 0.0141 loss: 0.0141 2022/09/21 07:57:05 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 07:57:05 - mmengine - INFO - Epoch(train) [3][22700/42151] lr: 3.0000e-04 eta: 22:17:38 time: 0.5726 data_time: 0.1394 memory: 14682 loss_ce: 0.0136 loss: 0.0136 2022/09/21 07:57:58 - mmengine - INFO - Epoch(train) [3][22800/42151] lr: 3.0000e-04 eta: 22:16:40 time: 0.5729 data_time: 0.1385 memory: 14682 loss_ce: 0.0141 loss: 0.0141 2022/09/21 07:58:54 - mmengine - INFO - Epoch(train) [3][22900/42151] lr: 3.0000e-04 eta: 22:15:45 time: 0.5786 data_time: 0.1962 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 07:59:47 - mmengine - INFO - Epoch(train) [3][23000/42151] lr: 3.0000e-04 eta: 22:14:48 time: 0.5879 data_time: 0.1931 memory: 14682 loss_ce: 0.0136 loss: 0.0136 2022/09/21 08:00:40 - mmengine - INFO - Epoch(train) [3][23100/42151] lr: 3.0000e-04 eta: 22:13:50 time: 0.5393 data_time: 0.1551 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 08:01:35 - mmengine - INFO - Epoch(train) [3][23200/42151] lr: 3.0000e-04 eta: 22:12:55 time: 0.5504 data_time: 0.1496 memory: 14682 loss_ce: 0.0145 loss: 0.0145 2022/09/21 08:02:28 - mmengine - INFO - Epoch(train) [3][23300/42151] lr: 3.0000e-04 eta: 22:11:58 time: 0.5781 data_time: 0.1723 memory: 14682 loss_ce: 0.0137 loss: 0.0137 2022/09/21 08:03:22 - mmengine - INFO - Epoch(train) [3][23400/42151] lr: 3.0000e-04 eta: 22:11:01 time: 0.5220 data_time: 0.1426 memory: 14682 loss_ce: 0.0131 loss: 0.0131 2022/09/21 08:04:19 - mmengine - INFO - Epoch(train) [3][23500/42151] lr: 3.0000e-04 eta: 22:10:08 time: 0.6275 data_time: 0.2300 memory: 14682 loss_ce: 0.0155 loss: 0.0155 2022/09/21 08:05:12 - mmengine - INFO - Epoch(train) [3][23600/42151] lr: 3.0000e-04 eta: 22:09:11 time: 0.5817 data_time: 0.2023 memory: 14682 loss_ce: 0.0154 loss: 0.0154 2022/09/21 08:06:04 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 08:06:05 - mmengine - INFO - Epoch(train) [3][23700/42151] lr: 3.0000e-04 eta: 22:08:13 time: 0.5164 data_time: 0.1365 memory: 14682 loss_ce: 0.0128 loss: 0.0128 2022/09/21 08:06:58 - mmengine - INFO - Epoch(train) [3][23800/42151] lr: 3.0000e-04 eta: 22:07:15 time: 0.5457 data_time: 0.1427 memory: 14682 loss_ce: 0.0142 loss: 0.0142 2022/09/21 08:07:50 - mmengine - INFO - Epoch(train) [3][23900/42151] lr: 3.0000e-04 eta: 22:06:16 time: 0.5465 data_time: 0.1669 memory: 14682 loss_ce: 0.0134 loss: 0.0134 2022/09/21 08:08:42 - mmengine - INFO - Epoch(train) [3][24000/42151] lr: 3.0000e-04 eta: 22:05:18 time: 0.5039 data_time: 0.1213 memory: 14682 loss_ce: 0.0150 loss: 0.0150 2022/09/21 08:09:37 - mmengine - INFO - Epoch(train) [3][24100/42151] lr: 3.0000e-04 eta: 22:04:22 time: 0.6507 data_time: 0.2383 memory: 14682 loss_ce: 0.0120 loss: 0.0120 2022/09/21 08:10:29 - mmengine - INFO - Epoch(train) [3][24200/42151] lr: 3.0000e-04 eta: 22:03:23 time: 0.5833 data_time: 0.2035 memory: 14682 loss_ce: 0.0134 loss: 0.0134 2022/09/21 08:11:22 - mmengine - INFO - Epoch(train) [3][24300/42151] lr: 3.0000e-04 eta: 22:02:25 time: 0.4997 data_time: 0.1210 memory: 14682 loss_ce: 0.0154 loss: 0.0154 2022/09/21 08:12:16 - mmengine - INFO - Epoch(train) [3][24400/42151] lr: 3.0000e-04 eta: 22:01:29 time: 0.5725 data_time: 0.1625 memory: 14682 loss_ce: 0.0152 loss: 0.0152 2022/09/21 08:13:09 - mmengine - INFO - Epoch(train) [3][24500/42151] lr: 3.0000e-04 eta: 22:00:31 time: 0.5533 data_time: 0.1728 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 08:14:02 - mmengine - INFO - Epoch(train) [3][24600/42151] lr: 3.0000e-04 eta: 21:59:34 time: 0.5138 data_time: 0.1340 memory: 14682 loss_ce: 0.0142 loss: 0.0142 2022/09/21 08:14:55 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 08:14:57 - mmengine - INFO - Epoch(train) [3][24700/42151] lr: 3.0000e-04 eta: 21:58:39 time: 0.6221 data_time: 0.2131 memory: 14682 loss_ce: 0.0139 loss: 0.0139 2022/09/21 08:15:50 - mmengine - INFO - Epoch(train) [3][24800/42151] lr: 3.0000e-04 eta: 21:57:41 time: 0.5823 data_time: 0.1948 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 08:16:42 - mmengine - INFO - Epoch(train) [3][24900/42151] lr: 3.0000e-04 eta: 21:56:42 time: 0.4978 data_time: 0.1195 memory: 14682 loss_ce: 0.0136 loss: 0.0136 2022/09/21 08:17:36 - mmengine - INFO - Epoch(train) [3][25000/42151] lr: 3.0000e-04 eta: 21:55:46 time: 0.5657 data_time: 0.1368 memory: 14682 loss_ce: 0.0163 loss: 0.0163 2022/09/21 08:18:28 - mmengine - INFO - Epoch(train) [3][25100/42151] lr: 3.0000e-04 eta: 21:54:47 time: 0.5276 data_time: 0.1318 memory: 14682 loss_ce: 0.0149 loss: 0.0149 2022/09/21 08:19:21 - mmengine - INFO - Epoch(train) [3][25200/42151] lr: 3.0000e-04 eta: 21:53:50 time: 0.5346 data_time: 0.1457 memory: 14682 loss_ce: 0.0156 loss: 0.0156 2022/09/21 08:20:16 - mmengine - INFO - Epoch(train) [3][25300/42151] lr: 3.0000e-04 eta: 21:52:55 time: 0.5862 data_time: 0.1907 memory: 14682 loss_ce: 0.0145 loss: 0.0145 2022/09/21 08:21:09 - mmengine - INFO - Epoch(train) [3][25400/42151] lr: 3.0000e-04 eta: 21:51:57 time: 0.6108 data_time: 0.2296 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 08:22:01 - mmengine - INFO - Epoch(train) [3][25500/42151] lr: 3.0000e-04 eta: 21:50:58 time: 0.5381 data_time: 0.1359 memory: 14682 loss_ce: 0.0125 loss: 0.0125 2022/09/21 08:22:54 - mmengine - INFO - Epoch(train) [3][25600/42151] lr: 3.0000e-04 eta: 21:50:01 time: 0.5302 data_time: 0.1266 memory: 14682 loss_ce: 0.0135 loss: 0.0135 2022/09/21 08:23:45 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 08:23:46 - mmengine - INFO - Epoch(train) [3][25700/42151] lr: 3.0000e-04 eta: 21:49:02 time: 0.5519 data_time: 0.1689 memory: 14682 loss_ce: 0.0151 loss: 0.0151 2022/09/21 08:24:38 - mmengine - INFO - Epoch(train) [3][25800/42151] lr: 3.0000e-04 eta: 21:48:03 time: 0.5054 data_time: 0.1214 memory: 14682 loss_ce: 0.0135 loss: 0.0135 2022/09/21 08:25:32 - mmengine - INFO - Epoch(train) [3][25900/42151] lr: 3.0000e-04 eta: 21:47:07 time: 0.5577 data_time: 0.1790 memory: 14682 loss_ce: 0.0131 loss: 0.0131 2022/09/21 08:26:30 - mmengine - INFO - Epoch(train) [3][26000/42151] lr: 3.0000e-04 eta: 21:46:15 time: 0.5914 data_time: 0.2078 memory: 14682 loss_ce: 0.0158 loss: 0.0158 2022/09/21 08:27:21 - mmengine - INFO - Epoch(train) [3][26100/42151] lr: 3.0000e-04 eta: 21:45:15 time: 0.5288 data_time: 0.1221 memory: 14682 loss_ce: 0.0133 loss: 0.0133 2022/09/21 08:28:15 - mmengine - INFO - Epoch(train) [3][26200/42151] lr: 3.0000e-04 eta: 21:44:19 time: 0.5046 data_time: 0.1266 memory: 14682 loss_ce: 0.0135 loss: 0.0135 2022/09/21 08:29:07 - mmengine - INFO - Epoch(train) [3][26300/42151] lr: 3.0000e-04 eta: 21:43:21 time: 0.5604 data_time: 0.1675 memory: 14682 loss_ce: 0.0141 loss: 0.0141 2022/09/21 08:30:00 - mmengine - INFO - Epoch(train) [3][26400/42151] lr: 3.0000e-04 eta: 21:42:22 time: 0.5148 data_time: 0.1318 memory: 14682 loss_ce: 0.0147 loss: 0.0147 2022/09/21 08:30:56 - mmengine - INFO - Epoch(train) [3][26500/42151] lr: 3.0000e-04 eta: 21:41:30 time: 0.5846 data_time: 0.2068 memory: 14682 loss_ce: 0.0129 loss: 0.0129 2022/09/21 08:31:50 - mmengine - INFO - Epoch(train) [3][26600/42151] lr: 3.0000e-04 eta: 21:40:33 time: 0.5937 data_time: 0.2125 memory: 14682 loss_ce: 0.0163 loss: 0.0163 2022/09/21 08:32:42 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 08:32:43 - mmengine - INFO - Epoch(train) [3][26700/42151] lr: 3.0000e-04 eta: 21:39:35 time: 0.5287 data_time: 0.1531 memory: 14682 loss_ce: 0.0131 loss: 0.0131 2022/09/21 08:33:38 - mmengine - INFO - Epoch(train) [3][26800/42151] lr: 3.0000e-04 eta: 21:38:40 time: 0.5186 data_time: 0.1307 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 08:34:31 - mmengine - INFO - Epoch(train) [3][26900/42151] lr: 3.0000e-04 eta: 21:37:43 time: 0.6206 data_time: 0.2106 memory: 14682 loss_ce: 0.0141 loss: 0.0141 2022/09/21 08:35:24 - mmengine - INFO - Epoch(train) [3][27000/42151] lr: 3.0000e-04 eta: 21:36:46 time: 0.5206 data_time: 0.1405 memory: 14682 loss_ce: 0.0144 loss: 0.0144 2022/09/21 08:36:19 - mmengine - INFO - Epoch(train) [3][27100/42151] lr: 3.0000e-04 eta: 21:35:51 time: 0.5605 data_time: 0.1807 memory: 14682 loss_ce: 0.0123 loss: 0.0123 2022/09/21 08:37:11 - mmengine - INFO - Epoch(train) [3][27200/42151] lr: 3.0000e-04 eta: 21:34:52 time: 0.6015 data_time: 0.1969 memory: 14682 loss_ce: 0.0144 loss: 0.0144 2022/09/21 08:38:05 - mmengine - INFO - Epoch(train) [3][27300/42151] lr: 3.0000e-04 eta: 21:33:56 time: 0.5302 data_time: 0.1248 memory: 14682 loss_ce: 0.0120 loss: 0.0120 2022/09/21 08:38:58 - mmengine - INFO - Epoch(train) [3][27400/42151] lr: 3.0000e-04 eta: 21:32:58 time: 0.5024 data_time: 0.1228 memory: 14682 loss_ce: 0.0134 loss: 0.0134 2022/09/21 08:39:56 - mmengine - INFO - Epoch(train) [3][27500/42151] lr: 3.0000e-04 eta: 21:32:07 time: 0.5723 data_time: 0.1704 memory: 14682 loss_ce: 0.0162 loss: 0.0162 2022/09/21 08:40:48 - mmengine - INFO - Epoch(train) [3][27600/42151] lr: 3.0000e-04 eta: 21:31:09 time: 0.5812 data_time: 0.1344 memory: 14682 loss_ce: 0.0131 loss: 0.0131 2022/09/21 08:41:41 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 08:41:43 - mmengine - INFO - Epoch(train) [3][27700/42151] lr: 3.0000e-04 eta: 21:30:13 time: 0.5780 data_time: 0.1813 memory: 14682 loss_ce: 0.0134 loss: 0.0134 2022/09/21 08:42:36 - mmengine - INFO - Epoch(train) [3][27800/42151] lr: 3.0000e-04 eta: 21:29:16 time: 0.5787 data_time: 0.2004 memory: 14682 loss_ce: 0.0145 loss: 0.0145 2022/09/21 08:43:29 - mmengine - INFO - Epoch(train) [3][27900/42151] lr: 3.0000e-04 eta: 21:28:18 time: 0.5357 data_time: 0.1512 memory: 14682 loss_ce: 0.0126 loss: 0.0126 2022/09/21 08:44:26 - mmengine - INFO - Epoch(train) [3][28000/42151] lr: 3.0000e-04 eta: 21:27:26 time: 0.5104 data_time: 0.1328 memory: 14682 loss_ce: 0.0128 loss: 0.0128 2022/09/21 08:45:19 - mmengine - INFO - Epoch(train) [3][28100/42151] lr: 3.0000e-04 eta: 21:26:29 time: 0.5819 data_time: 0.1568 memory: 14682 loss_ce: 0.0133 loss: 0.0133 2022/09/21 08:46:12 - mmengine - INFO - Epoch(train) [3][28200/42151] lr: 3.0000e-04 eta: 21:25:32 time: 0.5345 data_time: 0.1537 memory: 14682 loss_ce: 0.0165 loss: 0.0165 2022/09/21 08:47:08 - mmengine - INFO - Epoch(train) [3][28300/42151] lr: 3.0000e-04 eta: 21:24:38 time: 0.5639 data_time: 0.1853 memory: 14682 loss_ce: 0.0112 loss: 0.0112 2022/09/21 08:48:01 - mmengine - INFO - Epoch(train) [3][28400/42151] lr: 3.0000e-04 eta: 21:23:41 time: 0.6593 data_time: 0.2490 memory: 14682 loss_ce: 0.0148 loss: 0.0148 2022/09/21 08:48:54 - mmengine - INFO - Epoch(train) [3][28500/42151] lr: 3.0000e-04 eta: 21:22:43 time: 0.5463 data_time: 0.1670 memory: 14682 loss_ce: 0.0143 loss: 0.0143 2022/09/21 08:49:48 - mmengine - INFO - Epoch(train) [3][28600/42151] lr: 3.0000e-04 eta: 21:21:47 time: 0.5276 data_time: 0.1472 memory: 14682 loss_ce: 0.0158 loss: 0.0158 2022/09/21 08:50:40 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 08:50:41 - mmengine - INFO - Epoch(train) [3][28700/42151] lr: 3.0000e-04 eta: 21:20:49 time: 0.5366 data_time: 0.1539 memory: 14682 loss_ce: 0.0121 loss: 0.0121 2022/09/21 08:51:33 - mmengine - INFO - Epoch(train) [3][28800/42151] lr: 3.0000e-04 eta: 21:19:51 time: 0.5269 data_time: 0.1487 memory: 14682 loss_ce: 0.0143 loss: 0.0143 2022/09/21 08:52:30 - mmengine - INFO - Epoch(train) [3][28900/42151] lr: 3.0000e-04 eta: 21:18:59 time: 0.5989 data_time: 0.2168 memory: 14682 loss_ce: 0.0131 loss: 0.0131 2022/09/21 08:53:25 - mmengine - INFO - Epoch(train) [3][29000/42151] lr: 3.0000e-04 eta: 21:18:04 time: 0.7173 data_time: 0.2946 memory: 14682 loss_ce: 0.0148 loss: 0.0148 2022/09/21 08:54:19 - mmengine - INFO - Epoch(train) [3][29100/42151] lr: 3.0000e-04 eta: 21:17:08 time: 0.5396 data_time: 0.1500 memory: 14682 loss_ce: 0.0155 loss: 0.0155 2022/09/21 08:55:13 - mmengine - INFO - Epoch(train) [3][29200/42151] lr: 3.0000e-04 eta: 21:16:12 time: 0.5241 data_time: 0.1459 memory: 14682 loss_ce: 0.0114 loss: 0.0114 2022/09/21 08:56:07 - mmengine - INFO - Epoch(train) [3][29300/42151] lr: 3.0000e-04 eta: 21:15:15 time: 0.5407 data_time: 0.1636 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 08:56:59 - mmengine - INFO - Epoch(train) [3][29400/42151] lr: 3.0000e-04 eta: 21:14:17 time: 0.5442 data_time: 0.1668 memory: 14682 loss_ce: 0.0142 loss: 0.0142 2022/09/21 08:57:54 - mmengine - INFO - Epoch(train) [3][29500/42151] lr: 3.0000e-04 eta: 21:13:22 time: 0.5882 data_time: 0.2054 memory: 14682 loss_ce: 0.0158 loss: 0.0158 2022/09/21 08:58:46 - mmengine - INFO - Epoch(train) [3][29600/42151] lr: 3.0000e-04 eta: 21:12:24 time: 0.5717 data_time: 0.1937 memory: 14682 loss_ce: 0.0139 loss: 0.0139 2022/09/21 08:59:38 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 08:59:39 - mmengine - INFO - Epoch(train) [3][29700/42151] lr: 3.0000e-04 eta: 21:11:26 time: 0.5414 data_time: 0.1614 memory: 14682 loss_ce: 0.0157 loss: 0.0157 2022/09/21 09:00:33 - mmengine - INFO - Epoch(train) [3][29800/42151] lr: 3.0000e-04 eta: 21:10:30 time: 0.5343 data_time: 0.1219 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 09:01:28 - mmengine - INFO - Epoch(train) [3][29900/42151] lr: 3.0000e-04 eta: 21:09:35 time: 0.5796 data_time: 0.1758 memory: 14682 loss_ce: 0.0144 loss: 0.0144 2022/09/21 09:02:21 - mmengine - INFO - Epoch(train) [3][30000/42151] lr: 3.0000e-04 eta: 21:08:37 time: 0.5445 data_time: 0.1385 memory: 14682 loss_ce: 0.0134 loss: 0.0134 2022/09/21 09:03:14 - mmengine - INFO - Epoch(train) [3][30100/42151] lr: 3.0000e-04 eta: 21:07:41 time: 0.5659 data_time: 0.1791 memory: 14682 loss_ce: 0.0146 loss: 0.0146 2022/09/21 09:04:06 - mmengine - INFO - Epoch(train) [3][30200/42151] lr: 3.0000e-04 eta: 21:06:42 time: 0.5824 data_time: 0.2044 memory: 14682 loss_ce: 0.0142 loss: 0.0142 2022/09/21 09:04:59 - mmengine - INFO - Epoch(train) [3][30300/42151] lr: 3.0000e-04 eta: 21:05:45 time: 0.5003 data_time: 0.1211 memory: 14682 loss_ce: 0.0139 loss: 0.0139 2022/09/21 09:05:53 - mmengine - INFO - Epoch(train) [3][30400/42151] lr: 3.0000e-04 eta: 21:04:49 time: 0.5641 data_time: 0.1246 memory: 14682 loss_ce: 0.0134 loss: 0.0134 2022/09/21 09:06:45 - mmengine - INFO - Epoch(train) [3][30500/42151] lr: 3.0000e-04 eta: 21:03:50 time: 0.5321 data_time: 0.1490 memory: 14682 loss_ce: 0.0127 loss: 0.0127 2022/09/21 09:07:37 - mmengine - INFO - Epoch(train) [3][30600/42151] lr: 3.0000e-04 eta: 21:02:52 time: 0.5272 data_time: 0.1495 memory: 14682 loss_ce: 0.0135 loss: 0.0135 2022/09/21 09:08:29 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 09:08:32 - mmengine - INFO - Epoch(train) [3][30700/42151] lr: 3.0000e-04 eta: 21:01:56 time: 0.6066 data_time: 0.2133 memory: 14682 loss_ce: 0.0137 loss: 0.0137 2022/09/21 09:09:23 - mmengine - INFO - Epoch(train) [3][30800/42151] lr: 3.0000e-04 eta: 21:00:58 time: 0.5869 data_time: 0.1791 memory: 14682 loss_ce: 0.0154 loss: 0.0154 2022/09/21 09:10:15 - mmengine - INFO - Epoch(train) [3][30900/42151] lr: 3.0000e-04 eta: 20:59:59 time: 0.5094 data_time: 0.1312 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 09:11:10 - mmengine - INFO - Epoch(train) [3][31000/42151] lr: 3.0000e-04 eta: 20:59:04 time: 0.5141 data_time: 0.1359 memory: 14682 loss_ce: 0.0132 loss: 0.0132 2022/09/21 09:12:03 - mmengine - INFO - Epoch(train) [3][31100/42151] lr: 3.0000e-04 eta: 20:58:07 time: 0.5587 data_time: 0.1534 memory: 14682 loss_ce: 0.0127 loss: 0.0127 2022/09/21 09:12:55 - mmengine - INFO - Epoch(train) [3][31200/42151] lr: 3.0000e-04 eta: 20:57:09 time: 0.5241 data_time: 0.1435 memory: 14682 loss_ce: 0.0123 loss: 0.0123 2022/09/21 09:13:50 - mmengine - INFO - Epoch(train) [3][31300/42151] lr: 3.0000e-04 eta: 20:56:13 time: 0.5686 data_time: 0.1915 memory: 14682 loss_ce: 0.0133 loss: 0.0133 2022/09/21 09:14:43 - mmengine - INFO - Epoch(train) [3][31400/42151] lr: 3.0000e-04 eta: 20:55:16 time: 0.6084 data_time: 0.2036 memory: 14682 loss_ce: 0.0159 loss: 0.0159 2022/09/21 09:15:35 - mmengine - INFO - Epoch(train) [3][31500/42151] lr: 3.0000e-04 eta: 20:54:18 time: 0.5080 data_time: 0.1291 memory: 14682 loss_ce: 0.0137 loss: 0.0137 2022/09/21 09:16:29 - mmengine - INFO - Epoch(train) [3][31600/42151] lr: 3.0000e-04 eta: 20:53:22 time: 0.5150 data_time: 0.1382 memory: 14682 loss_ce: 0.0124 loss: 0.0124 2022/09/21 09:17:20 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 09:17:21 - mmengine - INFO - Epoch(train) [3][31700/42151] lr: 3.0000e-04 eta: 20:52:24 time: 0.5291 data_time: 0.1492 memory: 14682 loss_ce: 0.0137 loss: 0.0137 2022/09/21 09:18:13 - mmengine - INFO - Epoch(train) [3][31800/42151] lr: 3.0000e-04 eta: 20:51:26 time: 0.5083 data_time: 0.1233 memory: 14682 loss_ce: 0.0134 loss: 0.0134 2022/09/21 09:19:07 - mmengine - INFO - Epoch(train) [3][31900/42151] lr: 3.0000e-04 eta: 20:50:30 time: 0.5857 data_time: 0.2012 memory: 14682 loss_ce: 0.0151 loss: 0.0151 2022/09/21 09:19:59 - mmengine - INFO - Epoch(train) [3][32000/42151] lr: 3.0000e-04 eta: 20:49:31 time: 0.5593 data_time: 0.1800 memory: 14682 loss_ce: 0.0139 loss: 0.0139 2022/09/21 09:20:52 - mmengine - INFO - Epoch(train) [3][32100/42151] lr: 3.0000e-04 eta: 20:48:35 time: 0.5610 data_time: 0.1307 memory: 14682 loss_ce: 0.0132 loss: 0.0132 2022/09/21 09:21:46 - mmengine - INFO - Epoch(train) [3][32200/42151] lr: 3.0000e-04 eta: 20:47:38 time: 0.5279 data_time: 0.1373 memory: 14682 loss_ce: 0.0151 loss: 0.0151 2022/09/21 09:22:39 - mmengine - INFO - Epoch(train) [3][32300/42151] lr: 3.0000e-04 eta: 20:46:41 time: 0.5450 data_time: 0.1425 memory: 14682 loss_ce: 0.0133 loss: 0.0133 2022/09/21 09:23:33 - mmengine - INFO - Epoch(train) [3][32400/42151] lr: 3.0000e-04 eta: 20:45:45 time: 0.5490 data_time: 0.1299 memory: 14682 loss_ce: 0.0124 loss: 0.0124 2022/09/21 09:24:27 - mmengine - INFO - Epoch(train) [3][32500/42151] lr: 3.0000e-04 eta: 20:44:49 time: 0.6186 data_time: 0.2150 memory: 14682 loss_ce: 0.0145 loss: 0.0145 2022/09/21 09:25:19 - mmengine - INFO - Epoch(train) [3][32600/42151] lr: 3.0000e-04 eta: 20:43:51 time: 0.5429 data_time: 0.1661 memory: 14682 loss_ce: 0.0146 loss: 0.0146 2022/09/21 09:26:12 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 09:26:12 - mmengine - INFO - Epoch(train) [3][32700/42151] lr: 3.0000e-04 eta: 20:42:54 time: 0.5413 data_time: 0.1625 memory: 14682 loss_ce: 0.0125 loss: 0.0125 2022/09/21 09:27:06 - mmengine - INFO - Epoch(train) [3][32800/42151] lr: 3.0000e-04 eta: 20:41:57 time: 0.5184 data_time: 0.1347 memory: 14682 loss_ce: 0.0133 loss: 0.0133 2022/09/21 09:27:59 - mmengine - INFO - Epoch(train) [3][32900/42151] lr: 3.0000e-04 eta: 20:41:01 time: 0.5391 data_time: 0.1295 memory: 14682 loss_ce: 0.0158 loss: 0.0158 2022/09/21 09:28:52 - mmengine - INFO - Epoch(train) [3][33000/42151] lr: 3.0000e-04 eta: 20:40:03 time: 0.5123 data_time: 0.1345 memory: 14682 loss_ce: 0.0132 loss: 0.0132 2022/09/21 09:29:47 - mmengine - INFO - Epoch(train) [3][33100/42151] lr: 3.0000e-04 eta: 20:39:09 time: 0.5757 data_time: 0.1931 memory: 14682 loss_ce: 0.0155 loss: 0.0155 2022/09/21 09:30:40 - mmengine - INFO - Epoch(train) [3][33200/42151] lr: 3.0000e-04 eta: 20:38:11 time: 0.5721 data_time: 0.1936 memory: 14682 loss_ce: 0.0131 loss: 0.0131 2022/09/21 09:31:32 - mmengine - INFO - Epoch(train) [3][33300/42151] lr: 3.0000e-04 eta: 20:37:14 time: 0.5390 data_time: 0.1612 memory: 14682 loss_ce: 0.0125 loss: 0.0125 2022/09/21 09:32:36 - mmengine - INFO - Epoch(train) [3][33400/42151] lr: 3.0000e-04 eta: 20:36:29 time: 0.6826 data_time: 0.1540 memory: 14682 loss_ce: 0.0139 loss: 0.0139 2022/09/21 09:33:30 - mmengine - INFO - Epoch(train) [3][33500/42151] lr: 3.0000e-04 eta: 20:35:33 time: 0.5602 data_time: 0.1806 memory: 14682 loss_ce: 0.0130 loss: 0.0130 2022/09/21 09:34:22 - mmengine - INFO - Epoch(train) [3][33600/42151] lr: 3.0000e-04 eta: 20:34:35 time: 0.5166 data_time: 0.1361 memory: 14682 loss_ce: 0.0158 loss: 0.0158 2022/09/21 09:35:14 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 09:35:16 - mmengine - INFO - Epoch(train) [3][33700/42151] lr: 3.0000e-04 eta: 20:33:39 time: 0.5842 data_time: 0.1978 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 09:36:08 - mmengine - INFO - Epoch(train) [3][33800/42151] lr: 3.0000e-04 eta: 20:32:42 time: 0.6469 data_time: 0.2375 memory: 14682 loss_ce: 0.0122 loss: 0.0122 2022/09/21 09:37:01 - mmengine - INFO - Epoch(train) [3][33900/42151] lr: 3.0000e-04 eta: 20:31:44 time: 0.5153 data_time: 0.1357 memory: 14682 loss_ce: 0.0130 loss: 0.0130 2022/09/21 09:37:56 - mmengine - INFO - Epoch(train) [3][34000/42151] lr: 3.0000e-04 eta: 20:30:50 time: 0.5353 data_time: 0.1542 memory: 14682 loss_ce: 0.0123 loss: 0.0123 2022/09/21 09:38:50 - mmengine - INFO - Epoch(train) [3][34100/42151] lr: 3.0000e-04 eta: 20:29:53 time: 0.5199 data_time: 0.1434 memory: 14682 loss_ce: 0.0152 loss: 0.0152 2022/09/21 09:39:43 - mmengine - INFO - Epoch(train) [3][34200/42151] lr: 3.0000e-04 eta: 20:28:56 time: 0.5395 data_time: 0.1558 memory: 14682 loss_ce: 0.0133 loss: 0.0133 2022/09/21 09:40:40 - mmengine - INFO - Epoch(train) [3][34300/42151] lr: 3.0000e-04 eta: 20:28:04 time: 0.5693 data_time: 0.1894 memory: 14682 loss_ce: 0.0135 loss: 0.0135 2022/09/21 09:41:33 - mmengine - INFO - Epoch(train) [3][34400/42151] lr: 3.0000e-04 eta: 20:27:06 time: 0.5672 data_time: 0.1879 memory: 14682 loss_ce: 0.0145 loss: 0.0145 2022/09/21 09:42:25 - mmengine - INFO - Epoch(train) [3][34500/42151] lr: 3.0000e-04 eta: 20:26:09 time: 0.5479 data_time: 0.1467 memory: 14682 loss_ce: 0.0119 loss: 0.0119 2022/09/21 09:43:20 - mmengine - INFO - Epoch(train) [3][34600/42151] lr: 3.0000e-04 eta: 20:25:14 time: 0.5640 data_time: 0.1441 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 09:44:13 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 09:44:13 - mmengine - INFO - Epoch(train) [3][34700/42151] lr: 3.0000e-04 eta: 20:24:17 time: 0.5411 data_time: 0.1363 memory: 14682 loss_ce: 0.0127 loss: 0.0127 2022/09/21 09:45:07 - mmengine - INFO - Epoch(train) [3][34800/42151] lr: 3.0000e-04 eta: 20:23:21 time: 0.5249 data_time: 0.1279 memory: 14682 loss_ce: 0.0119 loss: 0.0119 2022/09/21 09:46:01 - mmengine - INFO - Epoch(train) [3][34900/42151] lr: 3.0000e-04 eta: 20:22:25 time: 0.5601 data_time: 0.1805 memory: 14682 loss_ce: 0.0146 loss: 0.0146 2022/09/21 09:46:54 - mmengine - INFO - Epoch(train) [3][35000/42151] lr: 3.0000e-04 eta: 20:21:29 time: 0.5650 data_time: 0.1871 memory: 14682 loss_ce: 0.0148 loss: 0.0148 2022/09/21 09:47:48 - mmengine - INFO - Epoch(train) [3][35100/42151] lr: 3.0000e-04 eta: 20:20:32 time: 0.5360 data_time: 0.1420 memory: 14682 loss_ce: 0.0135 loss: 0.0135 2022/09/21 09:48:42 - mmengine - INFO - Epoch(train) [3][35200/42151] lr: 3.0000e-04 eta: 20:19:37 time: 0.5345 data_time: 0.1313 memory: 14682 loss_ce: 0.0152 loss: 0.0152 2022/09/21 09:49:35 - mmengine - INFO - Epoch(train) [3][35300/42151] lr: 3.0000e-04 eta: 20:18:39 time: 0.5244 data_time: 0.1449 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 09:50:28 - mmengine - INFO - Epoch(train) [3][35400/42151] lr: 3.0000e-04 eta: 20:17:43 time: 0.5510 data_time: 0.1487 memory: 14682 loss_ce: 0.0142 loss: 0.0142 2022/09/21 09:51:22 - mmengine - INFO - Epoch(train) [3][35500/42151] lr: 3.0000e-04 eta: 20:16:47 time: 0.5942 data_time: 0.2166 memory: 14682 loss_ce: 0.0149 loss: 0.0149 2022/09/21 09:52:15 - mmengine - INFO - Epoch(train) [3][35600/42151] lr: 3.0000e-04 eta: 20:15:50 time: 0.5607 data_time: 0.1823 memory: 14682 loss_ce: 0.0118 loss: 0.0118 2022/09/21 09:53:08 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 09:53:09 - mmengine - INFO - Epoch(train) [3][35700/42151] lr: 3.0000e-04 eta: 20:14:54 time: 0.5527 data_time: 0.1380 memory: 14682 loss_ce: 0.0155 loss: 0.0155 2022/09/21 09:54:04 - mmengine - INFO - Epoch(train) [3][35800/42151] lr: 3.0000e-04 eta: 20:13:59 time: 0.5432 data_time: 0.1648 memory: 14682 loss_ce: 0.0135 loss: 0.0135 2022/09/21 09:54:57 - mmengine - INFO - Epoch(train) [3][35900/42151] lr: 3.0000e-04 eta: 20:13:02 time: 0.5242 data_time: 0.1447 memory: 14682 loss_ce: 0.0147 loss: 0.0147 2022/09/21 09:55:50 - mmengine - INFO - Epoch(train) [3][36000/42151] lr: 3.0000e-04 eta: 20:12:06 time: 0.5714 data_time: 0.1498 memory: 14682 loss_ce: 0.0125 loss: 0.0125 2022/09/21 09:56:46 - mmengine - INFO - Epoch(train) [3][36100/42151] lr: 3.0000e-04 eta: 20:11:11 time: 0.5912 data_time: 0.2015 memory: 14682 loss_ce: 0.0153 loss: 0.0153 2022/09/21 09:57:38 - mmengine - INFO - Epoch(train) [3][36200/42151] lr: 3.0000e-04 eta: 20:10:14 time: 0.5734 data_time: 0.1799 memory: 14682 loss_ce: 0.0152 loss: 0.0152 2022/09/21 09:58:31 - mmengine - INFO - Epoch(train) [3][36300/42151] lr: 3.0000e-04 eta: 20:09:17 time: 0.5214 data_time: 0.1431 memory: 14682 loss_ce: 0.0146 loss: 0.0146 2022/09/21 09:59:26 - mmengine - INFO - Epoch(train) [3][36400/42151] lr: 3.0000e-04 eta: 20:08:22 time: 0.5475 data_time: 0.1662 memory: 14682 loss_ce: 0.0153 loss: 0.0153 2022/09/21 10:00:18 - mmengine - INFO - Epoch(train) [3][36500/42151] lr: 3.0000e-04 eta: 20:07:25 time: 0.5464 data_time: 0.1408 memory: 14682 loss_ce: 0.0127 loss: 0.0127 2022/09/21 10:01:11 - mmengine - INFO - Epoch(train) [3][36600/42151] lr: 3.0000e-04 eta: 20:06:27 time: 0.5156 data_time: 0.1335 memory: 14682 loss_ce: 0.0133 loss: 0.0133 2022/09/21 10:02:04 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 10:02:06 - mmengine - INFO - Epoch(train) [3][36700/42151] lr: 3.0000e-04 eta: 20:05:33 time: 0.5951 data_time: 0.1889 memory: 14682 loss_ce: 0.0130 loss: 0.0130 2022/09/21 10:02:58 - mmengine - INFO - Epoch(train) [3][36800/42151] lr: 3.0000e-04 eta: 20:04:35 time: 0.5458 data_time: 0.1655 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 10:03:52 - mmengine - INFO - Epoch(train) [3][36900/42151] lr: 3.0000e-04 eta: 20:03:39 time: 0.5396 data_time: 0.1403 memory: 14682 loss_ce: 0.0149 loss: 0.0149 2022/09/21 10:04:46 - mmengine - INFO - Epoch(train) [3][37000/42151] lr: 3.0000e-04 eta: 20:02:43 time: 0.5564 data_time: 0.1419 memory: 14682 loss_ce: 0.0139 loss: 0.0139 2022/09/21 10:05:40 - mmengine - INFO - Epoch(train) [3][37100/42151] lr: 3.0000e-04 eta: 20:01:48 time: 0.6207 data_time: 0.1928 memory: 14682 loss_ce: 0.0127 loss: 0.0127 2022/09/21 10:06:35 - mmengine - INFO - Epoch(train) [3][37200/42151] lr: 3.0000e-04 eta: 20:00:53 time: 0.5402 data_time: 0.1332 memory: 14682 loss_ce: 0.0129 loss: 0.0129 2022/09/21 10:07:32 - mmengine - INFO - Epoch(train) [3][37300/42151] lr: 3.0000e-04 eta: 20:00:00 time: 0.6559 data_time: 0.2191 memory: 14682 loss_ce: 0.0150 loss: 0.0150 2022/09/21 10:08:27 - mmengine - INFO - Epoch(train) [3][37400/42151] lr: 3.0000e-04 eta: 19:59:06 time: 0.6336 data_time: 0.1923 memory: 14682 loss_ce: 0.0129 loss: 0.0129 2022/09/21 10:09:22 - mmengine - INFO - Epoch(train) [3][37500/42151] lr: 3.0000e-04 eta: 19:58:10 time: 0.6009 data_time: 0.1661 memory: 14682 loss_ce: 0.0127 loss: 0.0127 2022/09/21 10:10:17 - mmengine - INFO - Epoch(train) [3][37600/42151] lr: 3.0000e-04 eta: 19:57:16 time: 0.5934 data_time: 0.1443 memory: 14682 loss_ce: 0.0137 loss: 0.0137 2022/09/21 10:11:10 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 10:11:11 - mmengine - INFO - Epoch(train) [3][37700/42151] lr: 3.0000e-04 eta: 19:56:20 time: 0.6120 data_time: 0.1465 memory: 14682 loss_ce: 0.0153 loss: 0.0153 2022/09/21 10:12:04 - mmengine - INFO - Epoch(train) [3][37800/42151] lr: 3.0000e-04 eta: 19:55:23 time: 0.5309 data_time: 0.1512 memory: 14682 loss_ce: 0.0135 loss: 0.0135 2022/09/21 10:13:02 - mmengine - INFO - Epoch(train) [3][37900/42151] lr: 3.0000e-04 eta: 19:54:32 time: 0.6740 data_time: 0.2220 memory: 14682 loss_ce: 0.0133 loss: 0.0133 2022/09/21 10:13:57 - mmengine - INFO - Epoch(train) [3][38000/42151] lr: 3.0000e-04 eta: 19:53:37 time: 0.5906 data_time: 0.2044 memory: 14682 loss_ce: 0.0144 loss: 0.0144 2022/09/21 10:14:53 - mmengine - INFO - Epoch(train) [3][38100/42151] lr: 3.0000e-04 eta: 19:52:43 time: 0.5382 data_time: 0.1583 memory: 14682 loss_ce: 0.0126 loss: 0.0126 2022/09/21 10:15:49 - mmengine - INFO - Epoch(train) [3][38200/42151] lr: 3.0000e-04 eta: 19:51:50 time: 0.5985 data_time: 0.1800 memory: 14682 loss_ce: 0.0131 loss: 0.0131 2022/09/21 10:16:43 - mmengine - INFO - Epoch(train) [3][38300/42151] lr: 3.0000e-04 eta: 19:50:54 time: 0.5329 data_time: 0.1460 memory: 14682 loss_ce: 0.0146 loss: 0.0146 2022/09/21 10:17:38 - mmengine - INFO - Epoch(train) [3][38400/42151] lr: 3.0000e-04 eta: 19:50:00 time: 0.6548 data_time: 0.1558 memory: 14682 loss_ce: 0.0145 loss: 0.0145 2022/09/21 10:18:35 - mmengine - INFO - Epoch(train) [3][38500/42151] lr: 3.0000e-04 eta: 19:49:07 time: 0.6387 data_time: 0.2512 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 10:19:30 - mmengine - INFO - Epoch(train) [3][38600/42151] lr: 3.0000e-04 eta: 19:48:12 time: 0.6291 data_time: 0.2071 memory: 14682 loss_ce: 0.0142 loss: 0.0142 2022/09/21 10:20:24 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 10:20:24 - mmengine - INFO - Epoch(train) [3][38700/42151] lr: 3.0000e-04 eta: 19:47:17 time: 0.5396 data_time: 0.1368 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 10:21:21 - mmengine - INFO - Epoch(train) [3][38800/42151] lr: 3.0000e-04 eta: 19:46:24 time: 0.5993 data_time: 0.1611 memory: 14682 loss_ce: 0.0142 loss: 0.0142 2022/09/21 10:22:15 - mmengine - INFO - Epoch(train) [3][38900/42151] lr: 3.0000e-04 eta: 19:45:28 time: 0.5532 data_time: 0.1725 memory: 14682 loss_ce: 0.0128 loss: 0.0128 2022/09/21 10:23:11 - mmengine - INFO - Epoch(train) [3][39000/42151] lr: 3.0000e-04 eta: 19:44:34 time: 0.6073 data_time: 0.1772 memory: 14682 loss_ce: 0.0128 loss: 0.0128 2022/09/21 10:24:08 - mmengine - INFO - Epoch(train) [3][39100/42151] lr: 3.0000e-04 eta: 19:43:41 time: 0.5988 data_time: 0.2098 memory: 14682 loss_ce: 0.0133 loss: 0.0133 2022/09/21 10:25:02 - mmengine - INFO - Epoch(train) [3][39200/42151] lr: 3.0000e-04 eta: 19:42:46 time: 0.5690 data_time: 0.1884 memory: 14682 loss_ce: 0.0141 loss: 0.0141 2022/09/21 10:25:56 - mmengine - INFO - Epoch(train) [3][39300/42151] lr: 3.0000e-04 eta: 19:41:51 time: 0.5435 data_time: 0.1639 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 10:26:52 - mmengine - INFO - Epoch(train) [3][39400/42151] lr: 3.0000e-04 eta: 19:40:57 time: 0.5672 data_time: 0.1540 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 10:27:48 - mmengine - INFO - Epoch(train) [3][39500/42151] lr: 3.0000e-04 eta: 19:40:03 time: 0.5726 data_time: 0.1546 memory: 14682 loss_ce: 0.0141 loss: 0.0141 2022/09/21 10:28:44 - mmengine - INFO - Epoch(train) [3][39600/42151] lr: 3.0000e-04 eta: 19:39:09 time: 0.5350 data_time: 0.1518 memory: 14682 loss_ce: 0.0150 loss: 0.0150 2022/09/21 10:29:37 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 10:29:39 - mmengine - INFO - Epoch(train) [3][39700/42151] lr: 3.0000e-04 eta: 19:38:15 time: 0.5476 data_time: 0.1698 memory: 14682 loss_ce: 0.0125 loss: 0.0125 2022/09/21 10:30:33 - mmengine - INFO - Epoch(train) [3][39800/42151] lr: 3.0000e-04 eta: 19:37:19 time: 0.6436 data_time: 0.2035 memory: 14682 loss_ce: 0.0144 loss: 0.0144 2022/09/21 10:31:27 - mmengine - INFO - Epoch(train) [3][39900/42151] lr: 3.0000e-04 eta: 19:36:23 time: 0.5215 data_time: 0.1422 memory: 14682 loss_ce: 0.0123 loss: 0.0123 2022/09/21 10:32:22 - mmengine - INFO - Epoch(train) [3][40000/42151] lr: 3.0000e-04 eta: 19:35:28 time: 0.5776 data_time: 0.1578 memory: 14682 loss_ce: 0.0129 loss: 0.0129 2022/09/21 10:33:15 - mmengine - INFO - Epoch(train) [3][40100/42151] lr: 3.0000e-04 eta: 19:34:31 time: 0.5282 data_time: 0.1447 memory: 14682 loss_ce: 0.0146 loss: 0.0146 2022/09/21 10:34:10 - mmengine - INFO - Epoch(train) [3][40200/42151] lr: 3.0000e-04 eta: 19:33:36 time: 0.5572 data_time: 0.1507 memory: 14682 loss_ce: 0.0123 loss: 0.0123 2022/09/21 10:35:07 - mmengine - INFO - Epoch(train) [3][40300/42151] lr: 3.0000e-04 eta: 19:32:44 time: 0.6022 data_time: 0.2205 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 10:36:02 - mmengine - INFO - Epoch(train) [3][40400/42151] lr: 3.0000e-04 eta: 19:31:49 time: 0.5858 data_time: 0.2033 memory: 14682 loss_ce: 0.0136 loss: 0.0136 2022/09/21 10:36:57 - mmengine - INFO - Epoch(train) [3][40500/42151] lr: 3.0000e-04 eta: 19:30:54 time: 0.5402 data_time: 0.1587 memory: 14682 loss_ce: 0.0133 loss: 0.0133 2022/09/21 10:37:53 - mmengine - INFO - Epoch(train) [3][40600/42151] lr: 3.0000e-04 eta: 19:30:01 time: 0.5666 data_time: 0.1772 memory: 14682 loss_ce: 0.0150 loss: 0.0150 2022/09/21 10:38:48 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 10:38:49 - mmengine - INFO - Epoch(train) [3][40700/42151] lr: 3.0000e-04 eta: 19:29:07 time: 0.5328 data_time: 0.1517 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 10:39:44 - mmengine - INFO - Epoch(train) [3][40800/42151] lr: 3.0000e-04 eta: 19:28:12 time: 0.5462 data_time: 0.1635 memory: 14682 loss_ce: 0.0145 loss: 0.0145 2022/09/21 10:40:41 - mmengine - INFO - Epoch(train) [3][40900/42151] lr: 3.0000e-04 eta: 19:27:19 time: 0.5973 data_time: 0.2190 memory: 14682 loss_ce: 0.0135 loss: 0.0135 2022/09/21 10:41:36 - mmengine - INFO - Epoch(train) [3][41000/42151] lr: 3.0000e-04 eta: 19:26:25 time: 0.6366 data_time: 0.2479 memory: 14682 loss_ce: 0.0138 loss: 0.0138 2022/09/21 10:42:30 - mmengine - INFO - Epoch(train) [3][41100/42151] lr: 3.0000e-04 eta: 19:25:30 time: 0.5864 data_time: 0.1450 memory: 14682 loss_ce: 0.0112 loss: 0.0112 2022/09/21 10:43:27 - mmengine - INFO - Epoch(train) [3][41200/42151] lr: 3.0000e-04 eta: 19:24:36 time: 0.5457 data_time: 0.1613 memory: 14682 loss_ce: 0.0117 loss: 0.0117 2022/09/21 10:44:22 - mmengine - INFO - Epoch(train) [3][41300/42151] lr: 3.0000e-04 eta: 19:23:42 time: 0.5438 data_time: 0.1529 memory: 14682 loss_ce: 0.0125 loss: 0.0125 2022/09/21 10:45:17 - mmengine - INFO - Epoch(train) [3][41400/42151] lr: 3.0000e-04 eta: 19:22:47 time: 0.5183 data_time: 0.1332 memory: 14682 loss_ce: 0.0134 loss: 0.0134 2022/09/21 10:46:15 - mmengine - INFO - Epoch(train) [3][41500/42151] lr: 3.0000e-04 eta: 19:21:55 time: 0.5755 data_time: 0.1939 memory: 14682 loss_ce: 0.0144 loss: 0.0144 2022/09/21 10:47:09 - mmengine - INFO - Epoch(train) [3][41600/42151] lr: 3.0000e-04 eta: 19:21:00 time: 0.5980 data_time: 0.2177 memory: 14682 loss_ce: 0.0140 loss: 0.0140 2022/09/21 10:48:03 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 10:48:04 - mmengine - INFO - Epoch(train) [3][41700/42151] lr: 3.0000e-04 eta: 19:20:05 time: 0.5823 data_time: 0.1448 memory: 14682 loss_ce: 0.0127 loss: 0.0127 2022/09/21 10:49:00 - mmengine - INFO - Epoch(train) [3][41800/42151] lr: 3.0000e-04 eta: 19:19:11 time: 0.5438 data_time: 0.1641 memory: 14682 loss_ce: 0.0129 loss: 0.0129 2022/09/21 10:49:55 - mmengine - INFO - Epoch(train) [3][41900/42151] lr: 3.0000e-04 eta: 19:18:17 time: 0.6513 data_time: 0.1578 memory: 14682 loss_ce: 0.0127 loss: 0.0127 2022/09/21 10:50:53 - mmengine - INFO - Epoch(train) [3][42000/42151] lr: 3.0000e-04 eta: 19:17:24 time: 0.6154 data_time: 0.1690 memory: 14682 loss_ce: 0.0116 loss: 0.0116 2022/09/21 10:51:51 - mmengine - INFO - Epoch(train) [3][42100/42151] lr: 3.0000e-04 eta: 19:16:33 time: 0.6054 data_time: 0.1943 memory: 14682 loss_ce: 0.0141 loss: 0.0141 2022/09/21 10:52:16 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 10:52:16 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/09/21 10:53:05 - mmengine - INFO - Epoch(val) [3][100/7672] eta: 0:41:17 time: 0.3272 data_time: 0.0010 memory: 14682 2022/09/21 10:53:40 - mmengine - INFO - Epoch(val) [3][200/7672] eta: 0:39:51 time: 0.3201 data_time: 0.0009 memory: 1304 2022/09/21 10:54:14 - mmengine - INFO - Epoch(val) [3][300/7672] eta: 0:27:37 time: 0.2248 data_time: 0.0009 memory: 1304 2022/09/21 10:54:37 - mmengine - INFO - Epoch(val) [3][400/7672] eta: 0:25:50 time: 0.2132 data_time: 0.0009 memory: 1304 2022/09/21 10:55:01 - mmengine - INFO - Epoch(val) [3][500/7672] eta: 0:26:58 time: 0.2257 data_time: 0.0020 memory: 1304 2022/09/21 10:55:24 - mmengine - INFO - Epoch(val) [3][600/7672] eta: 0:25:44 time: 0.2183 data_time: 0.0020 memory: 1304 2022/09/21 10:55:47 - mmengine - INFO - Epoch(val) [3][700/7672] eta: 0:23:43 time: 0.2041 data_time: 0.0009 memory: 1304 2022/09/21 10:56:10 - mmengine - INFO - Epoch(val) [3][800/7672] eta: 0:28:26 time: 0.2483 data_time: 0.0015 memory: 1304 2022/09/21 10:56:33 - mmengine - INFO - Epoch(val) [3][900/7672] eta: 0:25:37 time: 0.2270 data_time: 0.0010 memory: 1304 2022/09/21 10:56:57 - mmengine - INFO - Epoch(val) [3][1000/7672] eta: 0:26:29 time: 0.2382 data_time: 0.0009 memory: 1304 2022/09/21 10:57:21 - mmengine - INFO - Epoch(val) [3][1100/7672] eta: 0:26:14 time: 0.2395 data_time: 0.0009 memory: 1304 2022/09/21 10:57:44 - mmengine - INFO - Epoch(val) [3][1200/7672] eta: 0:24:33 time: 0.2276 data_time: 0.0052 memory: 1304 2022/09/21 10:58:07 - mmengine - INFO - Epoch(val) [3][1300/7672] eta: 0:23:27 time: 0.2209 data_time: 0.0009 memory: 1304 2022/09/21 10:58:31 - mmengine - INFO - Epoch(val) [3][1400/7672] eta: 0:24:06 time: 0.2306 data_time: 0.0010 memory: 1304 2022/09/21 10:58:54 - mmengine - INFO - Epoch(val) [3][1500/7672] eta: 0:23:36 time: 0.2295 data_time: 0.0010 memory: 1304 2022/09/21 10:59:17 - mmengine - INFO - Epoch(val) [3][1600/7672] eta: 0:22:24 time: 0.2214 data_time: 0.0009 memory: 1304 2022/09/21 10:59:40 - mmengine - INFO - Epoch(val) [3][1700/7672] eta: 0:22:37 time: 0.2273 data_time: 0.0009 memory: 1304 2022/09/21 11:00:03 - mmengine - INFO - Epoch(val) [3][1800/7672] eta: 0:27:23 time: 0.2798 data_time: 0.0035 memory: 1304 2022/09/21 11:00:26 - mmengine - INFO - Epoch(val) [3][1900/7672] eta: 0:21:22 time: 0.2222 data_time: 0.0009 memory: 1304 2022/09/21 11:00:50 - mmengine - INFO - Epoch(val) [3][2000/7672] eta: 0:20:27 time: 0.2165 data_time: 0.0010 memory: 1304 2022/09/21 11:01:13 - mmengine - INFO - Epoch(val) [3][2100/7672] eta: 0:23:29 time: 0.2529 data_time: 0.0043 memory: 1304 2022/09/21 11:01:36 - mmengine - INFO - Epoch(val) [3][2200/7672] eta: 0:21:17 time: 0.2335 data_time: 0.0009 memory: 1304 2022/09/21 11:01:58 - mmengine - INFO - Epoch(val) [3][2300/7672] eta: 0:18:27 time: 0.2063 data_time: 0.0009 memory: 1304 2022/09/21 11:02:21 - mmengine - INFO - Epoch(val) [3][2400/7672] eta: 0:25:32 time: 0.2908 data_time: 0.0011 memory: 1304 2022/09/21 11:02:43 - mmengine - INFO - Epoch(val) [3][2500/7672] eta: 0:19:38 time: 0.2278 data_time: 0.0052 memory: 1304 2022/09/21 11:03:07 - mmengine - INFO - Epoch(val) [3][2600/7672] eta: 0:19:35 time: 0.2317 data_time: 0.0009 memory: 1304 2022/09/21 11:03:31 - mmengine - INFO - Epoch(val) [3][2700/7672] eta: 0:17:35 time: 0.2123 data_time: 0.0009 memory: 1304 2022/09/21 11:03:55 - mmengine - INFO - Epoch(val) [3][2800/7672] eta: 0:17:24 time: 0.2144 data_time: 0.0022 memory: 1304 2022/09/21 11:04:19 - mmengine - INFO - Epoch(val) [3][2900/7672] eta: 0:18:11 time: 0.2287 data_time: 0.0009 memory: 1304 2022/09/21 11:04:42 - mmengine - INFO - Epoch(val) [3][3000/7672] eta: 0:18:19 time: 0.2353 data_time: 0.0013 memory: 1304 2022/09/21 11:05:07 - mmengine - INFO - Epoch(val) [3][3100/7672] eta: 0:18:28 time: 0.2424 data_time: 0.0032 memory: 1304 2022/09/21 11:05:31 - mmengine - INFO - Epoch(val) [3][3200/7672] eta: 0:16:32 time: 0.2220 data_time: 0.0009 memory: 1304 2022/09/21 11:05:54 - mmengine - INFO - Epoch(val) [3][3300/7672] eta: 0:17:40 time: 0.2426 data_time: 0.0020 memory: 1304 2022/09/21 11:06:18 - mmengine - INFO - Epoch(val) [3][3400/7672] eta: 0:16:03 time: 0.2255 data_time: 0.0009 memory: 1304 2022/09/21 11:06:42 - mmengine - INFO - Epoch(val) [3][3500/7672] eta: 0:17:01 time: 0.2448 data_time: 0.0010 memory: 1304 2022/09/21 11:07:06 - mmengine - INFO - Epoch(val) [3][3600/7672] eta: 0:14:47 time: 0.2180 data_time: 0.0029 memory: 1304 2022/09/21 11:07:30 - mmengine - INFO - Epoch(val) [3][3700/7672] eta: 0:15:32 time: 0.2348 data_time: 0.0010 memory: 1304 2022/09/21 11:07:54 - mmengine - INFO - Epoch(val) [3][3800/7672] eta: 0:14:50 time: 0.2300 data_time: 0.0016 memory: 1304 2022/09/21 11:08:17 - mmengine - INFO - Epoch(val) [3][3900/7672] eta: 0:13:43 time: 0.2182 data_time: 0.0010 memory: 1304 2022/09/21 11:08:40 - mmengine - INFO - Epoch(val) [3][4000/7672] eta: 0:12:57 time: 0.2117 data_time: 0.0008 memory: 1304 2022/09/21 11:09:03 - mmengine - INFO - Epoch(val) [3][4100/7672] eta: 0:18:02 time: 0.3029 data_time: 0.0058 memory: 1304 2022/09/21 11:09:26 - mmengine - INFO - Epoch(val) [3][4200/7672] eta: 0:12:38 time: 0.2185 data_time: 0.0025 memory: 1304 2022/09/21 11:09:49 - mmengine - INFO - Epoch(val) [3][4300/7672] eta: 0:12:13 time: 0.2177 data_time: 0.0010 memory: 1304 2022/09/21 11:10:11 - mmengine - INFO - Epoch(val) [3][4400/7672] eta: 0:11:07 time: 0.2039 data_time: 0.0008 memory: 1304 2022/09/21 11:10:33 - mmengine - INFO - Epoch(val) [3][4500/7672] eta: 0:10:59 time: 0.2078 data_time: 0.0023 memory: 1304 2022/09/21 11:10:54 - mmengine - INFO - Epoch(val) [3][4600/7672] eta: 0:12:01 time: 0.2349 data_time: 0.0039 memory: 1304 2022/09/21 11:11:15 - mmengine - INFO - Epoch(val) [3][4700/7672] eta: 0:10:29 time: 0.2120 data_time: 0.0025 memory: 1304 2022/09/21 11:11:37 - mmengine - INFO - Epoch(val) [3][4800/7672] eta: 0:09:50 time: 0.2055 data_time: 0.0008 memory: 1304 2022/09/21 11:11:58 - mmengine - INFO - Epoch(val) [3][4900/7672] eta: 0:09:34 time: 0.2071 data_time: 0.0008 memory: 1304 2022/09/21 11:12:19 - mmengine - INFO - Epoch(val) [3][5000/7672] eta: 0:09:02 time: 0.2029 data_time: 0.0008 memory: 1304 2022/09/21 11:12:40 - mmengine - INFO - Epoch(val) [3][5100/7672] eta: 0:08:48 time: 0.2056 data_time: 0.0008 memory: 1304 2022/09/21 11:13:02 - mmengine - INFO - Epoch(val) [3][5200/7672] eta: 0:08:47 time: 0.2133 data_time: 0.0008 memory: 1304 2022/09/21 11:13:23 - mmengine - INFO - Epoch(val) [3][5300/7672] eta: 0:07:59 time: 0.2020 data_time: 0.0008 memory: 1304 2022/09/21 11:13:45 - mmengine - INFO - Epoch(val) [3][5400/7672] eta: 0:09:12 time: 0.2431 data_time: 0.0025 memory: 1304 2022/09/21 11:14:06 - mmengine - INFO - Epoch(val) [3][5500/7672] eta: 0:07:29 time: 0.2068 data_time: 0.0023 memory: 1304 2022/09/21 11:14:27 - mmengine - INFO - Epoch(val) [3][5600/7672] eta: 0:07:12 time: 0.2086 data_time: 0.0008 memory: 1304 2022/09/21 11:14:49 - mmengine - INFO - Epoch(val) [3][5700/7672] eta: 0:06:50 time: 0.2079 data_time: 0.0008 memory: 1304 2022/09/21 11:15:09 - mmengine - INFO - Epoch(val) [3][5800/7672] eta: 0:06:28 time: 0.2077 data_time: 0.0008 memory: 1304 2022/09/21 11:15:31 - mmengine - INFO - Epoch(val) [3][5900/7672] eta: 0:06:11 time: 0.2095 data_time: 0.0008 memory: 1304 2022/09/21 11:15:52 - mmengine - INFO - Epoch(val) [3][6000/7672] eta: 0:05:43 time: 0.2056 data_time: 0.0008 memory: 1304 2022/09/21 11:16:13 - mmengine - INFO - Epoch(val) [3][6100/7672] eta: 0:06:14 time: 0.2379 data_time: 0.0070 memory: 1304 2022/09/21 11:16:35 - mmengine - INFO - Epoch(val) [3][6200/7672] eta: 0:05:03 time: 0.2060 data_time: 0.0008 memory: 1304 2022/09/21 11:16:57 - mmengine - INFO - Epoch(val) [3][6300/7672] eta: 0:05:02 time: 0.2202 data_time: 0.0020 memory: 1304 2022/09/21 11:17:18 - mmengine - INFO - Epoch(val) [3][6400/7672] eta: 0:04:20 time: 0.2048 data_time: 0.0008 memory: 1304 2022/09/21 11:17:40 - mmengine - INFO - Epoch(val) [3][6500/7672] eta: 0:04:04 time: 0.2090 data_time: 0.0008 memory: 1304 2022/09/21 11:18:01 - mmengine - INFO - Epoch(val) [3][6600/7672] eta: 0:03:40 time: 0.2054 data_time: 0.0008 memory: 1304 2022/09/21 11:18:22 - mmengine - INFO - Epoch(val) [3][6700/7672] eta: 0:03:20 time: 0.2063 data_time: 0.0008 memory: 1304 2022/09/21 11:18:43 - mmengine - INFO - Epoch(val) [3][6800/7672] eta: 0:03:01 time: 0.2086 data_time: 0.0010 memory: 1304 2022/09/21 11:19:05 - mmengine - INFO - Epoch(val) [3][6900/7672] eta: 0:02:41 time: 0.2094 data_time: 0.0020 memory: 1304 2022/09/21 11:19:26 - mmengine - INFO - Epoch(val) [3][7000/7672] eta: 0:02:19 time: 0.2073 data_time: 0.0023 memory: 1304 2022/09/21 11:19:48 - mmengine - INFO - Epoch(val) [3][7100/7672] eta: 0:01:59 time: 0.2081 data_time: 0.0008 memory: 1304 2022/09/21 11:20:09 - mmengine - INFO - Epoch(val) [3][7200/7672] eta: 0:01:36 time: 0.2036 data_time: 0.0008 memory: 1304 2022/09/21 11:20:30 - mmengine - INFO - Epoch(val) [3][7300/7672] eta: 0:01:17 time: 0.2080 data_time: 0.0008 memory: 1304 2022/09/21 11:20:52 - mmengine - INFO - Epoch(val) [3][7400/7672] eta: 0:00:56 time: 0.2080 data_time: 0.0009 memory: 1304 2022/09/21 11:21:13 - mmengine - INFO - Epoch(val) [3][7500/7672] eta: 0:00:35 time: 0.2040 data_time: 0.0008 memory: 1304 2022/09/21 11:21:34 - mmengine - INFO - Epoch(val) [3][7600/7672] eta: 0:00:15 time: 0.2103 data_time: 0.0008 memory: 1304 2022/09/21 11:21:49 - mmengine - INFO - Epoch(val) [3][7672/7672] CUTE80/recog/word_acc_ignore_case_symbol: 0.8542 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9380 SVT/recog/word_acc_ignore_case_symbol: 0.8794 SVTP/recog/word_acc_ignore_case_symbol: 0.7643 IC13/recog/word_acc_ignore_case_symbol: 0.9340 IC15/recog/word_acc_ignore_case_symbol: 0.7029 2022/09/21 11:22:52 - mmengine - INFO - Epoch(train) [4][100/42151] lr: 3.0000e-05 eta: 19:15:14 time: 0.6142 data_time: 0.2294 memory: 14682 loss_ce: 0.0131 loss: 0.0131 2022/09/21 11:23:46 - mmengine - INFO - Epoch(train) [4][200/42151] lr: 3.0000e-05 eta: 19:14:18 time: 0.5895 data_time: 0.1769 memory: 14682 loss_ce: 0.0117 loss: 0.0117 2022/09/21 11:24:41 - mmengine - INFO - Epoch(train) [4][300/42151] lr: 3.0000e-05 eta: 19:13:23 time: 0.6014 data_time: 0.2222 memory: 14682 loss_ce: 0.0114 loss: 0.0114 2022/09/21 11:25:36 - mmengine - INFO - Epoch(train) [4][400/42151] lr: 3.0000e-05 eta: 19:12:29 time: 0.5857 data_time: 0.1655 memory: 14682 loss_ce: 0.0111 loss: 0.0111 2022/09/21 11:26:29 - mmengine - INFO - Epoch(train) [4][500/42151] lr: 3.0000e-05 eta: 19:11:32 time: 0.4460 data_time: 0.0643 memory: 14682 loss_ce: 0.0108 loss: 0.0108 2022/09/21 11:26:55 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 11:27:23 - mmengine - INFO - Epoch(train) [4][600/42151] lr: 3.0000e-05 eta: 19:10:36 time: 0.4010 data_time: 0.0153 memory: 14682 loss_ce: 0.0115 loss: 0.0115 2022/09/21 11:28:20 - mmengine - INFO - Epoch(train) [4][700/42151] lr: 3.0000e-05 eta: 19:09:43 time: 0.5559 data_time: 0.1718 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 11:29:15 - mmengine - INFO - Epoch(train) [4][800/42151] lr: 3.0000e-05 eta: 19:08:48 time: 0.6410 data_time: 0.2089 memory: 14682 loss_ce: 0.0105 loss: 0.0105 2022/09/21 11:30:10 - mmengine - INFO - Epoch(train) [4][900/42151] lr: 3.0000e-05 eta: 19:07:54 time: 0.6553 data_time: 0.2396 memory: 14682 loss_ce: 0.0109 loss: 0.0109 2022/09/21 11:31:05 - mmengine - INFO - Epoch(train) [4][1000/42151] lr: 3.0000e-05 eta: 19:06:59 time: 0.5699 data_time: 0.1541 memory: 14682 loss_ce: 0.0108 loss: 0.0108 2022/09/21 11:31:59 - mmengine - INFO - Epoch(train) [4][1100/42151] lr: 3.0000e-05 eta: 19:06:03 time: 0.4757 data_time: 0.0720 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 11:32:52 - mmengine - INFO - Epoch(train) [4][1200/42151] lr: 3.0000e-05 eta: 19:05:07 time: 0.3944 data_time: 0.0156 memory: 14682 loss_ce: 0.0105 loss: 0.0105 2022/09/21 11:33:49 - mmengine - INFO - Epoch(train) [4][1300/42151] lr: 3.0000e-05 eta: 19:04:14 time: 0.6130 data_time: 0.1690 memory: 14682 loss_ce: 0.0124 loss: 0.0124 2022/09/21 11:34:42 - mmengine - INFO - Epoch(train) [4][1400/42151] lr: 3.0000e-05 eta: 19:03:18 time: 0.5802 data_time: 0.1376 memory: 14682 loss_ce: 0.0113 loss: 0.0113 2022/09/21 11:35:37 - mmengine - INFO - Epoch(train) [4][1500/42151] lr: 3.0000e-05 eta: 19:02:22 time: 0.6041 data_time: 0.1898 memory: 14682 loss_ce: 0.0106 loss: 0.0106 2022/09/21 11:36:03 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 11:36:32 - mmengine - INFO - Epoch(train) [4][1600/42151] lr: 3.0000e-05 eta: 19:01:28 time: 0.5273 data_time: 0.1479 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 11:37:26 - mmengine - INFO - Epoch(train) [4][1700/42151] lr: 3.0000e-05 eta: 19:00:32 time: 0.4789 data_time: 0.0596 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 11:38:20 - mmengine - INFO - Epoch(train) [4][1800/42151] lr: 3.0000e-05 eta: 18:59:36 time: 0.3951 data_time: 0.0151 memory: 14682 loss_ce: 0.0106 loss: 0.0106 2022/09/21 11:39:15 - mmengine - INFO - Epoch(train) [4][1900/42151] lr: 3.0000e-05 eta: 18:58:41 time: 0.5340 data_time: 0.1533 memory: 14682 loss_ce: 0.0105 loss: 0.0105 2022/09/21 11:40:11 - mmengine - INFO - Epoch(train) [4][2000/42151] lr: 3.0000e-05 eta: 18:57:47 time: 0.6474 data_time: 0.1616 memory: 14682 loss_ce: 0.0125 loss: 0.0125 2022/09/21 11:41:06 - mmengine - INFO - Epoch(train) [4][2100/42151] lr: 3.0000e-05 eta: 18:56:52 time: 0.6707 data_time: 0.2359 memory: 14682 loss_ce: 0.0111 loss: 0.0111 2022/09/21 11:42:01 - mmengine - INFO - Epoch(train) [4][2200/42151] lr: 3.0000e-05 eta: 18:55:58 time: 0.5399 data_time: 0.1586 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 11:42:56 - mmengine - INFO - Epoch(train) [4][2300/42151] lr: 3.0000e-05 eta: 18:55:03 time: 0.4420 data_time: 0.0596 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 11:43:49 - mmengine - INFO - Epoch(train) [4][2400/42151] lr: 3.0000e-05 eta: 18:54:07 time: 0.3951 data_time: 0.0156 memory: 14682 loss_ce: 0.0105 loss: 0.0105 2022/09/21 11:44:45 - mmengine - INFO - Epoch(train) [4][2500/42151] lr: 3.0000e-05 eta: 18:53:13 time: 0.5868 data_time: 0.1591 memory: 14682 loss_ce: 0.0112 loss: 0.0112 2022/09/21 11:45:11 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 11:45:40 - mmengine - INFO - Epoch(train) [4][2600/42151] lr: 3.0000e-05 eta: 18:52:18 time: 0.5519 data_time: 0.1722 memory: 14682 loss_ce: 0.0101 loss: 0.0101 2022/09/21 11:46:35 - mmengine - INFO - Epoch(train) [4][2700/42151] lr: 3.0000e-05 eta: 18:51:23 time: 0.5900 data_time: 0.2110 memory: 14682 loss_ce: 0.0103 loss: 0.0103 2022/09/21 11:47:30 - mmengine - INFO - Epoch(train) [4][2800/42151] lr: 3.0000e-05 eta: 18:50:28 time: 0.5726 data_time: 0.1911 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 11:48:24 - mmengine - INFO - Epoch(train) [4][2900/42151] lr: 3.0000e-05 eta: 18:49:33 time: 0.4821 data_time: 0.0969 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 11:49:19 - mmengine - INFO - Epoch(train) [4][3000/42151] lr: 3.0000e-05 eta: 18:48:38 time: 0.4049 data_time: 0.0272 memory: 14682 loss_ce: 0.0103 loss: 0.0103 2022/09/21 11:50:16 - mmengine - INFO - Epoch(train) [4][3100/42151] lr: 3.0000e-05 eta: 18:47:45 time: 0.6122 data_time: 0.1855 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 11:51:11 - mmengine - INFO - Epoch(train) [4][3200/42151] lr: 3.0000e-05 eta: 18:46:50 time: 0.6135 data_time: 0.1980 memory: 14682 loss_ce: 0.0104 loss: 0.0104 2022/09/21 11:52:07 - mmengine - INFO - Epoch(train) [4][3300/42151] lr: 3.0000e-05 eta: 18:45:57 time: 0.6327 data_time: 0.2461 memory: 14682 loss_ce: 0.0116 loss: 0.0116 2022/09/21 11:53:02 - mmengine - INFO - Epoch(train) [4][3400/42151] lr: 3.0000e-05 eta: 18:45:02 time: 0.5528 data_time: 0.1735 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/21 11:53:58 - mmengine - INFO - Epoch(train) [4][3500/42151] lr: 3.0000e-05 eta: 18:44:08 time: 0.5056 data_time: 0.1047 memory: 14682 loss_ce: 0.0106 loss: 0.0106 2022/09/21 11:54:23 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 11:54:52 - mmengine - INFO - Epoch(train) [4][3600/42151] lr: 3.0000e-05 eta: 18:43:13 time: 0.4201 data_time: 0.0361 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/21 11:55:49 - mmengine - INFO - Epoch(train) [4][3700/42151] lr: 3.0000e-05 eta: 18:42:19 time: 0.6264 data_time: 0.2309 memory: 14682 loss_ce: 0.0109 loss: 0.0109 2022/09/21 11:56:43 - mmengine - INFO - Epoch(train) [4][3800/42151] lr: 3.0000e-05 eta: 18:41:24 time: 0.5565 data_time: 0.1785 memory: 14682 loss_ce: 0.0112 loss: 0.0112 2022/09/21 11:57:38 - mmengine - INFO - Epoch(train) [4][3900/42151] lr: 3.0000e-05 eta: 18:40:29 time: 0.6266 data_time: 0.2046 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 11:58:33 - mmengine - INFO - Epoch(train) [4][4000/42151] lr: 3.0000e-05 eta: 18:39:34 time: 0.5302 data_time: 0.1495 memory: 14682 loss_ce: 0.0113 loss: 0.0113 2022/09/21 11:59:27 - mmengine - INFO - Epoch(train) [4][4100/42151] lr: 3.0000e-05 eta: 18:38:38 time: 0.4438 data_time: 0.0575 memory: 14682 loss_ce: 0.0101 loss: 0.0101 2022/09/21 12:00:26 - mmengine - INFO - Epoch(train) [4][4200/42151] lr: 3.0000e-05 eta: 18:37:48 time: 0.4069 data_time: 0.0276 memory: 14682 loss_ce: 0.0113 loss: 0.0113 2022/09/21 12:01:23 - mmengine - INFO - Epoch(train) [4][4300/42151] lr: 3.0000e-05 eta: 18:36:54 time: 0.7003 data_time: 0.2651 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 12:02:17 - mmengine - INFO - Epoch(train) [4][4400/42151] lr: 3.0000e-05 eta: 18:35:59 time: 0.5726 data_time: 0.1907 memory: 14682 loss_ce: 0.0108 loss: 0.0108 2022/09/21 12:03:13 - mmengine - INFO - Epoch(train) [4][4500/42151] lr: 3.0000e-05 eta: 18:35:05 time: 0.5901 data_time: 0.2080 memory: 14682 loss_ce: 0.0108 loss: 0.0108 2022/09/21 12:03:39 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 12:04:07 - mmengine - INFO - Epoch(train) [4][4600/42151] lr: 3.0000e-05 eta: 18:34:10 time: 0.5206 data_time: 0.1406 memory: 14682 loss_ce: 0.0103 loss: 0.0103 2022/09/21 12:05:02 - mmengine - INFO - Epoch(train) [4][4700/42151] lr: 3.0000e-05 eta: 18:33:15 time: 0.4416 data_time: 0.0559 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 12:05:57 - mmengine - INFO - Epoch(train) [4][4800/42151] lr: 3.0000e-05 eta: 18:32:20 time: 0.4143 data_time: 0.0313 memory: 14682 loss_ce: 0.0101 loss: 0.0101 2022/09/21 12:06:53 - mmengine - INFO - Epoch(train) [4][4900/42151] lr: 3.0000e-05 eta: 18:31:26 time: 0.5928 data_time: 0.2095 memory: 14682 loss_ce: 0.0110 loss: 0.0110 2022/09/21 12:07:47 - mmengine - INFO - Epoch(train) [4][5000/42151] lr: 3.0000e-05 eta: 18:30:30 time: 0.5769 data_time: 0.1975 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 12:08:42 - mmengine - INFO - Epoch(train) [4][5100/42151] lr: 3.0000e-05 eta: 18:29:35 time: 0.6334 data_time: 0.2318 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/21 12:09:37 - mmengine - INFO - Epoch(train) [4][5200/42151] lr: 3.0000e-05 eta: 18:28:41 time: 0.5343 data_time: 0.1482 memory: 14682 loss_ce: 0.0115 loss: 0.0115 2022/09/21 12:10:32 - mmengine - INFO - Epoch(train) [4][5300/42151] lr: 3.0000e-05 eta: 18:27:46 time: 0.4363 data_time: 0.0544 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 12:11:26 - mmengine - INFO - Epoch(train) [4][5400/42151] lr: 3.0000e-05 eta: 18:26:50 time: 0.4261 data_time: 0.0372 memory: 14682 loss_ce: 0.0102 loss: 0.0102 2022/09/21 12:12:24 - mmengine - INFO - Epoch(train) [4][5500/42151] lr: 3.0000e-05 eta: 18:25:58 time: 0.6649 data_time: 0.2203 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 12:12:50 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 12:13:21 - mmengine - INFO - Epoch(train) [4][5600/42151] lr: 3.0000e-05 eta: 18:25:05 time: 0.6483 data_time: 0.2187 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 12:14:17 - mmengine - INFO - Epoch(train) [4][5700/42151] lr: 3.0000e-05 eta: 18:24:11 time: 0.5947 data_time: 0.2125 memory: 14682 loss_ce: 0.0114 loss: 0.0114 2022/09/21 12:15:13 - mmengine - INFO - Epoch(train) [4][5800/42151] lr: 3.0000e-05 eta: 18:23:18 time: 0.6036 data_time: 0.1729 memory: 14682 loss_ce: 0.0107 loss: 0.0107 2022/09/21 12:16:09 - mmengine - INFO - Epoch(train) [4][5900/42151] lr: 3.0000e-05 eta: 18:22:24 time: 0.5109 data_time: 0.0558 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 12:17:05 - mmengine - INFO - Epoch(train) [4][6000/42151] lr: 3.0000e-05 eta: 18:21:29 time: 0.4070 data_time: 0.0270 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/21 12:18:02 - mmengine - INFO - Epoch(train) [4][6100/42151] lr: 3.0000e-05 eta: 18:20:36 time: 0.5950 data_time: 0.2105 memory: 14682 loss_ce: 0.0121 loss: 0.0121 2022/09/21 12:18:56 - mmengine - INFO - Epoch(train) [4][6200/42151] lr: 3.0000e-05 eta: 18:19:41 time: 0.5983 data_time: 0.2159 memory: 14682 loss_ce: 0.0105 loss: 0.0105 2022/09/21 12:19:52 - mmengine - INFO - Epoch(train) [4][6300/42151] lr: 3.0000e-05 eta: 18:18:47 time: 0.6358 data_time: 0.2334 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 12:20:47 - mmengine - INFO - Epoch(train) [4][6400/42151] lr: 3.0000e-05 eta: 18:17:52 time: 0.5467 data_time: 0.1641 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 12:21:43 - mmengine - INFO - Epoch(train) [4][6500/42151] lr: 3.0000e-05 eta: 18:16:59 time: 0.4471 data_time: 0.0591 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 12:22:09 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 12:22:38 - mmengine - INFO - Epoch(train) [4][6600/42151] lr: 3.0000e-05 eta: 18:16:04 time: 0.3954 data_time: 0.0154 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 12:23:36 - mmengine - INFO - Epoch(train) [4][6700/42151] lr: 3.0000e-05 eta: 18:15:11 time: 0.6374 data_time: 0.2476 memory: 14682 loss_ce: 0.0104 loss: 0.0104 2022/09/21 12:24:30 - mmengine - INFO - Epoch(train) [4][6800/42151] lr: 3.0000e-05 eta: 18:14:16 time: 0.6512 data_time: 0.2248 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 12:25:27 - mmengine - INFO - Epoch(train) [4][6900/42151] lr: 3.0000e-05 eta: 18:13:23 time: 0.6014 data_time: 0.2190 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 12:26:23 - mmengine - INFO - Epoch(train) [4][7000/42151] lr: 3.0000e-05 eta: 18:12:29 time: 0.6074 data_time: 0.1779 memory: 14682 loss_ce: 0.0102 loss: 0.0102 2022/09/21 12:27:19 - mmengine - INFO - Epoch(train) [4][7100/42151] lr: 3.0000e-05 eta: 18:11:35 time: 0.4421 data_time: 0.0597 memory: 14682 loss_ce: 0.0102 loss: 0.0102 2022/09/21 12:28:24 - mmengine - INFO - Epoch(train) [4][7200/42151] lr: 3.0000e-05 eta: 18:10:49 time: 0.3986 data_time: 0.0156 memory: 14682 loss_ce: 0.0113 loss: 0.0113 2022/09/21 12:29:21 - mmengine - INFO - Epoch(train) [4][7300/42151] lr: 3.0000e-05 eta: 18:09:56 time: 0.6540 data_time: 0.2309 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 12:30:15 - mmengine - INFO - Epoch(train) [4][7400/42151] lr: 3.0000e-05 eta: 18:09:00 time: 0.6318 data_time: 0.2507 memory: 14682 loss_ce: 0.0104 loss: 0.0104 2022/09/21 12:31:10 - mmengine - INFO - Epoch(train) [4][7500/42151] lr: 3.0000e-05 eta: 18:08:06 time: 0.6085 data_time: 0.2258 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 12:31:36 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 12:32:04 - mmengine - INFO - Epoch(train) [4][7600/42151] lr: 3.0000e-05 eta: 18:07:10 time: 0.5659 data_time: 0.1526 memory: 14682 loss_ce: 0.0115 loss: 0.0115 2022/09/21 12:32:58 - mmengine - INFO - Epoch(train) [4][7700/42151] lr: 3.0000e-05 eta: 18:06:15 time: 0.4612 data_time: 0.0778 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 12:33:52 - mmengine - INFO - Epoch(train) [4][7800/42151] lr: 3.0000e-05 eta: 18:05:18 time: 0.3967 data_time: 0.0153 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 12:34:48 - mmengine - INFO - Epoch(train) [4][7900/42151] lr: 3.0000e-05 eta: 18:04:24 time: 0.5971 data_time: 0.2153 memory: 14682 loss_ce: 0.0102 loss: 0.0102 2022/09/21 12:35:42 - mmengine - INFO - Epoch(train) [4][8000/42151] lr: 3.0000e-05 eta: 18:03:29 time: 0.6212 data_time: 0.2375 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 12:36:37 - mmengine - INFO - Epoch(train) [4][8100/42151] lr: 3.0000e-05 eta: 18:02:34 time: 0.6200 data_time: 0.2349 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 12:37:33 - mmengine - INFO - Epoch(train) [4][8200/42151] lr: 3.0000e-05 eta: 18:01:40 time: 0.6282 data_time: 0.2049 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 12:38:27 - mmengine - INFO - Epoch(train) [4][8300/42151] lr: 3.0000e-05 eta: 18:00:45 time: 0.4397 data_time: 0.0609 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/21 12:39:21 - mmengine - INFO - Epoch(train) [4][8400/42151] lr: 3.0000e-05 eta: 17:59:49 time: 0.3955 data_time: 0.0151 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 12:40:17 - mmengine - INFO - Epoch(train) [4][8500/42151] lr: 3.0000e-05 eta: 17:58:55 time: 0.5659 data_time: 0.1863 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/21 12:40:42 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 12:41:11 - mmengine - INFO - Epoch(train) [4][8600/42151] lr: 3.0000e-05 eta: 17:57:59 time: 0.6040 data_time: 0.2163 memory: 14682 loss_ce: 0.0104 loss: 0.0104 2022/09/21 12:42:06 - mmengine - INFO - Epoch(train) [4][8700/42151] lr: 3.0000e-05 eta: 17:57:04 time: 0.6254 data_time: 0.2417 memory: 14682 loss_ce: 0.0107 loss: 0.0107 2022/09/21 12:43:01 - mmengine - INFO - Epoch(train) [4][8800/42151] lr: 3.0000e-05 eta: 17:56:09 time: 0.5975 data_time: 0.2013 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 12:43:55 - mmengine - INFO - Epoch(train) [4][8900/42151] lr: 3.0000e-05 eta: 17:55:14 time: 0.4491 data_time: 0.0666 memory: 14682 loss_ce: 0.0104 loss: 0.0104 2022/09/21 12:44:49 - mmengine - INFO - Epoch(train) [4][9000/42151] lr: 3.0000e-05 eta: 17:54:18 time: 0.3934 data_time: 0.0147 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 12:45:45 - mmengine - INFO - Epoch(train) [4][9100/42151] lr: 3.0000e-05 eta: 17:53:24 time: 0.6368 data_time: 0.2079 memory: 14682 loss_ce: 0.0111 loss: 0.0111 2022/09/21 12:46:39 - mmengine - INFO - Epoch(train) [4][9200/42151] lr: 3.0000e-05 eta: 17:52:29 time: 0.5982 data_time: 0.2189 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 12:47:33 - mmengine - INFO - Epoch(train) [4][9300/42151] lr: 3.0000e-05 eta: 17:51:33 time: 0.6127 data_time: 0.2341 memory: 14682 loss_ce: 0.0109 loss: 0.0109 2022/09/21 12:48:28 - mmengine - INFO - Epoch(train) [4][9400/42151] lr: 3.0000e-05 eta: 17:50:38 time: 0.5499 data_time: 0.1679 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 12:49:23 - mmengine - INFO - Epoch(train) [4][9500/42151] lr: 3.0000e-05 eta: 17:49:43 time: 0.4881 data_time: 0.0614 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 12:49:49 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 12:50:17 - mmengine - INFO - Epoch(train) [4][9600/42151] lr: 3.0000e-05 eta: 17:48:47 time: 0.4061 data_time: 0.0163 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 12:51:13 - mmengine - INFO - Epoch(train) [4][9700/42151] lr: 3.0000e-05 eta: 17:47:54 time: 0.5857 data_time: 0.1912 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 12:52:08 - mmengine - INFO - Epoch(train) [4][9800/42151] lr: 3.0000e-05 eta: 17:46:59 time: 0.5786 data_time: 0.1961 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 12:53:03 - mmengine - INFO - Epoch(train) [4][9900/42151] lr: 3.0000e-05 eta: 17:46:04 time: 0.5992 data_time: 0.2207 memory: 14682 loss_ce: 0.0113 loss: 0.0113 2022/09/21 12:53:58 - mmengine - INFO - Epoch(train) [4][10000/42151] lr: 3.0000e-05 eta: 17:45:09 time: 0.5684 data_time: 0.1871 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 12:54:51 - mmengine - INFO - Epoch(train) [4][10100/42151] lr: 3.0000e-05 eta: 17:44:13 time: 0.4426 data_time: 0.0647 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 12:55:45 - mmengine - INFO - Epoch(train) [4][10200/42151] lr: 3.0000e-05 eta: 17:43:17 time: 0.3952 data_time: 0.0146 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 12:56:40 - mmengine - INFO - Epoch(train) [4][10300/42151] lr: 3.0000e-05 eta: 17:42:22 time: 0.5616 data_time: 0.1831 memory: 14682 loss_ce: 0.0103 loss: 0.0103 2022/09/21 12:57:34 - mmengine - INFO - Epoch(train) [4][10400/42151] lr: 3.0000e-05 eta: 17:41:26 time: 0.5505 data_time: 0.1764 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 12:58:28 - mmengine - INFO - Epoch(train) [4][10500/42151] lr: 3.0000e-05 eta: 17:40:30 time: 0.5918 data_time: 0.2089 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 12:58:53 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 12:59:22 - mmengine - INFO - Epoch(train) [4][10600/42151] lr: 3.0000e-05 eta: 17:39:35 time: 0.5409 data_time: 0.1607 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 13:00:17 - mmengine - INFO - Epoch(train) [4][10700/42151] lr: 3.0000e-05 eta: 17:38:40 time: 0.4602 data_time: 0.0655 memory: 14682 loss_ce: 0.0101 loss: 0.0101 2022/09/21 13:01:10 - mmengine - INFO - Epoch(train) [4][10800/42151] lr: 3.0000e-05 eta: 17:37:44 time: 0.3949 data_time: 0.0169 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 13:02:08 - mmengine - INFO - Epoch(train) [4][10900/42151] lr: 3.0000e-05 eta: 17:36:51 time: 0.5985 data_time: 0.1711 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 13:03:01 - mmengine - INFO - Epoch(train) [4][11000/42151] lr: 3.0000e-05 eta: 17:35:55 time: 0.5607 data_time: 0.1820 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 13:03:58 - mmengine - INFO - Epoch(train) [4][11100/42151] lr: 3.0000e-05 eta: 17:35:02 time: 0.6806 data_time: 0.2543 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 13:04:52 - mmengine - INFO - Epoch(train) [4][11200/42151] lr: 3.0000e-05 eta: 17:34:07 time: 0.5636 data_time: 0.1797 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 13:05:48 - mmengine - INFO - Epoch(train) [4][11300/42151] lr: 3.0000e-05 eta: 17:33:12 time: 0.4542 data_time: 0.0693 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 13:06:43 - mmengine - INFO - Epoch(train) [4][11400/42151] lr: 3.0000e-05 eta: 17:32:17 time: 0.3971 data_time: 0.0144 memory: 14682 loss_ce: 0.0109 loss: 0.0109 2022/09/21 13:07:40 - mmengine - INFO - Epoch(train) [4][11500/42151] lr: 3.0000e-05 eta: 17:31:25 time: 0.6069 data_time: 0.2005 memory: 14682 loss_ce: 0.0103 loss: 0.0103 2022/09/21 13:08:06 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 13:08:36 - mmengine - INFO - Epoch(train) [4][11600/42151] lr: 3.0000e-05 eta: 17:30:30 time: 0.6004 data_time: 0.1946 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 13:09:32 - mmengine - INFO - Epoch(train) [4][11700/42151] lr: 3.0000e-05 eta: 17:29:36 time: 0.5807 data_time: 0.2028 memory: 14682 loss_ce: 0.0108 loss: 0.0108 2022/09/21 13:10:28 - mmengine - INFO - Epoch(train) [4][11800/42151] lr: 3.0000e-05 eta: 17:28:42 time: 0.6062 data_time: 0.1803 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 13:11:24 - mmengine - INFO - Epoch(train) [4][11900/42151] lr: 3.0000e-05 eta: 17:27:48 time: 0.5063 data_time: 0.0903 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 13:12:19 - mmengine - INFO - Epoch(train) [4][12000/42151] lr: 3.0000e-05 eta: 17:26:54 time: 0.3964 data_time: 0.0166 memory: 14682 loss_ce: 0.0106 loss: 0.0106 2022/09/21 13:13:17 - mmengine - INFO - Epoch(train) [4][12100/42151] lr: 3.0000e-05 eta: 17:26:01 time: 0.5805 data_time: 0.1911 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 13:14:11 - mmengine - INFO - Epoch(train) [4][12200/42151] lr: 3.0000e-05 eta: 17:25:06 time: 0.5806 data_time: 0.2003 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 13:15:08 - mmengine - INFO - Epoch(train) [4][12300/42151] lr: 3.0000e-05 eta: 17:24:12 time: 0.6097 data_time: 0.2257 memory: 14682 loss_ce: 0.0106 loss: 0.0106 2022/09/21 13:16:02 - mmengine - INFO - Epoch(train) [4][12400/42151] lr: 3.0000e-05 eta: 17:23:17 time: 0.5569 data_time: 0.1719 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 13:16:57 - mmengine - INFO - Epoch(train) [4][12500/42151] lr: 3.0000e-05 eta: 17:22:22 time: 0.4811 data_time: 0.0564 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 13:17:23 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 13:17:51 - mmengine - INFO - Epoch(train) [4][12600/42151] lr: 3.0000e-05 eta: 17:21:27 time: 0.3971 data_time: 0.0154 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 13:18:49 - mmengine - INFO - Epoch(train) [4][12700/42151] lr: 3.0000e-05 eta: 17:20:34 time: 0.6412 data_time: 0.2335 memory: 14682 loss_ce: 0.0106 loss: 0.0106 2022/09/21 13:19:44 - mmengine - INFO - Epoch(train) [4][12800/42151] lr: 3.0000e-05 eta: 17:19:39 time: 0.5964 data_time: 0.2128 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 13:20:42 - mmengine - INFO - Epoch(train) [4][12900/42151] lr: 3.0000e-05 eta: 17:18:46 time: 0.6311 data_time: 0.2493 memory: 14682 loss_ce: 0.0105 loss: 0.0105 2022/09/21 13:21:37 - mmengine - INFO - Epoch(train) [4][13000/42151] lr: 3.0000e-05 eta: 17:17:52 time: 0.6085 data_time: 0.2056 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 13:22:32 - mmengine - INFO - Epoch(train) [4][13100/42151] lr: 3.0000e-05 eta: 17:16:57 time: 0.4344 data_time: 0.0544 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/21 13:23:27 - mmengine - INFO - Epoch(train) [4][13200/42151] lr: 3.0000e-05 eta: 17:16:02 time: 0.4401 data_time: 0.0329 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 13:24:23 - mmengine - INFO - Epoch(train) [4][13300/42151] lr: 3.0000e-05 eta: 17:15:08 time: 0.5894 data_time: 0.2059 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 13:25:19 - mmengine - INFO - Epoch(train) [4][13400/42151] lr: 3.0000e-05 eta: 17:14:15 time: 0.6254 data_time: 0.2264 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 13:26:17 - mmengine - INFO - Epoch(train) [4][13500/42151] lr: 3.0000e-05 eta: 17:13:22 time: 0.6619 data_time: 0.2282 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 13:26:44 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 13:27:14 - mmengine - INFO - Epoch(train) [4][13600/42151] lr: 3.0000e-05 eta: 17:12:28 time: 0.5600 data_time: 0.1769 memory: 14682 loss_ce: 0.0117 loss: 0.0117 2022/09/21 13:28:10 - mmengine - INFO - Epoch(train) [4][13700/42151] lr: 3.0000e-05 eta: 17:11:34 time: 0.4378 data_time: 0.0553 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 13:29:07 - mmengine - INFO - Epoch(train) [4][13800/42151] lr: 3.0000e-05 eta: 17:10:41 time: 0.4134 data_time: 0.0161 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 13:30:06 - mmengine - INFO - Epoch(train) [4][13900/42151] lr: 3.0000e-05 eta: 17:09:50 time: 0.6465 data_time: 0.2183 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 13:31:05 - mmengine - INFO - Epoch(train) [4][14000/42151] lr: 3.0000e-05 eta: 17:08:58 time: 0.6457 data_time: 0.2489 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/21 13:32:04 - mmengine - INFO - Epoch(train) [4][14100/42151] lr: 3.0000e-05 eta: 17:08:06 time: 0.6022 data_time: 0.2187 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 13:33:01 - mmengine - INFO - Epoch(train) [4][14200/42151] lr: 3.0000e-05 eta: 17:07:13 time: 0.5867 data_time: 0.1887 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 13:33:58 - mmengine - INFO - Epoch(train) [4][14300/42151] lr: 3.0000e-05 eta: 17:06:20 time: 0.5003 data_time: 0.0600 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 13:34:54 - mmengine - INFO - Epoch(train) [4][14400/42151] lr: 3.0000e-05 eta: 17:05:25 time: 0.3972 data_time: 0.0148 memory: 14682 loss_ce: 0.0106 loss: 0.0106 2022/09/21 13:35:52 - mmengine - INFO - Epoch(train) [4][14500/42151] lr: 3.0000e-05 eta: 17:04:33 time: 0.5820 data_time: 0.1993 memory: 14682 loss_ce: 0.0104 loss: 0.0104 2022/09/21 13:36:18 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 13:36:47 - mmengine - INFO - Epoch(train) [4][14600/42151] lr: 3.0000e-05 eta: 17:03:39 time: 0.5756 data_time: 0.1925 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 13:37:44 - mmengine - INFO - Epoch(train) [4][14700/42151] lr: 3.0000e-05 eta: 17:02:45 time: 0.6061 data_time: 0.2045 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 13:38:41 - mmengine - INFO - Epoch(train) [4][14800/42151] lr: 3.0000e-05 eta: 17:01:52 time: 0.6119 data_time: 0.1861 memory: 14682 loss_ce: 0.0105 loss: 0.0105 2022/09/21 13:39:36 - mmengine - INFO - Epoch(train) [4][14900/42151] lr: 3.0000e-05 eta: 17:00:57 time: 0.4362 data_time: 0.0558 memory: 14682 loss_ce: 0.0115 loss: 0.0115 2022/09/21 13:40:33 - mmengine - INFO - Epoch(train) [4][15000/42151] lr: 3.0000e-05 eta: 17:00:04 time: 0.4252 data_time: 0.0243 memory: 14682 loss_ce: 0.0104 loss: 0.0104 2022/09/21 13:41:33 - mmengine - INFO - Epoch(train) [4][15100/42151] lr: 3.0000e-05 eta: 16:59:12 time: 0.6235 data_time: 0.2181 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 13:42:29 - mmengine - INFO - Epoch(train) [4][15200/42151] lr: 3.0000e-05 eta: 16:58:18 time: 0.6380 data_time: 0.2085 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 13:43:26 - mmengine - INFO - Epoch(train) [4][15300/42151] lr: 3.0000e-05 eta: 16:57:25 time: 0.6018 data_time: 0.2185 memory: 14682 loss_ce: 0.0110 loss: 0.0110 2022/09/21 13:44:24 - mmengine - INFO - Epoch(train) [4][15400/42151] lr: 3.0000e-05 eta: 16:56:33 time: 0.6416 data_time: 0.2483 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 13:45:20 - mmengine - INFO - Epoch(train) [4][15500/42151] lr: 3.0000e-05 eta: 16:55:38 time: 0.4555 data_time: 0.0627 memory: 14682 loss_ce: 0.0104 loss: 0.0104 2022/09/21 13:45:47 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 13:46:15 - mmengine - INFO - Epoch(train) [4][15600/42151] lr: 3.0000e-05 eta: 16:54:44 time: 0.4081 data_time: 0.0197 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 13:47:13 - mmengine - INFO - Epoch(train) [4][15700/42151] lr: 3.0000e-05 eta: 16:53:51 time: 0.6078 data_time: 0.2078 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 13:48:09 - mmengine - INFO - Epoch(train) [4][15800/42151] lr: 3.0000e-05 eta: 16:52:57 time: 0.6560 data_time: 0.2010 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 13:49:06 - mmengine - INFO - Epoch(train) [4][15900/42151] lr: 3.0000e-05 eta: 16:52:04 time: 0.6255 data_time: 0.2418 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 13:50:03 - mmengine - INFO - Epoch(train) [4][16000/42151] lr: 3.0000e-05 eta: 16:51:10 time: 0.5737 data_time: 0.1735 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/21 13:50:59 - mmengine - INFO - Epoch(train) [4][16100/42151] lr: 3.0000e-05 eta: 16:50:16 time: 0.4705 data_time: 0.0756 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/21 13:51:57 - mmengine - INFO - Epoch(train) [4][16200/42151] lr: 3.0000e-05 eta: 16:49:24 time: 0.4007 data_time: 0.0167 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 13:52:56 - mmengine - INFO - Epoch(train) [4][16300/42151] lr: 3.0000e-05 eta: 16:48:32 time: 0.5744 data_time: 0.1865 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 13:53:52 - mmengine - INFO - Epoch(train) [4][16400/42151] lr: 3.0000e-05 eta: 16:47:37 time: 0.6205 data_time: 0.2024 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 13:54:49 - mmengine - INFO - Epoch(train) [4][16500/42151] lr: 3.0000e-05 eta: 16:46:44 time: 0.6384 data_time: 0.2361 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 13:55:16 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 13:55:45 - mmengine - INFO - Epoch(train) [4][16600/42151] lr: 3.0000e-05 eta: 16:45:50 time: 0.5642 data_time: 0.1748 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 13:56:41 - mmengine - INFO - Epoch(train) [4][16700/42151] lr: 3.0000e-05 eta: 16:44:56 time: 0.4790 data_time: 0.0755 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/21 13:57:38 - mmengine - INFO - Epoch(train) [4][16800/42151] lr: 3.0000e-05 eta: 16:44:02 time: 0.4037 data_time: 0.0197 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 13:58:39 - mmengine - INFO - Epoch(train) [4][16900/42151] lr: 3.0000e-05 eta: 16:43:12 time: 0.6483 data_time: 0.2555 memory: 14682 loss_ce: 0.0108 loss: 0.0108 2022/09/21 13:59:34 - mmengine - INFO - Epoch(train) [4][17000/42151] lr: 3.0000e-05 eta: 16:42:17 time: 0.5859 data_time: 0.1985 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 14:00:31 - mmengine - INFO - Epoch(train) [4][17100/42151] lr: 3.0000e-05 eta: 16:41:24 time: 0.6430 data_time: 0.2509 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 14:01:28 - mmengine - INFO - Epoch(train) [4][17200/42151] lr: 3.0000e-05 eta: 16:40:30 time: 0.5533 data_time: 0.1718 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/21 14:02:24 - mmengine - INFO - Epoch(train) [4][17300/42151] lr: 3.0000e-05 eta: 16:39:36 time: 0.4602 data_time: 0.0783 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 14:03:22 - mmengine - INFO - Epoch(train) [4][17400/42151] lr: 3.0000e-05 eta: 16:38:43 time: 0.4018 data_time: 0.0150 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 14:04:21 - mmengine - INFO - Epoch(train) [4][17500/42151] lr: 3.0000e-05 eta: 16:37:51 time: 0.6738 data_time: 0.2360 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 14:04:48 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 14:05:17 - mmengine - INFO - Epoch(train) [4][17600/42151] lr: 3.0000e-05 eta: 16:36:57 time: 0.6269 data_time: 0.2322 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 14:06:16 - mmengine - INFO - Epoch(train) [4][17700/42151] lr: 3.0000e-05 eta: 16:36:05 time: 0.6455 data_time: 0.2612 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 14:07:11 - mmengine - INFO - Epoch(train) [4][17800/42151] lr: 3.0000e-05 eta: 16:35:10 time: 0.5545 data_time: 0.1681 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 14:08:07 - mmengine - INFO - Epoch(train) [4][17900/42151] lr: 3.0000e-05 eta: 16:34:16 time: 0.4646 data_time: 0.0657 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 14:09:03 - mmengine - INFO - Epoch(train) [4][18000/42151] lr: 3.0000e-05 eta: 16:33:22 time: 0.4712 data_time: 0.0187 memory: 14682 loss_ce: 0.0113 loss: 0.0113 2022/09/21 14:10:03 - mmengine - INFO - Epoch(train) [4][18100/42151] lr: 3.0000e-05 eta: 16:32:30 time: 0.6155 data_time: 0.2287 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 14:10:59 - mmengine - INFO - Epoch(train) [4][18200/42151] lr: 3.0000e-05 eta: 16:31:36 time: 0.6344 data_time: 0.2259 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 14:11:57 - mmengine - INFO - Epoch(train) [4][18300/42151] lr: 3.0000e-05 eta: 16:30:43 time: 0.6422 data_time: 0.2601 memory: 14682 loss_ce: 0.0116 loss: 0.0116 2022/09/21 14:12:55 - mmengine - INFO - Epoch(train) [4][18400/42151] lr: 3.0000e-05 eta: 16:29:51 time: 0.6322 data_time: 0.1796 memory: 14682 loss_ce: 0.0107 loss: 0.0107 2022/09/21 14:13:51 - mmengine - INFO - Epoch(train) [4][18500/42151] lr: 3.0000e-05 eta: 16:28:57 time: 0.4697 data_time: 0.0848 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 14:14:22 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 14:14:52 - mmengine - INFO - Epoch(train) [4][18600/42151] lr: 3.0000e-05 eta: 16:28:06 time: 0.4008 data_time: 0.0158 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 14:15:52 - mmengine - INFO - Epoch(train) [4][18700/42151] lr: 3.0000e-05 eta: 16:27:15 time: 0.5852 data_time: 0.2010 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 14:16:47 - mmengine - INFO - Epoch(train) [4][18800/42151] lr: 3.0000e-05 eta: 16:26:20 time: 0.5361 data_time: 0.1558 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 14:17:43 - mmengine - INFO - Epoch(train) [4][18900/42151] lr: 3.0000e-05 eta: 16:25:26 time: 0.6234 data_time: 0.2067 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 14:18:46 - mmengine - INFO - Epoch(train) [4][19000/42151] lr: 3.0000e-05 eta: 16:24:37 time: 1.3804 data_time: 0.6663 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 14:19:42 - mmengine - INFO - Epoch(train) [4][19100/42151] lr: 3.0000e-05 eta: 16:23:43 time: 0.4783 data_time: 0.0589 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 14:20:38 - mmengine - INFO - Epoch(train) [4][19200/42151] lr: 3.0000e-05 eta: 16:22:48 time: 0.4021 data_time: 0.0148 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 14:21:35 - mmengine - INFO - Epoch(train) [4][19300/42151] lr: 3.0000e-05 eta: 16:21:54 time: 0.6125 data_time: 0.1697 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 14:22:36 - mmengine - INFO - Epoch(train) [4][19400/42151] lr: 3.0000e-05 eta: 16:21:04 time: 1.0477 data_time: 0.1439 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 14:23:39 - mmengine - INFO - Epoch(train) [4][19500/42151] lr: 3.0000e-05 eta: 16:20:15 time: 0.6254 data_time: 0.2282 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 14:24:08 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 14:24:39 - mmengine - INFO - Epoch(train) [4][19600/42151] lr: 3.0000e-05 eta: 16:19:23 time: 0.5655 data_time: 0.1807 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 14:25:36 - mmengine - INFO - Epoch(train) [4][19700/42151] lr: 3.0000e-05 eta: 16:18:29 time: 0.5310 data_time: 0.0865 memory: 14682 loss_ce: 0.0105 loss: 0.0105 2022/09/21 14:26:30 - mmengine - INFO - Epoch(train) [4][19800/42151] lr: 3.0000e-05 eta: 16:17:34 time: 0.4273 data_time: 0.0159 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 14:27:27 - mmengine - INFO - Epoch(train) [4][19900/42151] lr: 3.0000e-05 eta: 16:16:40 time: 0.6204 data_time: 0.2022 memory: 14682 loss_ce: 0.0106 loss: 0.0106 2022/09/21 14:28:21 - mmengine - INFO - Epoch(train) [4][20000/42151] lr: 3.0000e-05 eta: 16:15:44 time: 0.5457 data_time: 0.1620 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 14:29:16 - mmengine - INFO - Epoch(train) [4][20100/42151] lr: 3.0000e-05 eta: 16:14:50 time: 0.6020 data_time: 0.2175 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 14:30:12 - mmengine - INFO - Epoch(train) [4][20200/42151] lr: 3.0000e-05 eta: 16:13:55 time: 0.5721 data_time: 0.1518 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 14:31:07 - mmengine - INFO - Epoch(train) [4][20300/42151] lr: 3.0000e-05 eta: 16:13:00 time: 0.4515 data_time: 0.0660 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 14:32:03 - mmengine - INFO - Epoch(train) [4][20400/42151] lr: 3.0000e-05 eta: 16:12:06 time: 0.4201 data_time: 0.0328 memory: 14682 loss_ce: 0.0114 loss: 0.0114 2022/09/21 14:33:01 - mmengine - INFO - Epoch(train) [4][20500/42151] lr: 3.0000e-05 eta: 16:11:13 time: 0.5860 data_time: 0.1733 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 14:33:27 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 14:33:56 - mmengine - INFO - Epoch(train) [4][20600/42151] lr: 3.0000e-05 eta: 16:10:18 time: 0.5843 data_time: 0.1952 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 14:34:53 - mmengine - INFO - Epoch(train) [4][20700/42151] lr: 3.0000e-05 eta: 16:09:24 time: 0.7365 data_time: 0.2392 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 14:35:49 - mmengine - INFO - Epoch(train) [4][20800/42151] lr: 3.0000e-05 eta: 16:08:30 time: 0.5423 data_time: 0.1609 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 14:36:44 - mmengine - INFO - Epoch(train) [4][20900/42151] lr: 3.0000e-05 eta: 16:07:35 time: 0.4885 data_time: 0.1035 memory: 14682 loss_ce: 0.0114 loss: 0.0114 2022/09/21 14:37:40 - mmengine - INFO - Epoch(train) [4][21000/42151] lr: 3.0000e-05 eta: 16:06:41 time: 0.4341 data_time: 0.0279 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/21 14:38:37 - mmengine - INFO - Epoch(train) [4][21100/42151] lr: 3.0000e-05 eta: 16:05:47 time: 0.6073 data_time: 0.1396 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 14:39:34 - mmengine - INFO - Epoch(train) [4][21200/42151] lr: 3.0000e-05 eta: 16:04:54 time: 0.6539 data_time: 0.1814 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 14:40:33 - mmengine - INFO - Epoch(train) [4][21300/42151] lr: 3.0000e-05 eta: 16:04:02 time: 0.6877 data_time: 0.2983 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 14:41:32 - mmengine - INFO - Epoch(train) [4][21400/42151] lr: 3.0000e-05 eta: 16:03:09 time: 0.6680 data_time: 0.2195 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 14:42:29 - mmengine - INFO - Epoch(train) [4][21500/42151] lr: 3.0000e-05 eta: 16:02:15 time: 0.4919 data_time: 0.1063 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 14:42:58 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 14:43:27 - mmengine - INFO - Epoch(train) [4][21600/42151] lr: 3.0000e-05 eta: 16:01:22 time: 0.4220 data_time: 0.0281 memory: 14682 loss_ce: 0.0102 loss: 0.0102 2022/09/21 14:44:26 - mmengine - INFO - Epoch(train) [4][21700/42151] lr: 3.0000e-05 eta: 16:00:31 time: 0.5916 data_time: 0.1921 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 14:45:25 - mmengine - INFO - Epoch(train) [4][21800/42151] lr: 3.0000e-05 eta: 15:59:38 time: 0.6421 data_time: 0.1672 memory: 14682 loss_ce: 0.0101 loss: 0.0101 2022/09/21 14:46:23 - mmengine - INFO - Epoch(train) [4][21900/42151] lr: 3.0000e-05 eta: 15:58:45 time: 0.6162 data_time: 0.2245 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 14:47:21 - mmengine - INFO - Epoch(train) [4][22000/42151] lr: 3.0000e-05 eta: 15:57:52 time: 0.6163 data_time: 0.2151 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 14:48:17 - mmengine - INFO - Epoch(train) [4][22100/42151] lr: 3.0000e-05 eta: 15:56:58 time: 0.5108 data_time: 0.1213 memory: 14682 loss_ce: 0.0105 loss: 0.0105 2022/09/21 14:49:13 - mmengine - INFO - Epoch(train) [4][22200/42151] lr: 3.0000e-05 eta: 15:56:03 time: 0.3974 data_time: 0.0145 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 14:50:10 - mmengine - INFO - Epoch(train) [4][22300/42151] lr: 3.0000e-05 eta: 15:55:10 time: 0.5447 data_time: 0.1634 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 14:51:05 - mmengine - INFO - Epoch(train) [4][22400/42151] lr: 3.0000e-05 eta: 15:54:15 time: 0.6070 data_time: 0.1554 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 14:52:03 - mmengine - INFO - Epoch(train) [4][22500/42151] lr: 3.0000e-05 eta: 15:53:21 time: 0.6570 data_time: 0.2267 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 14:52:30 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 14:52:59 - mmengine - INFO - Epoch(train) [4][22600/42151] lr: 3.0000e-05 eta: 15:52:27 time: 0.5825 data_time: 0.1668 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 14:53:55 - mmengine - INFO - Epoch(train) [4][22700/42151] lr: 3.0000e-05 eta: 15:51:33 time: 0.4732 data_time: 0.0653 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 14:54:52 - mmengine - INFO - Epoch(train) [4][22800/42151] lr: 3.0000e-05 eta: 15:50:39 time: 0.4129 data_time: 0.0171 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 14:55:51 - mmengine - INFO - Epoch(train) [4][22900/42151] lr: 3.0000e-05 eta: 15:49:47 time: 0.5875 data_time: 0.2025 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 14:56:48 - mmengine - INFO - Epoch(train) [4][23000/42151] lr: 3.0000e-05 eta: 15:48:53 time: 0.6102 data_time: 0.1841 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 14:57:46 - mmengine - INFO - Epoch(train) [4][23100/42151] lr: 3.0000e-05 eta: 15:48:00 time: 0.6187 data_time: 0.1979 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 14:58:43 - mmengine - INFO - Epoch(train) [4][23200/42151] lr: 3.0000e-05 eta: 15:47:07 time: 0.6018 data_time: 0.1999 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 14:59:40 - mmengine - INFO - Epoch(train) [4][23300/42151] lr: 3.0000e-05 eta: 15:46:13 time: 0.4823 data_time: 0.0665 memory: 14682 loss_ce: 0.0104 loss: 0.0104 2022/09/21 15:00:38 - mmengine - INFO - Epoch(train) [4][23400/42151] lr: 3.0000e-05 eta: 15:45:20 time: 0.4309 data_time: 0.0185 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 15:01:36 - mmengine - INFO - Epoch(train) [4][23500/42151] lr: 3.0000e-05 eta: 15:44:27 time: 0.5847 data_time: 0.1924 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 15:02:02 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 15:02:32 - mmengine - INFO - Epoch(train) [4][23600/42151] lr: 3.0000e-05 eta: 15:43:32 time: 0.5523 data_time: 0.1702 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 15:03:29 - mmengine - INFO - Epoch(train) [4][23700/42151] lr: 3.0000e-05 eta: 15:42:38 time: 0.6447 data_time: 0.2223 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 15:04:26 - mmengine - INFO - Epoch(train) [4][23800/42151] lr: 3.0000e-05 eta: 15:41:45 time: 0.5531 data_time: 0.1724 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 15:05:22 - mmengine - INFO - Epoch(train) [4][23900/42151] lr: 3.0000e-05 eta: 15:40:50 time: 0.4568 data_time: 0.0683 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 15:06:18 - mmengine - INFO - Epoch(train) [4][24000/42151] lr: 3.0000e-05 eta: 15:39:56 time: 0.4319 data_time: 0.0272 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 15:07:16 - mmengine - INFO - Epoch(train) [4][24100/42151] lr: 3.0000e-05 eta: 15:39:03 time: 0.6102 data_time: 0.1570 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 15:08:12 - mmengine - INFO - Epoch(train) [4][24200/42151] lr: 3.0000e-05 eta: 15:38:08 time: 0.5932 data_time: 0.2109 memory: 14682 loss_ce: 0.0101 loss: 0.0101 2022/09/21 15:09:10 - mmengine - INFO - Epoch(train) [4][24300/42151] lr: 3.0000e-05 eta: 15:37:15 time: 0.6995 data_time: 0.2463 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 15:10:06 - mmengine - INFO - Epoch(train) [4][24400/42151] lr: 3.0000e-05 eta: 15:36:21 time: 0.5365 data_time: 0.1437 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 15:11:02 - mmengine - INFO - Epoch(train) [4][24500/42151] lr: 3.0000e-05 eta: 15:35:26 time: 0.4782 data_time: 0.0952 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 15:11:29 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 15:11:59 - mmengine - INFO - Epoch(train) [4][24600/42151] lr: 3.0000e-05 eta: 15:34:32 time: 0.4489 data_time: 0.0175 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 15:12:56 - mmengine - INFO - Epoch(train) [4][24700/42151] lr: 3.0000e-05 eta: 15:33:39 time: 0.5912 data_time: 0.1537 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 15:13:53 - mmengine - INFO - Epoch(train) [4][24800/42151] lr: 3.0000e-05 eta: 15:32:45 time: 0.6073 data_time: 0.2170 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 15:14:51 - mmengine - INFO - Epoch(train) [4][24900/42151] lr: 3.0000e-05 eta: 15:31:52 time: 0.6948 data_time: 0.2634 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 15:15:49 - mmengine - INFO - Epoch(train) [4][25000/42151] lr: 3.0000e-05 eta: 15:30:58 time: 0.5736 data_time: 0.1594 memory: 14682 loss_ce: 0.0102 loss: 0.0102 2022/09/21 15:16:46 - mmengine - INFO - Epoch(train) [4][25100/42151] lr: 3.0000e-05 eta: 15:30:05 time: 0.5047 data_time: 0.1091 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 15:17:42 - mmengine - INFO - Epoch(train) [4][25200/42151] lr: 3.0000e-05 eta: 15:29:10 time: 0.3964 data_time: 0.0164 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 15:18:39 - mmengine - INFO - Epoch(train) [4][25300/42151] lr: 3.0000e-05 eta: 15:28:16 time: 0.5559 data_time: 0.1744 memory: 14682 loss_ce: 0.0110 loss: 0.0110 2022/09/21 15:19:39 - mmengine - INFO - Epoch(train) [4][25400/42151] lr: 3.0000e-05 eta: 15:27:25 time: 0.6759 data_time: 0.1796 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 15:20:36 - mmengine - INFO - Epoch(train) [4][25500/42151] lr: 3.0000e-05 eta: 15:26:31 time: 0.6035 data_time: 0.2039 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 15:21:04 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 15:21:34 - mmengine - INFO - Epoch(train) [4][25600/42151] lr: 3.0000e-05 eta: 15:25:38 time: 0.5343 data_time: 0.1506 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 15:22:30 - mmengine - INFO - Epoch(train) [4][25700/42151] lr: 3.0000e-05 eta: 15:24:43 time: 0.5136 data_time: 0.1230 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 15:23:28 - mmengine - INFO - Epoch(train) [4][25800/42151] lr: 3.0000e-05 eta: 15:23:50 time: 0.4722 data_time: 0.0211 memory: 14682 loss_ce: 0.0105 loss: 0.0105 2022/09/21 15:24:26 - mmengine - INFO - Epoch(train) [4][25900/42151] lr: 3.0000e-05 eta: 15:22:57 time: 0.5831 data_time: 0.1912 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 15:25:22 - mmengine - INFO - Epoch(train) [4][26000/42151] lr: 3.0000e-05 eta: 15:22:02 time: 0.5934 data_time: 0.1568 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 15:26:19 - mmengine - INFO - Epoch(train) [4][26100/42151] lr: 3.0000e-05 eta: 15:21:09 time: 0.6508 data_time: 0.2374 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 15:27:17 - mmengine - INFO - Epoch(train) [4][26200/42151] lr: 3.0000e-05 eta: 15:20:15 time: 0.5467 data_time: 0.1602 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 15:28:16 - mmengine - INFO - Epoch(train) [4][26300/42151] lr: 3.0000e-05 eta: 15:19:23 time: 0.5160 data_time: 0.0788 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 15:29:13 - mmengine - INFO - Epoch(train) [4][26400/42151] lr: 3.0000e-05 eta: 15:18:29 time: 0.3963 data_time: 0.0150 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 15:30:11 - mmengine - INFO - Epoch(train) [4][26500/42151] lr: 3.0000e-05 eta: 15:17:36 time: 0.6280 data_time: 0.2257 memory: 14682 loss_ce: 0.0107 loss: 0.0107 2022/09/21 15:30:39 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 15:31:10 - mmengine - INFO - Epoch(train) [4][26600/42151] lr: 3.0000e-05 eta: 15:16:43 time: 0.5628 data_time: 0.1815 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 15:32:07 - mmengine - INFO - Epoch(train) [4][26700/42151] lr: 3.0000e-05 eta: 15:15:49 time: 0.5785 data_time: 0.1677 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 15:33:03 - mmengine - INFO - Epoch(train) [4][26800/42151] lr: 3.0000e-05 eta: 15:14:55 time: 0.5708 data_time: 0.1738 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 15:34:00 - mmengine - INFO - Epoch(train) [4][26900/42151] lr: 3.0000e-05 eta: 15:14:01 time: 0.5103 data_time: 0.0706 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 15:34:56 - mmengine - INFO - Epoch(train) [4][27000/42151] lr: 3.0000e-05 eta: 15:13:06 time: 0.4076 data_time: 0.0170 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 15:35:55 - mmengine - INFO - Epoch(train) [4][27100/42151] lr: 3.0000e-05 eta: 15:12:14 time: 0.5998 data_time: 0.2157 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 15:36:51 - mmengine - INFO - Epoch(train) [4][27200/42151] lr: 3.0000e-05 eta: 15:11:19 time: 0.5464 data_time: 0.1637 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 15:40:26 - mmengine - INFO - Epoch(train) [4][27300/42151] lr: 3.0000e-05 eta: 15:12:07 time: 0.6109 data_time: 0.1745 memory: 14682 loss_ce: 0.0073 loss: 0.0073 2022/09/21 15:41:23 - mmengine - INFO - Epoch(train) [4][27400/42151] lr: 3.0000e-05 eta: 15:11:13 time: 0.5640 data_time: 0.1679 memory: 14682 loss_ce: 0.0101 loss: 0.0101 2022/09/21 15:42:25 - mmengine - INFO - Epoch(train) [4][27500/42151] lr: 3.0000e-05 eta: 15:10:22 time: 0.5504 data_time: 0.1188 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 15:42:59 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 15:43:34 - mmengine - INFO - Epoch(train) [4][27600/42151] lr: 3.0000e-05 eta: 15:09:36 time: 0.4515 data_time: 0.0292 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 15:44:41 - mmengine - INFO - Epoch(train) [4][27700/42151] lr: 3.0000e-05 eta: 15:08:48 time: 0.6006 data_time: 0.1766 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 15:45:43 - mmengine - INFO - Epoch(train) [4][27800/42151] lr: 3.0000e-05 eta: 15:07:58 time: 0.6249 data_time: 0.2225 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 15:49:07 - mmengine - INFO - Epoch(train) [4][27900/42151] lr: 3.0000e-05 eta: 15:08:37 time: 0.8196 data_time: 0.3671 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 15:50:15 - mmengine - INFO - Epoch(train) [4][28000/42151] lr: 3.0000e-05 eta: 15:07:50 time: 0.5573 data_time: 0.1651 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 15:51:11 - mmengine - INFO - Epoch(train) [4][28100/42151] lr: 3.0000e-05 eta: 15:06:55 time: 0.5224 data_time: 0.1132 memory: 14682 loss_ce: 0.0107 loss: 0.0107 2022/09/21 15:52:07 - mmengine - INFO - Epoch(train) [4][28200/42151] lr: 3.0000e-05 eta: 15:06:00 time: 0.4282 data_time: 0.0177 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 15:53:07 - mmengine - INFO - Epoch(train) [4][28300/42151] lr: 3.0000e-05 eta: 15:05:07 time: 0.5682 data_time: 0.1424 memory: 14682 loss_ce: 0.0111 loss: 0.0111 2022/09/21 15:54:04 - mmengine - INFO - Epoch(train) [4][28400/42151] lr: 3.0000e-05 eta: 15:04:13 time: 0.6950 data_time: 0.2395 memory: 14682 loss_ce: 0.0103 loss: 0.0103 2022/09/21 15:55:03 - mmengine - INFO - Epoch(train) [4][28500/42151] lr: 3.0000e-05 eta: 15:03:20 time: 0.6516 data_time: 0.2525 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 15:55:29 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 15:55:59 - mmengine - INFO - Epoch(train) [4][28600/42151] lr: 3.0000e-05 eta: 15:02:25 time: 0.5598 data_time: 0.1434 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 15:56:56 - mmengine - INFO - Epoch(train) [4][28700/42151] lr: 3.0000e-05 eta: 15:01:31 time: 0.4754 data_time: 0.0950 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 15:57:51 - mmengine - INFO - Epoch(train) [4][28800/42151] lr: 3.0000e-05 eta: 15:00:35 time: 0.4123 data_time: 0.0190 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 15:58:49 - mmengine - INFO - Epoch(train) [4][28900/42151] lr: 3.0000e-05 eta: 14:59:42 time: 0.5892 data_time: 0.1716 memory: 14682 loss_ce: 0.0105 loss: 0.0105 2022/09/21 15:59:46 - mmengine - INFO - Epoch(train) [4][29000/42151] lr: 3.0000e-05 eta: 14:58:47 time: 0.5966 data_time: 0.1860 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 16:00:42 - mmengine - INFO - Epoch(train) [4][29100/42151] lr: 3.0000e-05 eta: 14:57:52 time: 0.6158 data_time: 0.1969 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 16:01:38 - mmengine - INFO - Epoch(train) [4][29200/42151] lr: 3.0000e-05 eta: 14:56:57 time: 0.5412 data_time: 0.1476 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 16:02:36 - mmengine - INFO - Epoch(train) [4][29300/42151] lr: 3.0000e-05 eta: 14:56:04 time: 0.5484 data_time: 0.1292 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 16:03:45 - mmengine - INFO - Epoch(train) [4][29400/42151] lr: 3.0000e-05 eta: 14:55:17 time: 0.4574 data_time: 0.0220 memory: 14682 loss_ce: 0.0110 loss: 0.0110 2022/09/21 16:04:55 - mmengine - INFO - Epoch(train) [4][29500/42151] lr: 3.0000e-05 eta: 14:54:31 time: 0.6509 data_time: 0.2107 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 16:05:21 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 16:05:54 - mmengine - INFO - Epoch(train) [4][29600/42151] lr: 3.0000e-05 eta: 14:53:37 time: 0.7182 data_time: 0.2269 memory: 14682 loss_ce: 0.0105 loss: 0.0105 2022/09/21 16:07:12 - mmengine - INFO - Epoch(train) [4][29700/42151] lr: 3.0000e-05 eta: 14:52:56 time: 0.7691 data_time: 0.3321 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 16:08:27 - mmengine - INFO - Epoch(train) [4][29800/42151] lr: 3.0000e-05 eta: 14:52:13 time: 0.8131 data_time: 0.2930 memory: 14682 loss_ce: 0.0102 loss: 0.0102 2022/09/21 16:09:39 - mmengine - INFO - Epoch(train) [4][29900/42151] lr: 3.0000e-05 eta: 14:51:28 time: 0.5141 data_time: 0.0766 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 16:10:38 - mmengine - INFO - Epoch(train) [4][30000/42151] lr: 3.0000e-05 eta: 14:50:34 time: 0.4362 data_time: 0.0218 memory: 14682 loss_ce: 0.0111 loss: 0.0111 2022/09/21 16:11:43 - mmengine - INFO - Epoch(train) [4][30100/42151] lr: 3.0000e-05 eta: 14:49:45 time: 0.8601 data_time: 0.3102 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 16:12:44 - mmengine - INFO - Epoch(train) [4][30200/42151] lr: 3.0000e-05 eta: 14:48:52 time: 0.5750 data_time: 0.1759 memory: 14682 loss_ce: 0.0107 loss: 0.0107 2022/09/21 16:13:46 - mmengine - INFO - Epoch(train) [4][30300/42151] lr: 3.0000e-05 eta: 14:48:01 time: 0.8236 data_time: 0.3332 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 16:14:55 - mmengine - INFO - Epoch(train) [4][30400/42151] lr: 3.0000e-05 eta: 14:47:14 time: 0.7148 data_time: 0.2432 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 16:16:02 - mmengine - INFO - Epoch(train) [4][30500/42151] lr: 3.0000e-05 eta: 14:46:26 time: 0.5157 data_time: 0.0767 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 16:16:35 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 16:17:07 - mmengine - INFO - Epoch(train) [4][30600/42151] lr: 3.0000e-05 eta: 14:45:36 time: 0.3952 data_time: 0.0150 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 16:18:15 - mmengine - INFO - Epoch(train) [4][30700/42151] lr: 3.0000e-05 eta: 14:44:48 time: 0.8316 data_time: 0.3524 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 16:19:19 - mmengine - INFO - Epoch(train) [4][30800/42151] lr: 3.0000e-05 eta: 14:43:58 time: 0.5754 data_time: 0.1814 memory: 14682 loss_ce: 0.0101 loss: 0.0101 2022/09/21 16:20:19 - mmengine - INFO - Epoch(train) [4][30900/42151] lr: 3.0000e-05 eta: 14:43:05 time: 0.7438 data_time: 0.2479 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 16:21:26 - mmengine - INFO - Epoch(train) [4][31000/42151] lr: 3.0000e-05 eta: 14:42:17 time: 0.6532 data_time: 0.2140 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 16:22:31 - mmengine - INFO - Epoch(train) [4][31100/42151] lr: 3.0000e-05 eta: 14:41:27 time: 0.4496 data_time: 0.0680 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 16:23:34 - mmengine - INFO - Epoch(train) [4][31200/42151] lr: 3.0000e-05 eta: 14:40:36 time: 0.3989 data_time: 0.0152 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 16:24:33 - mmengine - INFO - Epoch(train) [4][31300/42151] lr: 3.0000e-05 eta: 14:39:43 time: 0.6139 data_time: 0.1621 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 16:25:29 - mmengine - INFO - Epoch(train) [4][31400/42151] lr: 3.0000e-05 eta: 14:38:47 time: 0.5700 data_time: 0.1864 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 16:26:35 - mmengine - INFO - Epoch(train) [4][31500/42151] lr: 3.0000e-05 eta: 14:37:58 time: 0.7212 data_time: 0.2760 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 16:27:06 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 16:27:37 - mmengine - INFO - Epoch(train) [4][31600/42151] lr: 3.0000e-05 eta: 14:37:07 time: 0.6491 data_time: 0.1860 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 16:28:39 - mmengine - INFO - Epoch(train) [4][31700/42151] lr: 3.0000e-05 eta: 14:36:15 time: 0.5641 data_time: 0.1262 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 16:30:16 - mmengine - INFO - Epoch(train) [4][31800/42151] lr: 3.0000e-05 eta: 14:35:45 time: 0.4696 data_time: 0.0306 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 16:31:15 - mmengine - INFO - Epoch(train) [4][31900/42151] lr: 3.0000e-05 eta: 14:34:51 time: 0.6023 data_time: 0.2072 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 16:32:20 - mmengine - INFO - Epoch(train) [4][32000/42151] lr: 3.0000e-05 eta: 14:34:01 time: 0.6263 data_time: 0.1721 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 16:33:28 - mmengine - INFO - Epoch(train) [4][32100/42151] lr: 3.0000e-05 eta: 14:33:13 time: 0.7266 data_time: 0.2714 memory: 14682 loss_ce: 0.0101 loss: 0.0101 2022/09/21 16:34:28 - mmengine - INFO - Epoch(train) [4][32200/42151] lr: 3.0000e-05 eta: 14:32:20 time: 0.5244 data_time: 0.1340 memory: 14682 loss_ce: 0.0108 loss: 0.0108 2022/09/21 16:35:26 - mmengine - INFO - Epoch(train) [4][32300/42151] lr: 3.0000e-05 eta: 14:31:26 time: 0.5820 data_time: 0.0901 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 16:36:25 - mmengine - INFO - Epoch(train) [4][32400/42151] lr: 3.0000e-05 eta: 14:30:32 time: 0.4747 data_time: 0.0251 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 16:37:21 - mmengine - INFO - Epoch(train) [4][32500/42151] lr: 3.0000e-05 eta: 14:29:37 time: 0.5847 data_time: 0.1714 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 16:37:46 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 16:38:16 - mmengine - INFO - Epoch(train) [4][32600/42151] lr: 3.0000e-05 eta: 14:28:41 time: 0.5572 data_time: 0.1426 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 16:39:11 - mmengine - INFO - Epoch(train) [4][32700/42151] lr: 3.0000e-05 eta: 14:27:45 time: 0.5994 data_time: 0.2185 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 16:40:07 - mmengine - INFO - Epoch(train) [4][32800/42151] lr: 3.0000e-05 eta: 14:26:50 time: 0.5639 data_time: 0.1749 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 16:41:03 - mmengine - INFO - Epoch(train) [4][32900/42151] lr: 3.0000e-05 eta: 14:25:55 time: 0.4820 data_time: 0.0963 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 16:42:00 - mmengine - INFO - Epoch(train) [4][33000/42151] lr: 3.0000e-05 eta: 14:25:00 time: 0.4353 data_time: 0.0515 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 16:42:58 - mmengine - INFO - Epoch(train) [4][33100/42151] lr: 3.0000e-05 eta: 14:24:06 time: 0.7160 data_time: 0.2153 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 16:43:55 - mmengine - INFO - Epoch(train) [4][33200/42151] lr: 3.0000e-05 eta: 14:23:11 time: 0.6100 data_time: 0.1620 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 16:44:50 - mmengine - INFO - Epoch(train) [4][33300/42151] lr: 3.0000e-05 eta: 14:22:15 time: 0.6309 data_time: 0.1749 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 16:45:45 - mmengine - INFO - Epoch(train) [4][33400/42151] lr: 3.0000e-05 eta: 14:21:20 time: 0.5770 data_time: 0.1642 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 16:46:47 - mmengine - INFO - Epoch(train) [4][33500/42151] lr: 3.0000e-05 eta: 14:20:28 time: 0.5383 data_time: 0.0760 memory: 14682 loss_ce: 0.0105 loss: 0.0105 2022/09/21 16:47:15 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 16:47:50 - mmengine - INFO - Epoch(train) [4][33600/42151] lr: 3.0000e-05 eta: 14:19:36 time: 0.4480 data_time: 0.0241 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 16:48:50 - mmengine - INFO - Epoch(train) [4][33700/42151] lr: 3.0000e-05 eta: 14:18:43 time: 0.8095 data_time: 0.3393 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 16:50:00 - mmengine - INFO - Epoch(train) [4][33800/42151] lr: 3.0000e-05 eta: 14:17:56 time: 0.7229 data_time: 0.2789 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 16:51:09 - mmengine - INFO - Epoch(train) [4][33900/42151] lr: 3.0000e-05 eta: 14:17:08 time: 0.9070 data_time: 0.4184 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 16:52:22 - mmengine - INFO - Epoch(train) [4][34000/42151] lr: 3.0000e-05 eta: 14:16:23 time: 0.7627 data_time: 0.3114 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 16:53:34 - mmengine - INFO - Epoch(train) [4][34100/42151] lr: 3.0000e-05 eta: 14:15:36 time: 0.5472 data_time: 0.1240 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/21 16:54:35 - mmengine - INFO - Epoch(train) [4][34200/42151] lr: 3.0000e-05 eta: 14:14:44 time: 0.5028 data_time: 0.1002 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 16:55:43 - mmengine - INFO - Epoch(train) [4][34300/42151] lr: 3.0000e-05 eta: 14:13:55 time: 0.6718 data_time: 0.1891 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 16:56:49 - mmengine - INFO - Epoch(train) [4][34400/42151] lr: 3.0000e-05 eta: 14:13:06 time: 0.7718 data_time: 0.2363 memory: 14682 loss_ce: 0.0105 loss: 0.0105 2022/09/21 16:57:54 - mmengine - INFO - Epoch(train) [4][34500/42151] lr: 3.0000e-05 eta: 14:12:15 time: 0.8540 data_time: 0.3250 memory: 14682 loss_ce: 0.0109 loss: 0.0109 2022/09/21 16:58:25 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 16:59:02 - mmengine - INFO - Epoch(train) [4][34600/42151] lr: 3.0000e-05 eta: 14:11:27 time: 0.6969 data_time: 0.2426 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 17:00:05 - mmengine - INFO - Epoch(train) [4][34700/42151] lr: 3.0000e-05 eta: 14:10:35 time: 0.4868 data_time: 0.0665 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 17:01:08 - mmengine - INFO - Epoch(train) [4][34800/42151] lr: 3.0000e-05 eta: 14:09:44 time: 0.4999 data_time: 0.0409 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 17:02:14 - mmengine - INFO - Epoch(train) [4][34900/42151] lr: 3.0000e-05 eta: 14:08:54 time: 0.5450 data_time: 0.1549 memory: 14682 loss_ce: 0.0108 loss: 0.0108 2022/09/21 17:03:16 - mmengine - INFO - Epoch(train) [4][35000/42151] lr: 3.0000e-05 eta: 14:08:02 time: 0.7019 data_time: 0.2722 memory: 14682 loss_ce: 0.0104 loss: 0.0104 2022/09/21 17:04:28 - mmengine - INFO - Epoch(train) [4][35100/42151] lr: 3.0000e-05 eta: 14:07:15 time: 0.8315 data_time: 0.3663 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 17:05:27 - mmengine - INFO - Epoch(train) [4][35200/42151] lr: 3.0000e-05 eta: 14:06:22 time: 0.6046 data_time: 0.1590 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 17:06:29 - mmengine - INFO - Epoch(train) [4][35300/42151] lr: 3.0000e-05 eta: 14:05:30 time: 0.5158 data_time: 0.1151 memory: 14682 loss_ce: 0.0116 loss: 0.0116 2022/09/21 17:07:31 - mmengine - INFO - Epoch(train) [4][35400/42151] lr: 3.0000e-05 eta: 14:04:38 time: 0.4917 data_time: 0.0809 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 17:08:33 - mmengine - INFO - Epoch(train) [4][35500/42151] lr: 3.0000e-05 eta: 14:03:45 time: 0.6691 data_time: 0.1804 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 17:08:58 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 17:09:29 - mmengine - INFO - Epoch(train) [4][35600/42151] lr: 3.0000e-05 eta: 14:02:50 time: 0.5788 data_time: 0.1569 memory: 14682 loss_ce: 0.0102 loss: 0.0102 2022/09/21 17:10:25 - mmengine - INFO - Epoch(train) [4][35700/42151] lr: 3.0000e-05 eta: 14:01:54 time: 0.6160 data_time: 0.1927 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 17:11:21 - mmengine - INFO - Epoch(train) [4][35800/42151] lr: 3.0000e-05 eta: 14:00:59 time: 0.5373 data_time: 0.1533 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 17:12:17 - mmengine - INFO - Epoch(train) [4][35900/42151] lr: 3.0000e-05 eta: 14:00:04 time: 0.4906 data_time: 0.0649 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 17:13:14 - mmengine - INFO - Epoch(train) [4][36000/42151] lr: 3.0000e-05 eta: 13:59:08 time: 0.4444 data_time: 0.0245 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 17:14:12 - mmengine - INFO - Epoch(train) [4][36100/42151] lr: 3.0000e-05 eta: 13:58:14 time: 0.5719 data_time: 0.1809 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/21 17:15:07 - mmengine - INFO - Epoch(train) [4][36200/42151] lr: 3.0000e-05 eta: 13:57:18 time: 0.5776 data_time: 0.1742 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 17:16:08 - mmengine - INFO - Epoch(train) [4][36300/42151] lr: 3.0000e-05 eta: 13:56:25 time: 0.6586 data_time: 0.2387 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 17:17:05 - mmengine - INFO - Epoch(train) [4][36400/42151] lr: 3.0000e-05 eta: 13:55:30 time: 0.5603 data_time: 0.1719 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 17:18:03 - mmengine - INFO - Epoch(train) [4][36500/42151] lr: 3.0000e-05 eta: 13:54:36 time: 0.5086 data_time: 0.1096 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/21 17:18:32 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 17:19:02 - mmengine - INFO - Epoch(train) [4][36600/42151] lr: 3.0000e-05 eta: 13:53:42 time: 0.4806 data_time: 0.0536 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 17:19:59 - mmengine - INFO - Epoch(train) [4][36700/42151] lr: 3.0000e-05 eta: 13:52:47 time: 0.6132 data_time: 0.1502 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 17:20:57 - mmengine - INFO - Epoch(train) [4][36800/42151] lr: 3.0000e-05 eta: 13:51:52 time: 0.6004 data_time: 0.1661 memory: 14682 loss_ce: 0.0120 loss: 0.0120 2022/09/21 17:21:53 - mmengine - INFO - Epoch(train) [4][36900/42151] lr: 3.0000e-05 eta: 13:50:57 time: 0.6098 data_time: 0.1896 memory: 14682 loss_ce: 0.0109 loss: 0.0109 2022/09/21 17:22:50 - mmengine - INFO - Epoch(train) [4][37000/42151] lr: 3.0000e-05 eta: 13:50:02 time: 0.5368 data_time: 0.1365 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 17:23:46 - mmengine - INFO - Epoch(train) [4][37100/42151] lr: 3.0000e-05 eta: 13:49:07 time: 0.4851 data_time: 0.0659 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/21 17:24:42 - mmengine - INFO - Epoch(train) [4][37200/42151] lr: 3.0000e-05 eta: 13:48:11 time: 0.4531 data_time: 0.0263 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 17:25:39 - mmengine - INFO - Epoch(train) [4][37300/42151] lr: 3.0000e-05 eta: 13:47:16 time: 0.5687 data_time: 0.1734 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 17:26:35 - mmengine - INFO - Epoch(train) [4][37400/42151] lr: 3.0000e-05 eta: 13:46:20 time: 0.5881 data_time: 0.1927 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 17:27:33 - mmengine - INFO - Epoch(train) [4][37500/42151] lr: 3.0000e-05 eta: 13:45:26 time: 0.7638 data_time: 0.2926 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 17:28:00 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 17:28:30 - mmengine - INFO - Epoch(train) [4][37600/42151] lr: 3.0000e-05 eta: 13:44:31 time: 0.5489 data_time: 0.1476 memory: 14682 loss_ce: 0.0103 loss: 0.0103 2022/09/21 17:29:27 - mmengine - INFO - Epoch(train) [4][37700/42151] lr: 3.0000e-05 eta: 13:43:36 time: 0.5021 data_time: 0.1157 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 17:30:23 - mmengine - INFO - Epoch(train) [4][37800/42151] lr: 3.0000e-05 eta: 13:42:40 time: 0.4625 data_time: 0.0755 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 17:31:21 - mmengine - INFO - Epoch(train) [4][37900/42151] lr: 3.0000e-05 eta: 13:41:46 time: 0.5845 data_time: 0.1631 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 17:32:18 - mmengine - INFO - Epoch(train) [4][38000/42151] lr: 3.0000e-05 eta: 13:40:51 time: 0.6108 data_time: 0.1801 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 17:33:15 - mmengine - INFO - Epoch(train) [4][38100/42151] lr: 3.0000e-05 eta: 13:39:56 time: 0.7109 data_time: 0.2251 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 17:34:11 - mmengine - INFO - Epoch(train) [4][38200/42151] lr: 3.0000e-05 eta: 13:39:01 time: 0.5290 data_time: 0.1426 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 17:35:09 - mmengine - INFO - Epoch(train) [4][38300/42151] lr: 3.0000e-05 eta: 13:38:06 time: 0.4900 data_time: 0.0685 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 17:36:06 - mmengine - INFO - Epoch(train) [4][38400/42151] lr: 3.0000e-05 eta: 13:37:11 time: 0.4313 data_time: 0.0244 memory: 14682 loss_ce: 0.0101 loss: 0.0101 2022/09/21 17:37:03 - mmengine - INFO - Epoch(train) [4][38500/42151] lr: 3.0000e-05 eta: 13:36:16 time: 0.5748 data_time: 0.1689 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 17:37:27 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 17:37:58 - mmengine - INFO - Epoch(train) [4][38600/42151] lr: 3.0000e-05 eta: 13:35:20 time: 0.5790 data_time: 0.1932 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 17:38:55 - mmengine - INFO - Epoch(train) [4][38700/42151] lr: 3.0000e-05 eta: 13:34:25 time: 0.6782 data_time: 0.2582 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 17:39:51 - mmengine - INFO - Epoch(train) [4][38800/42151] lr: 3.0000e-05 eta: 13:33:29 time: 0.5381 data_time: 0.1500 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 17:40:48 - mmengine - INFO - Epoch(train) [4][38900/42151] lr: 3.0000e-05 eta: 13:32:35 time: 0.4770 data_time: 0.0961 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 17:41:44 - mmengine - INFO - Epoch(train) [4][39000/42151] lr: 3.0000e-05 eta: 13:31:39 time: 0.4396 data_time: 0.0510 memory: 14682 loss_ce: 0.0101 loss: 0.0101 2022/09/21 17:42:42 - mmengine - INFO - Epoch(train) [4][39100/42151] lr: 3.0000e-05 eta: 13:30:44 time: 0.5576 data_time: 0.1238 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 17:43:39 - mmengine - INFO - Epoch(train) [4][39200/42151] lr: 3.0000e-05 eta: 13:29:49 time: 0.6453 data_time: 0.2070 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 17:44:35 - mmengine - INFO - Epoch(train) [4][39300/42151] lr: 3.0000e-05 eta: 13:28:54 time: 0.6351 data_time: 0.2221 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 17:45:34 - mmengine - INFO - Epoch(train) [4][39400/42151] lr: 3.0000e-05 eta: 13:28:00 time: 0.5251 data_time: 0.1385 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 17:46:33 - mmengine - INFO - Epoch(train) [4][39500/42151] lr: 3.0000e-05 eta: 13:27:06 time: 0.5120 data_time: 0.0918 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 17:47:01 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 17:47:29 - mmengine - INFO - Epoch(train) [4][39600/42151] lr: 3.0000e-05 eta: 13:26:10 time: 0.4334 data_time: 0.0283 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 17:48:28 - mmengine - INFO - Epoch(train) [4][39700/42151] lr: 3.0000e-05 eta: 13:25:16 time: 0.5668 data_time: 0.1838 memory: 14682 loss_ce: 0.0107 loss: 0.0107 2022/09/21 17:49:26 - mmengine - INFO - Epoch(train) [4][39800/42151] lr: 3.0000e-05 eta: 13:24:22 time: 0.6308 data_time: 0.2098 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 17:50:25 - mmengine - INFO - Epoch(train) [4][39900/42151] lr: 3.0000e-05 eta: 13:23:27 time: 0.7872 data_time: 0.2921 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 17:51:23 - mmengine - INFO - Epoch(train) [4][40000/42151] lr: 3.0000e-05 eta: 13:22:33 time: 0.5617 data_time: 0.1536 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 17:52:20 - mmengine - INFO - Epoch(train) [4][40100/42151] lr: 3.0000e-05 eta: 13:21:38 time: 0.4834 data_time: 0.1019 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 17:53:16 - mmengine - INFO - Epoch(train) [4][40200/42151] lr: 3.0000e-05 eta: 13:20:43 time: 0.4413 data_time: 0.0534 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 17:54:17 - mmengine - INFO - Epoch(train) [4][40300/42151] lr: 3.0000e-05 eta: 13:19:49 time: 0.6568 data_time: 0.1505 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 17:55:16 - mmengine - INFO - Epoch(train) [4][40400/42151] lr: 3.0000e-05 eta: 13:18:55 time: 0.6348 data_time: 0.1802 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/21 17:56:14 - mmengine - INFO - Epoch(train) [4][40500/42151] lr: 3.0000e-05 eta: 13:18:01 time: 0.6752 data_time: 0.2302 memory: 14682 loss_ce: 0.0114 loss: 0.0114 2022/09/21 17:56:43 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 17:57:13 - mmengine - INFO - Epoch(train) [4][40600/42151] lr: 3.0000e-05 eta: 13:17:07 time: 0.5375 data_time: 0.1421 memory: 14682 loss_ce: 0.0108 loss: 0.0108 2022/09/21 17:58:10 - mmengine - INFO - Epoch(train) [4][40700/42151] lr: 3.0000e-05 eta: 13:16:12 time: 0.4987 data_time: 0.0763 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 17:59:07 - mmengine - INFO - Epoch(train) [4][40800/42151] lr: 3.0000e-05 eta: 13:15:17 time: 0.4456 data_time: 0.0248 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 18:00:14 - mmengine - INFO - Epoch(train) [4][40900/42151] lr: 3.0000e-05 eta: 13:14:27 time: 0.5715 data_time: 0.1893 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 18:01:12 - mmengine - INFO - Epoch(train) [4][41000/42151] lr: 3.0000e-05 eta: 13:13:32 time: 0.5880 data_time: 0.1968 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 18:02:11 - mmengine - INFO - Epoch(train) [4][41100/42151] lr: 3.0000e-05 eta: 13:12:38 time: 0.6429 data_time: 0.2519 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 18:03:09 - mmengine - INFO - Epoch(train) [4][41200/42151] lr: 3.0000e-05 eta: 13:11:43 time: 0.5295 data_time: 0.1466 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 18:04:08 - mmengine - INFO - Epoch(train) [4][41300/42151] lr: 3.0000e-05 eta: 13:10:49 time: 0.5570 data_time: 0.1038 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 18:05:04 - mmengine - INFO - Epoch(train) [4][41400/42151] lr: 3.0000e-05 eta: 13:09:54 time: 0.4930 data_time: 0.0709 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 18:06:04 - mmengine - INFO - Epoch(train) [4][41500/42151] lr: 3.0000e-05 eta: 13:09:00 time: 0.5819 data_time: 0.1353 memory: 14682 loss_ce: 0.0105 loss: 0.0105 2022/09/21 18:06:32 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 18:07:03 - mmengine - INFO - Epoch(train) [4][41600/42151] lr: 3.0000e-05 eta: 13:08:06 time: 0.6391 data_time: 0.1490 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/21 18:08:03 - mmengine - INFO - Epoch(train) [4][41700/42151] lr: 3.0000e-05 eta: 13:07:12 time: 0.6793 data_time: 0.2264 memory: 14682 loss_ce: 0.0106 loss: 0.0106 2022/09/21 18:09:00 - mmengine - INFO - Epoch(train) [4][41800/42151] lr: 3.0000e-05 eta: 13:06:18 time: 0.5290 data_time: 0.1430 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 18:09:59 - mmengine - INFO - Epoch(train) [4][41900/42151] lr: 3.0000e-05 eta: 13:05:23 time: 0.5028 data_time: 0.0681 memory: 14682 loss_ce: 0.0102 loss: 0.0102 2022/09/21 18:10:57 - mmengine - INFO - Epoch(train) [4][42000/42151] lr: 3.0000e-05 eta: 13:04:28 time: 0.4790 data_time: 0.0335 memory: 14682 loss_ce: 0.0103 loss: 0.0103 2022/09/21 18:11:57 - mmengine - INFO - Epoch(train) [4][42100/42151] lr: 3.0000e-05 eta: 13:03:35 time: 0.5944 data_time: 0.1891 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 18:12:23 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 18:12:23 - mmengine - INFO - Saving checkpoint at 4 epochs 2022/09/21 18:13:11 - mmengine - INFO - Epoch(val) [4][100/7672] eta: 0:41:20 time: 0.3276 data_time: 0.0014 memory: 14682 2022/09/21 18:13:48 - mmengine - INFO - Epoch(val) [4][200/7672] eta: 0:38:29 time: 0.3091 data_time: 0.0012 memory: 1304 2022/09/21 18:14:23 - mmengine - INFO - Epoch(val) [4][300/7672] eta: 0:28:10 time: 0.2293 data_time: 0.0009 memory: 1304 2022/09/21 18:14:48 - mmengine - INFO - Epoch(val) [4][400/7672] eta: 0:28:16 time: 0.2333 data_time: 0.0012 memory: 1304 2022/09/21 18:15:12 - mmengine - INFO - Epoch(val) [4][500/7672] eta: 0:28:22 time: 0.2374 data_time: 0.0009 memory: 1304 2022/09/21 18:15:37 - mmengine - INFO - Epoch(val) [4][600/7672] eta: 0:26:49 time: 0.2276 data_time: 0.0009 memory: 1304 2022/09/21 18:16:02 - mmengine - INFO - Epoch(val) [4][700/7672] eta: 0:26:00 time: 0.2239 data_time: 0.0009 memory: 1304 2022/09/21 18:16:27 - mmengine - INFO - Epoch(val) [4][800/7672] eta: 0:25:37 time: 0.2238 data_time: 0.0009 memory: 1304 2022/09/21 18:16:50 - mmengine - INFO - Epoch(val) [4][900/7672] eta: 0:25:31 time: 0.2261 data_time: 0.0019 memory: 1304 2022/09/21 18:17:16 - mmengine - INFO - Epoch(val) [4][1000/7672] eta: 0:25:04 time: 0.2254 data_time: 0.0014 memory: 1304 2022/09/21 18:17:38 - mmengine - INFO - Epoch(val) [4][1100/7672] eta: 0:23:07 time: 0.2110 data_time: 0.0009 memory: 1304 2022/09/21 18:18:04 - mmengine - INFO - Epoch(val) [4][1200/7672] eta: 0:27:19 time: 0.2534 data_time: 0.0042 memory: 1304 2022/09/21 18:18:29 - mmengine - INFO - Epoch(val) [4][1300/7672] eta: 0:26:44 time: 0.2518 data_time: 0.0011 memory: 1304 2022/09/21 18:18:53 - mmengine - INFO - Epoch(val) [4][1400/7672] eta: 0:21:39 time: 0.2072 data_time: 0.0009 memory: 1304 2022/09/21 18:19:16 - mmengine - INFO - Epoch(val) [4][1500/7672] eta: 0:25:31 time: 0.2482 data_time: 0.0018 memory: 1304 2022/09/21 18:19:46 - mmengine - INFO - Epoch(val) [4][1600/7672] eta: 0:26:18 time: 0.2600 data_time: 0.0027 memory: 1304 2022/09/21 18:20:11 - mmengine - INFO - Epoch(val) [4][1700/7672] eta: 0:23:44 time: 0.2386 data_time: 0.0010 memory: 1304 2022/09/21 18:20:37 - mmengine - INFO - Epoch(val) [4][1800/7672] eta: 0:19:31 time: 0.1995 data_time: 0.0009 memory: 1304 2022/09/21 18:21:00 - mmengine - INFO - Epoch(val) [4][1900/7672] eta: 0:21:16 time: 0.2212 data_time: 0.0009 memory: 1304 2022/09/21 18:21:24 - mmengine - INFO - Epoch(val) [4][2000/7672] eta: 0:22:56 time: 0.2426 data_time: 0.0045 memory: 1304 2022/09/21 18:21:49 - mmengine - INFO - Epoch(val) [4][2100/7672] eta: 0:19:40 time: 0.2119 data_time: 0.0009 memory: 1304 2022/09/21 18:22:12 - mmengine - INFO - Epoch(val) [4][2200/7672] eta: 0:19:16 time: 0.2113 data_time: 0.0009 memory: 1304 2022/09/21 18:22:36 - mmengine - INFO - Epoch(val) [4][2300/7672] eta: 0:21:11 time: 0.2368 data_time: 0.0019 memory: 1304 2022/09/21 18:23:01 - mmengine - INFO - Epoch(val) [4][2400/7672] eta: 0:21:55 time: 0.2495 data_time: 0.0010 memory: 1304 2022/09/21 18:23:25 - mmengine - INFO - Epoch(val) [4][2500/7672] eta: 0:21:13 time: 0.2462 data_time: 0.0042 memory: 1304 2022/09/21 18:23:50 - mmengine - INFO - Epoch(val) [4][2600/7672] eta: 0:20:01 time: 0.2368 data_time: 0.0010 memory: 1304 2022/09/21 18:24:14 - mmengine - INFO - Epoch(val) [4][2700/7672] eta: 0:21:01 time: 0.2537 data_time: 0.0044 memory: 1304 2022/09/21 18:24:37 - mmengine - INFO - Epoch(val) [4][2800/7672] eta: 0:22:13 time: 0.2737 data_time: 0.0011 memory: 1304 2022/09/21 18:25:01 - mmengine - INFO - Epoch(val) [4][2900/7672] eta: 0:18:07 time: 0.2280 data_time: 0.0017 memory: 1304 2022/09/21 18:25:26 - mmengine - INFO - Epoch(val) [4][3000/7672] eta: 0:18:08 time: 0.2329 data_time: 0.0051 memory: 1304 2022/09/21 18:25:50 - mmengine - INFO - Epoch(val) [4][3100/7672] eta: 0:17:14 time: 0.2263 data_time: 0.0011 memory: 1304 2022/09/21 18:26:14 - mmengine - INFO - Epoch(val) [4][3200/7672] eta: 0:17:52 time: 0.2398 data_time: 0.0017 memory: 1304 2022/09/21 18:26:38 - mmengine - INFO - Epoch(val) [4][3300/7672] eta: 0:16:32 time: 0.2271 data_time: 0.0009 memory: 1304 2022/09/21 18:27:03 - mmengine - INFO - Epoch(val) [4][3400/7672] eta: 0:18:23 time: 0.2583 data_time: 0.0030 memory: 1304 2022/09/21 18:27:28 - mmengine - INFO - Epoch(val) [4][3500/7672] eta: 0:15:58 time: 0.2298 data_time: 0.0010 memory: 1304 2022/09/21 18:27:52 - mmengine - INFO - Epoch(val) [4][3600/7672] eta: 0:15:22 time: 0.2265 data_time: 0.0009 memory: 1304 2022/09/21 18:28:17 - mmengine - INFO - Epoch(val) [4][3700/7672] eta: 0:17:51 time: 0.2698 data_time: 0.0023 memory: 1304 2022/09/21 18:28:42 - mmengine - INFO - Epoch(val) [4][3800/7672] eta: 0:16:17 time: 0.2525 data_time: 0.0010 memory: 1304 2022/09/21 18:29:06 - mmengine - INFO - Epoch(val) [4][3900/7672] eta: 0:15:14 time: 0.2424 data_time: 0.0041 memory: 1304 2022/09/21 18:29:30 - mmengine - INFO - Epoch(val) [4][4000/7672] eta: 0:16:02 time: 0.2622 data_time: 0.0011 memory: 1304 2022/09/21 18:29:55 - mmengine - INFO - Epoch(val) [4][4100/7672] eta: 0:14:25 time: 0.2424 data_time: 0.0011 memory: 1304 2022/09/21 18:30:19 - mmengine - INFO - Epoch(val) [4][4200/7672] eta: 0:15:17 time: 0.2643 data_time: 0.0027 memory: 1304 2022/09/21 18:30:44 - mmengine - INFO - Epoch(val) [4][4300/7672] eta: 0:12:40 time: 0.2254 data_time: 0.0011 memory: 1304 2022/09/21 18:31:08 - mmengine - INFO - Epoch(val) [4][4400/7672] eta: 0:12:20 time: 0.2264 data_time: 0.0009 memory: 1304 2022/09/21 18:31:33 - mmengine - INFO - Epoch(val) [4][4500/7672] eta: 0:12:14 time: 0.2314 data_time: 0.0009 memory: 1304 2022/09/21 18:31:57 - mmengine - INFO - Epoch(val) [4][4600/7672] eta: 0:10:39 time: 0.2081 data_time: 0.0009 memory: 1304 2022/09/21 18:32:21 - mmengine - INFO - Epoch(val) [4][4700/7672] eta: 0:13:05 time: 0.2641 data_time: 0.0012 memory: 1304 2022/09/21 18:32:45 - mmengine - INFO - Epoch(val) [4][4800/7672] eta: 0:11:28 time: 0.2396 data_time: 0.0030 memory: 1304 2022/09/21 18:33:10 - mmengine - INFO - Epoch(val) [4][4900/7672] eta: 0:12:09 time: 0.2632 data_time: 0.0030 memory: 1304 2022/09/21 18:33:34 - mmengine - INFO - Epoch(val) [4][5000/7672] eta: 0:09:52 time: 0.2218 data_time: 0.0009 memory: 1304 2022/09/21 18:33:59 - mmengine - INFO - Epoch(val) [4][5100/7672] eta: 0:09:01 time: 0.2107 data_time: 0.0008 memory: 1304 2022/09/21 18:34:23 - mmengine - INFO - Epoch(val) [4][5200/7672] eta: 0:10:36 time: 0.2576 data_time: 0.0010 memory: 1304 2022/09/21 18:34:47 - mmengine - INFO - Epoch(val) [4][5300/7672] eta: 0:12:32 time: 0.3172 data_time: 0.0047 memory: 1304 2022/09/21 18:35:12 - mmengine - INFO - Epoch(val) [4][5400/7672] eta: 0:08:29 time: 0.2244 data_time: 0.0009 memory: 1304 2022/09/21 18:35:37 - mmengine - INFO - Epoch(val) [4][5500/7672] eta: 0:09:59 time: 0.2760 data_time: 0.0028 memory: 1304 2022/09/21 18:36:03 - mmengine - INFO - Epoch(val) [4][5600/7672] eta: 0:09:47 time: 0.2837 data_time: 0.0014 memory: 1304 2022/09/21 18:36:33 - mmengine - INFO - Epoch(val) [4][5700/7672] eta: 0:08:34 time: 0.2610 data_time: 0.0011 memory: 1304 2022/09/21 18:37:00 - mmengine - INFO - Epoch(val) [4][5800/7672] eta: 0:07:12 time: 0.2312 data_time: 0.0030 memory: 1304 2022/09/21 18:37:23 - mmengine - INFO - Epoch(val) [4][5900/7672] eta: 0:07:18 time: 0.2476 data_time: 0.0010 memory: 1304 2022/09/21 18:37:49 - mmengine - INFO - Epoch(val) [4][6000/7672] eta: 0:06:33 time: 0.2354 data_time: 0.0010 memory: 1304 2022/09/21 18:38:13 - mmengine - INFO - Epoch(val) [4][6100/7672] eta: 0:06:12 time: 0.2367 data_time: 0.0010 memory: 1304 2022/09/21 18:38:37 - mmengine - INFO - Epoch(val) [4][6200/7672] eta: 0:05:40 time: 0.2311 data_time: 0.0015 memory: 1304 2022/09/21 18:39:01 - mmengine - INFO - Epoch(val) [4][6300/7672] eta: 0:05:00 time: 0.2187 data_time: 0.0018 memory: 1304 2022/09/21 18:39:25 - mmengine - INFO - Epoch(val) [4][6400/7672] eta: 0:05:44 time: 0.2711 data_time: 0.0011 memory: 1304 2022/09/21 18:39:49 - mmengine - INFO - Epoch(val) [4][6500/7672] eta: 0:04:17 time: 0.2195 data_time: 0.0010 memory: 1304 2022/09/21 18:40:12 - mmengine - INFO - Epoch(val) [4][6600/7672] eta: 0:03:47 time: 0.2126 data_time: 0.0012 memory: 1304 2022/09/21 18:40:35 - mmengine - INFO - Epoch(val) [4][6700/7672] eta: 0:03:43 time: 0.2298 data_time: 0.0010 memory: 1304 2022/09/21 18:40:59 - mmengine - INFO - Epoch(val) [4][6800/7672] eta: 0:03:01 time: 0.2083 data_time: 0.0009 memory: 1304 2022/09/21 18:41:23 - mmengine - INFO - Epoch(val) [4][6900/7672] eta: 0:03:20 time: 0.2594 data_time: 0.0012 memory: 1304 2022/09/21 18:41:49 - mmengine - INFO - Epoch(val) [4][7000/7672] eta: 0:03:02 time: 0.2711 data_time: 0.0014 memory: 1304 2022/09/21 18:42:13 - mmengine - INFO - Epoch(val) [4][7100/7672] eta: 0:02:07 time: 0.2235 data_time: 0.0009 memory: 1304 2022/09/21 18:42:37 - mmengine - INFO - Epoch(val) [4][7200/7672] eta: 0:01:53 time: 0.2400 data_time: 0.0082 memory: 1304 2022/09/21 18:43:01 - mmengine - INFO - Epoch(val) [4][7300/7672] eta: 0:01:34 time: 0.2535 data_time: 0.0027 memory: 1304 2022/09/21 18:43:28 - mmengine - INFO - Epoch(val) [4][7400/7672] eta: 0:01:14 time: 0.2736 data_time: 0.0042 memory: 1304 2022/09/21 18:43:54 - mmengine - INFO - Epoch(val) [4][7500/7672] eta: 0:00:39 time: 0.2276 data_time: 0.0009 memory: 1304 2022/09/21 18:44:17 - mmengine - INFO - Epoch(val) [4][7600/7672] eta: 0:00:15 time: 0.2171 data_time: 0.0020 memory: 1304 2022/09/21 18:44:34 - mmengine - INFO - Epoch(val) [4][7672/7672] CUTE80/recog/word_acc_ignore_case_symbol: 0.8854 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9470 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: 0.9399 IC15/recog/word_acc_ignore_case_symbol: 0.7357 2022/09/21 18:45:46 - mmengine - INFO - Epoch(train) [5][100/42151] lr: 3.0000e-06 eta: 13:02:17 time: 0.7578 data_time: 0.3441 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 18:46:44 - mmengine - INFO - Epoch(train) [5][200/42151] lr: 3.0000e-06 eta: 13:01:23 time: 0.6567 data_time: 0.2683 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 18:47:46 - mmengine - INFO - Epoch(train) [5][300/42151] lr: 3.0000e-06 eta: 13:00:30 time: 0.5884 data_time: 0.1487 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 18:48:41 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 18:48:43 - mmengine - INFO - Epoch(train) [5][400/42151] lr: 3.0000e-06 eta: 12:59:35 time: 0.4236 data_time: 0.0055 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 18:49:44 - mmengine - INFO - Epoch(train) [5][500/42151] lr: 3.0000e-06 eta: 12:58:41 time: 0.4982 data_time: 0.0651 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 18:50:41 - mmengine - INFO - Epoch(train) [5][600/42151] lr: 3.0000e-06 eta: 12:57:47 time: 0.6082 data_time: 0.2136 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 18:51:45 - mmengine - INFO - Epoch(train) [5][700/42151] lr: 3.0000e-06 eta: 12:56:55 time: 0.6908 data_time: 0.2315 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 18:52:44 - mmengine - INFO - Epoch(train) [5][800/42151] lr: 3.0000e-06 eta: 12:56:01 time: 0.6636 data_time: 0.2595 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 18:53:42 - mmengine - INFO - Epoch(train) [5][900/42151] lr: 3.0000e-06 eta: 12:55:06 time: 0.4513 data_time: 0.0656 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/21 18:54:39 - mmengine - INFO - Epoch(train) [5][1000/42151] lr: 3.0000e-06 eta: 12:54:11 time: 0.4012 data_time: 0.0060 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 18:55:39 - mmengine - INFO - Epoch(train) [5][1100/42151] lr: 3.0000e-06 eta: 12:53:17 time: 0.4928 data_time: 0.0964 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 18:56:35 - mmengine - INFO - Epoch(train) [5][1200/42151] lr: 3.0000e-06 eta: 12:52:21 time: 0.5145 data_time: 0.1343 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/21 18:57:33 - mmengine - INFO - Epoch(train) [5][1300/42151] lr: 3.0000e-06 eta: 12:51:27 time: 0.7098 data_time: 0.2185 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 18:58:27 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 18:58:29 - mmengine - INFO - Epoch(train) [5][1400/42151] lr: 3.0000e-06 eta: 12:50:31 time: 0.7177 data_time: 0.2705 memory: 14682 loss_ce: 0.0069 loss: 0.0069 2022/09/21 18:59:26 - mmengine - INFO - Epoch(train) [5][1500/42151] lr: 3.0000e-06 eta: 12:49:36 time: 0.4972 data_time: 0.1066 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 19:00:24 - mmengine - INFO - Epoch(train) [5][1600/42151] lr: 3.0000e-06 eta: 12:48:41 time: 0.4418 data_time: 0.0062 memory: 14682 loss_ce: 0.0102 loss: 0.0102 2022/09/21 19:01:27 - mmengine - INFO - Epoch(train) [5][1700/42151] lr: 3.0000e-06 eta: 12:47:49 time: 0.4950 data_time: 0.1136 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 19:02:30 - mmengine - INFO - Epoch(train) [5][1800/42151] lr: 3.0000e-06 eta: 12:46:56 time: 0.5863 data_time: 0.1382 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 19:03:34 - mmengine - INFO - Epoch(train) [5][1900/42151] lr: 3.0000e-06 eta: 12:46:05 time: 0.9027 data_time: 0.3842 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 19:04:34 - mmengine - INFO - Epoch(train) [5][2000/42151] lr: 3.0000e-06 eta: 12:45:11 time: 0.8487 data_time: 0.3961 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 19:05:31 - mmengine - INFO - Epoch(train) [5][2100/42151] lr: 3.0000e-06 eta: 12:44:15 time: 0.4688 data_time: 0.0664 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 19:06:28 - mmengine - INFO - Epoch(train) [5][2200/42151] lr: 3.0000e-06 eta: 12:43:20 time: 0.4420 data_time: 0.0078 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 19:07:30 - mmengine - INFO - Epoch(train) [5][2300/42151] lr: 3.0000e-06 eta: 12:42:28 time: 0.4885 data_time: 0.0923 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 19:08:24 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 19:08:26 - mmengine - INFO - Epoch(train) [5][2400/42151] lr: 3.0000e-06 eta: 12:41:32 time: 0.4977 data_time: 0.1183 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/21 19:09:24 - mmengine - INFO - Epoch(train) [5][2500/42151] lr: 3.0000e-06 eta: 12:40:37 time: 0.6295 data_time: 0.2100 memory: 14682 loss_ce: 0.0072 loss: 0.0072 2022/09/21 19:10:20 - mmengine - INFO - Epoch(train) [5][2600/42151] lr: 3.0000e-06 eta: 12:39:41 time: 0.7850 data_time: 0.2833 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 19:11:15 - mmengine - INFO - Epoch(train) [5][2700/42151] lr: 3.0000e-06 eta: 12:38:45 time: 0.4650 data_time: 0.0838 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/21 19:12:12 - mmengine - INFO - Epoch(train) [5][2800/42151] lr: 3.0000e-06 eta: 12:37:50 time: 0.4250 data_time: 0.0065 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 19:13:07 - mmengine - INFO - Epoch(train) [5][2900/42151] lr: 3.0000e-06 eta: 12:36:54 time: 0.5209 data_time: 0.0918 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 19:14:13 - mmengine - INFO - Epoch(train) [5][3000/42151] lr: 3.0000e-06 eta: 12:36:03 time: 0.6009 data_time: 0.1789 memory: 14682 loss_ce: 0.0064 loss: 0.0064 2022/09/21 19:15:20 - mmengine - INFO - Epoch(train) [5][3100/42151] lr: 3.0000e-06 eta: 12:35:12 time: 0.8768 data_time: 0.4011 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 19:16:21 - mmengine - INFO - Epoch(train) [5][3200/42151] lr: 3.0000e-06 eta: 12:34:19 time: 0.7618 data_time: 0.3724 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 19:17:19 - mmengine - INFO - Epoch(train) [5][3300/42151] lr: 3.0000e-06 eta: 12:33:24 time: 0.5271 data_time: 0.1152 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 19:18:17 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 19:18:19 - mmengine - INFO - Epoch(train) [5][3400/42151] lr: 3.0000e-06 eta: 12:32:30 time: 0.4713 data_time: 0.0096 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 19:19:19 - mmengine - INFO - Epoch(train) [5][3500/42151] lr: 3.0000e-06 eta: 12:31:36 time: 0.5124 data_time: 0.1081 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 19:20:19 - mmengine - INFO - Epoch(train) [5][3600/42151] lr: 3.0000e-06 eta: 12:30:42 time: 0.5313 data_time: 0.0939 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 19:21:29 - mmengine - INFO - Epoch(train) [5][3700/42151] lr: 3.0000e-06 eta: 12:29:53 time: 0.9670 data_time: 0.5077 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 19:22:29 - mmengine - INFO - Epoch(train) [5][3800/42151] lr: 3.0000e-06 eta: 12:28:59 time: 0.7345 data_time: 0.3359 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 19:23:28 - mmengine - INFO - Epoch(train) [5][3900/42151] lr: 3.0000e-06 eta: 12:28:05 time: 0.4712 data_time: 0.0795 memory: 14682 loss_ce: 0.0101 loss: 0.0101 2022/09/21 19:24:29 - mmengine - INFO - Epoch(train) [5][4000/42151] lr: 3.0000e-06 eta: 12:27:11 time: 0.3991 data_time: 0.0067 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 19:25:27 - mmengine - INFO - Epoch(train) [5][4100/42151] lr: 3.0000e-06 eta: 12:26:16 time: 0.4730 data_time: 0.0820 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 19:26:33 - mmengine - INFO - Epoch(train) [5][4200/42151] lr: 3.0000e-06 eta: 12:25:25 time: 0.5160 data_time: 0.0953 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/21 19:27:37 - mmengine - INFO - Epoch(train) [5][4300/42151] lr: 3.0000e-06 eta: 12:24:33 time: 0.6123 data_time: 0.2247 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 19:28:34 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 19:28:36 - mmengine - INFO - Epoch(train) [5][4400/42151] lr: 3.0000e-06 eta: 12:23:39 time: 0.8348 data_time: 0.3059 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 19:29:36 - mmengine - INFO - Epoch(train) [5][4500/42151] lr: 3.0000e-06 eta: 12:22:45 time: 0.4957 data_time: 0.1009 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 19:30:34 - mmengine - INFO - Epoch(train) [5][4600/42151] lr: 3.0000e-06 eta: 12:21:50 time: 0.4239 data_time: 0.0068 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 19:31:37 - mmengine - INFO - Epoch(train) [5][4700/42151] lr: 3.0000e-06 eta: 12:20:58 time: 0.5653 data_time: 0.0953 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/21 19:32:38 - mmengine - INFO - Epoch(train) [5][4800/42151] lr: 3.0000e-06 eta: 12:20:04 time: 0.5930 data_time: 0.1788 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 19:33:39 - mmengine - INFO - Epoch(train) [5][4900/42151] lr: 3.0000e-06 eta: 12:19:11 time: 0.6982 data_time: 0.2802 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/21 19:34:47 - mmengine - INFO - Epoch(train) [5][5000/42151] lr: 3.0000e-06 eta: 12:18:20 time: 0.9246 data_time: 0.4731 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/21 19:35:51 - mmengine - INFO - Epoch(train) [5][5100/42151] lr: 3.0000e-06 eta: 12:17:28 time: 0.4818 data_time: 0.0639 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/21 19:36:58 - mmengine - INFO - Epoch(train) [5][5200/42151] lr: 3.0000e-06 eta: 12:16:37 time: 0.5781 data_time: 0.0088 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/21 19:38:04 - mmengine - INFO - Epoch(train) [5][5300/42151] lr: 3.0000e-06 eta: 12:15:46 time: 0.5443 data_time: 0.0679 memory: 14682 loss_ce: 0.0075 loss: 0.0075 2022/09/21 19:39:03 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 19:39:06 - mmengine - INFO - Epoch(train) [5][5400/42151] lr: 3.0000e-06 eta: 12:14:53 time: 0.6564 data_time: 0.1711 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 19:40:12 - mmengine - INFO - Epoch(train) [5][5500/42151] lr: 3.0000e-06 eta: 12:14:01 time: 0.9804 data_time: 0.5370 memory: 14682 loss_ce: 0.0061 loss: 0.0061 2022/09/21 19:41:09 - mmengine - INFO - Epoch(train) [5][5600/42151] lr: 3.0000e-06 eta: 12:13:06 time: 0.7495 data_time: 0.3413 memory: 14682 loss_ce: 0.0075 loss: 0.0075 2022/09/21 19:42:12 - mmengine - INFO - Epoch(train) [5][5700/42151] lr: 3.0000e-06 eta: 12:12:13 time: 0.5199 data_time: 0.0930 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 19:43:13 - mmengine - INFO - Epoch(train) [5][5800/42151] lr: 3.0000e-06 eta: 12:11:20 time: 0.4941 data_time: 0.0072 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/21 19:44:17 - mmengine - INFO - Epoch(train) [5][5900/42151] lr: 3.0000e-06 eta: 12:10:27 time: 0.6086 data_time: 0.1608 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 19:45:25 - mmengine - INFO - Epoch(train) [5][6000/42151] lr: 3.0000e-06 eta: 12:09:37 time: 0.7826 data_time: 0.2416 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 19:46:30 - mmengine - INFO - Epoch(train) [5][6100/42151] lr: 3.0000e-06 eta: 12:08:45 time: 0.7738 data_time: 0.3649 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 19:47:30 - mmengine - INFO - Epoch(train) [5][6200/42151] lr: 3.0000e-06 eta: 12:07:51 time: 0.6962 data_time: 0.2768 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 19:48:39 - mmengine - INFO - Epoch(train) [5][6300/42151] lr: 3.0000e-06 eta: 12:07:01 time: 0.4723 data_time: 0.0729 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 19:49:36 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 19:49:38 - mmengine - INFO - Epoch(train) [5][6400/42151] lr: 3.0000e-06 eta: 12:06:06 time: 0.4322 data_time: 0.0069 memory: 14682 loss_ce: 0.0071 loss: 0.0071 2022/09/21 19:50:45 - mmengine - INFO - Epoch(train) [5][6500/42151] lr: 3.0000e-06 eta: 12:05:15 time: 0.5451 data_time: 0.0830 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 19:51:51 - mmengine - INFO - Epoch(train) [5][6600/42151] lr: 3.0000e-06 eta: 12:04:24 time: 0.6067 data_time: 0.1322 memory: 14682 loss_ce: 0.0073 loss: 0.0073 2022/09/21 19:52:56 - mmengine - INFO - Epoch(train) [5][6700/42151] lr: 3.0000e-06 eta: 12:03:31 time: 0.7350 data_time: 0.3188 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/21 19:53:57 - mmengine - INFO - Epoch(train) [5][6800/42151] lr: 3.0000e-06 eta: 12:02:38 time: 0.7049 data_time: 0.3095 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 19:55:01 - mmengine - INFO - Epoch(train) [5][6900/42151] lr: 3.0000e-06 eta: 12:01:45 time: 0.6661 data_time: 0.2221 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 19:56:04 - mmengine - INFO - Epoch(train) [5][7000/42151] lr: 3.0000e-06 eta: 12:00:52 time: 0.4764 data_time: 0.0070 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 19:57:04 - mmengine - INFO - Epoch(train) [5][7100/42151] lr: 3.0000e-06 eta: 11:59:58 time: 0.5283 data_time: 0.1242 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 19:58:09 - mmengine - INFO - Epoch(train) [5][7200/42151] lr: 3.0000e-06 eta: 11:59:06 time: 0.5949 data_time: 0.1763 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 19:59:19 - mmengine - INFO - Epoch(train) [5][7300/42151] lr: 3.0000e-06 eta: 11:58:17 time: 0.9103 data_time: 0.4627 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/21 20:00:30 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 20:00:31 - mmengine - INFO - Epoch(train) [5][7400/42151] lr: 3.0000e-06 eta: 11:57:28 time: 0.9975 data_time: 0.4856 memory: 14682 loss_ce: 0.0071 loss: 0.0071 2022/09/21 20:01:36 - mmengine - INFO - Epoch(train) [5][7500/42151] lr: 3.0000e-06 eta: 11:56:35 time: 0.5345 data_time: 0.0878 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 20:02:39 - mmengine - INFO - Epoch(train) [5][7600/42151] lr: 3.0000e-06 eta: 11:55:42 time: 0.4332 data_time: 0.0059 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/21 20:03:40 - mmengine - INFO - Epoch(train) [5][7700/42151] lr: 3.0000e-06 eta: 11:54:48 time: 0.4552 data_time: 0.0632 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 20:04:46 - mmengine - INFO - Epoch(train) [5][7800/42151] lr: 3.0000e-06 eta: 11:53:57 time: 0.6820 data_time: 0.1388 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/21 20:05:52 - mmengine - INFO - Epoch(train) [5][7900/42151] lr: 3.0000e-06 eta: 11:53:05 time: 0.7868 data_time: 0.3094 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/21 20:06:55 - mmengine - INFO - Epoch(train) [5][8000/42151] lr: 3.0000e-06 eta: 11:52:12 time: 1.0484 data_time: 0.4777 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/21 20:07:53 - mmengine - INFO - Epoch(train) [5][8100/42151] lr: 3.0000e-06 eta: 11:51:17 time: 0.5529 data_time: 0.1371 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/21 20:08:55 - mmengine - INFO - Epoch(train) [5][8200/42151] lr: 3.0000e-06 eta: 11:50:24 time: 0.4663 data_time: 0.0059 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 20:10:01 - mmengine - INFO - Epoch(train) [5][8300/42151] lr: 3.0000e-06 eta: 11:49:32 time: 0.5324 data_time: 0.1064 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 20:11:04 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 20:11:06 - mmengine - INFO - Epoch(train) [5][8400/42151] lr: 3.0000e-06 eta: 11:48:39 time: 0.6146 data_time: 0.1649 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 20:12:12 - mmengine - INFO - Epoch(train) [5][8500/42151] lr: 3.0000e-06 eta: 11:47:48 time: 0.6477 data_time: 0.2636 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 20:13:13 - mmengine - INFO - Epoch(train) [5][8600/42151] lr: 3.0000e-06 eta: 11:46:54 time: 0.6832 data_time: 0.2828 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 20:14:14 - mmengine - INFO - Epoch(train) [5][8700/42151] lr: 3.0000e-06 eta: 11:46:00 time: 0.5217 data_time: 0.1261 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 20:15:13 - mmengine - INFO - Epoch(train) [5][8800/42151] lr: 3.0000e-06 eta: 11:45:05 time: 0.3889 data_time: 0.0069 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 20:16:18 - mmengine - INFO - Epoch(train) [5][8900/42151] lr: 3.0000e-06 eta: 11:44:13 time: 0.6083 data_time: 0.0818 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 20:17:18 - mmengine - INFO - Epoch(train) [5][9000/42151] lr: 3.0000e-06 eta: 11:43:19 time: 0.5100 data_time: 0.1080 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 20:18:33 - mmengine - INFO - Epoch(train) [5][9100/42151] lr: 3.0000e-06 eta: 11:42:30 time: 1.0883 data_time: 0.3847 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 20:19:35 - mmengine - INFO - Epoch(train) [5][9200/42151] lr: 3.0000e-06 eta: 11:41:37 time: 0.6525 data_time: 0.2650 memory: 14682 loss_ce: 0.0071 loss: 0.0071 2022/09/21 20:20:35 - mmengine - INFO - Epoch(train) [5][9300/42151] lr: 3.0000e-06 eta: 11:40:42 time: 0.6749 data_time: 0.2036 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 20:21:31 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 20:21:32 - mmengine - INFO - Epoch(train) [5][9400/42151] lr: 3.0000e-06 eta: 11:39:47 time: 0.4154 data_time: 0.0074 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 20:22:44 - mmengine - INFO - Epoch(train) [5][9500/42151] lr: 3.0000e-06 eta: 11:38:57 time: 0.5578 data_time: 0.1541 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/21 20:23:47 - mmengine - INFO - Epoch(train) [5][9600/42151] lr: 3.0000e-06 eta: 11:38:04 time: 0.5983 data_time: 0.1499 memory: 14682 loss_ce: 0.0075 loss: 0.0075 2022/09/21 20:24:51 - mmengine - INFO - Epoch(train) [5][9700/42151] lr: 3.0000e-06 eta: 11:37:12 time: 0.6424 data_time: 0.2463 memory: 14682 loss_ce: 0.0075 loss: 0.0075 2022/09/21 20:25:50 - mmengine - INFO - Epoch(train) [5][9800/42151] lr: 3.0000e-06 eta: 11:36:16 time: 0.6705 data_time: 0.2675 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 20:26:48 - mmengine - INFO - Epoch(train) [5][9900/42151] lr: 3.0000e-06 eta: 11:35:21 time: 0.5355 data_time: 0.0843 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 20:27:50 - mmengine - INFO - Epoch(train) [5][10000/42151] lr: 3.0000e-06 eta: 11:34:27 time: 0.3920 data_time: 0.0057 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 20:28:49 - mmengine - INFO - Epoch(train) [5][10100/42151] lr: 3.0000e-06 eta: 11:33:33 time: 0.4946 data_time: 0.1075 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 20:29:48 - mmengine - INFO - Epoch(train) [5][10200/42151] lr: 3.0000e-06 eta: 11:32:38 time: 0.4799 data_time: 0.0793 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 20:30:52 - mmengine - INFO - Epoch(train) [5][10300/42151] lr: 3.0000e-06 eta: 11:31:45 time: 0.8331 data_time: 0.3839 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 20:31:53 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 20:31:55 - mmengine - INFO - Epoch(train) [5][10400/42151] lr: 3.0000e-06 eta: 11:30:51 time: 0.8307 data_time: 0.3216 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 20:33:02 - mmengine - INFO - Epoch(train) [5][10500/42151] lr: 3.0000e-06 eta: 11:30:00 time: 0.6961 data_time: 0.1886 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 20:34:07 - mmengine - INFO - Epoch(train) [5][10600/42151] lr: 3.0000e-06 eta: 11:29:08 time: 0.4349 data_time: 0.0102 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/21 20:35:07 - mmengine - INFO - Epoch(train) [5][10700/42151] lr: 3.0000e-06 eta: 11:28:13 time: 0.5674 data_time: 0.1208 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/21 20:36:10 - mmengine - INFO - Epoch(train) [5][10800/42151] lr: 3.0000e-06 eta: 11:27:20 time: 0.6233 data_time: 0.1377 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/21 20:37:17 - mmengine - INFO - Epoch(train) [5][10900/42151] lr: 3.0000e-06 eta: 11:26:28 time: 0.7730 data_time: 0.3535 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 20:38:20 - mmengine - INFO - Epoch(train) [5][11000/42151] lr: 3.0000e-06 eta: 11:25:35 time: 0.9121 data_time: 0.4611 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 20:39:25 - mmengine - INFO - Epoch(train) [5][11100/42151] lr: 3.0000e-06 eta: 11:24:42 time: 0.5057 data_time: 0.1006 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 20:40:32 - mmengine - INFO - Epoch(train) [5][11200/42151] lr: 3.0000e-06 eta: 11:23:51 time: 0.4347 data_time: 0.0081 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 20:41:38 - mmengine - INFO - Epoch(train) [5][11300/42151] lr: 3.0000e-06 eta: 11:22:58 time: 0.5672 data_time: 0.1547 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 20:42:40 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 20:42:42 - mmengine - INFO - Epoch(train) [5][11400/42151] lr: 3.0000e-06 eta: 11:22:05 time: 0.5528 data_time: 0.1393 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/21 20:43:46 - mmengine - INFO - Epoch(train) [5][11500/42151] lr: 3.0000e-06 eta: 11:21:12 time: 0.6423 data_time: 0.2145 memory: 14682 loss_ce: 0.0072 loss: 0.0072 2022/09/21 20:44:47 - mmengine - INFO - Epoch(train) [5][11600/42151] lr: 3.0000e-06 eta: 11:20:18 time: 0.8710 data_time: 0.4173 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 20:45:50 - mmengine - INFO - Epoch(train) [5][11700/42151] lr: 3.0000e-06 eta: 11:19:25 time: 0.5303 data_time: 0.0731 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 20:46:52 - mmengine - INFO - Epoch(train) [5][11800/42151] lr: 3.0000e-06 eta: 11:18:31 time: 0.3899 data_time: 0.0056 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 20:47:54 - mmengine - INFO - Epoch(train) [5][11900/42151] lr: 3.0000e-06 eta: 11:17:37 time: 0.4554 data_time: 0.0731 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/21 20:48:59 - mmengine - INFO - Epoch(train) [5][12000/42151] lr: 3.0000e-06 eta: 11:16:45 time: 0.6876 data_time: 0.1803 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 20:50:13 - mmengine - INFO - Epoch(train) [5][12100/42151] lr: 3.0000e-06 eta: 11:15:55 time: 0.8213 data_time: 0.3855 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 20:51:22 - mmengine - INFO - Epoch(train) [5][12200/42151] lr: 3.0000e-06 eta: 11:15:04 time: 0.7996 data_time: 0.3375 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 20:52:27 - mmengine - INFO - Epoch(train) [5][12300/42151] lr: 3.0000e-06 eta: 11:14:11 time: 0.5540 data_time: 0.1503 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 20:53:25 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 20:53:27 - mmengine - INFO - Epoch(train) [5][12400/42151] lr: 3.0000e-06 eta: 11:13:17 time: 0.4109 data_time: 0.0089 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 20:54:35 - mmengine - INFO - Epoch(train) [5][12500/42151] lr: 3.0000e-06 eta: 11:12:25 time: 0.5155 data_time: 0.1247 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 20:55:34 - mmengine - INFO - Epoch(train) [5][12600/42151] lr: 3.0000e-06 eta: 11:11:30 time: 0.6320 data_time: 0.1775 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 20:56:45 - mmengine - INFO - Epoch(train) [5][12700/42151] lr: 3.0000e-06 eta: 11:10:40 time: 0.8716 data_time: 0.3962 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 20:57:48 - mmengine - INFO - Epoch(train) [5][12800/42151] lr: 3.0000e-06 eta: 11:09:46 time: 0.7548 data_time: 0.3230 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 20:58:47 - mmengine - INFO - Epoch(train) [5][12900/42151] lr: 3.0000e-06 eta: 11:08:51 time: 0.5075 data_time: 0.0868 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 20:59:51 - mmengine - INFO - Epoch(train) [5][13000/42151] lr: 3.0000e-06 eta: 11:07:58 time: 0.3983 data_time: 0.0062 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 21:00:56 - mmengine - INFO - Epoch(train) [5][13100/42151] lr: 3.0000e-06 eta: 11:07:05 time: 0.5339 data_time: 0.1126 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 21:02:01 - mmengine - INFO - Epoch(train) [5][13200/42151] lr: 3.0000e-06 eta: 11:06:12 time: 0.5375 data_time: 0.0855 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/21 21:03:08 - mmengine - INFO - Epoch(train) [5][13300/42151] lr: 3.0000e-06 eta: 11:05:20 time: 0.6750 data_time: 0.2717 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 21:04:10 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 21:04:12 - mmengine - INFO - Epoch(train) [5][13400/42151] lr: 3.0000e-06 eta: 11:04:27 time: 0.8235 data_time: 0.3540 memory: 14682 loss_ce: 0.0072 loss: 0.0072 2022/09/21 21:05:13 - mmengine - INFO - Epoch(train) [5][13500/42151] lr: 3.0000e-06 eta: 11:03:33 time: 0.5580 data_time: 0.0896 memory: 14682 loss_ce: 0.0073 loss: 0.0073 2022/09/21 21:06:25 - mmengine - INFO - Epoch(train) [5][13600/42151] lr: 3.0000e-06 eta: 11:02:42 time: 0.4552 data_time: 0.0081 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 21:07:32 - mmengine - INFO - Epoch(train) [5][13700/42151] lr: 3.0000e-06 eta: 11:01:51 time: 0.6017 data_time: 0.1431 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/21 21:08:41 - mmengine - INFO - Epoch(train) [5][13800/42151] lr: 3.0000e-06 eta: 11:00:59 time: 0.6060 data_time: 0.1691 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 21:09:46 - mmengine - INFO - Epoch(train) [5][13900/42151] lr: 3.0000e-06 eta: 11:00:06 time: 0.7609 data_time: 0.3148 memory: 14682 loss_ce: 0.0069 loss: 0.0069 2022/09/21 21:10:54 - mmengine - INFO - Epoch(train) [5][14000/42151] lr: 3.0000e-06 eta: 10:59:14 time: 0.8238 data_time: 0.3803 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/21 21:11:55 - mmengine - INFO - Epoch(train) [5][14100/42151] lr: 3.0000e-06 eta: 10:58:20 time: 0.5497 data_time: 0.0817 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 21:13:03 - mmengine - INFO - Epoch(train) [5][14200/42151] lr: 3.0000e-06 eta: 10:57:28 time: 0.4454 data_time: 0.0084 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/21 21:14:09 - mmengine - INFO - Epoch(train) [5][14300/42151] lr: 3.0000e-06 eta: 10:56:35 time: 0.5291 data_time: 0.1067 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 21:15:09 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 21:15:11 - mmengine - INFO - Epoch(train) [5][14400/42151] lr: 3.0000e-06 eta: 10:55:41 time: 0.5010 data_time: 0.0977 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 21:16:17 - mmengine - INFO - Epoch(train) [5][14500/42151] lr: 3.0000e-06 eta: 10:54:49 time: 0.8175 data_time: 0.3986 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 21:17:20 - mmengine - INFO - Epoch(train) [5][14600/42151] lr: 3.0000e-06 eta: 10:53:55 time: 0.6301 data_time: 0.2342 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 21:18:30 - mmengine - INFO - Epoch(train) [5][14700/42151] lr: 3.0000e-06 eta: 10:53:04 time: 0.5353 data_time: 0.1019 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 21:19:33 - mmengine - INFO - Epoch(train) [5][14800/42151] lr: 3.0000e-06 eta: 10:52:10 time: 0.4559 data_time: 0.0146 memory: 14682 loss_ce: 0.0075 loss: 0.0075 2022/09/21 21:20:39 - mmengine - INFO - Epoch(train) [5][14900/42151] lr: 3.0000e-06 eta: 10:51:17 time: 0.4563 data_time: 0.0707 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/21 21:21:44 - mmengine - INFO - Epoch(train) [5][15000/42151] lr: 3.0000e-06 eta: 10:50:24 time: 0.5742 data_time: 0.1381 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 21:22:49 - mmengine - INFO - Epoch(train) [5][15100/42151] lr: 3.0000e-06 eta: 10:49:31 time: 0.7270 data_time: 0.3291 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/21 21:23:47 - mmengine - INFO - Epoch(train) [5][15200/42151] lr: 3.0000e-06 eta: 10:48:36 time: 0.7304 data_time: 0.3211 memory: 14682 loss_ce: 0.0103 loss: 0.0103 2022/09/21 21:24:51 - mmengine - INFO - Epoch(train) [5][15300/42151] lr: 3.0000e-06 eta: 10:47:42 time: 0.5395 data_time: 0.1326 memory: 14682 loss_ce: 0.0064 loss: 0.0064 2022/09/21 21:25:53 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 21:25:55 - mmengine - INFO - Epoch(train) [5][15400/42151] lr: 3.0000e-06 eta: 10:46:49 time: 0.4464 data_time: 0.0123 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/21 21:27:07 - mmengine - INFO - Epoch(train) [5][15500/42151] lr: 3.0000e-06 eta: 10:45:58 time: 0.7417 data_time: 0.1069 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 21:28:10 - mmengine - INFO - Epoch(train) [5][15600/42151] lr: 3.0000e-06 eta: 10:45:04 time: 0.6167 data_time: 0.1929 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/21 21:29:17 - mmengine - INFO - Epoch(train) [5][15700/42151] lr: 3.0000e-06 eta: 10:44:12 time: 0.7237 data_time: 0.2850 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/21 21:30:28 - mmengine - INFO - Epoch(train) [5][15800/42151] lr: 3.0000e-06 eta: 10:43:21 time: 0.9293 data_time: 0.4323 memory: 14682 loss_ce: 0.0072 loss: 0.0072 2022/09/21 21:31:28 - mmengine - INFO - Epoch(train) [5][15900/42151] lr: 3.0000e-06 eta: 10:42:26 time: 0.7166 data_time: 0.1437 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 21:32:36 - mmengine - INFO - Epoch(train) [5][16000/42151] lr: 3.0000e-06 eta: 10:41:34 time: 0.4973 data_time: 0.0122 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/21 21:33:44 - mmengine - INFO - Epoch(train) [5][16100/42151] lr: 3.0000e-06 eta: 10:40:42 time: 0.7489 data_time: 0.2430 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 21:34:47 - mmengine - INFO - Epoch(train) [5][16200/42151] lr: 3.0000e-06 eta: 10:39:48 time: 0.5400 data_time: 0.1404 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 21:35:52 - mmengine - INFO - Epoch(train) [5][16300/42151] lr: 3.0000e-06 eta: 10:38:55 time: 0.7301 data_time: 0.3044 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 21:36:47 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 21:36:48 - mmengine - INFO - Epoch(train) [5][16400/42151] lr: 3.0000e-06 eta: 10:37:58 time: 0.7006 data_time: 0.3014 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 21:37:55 - mmengine - INFO - Epoch(train) [5][16500/42151] lr: 3.0000e-06 eta: 10:37:05 time: 0.6017 data_time: 0.1477 memory: 14682 loss_ce: 0.0073 loss: 0.0073 2022/09/21 21:39:00 - mmengine - INFO - Epoch(train) [5][16600/42151] lr: 3.0000e-06 eta: 10:36:12 time: 0.3979 data_time: 0.0061 memory: 14682 loss_ce: 0.0069 loss: 0.0069 2022/09/21 21:40:03 - mmengine - INFO - Epoch(train) [5][16700/42151] lr: 3.0000e-06 eta: 10:35:18 time: 0.6285 data_time: 0.2002 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/21 21:41:14 - mmengine - INFO - Epoch(train) [5][16800/42151] lr: 3.0000e-06 eta: 10:34:27 time: 0.6434 data_time: 0.1506 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 21:42:16 - mmengine - INFO - Epoch(train) [5][16900/42151] lr: 3.0000e-06 eta: 10:33:33 time: 0.7183 data_time: 0.2889 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 21:43:21 - mmengine - INFO - Epoch(train) [5][17000/42151] lr: 3.0000e-06 eta: 10:32:40 time: 0.6479 data_time: 0.2264 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 21:44:25 - mmengine - INFO - Epoch(train) [5][17100/42151] lr: 3.0000e-06 eta: 10:31:46 time: 0.6035 data_time: 0.1643 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 21:45:28 - mmengine - INFO - Epoch(train) [5][17200/42151] lr: 3.0000e-06 eta: 10:30:52 time: 0.6141 data_time: 0.0095 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 21:46:29 - mmengine - INFO - Epoch(train) [5][17300/42151] lr: 3.0000e-06 eta: 10:29:57 time: 0.6065 data_time: 0.0740 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 21:47:29 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 21:47:31 - mmengine - INFO - Epoch(train) [5][17400/42151] lr: 3.0000e-06 eta: 10:29:03 time: 0.7191 data_time: 0.1774 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 21:48:41 - mmengine - INFO - Epoch(train) [5][17500/42151] lr: 3.0000e-06 eta: 10:28:11 time: 0.8803 data_time: 0.4571 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/21 21:49:48 - mmengine - INFO - Epoch(train) [5][17600/42151] lr: 3.0000e-06 eta: 10:27:18 time: 0.8452 data_time: 0.3703 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 21:50:52 - mmengine - INFO - Epoch(train) [5][17700/42151] lr: 3.0000e-06 eta: 10:26:25 time: 0.5138 data_time: 0.0950 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/21 21:51:52 - mmengine - INFO - Epoch(train) [5][17800/42151] lr: 3.0000e-06 eta: 10:25:29 time: 0.4051 data_time: 0.0067 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 21:53:00 - mmengine - INFO - Epoch(train) [5][17900/42151] lr: 3.0000e-06 eta: 10:24:37 time: 0.5465 data_time: 0.1389 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/21 21:54:04 - mmengine - INFO - Epoch(train) [5][18000/42151] lr: 3.0000e-06 eta: 10:23:43 time: 0.5472 data_time: 0.1533 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/21 21:55:14 - mmengine - INFO - Epoch(train) [5][18100/42151] lr: 3.0000e-06 eta: 10:22:52 time: 0.9524 data_time: 0.4537 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/21 21:56:18 - mmengine - INFO - Epoch(train) [5][18200/42151] lr: 3.0000e-06 eta: 10:21:58 time: 0.8228 data_time: 0.3513 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 21:57:26 - mmengine - INFO - Epoch(train) [5][18300/42151] lr: 3.0000e-06 eta: 10:21:05 time: 0.5984 data_time: 0.1728 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 21:58:24 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 21:58:27 - mmengine - INFO - Epoch(train) [5][18400/42151] lr: 3.0000e-06 eta: 10:20:10 time: 0.4615 data_time: 0.0117 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/21 21:59:33 - mmengine - INFO - Epoch(train) [5][18500/42151] lr: 3.0000e-06 eta: 10:19:17 time: 0.5378 data_time: 0.1172 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 22:00:35 - mmengine - INFO - Epoch(train) [5][18600/42151] lr: 3.0000e-06 eta: 10:18:23 time: 0.5648 data_time: 0.1048 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 22:01:46 - mmengine - INFO - Epoch(train) [5][18700/42151] lr: 3.0000e-06 eta: 10:17:31 time: 0.9267 data_time: 0.4472 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 22:02:52 - mmengine - INFO - Epoch(train) [5][18800/42151] lr: 3.0000e-06 eta: 10:16:38 time: 0.8956 data_time: 0.3332 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 22:04:00 - mmengine - INFO - Epoch(train) [5][18900/42151] lr: 3.0000e-06 eta: 10:15:46 time: 0.6412 data_time: 0.1902 memory: 14682 loss_ce: 0.0073 loss: 0.0073 2022/09/21 22:05:00 - mmengine - INFO - Epoch(train) [5][19000/42151] lr: 3.0000e-06 eta: 10:14:50 time: 0.4089 data_time: 0.0083 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 22:06:03 - mmengine - INFO - Epoch(train) [5][19100/42151] lr: 3.0000e-06 eta: 10:13:56 time: 0.5109 data_time: 0.1025 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/21 22:07:08 - mmengine - INFO - Epoch(train) [5][19200/42151] lr: 3.0000e-06 eta: 10:13:02 time: 0.5569 data_time: 0.1413 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/21 22:08:13 - mmengine - INFO - Epoch(train) [5][19300/42151] lr: 3.0000e-06 eta: 10:12:09 time: 0.8002 data_time: 0.3894 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 22:09:18 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 22:09:20 - mmengine - INFO - Epoch(train) [5][19400/42151] lr: 3.0000e-06 eta: 10:11:16 time: 0.7169 data_time: 0.2496 memory: 14682 loss_ce: 0.0070 loss: 0.0070 2022/09/21 22:10:27 - mmengine - INFO - Epoch(train) [5][19500/42151] lr: 3.0000e-06 eta: 10:10:23 time: 0.5479 data_time: 0.1276 memory: 14682 loss_ce: 0.0071 loss: 0.0071 2022/09/21 22:11:30 - mmengine - INFO - Epoch(train) [5][19600/42151] lr: 3.0000e-06 eta: 10:09:29 time: 0.4771 data_time: 0.0064 memory: 14682 loss_ce: 0.0070 loss: 0.0070 2022/09/21 22:12:35 - mmengine - INFO - Epoch(train) [5][19700/42151] lr: 3.0000e-06 eta: 10:08:35 time: 0.5608 data_time: 0.1047 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 22:13:37 - mmengine - INFO - Epoch(train) [5][19800/42151] lr: 3.0000e-06 eta: 10:07:40 time: 0.4936 data_time: 0.0945 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 22:14:44 - mmengine - INFO - Epoch(train) [5][19900/42151] lr: 3.0000e-06 eta: 10:06:48 time: 0.8770 data_time: 0.3467 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/21 22:15:54 - mmengine - INFO - Epoch(train) [5][20000/42151] lr: 3.0000e-06 eta: 10:05:56 time: 0.8336 data_time: 0.3731 memory: 14682 loss_ce: 0.0071 loss: 0.0071 2022/09/21 22:16:58 - mmengine - INFO - Epoch(train) [5][20100/42151] lr: 3.0000e-06 eta: 10:05:02 time: 0.5554 data_time: 0.0973 memory: 14682 loss_ce: 0.0070 loss: 0.0070 2022/09/21 22:17:59 - mmengine - INFO - Epoch(train) [5][20200/42151] lr: 3.0000e-06 eta: 10:04:06 time: 0.4035 data_time: 0.0062 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/21 22:19:02 - mmengine - INFO - Epoch(train) [5][20300/42151] lr: 3.0000e-06 eta: 10:03:12 time: 0.5169 data_time: 0.1157 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 22:20:03 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 22:20:05 - mmengine - INFO - Epoch(train) [5][20400/42151] lr: 3.0000e-06 eta: 10:02:18 time: 0.5293 data_time: 0.1348 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/21 22:21:12 - mmengine - INFO - Epoch(train) [5][20500/42151] lr: 3.0000e-06 eta: 10:01:25 time: 0.6723 data_time: 0.2560 memory: 14682 loss_ce: 0.0069 loss: 0.0069 2022/09/21 22:22:14 - mmengine - INFO - Epoch(train) [5][20600/42151] lr: 3.0000e-06 eta: 10:00:30 time: 0.7137 data_time: 0.3162 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 22:23:14 - mmengine - INFO - Epoch(train) [5][20700/42151] lr: 3.0000e-06 eta: 9:59:34 time: 0.4616 data_time: 0.0756 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 22:24:20 - mmengine - INFO - Epoch(train) [5][20800/42151] lr: 3.0000e-06 eta: 9:58:41 time: 0.4459 data_time: 0.0071 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/21 22:25:19 - mmengine - INFO - Epoch(train) [5][20900/42151] lr: 3.0000e-06 eta: 9:57:45 time: 0.5876 data_time: 0.1530 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/21 22:26:31 - mmengine - INFO - Epoch(train) [5][21000/42151] lr: 3.0000e-06 eta: 9:56:54 time: 0.6434 data_time: 0.1852 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 22:27:43 - mmengine - INFO - Epoch(train) [5][21100/42151] lr: 3.0000e-06 eta: 9:56:02 time: 0.7330 data_time: 0.2956 memory: 14682 loss_ce: 0.0070 loss: 0.0070 2022/09/21 22:28:50 - mmengine - INFO - Epoch(train) [5][21200/42151] lr: 3.0000e-06 eta: 9:55:09 time: 1.0678 data_time: 0.4271 memory: 14682 loss_ce: 0.0098 loss: 0.0098 2022/09/21 22:29:54 - mmengine - INFO - Epoch(train) [5][21300/42151] lr: 3.0000e-06 eta: 9:54:15 time: 0.5867 data_time: 0.1478 memory: 14682 loss_ce: 0.0067 loss: 0.0067 2022/09/21 22:30:55 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 22:30:57 - mmengine - INFO - Epoch(train) [5][21400/42151] lr: 3.0000e-06 eta: 9:53:21 time: 0.3922 data_time: 0.0069 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 22:32:00 - mmengine - INFO - Epoch(train) [5][21500/42151] lr: 3.0000e-06 eta: 9:52:26 time: 0.5605 data_time: 0.1358 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 22:33:04 - mmengine - INFO - Epoch(train) [5][21600/42151] lr: 3.0000e-06 eta: 9:51:32 time: 0.5642 data_time: 0.1347 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/21 22:34:07 - mmengine - INFO - Epoch(train) [5][21700/42151] lr: 3.0000e-06 eta: 9:50:37 time: 0.7754 data_time: 0.3508 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 22:35:13 - mmengine - INFO - Epoch(train) [5][21800/42151] lr: 3.0000e-06 eta: 9:49:44 time: 0.9716 data_time: 0.4520 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 22:36:11 - mmengine - INFO - Epoch(train) [5][21900/42151] lr: 3.0000e-06 eta: 9:48:47 time: 0.6359 data_time: 0.1592 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/21 22:37:14 - mmengine - INFO - Epoch(train) [5][22000/42151] lr: 3.0000e-06 eta: 9:47:53 time: 0.4165 data_time: 0.0076 memory: 14682 loss_ce: 0.0073 loss: 0.0073 2022/09/21 22:38:20 - mmengine - INFO - Epoch(train) [5][22100/42151] lr: 3.0000e-06 eta: 9:46:59 time: 0.5225 data_time: 0.1094 memory: 14682 loss_ce: 0.0064 loss: 0.0064 2022/09/21 22:39:25 - mmengine - INFO - Epoch(train) [5][22200/42151] lr: 3.0000e-06 eta: 9:46:05 time: 0.5285 data_time: 0.1367 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 22:40:38 - mmengine - INFO - Epoch(train) [5][22300/42151] lr: 3.0000e-06 eta: 9:45:14 time: 1.0281 data_time: 0.4397 memory: 14682 loss_ce: 0.0073 loss: 0.0073 2022/09/21 22:41:46 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 22:41:47 - mmengine - INFO - Epoch(train) [5][22400/42151] lr: 3.0000e-06 eta: 9:44:22 time: 0.8650 data_time: 0.3982 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 22:42:53 - mmengine - INFO - Epoch(train) [5][22500/42151] lr: 3.0000e-06 eta: 9:43:28 time: 0.5810 data_time: 0.1002 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 22:44:00 - mmengine - INFO - Epoch(train) [5][22600/42151] lr: 3.0000e-06 eta: 9:42:35 time: 0.4230 data_time: 0.0088 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 22:45:02 - mmengine - INFO - Epoch(train) [5][22700/42151] lr: 3.0000e-06 eta: 9:41:40 time: 0.4857 data_time: 0.0753 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/21 22:46:05 - mmengine - INFO - Epoch(train) [5][22800/42151] lr: 3.0000e-06 eta: 9:40:45 time: 0.6141 data_time: 0.1415 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 22:47:12 - mmengine - INFO - Epoch(train) [5][22900/42151] lr: 3.0000e-06 eta: 9:39:52 time: 0.8236 data_time: 0.4014 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/21 22:48:23 - mmengine - INFO - Epoch(train) [5][23000/42151] lr: 3.0000e-06 eta: 9:38:59 time: 0.9133 data_time: 0.4700 memory: 14682 loss_ce: 0.0075 loss: 0.0075 2022/09/21 22:49:34 - mmengine - INFO - Epoch(train) [5][23100/42151] lr: 3.0000e-06 eta: 9:38:07 time: 0.5914 data_time: 0.1505 memory: 14682 loss_ce: 0.0066 loss: 0.0066 2022/09/21 22:50:44 - mmengine - INFO - Epoch(train) [5][23200/42151] lr: 3.0000e-06 eta: 9:37:15 time: 0.4544 data_time: 0.0127 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 22:51:48 - mmengine - INFO - Epoch(train) [5][23300/42151] lr: 3.0000e-06 eta: 9:36:21 time: 0.5273 data_time: 0.1115 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 22:52:51 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 22:52:53 - mmengine - INFO - Epoch(train) [5][23400/42151] lr: 3.0000e-06 eta: 9:35:26 time: 0.5426 data_time: 0.1346 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/21 22:54:01 - mmengine - INFO - Epoch(train) [5][23500/42151] lr: 3.0000e-06 eta: 9:34:33 time: 0.7407 data_time: 0.2904 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 22:55:08 - mmengine - INFO - Epoch(train) [5][23600/42151] lr: 3.0000e-06 eta: 9:33:40 time: 1.0744 data_time: 0.4984 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/21 22:56:08 - mmengine - INFO - Epoch(train) [5][23700/42151] lr: 3.0000e-06 eta: 9:32:44 time: 0.5357 data_time: 0.1183 memory: 14682 loss_ce: 0.0100 loss: 0.0100 2022/09/21 22:57:10 - mmengine - INFO - Epoch(train) [5][23800/42151] lr: 3.0000e-06 eta: 9:31:49 time: 0.4168 data_time: 0.0076 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 22:58:20 - mmengine - INFO - Epoch(train) [5][23900/42151] lr: 3.0000e-06 eta: 9:30:56 time: 0.5406 data_time: 0.1049 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 22:59:31 - mmengine - INFO - Epoch(train) [5][24000/42151] lr: 3.0000e-06 eta: 9:30:04 time: 0.6110 data_time: 0.1205 memory: 14682 loss_ce: 0.0066 loss: 0.0066 2022/09/21 23:00:42 - mmengine - INFO - Epoch(train) [5][24100/42151] lr: 3.0000e-06 eta: 9:29:12 time: 0.9427 data_time: 0.3812 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 23:01:51 - mmengine - INFO - Epoch(train) [5][24200/42151] lr: 3.0000e-06 eta: 9:28:19 time: 0.7243 data_time: 0.3192 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 23:02:53 - mmengine - INFO - Epoch(train) [5][24300/42151] lr: 3.0000e-06 eta: 9:27:24 time: 0.6337 data_time: 0.1592 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 23:03:56 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 23:03:57 - mmengine - INFO - Epoch(train) [5][24400/42151] lr: 3.0000e-06 eta: 9:26:29 time: 0.4050 data_time: 0.0074 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/21 23:05:03 - mmengine - INFO - Epoch(train) [5][24500/42151] lr: 3.0000e-06 eta: 9:25:36 time: 0.6363 data_time: 0.1890 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 23:06:13 - mmengine - INFO - Epoch(train) [5][24600/42151] lr: 3.0000e-06 eta: 9:24:43 time: 0.7112 data_time: 0.2109 memory: 14682 loss_ce: 0.0073 loss: 0.0073 2022/09/21 23:07:21 - mmengine - INFO - Epoch(train) [5][24700/42151] lr: 3.0000e-06 eta: 9:23:50 time: 0.6365 data_time: 0.2470 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/21 23:08:24 - mmengine - INFO - Epoch(train) [5][24800/42151] lr: 3.0000e-06 eta: 9:22:55 time: 0.8612 data_time: 0.3692 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/21 23:09:26 - mmengine - INFO - Epoch(train) [5][24900/42151] lr: 3.0000e-06 eta: 9:21:59 time: 0.6189 data_time: 0.0792 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/21 23:10:29 - mmengine - INFO - Epoch(train) [5][25000/42151] lr: 3.0000e-06 eta: 9:21:04 time: 0.3864 data_time: 0.0064 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 23:11:34 - mmengine - INFO - Epoch(train) [5][25100/42151] lr: 3.0000e-06 eta: 9:20:10 time: 0.5522 data_time: 0.1369 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/21 23:12:44 - mmengine - INFO - Epoch(train) [5][25200/42151] lr: 3.0000e-06 eta: 9:19:18 time: 0.5097 data_time: 0.0871 memory: 14682 loss_ce: 0.0075 loss: 0.0075 2022/09/21 23:13:54 - mmengine - INFO - Epoch(train) [5][25300/42151] lr: 3.0000e-06 eta: 9:18:25 time: 0.7917 data_time: 0.3584 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/21 23:14:58 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 23:15:00 - mmengine - INFO - Epoch(train) [5][25400/42151] lr: 3.0000e-06 eta: 9:17:31 time: 0.6761 data_time: 0.2739 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 23:16:05 - mmengine - INFO - Epoch(train) [5][25500/42151] lr: 3.0000e-06 eta: 9:16:36 time: 0.5951 data_time: 0.1541 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/21 23:17:06 - mmengine - INFO - Epoch(train) [5][25600/42151] lr: 3.0000e-06 eta: 9:15:41 time: 0.3936 data_time: 0.0070 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 23:18:15 - mmengine - INFO - Epoch(train) [5][25700/42151] lr: 3.0000e-06 eta: 9:14:48 time: 0.7050 data_time: 0.1932 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/21 23:19:23 - mmengine - INFO - Epoch(train) [5][25800/42151] lr: 3.0000e-06 eta: 9:13:54 time: 0.6764 data_time: 0.1182 memory: 14682 loss_ce: 0.0075 loss: 0.0075 2022/09/21 23:20:33 - mmengine - INFO - Epoch(train) [5][25900/42151] lr: 3.0000e-06 eta: 9:13:01 time: 0.6756 data_time: 0.2427 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/21 23:21:35 - mmengine - INFO - Epoch(train) [5][26000/42151] lr: 3.0000e-06 eta: 9:12:06 time: 0.8162 data_time: 0.3394 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/21 23:22:37 - mmengine - INFO - Epoch(train) [5][26100/42151] lr: 3.0000e-06 eta: 9:11:11 time: 0.5443 data_time: 0.1216 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 23:23:38 - mmengine - INFO - Epoch(train) [5][26200/42151] lr: 3.0000e-06 eta: 9:10:15 time: 0.3981 data_time: 0.0064 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 23:24:46 - mmengine - INFO - Epoch(train) [5][26300/42151] lr: 3.0000e-06 eta: 9:09:22 time: 0.6327 data_time: 0.1891 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/21 23:25:50 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 23:25:52 - mmengine - INFO - Epoch(train) [5][26400/42151] lr: 3.0000e-06 eta: 9:08:27 time: 0.6220 data_time: 0.1879 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 23:27:03 - mmengine - INFO - Epoch(train) [5][26500/42151] lr: 3.0000e-06 eta: 9:07:35 time: 0.8934 data_time: 0.4495 memory: 14682 loss_ce: 0.0071 loss: 0.0071 2022/09/21 23:28:10 - mmengine - INFO - Epoch(train) [5][26600/42151] lr: 3.0000e-06 eta: 9:06:41 time: 0.7921 data_time: 0.3622 memory: 14682 loss_ce: 0.0071 loss: 0.0071 2022/09/21 23:29:16 - mmengine - INFO - Epoch(train) [5][26700/42151] lr: 3.0000e-06 eta: 9:05:47 time: 0.6021 data_time: 0.1551 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 23:30:23 - mmengine - INFO - Epoch(train) [5][26800/42151] lr: 3.0000e-06 eta: 9:04:53 time: 0.3897 data_time: 0.0055 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 23:31:28 - mmengine - INFO - Epoch(train) [5][26900/42151] lr: 3.0000e-06 eta: 9:03:58 time: 0.5821 data_time: 0.1054 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 23:32:33 - mmengine - INFO - Epoch(train) [5][27000/42151] lr: 3.0000e-06 eta: 9:03:04 time: 0.6185 data_time: 0.1737 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/21 23:33:39 - mmengine - INFO - Epoch(train) [5][27100/42151] lr: 3.0000e-06 eta: 9:02:10 time: 0.8377 data_time: 0.2842 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/21 23:34:44 - mmengine - INFO - Epoch(train) [5][27200/42151] lr: 3.0000e-06 eta: 9:01:15 time: 0.6891 data_time: 0.2711 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 23:35:53 - mmengine - INFO - Epoch(train) [5][27300/42151] lr: 3.0000e-06 eta: 9:00:22 time: 0.6568 data_time: 0.1443 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/21 23:37:05 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 23:37:07 - mmengine - INFO - Epoch(train) [5][27400/42151] lr: 3.0000e-06 eta: 8:59:30 time: 0.4413 data_time: 0.0142 memory: 14682 loss_ce: 0.0066 loss: 0.0066 2022/09/21 23:38:13 - mmengine - INFO - Epoch(train) [5][27500/42151] lr: 3.0000e-06 eta: 8:58:36 time: 0.5979 data_time: 0.1140 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/21 23:39:17 - mmengine - INFO - Epoch(train) [5][27600/42151] lr: 3.0000e-06 eta: 8:57:41 time: 0.7283 data_time: 0.1884 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 23:40:23 - mmengine - INFO - Epoch(train) [5][27700/42151] lr: 3.0000e-06 eta: 8:56:46 time: 0.9753 data_time: 0.4495 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/21 23:41:35 - mmengine - INFO - Epoch(train) [5][27800/42151] lr: 3.0000e-06 eta: 8:55:54 time: 0.7950 data_time: 0.3668 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/21 23:42:42 - mmengine - INFO - Epoch(train) [5][27900/42151] lr: 3.0000e-06 eta: 8:55:00 time: 0.5349 data_time: 0.1302 memory: 14682 loss_ce: 0.0065 loss: 0.0065 2022/09/21 23:43:43 - mmengine - INFO - Epoch(train) [5][28000/42151] lr: 3.0000e-06 eta: 8:54:04 time: 0.4350 data_time: 0.0093 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/21 23:44:52 - mmengine - INFO - Epoch(train) [5][28100/42151] lr: 3.0000e-06 eta: 8:53:11 time: 0.4881 data_time: 0.1024 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/21 23:46:01 - mmengine - INFO - Epoch(train) [5][28200/42151] lr: 3.0000e-06 eta: 8:52:17 time: 0.5226 data_time: 0.0810 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/21 23:47:07 - mmengine - INFO - Epoch(train) [5][28300/42151] lr: 3.0000e-06 eta: 8:51:23 time: 0.7168 data_time: 0.2944 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/21 23:48:17 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 23:48:19 - mmengine - INFO - Epoch(train) [5][28400/42151] lr: 3.0000e-06 eta: 8:50:30 time: 0.9972 data_time: 0.4852 memory: 14682 loss_ce: 0.0065 loss: 0.0065 2022/09/21 23:49:25 - mmengine - INFO - Epoch(train) [5][28500/42151] lr: 3.0000e-06 eta: 8:49:36 time: 0.5575 data_time: 0.1496 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/21 23:50:27 - mmengine - INFO - Epoch(train) [5][28600/42151] lr: 3.0000e-06 eta: 8:48:40 time: 0.4399 data_time: 0.0089 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/21 23:51:37 - mmengine - INFO - Epoch(train) [5][28700/42151] lr: 3.0000e-06 eta: 8:47:47 time: 0.6221 data_time: 0.0697 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/21 23:52:42 - mmengine - INFO - Epoch(train) [5][28800/42151] lr: 3.0000e-06 eta: 8:46:52 time: 0.5403 data_time: 0.1422 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/21 23:53:58 - mmengine - INFO - Epoch(train) [5][28900/42151] lr: 3.0000e-06 eta: 8:46:00 time: 0.8434 data_time: 0.3357 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/21 23:55:11 - mmengine - INFO - Epoch(train) [5][29000/42151] lr: 3.0000e-06 eta: 8:45:08 time: 0.8545 data_time: 0.3363 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/21 23:56:15 - mmengine - INFO - Epoch(train) [5][29100/42151] lr: 3.0000e-06 eta: 8:44:13 time: 0.5252 data_time: 0.1306 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/21 23:57:25 - mmengine - INFO - Epoch(train) [5][29200/42151] lr: 3.0000e-06 eta: 8:43:20 time: 0.4436 data_time: 0.0127 memory: 14682 loss_ce: 0.0072 loss: 0.0072 2022/09/21 23:58:30 - mmengine - INFO - Epoch(train) [5][29300/42151] lr: 3.0000e-06 eta: 8:42:25 time: 0.6372 data_time: 0.1436 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/21 23:59:33 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/21 23:59:35 - mmengine - INFO - Epoch(train) [5][29400/42151] lr: 3.0000e-06 eta: 8:41:30 time: 0.5371 data_time: 0.1287 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/22 00:00:44 - mmengine - INFO - Epoch(train) [5][29500/42151] lr: 3.0000e-06 eta: 8:40:36 time: 0.7966 data_time: 0.3374 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/22 00:01:48 - mmengine - INFO - Epoch(train) [5][29600/42151] lr: 3.0000e-06 eta: 8:39:41 time: 0.8257 data_time: 0.3765 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/22 00:02:58 - mmengine - INFO - Epoch(train) [5][29700/42151] lr: 3.0000e-06 eta: 8:38:48 time: 0.7234 data_time: 0.2450 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/22 00:04:10 - mmengine - INFO - Epoch(train) [5][29800/42151] lr: 3.0000e-06 eta: 8:37:55 time: 0.5039 data_time: 0.0121 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/22 00:05:15 - mmengine - INFO - Epoch(train) [5][29900/42151] lr: 3.0000e-06 eta: 8:37:00 time: 0.4976 data_time: 0.0835 memory: 14682 loss_ce: 0.0069 loss: 0.0069 2022/09/22 00:06:25 - mmengine - INFO - Epoch(train) [5][30000/42151] lr: 3.0000e-06 eta: 8:36:07 time: 0.6349 data_time: 0.1942 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/22 00:07:30 - mmengine - INFO - Epoch(train) [5][30100/42151] lr: 3.0000e-06 eta: 8:35:11 time: 0.6160 data_time: 0.2261 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/22 00:08:40 - mmengine - INFO - Epoch(train) [5][30200/42151] lr: 3.0000e-06 eta: 8:34:18 time: 1.0119 data_time: 0.4829 memory: 14682 loss_ce: 0.0065 loss: 0.0065 2022/09/22 00:09:37 - mmengine - INFO - Epoch(train) [5][30300/42151] lr: 3.0000e-06 eta: 8:33:21 time: 0.5732 data_time: 0.1324 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/22 00:10:49 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 00:10:51 - mmengine - INFO - Epoch(train) [5][30400/42151] lr: 3.0000e-06 eta: 8:32:28 time: 0.4623 data_time: 0.0090 memory: 14682 loss_ce: 0.0072 loss: 0.0072 2022/09/22 00:12:08 - mmengine - INFO - Epoch(train) [5][30500/42151] lr: 3.0000e-06 eta: 8:31:37 time: 0.7347 data_time: 0.2646 memory: 14682 loss_ce: 0.0066 loss: 0.0066 2022/09/22 00:13:27 - mmengine - INFO - Epoch(train) [5][30600/42151] lr: 3.0000e-06 eta: 8:30:46 time: 0.9005 data_time: 0.3384 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/22 00:14:42 - mmengine - INFO - Epoch(train) [5][30700/42151] lr: 3.0000e-06 eta: 8:29:53 time: 1.0120 data_time: 0.4665 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/22 00:15:49 - mmengine - INFO - Epoch(train) [5][30800/42151] lr: 3.0000e-06 eta: 8:28:59 time: 0.8870 data_time: 0.3922 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/22 00:17:03 - mmengine - INFO - Epoch(train) [5][30900/42151] lr: 3.0000e-06 eta: 8:28:06 time: 0.7750 data_time: 0.2013 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/22 00:18:09 - mmengine - INFO - Epoch(train) [5][31000/42151] lr: 3.0000e-06 eta: 8:27:11 time: 0.4015 data_time: 0.0068 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/22 00:19:21 - mmengine - INFO - Epoch(train) [5][31100/42151] lr: 3.0000e-06 eta: 8:26:18 time: 0.7410 data_time: 0.1702 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/22 00:20:22 - mmengine - INFO - Epoch(train) [5][31200/42151] lr: 3.0000e-06 eta: 8:25:22 time: 0.6639 data_time: 0.1649 memory: 14682 loss_ce: 0.0106 loss: 0.0106 2022/09/22 00:21:32 - mmengine - INFO - Epoch(train) [5][31300/42151] lr: 3.0000e-06 eta: 8:24:29 time: 0.6880 data_time: 0.2929 memory: 14682 loss_ce: 0.0069 loss: 0.0069 2022/09/22 00:22:34 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 00:22:36 - mmengine - INFO - Epoch(train) [5][31400/42151] lr: 3.0000e-06 eta: 8:23:33 time: 0.8512 data_time: 0.3107 memory: 14682 loss_ce: 0.0071 loss: 0.0071 2022/09/22 00:23:37 - mmengine - INFO - Epoch(train) [5][31500/42151] lr: 3.0000e-06 eta: 8:22:37 time: 0.5336 data_time: 0.0961 memory: 14682 loss_ce: 0.0071 loss: 0.0071 2022/09/22 00:24:53 - mmengine - INFO - Epoch(train) [5][31600/42151] lr: 3.0000e-06 eta: 8:21:45 time: 0.6862 data_time: 0.0203 memory: 14682 loss_ce: 0.0064 loss: 0.0064 2022/09/22 00:26:06 - mmengine - INFO - Epoch(train) [5][31700/42151] lr: 3.0000e-06 eta: 8:20:52 time: 0.7021 data_time: 0.2617 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/22 00:27:10 - mmengine - INFO - Epoch(train) [5][31800/42151] lr: 3.0000e-06 eta: 8:19:57 time: 0.5913 data_time: 0.1348 memory: 14682 loss_ce: 0.0073 loss: 0.0073 2022/09/22 00:28:15 - mmengine - INFO - Epoch(train) [5][31900/42151] lr: 3.0000e-06 eta: 8:19:01 time: 0.6972 data_time: 0.2801 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/22 00:29:15 - mmengine - INFO - Epoch(train) [5][32000/42151] lr: 3.0000e-06 eta: 8:18:05 time: 0.7120 data_time: 0.2731 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/22 00:30:22 - mmengine - INFO - Epoch(train) [5][32100/42151] lr: 3.0000e-06 eta: 8:17:11 time: 0.5543 data_time: 0.1426 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/22 00:31:23 - mmengine - INFO - Epoch(train) [5][32200/42151] lr: 3.0000e-06 eta: 8:16:15 time: 0.4644 data_time: 0.0066 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/22 00:32:30 - mmengine - INFO - Epoch(train) [5][32300/42151] lr: 3.0000e-06 eta: 8:15:20 time: 0.6292 data_time: 0.1029 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/22 00:33:35 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 00:33:37 - mmengine - INFO - Epoch(train) [5][32400/42151] lr: 3.0000e-06 eta: 8:14:25 time: 0.5325 data_time: 0.0845 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/22 00:34:46 - mmengine - INFO - Epoch(train) [5][32500/42151] lr: 3.0000e-06 eta: 8:13:31 time: 1.0147 data_time: 0.4489 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/22 00:35:47 - mmengine - INFO - Epoch(train) [5][32600/42151] lr: 3.0000e-06 eta: 8:12:35 time: 0.7776 data_time: 0.3830 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 00:36:52 - mmengine - INFO - Epoch(train) [5][32700/42151] lr: 3.0000e-06 eta: 8:11:40 time: 0.5996 data_time: 0.1711 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/22 00:38:03 - mmengine - INFO - Epoch(train) [5][32800/42151] lr: 3.0000e-06 eta: 8:10:46 time: 0.4519 data_time: 0.0102 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/22 00:39:05 - mmengine - INFO - Epoch(train) [5][32900/42151] lr: 3.0000e-06 eta: 8:09:50 time: 0.4843 data_time: 0.0862 memory: 14682 loss_ce: 0.0071 loss: 0.0071 2022/09/22 00:40:05 - mmengine - INFO - Epoch(train) [5][33000/42151] lr: 3.0000e-06 eta: 8:08:53 time: 0.6875 data_time: 0.1785 memory: 14682 loss_ce: 0.0075 loss: 0.0075 2022/09/22 00:41:11 - mmengine - INFO - Epoch(train) [5][33100/42151] lr: 3.0000e-06 eta: 8:07:59 time: 0.6528 data_time: 0.1975 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/22 00:42:16 - mmengine - INFO - Epoch(train) [5][33200/42151] lr: 3.0000e-06 eta: 8:07:03 time: 0.6600 data_time: 0.2371 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/22 00:43:20 - mmengine - INFO - Epoch(train) [5][33300/42151] lr: 3.0000e-06 eta: 8:06:08 time: 0.6112 data_time: 0.1889 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/22 00:44:22 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 00:44:23 - mmengine - INFO - Epoch(train) [5][33400/42151] lr: 3.0000e-06 eta: 8:05:12 time: 0.4987 data_time: 0.0080 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/22 00:45:25 - mmengine - INFO - Epoch(train) [5][33500/42151] lr: 3.0000e-06 eta: 8:04:16 time: 0.6533 data_time: 0.1470 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/22 00:46:28 - mmengine - INFO - Epoch(train) [5][33600/42151] lr: 3.0000e-06 eta: 8:03:20 time: 0.5532 data_time: 0.1336 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/22 00:47:31 - mmengine - INFO - Epoch(train) [5][33700/42151] lr: 3.0000e-06 eta: 8:02:25 time: 0.6957 data_time: 0.2375 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/22 00:48:36 - mmengine - INFO - Epoch(train) [5][33800/42151] lr: 3.0000e-06 eta: 8:01:29 time: 0.8504 data_time: 0.2829 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/22 00:49:38 - mmengine - INFO - Epoch(train) [5][33900/42151] lr: 3.0000e-06 eta: 8:00:33 time: 0.6623 data_time: 0.2371 memory: 14682 loss_ce: 0.0064 loss: 0.0064 2022/09/22 00:50:40 - mmengine - INFO - Epoch(train) [5][34000/42151] lr: 3.0000e-06 eta: 7:59:37 time: 0.3828 data_time: 0.0049 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/22 00:51:40 - mmengine - INFO - Epoch(train) [5][34100/42151] lr: 3.0000e-06 eta: 7:58:41 time: 0.4826 data_time: 0.0992 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/22 00:52:38 - mmengine - INFO - Epoch(train) [5][34200/42151] lr: 3.0000e-06 eta: 7:57:44 time: 0.5583 data_time: 0.1480 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/22 00:53:48 - mmengine - INFO - Epoch(train) [5][34300/42151] lr: 3.0000e-06 eta: 7:56:50 time: 0.7767 data_time: 0.2713 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/22 00:54:44 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 00:54:47 - mmengine - INFO - Epoch(train) [5][34400/42151] lr: 3.0000e-06 eta: 7:55:53 time: 0.8003 data_time: 0.2814 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/22 00:55:56 - mmengine - INFO - Epoch(train) [5][34500/42151] lr: 3.0000e-06 eta: 7:54:58 time: 0.5977 data_time: 0.1320 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 00:57:00 - mmengine - INFO - Epoch(train) [5][34600/42151] lr: 3.0000e-06 eta: 7:54:03 time: 0.4195 data_time: 0.0066 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 00:58:01 - mmengine - INFO - Epoch(train) [5][34700/42151] lr: 3.0000e-06 eta: 7:53:07 time: 0.6059 data_time: 0.1454 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/22 00:59:09 - mmengine - INFO - Epoch(train) [5][34800/42151] lr: 3.0000e-06 eta: 7:52:12 time: 0.5896 data_time: 0.1614 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/22 01:00:15 - mmengine - INFO - Epoch(train) [5][34900/42151] lr: 3.0000e-06 eta: 7:51:17 time: 0.7081 data_time: 0.2930 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/22 01:01:23 - mmengine - INFO - Epoch(train) [5][35000/42151] lr: 3.0000e-06 eta: 7:50:22 time: 1.0490 data_time: 0.5205 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/22 01:02:21 - mmengine - INFO - Epoch(train) [5][35100/42151] lr: 3.0000e-06 eta: 7:49:25 time: 0.5610 data_time: 0.1708 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/22 01:03:22 - mmengine - INFO - Epoch(train) [5][35200/42151] lr: 3.0000e-06 eta: 7:48:29 time: 0.4417 data_time: 0.0122 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 01:04:24 - mmengine - INFO - Epoch(train) [5][35300/42151] lr: 3.0000e-06 eta: 7:47:33 time: 0.7129 data_time: 0.1942 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/22 01:05:25 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 01:05:26 - mmengine - INFO - Epoch(train) [5][35400/42151] lr: 3.0000e-06 eta: 7:46:37 time: 0.6223 data_time: 0.0999 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/22 01:06:30 - mmengine - INFO - Epoch(train) [5][35500/42151] lr: 3.0000e-06 eta: 7:45:41 time: 0.6472 data_time: 0.2284 memory: 14682 loss_ce: 0.0072 loss: 0.0072 2022/09/22 01:07:37 - mmengine - INFO - Epoch(train) [5][35600/42151] lr: 3.0000e-06 eta: 7:44:46 time: 0.8751 data_time: 0.3562 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/22 01:08:43 - mmengine - INFO - Epoch(train) [5][35700/42151] lr: 3.0000e-06 eta: 7:43:51 time: 0.5474 data_time: 0.1141 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/22 01:09:44 - mmengine - INFO - Epoch(train) [5][35800/42151] lr: 3.0000e-06 eta: 7:42:54 time: 0.4846 data_time: 0.0149 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/22 01:10:47 - mmengine - INFO - Epoch(train) [5][35900/42151] lr: 3.0000e-06 eta: 7:41:58 time: 0.5588 data_time: 0.1524 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/22 01:11:54 - mmengine - INFO - Epoch(train) [5][36000/42151] lr: 3.0000e-06 eta: 7:41:03 time: 0.6763 data_time: 0.1759 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/22 01:12:56 - mmengine - INFO - Epoch(train) [5][36100/42151] lr: 3.0000e-06 eta: 7:40:07 time: 0.7336 data_time: 0.2598 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/22 01:13:59 - mmengine - INFO - Epoch(train) [5][36200/42151] lr: 3.0000e-06 eta: 7:39:11 time: 0.8995 data_time: 0.3870 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/22 01:15:04 - mmengine - INFO - Epoch(train) [5][36300/42151] lr: 3.0000e-06 eta: 7:38:16 time: 0.5246 data_time: 0.0977 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/22 01:16:07 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 01:16:09 - mmengine - INFO - Epoch(train) [5][36400/42151] lr: 3.0000e-06 eta: 7:37:20 time: 0.4333 data_time: 0.0095 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/22 01:17:16 - mmengine - INFO - Epoch(train) [5][36500/42151] lr: 3.0000e-06 eta: 7:36:25 time: 0.4959 data_time: 0.0678 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 01:18:16 - mmengine - INFO - Epoch(train) [5][36600/42151] lr: 3.0000e-06 eta: 7:35:29 time: 0.7199 data_time: 0.2493 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/22 01:19:22 - mmengine - INFO - Epoch(train) [5][36700/42151] lr: 3.0000e-06 eta: 7:34:33 time: 0.8629 data_time: 0.3707 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/22 01:20:26 - mmengine - INFO - Epoch(train) [5][36800/42151] lr: 3.0000e-06 eta: 7:33:38 time: 0.8574 data_time: 0.3138 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/22 01:21:30 - mmengine - INFO - Epoch(train) [5][36900/42151] lr: 3.0000e-06 eta: 7:32:42 time: 0.7355 data_time: 0.1331 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/22 01:22:37 - mmengine - INFO - Epoch(train) [5][37000/42151] lr: 3.0000e-06 eta: 7:31:47 time: 0.4979 data_time: 0.0090 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/22 01:23:36 - mmengine - INFO - Epoch(train) [5][37100/42151] lr: 3.0000e-06 eta: 7:30:50 time: 0.5669 data_time: 0.1217 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/22 01:24:40 - mmengine - INFO - Epoch(train) [5][37200/42151] lr: 3.0000e-06 eta: 7:29:54 time: 0.7170 data_time: 0.1784 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/22 01:25:46 - mmengine - INFO - Epoch(train) [5][37300/42151] lr: 3.0000e-06 eta: 7:28:58 time: 0.7539 data_time: 0.2841 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/22 01:26:46 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 01:26:48 - mmengine - INFO - Epoch(train) [5][37400/42151] lr: 3.0000e-06 eta: 7:28:02 time: 0.9027 data_time: 0.3633 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/22 01:27:52 - mmengine - INFO - Epoch(train) [5][37500/42151] lr: 3.0000e-06 eta: 7:27:07 time: 0.5732 data_time: 0.1237 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/22 01:28:54 - mmengine - INFO - Epoch(train) [5][37600/42151] lr: 3.0000e-06 eta: 7:26:10 time: 0.4378 data_time: 0.0061 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/22 01:30:02 - mmengine - INFO - Epoch(train) [5][37700/42151] lr: 3.0000e-06 eta: 7:25:15 time: 0.5766 data_time: 0.0940 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/22 01:31:05 - mmengine - INFO - Epoch(train) [5][37800/42151] lr: 3.0000e-06 eta: 7:24:19 time: 0.5824 data_time: 0.1049 memory: 14682 loss_ce: 0.0110 loss: 0.0110 2022/09/22 01:32:10 - mmengine - INFO - Epoch(train) [5][37900/42151] lr: 3.0000e-06 eta: 7:23:24 time: 0.9090 data_time: 0.3984 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/22 01:33:11 - mmengine - INFO - Epoch(train) [5][38000/42151] lr: 3.0000e-06 eta: 7:22:27 time: 0.7275 data_time: 0.3101 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/22 01:34:08 - mmengine - INFO - Epoch(train) [5][38100/42151] lr: 3.0000e-06 eta: 7:21:30 time: 0.5571 data_time: 0.1237 memory: 14682 loss_ce: 0.0075 loss: 0.0075 2022/09/22 01:35:09 - mmengine - INFO - Epoch(train) [5][38200/42151] lr: 3.0000e-06 eta: 7:20:33 time: 0.4424 data_time: 0.0078 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/22 01:36:08 - mmengine - INFO - Epoch(train) [5][38300/42151] lr: 3.0000e-06 eta: 7:19:36 time: 0.5047 data_time: 0.0763 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/22 01:37:09 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 01:37:11 - mmengine - INFO - Epoch(train) [5][38400/42151] lr: 3.0000e-06 eta: 7:18:40 time: 0.5985 data_time: 0.1372 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/22 01:38:20 - mmengine - INFO - Epoch(train) [5][38500/42151] lr: 3.0000e-06 eta: 7:17:45 time: 0.7956 data_time: 0.2807 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/22 01:39:20 - mmengine - INFO - Epoch(train) [5][38600/42151] lr: 3.0000e-06 eta: 7:16:48 time: 0.7855 data_time: 0.2981 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/22 01:40:22 - mmengine - INFO - Epoch(train) [5][38700/42151] lr: 3.0000e-06 eta: 7:15:52 time: 0.5051 data_time: 0.1254 memory: 14682 loss_ce: 0.0069 loss: 0.0069 2022/09/22 01:41:20 - mmengine - INFO - Epoch(train) [5][38800/42151] lr: 3.0000e-06 eta: 7:14:55 time: 0.3872 data_time: 0.0060 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/22 01:42:19 - mmengine - INFO - Epoch(train) [5][38900/42151] lr: 3.0000e-06 eta: 7:13:58 time: 0.5531 data_time: 0.1234 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 01:43:20 - mmengine - INFO - Epoch(train) [5][39000/42151] lr: 3.0000e-06 eta: 7:13:01 time: 0.6503 data_time: 0.1595 memory: 14682 loss_ce: 0.0075 loss: 0.0075 2022/09/22 01:44:22 - mmengine - INFO - Epoch(train) [5][39100/42151] lr: 3.0000e-06 eta: 7:12:05 time: 0.7014 data_time: 0.2720 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/22 01:45:25 - mmengine - INFO - Epoch(train) [5][39200/42151] lr: 3.0000e-06 eta: 7:11:09 time: 0.8154 data_time: 0.4041 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/22 01:46:23 - mmengine - INFO - Epoch(train) [5][39300/42151] lr: 3.0000e-06 eta: 7:10:11 time: 0.5803 data_time: 0.1665 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/22 01:47:26 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 01:47:30 - mmengine - INFO - Epoch(train) [5][39400/42151] lr: 3.0000e-06 eta: 7:09:16 time: 0.6846 data_time: 0.0121 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/22 01:48:32 - mmengine - INFO - Epoch(train) [5][39500/42151] lr: 3.0000e-06 eta: 7:08:20 time: 0.6010 data_time: 0.1293 memory: 14682 loss_ce: 0.0106 loss: 0.0106 2022/09/22 01:49:34 - mmengine - INFO - Epoch(train) [5][39600/42151] lr: 3.0000e-06 eta: 7:07:23 time: 0.6130 data_time: 0.1794 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 01:50:37 - mmengine - INFO - Epoch(train) [5][39700/42151] lr: 3.0000e-06 eta: 7:06:27 time: 0.6888 data_time: 0.2785 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/22 01:51:40 - mmengine - INFO - Epoch(train) [5][39800/42151] lr: 3.0000e-06 eta: 7:05:31 time: 0.6851 data_time: 0.2538 memory: 14682 loss_ce: 0.0068 loss: 0.0068 2022/09/22 01:52:40 - mmengine - INFO - Epoch(train) [5][39900/42151] lr: 3.0000e-06 eta: 7:04:34 time: 0.6375 data_time: 0.1147 memory: 14682 loss_ce: 0.0073 loss: 0.0073 2022/09/22 01:53:44 - mmengine - INFO - Epoch(train) [5][40000/42151] lr: 3.0000e-06 eta: 7:03:38 time: 0.4335 data_time: 0.0084 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/22 01:54:43 - mmengine - INFO - Epoch(train) [5][40100/42151] lr: 3.0000e-06 eta: 7:02:41 time: 0.5511 data_time: 0.1139 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/22 01:55:49 - mmengine - INFO - Epoch(train) [5][40200/42151] lr: 3.0000e-06 eta: 7:01:46 time: 0.6321 data_time: 0.1318 memory: 14682 loss_ce: 0.0070 loss: 0.0070 2022/09/22 01:56:56 - mmengine - INFO - Epoch(train) [5][40300/42151] lr: 3.0000e-06 eta: 7:00:50 time: 0.7769 data_time: 0.3576 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/22 01:57:53 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 01:57:55 - mmengine - INFO - Epoch(train) [5][40400/42151] lr: 3.0000e-06 eta: 6:59:53 time: 0.8321 data_time: 0.3361 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/22 01:58:55 - mmengine - INFO - Epoch(train) [5][40500/42151] lr: 3.0000e-06 eta: 6:58:56 time: 0.5239 data_time: 0.1329 memory: 14682 loss_ce: 0.0071 loss: 0.0071 2022/09/22 01:59:59 - mmengine - INFO - Epoch(train) [5][40600/42151] lr: 3.0000e-06 eta: 6:58:00 time: 0.4139 data_time: 0.0078 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/22 02:01:00 - mmengine - INFO - Epoch(train) [5][40700/42151] lr: 3.0000e-06 eta: 6:57:04 time: 0.5170 data_time: 0.0764 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/22 02:02:05 - mmengine - INFO - Epoch(train) [5][40800/42151] lr: 3.0000e-06 eta: 6:56:08 time: 0.6249 data_time: 0.1653 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/22 02:03:05 - mmengine - INFO - Epoch(train) [5][40900/42151] lr: 3.0000e-06 eta: 6:55:11 time: 0.6364 data_time: 0.2098 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/22 02:04:04 - mmengine - INFO - Epoch(train) [5][41000/42151] lr: 3.0000e-06 eta: 6:54:14 time: 0.6528 data_time: 0.2589 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/22 02:05:05 - mmengine - INFO - Epoch(train) [5][41100/42151] lr: 3.0000e-06 eta: 6:53:17 time: 0.5174 data_time: 0.1368 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/22 02:06:04 - mmengine - INFO - Epoch(train) [5][41200/42151] lr: 3.0000e-06 eta: 6:52:20 time: 0.3985 data_time: 0.0096 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/22 02:07:09 - mmengine - INFO - Epoch(train) [5][41300/42151] lr: 3.0000e-06 eta: 6:51:24 time: 0.5939 data_time: 0.1489 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 02:08:05 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 02:08:07 - mmengine - INFO - Epoch(train) [5][41400/42151] lr: 3.0000e-06 eta: 6:50:27 time: 0.5423 data_time: 0.1403 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/22 02:09:15 - mmengine - INFO - Epoch(train) [5][41500/42151] lr: 3.0000e-06 eta: 6:49:32 time: 0.8779 data_time: 0.3598 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/22 02:10:14 - mmengine - INFO - Epoch(train) [5][41600/42151] lr: 3.0000e-06 eta: 6:48:35 time: 0.7591 data_time: 0.2931 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/22 02:11:14 - mmengine - INFO - Epoch(train) [5][41700/42151] lr: 3.0000e-06 eta: 6:47:38 time: 0.6033 data_time: 0.1317 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/22 02:12:14 - mmengine - INFO - Epoch(train) [5][41800/42151] lr: 3.0000e-06 eta: 6:46:41 time: 0.4085 data_time: 0.0081 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/22 02:13:13 - mmengine - INFO - Epoch(train) [5][41900/42151] lr: 3.0000e-06 eta: 6:45:44 time: 0.6660 data_time: 0.1306 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/22 02:14:16 - mmengine - INFO - Epoch(train) [5][42000/42151] lr: 3.0000e-06 eta: 6:44:47 time: 0.4911 data_time: 0.0858 memory: 14682 loss_ce: 0.0094 loss: 0.0094 2022/09/22 02:15:19 - mmengine - INFO - Epoch(train) [5][42100/42151] lr: 3.0000e-06 eta: 6:43:51 time: 0.6981 data_time: 0.2736 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/22 02:15:44 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 02:15:44 - mmengine - INFO - Saving checkpoint at 5 epochs 2022/09/22 02:16:42 - mmengine - INFO - Epoch(val) [5][100/7672] eta: 0:58:10 time: 0.4610 data_time: 0.0025 memory: 14682 2022/09/22 02:17:27 - mmengine - INFO - Epoch(val) [5][200/7672] eta: 0:58:23 time: 0.4689 data_time: 0.0031 memory: 1304 2022/09/22 02:18:08 - mmengine - INFO - Epoch(val) [5][300/7672] eta: 0:27:00 time: 0.2199 data_time: 0.0009 memory: 1304 2022/09/22 02:18:33 - mmengine - INFO - Epoch(val) [5][400/7672] eta: 0:30:16 time: 0.2498 data_time: 0.0009 memory: 1304 2022/09/22 02:18:59 - mmengine - INFO - Epoch(val) [5][500/7672] eta: 0:25:12 time: 0.2109 data_time: 0.0009 memory: 1304 2022/09/22 02:19:24 - mmengine - INFO - Epoch(val) [5][600/7672] eta: 0:30:11 time: 0.2562 data_time: 0.0011 memory: 1304 2022/09/22 02:19:53 - mmengine - INFO - Epoch(val) [5][700/7672] eta: 0:39:57 time: 0.3439 data_time: 0.0083 memory: 1304 2022/09/22 02:20:16 - mmengine - INFO - Epoch(val) [5][800/7672] eta: 0:24:39 time: 0.2153 data_time: 0.0009 memory: 1304 2022/09/22 02:20:42 - mmengine - INFO - Epoch(val) [5][900/7672] eta: 0:31:07 time: 0.2758 data_time: 0.0034 memory: 1304 2022/09/22 02:21:09 - mmengine - INFO - Epoch(val) [5][1000/7672] eta: 0:30:17 time: 0.2724 data_time: 0.0015 memory: 1304 2022/09/22 02:21:37 - mmengine - INFO - Epoch(val) [5][1100/7672] eta: 0:26:20 time: 0.2406 data_time: 0.0009 memory: 1304 2022/09/22 02:22:05 - mmengine - INFO - Epoch(val) [5][1200/7672] eta: 0:29:36 time: 0.2745 data_time: 0.0011 memory: 1304 2022/09/22 02:22:28 - mmengine - INFO - Epoch(val) [5][1300/7672] eta: 0:21:15 time: 0.2002 data_time: 0.0009 memory: 1304 2022/09/22 02:22:50 - mmengine - INFO - Epoch(val) [5][1400/7672] eta: 0:27:35 time: 0.2639 data_time: 0.0035 memory: 1304 2022/09/22 02:23:15 - mmengine - INFO - Epoch(val) [5][1500/7672] eta: 0:20:16 time: 0.1972 data_time: 0.0008 memory: 1304 2022/09/22 02:23:38 - mmengine - INFO - Epoch(val) [5][1600/7672] eta: 0:20:03 time: 0.1982 data_time: 0.0008 memory: 1304 2022/09/22 02:24:02 - mmengine - INFO - Epoch(val) [5][1700/7672] eta: 0:34:44 time: 0.3491 data_time: 0.0065 memory: 1304 2022/09/22 02:24:27 - mmengine - INFO - Epoch(val) [5][1800/7672] eta: 0:20:57 time: 0.2141 data_time: 0.0009 memory: 1304 2022/09/22 02:24:54 - mmengine - INFO - Epoch(val) [5][1900/7672] eta: 0:24:44 time: 0.2572 data_time: 0.0011 memory: 1304 2022/09/22 02:25:20 - mmengine - INFO - Epoch(val) [5][2000/7672] eta: 0:24:18 time: 0.2571 data_time: 0.0011 memory: 1304 2022/09/22 02:25:46 - mmengine - INFO - Epoch(val) [5][2100/7672] eta: 0:18:53 time: 0.2034 data_time: 0.0009 memory: 1304 2022/09/22 02:26:07 - mmengine - INFO - Epoch(val) [5][2200/7672] eta: 0:19:25 time: 0.2130 data_time: 0.0010 memory: 1304 2022/09/22 02:26:29 - mmengine - INFO - Epoch(val) [5][2300/7672] eta: 0:19:12 time: 0.2146 data_time: 0.0009 memory: 1304 2022/09/22 02:26:54 - mmengine - INFO - Epoch(val) [5][2400/7672] eta: 0:21:42 time: 0.2471 data_time: 0.0013 memory: 1304 2022/09/22 02:27:21 - mmengine - INFO - Epoch(val) [5][2500/7672] eta: 0:25:36 time: 0.2972 data_time: 0.0056 memory: 1304 2022/09/22 02:27:49 - mmengine - INFO - Epoch(val) [5][2600/7672] eta: 0:23:43 time: 0.2806 data_time: 0.0010 memory: 1304 2022/09/22 02:28:14 - mmengine - INFO - Epoch(val) [5][2700/7672] eta: 0:20:09 time: 0.2432 data_time: 0.0019 memory: 1304 2022/09/22 02:28:35 - mmengine - INFO - Epoch(val) [5][2800/7672] eta: 0:18:06 time: 0.2229 data_time: 0.0009 memory: 1304 2022/09/22 02:28:56 - mmengine - INFO - Epoch(val) [5][2900/7672] eta: 0:16:32 time: 0.2081 data_time: 0.0012 memory: 1304 2022/09/22 02:29:22 - mmengine - INFO - Epoch(val) [5][3000/7672] eta: 0:22:36 time: 0.2903 data_time: 0.0041 memory: 1304 2022/09/22 02:29:48 - mmengine - INFO - Epoch(val) [5][3100/7672] eta: 0:17:26 time: 0.2289 data_time: 0.0009 memory: 1304 2022/09/22 02:30:09 - mmengine - INFO - Epoch(val) [5][3200/7672] eta: 0:16:05 time: 0.2159 data_time: 0.0009 memory: 1304 2022/09/22 02:30:33 - mmengine - INFO - Epoch(val) [5][3300/7672] eta: 0:20:27 time: 0.2807 data_time: 0.0045 memory: 1304 2022/09/22 02:30:55 - mmengine - INFO - Epoch(val) [5][3400/7672] eta: 0:14:42 time: 0.2065 data_time: 0.0009 memory: 1304 2022/09/22 02:31:18 - mmengine - INFO - Epoch(val) [5][3500/7672] eta: 0:15:01 time: 0.2162 data_time: 0.0009 memory: 1304 2022/09/22 02:31:43 - mmengine - INFO - Epoch(val) [5][3600/7672] eta: 0:17:23 time: 0.2562 data_time: 0.0011 memory: 1304 2022/09/22 02:32:09 - mmengine - INFO - Epoch(val) [5][3700/7672] eta: 0:17:21 time: 0.2621 data_time: 0.0009 memory: 1304 2022/09/22 02:32:34 - mmengine - INFO - Epoch(val) [5][3800/7672] eta: 0:19:28 time: 0.3018 data_time: 0.0032 memory: 1304 2022/09/22 02:33:00 - mmengine - INFO - Epoch(val) [5][3900/7672] eta: 0:17:11 time: 0.2734 data_time: 0.0013 memory: 1304 2022/09/22 02:33:26 - mmengine - INFO - Epoch(val) [5][4000/7672] eta: 0:12:59 time: 0.2124 data_time: 0.0025 memory: 1304 2022/09/22 02:33:48 - mmengine - INFO - Epoch(val) [5][4100/7672] eta: 0:11:57 time: 0.2008 data_time: 0.0010 memory: 1304 2022/09/22 02:34:09 - mmengine - INFO - Epoch(val) [5][4200/7672] eta: 0:12:04 time: 0.2087 data_time: 0.0009 memory: 1304 2022/09/22 02:34:31 - mmengine - INFO - Epoch(val) [5][4300/7672] eta: 0:12:44 time: 0.2268 data_time: 0.0009 memory: 1304 2022/09/22 02:34:55 - mmengine - INFO - Epoch(val) [5][4400/7672] eta: 0:11:06 time: 0.2037 data_time: 0.0008 memory: 1304 2022/09/22 02:35:19 - mmengine - INFO - Epoch(val) [5][4500/7672] eta: 0:12:27 time: 0.2357 data_time: 0.0009 memory: 1304 2022/09/22 02:35:45 - mmengine - INFO - Epoch(val) [5][4600/7672] eta: 0:11:57 time: 0.2334 data_time: 0.0009 memory: 1304 2022/09/22 02:36:06 - mmengine - INFO - Epoch(val) [5][4700/7672] eta: 0:09:58 time: 0.2014 data_time: 0.0008 memory: 1304 2022/09/22 02:36:27 - mmengine - INFO - Epoch(val) [5][4800/7672] eta: 0:09:20 time: 0.1952 data_time: 0.0008 memory: 1304 2022/09/22 02:36:51 - mmengine - INFO - Epoch(val) [5][4900/7672] eta: 0:09:29 time: 0.2054 data_time: 0.0023 memory: 1304 2022/09/22 02:37:13 - mmengine - INFO - Epoch(val) [5][5000/7672] eta: 0:11:45 time: 0.2639 data_time: 0.0076 memory: 1304 2022/09/22 02:37:35 - mmengine - INFO - Epoch(val) [5][5100/7672] eta: 0:10:20 time: 0.2414 data_time: 0.0028 memory: 1304 2022/09/22 02:38:01 - mmengine - INFO - Epoch(val) [5][5200/7672] eta: 0:08:44 time: 0.2123 data_time: 0.0009 memory: 1304 2022/09/22 02:38:24 - mmengine - INFO - Epoch(val) [5][5300/7672] eta: 0:08:26 time: 0.2136 data_time: 0.0009 memory: 1304 2022/09/22 02:38:45 - mmengine - INFO - Epoch(val) [5][5400/7672] eta: 0:07:19 time: 0.1933 data_time: 0.0024 memory: 1304 2022/09/22 02:39:06 - mmengine - INFO - Epoch(val) [5][5500/7672] eta: 0:07:03 time: 0.1949 data_time: 0.0009 memory: 1304 2022/09/22 02:39:30 - mmengine - INFO - Epoch(val) [5][5600/7672] eta: 0:08:49 time: 0.2558 data_time: 0.0010 memory: 1304 2022/09/22 02:39:55 - mmengine - INFO - Epoch(val) [5][5700/7672] eta: 0:08:22 time: 0.2546 data_time: 0.0010 memory: 1304 2022/09/22 02:40:18 - mmengine - INFO - Epoch(val) [5][5800/7672] eta: 0:07:39 time: 0.2454 data_time: 0.0010 memory: 1304 2022/09/22 02:40:42 - mmengine - INFO - Epoch(val) [5][5900/7672] eta: 0:06:29 time: 0.2195 data_time: 0.0009 memory: 1304 2022/09/22 02:41:03 - mmengine - INFO - Epoch(val) [5][6000/7672] eta: 0:06:46 time: 0.2432 data_time: 0.0051 memory: 1304 2022/09/22 02:41:25 - mmengine - INFO - Epoch(val) [5][6100/7672] eta: 0:05:12 time: 0.1987 data_time: 0.0009 memory: 1304 2022/09/22 02:41:45 - mmengine - INFO - Epoch(val) [5][6200/7672] eta: 0:05:08 time: 0.2096 data_time: 0.0009 memory: 1304 2022/09/22 02:42:09 - mmengine - INFO - Epoch(val) [5][6300/7672] eta: 0:05:12 time: 0.2276 data_time: 0.0009 memory: 1304 2022/09/22 02:42:35 - mmengine - INFO - Epoch(val) [5][6400/7672] eta: 0:05:14 time: 0.2473 data_time: 0.0010 memory: 1304 2022/09/22 02:42:58 - mmengine - INFO - Epoch(val) [5][6500/7672] eta: 0:03:57 time: 0.2023 data_time: 0.0008 memory: 1304 2022/09/22 02:43:21 - mmengine - INFO - Epoch(val) [5][6600/7672] eta: 0:03:43 time: 0.2085 data_time: 0.0008 memory: 1304 2022/09/22 02:43:43 - mmengine - INFO - Epoch(val) [5][6700/7672] eta: 0:03:25 time: 0.2109 data_time: 0.0035 memory: 1304 2022/09/22 02:44:04 - mmengine - INFO - Epoch(val) [5][6800/7672] eta: 0:03:13 time: 0.2215 data_time: 0.0024 memory: 1304 2022/09/22 02:44:26 - mmengine - INFO - Epoch(val) [5][6900/7672] eta: 0:02:59 time: 0.2322 data_time: 0.0029 memory: 1304 2022/09/22 02:44:48 - mmengine - INFO - Epoch(val) [5][7000/7672] eta: 0:02:43 time: 0.2432 data_time: 0.0010 memory: 1304 2022/09/22 02:45:13 - mmengine - INFO - Epoch(val) [5][7100/7672] eta: 0:02:41 time: 0.2821 data_time: 0.0055 memory: 1304 2022/09/22 02:45:39 - mmengine - INFO - Epoch(val) [5][7200/7672] eta: 0:01:59 time: 0.2531 data_time: 0.0010 memory: 1304 2022/09/22 02:46:03 - mmengine - INFO - Epoch(val) [5][7300/7672] eta: 0:01:22 time: 0.2230 data_time: 0.0022 memory: 1304 2022/09/22 02:46:24 - mmengine - INFO - Epoch(val) [5][7400/7672] eta: 0:01:03 time: 0.2352 data_time: 0.0045 memory: 1304 2022/09/22 02:46:47 - mmengine - INFO - Epoch(val) [5][7500/7672] eta: 0:00:35 time: 0.2080 data_time: 0.0009 memory: 1304 2022/09/22 02:47:08 - mmengine - INFO - Epoch(val) [5][7600/7672] eta: 0:00:13 time: 0.1937 data_time: 0.0009 memory: 1304 2022/09/22 02:47:23 - mmengine - INFO - Epoch(val) [5][7672/7672] CUTE80/recog/word_acc_ignore_case_symbol: 0.8750 IIIT5K/recog/word_acc_ignore_case_symbol: 0.9473 SVT/recog/word_acc_ignore_case_symbol: 0.8918 SVTP/recog/word_acc_ignore_case_symbol: 0.7969 IC13/recog/word_acc_ignore_case_symbol: 0.9438 IC15/recog/word_acc_ignore_case_symbol: 0.7357 2022/09/22 02:48:28 - mmengine - INFO - Epoch(train) [6][100/42151] lr: 3.0000e-06 eta: 6:42:25 time: 0.6391 data_time: 0.2111 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/22 02:49:27 - mmengine - INFO - Epoch(train) [6][200/42151] lr: 3.0000e-06 eta: 6:41:28 time: 0.7470 data_time: 0.2289 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/22 02:49:51 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 02:50:22 - mmengine - INFO - Epoch(train) [6][300/42151] lr: 3.0000e-06 eta: 6:40:30 time: 0.5360 data_time: 0.1353 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/22 02:51:14 - mmengine - INFO - Epoch(train) [6][400/42151] lr: 3.0000e-06 eta: 6:39:32 time: 0.4847 data_time: 0.0723 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/22 02:52:13 - mmengine - INFO - Epoch(train) [6][500/42151] lr: 3.0000e-06 eta: 6:38:34 time: 0.4915 data_time: 0.0442 memory: 14682 loss_ce: 0.0065 loss: 0.0065 2022/09/22 02:53:09 - mmengine - INFO - Epoch(train) [6][600/42151] lr: 3.0000e-06 eta: 6:37:37 time: 0.4098 data_time: 0.0336 memory: 14682 loss_ce: 0.0073 loss: 0.0073 2022/09/22 02:54:04 - mmengine - INFO - Epoch(train) [6][700/42151] lr: 3.0000e-06 eta: 6:36:39 time: 0.5742 data_time: 0.1940 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/22 02:54:56 - mmengine - INFO - Epoch(train) [6][800/42151] lr: 3.0000e-06 eta: 6:35:40 time: 0.6289 data_time: 0.1598 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/22 02:55:50 - mmengine - INFO - Epoch(train) [6][900/42151] lr: 3.0000e-06 eta: 6:34:42 time: 0.4918 data_time: 0.1136 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/22 02:56:43 - mmengine - INFO - Epoch(train) [6][1000/42151] lr: 3.0000e-06 eta: 6:33:44 time: 0.4885 data_time: 0.1145 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/22 02:57:35 - mmengine - INFO - Epoch(train) [6][1100/42151] lr: 3.0000e-06 eta: 6:32:45 time: 0.4199 data_time: 0.0235 memory: 14682 loss_ce: 0.0070 loss: 0.0070 2022/09/22 02:58:27 - mmengine - INFO - Epoch(train) [6][1200/42151] lr: 3.0000e-06 eta: 6:31:47 time: 0.4072 data_time: 0.0305 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/22 02:58:52 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 02:59:22 - mmengine - INFO - Epoch(train) [6][1300/42151] lr: 3.0000e-06 eta: 6:30:49 time: 0.5646 data_time: 0.1656 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/22 03:00:14 - mmengine - INFO - Epoch(train) [6][1400/42151] lr: 3.0000e-06 eta: 6:29:51 time: 0.5601 data_time: 0.1441 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/22 03:01:08 - mmengine - INFO - Epoch(train) [6][1500/42151] lr: 3.0000e-06 eta: 6:28:52 time: 0.4743 data_time: 0.0912 memory: 14682 loss_ce: 0.0068 loss: 0.0068 2022/09/22 03:02:00 - mmengine - INFO - Epoch(train) [6][1600/42151] lr: 3.0000e-06 eta: 6:27:54 time: 0.5127 data_time: 0.1000 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/22 03:02:53 - mmengine - INFO - Epoch(train) [6][1700/42151] lr: 3.0000e-06 eta: 6:26:56 time: 0.4308 data_time: 0.0237 memory: 14682 loss_ce: 0.0068 loss: 0.0068 2022/09/22 03:03:47 - mmengine - INFO - Epoch(train) [6][1800/42151] lr: 3.0000e-06 eta: 6:25:58 time: 0.4703 data_time: 0.0392 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/22 03:04:41 - mmengine - INFO - Epoch(train) [6][1900/42151] lr: 3.0000e-06 eta: 6:25:00 time: 0.5479 data_time: 0.1688 memory: 14682 loss_ce: 0.0068 loss: 0.0068 2022/09/22 03:05:33 - mmengine - INFO - Epoch(train) [6][2000/42151] lr: 3.0000e-06 eta: 6:24:01 time: 0.5269 data_time: 0.1509 memory: 14682 loss_ce: 0.0075 loss: 0.0075 2022/09/22 03:06:28 - mmengine - INFO - Epoch(train) [6][2100/42151] lr: 3.0000e-06 eta: 6:23:04 time: 0.5363 data_time: 0.1379 memory: 14682 loss_ce: 0.0067 loss: 0.0067 2022/09/22 03:07:21 - mmengine - INFO - Epoch(train) [6][2200/42151] lr: 3.0000e-06 eta: 6:22:05 time: 0.5274 data_time: 0.1310 memory: 14682 loss_ce: 0.0069 loss: 0.0069 2022/09/22 03:07:45 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 03:08:14 - mmengine - INFO - Epoch(train) [6][2300/42151] lr: 3.0000e-06 eta: 6:21:07 time: 0.4216 data_time: 0.0222 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/22 03:09:06 - mmengine - INFO - Epoch(train) [6][2400/42151] lr: 3.0000e-06 eta: 6:20:09 time: 0.4268 data_time: 0.0314 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/22 03:10:00 - mmengine - INFO - Epoch(train) [6][2500/42151] lr: 3.0000e-06 eta: 6:19:11 time: 0.6020 data_time: 0.1613 memory: 14682 loss_ce: 0.0073 loss: 0.0073 2022/09/22 03:10:53 - mmengine - INFO - Epoch(train) [6][2600/42151] lr: 3.0000e-06 eta: 6:18:12 time: 0.5488 data_time: 0.1469 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/22 03:11:46 - mmengine - INFO - Epoch(train) [6][2700/42151] lr: 3.0000e-06 eta: 6:17:14 time: 0.4837 data_time: 0.1095 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/22 03:12:39 - mmengine - INFO - Epoch(train) [6][2800/42151] lr: 3.0000e-06 eta: 6:16:16 time: 0.5681 data_time: 0.1376 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/22 03:13:31 - mmengine - INFO - Epoch(train) [6][2900/42151] lr: 3.0000e-06 eta: 6:15:18 time: 0.4075 data_time: 0.0325 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/22 03:14:24 - mmengine - INFO - Epoch(train) [6][3000/42151] lr: 3.0000e-06 eta: 6:14:19 time: 0.4374 data_time: 0.0404 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/22 03:15:18 - mmengine - INFO - Epoch(train) [6][3100/42151] lr: 3.0000e-06 eta: 6:13:22 time: 0.6068 data_time: 0.1565 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/22 03:16:10 - mmengine - INFO - Epoch(train) [6][3200/42151] lr: 3.0000e-06 eta: 6:12:23 time: 0.5139 data_time: 0.1386 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/22 03:16:34 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 03:17:03 - mmengine - INFO - Epoch(train) [6][3300/42151] lr: 3.0000e-06 eta: 6:11:25 time: 0.4824 data_time: 0.1033 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/22 03:17:54 - mmengine - INFO - Epoch(train) [6][3400/42151] lr: 3.0000e-06 eta: 6:10:26 time: 0.4848 data_time: 0.1090 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/22 03:18:47 - mmengine - INFO - Epoch(train) [6][3500/42151] lr: 3.0000e-06 eta: 6:09:28 time: 0.4367 data_time: 0.0478 memory: 14682 loss_ce: 0.0068 loss: 0.0068 2022/09/22 03:19:39 - mmengine - INFO - Epoch(train) [6][3600/42151] lr: 3.0000e-06 eta: 6:08:30 time: 0.4795 data_time: 0.0470 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 03:20:32 - mmengine - INFO - Epoch(train) [6][3700/42151] lr: 3.0000e-06 eta: 6:07:32 time: 0.5549 data_time: 0.1755 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/22 03:21:24 - mmengine - INFO - Epoch(train) [6][3800/42151] lr: 3.0000e-06 eta: 6:06:33 time: 0.5365 data_time: 0.1598 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/22 03:22:17 - mmengine - INFO - Epoch(train) [6][3900/42151] lr: 3.0000e-06 eta: 6:05:35 time: 0.5321 data_time: 0.1021 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/22 03:23:10 - mmengine - INFO - Epoch(train) [6][4000/42151] lr: 3.0000e-06 eta: 6:04:37 time: 0.5375 data_time: 0.1252 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 03:24:02 - mmengine - INFO - Epoch(train) [6][4100/42151] lr: 3.0000e-06 eta: 6:03:39 time: 0.4092 data_time: 0.0342 memory: 14682 loss_ce: 0.0071 loss: 0.0071 2022/09/22 03:24:55 - mmengine - INFO - Epoch(train) [6][4200/42151] lr: 3.0000e-06 eta: 6:02:41 time: 0.4448 data_time: 0.0696 memory: 14682 loss_ce: 0.0070 loss: 0.0070 2022/09/22 03:25:19 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 03:25:49 - mmengine - INFO - Epoch(train) [6][4300/42151] lr: 3.0000e-06 eta: 6:01:43 time: 0.5846 data_time: 0.1694 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/22 03:26:41 - mmengine - INFO - Epoch(train) [6][4400/42151] lr: 3.0000e-06 eta: 6:00:45 time: 0.6194 data_time: 0.1666 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/22 03:27:33 - mmengine - INFO - Epoch(train) [6][4500/42151] lr: 3.0000e-06 eta: 5:59:46 time: 0.4937 data_time: 0.0859 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/22 03:28:25 - mmengine - INFO - Epoch(train) [6][4600/42151] lr: 3.0000e-06 eta: 5:58:48 time: 0.5017 data_time: 0.1232 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/22 03:29:17 - mmengine - INFO - Epoch(train) [6][4700/42151] lr: 3.0000e-06 eta: 5:57:50 time: 0.4915 data_time: 0.0427 memory: 14682 loss_ce: 0.0070 loss: 0.0070 2022/09/22 03:30:09 - mmengine - INFO - Epoch(train) [6][4800/42151] lr: 3.0000e-06 eta: 5:56:52 time: 0.4538 data_time: 0.0733 memory: 14682 loss_ce: 0.0086 loss: 0.0086 2022/09/22 03:31:02 - mmengine - INFO - Epoch(train) [6][4900/42151] lr: 3.0000e-06 eta: 5:55:53 time: 0.5155 data_time: 0.1400 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 03:31:53 - mmengine - INFO - Epoch(train) [6][5000/42151] lr: 3.0000e-06 eta: 5:54:55 time: 0.5500 data_time: 0.1639 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/22 03:32:46 - mmengine - INFO - Epoch(train) [6][5100/42151] lr: 3.0000e-06 eta: 5:53:57 time: 0.4858 data_time: 0.1088 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/22 03:33:38 - mmengine - INFO - Epoch(train) [6][5200/42151] lr: 3.0000e-06 eta: 5:52:59 time: 0.5137 data_time: 0.1032 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/22 03:34:02 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 03:34:29 - mmengine - INFO - Epoch(train) [6][5300/42151] lr: 3.0000e-06 eta: 5:52:00 time: 0.4073 data_time: 0.0338 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/22 03:35:22 - mmengine - INFO - Epoch(train) [6][5400/42151] lr: 3.0000e-06 eta: 5:51:02 time: 0.4174 data_time: 0.0407 memory: 14682 loss_ce: 0.0066 loss: 0.0066 2022/09/22 03:36:14 - mmengine - INFO - Epoch(train) [6][5500/42151] lr: 3.0000e-06 eta: 5:50:04 time: 0.5406 data_time: 0.1657 memory: 14682 loss_ce: 0.0071 loss: 0.0071 2022/09/22 03:37:06 - mmengine - INFO - Epoch(train) [6][5600/42151] lr: 3.0000e-06 eta: 5:49:06 time: 0.5213 data_time: 0.1431 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/22 03:37:59 - mmengine - INFO - Epoch(train) [6][5700/42151] lr: 3.0000e-06 eta: 5:48:08 time: 0.5479 data_time: 0.1322 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/22 03:38:51 - mmengine - INFO - Epoch(train) [6][5800/42151] lr: 3.0000e-06 eta: 5:47:09 time: 0.5203 data_time: 0.1165 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/22 03:39:43 - mmengine - INFO - Epoch(train) [6][5900/42151] lr: 3.0000e-06 eta: 5:46:11 time: 0.4101 data_time: 0.0315 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/22 03:40:35 - mmengine - INFO - Epoch(train) [6][6000/42151] lr: 3.0000e-06 eta: 5:45:13 time: 0.4398 data_time: 0.0613 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/22 03:41:29 - mmengine - INFO - Epoch(train) [6][6100/42151] lr: 3.0000e-06 eta: 5:44:15 time: 0.5374 data_time: 0.1384 memory: 14682 loss_ce: 0.0066 loss: 0.0066 2022/09/22 03:42:22 - mmengine - INFO - Epoch(train) [6][6200/42151] lr: 3.0000e-06 eta: 5:43:17 time: 0.5912 data_time: 0.1561 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 03:42:46 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 03:43:15 - mmengine - INFO - Epoch(train) [6][6300/42151] lr: 3.0000e-06 eta: 5:42:19 time: 0.5017 data_time: 0.1032 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/22 03:44:07 - mmengine - INFO - Epoch(train) [6][6400/42151] lr: 3.0000e-06 eta: 5:41:21 time: 0.4791 data_time: 0.1061 memory: 14682 loss_ce: 0.0073 loss: 0.0073 2022/09/22 03:45:00 - mmengine - INFO - Epoch(train) [6][6500/42151] lr: 3.0000e-06 eta: 5:40:23 time: 0.4790 data_time: 0.0323 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/22 03:45:53 - mmengine - INFO - Epoch(train) [6][6600/42151] lr: 3.0000e-06 eta: 5:39:25 time: 0.4587 data_time: 0.0830 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/22 03:46:46 - mmengine - INFO - Epoch(train) [6][6700/42151] lr: 3.0000e-06 eta: 5:38:27 time: 0.5249 data_time: 0.1487 memory: 14682 loss_ce: 0.0073 loss: 0.0073 2022/09/22 03:47:39 - mmengine - INFO - Epoch(train) [6][6800/42151] lr: 3.0000e-06 eta: 5:37:29 time: 0.5926 data_time: 0.1358 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/22 03:48:33 - mmengine - INFO - Epoch(train) [6][6900/42151] lr: 3.0000e-06 eta: 5:36:31 time: 0.5280 data_time: 0.1470 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/22 03:49:27 - mmengine - INFO - Epoch(train) [6][7000/42151] lr: 3.0000e-06 eta: 5:35:33 time: 0.4891 data_time: 0.1136 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/22 03:50:19 - mmengine - INFO - Epoch(train) [6][7100/42151] lr: 3.0000e-06 eta: 5:34:35 time: 0.4157 data_time: 0.0318 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/22 03:51:11 - mmengine - INFO - Epoch(train) [6][7200/42151] lr: 3.0000e-06 eta: 5:33:37 time: 0.4473 data_time: 0.0427 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/22 03:51:35 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 03:52:05 - mmengine - INFO - Epoch(train) [6][7300/42151] lr: 3.0000e-06 eta: 5:32:39 time: 0.5986 data_time: 0.1938 memory: 14682 loss_ce: 0.0073 loss: 0.0073 2022/09/22 03:52:57 - mmengine - INFO - Epoch(train) [6][7400/42151] lr: 3.0000e-06 eta: 5:31:41 time: 0.5210 data_time: 0.1451 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/22 03:53:50 - mmengine - INFO - Epoch(train) [6][7500/42151] lr: 3.0000e-06 eta: 5:30:43 time: 0.5187 data_time: 0.1132 memory: 14682 loss_ce: 0.0085 loss: 0.0085 2022/09/22 03:54:42 - mmengine - INFO - Epoch(train) [6][7600/42151] lr: 3.0000e-06 eta: 5:29:45 time: 0.5239 data_time: 0.1219 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/22 03:55:35 - mmengine - INFO - Epoch(train) [6][7700/42151] lr: 3.0000e-06 eta: 5:28:47 time: 0.4109 data_time: 0.0337 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/22 03:56:27 - mmengine - INFO - Epoch(train) [6][7800/42151] lr: 3.0000e-06 eta: 5:27:49 time: 0.4549 data_time: 0.0779 memory: 14682 loss_ce: 0.0088 loss: 0.0088 2022/09/22 03:57:21 - mmengine - INFO - Epoch(train) [6][7900/42151] lr: 3.0000e-06 eta: 5:26:51 time: 0.6011 data_time: 0.1671 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/22 03:58:13 - mmengine - INFO - Epoch(train) [6][8000/42151] lr: 3.0000e-06 eta: 5:25:53 time: 0.5417 data_time: 0.1493 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/22 03:59:07 - mmengine - INFO - Epoch(train) [6][8100/42151] lr: 3.0000e-06 eta: 5:24:55 time: 0.5036 data_time: 0.0910 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/22 04:00:00 - mmengine - INFO - Epoch(train) [6][8200/42151] lr: 3.0000e-06 eta: 5:23:58 time: 0.5309 data_time: 0.1130 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/22 04:00:25 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 04:00:54 - mmengine - INFO - Epoch(train) [6][8300/42151] lr: 3.0000e-06 eta: 5:23:00 time: 0.4306 data_time: 0.0310 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/22 04:01:46 - mmengine - INFO - Epoch(train) [6][8400/42151] lr: 3.0000e-06 eta: 5:22:02 time: 0.4505 data_time: 0.0742 memory: 14682 loss_ce: 0.0067 loss: 0.0067 2022/09/22 04:02:40 - mmengine - INFO - Epoch(train) [6][8500/42151] lr: 3.0000e-06 eta: 5:21:04 time: 0.5428 data_time: 0.1577 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/22 04:03:33 - mmengine - INFO - Epoch(train) [6][8600/42151] lr: 3.0000e-06 eta: 5:20:06 time: 0.5998 data_time: 0.1563 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/22 04:04:26 - mmengine - INFO - Epoch(train) [6][8700/42151] lr: 3.0000e-06 eta: 5:19:08 time: 0.4915 data_time: 0.1126 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/22 04:05:18 - mmengine - INFO - Epoch(train) [6][8800/42151] lr: 3.0000e-06 eta: 5:18:10 time: 0.4895 data_time: 0.1145 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/22 04:06:09 - mmengine - INFO - Epoch(train) [6][8900/42151] lr: 3.0000e-06 eta: 5:17:12 time: 0.4083 data_time: 0.0319 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 04:07:01 - mmengine - INFO - Epoch(train) [6][9000/42151] lr: 3.0000e-06 eta: 5:16:14 time: 0.4262 data_time: 0.0414 memory: 14682 loss_ce: 0.0071 loss: 0.0071 2022/09/22 04:07:54 - mmengine - INFO - Epoch(train) [6][9100/42151] lr: 3.0000e-06 eta: 5:15:16 time: 0.5607 data_time: 0.1715 memory: 14682 loss_ce: 0.0073 loss: 0.0073 2022/09/22 04:08:46 - mmengine - INFO - Epoch(train) [6][9200/42151] lr: 3.0000e-06 eta: 5:14:18 time: 0.5273 data_time: 0.1531 memory: 14682 loss_ce: 0.0099 loss: 0.0099 2022/09/22 04:09:10 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 04:09:39 - mmengine - INFO - Epoch(train) [6][9300/42151] lr: 3.0000e-06 eta: 5:13:20 time: 0.4845 data_time: 0.1063 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/22 04:10:32 - mmengine - INFO - Epoch(train) [6][9400/42151] lr: 3.0000e-06 eta: 5:12:22 time: 0.5888 data_time: 0.1129 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/22 04:11:25 - mmengine - INFO - Epoch(train) [6][9500/42151] lr: 3.0000e-06 eta: 5:11:24 time: 0.4812 data_time: 0.0973 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 04:12:16 - mmengine - INFO - Epoch(train) [6][9600/42151] lr: 3.0000e-06 eta: 5:10:26 time: 0.4525 data_time: 0.0726 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/22 04:13:10 - mmengine - INFO - Epoch(train) [6][9700/42151] lr: 3.0000e-06 eta: 5:09:28 time: 0.5116 data_time: 0.1365 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 04:14:02 - mmengine - INFO - Epoch(train) [6][9800/42151] lr: 3.0000e-06 eta: 5:08:30 time: 0.5638 data_time: 0.1562 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/22 04:14:55 - mmengine - INFO - Epoch(train) [6][9900/42151] lr: 3.0000e-06 eta: 5:07:33 time: 0.4848 data_time: 0.1103 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/22 04:15:47 - mmengine - INFO - Epoch(train) [6][10000/42151] lr: 3.0000e-06 eta: 5:06:35 time: 0.5074 data_time: 0.1303 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 04:16:39 - mmengine - INFO - Epoch(train) [6][10100/42151] lr: 3.0000e-06 eta: 5:05:37 time: 0.4371 data_time: 0.0624 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/22 04:17:32 - mmengine - INFO - Epoch(train) [6][10200/42151] lr: 3.0000e-06 eta: 5:04:39 time: 0.4243 data_time: 0.0476 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/22 04:17:56 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 04:18:26 - mmengine - INFO - Epoch(train) [6][10300/42151] lr: 3.0000e-06 eta: 5:03:41 time: 0.6106 data_time: 0.1825 memory: 14682 loss_ce: 0.0075 loss: 0.0075 2022/09/22 04:19:18 - mmengine - INFO - Epoch(train) [6][10400/42151] lr: 3.0000e-06 eta: 5:02:43 time: 0.5405 data_time: 0.1641 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/22 04:20:11 - mmengine - INFO - Epoch(train) [6][10500/42151] lr: 3.0000e-06 eta: 5:01:45 time: 0.5098 data_time: 0.0885 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/22 04:21:03 - mmengine - INFO - Epoch(train) [6][10600/42151] lr: 3.0000e-06 eta: 5:00:47 time: 0.4976 data_time: 0.0991 memory: 14682 loss_ce: 0.0072 loss: 0.0072 2022/09/22 04:21:55 - mmengine - INFO - Epoch(train) [6][10700/42151] lr: 3.0000e-06 eta: 4:59:49 time: 0.5013 data_time: 0.0499 memory: 14682 loss_ce: 0.0070 loss: 0.0070 2022/09/22 04:22:47 - mmengine - INFO - Epoch(train) [6][10800/42151] lr: 3.0000e-06 eta: 4:58:51 time: 0.4301 data_time: 0.0539 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/22 04:23:41 - mmengine - INFO - Epoch(train) [6][10900/42151] lr: 3.0000e-06 eta: 4:57:54 time: 0.5269 data_time: 0.1492 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/22 04:24:33 - mmengine - INFO - Epoch(train) [6][11000/42151] lr: 3.0000e-06 eta: 4:56:56 time: 0.5719 data_time: 0.1389 memory: 14682 loss_ce: 0.0065 loss: 0.0065 2022/09/22 04:25:26 - mmengine - INFO - Epoch(train) [6][11100/42151] lr: 3.0000e-06 eta: 4:55:58 time: 0.4956 data_time: 0.1141 memory: 14682 loss_ce: 0.0097 loss: 0.0097 2022/09/22 04:26:17 - mmengine - INFO - Epoch(train) [6][11200/42151] lr: 3.0000e-06 eta: 4:55:00 time: 0.5049 data_time: 0.0918 memory: 14682 loss_ce: 0.0075 loss: 0.0075 2022/09/22 04:26:41 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 04:27:09 - mmengine - INFO - Epoch(train) [6][11300/42151] lr: 3.0000e-06 eta: 4:54:02 time: 0.4677 data_time: 0.0895 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/22 04:28:01 - mmengine - INFO - Epoch(train) [6][11400/42151] lr: 3.0000e-06 eta: 4:53:04 time: 0.4602 data_time: 0.0797 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/22 04:28:54 - mmengine - INFO - Epoch(train) [6][11500/42151] lr: 3.0000e-06 eta: 4:52:06 time: 0.5632 data_time: 0.1376 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 04:29:45 - mmengine - INFO - Epoch(train) [6][11600/42151] lr: 3.0000e-06 eta: 4:51:08 time: 0.5224 data_time: 0.1455 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/22 04:30:39 - mmengine - INFO - Epoch(train) [6][11700/42151] lr: 3.0000e-06 eta: 4:50:11 time: 0.4945 data_time: 0.1187 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/22 04:31:30 - mmengine - INFO - Epoch(train) [6][11800/42151] lr: 3.0000e-06 eta: 4:49:13 time: 0.4969 data_time: 0.1232 memory: 14682 loss_ce: 0.0072 loss: 0.0072 2022/09/22 04:32:22 - mmengine - INFO - Epoch(train) [6][11900/42151] lr: 3.0000e-06 eta: 4:48:15 time: 0.4568 data_time: 0.0803 memory: 14682 loss_ce: 0.0083 loss: 0.0083 2022/09/22 04:33:15 - mmengine - INFO - Epoch(train) [6][12000/42151] lr: 3.0000e-06 eta: 4:47:17 time: 0.5172 data_time: 0.0884 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/22 04:34:09 - mmengine - INFO - Epoch(train) [6][12100/42151] lr: 3.0000e-06 eta: 4:46:20 time: 0.5090 data_time: 0.1337 memory: 14682 loss_ce: 0.0072 loss: 0.0072 2022/09/22 04:35:01 - mmengine - INFO - Epoch(train) [6][12200/42151] lr: 3.0000e-06 eta: 4:45:22 time: 0.5158 data_time: 0.1420 memory: 14682 loss_ce: 0.0093 loss: 0.0093 2022/09/22 04:35:25 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 04:35:56 - mmengine - INFO - Epoch(train) [6][12300/42151] lr: 3.0000e-06 eta: 4:44:24 time: 0.5809 data_time: 0.1297 memory: 14682 loss_ce: 0.0074 loss: 0.0074 2022/09/22 04:36:47 - mmengine - INFO - Epoch(train) [6][12400/42151] lr: 3.0000e-06 eta: 4:43:26 time: 0.4894 data_time: 0.1126 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/22 04:37:39 - mmengine - INFO - Epoch(train) [6][12500/42151] lr: 3.0000e-06 eta: 4:42:28 time: 0.4249 data_time: 0.0494 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/22 04:38:31 - mmengine - INFO - Epoch(train) [6][12600/42151] lr: 3.0000e-06 eta: 4:41:30 time: 0.4236 data_time: 0.0486 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/22 04:39:25 - mmengine - INFO - Epoch(train) [6][12700/42151] lr: 3.0000e-06 eta: 4:40:33 time: 0.5788 data_time: 0.1927 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/22 04:40:16 - mmengine - INFO - Epoch(train) [6][12800/42151] lr: 3.0000e-06 eta: 4:39:35 time: 0.5900 data_time: 0.1692 memory: 14682 loss_ce: 0.0092 loss: 0.0092 2022/09/22 04:41:07 - mmengine - INFO - Epoch(train) [6][12900/42151] lr: 3.0000e-06 eta: 4:38:37 time: 0.4790 data_time: 0.0821 memory: 14682 loss_ce: 0.0096 loss: 0.0096 2022/09/22 04:41:58 - mmengine - INFO - Epoch(train) [6][13000/42151] lr: 3.0000e-06 eta: 4:37:39 time: 0.4793 data_time: 0.0729 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/22 04:42:50 - mmengine - INFO - Epoch(train) [6][13100/42151] lr: 3.0000e-06 eta: 4:36:41 time: 0.4293 data_time: 0.0505 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/22 04:43:43 - mmengine - INFO - Epoch(train) [6][13200/42151] lr: 3.0000e-06 eta: 4:35:43 time: 0.4359 data_time: 0.0594 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/22 04:44:07 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 04:44:36 - mmengine - INFO - Epoch(train) [6][13300/42151] lr: 3.0000e-06 eta: 4:34:46 time: 0.5429 data_time: 0.1438 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/22 04:45:27 - mmengine - INFO - Epoch(train) [6][13400/42151] lr: 3.0000e-06 eta: 4:33:48 time: 0.5308 data_time: 0.1551 memory: 14682 loss_ce: 0.0073 loss: 0.0073 2022/09/22 04:46:20 - mmengine - INFO - Epoch(train) [6][13500/42151] lr: 3.0000e-06 eta: 4:32:50 time: 0.4856 data_time: 0.0914 memory: 14682 loss_ce: 0.0075 loss: 0.0075 2022/09/22 04:47:10 - mmengine - INFO - Epoch(train) [6][13600/42151] lr: 3.0000e-06 eta: 4:31:52 time: 0.4700 data_time: 0.0939 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/22 04:48:02 - mmengine - INFO - Epoch(train) [6][13700/42151] lr: 3.0000e-06 eta: 4:30:54 time: 0.4401 data_time: 0.0662 memory: 14682 loss_ce: 0.0090 loss: 0.0090 2022/09/22 04:48:53 - mmengine - INFO - Epoch(train) [6][13800/42151] lr: 3.0000e-06 eta: 4:29:56 time: 0.4463 data_time: 0.0694 memory: 14682 loss_ce: 0.0072 loss: 0.0072 2022/09/22 04:49:46 - mmengine - INFO - Epoch(train) [6][13900/42151] lr: 3.0000e-06 eta: 4:28:59 time: 0.5234 data_time: 0.1481 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/22 04:50:38 - mmengine - INFO - Epoch(train) [6][14000/42151] lr: 3.0000e-06 eta: 4:28:01 time: 0.5693 data_time: 0.1561 memory: 14682 loss_ce: 0.0069 loss: 0.0069 2022/09/22 04:51:30 - mmengine - INFO - Epoch(train) [6][14100/42151] lr: 3.0000e-06 eta: 4:27:03 time: 0.5000 data_time: 0.1236 memory: 14682 loss_ce: 0.0072 loss: 0.0072 2022/09/22 04:52:22 - mmengine - INFO - Epoch(train) [6][14200/42151] lr: 3.0000e-06 eta: 4:26:05 time: 0.4934 data_time: 0.1201 memory: 14682 loss_ce: 0.0095 loss: 0.0095 2022/09/22 04:52:45 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 04:53:13 - mmengine - INFO - Epoch(train) [6][14300/42151] lr: 3.0000e-06 eta: 4:25:08 time: 0.4438 data_time: 0.0708 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/22 04:54:03 - mmengine - INFO - Epoch(train) [6][14400/42151] lr: 3.0000e-06 eta: 4:24:10 time: 0.4431 data_time: 0.0696 memory: 14682 loss_ce: 0.0081 loss: 0.0081 2022/09/22 04:54:57 - mmengine - INFO - Epoch(train) [6][14500/42151] lr: 3.0000e-06 eta: 4:23:12 time: 0.6050 data_time: 0.1682 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/22 04:55:47 - mmengine - INFO - Epoch(train) [6][14600/42151] lr: 3.0000e-06 eta: 4:22:14 time: 0.5107 data_time: 0.1362 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/22 04:56:38 - mmengine - INFO - Epoch(train) [6][14700/42151] lr: 3.0000e-06 eta: 4:21:16 time: 0.5063 data_time: 0.1140 memory: 14682 loss_ce: 0.0084 loss: 0.0084 2022/09/22 04:57:29 - mmengine - INFO - Epoch(train) [6][14800/42151] lr: 3.0000e-06 eta: 4:20:18 time: 0.4804 data_time: 0.1001 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/22 04:58:19 - mmengine - INFO - Epoch(train) [6][14900/42151] lr: 3.0000e-06 eta: 4:19:20 time: 0.4150 data_time: 0.0406 memory: 14682 loss_ce: 0.0080 loss: 0.0080 2022/09/22 04:59:10 - mmengine - INFO - Epoch(train) [6][15000/42151] lr: 3.0000e-06 eta: 4:18:22 time: 0.4213 data_time: 0.0480 memory: 14682 loss_ce: 0.0089 loss: 0.0089 2022/09/22 05:00:02 - mmengine - INFO - Epoch(train) [6][15100/42151] lr: 3.0000e-06 eta: 4:17:25 time: 0.5343 data_time: 0.1603 memory: 14682 loss_ce: 0.0076 loss: 0.0076 2022/09/22 05:00:52 - mmengine - INFO - Epoch(train) [6][15200/42151] lr: 3.0000e-06 eta: 4:16:27 time: 0.5371 data_time: 0.1630 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/22 05:01:15 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 05:01:43 - mmengine - INFO - Epoch(train) [6][15300/42151] lr: 3.0000e-06 eta: 4:15:29 time: 0.4645 data_time: 0.0910 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/22 05:02:34 - mmengine - INFO - Epoch(train) [6][15400/42151] lr: 3.0000e-06 eta: 4:14:31 time: 0.4821 data_time: 0.0774 memory: 14682 loss_ce: 0.0079 loss: 0.0079 2022/09/22 05:03:26 - mmengine - INFO - Epoch(train) [6][15500/42151] lr: 3.0000e-06 eta: 4:13:33 time: 0.4370 data_time: 0.0483 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/22 05:04:16 - mmengine - INFO - Epoch(train) [6][15600/42151] lr: 3.0000e-06 eta: 4:12:36 time: 0.4633 data_time: 0.0553 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/22 05:05:08 - mmengine - INFO - Epoch(train) [6][15700/42151] lr: 3.0000e-06 eta: 4:11:38 time: 0.5437 data_time: 0.1374 memory: 14682 loss_ce: 0.0078 loss: 0.0078 2022/09/22 05:05:59 - mmengine - INFO - Epoch(train) [6][15800/42151] lr: 3.0000e-06 eta: 4:10:40 time: 0.5337 data_time: 0.1368 memory: 14682 loss_ce: 0.0072 loss: 0.0072 2022/09/22 05:06:51 - mmengine - INFO - Epoch(train) [6][15900/42151] lr: 3.0000e-06 eta: 4:09:42 time: 0.4959 data_time: 0.1189 memory: 14682 loss_ce: 0.0077 loss: 0.0077 2022/09/22 05:07:41 - mmengine - INFO - Epoch(train) [6][16000/42151] lr: 3.0000e-06 eta: 4:08:45 time: 0.4844 data_time: 0.1068 memory: 14682 loss_ce: 0.0082 loss: 0.0082 2022/09/22 05:08:32 - mmengine - INFO - Epoch(train) [6][16100/42151] lr: 3.0000e-06 eta: 4:07:47 time: 0.4362 data_time: 0.0623 memory: 14682 loss_ce: 0.0087 loss: 0.0087 2022/09/22 05:09:24 - mmengine - INFO - Epoch(train) [6][16200/42151] lr: 3.0000e-06 eta: 4:06:49 time: 0.4726 data_time: 0.0925 memory: 14682 loss_ce: 0.0091 loss: 0.0091 2022/09/22 05:09:48 - mmengine - INFO - Exp name: nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358 2022/09/22 05:10:17 - mmengine - INFO - Epoch(train) [6][16300/42151] lr: 3.0000e-06 eta: 4:05:52 time: 0.5328 data_time: 0.1485 memory: 14682 loss_ce: 0.0075 loss: 0.0075