2023/02/23 19:45:56 - 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: 42 GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/petrelfs/share/cuda-11.2 NVCC: Cuda compilation tools, release 11.2, V11.2.152 GCC: gcc (GCC) 5.4.0 PyTorch: 1.12.1 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201402 - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.3 - 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_61,code=sm_61;-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;-gencode;arch=compute_37,code=compute_37 - CuDNN 8.3.2 (built against CUDA 11.5) - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.13.1 OpenCV: 4.6.0 MMEngine: 0.5.0 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: 42 Distributed launcher: slurm Distributed training: True GPU number: 8 ------------------------------------------------------------ 2023/02/23 19:45:58 - mmengine - INFO - Config: custom_imports = dict( imports=['projects.SPTS.spts'], allow_failed_imports=False) file_client_args = dict(backend='disk') dictionary = dict( type='SPTSDictionary', dict_file= 'mmocr/projects/SPTS/config/spts/../../dicts/spts.txt', with_start=True, with_end=True, with_seq_end=True, same_start_end=False, with_padding=True, with_unknown=True, unknown_token=None) num_bins = 1000 model = dict( type='SPTS', data_preprocessor=dict( type='TextDetDataPreprocessor', mean=[0, 0, 0], std=[255, 255, 255], bgr_to_rgb=True), backbone=dict( type='mmdet.ResNet', depth=50, num_stages=4, out_indices=(3, ), frozen_stages=-1, norm_cfg=dict(type='BN', requires_grad=False), norm_eval=True, init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), style='pytorch'), encoder=dict(type='SPTSEncoder', d_backbone=2048, d_model=256), decoder=dict( type='SPTSDecoder', dictionary=dict( type='SPTSDictionary', dict_file= 'mmocr/projects/SPTS/config/spts/../../dicts/spts.txt', with_start=True, with_end=True, with_seq_end=True, same_start_end=False, with_padding=True, with_unknown=True, unknown_token=None), num_bins=1000, d_model=256, dropout=0.1, max_num_text=60, module_loss=dict( type='SPTSModuleLoss', num_bins=1000, ignore_first_char=True), postprocessor=dict(type='SPTSPostprocessor', num_bins=1000))) test_pipeline = [ dict( type='LoadImageFromFile', file_client_args=dict(backend='disk'), color_type='color_ignore_orientation'), dict( type='RescaleToShortSide', short_side_lens=[1000], long_side_bound=1824), dict( type='LoadOCRAnnotationsWithBezier', with_bbox=True, with_label=True, with_bezier=True, with_text=True), dict(type='Bezier2Polygon'), dict( type='ConvertText', dictionary=dict( type='SPTSDictionary', dict_file= 'mmocr/projects/SPTS/config/spts/../../dicts/spts.txt', with_start=True, with_end=True, with_seq_end=True, same_start_end=False, with_padding=True, with_unknown=True, unknown_token=None, num_bins=0)), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) ] train_pipeline = [ dict( type='LoadImageFromFile', file_client_args=dict(backend='disk'), color_type='color_ignore_orientation'), dict( type='LoadOCRAnnotationsWithBezier', with_bbox=True, with_label=True, with_bezier=True, with_text=True), dict(type='Bezier2Polygon'), dict(type='FixInvalidPolygon'), dict( type='ConvertText', dictionary=dict( type='SPTSDictionary', dict_file= 'mmocr/projects/SPTS/config/spts/../../dicts/spts.txt', with_start=True, with_end=True, with_seq_end=True, same_start_end=False, with_padding=True, with_unknown=True, unknown_token=None, num_bins=0)), dict(type='RemoveIgnored'), dict(type='RandomCrop', min_side_ratio=0.5), dict( type='RandomApply', transforms=[ dict( type='RandomRotate', max_angle=30, pad_with_fixed_color=True, use_canvas=True) ], prob=0.3), dict(type='FixInvalidPolygon'), dict( type='RandomChoiceResize', scales=[(640, 1600), (672, 1600), (704, 1600), (736, 1600), (768, 1600), (800, 1600), (832, 1600), (864, 1600), (896, 1600)], keep_ratio=True), dict( type='RandomApply', transforms=[ dict( type='TorchVisionWrapper', op='ColorJitter', brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5) ], prob=0.5), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) ] icdar2013_textspotting_data_root = 'data/icdar2013' icdar2013_textspotting_train = dict( type='AdelDataset', data_root='data/icdar2013', ann_file='ic13_train.json', data_prefix=dict(img_path='train_images/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None) icdar2013_textspotting_test = dict( type='AdelDataset', data_root='data/icdar2013', data_prefix=dict(img_path='test_images/'), ann_file='ic13_test.json', test_mode=True, pipeline=None) icdar2015_textspotting_data_root = 'data/icdar2015' icdar2015_textspotting_train = dict( type='AdelDataset', data_root='data/icdar2015', ann_file='ic15_train.json', data_prefix=dict(img_path='train_images/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None) icdar2015_textspotting_test = dict( type='AdelDataset', data_root='data/icdar2015', data_prefix=dict(img_path='test_images/'), ann_file='ic15_test.json', test_mode=True, pipeline=None) ctw1500_textspotting_data_root = 'data/CTW1500' ctw1500_textspotting_train = dict( type='AdelDataset', data_root='data/CTW1500', ann_file='annotations/train_ctw1500_maxlen25_v2.json', data_prefix=dict(img_path='ctwtrain_text_image/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None) ctw1500_textspotting_test = dict( type='AdelDataset', data_root='data/CTW1500', ann_file='annotations/test_ctw1500_maxlen25.json', data_prefix=dict(img_path='ctwtest_text_image/'), test_mode=True, pipeline=None) totaltext_textspotting_data_root = 'data/totaltext' totaltext_textspotting_train = dict( type='AdelDataset', data_root='data/totaltext', ann_file='train.json', data_prefix=dict(img_path='train_images/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None) totaltext_textspotting_test = dict( type='AdelDataset', data_root='data/totaltext', ann_file='test.json', data_prefix=dict(img_path='test_images/'), test_mode=True, pipeline=None) syntext1_textspotting_data_root = 'data/syntext1' syntext1_textspotting_train = dict( type='AdelDataset', data_root='data/syntext1', ann_file='train.json', data_prefix=dict(img_path='syntext_word_eng/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None) syntext2_textspotting_data_root = 'data/syntext2' syntext2_textspotting_train = dict( type='AdelDataset', data_root='data/syntext2', ann_file='train.json', data_prefix=dict(img_path='emcs_imgs/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None) mlt_textspotting_data_root = 'data/mlt2017' mlt_textspotting_train = dict( type='AdelDataset', data_root='data/mlt2017', ann_file='train.json', data_prefix=dict(img_path='MLT_train_images/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None) default_scope = 'mmocr' env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) randomness = dict(seed=42) default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=100), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=2), 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 = True val_evaluator = None test_evaluator = None vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='TextSpottingLocalVisualizer', name='visualizer', vis_backends=[dict(type='LocalVisBackend')]) num_epochs = 150 lr = 0.001 min_lr = 5e-05 optim_wrapper = dict( type='AmpOptimWrapper', optimizer=dict(type='AdamW', lr=0.001, weight_decay=0.0001), paramwise_cfg=dict(custom_keys=dict(backbone=dict(lr_mult=0.1))), loss_scale='dynamic') train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=150, val_interval=5) param_scheduler = [ dict(type='LinearLR', end=5, start_factor=0.2, by_epoch=True), dict(type='LinearLR', begin=5, end=147, end_factor=0.05, by_epoch=True) ] train_list = [ dict( type='AdelDataset', data_root='data/icdar2013', ann_file='ic13_train.json', data_prefix=dict(img_path='train_images/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None), dict( type='AdelDataset', data_root='data/icdar2015', ann_file='ic15_train.json', data_prefix=dict(img_path='train_images/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None), dict( type='AdelDataset', data_root='data/mlt2017', ann_file='train.json', data_prefix=dict(img_path='MLT_train_images/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None), dict( type='AdelDataset', data_root='data/totaltext', ann_file='train.json', data_prefix=dict(img_path='train_images/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None), dict( type='AdelDataset', data_root='data/syntext1', ann_file='train.json', data_prefix=dict(img_path='syntext_word_eng/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None), dict( type='AdelDataset', data_root='data/syntext2', ann_file='train.json', data_prefix=dict(img_path='emcs_imgs/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None) ] test_list = [ dict( type='AdelDataset', data_root='data/icdar2013', data_prefix=dict(img_path='test_images/'), ann_file='ic13_test.json', test_mode=True, pipeline=None), dict( type='AdelDataset', data_root='data/totaltext', ann_file='test.json', data_prefix=dict(img_path='test_images/'), test_mode=True, pipeline=None), dict( type='AdelDataset', data_root='data/CTW1500', ann_file='annotations/test_ctw1500_maxlen25.json', data_prefix=dict(img_path='ctwtest_text_image/'), test_mode=True, pipeline=None) ] train_dataset = dict( type='ConcatDataset', datasets=[ dict( type='AdelDataset', data_root='data/icdar2013', ann_file='ic13_train.json', data_prefix=dict(img_path='train_images/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None), dict( type='AdelDataset', data_root='data/icdar2015', ann_file='ic15_train.json', data_prefix=dict(img_path='train_images/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None), dict( type='AdelDataset', data_root='data/mlt2017', ann_file='train.json', data_prefix=dict(img_path='MLT_train_images/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None), dict( type='AdelDataset', data_root='data/totaltext', ann_file='train.json', data_prefix=dict(img_path='train_images/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None), dict( type='AdelDataset', data_root='data/syntext1', ann_file='train.json', data_prefix=dict(img_path='syntext_word_eng/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None), dict( type='AdelDataset', data_root='data/syntext2', ann_file='train.json', data_prefix=dict(img_path='emcs_imgs/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None) ], pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='disk'), color_type='color_ignore_orientation'), dict( type='LoadOCRAnnotationsWithBezier', with_bbox=True, with_label=True, with_bezier=True, with_text=True), dict(type='Bezier2Polygon'), dict(type='FixInvalidPolygon'), dict( type='ConvertText', dictionary=dict( type='SPTSDictionary', dict_file= 'mmocr/projects/SPTS/config/spts/../../dicts/spts.txt', with_start=True, with_end=True, with_seq_end=True, same_start_end=False, with_padding=True, with_unknown=True, unknown_token=None, num_bins=0)), dict(type='RemoveIgnored'), dict(type='RandomCrop', min_side_ratio=0.5), dict( type='RandomApply', transforms=[ dict( type='RandomRotate', max_angle=30, pad_with_fixed_color=True, use_canvas=True) ], prob=0.3), dict(type='FixInvalidPolygon'), dict( type='RandomChoiceResize', scales=[(640, 1600), (672, 1600), (704, 1600), (736, 1600), (768, 1600), (800, 1600), (832, 1600), (864, 1600), (896, 1600)], keep_ratio=True), dict( type='RandomApply', transforms=[ dict( type='TorchVisionWrapper', op='ColorJitter', brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5) ], prob=0.5), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) ]) train_dataloader = dict( batch_size=8, num_workers=8, pin_memory=True, persistent_workers=True, sampler=dict(type='RepeatAugSampler', shuffle=True, num_repeats=2), dataset=dict( type='ConcatDataset', datasets=[ dict( type='AdelDataset', data_root='data/icdar2013', ann_file='ic13_train.json', data_prefix=dict(img_path='train_images/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None), dict( type='AdelDataset', data_root='data/icdar2015', ann_file='ic15_train.json', data_prefix=dict(img_path='train_images/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None), dict( type='AdelDataset', data_root='data/mlt2017', ann_file='train.json', data_prefix=dict(img_path='MLT_train_images/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None), dict( type='AdelDataset', data_root='data/totaltext', ann_file='train.json', data_prefix=dict(img_path='train_images/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None), dict( type='AdelDataset', data_root='data/syntext1', ann_file='train.json', data_prefix=dict(img_path='syntext_word_eng/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None), dict( type='AdelDataset', data_root='data/syntext2', ann_file='train.json', data_prefix=dict(img_path='emcs_imgs/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None) ], pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='disk'), color_type='color_ignore_orientation'), dict( type='LoadOCRAnnotationsWithBezier', with_bbox=True, with_label=True, with_bezier=True, with_text=True), dict(type='Bezier2Polygon'), dict(type='FixInvalidPolygon'), dict( type='ConvertText', dictionary=dict( type='SPTSDictionary', dict_file= 'mmocr/projects/SPTS/config/spts/../../dicts/spts.txt', with_start=True, with_end=True, with_seq_end=True, same_start_end=False, with_padding=True, with_unknown=True, unknown_token=None, num_bins=0)), dict(type='RemoveIgnored'), dict(type='RandomCrop', min_side_ratio=0.5), dict( type='RandomApply', transforms=[ dict( type='RandomRotate', max_angle=30, pad_with_fixed_color=True, use_canvas=True) ], prob=0.3), dict(type='FixInvalidPolygon'), dict( type='RandomChoiceResize', scales=[(640, 1600), (672, 1600), (704, 1600), (736, 1600), (768, 1600), (800, 1600), (832, 1600), (864, 1600), (896, 1600)], keep_ratio=True), dict( type='RandomApply', transforms=[ dict( type='TorchVisionWrapper', op='ColorJitter', brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5) ], prob=0.5), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) ])) launcher = 'slurm' work_dir = './work_dirs/spts_resnet50_150e_pretrain-spts' 2023/02/23 19:45:58 - mmengine - WARNING - The "visualizer" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/02/23 19:45:58 - mmengine - WARNING - The "vis_backend" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/02/23 19:46:02 - mmengine - WARNING - The "model" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/02/23 19:46:02 - mmengine - WARNING - The "model" registry in mmdet did not set import location. Fallback to call `mmdet.utils.register_all_modules` instead. 2023/02/23 19:46:02 - mmengine - WARNING - The "task util" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/02/23 19:46:02 - mmengine - WARNING - The "hook" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/02/23 19:46:02 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (BELOW_NORMAL) LoggerHook -------------------- before_train: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_train_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) DistSamplerSeedHook -------------------- before_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (NORMAL ) SyncBuffersHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val_epoch: (NORMAL ) IterTimerHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (NORMAL ) VisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_test_epoch: (NORMAL ) IterTimerHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (NORMAL ) VisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- 2023/02/23 19:46:05 - mmengine - WARNING - The "loop" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/02/23 19:46:05 - mmengine - WARNING - The "dataset" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/02/23 19:46:05 - mmengine - WARNING - The "transform" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/02/23 19:46:55 - mmengine - WARNING - The "data sampler" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/02/23 19:46:55 - mmengine - WARNING - The "optimizer constructor" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.conv1.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.conv1.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.conv1.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv1.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv1.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv1.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv2.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv2.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv2.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv3.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv3.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv3.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.0.downsample.0.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.0.downsample.0.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.0.downsample.0.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv1.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv1.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv1.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv2.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv2.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv2.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv3.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv3.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv3.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv1.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv1.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv1.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv2.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv2.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv2.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv3.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv3.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv3.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv1.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv1.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv1.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv2.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv2.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv2.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv3.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv3.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv3.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.0.downsample.0.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.0.downsample.0.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.0.downsample.0.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv1.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv1.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv1.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv2.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv2.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv2.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv3.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv3.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv3.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv1.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv1.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv1.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv2.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv2.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv2.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv3.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv3.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv3.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv1.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv1.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv1.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv2.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv2.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv2.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv3.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv3.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv3.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv1.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv1.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv1.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv2.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv2.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv2.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv3.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv3.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv3.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.0.downsample.0.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.0.downsample.0.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.0.downsample.0.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv1.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv1.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv1.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv2.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv2.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv2.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv3.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv3.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv3.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv1.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv1.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv1.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv2.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv2.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv2.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv3.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv3.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv3.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv1.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv1.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv1.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv2.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv2.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv2.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv3.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv3.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv3.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv1.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv1.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv1.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv2.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv2.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv2.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv3.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv3.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv3.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv1.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv1.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv1.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv2.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv2.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv2.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv3.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv3.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv3.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv1.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv1.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv1.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv2.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv2.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv2.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv3.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv3.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv3.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.0.downsample.0.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.0.downsample.0.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.0.downsample.0.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv1.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv1.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv1.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv2.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv2.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv2.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv3.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv3.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv3.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv1.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv1.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv1.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv2.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv2.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv2.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv3.weight:lr=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv3.weight:weight_decay=0.0001 2023/02/23 19:46:55 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv3.weight:lr_mult=0.1 2023/02/23 19:46:55 - mmengine - WARNING - The "optimizer" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/02/23 19:46:55 - mmengine - WARNING - The "optim wrapper" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/02/23 19:46:55 - mmengine - WARNING - The "parameter scheduler" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/02/23 19:46:58 - mmengine - WARNING - The "weight initializer" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/02/23 19:46:58 - mmengine - INFO - load model from: torchvision://resnet50 2023/02/23 19:46:58 - mmengine - INFO - Loads checkpoint by torchvision backend from path: torchvision://resnet50 2023/02/23 19:47:00 - mmengine - WARNING - The model and loaded state dict do not match exactly unexpected key in source state_dict: fc.weight, fc.bias Name of parameter - Initialization information backbone.conv1.weight - torch.Size([64, 3, 7, 7]): PretrainedInit: load from torchvision://resnet50 backbone.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn2.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn2.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn3.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn3.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.downsample.1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.downsample.1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn2.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn2.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn3.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn3.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn2.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn2.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn3.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn3.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn3.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn3.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.downsample.1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.downsample.1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn3.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn3.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn3.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn3.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn3.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn3.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.downsample.1.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.downsample.1.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn2.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn2.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn3.weight - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn3.bias - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.downsample.1.weight - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.downsample.1.bias - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn2.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn2.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn3.weight - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn3.bias - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn2.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn2.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn3.weight - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn3.bias - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 encoder.input_proj.weight - torch.Size([256, 2048, 1, 1]): The value is the same before and after calling `init_weights` of SPTS encoder.input_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.embedding.word_embeddings.weight - torch.Size([1100, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.embedding.position_embeddings.weight - torch.Size([1621, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.embedding.LayerNorm.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.embedding.LayerNorm.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.vocab_embed.layer-0.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.vocab_embed.layer-0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.vocab_embed.layer-1.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.vocab_embed.layer-1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.vocab_embed.layer-2.weight - torch.Size([1100, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.vocab_embed.layer-2.bias - torch.Size([1100]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.norm.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.norm.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.multihead_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.multihead_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.multihead_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.multihead_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.norm3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.norm3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.multihead_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.multihead_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.multihead_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.multihead_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.norm3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.norm3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.multihead_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.multihead_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.multihead_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.multihead_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.norm3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.norm3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.multihead_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.multihead_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.multihead_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.multihead_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.norm3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.norm3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.multihead_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.multihead_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.multihead_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.multihead_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.norm3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.norm3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.multihead_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.multihead_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.multihead_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.multihead_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.norm3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.norm3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.norm.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.norm.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS 2023/02/23 19:47:00 - mmengine - INFO - Auto resumed from the latest checkpoint mmocr/work_dirs/spts_resnet50_150e_pretrain-spts/epoch_3.pth. 2023/02/23 19:47:02 - mmengine - INFO - Load checkpoint from mmocr/work_dirs/spts_resnet50_150e_pretrain-spts/epoch_3.pth 2023/02/23 19:47:03 - mmengine - INFO - resumed epoch: 3, iter: 15141 2023/02/23 19:47:03 - mmengine - INFO - Checkpoints will be saved to mmocr/work_dirs/spts_resnet50_150e_pretrain-spts. 2023/02/23 19:48:44 - mmengine - INFO - Epoch(train) [4][ 100/5047] lr: 8.0000e-05 eta: 8 days, 16:52:22 time: 0.9338 data_time: 0.0013 memory: 44951 loss: 0.2604 loss_ce: 0.2604 2023/02/23 19:50:15 - mmengine - INFO - Epoch(train) [4][ 200/5047] lr: 8.0000e-05 eta: 8 days, 6:32:44 time: 0.8958 data_time: 0.0045 memory: 40819 loss: 0.2597 loss_ce: 0.2597 2023/02/23 19:51:47 - mmengine - INFO - Epoch(train) [4][ 300/5047] lr: 8.0000e-05 eta: 8 days, 3:27:24 time: 0.9441 data_time: 0.0014 memory: 42338 loss: 0.2374 loss_ce: 0.2374 2023/02/23 19:53:20 - mmengine - INFO - Epoch(train) [4][ 400/5047] lr: 8.0000e-05 eta: 8 days, 2:22:56 time: 0.9621 data_time: 0.0015 memory: 46352 loss: 0.2467 loss_ce: 0.2467 2023/02/23 19:54:51 - mmengine - INFO - Epoch(train) [4][ 500/5047] lr: 8.0000e-05 eta: 8 days, 1:04:52 time: 0.9141 data_time: 0.0019 memory: 46774 loss: 0.2412 loss_ce: 0.2412 2023/02/23 19:56:22 - mmengine - INFO - Epoch(train) [4][ 600/5047] lr: 8.0000e-05 eta: 8 days, 0:04:53 time: 0.9475 data_time: 0.0015 memory: 47072 loss: 0.2272 loss_ce: 0.2272 2023/02/23 19:57:55 - mmengine - INFO - Epoch(train) [4][ 700/5047] lr: 8.0000e-05 eta: 8 days, 0:01:58 time: 0.9771 data_time: 0.0037 memory: 43288 loss: 0.2155 loss_ce: 0.2155 2023/02/23 19:59:27 - mmengine - INFO - Epoch(train) [4][ 800/5047] lr: 8.0000e-05 eta: 7 days, 23:32:09 time: 0.8764 data_time: 0.0015 memory: 45255 loss: 0.2578 loss_ce: 0.2578 2023/02/23 20:00:20 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 20:00:58 - mmengine - INFO - Epoch(train) [4][ 900/5047] lr: 8.0000e-05 eta: 7 days, 23:01:16 time: 0.9475 data_time: 0.0015 memory: 43316 loss: 0.2300 loss_ce: 0.2300 2023/02/23 20:02:28 - mmengine - INFO - Epoch(train) [4][1000/5047] lr: 8.0000e-05 eta: 7 days, 22:31:48 time: 0.9743 data_time: 0.0016 memory: 47812 loss: 0.2514 loss_ce: 0.2514 2023/02/23 20:04:00 - mmengine - INFO - Epoch(train) [4][1100/5047] lr: 8.0000e-05 eta: 7 days, 22:21:09 time: 0.9419 data_time: 0.0038 memory: 43613 loss: 0.2345 loss_ce: 0.2345 2023/02/23 20:05:30 - mmengine - INFO - Epoch(train) [4][1200/5047] lr: 8.0000e-05 eta: 7 days, 21:53:18 time: 0.9217 data_time: 0.0015 memory: 43568 loss: 0.2476 loss_ce: 0.2476 2023/02/23 20:07:01 - mmengine - INFO - Epoch(train) [4][1300/5047] lr: 8.0000e-05 eta: 7 days, 21:34:30 time: 0.9063 data_time: 0.0025 memory: 43613 loss: 0.2444 loss_ce: 0.2444 2023/02/23 20:08:32 - mmengine - INFO - Epoch(train) [4][1400/5047] lr: 8.0000e-05 eta: 7 days, 21:24:07 time: 0.9101 data_time: 0.0026 memory: 43949 loss: 0.2606 loss_ce: 0.2606 2023/02/23 20:10:03 - mmengine - INFO - Epoch(train) [4][1500/5047] lr: 8.0000e-05 eta: 7 days, 21:18:52 time: 0.9492 data_time: 0.0013 memory: 55563 loss: 0.2446 loss_ce: 0.2446 2023/02/23 20:11:34 - mmengine - INFO - Epoch(train) [4][1600/5047] lr: 8.0000e-05 eta: 7 days, 21:08:51 time: 0.9071 data_time: 0.0023 memory: 43613 loss: 0.2177 loss_ce: 0.2177 2023/02/23 20:13:05 - mmengine - INFO - Epoch(train) [4][1700/5047] lr: 8.0000e-05 eta: 7 days, 20:59:21 time: 0.8960 data_time: 0.0016 memory: 51719 loss: 0.2354 loss_ce: 0.2354 2023/02/23 20:14:35 - mmengine - INFO - Epoch(train) [4][1800/5047] lr: 8.0000e-05 eta: 7 days, 20:42:51 time: 0.8740 data_time: 0.0015 memory: 45851 loss: 0.2324 loss_ce: 0.2324 2023/02/23 20:15:28 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 20:16:05 - mmengine - INFO - Epoch(train) [4][1900/5047] lr: 8.0000e-05 eta: 7 days, 20:29:15 time: 0.8815 data_time: 0.0053 memory: 40678 loss: 0.2307 loss_ce: 0.2307 2023/02/23 20:17:34 - mmengine - INFO - Epoch(train) [4][2000/5047] lr: 8.0000e-05 eta: 7 days, 20:10:12 time: 0.9678 data_time: 0.0020 memory: 43613 loss: 0.2252 loss_ce: 0.2252 2023/02/23 20:19:05 - mmengine - INFO - Epoch(train) [4][2100/5047] lr: 8.0000e-05 eta: 7 days, 20:08:17 time: 0.8969 data_time: 0.0017 memory: 45302 loss: 0.2978 loss_ce: 0.2978 2023/02/23 20:20:36 - mmengine - INFO - Epoch(train) [4][2200/5047] lr: 8.0000e-05 eta: 7 days, 20:00:56 time: 0.9396 data_time: 0.0015 memory: 45643 loss: 0.2489 loss_ce: 0.2489 2023/02/23 20:22:05 - mmengine - INFO - Epoch(train) [4][2300/5047] lr: 8.0000e-05 eta: 7 days, 19:49:58 time: 0.8851 data_time: 0.0016 memory: 55562 loss: 0.2270 loss_ce: 0.2270 2023/02/23 20:23:39 - mmengine - INFO - Epoch(train) [4][2400/5047] lr: 8.0000e-05 eta: 7 days, 19:57:00 time: 0.9466 data_time: 0.0015 memory: 50514 loss: 0.2484 loss_ce: 0.2484 2023/02/23 20:25:08 - mmengine - INFO - Epoch(train) [4][2500/5047] lr: 8.0000e-05 eta: 7 days, 19:47:03 time: 0.9080 data_time: 0.0027 memory: 42336 loss: 0.2548 loss_ce: 0.2548 2023/02/23 20:26:38 - mmengine - INFO - Epoch(train) [4][2600/5047] lr: 8.0000e-05 eta: 7 days, 19:39:22 time: 0.8844 data_time: 0.0022 memory: 40825 loss: 0.2472 loss_ce: 0.2472 2023/02/23 20:28:08 - mmengine - INFO - Epoch(train) [4][2700/5047] lr: 8.0000e-05 eta: 7 days, 19:30:39 time: 0.9910 data_time: 0.0017 memory: 50504 loss: 0.2245 loss_ce: 0.2245 2023/02/23 20:29:39 - mmengine - INFO - Epoch(train) [4][2800/5047] lr: 8.0000e-05 eta: 7 days, 19:25:04 time: 0.9219 data_time: 0.0027 memory: 43546 loss: 0.1883 loss_ce: 0.1883 2023/02/23 20:30:31 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 20:31:09 - mmengine - INFO - Epoch(train) [4][2900/5047] lr: 8.0000e-05 eta: 7 days, 19:18:14 time: 0.9399 data_time: 0.0016 memory: 52964 loss: 0.2321 loss_ce: 0.2321 2023/02/23 20:32:39 - mmengine - INFO - Epoch(train) [4][3000/5047] lr: 8.0000e-05 eta: 7 days, 19:13:30 time: 0.9246 data_time: 0.0025 memory: 39603 loss: 0.2313 loss_ce: 0.2313 2023/02/23 20:34:11 - mmengine - INFO - Epoch(train) [4][3100/5047] lr: 8.0000e-05 eta: 7 days, 19:15:27 time: 0.9318 data_time: 0.0017 memory: 42409 loss: 0.2264 loss_ce: 0.2264 2023/02/23 20:35:42 - mmengine - INFO - Epoch(train) [4][3200/5047] lr: 8.0000e-05 eta: 7 days, 19:11:57 time: 0.8757 data_time: 0.0044 memory: 55562 loss: 0.2211 loss_ce: 0.2211 2023/02/23 20:37:12 - mmengine - INFO - Epoch(train) [4][3300/5047] lr: 8.0000e-05 eta: 7 days, 19:07:20 time: 0.9027 data_time: 0.0016 memory: 52127 loss: 0.2299 loss_ce: 0.2299 2023/02/23 20:38:44 - mmengine - INFO - Epoch(train) [4][3400/5047] lr: 8.0000e-05 eta: 7 days, 19:05:27 time: 0.8790 data_time: 0.0019 memory: 46713 loss: 0.2154 loss_ce: 0.2154 2023/02/23 20:40:15 - mmengine - INFO - Epoch(train) [4][3500/5047] lr: 8.0000e-05 eta: 7 days, 19:03:04 time: 0.8813 data_time: 0.0016 memory: 48774 loss: 0.2320 loss_ce: 0.2320 2023/02/23 20:41:46 - mmengine - INFO - Epoch(train) [4][3600/5047] lr: 8.0000e-05 eta: 7 days, 19:01:47 time: 0.9157 data_time: 0.0016 memory: 40825 loss: 0.2314 loss_ce: 0.2314 2023/02/23 20:43:18 - mmengine - INFO - Epoch(train) [4][3700/5047] lr: 8.0000e-05 eta: 7 days, 19:03:05 time: 0.9597 data_time: 0.0016 memory: 55323 loss: 0.2176 loss_ce: 0.2176 2023/02/23 20:44:47 - mmengine - INFO - Epoch(train) [4][3800/5047] lr: 8.0000e-05 eta: 7 days, 18:56:03 time: 0.9183 data_time: 0.0015 memory: 39977 loss: 0.2365 loss_ce: 0.2365 2023/02/23 20:45:42 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 20:46:19 - mmengine - INFO - Epoch(train) [4][3900/5047] lr: 8.0000e-05 eta: 7 days, 18:56:41 time: 0.9372 data_time: 0.0038 memory: 43354 loss: 0.2128 loss_ce: 0.2128 2023/02/23 20:47:50 - mmengine - INFO - Epoch(train) [4][4000/5047] lr: 8.0000e-05 eta: 7 days, 18:54:26 time: 0.9682 data_time: 0.0017 memory: 49715 loss: 0.2080 loss_ce: 0.2080 2023/02/23 20:49:21 - mmengine - INFO - Epoch(train) [4][4100/5047] lr: 8.0000e-05 eta: 7 days, 18:52:20 time: 0.8941 data_time: 0.0025 memory: 42577 loss: 0.2395 loss_ce: 0.2395 2023/02/23 20:50:51 - mmengine - INFO - Epoch(train) [4][4200/5047] lr: 8.0000e-05 eta: 7 days, 18:46:28 time: 0.9388 data_time: 0.0027 memory: 55562 loss: 0.2211 loss_ce: 0.2211 2023/02/23 20:52:22 - mmengine - INFO - Epoch(train) [4][4300/5047] lr: 8.0000e-05 eta: 7 days, 18:46:03 time: 0.9246 data_time: 0.0057 memory: 41724 loss: 0.2100 loss_ce: 0.2100 2023/02/23 20:53:52 - mmengine - INFO - Epoch(train) [4][4400/5047] lr: 8.0000e-05 eta: 7 days, 18:41:32 time: 0.8807 data_time: 0.0016 memory: 49147 loss: 0.2181 loss_ce: 0.2181 2023/02/23 20:55:24 - mmengine - INFO - Epoch(train) [4][4500/5047] lr: 8.0000e-05 eta: 7 days, 18:41:17 time: 0.9186 data_time: 0.0017 memory: 47925 loss: 0.2025 loss_ce: 0.2025 2023/02/23 20:56:55 - mmengine - INFO - Epoch(train) [4][4600/5047] lr: 8.0000e-05 eta: 7 days, 18:39:25 time: 0.9376 data_time: 0.0016 memory: 44012 loss: 0.2349 loss_ce: 0.2349 2023/02/23 20:58:25 - mmengine - INFO - Epoch(train) [4][4700/5047] lr: 8.0000e-05 eta: 7 days, 18:33:39 time: 0.8714 data_time: 0.0028 memory: 42336 loss: 0.2206 loss_ce: 0.2206 2023/02/23 20:59:55 - mmengine - INFO - Epoch(train) [4][4800/5047] lr: 8.0000e-05 eta: 7 days, 18:30:54 time: 0.9323 data_time: 0.0015 memory: 44586 loss: 0.2272 loss_ce: 0.2272 2023/02/23 21:00:49 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 21:01:25 - mmengine - INFO - Epoch(train) [4][4900/5047] lr: 8.0000e-05 eta: 7 days, 18:25:13 time: 0.8786 data_time: 0.0017 memory: 45137 loss: 0.2230 loss_ce: 0.2230 2023/02/23 21:02:56 - mmengine - INFO - Epoch(train) [4][5000/5047] lr: 8.0000e-05 eta: 7 days, 18:24:46 time: 0.8991 data_time: 0.0019 memory: 55562 loss: 0.2089 loss_ce: 0.2089 2023/02/23 21:03:38 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 21:03:38 - mmengine - INFO - Saving checkpoint at 4 epochs 2023/02/23 21:05:14 - mmengine - INFO - Epoch(train) [5][ 100/5047] lr: 1.0000e-04 eta: 7 days, 18:18:57 time: 0.8560 data_time: 0.0016 memory: 52543 loss: 0.1845 loss_ce: 0.1845 2023/02/23 21:06:44 - mmengine - INFO - Epoch(train) [5][ 200/5047] lr: 1.0000e-04 eta: 7 days, 18:15:18 time: 0.9592 data_time: 0.0019 memory: 44956 loss: 0.2177 loss_ce: 0.2177 2023/02/23 21:08:16 - mmengine - INFO - Epoch(train) [5][ 300/5047] lr: 1.0000e-04 eta: 7 days, 18:16:03 time: 0.9598 data_time: 0.0021 memory: 48565 loss: 0.1895 loss_ce: 0.1895 2023/02/23 21:09:46 - mmengine - INFO - Epoch(train) [5][ 400/5047] lr: 1.0000e-04 eta: 7 days, 18:12:01 time: 0.8661 data_time: 0.0017 memory: 44617 loss: 0.2086 loss_ce: 0.2086 2023/02/23 21:11:15 - mmengine - INFO - Epoch(train) [5][ 500/5047] lr: 1.0000e-04 eta: 7 days, 18:07:13 time: 0.9175 data_time: 0.0033 memory: 43289 loss: 0.2087 loss_ce: 0.2087 2023/02/23 21:12:46 - mmengine - INFO - Epoch(train) [5][ 600/5047] lr: 1.0000e-04 eta: 7 days, 18:06:17 time: 0.9274 data_time: 0.0018 memory: 53021 loss: 0.2466 loss_ce: 0.2466 2023/02/23 21:14:16 - mmengine - INFO - Epoch(train) [5][ 700/5047] lr: 1.0000e-04 eta: 7 days, 18:01:08 time: 0.9066 data_time: 0.0017 memory: 41323 loss: 0.2003 loss_ce: 0.2003 2023/02/23 21:15:46 - mmengine - INFO - Epoch(train) [5][ 800/5047] lr: 1.0000e-04 eta: 7 days, 17:57:53 time: 0.9041 data_time: 0.0017 memory: 43947 loss: 0.2051 loss_ce: 0.2051 2023/02/23 21:15:57 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 21:17:15 - mmengine - INFO - Epoch(train) [5][ 900/5047] lr: 1.0000e-04 eta: 7 days, 17:53:31 time: 0.8919 data_time: 0.0016 memory: 45302 loss: 0.2146 loss_ce: 0.2146 2023/02/23 21:18:45 - mmengine - INFO - Epoch(train) [5][1000/5047] lr: 1.0000e-04 eta: 7 days, 17:49:11 time: 0.9177 data_time: 0.0022 memory: 44278 loss: 0.2110 loss_ce: 0.2110 2023/02/23 21:20:16 - mmengine - INFO - Epoch(train) [5][1100/5047] lr: 1.0000e-04 eta: 7 days, 17:47:30 time: 0.9236 data_time: 0.0015 memory: 45639 loss: 0.2418 loss_ce: 0.2418 2023/02/23 21:21:47 - mmengine - INFO - Epoch(train) [5][1200/5047] lr: 1.0000e-04 eta: 7 days, 17:45:37 time: 0.9291 data_time: 0.0030 memory: 43289 loss: 0.2351 loss_ce: 0.2351 2023/02/23 21:23:17 - mmengine - INFO - Epoch(train) [5][1300/5047] lr: 1.0000e-04 eta: 7 days, 17:42:25 time: 0.8892 data_time: 0.0019 memory: 46794 loss: 0.2065 loss_ce: 0.2065 2023/02/23 21:24:46 - mmengine - INFO - Epoch(train) [5][1400/5047] lr: 1.0000e-04 eta: 7 days, 17:38:13 time: 0.8900 data_time: 0.0018 memory: 52964 loss: 0.1973 loss_ce: 0.1973 2023/02/23 21:26:17 - mmengine - INFO - Epoch(train) [5][1500/5047] lr: 1.0000e-04 eta: 7 days, 17:36:40 time: 0.9109 data_time: 0.0016 memory: 45640 loss: 0.2264 loss_ce: 0.2264 2023/02/23 21:27:46 - mmengine - INFO - Epoch(train) [5][1600/5047] lr: 1.0000e-04 eta: 7 days, 17:32:49 time: 0.9432 data_time: 0.0016 memory: 49715 loss: 0.2326 loss_ce: 0.2326 2023/02/23 21:29:17 - mmengine - INFO - Epoch(train) [5][1700/5047] lr: 1.0000e-04 eta: 7 days, 17:30:11 time: 0.9073 data_time: 0.0018 memory: 43618 loss: 0.2647 loss_ce: 0.2647 2023/02/23 21:30:46 - mmengine - INFO - Epoch(train) [5][1800/5047] lr: 1.0000e-04 eta: 7 days, 17:25:59 time: 0.8663 data_time: 0.0015 memory: 43412 loss: 0.2296 loss_ce: 0.2296 2023/02/23 21:30:57 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 21:32:17 - mmengine - INFO - Epoch(train) [5][1900/5047] lr: 1.0000e-04 eta: 7 days, 17:25:27 time: 0.8886 data_time: 0.0021 memory: 48912 loss: 0.2383 loss_ce: 0.2383 2023/02/23 21:33:47 - mmengine - INFO - Epoch(train) [5][2000/5047] lr: 1.0000e-04 eta: 7 days, 17:21:07 time: 0.8929 data_time: 0.0020 memory: 42711 loss: 0.2266 loss_ce: 0.2266 2023/02/23 21:35:15 - mmengine - INFO - Epoch(train) [5][2100/5047] lr: 1.0000e-04 eta: 7 days, 17:14:43 time: 0.8476 data_time: 0.0053 memory: 42707 loss: 0.2514 loss_ce: 0.2514 2023/02/23 21:36:45 - mmengine - INFO - Epoch(train) [5][2200/5047] lr: 1.0000e-04 eta: 7 days, 17:11:45 time: 0.8792 data_time: 0.0029 memory: 43613 loss: 0.2405 loss_ce: 0.2405 2023/02/23 21:38:13 - mmengine - INFO - Epoch(train) [5][2300/5047] lr: 1.0000e-04 eta: 7 days, 17:06:40 time: 0.8646 data_time: 0.0015 memory: 44617 loss: 0.2047 loss_ce: 0.2047 2023/02/23 21:39:45 - mmengine - INFO - Epoch(train) [5][2400/5047] lr: 1.0000e-04 eta: 7 days, 17:06:35 time: 0.9405 data_time: 0.0028 memory: 55562 loss: 0.2307 loss_ce: 0.2307 2023/02/23 21:41:15 - mmengine - INFO - Epoch(train) [5][2500/5047] lr: 1.0000e-04 eta: 7 days, 17:04:10 time: 0.8712 data_time: 0.0019 memory: 44477 loss: 0.2071 loss_ce: 0.2071 2023/02/23 21:42:45 - mmengine - INFO - Epoch(train) [5][2600/5047] lr: 1.0000e-04 eta: 7 days, 17:02:12 time: 0.8842 data_time: 0.0052 memory: 48471 loss: 0.2214 loss_ce: 0.2214 2023/02/23 21:44:16 - mmengine - INFO - Epoch(train) [5][2700/5047] lr: 1.0000e-04 eta: 7 days, 17:00:37 time: 0.8895 data_time: 0.0017 memory: 42024 loss: 0.2205 loss_ce: 0.2205 2023/02/23 21:45:48 - mmengine - INFO - Epoch(train) [5][2800/5047] lr: 1.0000e-04 eta: 7 days, 17:01:27 time: 0.8956 data_time: 0.0017 memory: 45640 loss: 0.2145 loss_ce: 0.2145 2023/02/23 21:45:59 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 21:47:18 - mmengine - INFO - Epoch(train) [5][2900/5047] lr: 1.0000e-04 eta: 7 days, 16:57:38 time: 0.8978 data_time: 0.0017 memory: 42965 loss: 0.2172 loss_ce: 0.2172 2023/02/23 21:48:49 - mmengine - INFO - Epoch(train) [5][3000/5047] lr: 1.0000e-04 eta: 7 days, 16:57:15 time: 0.9155 data_time: 0.0016 memory: 55297 loss: 0.1974 loss_ce: 0.1974 2023/02/23 21:50:19 - mmengine - INFO - Epoch(train) [5][3100/5047] lr: 1.0000e-04 eta: 7 days, 16:55:06 time: 0.8758 data_time: 0.0031 memory: 44956 loss: 0.2013 loss_ce: 0.2013 2023/02/23 21:51:49 - mmengine - INFO - Epoch(train) [5][3200/5047] lr: 1.0000e-04 eta: 7 days, 16:52:28 time: 0.8811 data_time: 0.0018 memory: 46266 loss: 0.1951 loss_ce: 0.1951 2023/02/23 21:53:19 - mmengine - INFO - Epoch(train) [5][3300/5047] lr: 1.0000e-04 eta: 7 days, 16:50:00 time: 0.9162 data_time: 0.0021 memory: 49171 loss: 0.2061 loss_ce: 0.2061 2023/02/23 21:54:48 - mmengine - INFO - Epoch(train) [5][3400/5047] lr: 1.0000e-04 eta: 7 days, 16:45:41 time: 0.9149 data_time: 0.0017 memory: 39955 loss: 0.2157 loss_ce: 0.2157 2023/02/23 21:56:19 - mmengine - INFO - Epoch(train) [5][3500/5047] lr: 1.0000e-04 eta: 7 days, 16:44:28 time: 0.9261 data_time: 0.0015 memory: 49334 loss: 0.1852 loss_ce: 0.1852 2023/02/23 21:57:49 - mmengine - INFO - Epoch(train) [5][3600/5047] lr: 1.0000e-04 eta: 7 days, 16:42:03 time: 0.9210 data_time: 0.0019 memory: 40535 loss: 0.2090 loss_ce: 0.2090 2023/02/23 21:59:20 - mmengine - INFO - Epoch(train) [5][3700/5047] lr: 1.0000e-04 eta: 7 days, 16:40:54 time: 0.8856 data_time: 0.0035 memory: 43706 loss: 0.1983 loss_ce: 0.1983 2023/02/23 22:00:50 - mmengine - INFO - Epoch(train) [5][3800/5047] lr: 1.0000e-04 eta: 7 days, 16:38:37 time: 0.9150 data_time: 0.0016 memory: 44539 loss: 0.2211 loss_ce: 0.2211 2023/02/23 22:01:01 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 22:02:19 - mmengine - INFO - Epoch(train) [5][3900/5047] lr: 1.0000e-04 eta: 7 days, 16:34:16 time: 0.8928 data_time: 0.0019 memory: 55562 loss: 0.1948 loss_ce: 0.1948 2023/02/23 22:03:49 - mmengine - INFO - Epoch(train) [5][4000/5047] lr: 1.0000e-04 eta: 7 days, 16:31:30 time: 0.8960 data_time: 0.0019 memory: 44238 loss: 0.2094 loss_ce: 0.2094 2023/02/23 22:05:18 - mmengine - INFO - Epoch(train) [5][4100/5047] lr: 1.0000e-04 eta: 7 days, 16:29:00 time: 0.9271 data_time: 0.0015 memory: 46277 loss: 0.2279 loss_ce: 0.2279 2023/02/23 22:06:49 - mmengine - INFO - Epoch(train) [5][4200/5047] lr: 1.0000e-04 eta: 7 days, 16:26:58 time: 0.8804 data_time: 0.0018 memory: 44617 loss: 0.1992 loss_ce: 0.1992 2023/02/23 22:08:17 - mmengine - INFO - Epoch(train) [5][4300/5047] lr: 1.0000e-04 eta: 7 days, 16:22:23 time: 0.9157 data_time: 0.0029 memory: 44705 loss: 0.1809 loss_ce: 0.1809 2023/02/23 22:09:47 - mmengine - INFO - Epoch(train) [5][4400/5047] lr: 1.0000e-04 eta: 7 days, 16:20:40 time: 0.9167 data_time: 0.0017 memory: 45786 loss: 0.1832 loss_ce: 0.1832 2023/02/23 22:11:16 - mmengine - INFO - Epoch(train) [5][4500/5047] lr: 1.0000e-04 eta: 7 days, 16:17:09 time: 0.9043 data_time: 0.0046 memory: 42371 loss: 0.1947 loss_ce: 0.1947 2023/02/23 22:12:46 - mmengine - INFO - Epoch(train) [5][4600/5047] lr: 1.0000e-04 eta: 7 days, 16:14:26 time: 0.8694 data_time: 0.0017 memory: 55562 loss: 0.2213 loss_ce: 0.2213 2023/02/23 22:14:17 - mmengine - INFO - Epoch(train) [5][4700/5047] lr: 1.0000e-04 eta: 7 days, 16:12:54 time: 0.9268 data_time: 0.0016 memory: 54276 loss: 0.2231 loss_ce: 0.2231 2023/02/23 22:15:47 - mmengine - INFO - Epoch(train) [5][4800/5047] lr: 1.0000e-04 eta: 7 days, 16:11:02 time: 0.8859 data_time: 0.0015 memory: 46502 loss: 0.2171 loss_ce: 0.2171 2023/02/23 22:15:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 22:17:16 - mmengine - INFO - Epoch(train) [5][4900/5047] lr: 1.0000e-04 eta: 7 days, 16:07:15 time: 0.8880 data_time: 0.0016 memory: 44036 loss: 0.1969 loss_ce: 0.1969 2023/02/23 22:18:45 - mmengine - INFO - Epoch(train) [5][5000/5047] lr: 1.0000e-04 eta: 7 days, 16:03:49 time: 0.8582 data_time: 0.0019 memory: 46198 loss: 0.2067 loss_ce: 0.2067 2023/02/23 22:19:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 22:19:27 - mmengine - INFO - Saving checkpoint at 5 epochs 2023/02/23 22:21:00 - mmengine - INFO - Epoch(train) [6][ 100/5047] lr: 3.3333e-05 eta: 7 days, 15:59:22 time: 0.8760 data_time: 0.0017 memory: 48026 loss: 0.1844 loss_ce: 0.1844 2023/02/23 22:22:31 - mmengine - INFO - Epoch(train) [6][ 200/5047] lr: 3.3333e-05 eta: 7 days, 15:57:35 time: 0.8493 data_time: 0.0028 memory: 46853 loss: 0.1865 loss_ce: 0.1865 2023/02/23 22:24:01 - mmengine - INFO - Epoch(train) [6][ 300/5047] lr: 3.3333e-05 eta: 7 days, 15:55:27 time: 0.9408 data_time: 0.0019 memory: 55562 loss: 0.1603 loss_ce: 0.1603 2023/02/23 22:25:31 - mmengine - INFO - Epoch(train) [6][ 400/5047] lr: 3.3333e-05 eta: 7 days, 15:53:34 time: 0.9357 data_time: 0.0021 memory: 55562 loss: 0.1855 loss_ce: 0.1855 2023/02/23 22:27:02 - mmengine - INFO - Epoch(train) [6][ 500/5047] lr: 3.3333e-05 eta: 7 days, 15:52:16 time: 0.9115 data_time: 0.0053 memory: 39681 loss: 0.1829 loss_ce: 0.1829 2023/02/23 22:28:32 - mmengine - INFO - Epoch(train) [6][ 600/5047] lr: 3.3333e-05 eta: 7 days, 15:50:31 time: 0.9074 data_time: 0.0015 memory: 45785 loss: 0.1724 loss_ce: 0.1724 2023/02/23 22:30:02 - mmengine - INFO - Epoch(train) [6][ 700/5047] lr: 3.3333e-05 eta: 7 days, 15:48:04 time: 0.8847 data_time: 0.0015 memory: 41122 loss: 0.1626 loss_ce: 0.1626 2023/02/23 22:30:59 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 22:31:31 - mmengine - INFO - Epoch(train) [6][ 800/5047] lr: 3.3333e-05 eta: 7 days, 15:44:49 time: 0.9264 data_time: 0.0020 memory: 49234 loss: 0.1773 loss_ce: 0.1773 2023/02/23 22:32:59 - mmengine - INFO - Epoch(train) [6][ 900/5047] lr: 3.3333e-05 eta: 7 days, 15:40:47 time: 0.8918 data_time: 0.0020 memory: 51561 loss: 0.1670 loss_ce: 0.1670 2023/02/23 22:34:30 - mmengine - INFO - Epoch(train) [6][1000/5047] lr: 3.3333e-05 eta: 7 days, 15:39:39 time: 0.8994 data_time: 0.0023 memory: 44617 loss: 0.1821 loss_ce: 0.1821 2023/02/23 22:35:59 - mmengine - INFO - Epoch(train) [6][1100/5047] lr: 3.3333e-05 eta: 7 days, 15:36:44 time: 0.8855 data_time: 0.0016 memory: 42336 loss: 0.1775 loss_ce: 0.1775 2023/02/23 22:37:29 - mmengine - INFO - Epoch(train) [6][1200/5047] lr: 3.3333e-05 eta: 7 days, 15:34:58 time: 0.9048 data_time: 0.0016 memory: 55562 loss: 0.1751 loss_ce: 0.1751 2023/02/23 22:38:58 - mmengine - INFO - Epoch(train) [6][1300/5047] lr: 3.3333e-05 eta: 7 days, 15:31:51 time: 0.8750 data_time: 0.0018 memory: 41122 loss: 0.1769 loss_ce: 0.1769 2023/02/23 22:40:28 - mmengine - INFO - Epoch(train) [6][1400/5047] lr: 3.3333e-05 eta: 7 days, 15:30:18 time: 0.8944 data_time: 0.0028 memory: 44587 loss: 0.1878 loss_ce: 0.1878 2023/02/23 22:41:58 - mmengine - INFO - Epoch(train) [6][1500/5047] lr: 3.3333e-05 eta: 7 days, 15:28:22 time: 0.8827 data_time: 0.0019 memory: 51755 loss: 0.1939 loss_ce: 0.1939 2023/02/23 22:43:27 - mmengine - INFO - Epoch(train) [6][1600/5047] lr: 3.3333e-05 eta: 7 days, 15:25:15 time: 0.8561 data_time: 0.0024 memory: 41171 loss: 0.2143 loss_ce: 0.2143 2023/02/23 22:44:57 - mmengine - INFO - Epoch(train) [6][1700/5047] lr: 3.3333e-05 eta: 7 days, 15:23:12 time: 0.9102 data_time: 0.0042 memory: 55562 loss: 0.1814 loss_ce: 0.1814 2023/02/23 22:45:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 22:46:27 - mmengine - INFO - Epoch(train) [6][1800/5047] lr: 3.3333e-05 eta: 7 days, 15:20:41 time: 0.8530 data_time: 0.0056 memory: 47459 loss: 0.1762 loss_ce: 0.1762 2023/02/23 22:47:56 - mmengine - INFO - Epoch(train) [6][1900/5047] lr: 3.3333e-05 eta: 7 days, 15:18:18 time: 0.9428 data_time: 0.0019 memory: 45560 loss: 0.1886 loss_ce: 0.1886 2023/02/23 22:49:24 - mmengine - INFO - Epoch(train) [6][2000/5047] lr: 3.3333e-05 eta: 7 days, 15:14:20 time: 0.8964 data_time: 0.0022 memory: 43613 loss: 0.1905 loss_ce: 0.1905 2023/02/23 22:50:54 - mmengine - INFO - Epoch(train) [6][2100/5047] lr: 3.3333e-05 eta: 7 days, 15:12:27 time: 0.8994 data_time: 0.0018 memory: 43947 loss: 0.1855 loss_ce: 0.1855 2023/02/23 22:52:24 - mmengine - INFO - Epoch(train) [6][2200/5047] lr: 3.3333e-05 eta: 7 days, 15:10:22 time: 0.9049 data_time: 0.0027 memory: 45302 loss: 0.1552 loss_ce: 0.1552 2023/02/23 22:53:54 - mmengine - INFO - Epoch(train) [6][2300/5047] lr: 3.3333e-05 eta: 7 days, 15:07:59 time: 0.8721 data_time: 0.0020 memory: 42024 loss: 0.1756 loss_ce: 0.1756 2023/02/23 22:55:23 - mmengine - INFO - Epoch(train) [6][2400/5047] lr: 3.3333e-05 eta: 7 days, 15:05:36 time: 0.8485 data_time: 0.0016 memory: 44596 loss: 0.2113 loss_ce: 0.2113 2023/02/23 22:56:53 - mmengine - INFO - Epoch(train) [6][2500/5047] lr: 3.3333e-05 eta: 7 days, 15:03:36 time: 0.8430 data_time: 0.0025 memory: 49407 loss: 0.1825 loss_ce: 0.1825 2023/02/23 22:58:21 - mmengine - INFO - Epoch(train) [6][2600/5047] lr: 3.3333e-05 eta: 7 days, 15:00:03 time: 0.8888 data_time: 0.0017 memory: 50420 loss: 0.1980 loss_ce: 0.1980 2023/02/23 22:59:50 - mmengine - INFO - Epoch(train) [6][2700/5047] lr: 3.3333e-05 eta: 7 days, 14:57:20 time: 0.9208 data_time: 0.0033 memory: 43613 loss: 0.1658 loss_ce: 0.1658 2023/02/23 23:00:49 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 23:01:20 - mmengine - INFO - Epoch(train) [6][2800/5047] lr: 3.3333e-05 eta: 7 days, 14:54:56 time: 0.8683 data_time: 0.0016 memory: 55562 loss: 0.1830 loss_ce: 0.1830 2023/02/23 23:02:48 - mmengine - INFO - Epoch(train) [6][2900/5047] lr: 3.3333e-05 eta: 7 days, 14:52:02 time: 0.8845 data_time: 0.0052 memory: 38862 loss: 0.1783 loss_ce: 0.1783 2023/02/23 23:04:19 - mmengine - INFO - Epoch(train) [6][3000/5047] lr: 3.3333e-05 eta: 7 days, 14:50:25 time: 0.9194 data_time: 0.0018 memory: 43967 loss: 0.1802 loss_ce: 0.1802 2023/02/23 23:05:48 - mmengine - INFO - Epoch(train) [6][3100/5047] lr: 3.3333e-05 eta: 7 days, 14:48:16 time: 0.9451 data_time: 0.0016 memory: 42965 loss: 0.1757 loss_ce: 0.1757 2023/02/23 23:07:17 - mmengine - INFO - Epoch(train) [6][3200/5047] lr: 3.3333e-05 eta: 7 days, 14:45:45 time: 0.8720 data_time: 0.0018 memory: 41359 loss: 0.1857 loss_ce: 0.1857 2023/02/23 23:08:48 - mmengine - INFO - Epoch(train) [6][3300/5047] lr: 3.3333e-05 eta: 7 days, 14:44:03 time: 0.8364 data_time: 0.0025 memory: 44117 loss: 0.2066 loss_ce: 0.2066 2023/02/23 23:10:17 - mmengine - INFO - Epoch(train) [6][3400/5047] lr: 3.3333e-05 eta: 7 days, 14:41:40 time: 0.8865 data_time: 0.0016 memory: 42977 loss: 0.1691 loss_ce: 0.1691 2023/02/23 23:11:46 - mmengine - INFO - Epoch(train) [6][3500/5047] lr: 3.3333e-05 eta: 7 days, 14:39:25 time: 0.8923 data_time: 0.0015 memory: 44478 loss: 0.1908 loss_ce: 0.1908 2023/02/23 23:13:17 - mmengine - INFO - Epoch(train) [6][3600/5047] lr: 3.3333e-05 eta: 7 days, 14:38:03 time: 0.9071 data_time: 0.0061 memory: 42336 loss: 0.1907 loss_ce: 0.1907 2023/02/23 23:14:46 - mmengine - INFO - Epoch(train) [6][3700/5047] lr: 3.3333e-05 eta: 7 days, 14:35:15 time: 0.9031 data_time: 0.0086 memory: 47590 loss: 0.1731 loss_ce: 0.1731 2023/02/23 23:15:43 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 23:16:15 - mmengine - INFO - Epoch(train) [6][3800/5047] lr: 3.3333e-05 eta: 7 days, 14:33:19 time: 0.8921 data_time: 0.0016 memory: 41315 loss: 0.2019 loss_ce: 0.2019 2023/02/23 23:17:44 - mmengine - INFO - Epoch(train) [6][3900/5047] lr: 3.3333e-05 eta: 7 days, 14:30:11 time: 0.8843 data_time: 0.0016 memory: 51556 loss: 0.1786 loss_ce: 0.1786 2023/02/23 23:19:12 - mmengine - INFO - Epoch(train) [6][4000/5047] lr: 3.3333e-05 eta: 7 days, 14:26:55 time: 0.8840 data_time: 0.0018 memory: 42024 loss: 0.2030 loss_ce: 0.2030 2023/02/23 23:20:41 - mmengine - INFO - Epoch(train) [6][4100/5047] lr: 3.3333e-05 eta: 7 days, 14:24:25 time: 0.9027 data_time: 0.0018 memory: 45465 loss: 0.1747 loss_ce: 0.1747 2023/02/23 23:22:11 - mmengine - INFO - Epoch(train) [6][4200/5047] lr: 3.3333e-05 eta: 7 days, 14:22:30 time: 0.9014 data_time: 0.0030 memory: 42024 loss: 0.1820 loss_ce: 0.1820 2023/02/23 23:23:41 - mmengine - INFO - Epoch(train) [6][4300/5047] lr: 3.3333e-05 eta: 7 days, 14:20:42 time: 0.8670 data_time: 0.0019 memory: 50106 loss: 0.1752 loss_ce: 0.1752 2023/02/23 23:25:11 - mmengine - INFO - Epoch(train) [6][4400/5047] lr: 3.3333e-05 eta: 7 days, 14:19:34 time: 0.8728 data_time: 0.0015 memory: 42024 loss: 0.1739 loss_ce: 0.1739 2023/02/23 23:26:40 - mmengine - INFO - Epoch(train) [6][4500/5047] lr: 3.3333e-05 eta: 7 days, 14:16:57 time: 0.8865 data_time: 0.0020 memory: 44410 loss: 0.1938 loss_ce: 0.1938 2023/02/23 23:28:08 - mmengine - INFO - Epoch(train) [6][4600/5047] lr: 3.3333e-05 eta: 7 days, 14:13:18 time: 0.8777 data_time: 0.0015 memory: 41419 loss: 0.1808 loss_ce: 0.1808 2023/02/23 23:29:37 - mmengine - INFO - Epoch(train) [6][4700/5047] lr: 3.3333e-05 eta: 7 days, 14:11:09 time: 0.8662 data_time: 0.0017 memory: 39960 loss: 0.1801 loss_ce: 0.1801 2023/02/23 23:30:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 23:31:08 - mmengine - INFO - Epoch(train) [6][4800/5047] lr: 3.3333e-05 eta: 7 days, 14:09:53 time: 0.8837 data_time: 0.0016 memory: 48210 loss: 0.1853 loss_ce: 0.1853 2023/02/23 23:32:37 - mmengine - INFO - Epoch(train) [6][4900/5047] lr: 3.3333e-05 eta: 7 days, 14:07:09 time: 0.8839 data_time: 0.0016 memory: 43289 loss: 0.1744 loss_ce: 0.1744 2023/02/23 23:34:07 - mmengine - INFO - Epoch(train) [6][5000/5047] lr: 3.3333e-05 eta: 7 days, 14:05:28 time: 0.8822 data_time: 0.0017 memory: 51308 loss: 0.1676 loss_ce: 0.1676 2023/02/23 23:34:49 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 23:34:49 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/02/23 23:36:24 - mmengine - INFO - Epoch(train) [7][ 100/5047] lr: 3.3132e-05 eta: 7 days, 14:03:00 time: 0.9104 data_time: 0.0017 memory: 45302 loss: 0.1961 loss_ce: 0.1961 2023/02/23 23:37:54 - mmengine - INFO - Epoch(train) [7][ 200/5047] lr: 3.3132e-05 eta: 7 days, 14:01:24 time: 0.9578 data_time: 0.0019 memory: 42396 loss: 0.1600 loss_ce: 0.1600 2023/02/23 23:39:24 - mmengine - INFO - Epoch(train) [7][ 300/5047] lr: 3.3132e-05 eta: 7 days, 13:59:50 time: 0.8789 data_time: 0.0015 memory: 55562 loss: 0.2040 loss_ce: 0.2040 2023/02/23 23:40:53 - mmengine - INFO - Epoch(train) [7][ 400/5047] lr: 3.3132e-05 eta: 7 days, 13:57:04 time: 0.8532 data_time: 0.0056 memory: 43947 loss: 0.1704 loss_ce: 0.1704 2023/02/23 23:42:22 - mmengine - INFO - Epoch(train) [7][ 500/5047] lr: 3.3132e-05 eta: 7 days, 13:54:52 time: 0.9074 data_time: 0.0017 memory: 44956 loss: 0.1745 loss_ce: 0.1745 2023/02/23 23:43:51 - mmengine - INFO - Epoch(train) [7][ 600/5047] lr: 3.3132e-05 eta: 7 days, 13:52:51 time: 0.8696 data_time: 0.0017 memory: 42024 loss: 0.1739 loss_ce: 0.1739 2023/02/23 23:45:22 - mmengine - INFO - Epoch(train) [7][ 700/5047] lr: 3.3132e-05 eta: 7 days, 13:51:24 time: 0.9119 data_time: 0.0015 memory: 42649 loss: 0.1778 loss_ce: 0.1778 2023/02/23 23:45:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/23 23:46:50 - mmengine - INFO - Epoch(train) [7][ 800/5047] lr: 3.3132e-05 eta: 7 days, 13:48:40 time: 0.9286 data_time: 0.0026 memory: 50242 loss: 0.1780 loss_ce: 0.1780 2023/02/23 23:48:21 - mmengine - INFO - Epoch(train) [7][ 900/5047] lr: 3.3132e-05 eta: 7 days, 13:47:54 time: 0.9301 data_time: 0.0021 memory: 42372 loss: 0.1592 loss_ce: 0.1592 2023/02/23 23:49:50 - mmengine - INFO - Epoch(train) [7][1000/5047] lr: 3.3132e-05 eta: 7 days, 13:45:35 time: 0.9193 data_time: 0.0016 memory: 46005 loss: 0.1615 loss_ce: 0.1615 2023/02/23 23:51:20 - mmengine - INFO - Epoch(train) [7][1100/5047] lr: 3.3132e-05 eta: 7 days, 13:43:26 time: 0.8614 data_time: 0.0022 memory: 46501 loss: 0.1631 loss_ce: 0.1631 2023/02/23 23:52:48 - mmengine - INFO - Epoch(train) [7][1200/5047] lr: 3.3132e-05 eta: 7 days, 13:40:55 time: 0.8974 data_time: 0.0020 memory: 39791 loss: 0.1677 loss_ce: 0.1677 2023/02/23 23:54:17 - mmengine - INFO - Epoch(train) [7][1300/5047] lr: 3.3132e-05 eta: 7 days, 13:38:31 time: 0.8951 data_time: 0.0017 memory: 42552 loss: 0.1897 loss_ce: 0.1897 2023/02/23 23:55:48 - mmengine - INFO - Epoch(train) [7][1400/5047] lr: 3.3132e-05 eta: 7 days, 13:37:05 time: 0.9306 data_time: 0.0018 memory: 51308 loss: 0.1806 loss_ce: 0.1806 2023/02/23 23:57:17 - mmengine - INFO - Epoch(train) [7][1500/5047] lr: 3.3132e-05 eta: 7 days, 13:35:17 time: 0.8593 data_time: 0.0015 memory: 42941 loss: 0.1872 loss_ce: 0.1872 2023/02/23 23:58:46 - mmengine - INFO - Epoch(train) [7][1600/5047] lr: 3.3132e-05 eta: 7 days, 13:32:45 time: 0.8858 data_time: 0.0016 memory: 55562 loss: 0.1802 loss_ce: 0.1802 2023/02/24 00:00:15 - mmengine - INFO - Epoch(train) [7][1700/5047] lr: 3.3132e-05 eta: 7 days, 13:30:28 time: 0.8844 data_time: 0.0019 memory: 42336 loss: 0.1948 loss_ce: 0.1948 2023/02/24 00:00:31 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 00:01:44 - mmengine - INFO - Epoch(train) [7][1800/5047] lr: 3.3132e-05 eta: 7 days, 13:28:18 time: 0.8902 data_time: 0.0019 memory: 46713 loss: 0.1721 loss_ce: 0.1721 2023/02/24 00:03:13 - mmengine - INFO - Epoch(train) [7][1900/5047] lr: 3.3132e-05 eta: 7 days, 13:25:37 time: 0.8325 data_time: 0.0020 memory: 48130 loss: 0.1672 loss_ce: 0.1672 2023/02/24 00:04:42 - mmengine - INFO - Epoch(train) [7][2000/5047] lr: 3.3132e-05 eta: 7 days, 13:23:19 time: 0.8540 data_time: 0.0018 memory: 42649 loss: 0.1735 loss_ce: 0.1735 2023/02/24 00:06:10 - mmengine - INFO - Epoch(train) [7][2100/5047] lr: 3.3132e-05 eta: 7 days, 13:20:39 time: 0.9130 data_time: 0.0016 memory: 45786 loss: 0.1786 loss_ce: 0.1786 2023/02/24 00:07:39 - mmengine - INFO - Epoch(train) [7][2200/5047] lr: 3.3132e-05 eta: 7 days, 13:18:34 time: 0.9025 data_time: 0.0019 memory: 45643 loss: 0.1898 loss_ce: 0.1898 2023/02/24 00:09:07 - mmengine - INFO - Epoch(train) [7][2300/5047] lr: 3.3132e-05 eta: 7 days, 13:15:30 time: 0.8506 data_time: 0.0064 memory: 48329 loss: 0.1728 loss_ce: 0.1728 2023/02/24 00:10:36 - mmengine - INFO - Epoch(train) [7][2400/5047] lr: 3.3132e-05 eta: 7 days, 13:12:48 time: 0.8116 data_time: 0.0017 memory: 45302 loss: 0.1724 loss_ce: 0.1724 2023/02/24 00:12:05 - mmengine - INFO - Epoch(train) [7][2500/5047] lr: 3.3132e-05 eta: 7 days, 13:10:33 time: 0.9089 data_time: 0.0024 memory: 46355 loss: 0.1663 loss_ce: 0.1663 2023/02/24 00:13:32 - mmengine - INFO - Epoch(train) [7][2600/5047] lr: 3.3132e-05 eta: 7 days, 13:07:36 time: 0.8696 data_time: 0.0015 memory: 43404 loss: 0.1808 loss_ce: 0.1808 2023/02/24 00:15:01 - mmengine - INFO - Epoch(train) [7][2700/5047] lr: 3.3132e-05 eta: 7 days, 13:05:15 time: 0.8301 data_time: 0.0015 memory: 43613 loss: 0.1756 loss_ce: 0.1756 2023/02/24 00:15:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 00:16:31 - mmengine - INFO - Epoch(train) [7][2800/5047] lr: 3.3132e-05 eta: 7 days, 13:03:27 time: 0.9119 data_time: 0.0017 memory: 46005 loss: 0.1581 loss_ce: 0.1581 2023/02/24 00:18:00 - mmengine - INFO - Epoch(train) [7][2900/5047] lr: 3.3132e-05 eta: 7 days, 13:01:03 time: 0.8973 data_time: 0.0018 memory: 42336 loss: 0.1883 loss_ce: 0.1883 2023/02/24 00:19:27 - mmengine - INFO - Epoch(train) [7][3000/5047] lr: 3.3132e-05 eta: 7 days, 12:57:58 time: 0.8179 data_time: 0.0024 memory: 41164 loss: 0.1882 loss_ce: 0.1882 2023/02/24 00:20:56 - mmengine - INFO - Epoch(train) [7][3100/5047] lr: 3.3132e-05 eta: 7 days, 12:55:46 time: 0.8943 data_time: 0.0065 memory: 47813 loss: 0.1860 loss_ce: 0.1860 2023/02/24 00:22:26 - mmengine - INFO - Epoch(train) [7][3200/5047] lr: 3.3132e-05 eta: 7 days, 12:54:01 time: 0.8565 data_time: 0.0016 memory: 55562 loss: 0.1677 loss_ce: 0.1677 2023/02/24 00:23:56 - mmengine - INFO - Epoch(train) [7][3300/5047] lr: 3.3132e-05 eta: 7 days, 12:52:29 time: 0.8818 data_time: 0.0016 memory: 53842 loss: 0.1551 loss_ce: 0.1551 2023/02/24 00:25:25 - mmengine - INFO - Epoch(train) [7][3400/5047] lr: 3.3132e-05 eta: 7 days, 12:50:37 time: 0.8214 data_time: 0.0030 memory: 54205 loss: 0.1823 loss_ce: 0.1823 2023/02/24 00:26:56 - mmengine - INFO - Epoch(train) [7][3500/5047] lr: 3.3132e-05 eta: 7 days, 12:49:36 time: 0.9093 data_time: 0.0024 memory: 41270 loss: 0.1719 loss_ce: 0.1719 2023/02/24 00:28:24 - mmengine - INFO - Epoch(train) [7][3600/5047] lr: 3.3132e-05 eta: 7 days, 12:46:59 time: 0.8863 data_time: 0.0018 memory: 44524 loss: 0.1878 loss_ce: 0.1878 2023/02/24 00:29:52 - mmengine - INFO - Epoch(train) [7][3700/5047] lr: 3.3132e-05 eta: 7 days, 12:44:09 time: 0.8242 data_time: 0.0015 memory: 42965 loss: 0.1914 loss_ce: 0.1914 2023/02/24 00:30:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 00:31:21 - mmengine - INFO - Epoch(train) [7][3800/5047] lr: 3.3132e-05 eta: 7 days, 12:41:57 time: 0.8677 data_time: 0.0015 memory: 42336 loss: 0.1883 loss_ce: 0.1883 2023/02/24 00:32:49 - mmengine - INFO - Epoch(train) [7][3900/5047] lr: 3.3132e-05 eta: 7 days, 12:39:20 time: 0.8657 data_time: 0.0017 memory: 46132 loss: 0.1895 loss_ce: 0.1895 2023/02/24 00:34:19 - mmengine - INFO - Epoch(train) [7][4000/5047] lr: 3.3132e-05 eta: 7 days, 12:37:28 time: 0.9361 data_time: 0.0015 memory: 43289 loss: 0.1680 loss_ce: 0.1680 2023/02/24 00:35:47 - mmengine - INFO - Epoch(train) [7][4100/5047] lr: 3.3132e-05 eta: 7 days, 12:35:00 time: 0.8704 data_time: 0.0019 memory: 43587 loss: 0.1835 loss_ce: 0.1835 2023/02/24 00:37:16 - mmengine - INFO - Epoch(train) [7][4200/5047] lr: 3.3132e-05 eta: 7 days, 12:32:33 time: 0.8788 data_time: 0.0025 memory: 44852 loss: 0.1640 loss_ce: 0.1640 2023/02/24 00:38:43 - mmengine - INFO - Epoch(train) [7][4300/5047] lr: 3.3132e-05 eta: 7 days, 12:29:45 time: 0.8578 data_time: 0.0018 memory: 49715 loss: 0.1799 loss_ce: 0.1799 2023/02/24 00:40:14 - mmengine - INFO - Epoch(train) [7][4400/5047] lr: 3.3132e-05 eta: 7 days, 12:28:30 time: 0.8856 data_time: 0.0021 memory: 54473 loss: 0.1823 loss_ce: 0.1823 2023/02/24 00:41:43 - mmengine - INFO - Epoch(train) [7][4500/5047] lr: 3.3132e-05 eta: 7 days, 12:26:33 time: 0.8971 data_time: 0.0028 memory: 42965 loss: 0.1902 loss_ce: 0.1902 2023/02/24 00:43:11 - mmengine - INFO - Epoch(train) [7][4600/5047] lr: 3.3132e-05 eta: 7 days, 12:24:10 time: 0.8905 data_time: 0.0016 memory: 55562 loss: 0.1829 loss_ce: 0.1829 2023/02/24 00:44:40 - mmengine - INFO - Epoch(train) [7][4700/5047] lr: 3.3132e-05 eta: 7 days, 12:22:08 time: 0.9212 data_time: 0.0018 memory: 42649 loss: 0.1920 loss_ce: 0.1920 2023/02/24 00:44:56 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 00:46:09 - mmengine - INFO - Epoch(train) [7][4800/5047] lr: 3.3132e-05 eta: 7 days, 12:19:40 time: 0.9541 data_time: 0.0017 memory: 46713 loss: 0.1502 loss_ce: 0.1502 2023/02/24 00:47:37 - mmengine - INFO - Epoch(train) [7][4900/5047] lr: 3.3132e-05 eta: 7 days, 12:17:12 time: 0.8672 data_time: 0.0017 memory: 49715 loss: 0.1839 loss_ce: 0.1839 2023/02/24 00:49:07 - mmengine - INFO - Epoch(train) [7][5000/5047] lr: 3.3132e-05 eta: 7 days, 12:15:26 time: 0.8892 data_time: 0.0016 memory: 43289 loss: 0.1715 loss_ce: 0.1715 2023/02/24 00:49:48 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 00:49:48 - mmengine - INFO - Saving checkpoint at 7 epochs 2023/02/24 00:51:21 - mmengine - INFO - Epoch(train) [8][ 100/5047] lr: 3.2931e-05 eta: 7 days, 12:11:26 time: 0.8843 data_time: 0.0018 memory: 49334 loss: 0.1663 loss_ce: 0.1663 2023/02/24 00:52:50 - mmengine - INFO - Epoch(train) [8][ 200/5047] lr: 3.2931e-05 eta: 7 days, 12:09:29 time: 0.8192 data_time: 0.0038 memory: 45297 loss: 0.1812 loss_ce: 0.1812 2023/02/24 00:54:19 - mmengine - INFO - Epoch(train) [8][ 300/5047] lr: 3.2931e-05 eta: 7 days, 12:07:36 time: 0.9062 data_time: 0.0016 memory: 42336 loss: 0.1635 loss_ce: 0.1635 2023/02/24 00:55:49 - mmengine - INFO - Epoch(train) [8][ 400/5047] lr: 3.2931e-05 eta: 7 days, 12:05:55 time: 0.9033 data_time: 0.0027 memory: 48148 loss: 0.1671 loss_ce: 0.1671 2023/02/24 00:57:18 - mmengine - INFO - Epoch(train) [8][ 500/5047] lr: 3.2931e-05 eta: 7 days, 12:03:45 time: 0.9283 data_time: 0.0017 memory: 43947 loss: 0.1911 loss_ce: 0.1911 2023/02/24 00:58:46 - mmengine - INFO - Epoch(train) [8][ 600/5047] lr: 3.2931e-05 eta: 7 days, 12:01:21 time: 0.8923 data_time: 0.0019 memory: 46005 loss: 0.1534 loss_ce: 0.1534 2023/02/24 00:59:49 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 01:00:15 - mmengine - INFO - Epoch(train) [8][ 700/5047] lr: 3.2931e-05 eta: 7 days, 11:59:22 time: 0.8645 data_time: 0.0017 memory: 40017 loss: 0.1868 loss_ce: 0.1868 2023/02/24 01:01:44 - mmengine - INFO - Epoch(train) [8][ 800/5047] lr: 3.2931e-05 eta: 7 days, 11:57:27 time: 0.8580 data_time: 0.0020 memory: 45172 loss: 0.1744 loss_ce: 0.1744 2023/02/24 01:03:13 - mmengine - INFO - Epoch(train) [8][ 900/5047] lr: 3.2931e-05 eta: 7 days, 11:55:38 time: 0.8624 data_time: 0.0019 memory: 45302 loss: 0.1455 loss_ce: 0.1455 2023/02/24 01:04:43 - mmengine - INFO - Epoch(train) [8][1000/5047] lr: 3.2931e-05 eta: 7 days, 11:54:06 time: 0.8983 data_time: 0.0016 memory: 45786 loss: 0.1727 loss_ce: 0.1727 2023/02/24 01:06:11 - mmengine - INFO - Epoch(train) [8][1100/5047] lr: 3.2931e-05 eta: 7 days, 11:51:34 time: 0.8740 data_time: 0.0039 memory: 40264 loss: 0.1846 loss_ce: 0.1846 2023/02/24 01:07:40 - mmengine - INFO - Epoch(train) [8][1200/5047] lr: 3.2931e-05 eta: 7 days, 11:49:25 time: 0.8523 data_time: 0.0024 memory: 42336 loss: 0.1566 loss_ce: 0.1566 2023/02/24 01:09:09 - mmengine - INFO - Epoch(train) [8][1300/5047] lr: 3.2931e-05 eta: 7 days, 11:47:36 time: 0.8808 data_time: 0.0015 memory: 54232 loss: 0.1898 loss_ce: 0.1898 2023/02/24 01:10:37 - mmengine - INFO - Epoch(train) [8][1400/5047] lr: 3.2931e-05 eta: 7 days, 11:45:06 time: 0.8605 data_time: 0.0018 memory: 40051 loss: 0.1692 loss_ce: 0.1692 2023/02/24 01:12:05 - mmengine - INFO - Epoch(train) [8][1500/5047] lr: 3.2931e-05 eta: 7 days, 11:42:20 time: 0.9025 data_time: 0.0016 memory: 42270 loss: 0.1842 loss_ce: 0.1842 2023/02/24 01:13:33 - mmengine - INFO - Epoch(train) [8][1600/5047] lr: 3.2931e-05 eta: 7 days, 11:40:09 time: 0.8965 data_time: 0.0016 memory: 46713 loss: 0.1711 loss_ce: 0.1711 2023/02/24 01:14:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 01:15:01 - mmengine - INFO - Epoch(train) [8][1700/5047] lr: 3.2931e-05 eta: 7 days, 11:37:37 time: 0.8837 data_time: 0.0019 memory: 43056 loss: 0.1527 loss_ce: 0.1527 2023/02/24 01:16:31 - mmengine - INFO - Epoch(train) [8][1800/5047] lr: 3.2931e-05 eta: 7 days, 11:35:49 time: 0.8867 data_time: 0.0020 memory: 41984 loss: 0.1805 loss_ce: 0.1805 2023/02/24 01:17:59 - mmengine - INFO - Epoch(train) [8][1900/5047] lr: 3.2931e-05 eta: 7 days, 11:33:31 time: 0.8607 data_time: 0.0023 memory: 40563 loss: 0.1751 loss_ce: 0.1751 2023/02/24 01:19:28 - mmengine - INFO - Epoch(train) [8][2000/5047] lr: 3.2931e-05 eta: 7 days, 11:31:29 time: 0.8534 data_time: 0.0021 memory: 45293 loss: 0.1933 loss_ce: 0.1933 2023/02/24 01:20:57 - mmengine - INFO - Epoch(train) [8][2100/5047] lr: 3.2931e-05 eta: 7 days, 11:29:33 time: 0.9521 data_time: 0.0015 memory: 40241 loss: 0.1477 loss_ce: 0.1477 2023/02/24 01:22:38 - mmengine - INFO - Epoch(train) [8][2200/5047] lr: 3.2931e-05 eta: 7 days, 11:34:27 time: 2.2097 data_time: 0.0018 memory: 45734 loss: 0.1826 loss_ce: 0.1826 2023/02/24 01:24:08 - mmengine - INFO - Epoch(train) [8][2300/5047] lr: 3.2931e-05 eta: 7 days, 11:32:40 time: 0.9453 data_time: 0.0017 memory: 39727 loss: 0.1849 loss_ce: 0.1849 2023/02/24 01:25:36 - mmengine - INFO - Epoch(train) [8][2400/5047] lr: 3.2931e-05 eta: 7 days, 11:30:26 time: 0.8337 data_time: 0.0015 memory: 43992 loss: 0.1797 loss_ce: 0.1797 2023/02/24 01:27:04 - mmengine - INFO - Epoch(train) [8][2500/5047] lr: 3.2931e-05 eta: 7 days, 11:27:50 time: 0.9147 data_time: 0.0018 memory: 45643 loss: 0.1647 loss_ce: 0.1647 2023/02/24 01:28:31 - mmengine - INFO - Epoch(train) [8][2600/5047] lr: 3.2931e-05 eta: 7 days, 11:24:53 time: 0.8661 data_time: 0.0028 memory: 48822 loss: 0.1645 loss_ce: 0.1645 2023/02/24 01:29:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 01:29:59 - mmengine - INFO - Epoch(train) [8][2700/5047] lr: 3.2931e-05 eta: 7 days, 11:22:34 time: 0.9064 data_time: 0.0020 memory: 46324 loss: 0.1534 loss_ce: 0.1534 2023/02/24 01:31:27 - mmengine - INFO - Epoch(train) [8][2800/5047] lr: 3.2931e-05 eta: 7 days, 11:19:45 time: 0.8426 data_time: 0.0015 memory: 45643 loss: 0.1671 loss_ce: 0.1671 2023/02/24 01:32:54 - mmengine - INFO - Epoch(train) [8][2900/5047] lr: 3.2931e-05 eta: 7 days, 11:16:56 time: 0.8756 data_time: 0.0029 memory: 42046 loss: 0.1840 loss_ce: 0.1840 2023/02/24 01:34:22 - mmengine - INFO - Epoch(train) [8][3000/5047] lr: 3.2931e-05 eta: 7 days, 11:14:31 time: 0.8889 data_time: 0.0018 memory: 41419 loss: 0.1640 loss_ce: 0.1640 2023/02/24 01:35:50 - mmengine - INFO - Epoch(train) [8][3100/5047] lr: 3.2931e-05 eta: 7 days, 11:12:23 time: 0.8547 data_time: 0.0016 memory: 47380 loss: 0.1837 loss_ce: 0.1837 2023/02/24 01:37:18 - mmengine - INFO - Epoch(train) [8][3200/5047] lr: 3.2931e-05 eta: 7 days, 11:09:57 time: 0.8840 data_time: 0.0016 memory: 43802 loss: 0.1482 loss_ce: 0.1482 2023/02/24 01:38:48 - mmengine - INFO - Epoch(train) [8][3300/5047] lr: 3.2931e-05 eta: 7 days, 11:08:26 time: 0.8781 data_time: 0.0017 memory: 55562 loss: 0.1744 loss_ce: 0.1744 2023/02/24 01:40:16 - mmengine - INFO - Epoch(train) [8][3400/5047] lr: 3.2931e-05 eta: 7 days, 11:06:06 time: 0.8771 data_time: 0.0032 memory: 42649 loss: 0.1605 loss_ce: 0.1605 2023/02/24 01:41:43 - mmengine - INFO - Epoch(train) [8][3500/5047] lr: 3.2931e-05 eta: 7 days, 11:03:01 time: 0.8410 data_time: 0.0039 memory: 44722 loss: 0.1622 loss_ce: 0.1622 2023/02/24 01:43:11 - mmengine - INFO - Epoch(train) [8][3600/5047] lr: 3.2931e-05 eta: 7 days, 11:00:37 time: 0.8822 data_time: 0.0016 memory: 45785 loss: 0.1615 loss_ce: 0.1615 2023/02/24 01:44:13 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 01:44:38 - mmengine - INFO - Epoch(train) [8][3700/5047] lr: 3.2931e-05 eta: 7 days, 10:58:00 time: 0.8493 data_time: 0.0016 memory: 44617 loss: 0.1845 loss_ce: 0.1845 2023/02/24 01:46:05 - mmengine - INFO - Epoch(train) [8][3800/5047] lr: 3.2931e-05 eta: 7 days, 10:55:05 time: 0.8301 data_time: 0.0016 memory: 46920 loss: 0.1933 loss_ce: 0.1933 2023/02/24 01:47:34 - mmengine - INFO - Epoch(train) [8][3900/5047] lr: 3.2931e-05 eta: 7 days, 10:52:56 time: 0.8812 data_time: 0.0105 memory: 52862 loss: 0.1784 loss_ce: 0.1784 2023/02/24 01:49:01 - mmengine - INFO - Epoch(train) [8][4000/5047] lr: 3.2931e-05 eta: 7 days, 10:50:26 time: 0.8782 data_time: 0.0023 memory: 46355 loss: 0.1647 loss_ce: 0.1647 2023/02/24 01:50:29 - mmengine - INFO - Epoch(train) [8][4100/5047] lr: 3.2931e-05 eta: 7 days, 10:47:44 time: 0.8348 data_time: 0.0020 memory: 48565 loss: 0.1845 loss_ce: 0.1845 2023/02/24 01:51:56 - mmengine - INFO - Epoch(train) [8][4200/5047] lr: 3.2931e-05 eta: 7 days, 10:45:04 time: 0.8992 data_time: 0.0016 memory: 43613 loss: 0.1692 loss_ce: 0.1692 2023/02/24 01:53:24 - mmengine - INFO - Epoch(train) [8][4300/5047] lr: 3.2931e-05 eta: 7 days, 10:42:52 time: 0.8990 data_time: 0.0017 memory: 48360 loss: 0.1714 loss_ce: 0.1714 2023/02/24 01:54:52 - mmengine - INFO - Epoch(train) [8][4400/5047] lr: 3.2931e-05 eta: 7 days, 10:40:30 time: 0.8805 data_time: 0.0025 memory: 48053 loss: 0.1755 loss_ce: 0.1755 2023/02/24 01:56:20 - mmengine - INFO - Epoch(train) [8][4500/5047] lr: 3.2931e-05 eta: 7 days, 10:38:04 time: 0.8734 data_time: 0.0046 memory: 55562 loss: 0.1599 loss_ce: 0.1599 2023/02/24 01:57:48 - mmengine - INFO - Epoch(train) [8][4600/5047] lr: 3.2931e-05 eta: 7 days, 10:35:40 time: 0.8719 data_time: 0.0020 memory: 42649 loss: 0.1631 loss_ce: 0.1631 2023/02/24 01:58:50 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 01:59:15 - mmengine - INFO - Epoch(train) [8][4700/5047] lr: 3.2931e-05 eta: 7 days, 10:33:05 time: 0.8469 data_time: 0.0017 memory: 55562 loss: 0.1955 loss_ce: 0.1955 2023/02/24 02:00:42 - mmengine - INFO - Epoch(train) [8][4800/5047] lr: 3.2931e-05 eta: 7 days, 10:30:31 time: 0.8567 data_time: 0.0020 memory: 45832 loss: 0.1796 loss_ce: 0.1796 2023/02/24 02:02:11 - mmengine - INFO - Epoch(train) [8][4900/5047] lr: 3.2931e-05 eta: 7 days, 10:28:44 time: 0.9254 data_time: 0.0018 memory: 43508 loss: 0.1677 loss_ce: 0.1677 2023/02/24 02:03:40 - mmengine - INFO - Epoch(train) [8][5000/5047] lr: 3.2931e-05 eta: 7 days, 10:26:40 time: 0.8896 data_time: 0.0021 memory: 43613 loss: 0.1671 loss_ce: 0.1671 2023/02/24 02:04:20 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 02:04:20 - mmengine - INFO - Saving checkpoint at 8 epochs 2023/02/24 02:05:53 - mmengine - INFO - Epoch(train) [9][ 100/5047] lr: 3.2730e-05 eta: 7 days, 10:22:51 time: 0.8758 data_time: 0.0018 memory: 43511 loss: 0.1663 loss_ce: 0.1663 2023/02/24 02:07:20 - mmengine - INFO - Epoch(train) [9][ 200/5047] lr: 3.2730e-05 eta: 7 days, 10:20:04 time: 0.8423 data_time: 0.0019 memory: 45289 loss: 0.1627 loss_ce: 0.1627 2023/02/24 02:08:49 - mmengine - INFO - Epoch(train) [9][ 300/5047] lr: 3.2730e-05 eta: 7 days, 10:18:11 time: 0.9038 data_time: 0.0023 memory: 47074 loss: 0.1729 loss_ce: 0.1729 2023/02/24 02:10:17 - mmengine - INFO - Epoch(train) [9][ 400/5047] lr: 3.2730e-05 eta: 7 days, 10:15:50 time: 0.8785 data_time: 0.0056 memory: 55366 loss: 0.1614 loss_ce: 0.1614 2023/02/24 02:11:46 - mmengine - INFO - Epoch(train) [9][ 500/5047] lr: 3.2730e-05 eta: 7 days, 10:14:18 time: 0.8648 data_time: 0.0028 memory: 50906 loss: 0.1797 loss_ce: 0.1797 2023/02/24 02:13:14 - mmengine - INFO - Epoch(train) [9][ 600/5047] lr: 3.2730e-05 eta: 7 days, 10:11:56 time: 0.9507 data_time: 0.0021 memory: 41527 loss: 0.1691 loss_ce: 0.1691 2023/02/24 02:13:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 02:14:41 - mmengine - INFO - Epoch(train) [9][ 700/5047] lr: 3.2730e-05 eta: 7 days, 10:09:26 time: 0.8584 data_time: 0.0017 memory: 42024 loss: 0.1895 loss_ce: 0.1895 2023/02/24 02:16:08 - mmengine - INFO - Epoch(train) [9][ 800/5047] lr: 3.2730e-05 eta: 7 days, 10:06:39 time: 0.8737 data_time: 0.0018 memory: 49528 loss: 0.1790 loss_ce: 0.1790 2023/02/24 02:17:36 - mmengine - INFO - Epoch(train) [9][ 900/5047] lr: 3.2730e-05 eta: 7 days, 10:04:34 time: 0.9022 data_time: 0.0029 memory: 46713 loss: 0.1682 loss_ce: 0.1682 2023/02/24 02:19:05 - mmengine - INFO - Epoch(train) [9][1000/5047] lr: 3.2730e-05 eta: 7 days, 10:02:33 time: 0.8895 data_time: 0.0032 memory: 42574 loss: 0.1504 loss_ce: 0.1504 2023/02/24 02:20:33 - mmengine - INFO - Epoch(train) [9][1100/5047] lr: 3.2730e-05 eta: 7 days, 10:00:33 time: 0.9066 data_time: 0.0020 memory: 43289 loss: 0.1643 loss_ce: 0.1643 2023/02/24 02:22:02 - mmengine - INFO - Epoch(train) [9][1200/5047] lr: 3.2730e-05 eta: 7 days, 9:58:37 time: 0.9069 data_time: 0.0017 memory: 43196 loss: 0.1741 loss_ce: 0.1741 2023/02/24 02:23:31 - mmengine - INFO - Epoch(train) [9][1300/5047] lr: 3.2730e-05 eta: 7 days, 9:56:52 time: 0.9120 data_time: 0.0019 memory: 48188 loss: 0.1922 loss_ce: 0.1922 2023/02/24 02:24:56 - mmengine - INFO - Epoch(train) [9][1400/5047] lr: 3.2730e-05 eta: 7 days, 9:53:35 time: 0.8168 data_time: 0.0018 memory: 42336 loss: 0.1690 loss_ce: 0.1690 2023/02/24 02:26:25 - mmengine - INFO - Epoch(train) [9][1500/5047] lr: 3.2730e-05 eta: 7 days, 9:51:53 time: 0.8664 data_time: 0.0023 memory: 49696 loss: 0.1689 loss_ce: 0.1689 2023/02/24 02:27:55 - mmengine - INFO - Epoch(train) [9][1600/5047] lr: 3.2730e-05 eta: 7 days, 9:50:19 time: 0.8581 data_time: 0.0016 memory: 46182 loss: 0.1677 loss_ce: 0.1677 2023/02/24 02:28:16 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 02:29:24 - mmengine - INFO - Epoch(train) [9][1700/5047] lr: 3.2730e-05 eta: 7 days, 9:48:46 time: 0.9320 data_time: 0.0043 memory: 43289 loss: 0.1596 loss_ce: 0.1596 2023/02/24 02:30:55 - mmengine - INFO - Epoch(train) [9][1800/5047] lr: 3.2730e-05 eta: 7 days, 9:47:48 time: 0.9351 data_time: 0.0020 memory: 42309 loss: 0.1718 loss_ce: 0.1718 2023/02/24 02:32:23 - mmengine - INFO - Epoch(train) [9][1900/5047] lr: 3.2730e-05 eta: 7 days, 9:45:39 time: 0.8775 data_time: 0.0047 memory: 43289 loss: 0.1400 loss_ce: 0.1400 2023/02/24 02:33:52 - mmengine - INFO - Epoch(train) [9][2000/5047] lr: 3.2730e-05 eta: 7 days, 9:43:46 time: 0.8747 data_time: 0.0018 memory: 45302 loss: 0.1727 loss_ce: 0.1727 2023/02/24 02:35:21 - mmengine - INFO - Epoch(train) [9][2100/5047] lr: 3.2730e-05 eta: 7 days, 9:42:08 time: 0.8596 data_time: 0.0015 memory: 47193 loss: 0.1830 loss_ce: 0.1830 2023/02/24 02:36:48 - mmengine - INFO - Epoch(train) [9][2200/5047] lr: 3.2730e-05 eta: 7 days, 9:39:47 time: 0.9375 data_time: 0.0018 memory: 55562 loss: 0.1567 loss_ce: 0.1567 2023/02/24 02:38:18 - mmengine - INFO - Epoch(train) [9][2300/5047] lr: 3.2730e-05 eta: 7 days, 9:38:10 time: 0.8396 data_time: 0.0028 memory: 48149 loss: 0.1511 loss_ce: 0.1511 2023/02/24 02:39:46 - mmengine - INFO - Epoch(train) [9][2400/5047] lr: 3.2730e-05 eta: 7 days, 9:36:09 time: 0.8944 data_time: 0.0020 memory: 41724 loss: 0.1733 loss_ce: 0.1733 2023/02/24 02:41:15 - mmengine - INFO - Epoch(train) [9][2500/5047] lr: 3.2730e-05 eta: 7 days, 9:34:32 time: 0.8976 data_time: 0.0016 memory: 42105 loss: 0.1665 loss_ce: 0.1665 2023/02/24 02:42:45 - mmengine - INFO - Epoch(train) [9][2600/5047] lr: 3.2730e-05 eta: 7 days, 9:33:00 time: 0.8884 data_time: 0.0017 memory: 40048 loss: 0.1526 loss_ce: 0.1526 2023/02/24 02:43:06 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 02:44:14 - mmengine - INFO - Epoch(train) [9][2700/5047] lr: 3.2730e-05 eta: 7 days, 9:31:24 time: 0.9055 data_time: 0.0020 memory: 50106 loss: 0.1684 loss_ce: 0.1684 2023/02/24 02:45:43 - mmengine - INFO - Epoch(train) [9][2800/5047] lr: 3.2730e-05 eta: 7 days, 9:29:41 time: 0.8787 data_time: 0.0017 memory: 41382 loss: 0.1469 loss_ce: 0.1469 2023/02/24 02:47:10 - mmengine - INFO - Epoch(train) [9][2900/5047] lr: 3.2730e-05 eta: 7 days, 9:27:03 time: 0.8224 data_time: 0.0024 memory: 52863 loss: 0.1667 loss_ce: 0.1667 2023/02/24 02:48:40 - mmengine - INFO - Epoch(train) [9][3000/5047] lr: 3.2730e-05 eta: 7 days, 9:25:44 time: 0.8791 data_time: 0.0017 memory: 41419 loss: 0.1659 loss_ce: 0.1659 2023/02/24 02:50:08 - mmengine - INFO - Epoch(train) [9][3100/5047] lr: 3.2730e-05 eta: 7 days, 9:23:54 time: 0.9132 data_time: 0.0030 memory: 47447 loss: 0.1757 loss_ce: 0.1757 2023/02/24 02:51:36 - mmengine - INFO - Epoch(train) [9][3200/5047] lr: 3.2730e-05 eta: 7 days, 9:21:32 time: 0.8860 data_time: 0.0016 memory: 48070 loss: 0.1498 loss_ce: 0.1498 2023/02/24 02:53:04 - mmengine - INFO - Epoch(train) [9][3300/5047] lr: 3.2730e-05 eta: 7 days, 9:19:20 time: 0.8953 data_time: 0.0018 memory: 43613 loss: 0.1822 loss_ce: 0.1822 2023/02/24 02:54:33 - mmengine - INFO - Epoch(train) [9][3400/5047] lr: 3.2730e-05 eta: 7 days, 9:17:39 time: 0.8703 data_time: 0.0020 memory: 50106 loss: 0.1551 loss_ce: 0.1551 2023/02/24 02:56:01 - mmengine - INFO - Epoch(train) [9][3500/5047] lr: 3.2730e-05 eta: 7 days, 9:15:45 time: 0.8927 data_time: 0.0016 memory: 55562 loss: 0.1648 loss_ce: 0.1648 2023/02/24 02:57:29 - mmengine - INFO - Epoch(train) [9][3600/5047] lr: 3.2730e-05 eta: 7 days, 9:13:44 time: 0.8785 data_time: 0.0016 memory: 41834 loss: 0.1456 loss_ce: 0.1456 2023/02/24 02:57:50 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 02:58:57 - mmengine - INFO - Epoch(train) [9][3700/5047] lr: 3.2730e-05 eta: 7 days, 9:11:24 time: 0.8759 data_time: 0.0018 memory: 53809 loss: 0.1539 loss_ce: 0.1539 2023/02/24 03:00:25 - mmengine - INFO - Epoch(train) [9][3800/5047] lr: 3.2730e-05 eta: 7 days, 9:09:30 time: 0.8760 data_time: 0.0016 memory: 49312 loss: 0.1745 loss_ce: 0.1745 2023/02/24 03:01:54 - mmengine - INFO - Epoch(train) [9][3900/5047] lr: 3.2730e-05 eta: 7 days, 9:07:56 time: 0.8911 data_time: 0.0016 memory: 45302 loss: 0.1795 loss_ce: 0.1795 2023/02/24 03:03:24 - mmengine - INFO - Epoch(train) [9][4000/5047] lr: 3.2730e-05 eta: 7 days, 9:06:39 time: 0.9587 data_time: 0.0020 memory: 43847 loss: 0.1581 loss_ce: 0.1581 2023/02/24 03:04:51 - mmengine - INFO - Epoch(train) [9][4100/5047] lr: 3.2730e-05 eta: 7 days, 9:04:10 time: 0.8712 data_time: 0.0022 memory: 44632 loss: 0.1625 loss_ce: 0.1625 2023/02/24 03:06:20 - mmengine - INFO - Epoch(train) [9][4200/5047] lr: 3.2730e-05 eta: 7 days, 9:02:17 time: 0.8758 data_time: 0.0017 memory: 41122 loss: 0.1785 loss_ce: 0.1785 2023/02/24 03:07:48 - mmengine - INFO - Epoch(train) [9][4300/5047] lr: 3.2730e-05 eta: 7 days, 9:00:12 time: 0.8790 data_time: 0.0019 memory: 42965 loss: 0.1599 loss_ce: 0.1599 2023/02/24 03:09:15 - mmengine - INFO - Epoch(train) [9][4400/5047] lr: 3.2730e-05 eta: 7 days, 8:57:56 time: 0.8641 data_time: 0.0017 memory: 47925 loss: 0.1593 loss_ce: 0.1593 2023/02/24 03:10:43 - mmengine - INFO - Epoch(train) [9][4500/5047] lr: 3.2730e-05 eta: 7 days, 8:55:42 time: 0.8885 data_time: 0.0018 memory: 42649 loss: 0.1732 loss_ce: 0.1732 2023/02/24 03:12:11 - mmengine - INFO - Epoch(train) [9][4600/5047] lr: 3.2730e-05 eta: 7 days, 8:53:26 time: 0.8366 data_time: 0.0024 memory: 41419 loss: 0.1635 loss_ce: 0.1635 2023/02/24 03:12:32 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 03:13:38 - mmengine - INFO - Epoch(train) [9][4700/5047] lr: 3.2730e-05 eta: 7 days, 8:51:10 time: 0.8523 data_time: 0.0025 memory: 41419 loss: 0.1684 loss_ce: 0.1684 2023/02/24 03:15:08 - mmengine - INFO - Epoch(train) [9][4800/5047] lr: 3.2730e-05 eta: 7 days, 8:49:51 time: 0.8365 data_time: 0.0019 memory: 55562 loss: 0.1626 loss_ce: 0.1626 2023/02/24 03:16:36 - mmengine - INFO - Epoch(train) [9][4900/5047] lr: 3.2730e-05 eta: 7 days, 8:47:46 time: 0.8662 data_time: 0.0042 memory: 44860 loss: 0.1537 loss_ce: 0.1537 2023/02/24 03:18:04 - mmengine - INFO - Epoch(train) [9][5000/5047] lr: 3.2730e-05 eta: 7 days, 8:45:40 time: 0.8531 data_time: 0.0021 memory: 43947 loss: 0.1678 loss_ce: 0.1678 2023/02/24 03:18:44 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 03:18:44 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/02/24 03:20:17 - mmengine - INFO - Epoch(train) [10][ 100/5047] lr: 3.2530e-05 eta: 7 days, 8:42:10 time: 0.8560 data_time: 0.0016 memory: 52371 loss: 0.1767 loss_ce: 0.1767 2023/02/24 03:21:43 - mmengine - INFO - Epoch(train) [10][ 200/5047] lr: 3.2530e-05 eta: 7 days, 8:39:29 time: 0.8634 data_time: 0.0018 memory: 42965 loss: 0.1561 loss_ce: 0.1561 2023/02/24 03:23:10 - mmengine - INFO - Epoch(train) [10][ 300/5047] lr: 3.2530e-05 eta: 7 days, 8:37:03 time: 0.9043 data_time: 0.0019 memory: 42649 loss: 0.1835 loss_ce: 0.1835 2023/02/24 03:24:38 - mmengine - INFO - Epoch(train) [10][ 400/5047] lr: 3.2530e-05 eta: 7 days, 8:34:58 time: 0.8684 data_time: 0.0016 memory: 43947 loss: 0.1638 loss_ce: 0.1638 2023/02/24 03:26:07 - mmengine - INFO - Epoch(train) [10][ 500/5047] lr: 3.2530e-05 eta: 7 days, 8:33:28 time: 0.9151 data_time: 0.0018 memory: 40825 loss: 0.1830 loss_ce: 0.1830 2023/02/24 03:27:14 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 03:27:35 - mmengine - INFO - Epoch(train) [10][ 600/5047] lr: 3.2530e-05 eta: 7 days, 8:31:14 time: 0.8641 data_time: 0.0017 memory: 44617 loss: 0.1629 loss_ce: 0.1629 2023/02/24 03:29:04 - mmengine - INFO - Epoch(train) [10][ 700/5047] lr: 3.2530e-05 eta: 7 days, 8:29:36 time: 0.8554 data_time: 0.0016 memory: 49334 loss: 0.1757 loss_ce: 0.1757 2023/02/24 03:30:31 - mmengine - INFO - Epoch(train) [10][ 800/5047] lr: 3.2530e-05 eta: 7 days, 8:27:25 time: 0.8891 data_time: 0.0016 memory: 42649 loss: 0.1435 loss_ce: 0.1435 2023/02/24 03:31:59 - mmengine - INFO - Epoch(train) [10][ 900/5047] lr: 3.2530e-05 eta: 7 days, 8:25:23 time: 0.8863 data_time: 0.0017 memory: 52964 loss: 0.1863 loss_ce: 0.1863 2023/02/24 03:33:28 - mmengine - INFO - Epoch(train) [10][1000/5047] lr: 3.2530e-05 eta: 7 days, 8:23:37 time: 0.8762 data_time: 0.0016 memory: 43289 loss: 0.1686 loss_ce: 0.1686 2023/02/24 03:34:55 - mmengine - INFO - Epoch(train) [10][1100/5047] lr: 3.2530e-05 eta: 7 days, 8:21:19 time: 0.8985 data_time: 0.0018 memory: 54277 loss: 0.1483 loss_ce: 0.1483 2023/02/24 03:36:22 - mmengine - INFO - Epoch(train) [10][1200/5047] lr: 3.2530e-05 eta: 7 days, 8:18:53 time: 0.8584 data_time: 0.0017 memory: 42965 loss: 0.1651 loss_ce: 0.1651 2023/02/24 03:37:50 - mmengine - INFO - Epoch(train) [10][1300/5047] lr: 3.2530e-05 eta: 7 days, 8:16:48 time: 0.8702 data_time: 0.0020 memory: 40825 loss: 0.1540 loss_ce: 0.1540 2023/02/24 03:39:17 - mmengine - INFO - Epoch(train) [10][1400/5047] lr: 3.2530e-05 eta: 7 days, 8:14:36 time: 0.8644 data_time: 0.0020 memory: 43895 loss: 0.1762 loss_ce: 0.1762 2023/02/24 03:40:46 - mmengine - INFO - Epoch(train) [10][1500/5047] lr: 3.2530e-05 eta: 7 days, 8:13:05 time: 0.8590 data_time: 0.0056 memory: 55562 loss: 0.1554 loss_ce: 0.1554 2023/02/24 03:41:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 03:42:14 - mmengine - INFO - Epoch(train) [10][1600/5047] lr: 3.2530e-05 eta: 7 days, 8:10:56 time: 0.8158 data_time: 0.0022 memory: 42372 loss: 0.1667 loss_ce: 0.1667 2023/02/24 03:43:42 - mmengine - INFO - Epoch(train) [10][1700/5047] lr: 3.2530e-05 eta: 7 days, 8:08:59 time: 0.8412 data_time: 0.0017 memory: 50024 loss: 0.1632 loss_ce: 0.1632 2023/02/24 03:45:10 - mmengine - INFO - Epoch(train) [10][1800/5047] lr: 3.2530e-05 eta: 7 days, 8:06:55 time: 0.9013 data_time: 0.0020 memory: 40500 loss: 0.1571 loss_ce: 0.1571 2023/02/24 03:46:38 - mmengine - INFO - Epoch(train) [10][1900/5047] lr: 3.2530e-05 eta: 7 days, 8:05:04 time: 0.9003 data_time: 0.0019 memory: 43007 loss: 0.1590 loss_ce: 0.1590 2023/02/24 03:48:06 - mmengine - INFO - Epoch(train) [10][2000/5047] lr: 3.2530e-05 eta: 7 days, 8:03:07 time: 0.8517 data_time: 0.0015 memory: 55562 loss: 0.1615 loss_ce: 0.1615 2023/02/24 03:49:35 - mmengine - INFO - Epoch(train) [10][2100/5047] lr: 3.2530e-05 eta: 7 days, 8:01:24 time: 0.8770 data_time: 0.0044 memory: 46502 loss: 0.1688 loss_ce: 0.1688 2023/02/24 03:51:06 - mmengine - INFO - Epoch(train) [10][2200/5047] lr: 3.2530e-05 eta: 7 days, 8:00:29 time: 0.9138 data_time: 0.0025 memory: 54242 loss: 0.1464 loss_ce: 0.1464 2023/02/24 03:52:34 - mmengine - INFO - Epoch(train) [10][2300/5047] lr: 3.2530e-05 eta: 7 days, 7:58:38 time: 0.8966 data_time: 0.0020 memory: 48565 loss: 0.1675 loss_ce: 0.1675 2023/02/24 03:54:03 - mmengine - INFO - Epoch(train) [10][2400/5047] lr: 3.2530e-05 eta: 7 days, 7:56:52 time: 0.8592 data_time: 0.0018 memory: 52882 loss: 0.1864 loss_ce: 0.1864 2023/02/24 03:55:31 - mmengine - INFO - Epoch(train) [10][2500/5047] lr: 3.2530e-05 eta: 7 days, 7:54:51 time: 0.8714 data_time: 0.0019 memory: 47813 loss: 0.1566 loss_ce: 0.1566 2023/02/24 03:56:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 03:56:59 - mmengine - INFO - Epoch(train) [10][2600/5047] lr: 3.2530e-05 eta: 7 days, 7:53:09 time: 0.8608 data_time: 0.0021 memory: 42649 loss: 0.1755 loss_ce: 0.1755 2023/02/24 03:58:27 - mmengine - INFO - Epoch(train) [10][2700/5047] lr: 3.2530e-05 eta: 7 days, 7:50:56 time: 0.8617 data_time: 0.0019 memory: 42309 loss: 0.1754 loss_ce: 0.1754 2023/02/24 03:59:55 - mmengine - INFO - Epoch(train) [10][2800/5047] lr: 3.2530e-05 eta: 7 days, 7:49:09 time: 0.8697 data_time: 0.0020 memory: 46794 loss: 0.1484 loss_ce: 0.1484 2023/02/24 04:01:23 - mmengine - INFO - Epoch(train) [10][2900/5047] lr: 3.2530e-05 eta: 7 days, 7:47:03 time: 0.8760 data_time: 0.0025 memory: 42105 loss: 0.1831 loss_ce: 0.1831 2023/02/24 04:02:52 - mmengine - INFO - Epoch(train) [10][3000/5047] lr: 3.2530e-05 eta: 7 days, 7:45:39 time: 0.9170 data_time: 0.0041 memory: 43056 loss: 0.1482 loss_ce: 0.1482 2023/02/24 04:04:21 - mmengine - INFO - Epoch(train) [10][3100/5047] lr: 3.2530e-05 eta: 7 days, 7:43:50 time: 0.8640 data_time: 0.0021 memory: 43198 loss: 0.1559 loss_ce: 0.1559 2023/02/24 04:05:49 - mmengine - INFO - Epoch(train) [10][3200/5047] lr: 3.2530e-05 eta: 7 days, 7:42:05 time: 0.8127 data_time: 0.0018 memory: 42649 loss: 0.1511 loss_ce: 0.1511 2023/02/24 04:07:19 - mmengine - INFO - Epoch(train) [10][3300/5047] lr: 3.2530e-05 eta: 7 days, 7:40:51 time: 0.8204 data_time: 0.0031 memory: 43947 loss: 0.1717 loss_ce: 0.1717 2023/02/24 04:08:48 - mmengine - INFO - Epoch(train) [10][3400/5047] lr: 3.2530e-05 eta: 7 days, 7:39:12 time: 0.8538 data_time: 0.0018 memory: 44617 loss: 0.1695 loss_ce: 0.1695 2023/02/24 04:10:17 - mmengine - INFO - Epoch(train) [10][3500/5047] lr: 3.2530e-05 eta: 7 days, 7:37:27 time: 0.8826 data_time: 0.0018 memory: 51640 loss: 0.1542 loss_ce: 0.1542 2023/02/24 04:11:26 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 04:11:46 - mmengine - INFO - Epoch(train) [10][3600/5047] lr: 3.2530e-05 eta: 7 days, 7:36:01 time: 0.9225 data_time: 0.0016 memory: 42336 loss: 0.1601 loss_ce: 0.1601 2023/02/24 04:13:15 - mmengine - INFO - Epoch(train) [10][3700/5047] lr: 3.2530e-05 eta: 7 days, 7:34:32 time: 0.8991 data_time: 0.0016 memory: 55468 loss: 0.1548 loss_ce: 0.1548 2023/02/24 04:14:43 - mmengine - INFO - Epoch(train) [10][3800/5047] lr: 3.2530e-05 eta: 7 days, 7:32:26 time: 0.8939 data_time: 0.0016 memory: 49595 loss: 0.1684 loss_ce: 0.1684 2023/02/24 04:16:12 - mmengine - INFO - Epoch(train) [10][3900/5047] lr: 3.2530e-05 eta: 7 days, 7:30:45 time: 0.8601 data_time: 0.0019 memory: 42579 loss: 0.1760 loss_ce: 0.1760 2023/02/24 04:17:41 - mmengine - INFO - Epoch(train) [10][4000/5047] lr: 3.2530e-05 eta: 7 days, 7:29:22 time: 0.9061 data_time: 0.0016 memory: 43289 loss: 0.1795 loss_ce: 0.1795 2023/02/24 04:19:08 - mmengine - INFO - Epoch(train) [10][4100/5047] lr: 3.2530e-05 eta: 7 days, 7:27:09 time: 0.8944 data_time: 0.0018 memory: 52817 loss: 0.1503 loss_ce: 0.1503 2023/02/24 04:20:34 - mmengine - INFO - Epoch(train) [10][4200/5047] lr: 3.2530e-05 eta: 7 days, 7:24:28 time: 0.8725 data_time: 0.0016 memory: 42336 loss: 0.1396 loss_ce: 0.1396 2023/02/24 04:22:03 - mmengine - INFO - Epoch(train) [10][4300/5047] lr: 3.2530e-05 eta: 7 days, 7:22:54 time: 0.9089 data_time: 0.0019 memory: 55562 loss: 0.1597 loss_ce: 0.1597 2023/02/24 04:23:31 - mmengine - INFO - Epoch(train) [10][4400/5047] lr: 3.2530e-05 eta: 7 days, 7:21:02 time: 0.8554 data_time: 0.0016 memory: 45302 loss: 0.1466 loss_ce: 0.1466 2023/02/24 04:25:00 - mmengine - INFO - Epoch(train) [10][4500/5047] lr: 3.2530e-05 eta: 7 days, 7:19:15 time: 0.8686 data_time: 0.0017 memory: 53809 loss: 0.1697 loss_ce: 0.1697 2023/02/24 04:26:09 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 04:26:29 - mmengine - INFO - Epoch(train) [10][4600/5047] lr: 3.2530e-05 eta: 7 days, 7:17:37 time: 0.8916 data_time: 0.0020 memory: 44707 loss: 0.1743 loss_ce: 0.1743 2023/02/24 04:27:55 - mmengine - INFO - Epoch(train) [10][4700/5047] lr: 3.2530e-05 eta: 7 days, 7:15:13 time: 0.8715 data_time: 0.0028 memory: 48210 loss: 0.1591 loss_ce: 0.1591 2023/02/24 04:29:25 - mmengine - INFO - Epoch(train) [10][4800/5047] lr: 3.2530e-05 eta: 7 days, 7:14:05 time: 0.9102 data_time: 0.0017 memory: 44241 loss: 0.1479 loss_ce: 0.1479 2023/02/24 04:30:55 - mmengine - INFO - Epoch(train) [10][4900/5047] lr: 3.2530e-05 eta: 7 days, 7:12:40 time: 0.9231 data_time: 0.0021 memory: 55485 loss: 0.1639 loss_ce: 0.1639 2023/02/24 04:32:23 - mmengine - INFO - Epoch(train) [10][5000/5047] lr: 3.2530e-05 eta: 7 days, 7:10:47 time: 0.9629 data_time: 0.0022 memory: 53387 loss: 0.1511 loss_ce: 0.1511 2023/02/24 04:33:05 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 04:33:05 - mmengine - INFO - Saving checkpoint at 10 epochs 2023/02/24 04:34:37 - mmengine - INFO - Epoch(train) [11][ 100/5047] lr: 3.2329e-05 eta: 7 days, 7:08:06 time: 0.9028 data_time: 0.0021 memory: 53387 loss: 0.1714 loss_ce: 0.1714 2023/02/24 04:36:06 - mmengine - INFO - Epoch(train) [11][ 200/5047] lr: 3.2329e-05 eta: 7 days, 7:06:31 time: 0.8912 data_time: 0.0025 memory: 42965 loss: 0.1616 loss_ce: 0.1616 2023/02/24 04:37:35 - mmengine - INFO - Epoch(train) [11][ 300/5047] lr: 3.2329e-05 eta: 7 days, 7:04:52 time: 0.9155 data_time: 0.0016 memory: 40825 loss: 0.1491 loss_ce: 0.1491 2023/02/24 04:39:03 - mmengine - INFO - Epoch(train) [11][ 400/5047] lr: 3.2329e-05 eta: 7 days, 7:03:02 time: 0.8683 data_time: 0.0018 memory: 49068 loss: 0.1734 loss_ce: 0.1734 2023/02/24 04:40:32 - mmengine - INFO - Epoch(train) [11][ 500/5047] lr: 3.2329e-05 eta: 7 days, 7:01:21 time: 0.8955 data_time: 0.0019 memory: 47547 loss: 0.1555 loss_ce: 0.1555 2023/02/24 04:40:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 04:42:00 - mmengine - INFO - Epoch(train) [11][ 600/5047] lr: 3.2329e-05 eta: 7 days, 6:59:39 time: 0.8957 data_time: 0.0034 memory: 42649 loss: 0.1440 loss_ce: 0.1440 2023/02/24 04:43:28 - mmengine - INFO - Epoch(train) [11][ 700/5047] lr: 3.2329e-05 eta: 7 days, 6:57:36 time: 0.8767 data_time: 0.0034 memory: 45815 loss: 0.1428 loss_ce: 0.1428 2023/02/24 04:44:56 - mmengine - INFO - Epoch(train) [11][ 800/5047] lr: 3.2329e-05 eta: 7 days, 6:55:38 time: 0.8453 data_time: 0.0021 memory: 40825 loss: 0.1519 loss_ce: 0.1519 2023/02/24 04:46:24 - mmengine - INFO - Epoch(train) [11][ 900/5047] lr: 3.2329e-05 eta: 7 days, 6:53:56 time: 0.8452 data_time: 0.0017 memory: 45302 loss: 0.1422 loss_ce: 0.1422 2023/02/24 04:47:53 - mmengine - INFO - Epoch(train) [11][1000/5047] lr: 3.2329e-05 eta: 7 days, 6:52:17 time: 0.8754 data_time: 0.0019 memory: 43613 loss: 0.1459 loss_ce: 0.1459 2023/02/24 04:49:20 - mmengine - INFO - Epoch(train) [11][1100/5047] lr: 3.2329e-05 eta: 7 days, 6:49:54 time: 0.8846 data_time: 0.0019 memory: 55562 loss: 0.1526 loss_ce: 0.1526 2023/02/24 04:50:47 - mmengine - INFO - Epoch(train) [11][1200/5047] lr: 3.2329e-05 eta: 7 days, 6:47:49 time: 0.8966 data_time: 0.0044 memory: 39681 loss: 0.1497 loss_ce: 0.1497 2023/02/24 04:52:16 - mmengine - INFO - Epoch(train) [11][1300/5047] lr: 3.2329e-05 eta: 7 days, 6:46:10 time: 0.8855 data_time: 0.0017 memory: 52569 loss: 0.1678 loss_ce: 0.1678 2023/02/24 04:53:44 - mmengine - INFO - Epoch(train) [11][1400/5047] lr: 3.2329e-05 eta: 7 days, 6:44:19 time: 0.9029 data_time: 0.0019 memory: 40825 loss: 0.1799 loss_ce: 0.1799 2023/02/24 04:55:13 - mmengine - INFO - Epoch(train) [11][1500/5047] lr: 3.2329e-05 eta: 7 days, 6:42:56 time: 0.8838 data_time: 0.0019 memory: 43348 loss: 0.1519 loss_ce: 0.1519 2023/02/24 04:55:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 04:56:41 - mmengine - INFO - Epoch(train) [11][1600/5047] lr: 3.2329e-05 eta: 7 days, 6:40:50 time: 0.9081 data_time: 0.0026 memory: 49171 loss: 0.1654 loss_ce: 0.1654 2023/02/24 04:58:10 - mmengine - INFO - Epoch(train) [11][1700/5047] lr: 3.2329e-05 eta: 7 days, 6:39:27 time: 0.8679 data_time: 0.0017 memory: 45948 loss: 0.1422 loss_ce: 0.1422 2023/02/24 04:59:40 - mmengine - INFO - Epoch(train) [11][1800/5047] lr: 3.2329e-05 eta: 7 days, 6:38:03 time: 0.8822 data_time: 0.0045 memory: 55562 loss: 0.1620 loss_ce: 0.1620 2023/02/24 05:01:07 - mmengine - INFO - Epoch(train) [11][1900/5047] lr: 3.2329e-05 eta: 7 days, 6:36:04 time: 0.8900 data_time: 0.0018 memory: 42024 loss: 0.1415 loss_ce: 0.1415 2023/02/24 05:02:35 - mmengine - INFO - Epoch(train) [11][2000/5047] lr: 3.2329e-05 eta: 7 days, 6:33:59 time: 0.8627 data_time: 0.0055 memory: 41108 loss: 0.1547 loss_ce: 0.1547 2023/02/24 05:04:04 - mmengine - INFO - Epoch(train) [11][2100/5047] lr: 3.2329e-05 eta: 7 days, 6:32:27 time: 0.9008 data_time: 0.0024 memory: 44705 loss: 0.1831 loss_ce: 0.1831 2023/02/24 05:05:33 - mmengine - INFO - Epoch(train) [11][2200/5047] lr: 3.2329e-05 eta: 7 days, 6:30:57 time: 0.9245 data_time: 0.0016 memory: 47963 loss: 0.1643 loss_ce: 0.1643 2023/02/24 05:07:01 - mmengine - INFO - Epoch(train) [11][2300/5047] lr: 3.2329e-05 eta: 7 days, 6:29:03 time: 0.8863 data_time: 0.0032 memory: 45361 loss: 0.1524 loss_ce: 0.1524 2023/02/24 05:08:28 - mmengine - INFO - Epoch(train) [11][2400/5047] lr: 3.2329e-05 eta: 7 days, 6:27:04 time: 0.9403 data_time: 0.0021 memory: 54242 loss: 0.1598 loss_ce: 0.1598 2023/02/24 05:09:56 - mmengine - INFO - Epoch(train) [11][2500/5047] lr: 3.2329e-05 eta: 7 days, 6:25:10 time: 0.8455 data_time: 0.0028 memory: 46713 loss: 0.1507 loss_ce: 0.1507 2023/02/24 05:10:22 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 05:11:24 - mmengine - INFO - Epoch(train) [11][2600/5047] lr: 3.2329e-05 eta: 7 days, 6:23:16 time: 0.9086 data_time: 0.0019 memory: 47073 loss: 0.1660 loss_ce: 0.1660 2023/02/24 05:12:51 - mmengine - INFO - Epoch(train) [11][2700/5047] lr: 3.2329e-05 eta: 7 days, 6:21:15 time: 0.9052 data_time: 0.0017 memory: 40825 loss: 0.1605 loss_ce: 0.1605 2023/02/24 05:14:19 - mmengine - INFO - Epoch(train) [11][2800/5047] lr: 3.2329e-05 eta: 7 days, 6:19:18 time: 0.8352 data_time: 0.0016 memory: 42579 loss: 0.1557 loss_ce: 0.1557 2023/02/24 05:15:48 - mmengine - INFO - Epoch(train) [11][2900/5047] lr: 3.2329e-05 eta: 7 days, 6:17:36 time: 0.8626 data_time: 0.0017 memory: 42336 loss: 0.1531 loss_ce: 0.1531 2023/02/24 05:17:16 - mmengine - INFO - Epoch(train) [11][3000/5047] lr: 3.2329e-05 eta: 7 days, 6:15:48 time: 0.9533 data_time: 0.0019 memory: 54072 loss: 0.1563 loss_ce: 0.1563 2023/02/24 05:18:43 - mmengine - INFO - Epoch(train) [11][3100/5047] lr: 3.2329e-05 eta: 7 days, 6:13:47 time: 0.8905 data_time: 0.0020 memory: 47074 loss: 0.1811 loss_ce: 0.1811 2023/02/24 05:20:11 - mmengine - INFO - Epoch(train) [11][3200/5047] lr: 3.2329e-05 eta: 7 days, 6:11:55 time: 0.8895 data_time: 0.0018 memory: 40971 loss: 0.1648 loss_ce: 0.1648 2023/02/24 05:21:39 - mmengine - INFO - Epoch(train) [11][3300/5047] lr: 3.2329e-05 eta: 7 days, 6:10:11 time: 0.8643 data_time: 0.0017 memory: 42965 loss: 0.1772 loss_ce: 0.1772 2023/02/24 05:23:08 - mmengine - INFO - Epoch(train) [11][3400/5047] lr: 3.2329e-05 eta: 7 days, 6:08:33 time: 0.8610 data_time: 0.0044 memory: 55562 loss: 0.1663 loss_ce: 0.1663 2023/02/24 05:24:36 - mmengine - INFO - Epoch(train) [11][3500/5047] lr: 3.2329e-05 eta: 7 days, 6:06:38 time: 0.8738 data_time: 0.0016 memory: 42691 loss: 0.1714 loss_ce: 0.1714 2023/02/24 05:25:02 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 05:26:02 - mmengine - INFO - Epoch(train) [11][3600/5047] lr: 3.2329e-05 eta: 7 days, 6:04:06 time: 0.9101 data_time: 0.0024 memory: 52905 loss: 0.1623 loss_ce: 0.1623 2023/02/24 05:27:29 - mmengine - INFO - Epoch(train) [11][3700/5047] lr: 3.2329e-05 eta: 7 days, 6:02:16 time: 0.8580 data_time: 0.0019 memory: 42024 loss: 0.1532 loss_ce: 0.1532 2023/02/24 05:28:57 - mmengine - INFO - Epoch(train) [11][3800/5047] lr: 3.2329e-05 eta: 7 days, 6:00:25 time: 0.9218 data_time: 0.0017 memory: 51175 loss: 0.1354 loss_ce: 0.1354 2023/02/24 05:30:25 - mmengine - INFO - Epoch(train) [11][3900/5047] lr: 3.2329e-05 eta: 7 days, 5:58:32 time: 0.8949 data_time: 0.0019 memory: 47074 loss: 0.1603 loss_ce: 0.1603 2023/02/24 05:31:52 - mmengine - INFO - Epoch(train) [11][4000/5047] lr: 3.2329e-05 eta: 7 days, 5:56:25 time: 0.8660 data_time: 0.0017 memory: 45851 loss: 0.1552 loss_ce: 0.1552 2023/02/24 05:33:20 - mmengine - INFO - Epoch(train) [11][4100/5047] lr: 3.2329e-05 eta: 7 days, 5:54:35 time: 0.8420 data_time: 0.0020 memory: 51586 loss: 0.1466 loss_ce: 0.1466 2023/02/24 05:34:47 - mmengine - INFO - Epoch(train) [11][4200/5047] lr: 3.2329e-05 eta: 7 days, 5:52:25 time: 0.8280 data_time: 0.0043 memory: 47447 loss: 0.1534 loss_ce: 0.1534 2023/02/24 05:36:16 - mmengine - INFO - Epoch(train) [11][4300/5047] lr: 3.2329e-05 eta: 7 days, 5:50:52 time: 0.8344 data_time: 0.0018 memory: 40241 loss: 0.1616 loss_ce: 0.1616 2023/02/24 05:37:46 - mmengine - INFO - Epoch(train) [11][4400/5047] lr: 3.2329e-05 eta: 7 days, 5:49:33 time: 0.8967 data_time: 0.0016 memory: 47077 loss: 0.1732 loss_ce: 0.1732 2023/02/24 05:39:14 - mmengine - INFO - Epoch(train) [11][4500/5047] lr: 3.2329e-05 eta: 7 days, 5:47:49 time: 0.9095 data_time: 0.0023 memory: 50592 loss: 0.1489 loss_ce: 0.1489 2023/02/24 05:39:40 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 05:40:42 - mmengine - INFO - Epoch(train) [11][4600/5047] lr: 3.2329e-05 eta: 7 days, 5:46:06 time: 0.8667 data_time: 0.0027 memory: 44956 loss: 0.1636 loss_ce: 0.1636 2023/02/24 05:42:10 - mmengine - INFO - Epoch(train) [11][4700/5047] lr: 3.2329e-05 eta: 7 days, 5:44:14 time: 0.9000 data_time: 0.0016 memory: 45540 loss: 0.1699 loss_ce: 0.1699 2023/02/24 05:43:38 - mmengine - INFO - Epoch(train) [11][4800/5047] lr: 3.2329e-05 eta: 7 days, 5:42:20 time: 0.8607 data_time: 0.0018 memory: 43613 loss: 0.1482 loss_ce: 0.1482 2023/02/24 05:45:05 - mmengine - INFO - Epoch(train) [11][4900/5047] lr: 3.2329e-05 eta: 7 days, 5:40:17 time: 0.8445 data_time: 0.0018 memory: 43613 loss: 0.1749 loss_ce: 0.1749 2023/02/24 05:46:32 - mmengine - INFO - Epoch(train) [11][5000/5047] lr: 3.2329e-05 eta: 7 days, 5:38:07 time: 0.8801 data_time: 0.0018 memory: 44617 loss: 0.1399 loss_ce: 0.1399 2023/02/24 05:47:13 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 05:47:13 - mmengine - INFO - Saving checkpoint at 11 epochs 2023/02/24 05:48:47 - mmengine - INFO - Epoch(train) [12][ 100/5047] lr: 3.2128e-05 eta: 7 days, 5:35:41 time: 0.8974 data_time: 0.0017 memory: 50906 loss: 0.1507 loss_ce: 0.1507 2023/02/24 05:50:15 - mmengine - INFO - Epoch(train) [12][ 200/5047] lr: 3.2128e-05 eta: 7 days, 5:33:55 time: 0.9252 data_time: 0.0021 memory: 41479 loss: 0.1469 loss_ce: 0.1469 2023/02/24 05:51:42 - mmengine - INFO - Epoch(train) [12][ 300/5047] lr: 3.2128e-05 eta: 7 days, 5:31:42 time: 0.8343 data_time: 0.0031 memory: 41009 loss: 0.1667 loss_ce: 0.1667 2023/02/24 05:53:09 - mmengine - INFO - Epoch(train) [12][ 400/5047] lr: 3.2128e-05 eta: 7 days, 5:29:45 time: 0.9308 data_time: 0.0016 memory: 50349 loss: 0.1681 loss_ce: 0.1681 2023/02/24 05:54:21 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 05:54:35 - mmengine - INFO - Epoch(train) [12][ 500/5047] lr: 3.2128e-05 eta: 7 days, 5:27:18 time: 0.8428 data_time: 0.0037 memory: 45302 loss: 0.1640 loss_ce: 0.1640 2023/02/24 05:56:03 - mmengine - INFO - Epoch(train) [12][ 600/5047] lr: 3.2128e-05 eta: 7 days, 5:25:34 time: 0.8969 data_time: 0.0017 memory: 40740 loss: 0.1610 loss_ce: 0.1610 2023/02/24 05:57:33 - mmengine - INFO - Epoch(train) [12][ 700/5047] lr: 3.2128e-05 eta: 7 days, 5:24:23 time: 0.9072 data_time: 0.0020 memory: 42336 loss: 0.1552 loss_ce: 0.1552 2023/02/24 05:59:01 - mmengine - INFO - Epoch(train) [12][ 800/5047] lr: 3.2128e-05 eta: 7 days, 5:22:26 time: 0.8437 data_time: 0.0017 memory: 43773 loss: 0.1584 loss_ce: 0.1584 2023/02/24 06:00:27 - mmengine - INFO - Epoch(train) [12][ 900/5047] lr: 3.2128e-05 eta: 7 days, 5:20:12 time: 0.8325 data_time: 0.0025 memory: 44278 loss: 0.1711 loss_ce: 0.1711 2023/02/24 06:01:56 - mmengine - INFO - Epoch(train) [12][1000/5047] lr: 3.2128e-05 eta: 7 days, 5:18:47 time: 0.8976 data_time: 0.0072 memory: 48948 loss: 0.1505 loss_ce: 0.1505 2023/02/24 06:03:24 - mmengine - INFO - Epoch(train) [12][1100/5047] lr: 3.2128e-05 eta: 7 days, 5:16:49 time: 0.8244 data_time: 0.0021 memory: 41770 loss: 0.1430 loss_ce: 0.1430 2023/02/24 06:04:52 - mmengine - INFO - Epoch(train) [12][1200/5047] lr: 3.2128e-05 eta: 7 days, 5:14:58 time: 0.8628 data_time: 0.0022 memory: 43477 loss: 0.1524 loss_ce: 0.1524 2023/02/24 06:06:19 - mmengine - INFO - Epoch(train) [12][1300/5047] lr: 3.2128e-05 eta: 7 days, 5:12:53 time: 0.8920 data_time: 0.0039 memory: 45773 loss: 0.1453 loss_ce: 0.1453 2023/02/24 06:07:46 - mmengine - INFO - Epoch(train) [12][1400/5047] lr: 3.2128e-05 eta: 7 days, 5:11:05 time: 0.9285 data_time: 0.0047 memory: 51658 loss: 0.1510 loss_ce: 0.1510 2023/02/24 06:08:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 06:09:13 - mmengine - INFO - Epoch(train) [12][1500/5047] lr: 3.2128e-05 eta: 7 days, 5:09:02 time: 0.8801 data_time: 0.0017 memory: 48855 loss: 0.1644 loss_ce: 0.1644 2023/02/24 06:10:41 - mmengine - INFO - Epoch(train) [12][1600/5047] lr: 3.2128e-05 eta: 7 days, 5:07:00 time: 0.8765 data_time: 0.0026 memory: 42649 loss: 0.1518 loss_ce: 0.1518 2023/02/24 06:12:08 - mmengine - INFO - Epoch(train) [12][1700/5047] lr: 3.2128e-05 eta: 7 days, 5:05:06 time: 0.9101 data_time: 0.0018 memory: 42965 loss: 0.1633 loss_ce: 0.1633 2023/02/24 06:13:37 - mmengine - INFO - Epoch(train) [12][1800/5047] lr: 3.2128e-05 eta: 7 days, 5:03:36 time: 0.9996 data_time: 0.0019 memory: 53028 loss: 0.1540 loss_ce: 0.1540 2023/02/24 06:15:05 - mmengine - INFO - Epoch(train) [12][1900/5047] lr: 3.2128e-05 eta: 7 days, 5:01:55 time: 0.9208 data_time: 0.0018 memory: 51755 loss: 0.1710 loss_ce: 0.1710 2023/02/24 06:16:32 - mmengine - INFO - Epoch(train) [12][2000/5047] lr: 3.2128e-05 eta: 7 days, 4:59:53 time: 0.8701 data_time: 0.0017 memory: 44049 loss: 0.1575 loss_ce: 0.1575 2023/02/24 06:17:59 - mmengine - INFO - Epoch(train) [12][2100/5047] lr: 3.2128e-05 eta: 7 days, 4:57:50 time: 0.8621 data_time: 0.0017 memory: 54206 loss: 0.1748 loss_ce: 0.1748 2023/02/24 06:19:26 - mmengine - INFO - Epoch(train) [12][2200/5047] lr: 3.2128e-05 eta: 7 days, 4:55:44 time: 0.8206 data_time: 0.0016 memory: 47074 loss: 0.1670 loss_ce: 0.1670 2023/02/24 06:20:56 - mmengine - INFO - Epoch(train) [12][2300/5047] lr: 3.2128e-05 eta: 7 days, 4:54:30 time: 0.8980 data_time: 0.0018 memory: 38605 loss: 0.1409 loss_ce: 0.1409 2023/02/24 06:22:24 - mmengine - INFO - Epoch(train) [12][2400/5047] lr: 3.2128e-05 eta: 7 days, 4:52:43 time: 0.8854 data_time: 0.0024 memory: 43289 loss: 0.1555 loss_ce: 0.1555 2023/02/24 06:23:38 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 06:23:52 - mmengine - INFO - Epoch(train) [12][2500/5047] lr: 3.2128e-05 eta: 7 days, 4:50:59 time: 0.8372 data_time: 0.0025 memory: 55562 loss: 0.1749 loss_ce: 0.1749 2023/02/24 06:25:21 - mmengine - INFO - Epoch(train) [12][2600/5047] lr: 3.2128e-05 eta: 7 days, 4:49:21 time: 0.8961 data_time: 0.0017 memory: 54072 loss: 0.1613 loss_ce: 0.1613 2023/02/24 06:26:49 - mmengine - INFO - Epoch(train) [12][2700/5047] lr: 3.2128e-05 eta: 7 days, 4:47:39 time: 0.9034 data_time: 0.0020 memory: 42743 loss: 0.1381 loss_ce: 0.1381 2023/02/24 06:28:18 - mmengine - INFO - Epoch(train) [12][2800/5047] lr: 3.2128e-05 eta: 7 days, 4:46:04 time: 0.8730 data_time: 0.0016 memory: 43027 loss: 0.1578 loss_ce: 0.1578 2023/02/24 06:29:46 - mmengine - INFO - Epoch(train) [12][2900/5047] lr: 3.2128e-05 eta: 7 days, 4:44:19 time: 0.9209 data_time: 0.0020 memory: 42649 loss: 0.1470 loss_ce: 0.1470 2023/02/24 06:31:14 - mmengine - INFO - Epoch(train) [12][3000/5047] lr: 3.2128e-05 eta: 7 days, 4:42:41 time: 0.8932 data_time: 0.0019 memory: 42965 loss: 0.1692 loss_ce: 0.1692 2023/02/24 06:32:43 - mmengine - INFO - Epoch(train) [12][3100/5047] lr: 3.2128e-05 eta: 7 days, 4:41:06 time: 0.9098 data_time: 0.0019 memory: 42629 loss: 0.1571 loss_ce: 0.1571 2023/02/24 06:34:10 - mmengine - INFO - Epoch(train) [12][3200/5047] lr: 3.2128e-05 eta: 7 days, 4:39:15 time: 0.8719 data_time: 0.0027 memory: 47074 loss: 0.1505 loss_ce: 0.1505 2023/02/24 06:35:39 - mmengine - INFO - Epoch(train) [12][3300/5047] lr: 3.2128e-05 eta: 7 days, 4:37:42 time: 0.8676 data_time: 0.0017 memory: 42336 loss: 0.1612 loss_ce: 0.1612 2023/02/24 06:37:07 - mmengine - INFO - Epoch(train) [12][3400/5047] lr: 3.2128e-05 eta: 7 days, 4:35:51 time: 0.8540 data_time: 0.0030 memory: 41419 loss: 0.1589 loss_ce: 0.1589 2023/02/24 06:38:19 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 06:38:34 - mmengine - INFO - Epoch(train) [12][3500/5047] lr: 3.2128e-05 eta: 7 days, 4:33:55 time: 0.9146 data_time: 0.0075 memory: 39960 loss: 0.1385 loss_ce: 0.1385 2023/02/24 06:40:00 - mmengine - INFO - Epoch(train) [12][3600/5047] lr: 3.2128e-05 eta: 7 days, 4:31:42 time: 0.8753 data_time: 0.0030 memory: 45302 loss: 0.1608 loss_ce: 0.1608 2023/02/24 06:41:28 - mmengine - INFO - Epoch(train) [12][3700/5047] lr: 3.2128e-05 eta: 7 days, 4:29:54 time: 0.9067 data_time: 0.0016 memory: 45837 loss: 0.1665 loss_ce: 0.1665 2023/02/24 06:42:56 - mmengine - INFO - Epoch(train) [12][3800/5047] lr: 3.2128e-05 eta: 7 days, 4:28:07 time: 0.8923 data_time: 0.0054 memory: 51816 loss: 0.1786 loss_ce: 0.1786 2023/02/24 06:44:24 - mmengine - INFO - Epoch(train) [12][3900/5047] lr: 3.2128e-05 eta: 7 days, 4:26:20 time: 0.9239 data_time: 0.0017 memory: 42336 loss: 0.1294 loss_ce: 0.1294 2023/02/24 06:45:50 - mmengine - INFO - Epoch(train) [12][4000/5047] lr: 3.2128e-05 eta: 7 days, 4:24:12 time: 0.8488 data_time: 0.0017 memory: 46269 loss: 0.1620 loss_ce: 0.1620 2023/02/24 06:47:20 - mmengine - INFO - Epoch(train) [12][4100/5047] lr: 3.2128e-05 eta: 7 days, 4:22:57 time: 0.8557 data_time: 0.0042 memory: 44956 loss: 0.1616 loss_ce: 0.1616 2023/02/24 06:48:49 - mmengine - INFO - Epoch(train) [12][4200/5047] lr: 3.2128e-05 eta: 7 days, 4:21:24 time: 0.8805 data_time: 0.0036 memory: 45069 loss: 0.1721 loss_ce: 0.1721 2023/02/24 06:50:16 - mmengine - INFO - Epoch(train) [12][4300/5047] lr: 3.2128e-05 eta: 7 days, 4:19:31 time: 0.9286 data_time: 0.0017 memory: 48639 loss: 0.1512 loss_ce: 0.1512 2023/02/24 06:51:42 - mmengine - INFO - Epoch(train) [12][4400/5047] lr: 3.2128e-05 eta: 7 days, 4:17:12 time: 0.8642 data_time: 0.0017 memory: 42435 loss: 0.1624 loss_ce: 0.1624 2023/02/24 06:52:53 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 06:53:09 - mmengine - INFO - Epoch(train) [12][4500/5047] lr: 3.2128e-05 eta: 7 days, 4:15:03 time: 0.8821 data_time: 0.0016 memory: 41724 loss: 0.1568 loss_ce: 0.1568 2023/02/24 06:54:36 - mmengine - INFO - Epoch(train) [12][4600/5047] lr: 3.2128e-05 eta: 7 days, 4:13:09 time: 0.9270 data_time: 0.0017 memory: 46794 loss: 0.1635 loss_ce: 0.1635 2023/02/24 06:56:02 - mmengine - INFO - Epoch(train) [12][4700/5047] lr: 3.2128e-05 eta: 7 days, 4:10:54 time: 0.8016 data_time: 0.0016 memory: 51211 loss: 0.1614 loss_ce: 0.1614 2023/02/24 06:57:29 - mmengine - INFO - Epoch(train) [12][4800/5047] lr: 3.2128e-05 eta: 7 days, 4:08:58 time: 0.8116 data_time: 0.0037 memory: 44956 loss: 0.1544 loss_ce: 0.1544 2023/02/24 06:58:58 - mmengine - INFO - Epoch(train) [12][4900/5047] lr: 3.2128e-05 eta: 7 days, 4:07:25 time: 0.8931 data_time: 0.0031 memory: 44496 loss: 0.1695 loss_ce: 0.1695 2023/02/24 07:00:25 - mmengine - INFO - Epoch(train) [12][5000/5047] lr: 3.2128e-05 eta: 7 days, 4:05:24 time: 0.8903 data_time: 0.0025 memory: 50349 loss: 0.1691 loss_ce: 0.1691 2023/02/24 07:01:06 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 07:01:06 - mmengine - INFO - Saving checkpoint at 12 epochs 2023/02/24 07:02:38 - mmengine - INFO - Epoch(train) [13][ 100/5047] lr: 3.1927e-05 eta: 7 days, 4:02:39 time: 0.8463 data_time: 0.0017 memory: 43339 loss: 0.1533 loss_ce: 0.1533 2023/02/24 07:04:06 - mmengine - INFO - Epoch(train) [13][ 200/5047] lr: 3.1927e-05 eta: 7 days, 4:00:57 time: 0.8920 data_time: 0.0016 memory: 51637 loss: 0.1480 loss_ce: 0.1480 2023/02/24 07:05:34 - mmengine - INFO - Epoch(train) [13][ 300/5047] lr: 3.1927e-05 eta: 7 days, 3:59:03 time: 0.8580 data_time: 0.0020 memory: 55296 loss: 0.1551 loss_ce: 0.1551 2023/02/24 07:07:01 - mmengine - INFO - Epoch(train) [13][ 400/5047] lr: 3.1927e-05 eta: 7 days, 3:57:09 time: 0.8373 data_time: 0.0020 memory: 44956 loss: 0.1571 loss_ce: 0.1571 2023/02/24 07:07:32 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 07:08:27 - mmengine - INFO - Epoch(train) [13][ 500/5047] lr: 3.1927e-05 eta: 7 days, 3:55:03 time: 0.8331 data_time: 0.0020 memory: 44278 loss: 0.1205 loss_ce: 0.1205 2023/02/24 07:09:52 - mmengine - INFO - Epoch(train) [13][ 600/5047] lr: 3.1927e-05 eta: 7 days, 3:52:33 time: 0.8558 data_time: 0.0018 memory: 45506 loss: 0.1470 loss_ce: 0.1470 2023/02/24 07:11:22 - mmengine - INFO - Epoch(train) [13][ 700/5047] lr: 3.1927e-05 eta: 7 days, 3:51:12 time: 0.8969 data_time: 0.0020 memory: 40897 loss: 0.1623 loss_ce: 0.1623 2023/02/24 07:12:49 - mmengine - INFO - Epoch(train) [13][ 800/5047] lr: 3.1927e-05 eta: 7 days, 3:49:26 time: 0.8584 data_time: 0.0015 memory: 40500 loss: 0.1601 loss_ce: 0.1601 2023/02/24 07:14:17 - mmengine - INFO - Epoch(train) [13][ 900/5047] lr: 3.1927e-05 eta: 7 days, 3:47:34 time: 0.8520 data_time: 0.0017 memory: 42965 loss: 0.1620 loss_ce: 0.1620 2023/02/24 07:15:44 - mmengine - INFO - Epoch(train) [13][1000/5047] lr: 3.1927e-05 eta: 7 days, 3:45:36 time: 0.8873 data_time: 0.0021 memory: 38789 loss: 0.1653 loss_ce: 0.1653 2023/02/24 07:17:12 - mmengine - INFO - Epoch(train) [13][1100/5047] lr: 3.1927e-05 eta: 7 days, 3:44:01 time: 0.8917 data_time: 0.0019 memory: 55562 loss: 0.1573 loss_ce: 0.1573 2023/02/24 07:18:41 - mmengine - INFO - Epoch(train) [13][1200/5047] lr: 3.1927e-05 eta: 7 days, 3:42:22 time: 0.8971 data_time: 0.0051 memory: 42965 loss: 0.1498 loss_ce: 0.1498 2023/02/24 07:20:11 - mmengine - INFO - Epoch(train) [13][1300/5047] lr: 3.1927e-05 eta: 7 days, 3:41:11 time: 0.9253 data_time: 0.0028 memory: 48310 loss: 0.1664 loss_ce: 0.1664 2023/02/24 07:21:37 - mmengine - INFO - Epoch(train) [13][1400/5047] lr: 3.1927e-05 eta: 7 days, 3:39:02 time: 0.8430 data_time: 0.0020 memory: 42623 loss: 0.1652 loss_ce: 0.1652 2023/02/24 07:22:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 07:23:05 - mmengine - INFO - Epoch(train) [13][1500/5047] lr: 3.1927e-05 eta: 7 days, 3:37:27 time: 0.9275 data_time: 0.0018 memory: 41122 loss: 0.1514 loss_ce: 0.1514 2023/02/24 07:24:33 - mmengine - INFO - Epoch(train) [13][1600/5047] lr: 3.1927e-05 eta: 7 days, 3:35:35 time: 0.8702 data_time: 0.0024 memory: 50505 loss: 0.1705 loss_ce: 0.1705 2023/02/24 07:25:59 - mmengine - INFO - Epoch(train) [13][1700/5047] lr: 3.1927e-05 eta: 7 days, 3:33:22 time: 0.8373 data_time: 0.0017 memory: 43289 loss: 0.1399 loss_ce: 0.1399 2023/02/24 07:27:25 - mmengine - INFO - Epoch(train) [13][1800/5047] lr: 3.1927e-05 eta: 7 days, 3:31:13 time: 0.8832 data_time: 0.0018 memory: 40540 loss: 0.1766 loss_ce: 0.1766 2023/02/24 07:28:52 - mmengine - INFO - Epoch(train) [13][1900/5047] lr: 3.1927e-05 eta: 7 days, 3:29:19 time: 0.8515 data_time: 0.0029 memory: 46711 loss: 0.1529 loss_ce: 0.1529 2023/02/24 07:30:19 - mmengine - INFO - Epoch(train) [13][2000/5047] lr: 3.1927e-05 eta: 7 days, 3:27:18 time: 0.8590 data_time: 0.0028 memory: 44553 loss: 0.1475 loss_ce: 0.1475 2023/02/24 07:31:46 - mmengine - INFO - Epoch(train) [13][2100/5047] lr: 3.1927e-05 eta: 7 days, 3:25:25 time: 0.8636 data_time: 0.0018 memory: 50505 loss: 0.1391 loss_ce: 0.1391 2023/02/24 07:33:12 - mmengine - INFO - Epoch(train) [13][2200/5047] lr: 3.1927e-05 eta: 7 days, 3:23:20 time: 0.8419 data_time: 0.0027 memory: 42024 loss: 0.1658 loss_ce: 0.1658 2023/02/24 07:34:41 - mmengine - INFO - Epoch(train) [13][2300/5047] lr: 3.1927e-05 eta: 7 days, 3:21:43 time: 0.8945 data_time: 0.0019 memory: 51308 loss: 0.1445 loss_ce: 0.1445 2023/02/24 07:36:08 - mmengine - INFO - Epoch(train) [13][2400/5047] lr: 3.1927e-05 eta: 7 days, 3:19:57 time: 0.9320 data_time: 0.0027 memory: 44539 loss: 0.1637 loss_ce: 0.1637 2023/02/24 07:36:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 07:37:35 - mmengine - INFO - Epoch(train) [13][2500/5047] lr: 3.1927e-05 eta: 7 days, 3:18:01 time: 0.9080 data_time: 0.0030 memory: 52975 loss: 0.1549 loss_ce: 0.1549 2023/02/24 07:39:03 - mmengine - INFO - Epoch(train) [13][2600/5047] lr: 3.1927e-05 eta: 7 days, 3:16:06 time: 0.9262 data_time: 0.0045 memory: 43947 loss: 0.1473 loss_ce: 0.1473 2023/02/24 07:40:31 - mmengine - INFO - Epoch(train) [13][2700/5047] lr: 3.1927e-05 eta: 7 days, 3:14:35 time: 0.8979 data_time: 0.0017 memory: 41344 loss: 0.1478 loss_ce: 0.1478 2023/02/24 07:41:59 - mmengine - INFO - Epoch(train) [13][2800/5047] lr: 3.1927e-05 eta: 7 days, 3:12:51 time: 0.9100 data_time: 0.0019 memory: 53809 loss: 0.1700 loss_ce: 0.1700 2023/02/24 07:43:27 - mmengine - INFO - Epoch(train) [13][2900/5047] lr: 3.1927e-05 eta: 7 days, 3:11:06 time: 0.9257 data_time: 0.0017 memory: 42552 loss: 0.1679 loss_ce: 0.1679 2023/02/24 07:44:52 - mmengine - INFO - Epoch(train) [13][3000/5047] lr: 3.1927e-05 eta: 7 days, 3:08:46 time: 0.8457 data_time: 0.0022 memory: 39681 loss: 0.1597 loss_ce: 0.1597 2023/02/24 07:46:19 - mmengine - INFO - Epoch(train) [13][3100/5047] lr: 3.1927e-05 eta: 7 days, 3:06:45 time: 0.8536 data_time: 0.0017 memory: 46892 loss: 0.1579 loss_ce: 0.1579 2023/02/24 07:47:46 - mmengine - INFO - Epoch(train) [13][3200/5047] lr: 3.1927e-05 eta: 7 days, 3:04:53 time: 0.8778 data_time: 0.0016 memory: 42024 loss: 0.1473 loss_ce: 0.1473 2023/02/24 07:49:11 - mmengine - INFO - Epoch(train) [13][3300/5047] lr: 3.1927e-05 eta: 7 days, 3:02:31 time: 0.8272 data_time: 0.0019 memory: 49330 loss: 0.1644 loss_ce: 0.1644 2023/02/24 07:50:38 - mmengine - INFO - Epoch(train) [13][3400/5047] lr: 3.1927e-05 eta: 7 days, 3:00:37 time: 0.8747 data_time: 0.0017 memory: 43289 loss: 0.1667 loss_ce: 0.1667 2023/02/24 07:51:10 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 07:52:07 - mmengine - INFO - Epoch(train) [13][3500/5047] lr: 3.1927e-05 eta: 7 days, 2:59:05 time: 0.9202 data_time: 0.0018 memory: 41724 loss: 0.1461 loss_ce: 0.1461 2023/02/24 07:53:34 - mmengine - INFO - Epoch(train) [13][3600/5047] lr: 3.1927e-05 eta: 7 days, 2:57:13 time: 0.8680 data_time: 0.0038 memory: 42401 loss: 0.1674 loss_ce: 0.1674 2023/02/24 07:55:02 - mmengine - INFO - Epoch(train) [13][3700/5047] lr: 3.1927e-05 eta: 7 days, 2:55:30 time: 0.8876 data_time: 0.0018 memory: 41419 loss: 0.1617 loss_ce: 0.1617 2023/02/24 07:56:30 - mmengine - INFO - Epoch(train) [13][3800/5047] lr: 3.1927e-05 eta: 7 days, 2:53:48 time: 0.9011 data_time: 0.0021 memory: 43493 loss: 0.1468 loss_ce: 0.1468 2023/02/24 07:57:57 - mmengine - INFO - Epoch(train) [13][3900/5047] lr: 3.1927e-05 eta: 7 days, 2:51:55 time: 0.8861 data_time: 0.0021 memory: 51795 loss: 0.1521 loss_ce: 0.1521 2023/02/24 07:59:23 - mmengine - INFO - Epoch(train) [13][4000/5047] lr: 3.1927e-05 eta: 7 days, 2:49:53 time: 0.8620 data_time: 0.0019 memory: 42649 loss: 0.1759 loss_ce: 0.1759 2023/02/24 08:00:53 - mmengine - INFO - Epoch(train) [13][4100/5047] lr: 3.1927e-05 eta: 7 days, 2:48:33 time: 0.9788 data_time: 0.0054 memory: 46160 loss: 0.1465 loss_ce: 0.1465 2023/02/24 08:02:22 - mmengine - INFO - Epoch(train) [13][4200/5047] lr: 3.1927e-05 eta: 7 days, 2:47:09 time: 0.9648 data_time: 0.0017 memory: 42965 loss: 0.1389 loss_ce: 0.1389 2023/02/24 08:03:48 - mmengine - INFO - Epoch(train) [13][4300/5047] lr: 3.1927e-05 eta: 7 days, 2:45:02 time: 0.8045 data_time: 0.0022 memory: 45616 loss: 0.1556 loss_ce: 0.1556 2023/02/24 08:05:15 - mmengine - INFO - Epoch(train) [13][4400/5047] lr: 3.1927e-05 eta: 7 days, 2:43:12 time: 0.8521 data_time: 0.0017 memory: 45643 loss: 0.1466 loss_ce: 0.1466 2023/02/24 08:05:48 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 08:06:43 - mmengine - INFO - Epoch(train) [13][4500/5047] lr: 3.1927e-05 eta: 7 days, 2:41:35 time: 0.9013 data_time: 0.0016 memory: 50607 loss: 0.1558 loss_ce: 0.1558 2023/02/24 08:08:13 - mmengine - INFO - Epoch(train) [13][4600/5047] lr: 3.1927e-05 eta: 7 days, 2:40:16 time: 0.8899 data_time: 0.0020 memory: 43416 loss: 0.1563 loss_ce: 0.1563 2023/02/24 08:09:39 - mmengine - INFO - Epoch(train) [13][4700/5047] lr: 3.1927e-05 eta: 7 days, 2:38:14 time: 0.8591 data_time: 0.0029 memory: 46005 loss: 0.1640 loss_ce: 0.1640 2023/02/24 08:11:07 - mmengine - INFO - Epoch(train) [13][4800/5047] lr: 3.1927e-05 eta: 7 days, 2:36:22 time: 0.8764 data_time: 0.0026 memory: 41724 loss: 0.1743 loss_ce: 0.1743 2023/02/24 08:12:34 - mmengine - INFO - Epoch(train) [13][4900/5047] lr: 3.1927e-05 eta: 7 days, 2:34:36 time: 0.8453 data_time: 0.0017 memory: 55562 loss: 0.1511 loss_ce: 0.1511 2023/02/24 08:14:02 - mmengine - INFO - Epoch(train) [13][5000/5047] lr: 3.1927e-05 eta: 7 days, 2:32:57 time: 0.9327 data_time: 0.0017 memory: 49334 loss: 0.1755 loss_ce: 0.1755 2023/02/24 08:14:43 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 08:14:43 - mmengine - INFO - Saving checkpoint at 13 epochs 2023/02/24 08:16:16 - mmengine - INFO - Epoch(train) [14][ 100/5047] lr: 3.1726e-05 eta: 7 days, 2:30:24 time: 0.8924 data_time: 0.0026 memory: 47813 loss: 0.1597 loss_ce: 0.1597 2023/02/24 08:17:43 - mmengine - INFO - Epoch(train) [14][ 200/5047] lr: 3.1726e-05 eta: 7 days, 2:28:29 time: 0.8669 data_time: 0.0026 memory: 44973 loss: 0.1570 loss_ce: 0.1570 2023/02/24 08:19:10 - mmengine - INFO - Epoch(train) [14][ 300/5047] lr: 3.1726e-05 eta: 7 days, 2:26:29 time: 0.8535 data_time: 0.0090 memory: 51308 loss: 0.1488 loss_ce: 0.1488 2023/02/24 08:20:28 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 08:20:37 - mmengine - INFO - Epoch(train) [14][ 400/5047] lr: 3.1726e-05 eta: 7 days, 2:24:38 time: 0.8427 data_time: 0.0018 memory: 46355 loss: 0.1416 loss_ce: 0.1416 2023/02/24 08:22:05 - mmengine - INFO - Epoch(train) [14][ 500/5047] lr: 3.1726e-05 eta: 7 days, 2:22:59 time: 0.8618 data_time: 0.0019 memory: 54113 loss: 0.1724 loss_ce: 0.1724 2023/02/24 08:23:32 - mmengine - INFO - Epoch(train) [14][ 600/5047] lr: 3.1726e-05 eta: 7 days, 2:21:11 time: 0.8533 data_time: 0.0015 memory: 39398 loss: 0.1626 loss_ce: 0.1626 2023/02/24 08:24:59 - mmengine - INFO - Epoch(train) [14][ 700/5047] lr: 3.1726e-05 eta: 7 days, 2:19:13 time: 0.8980 data_time: 0.0018 memory: 43009 loss: 0.1610 loss_ce: 0.1610 2023/02/24 08:26:26 - mmengine - INFO - Epoch(train) [14][ 800/5047] lr: 3.1726e-05 eta: 7 days, 2:17:19 time: 0.8510 data_time: 0.0016 memory: 48273 loss: 0.1398 loss_ce: 0.1398 2023/02/24 08:27:51 - mmengine - INFO - Epoch(train) [14][ 900/5047] lr: 3.1726e-05 eta: 7 days, 2:15:05 time: 0.8314 data_time: 0.0021 memory: 39398 loss: 0.1503 loss_ce: 0.1503 2023/02/24 08:29:19 - mmengine - INFO - Epoch(train) [14][1000/5047] lr: 3.1726e-05 eta: 7 days, 2:13:30 time: 0.8656 data_time: 0.0026 memory: 55562 loss: 0.1435 loss_ce: 0.1435 2023/02/24 08:30:47 - mmengine - INFO - Epoch(train) [14][1100/5047] lr: 3.1726e-05 eta: 7 days, 2:11:49 time: 0.8442 data_time: 0.0017 memory: 44728 loss: 0.1639 loss_ce: 0.1639 2023/02/24 08:32:14 - mmengine - INFO - Epoch(train) [14][1200/5047] lr: 3.1726e-05 eta: 7 days, 2:09:55 time: 0.8373 data_time: 0.0022 memory: 51657 loss: 0.1600 loss_ce: 0.1600 2023/02/24 08:33:41 - mmengine - INFO - Epoch(train) [14][1300/5047] lr: 3.1726e-05 eta: 7 days, 2:08:07 time: 0.8527 data_time: 0.0016 memory: 40697 loss: 0.1444 loss_ce: 0.1444 2023/02/24 08:34:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 08:35:08 - mmengine - INFO - Epoch(train) [14][1400/5047] lr: 3.1726e-05 eta: 7 days, 2:06:04 time: 0.8767 data_time: 0.0028 memory: 43613 loss: 0.1601 loss_ce: 0.1601 2023/02/24 08:36:35 - mmengine - INFO - Epoch(train) [14][1500/5047] lr: 3.1726e-05 eta: 7 days, 2:04:21 time: 0.8567 data_time: 0.0023 memory: 43947 loss: 0.1742 loss_ce: 0.1742 2023/02/24 08:38:03 - mmengine - INFO - Epoch(train) [14][1600/5047] lr: 3.1726e-05 eta: 7 days, 2:02:36 time: 0.8676 data_time: 0.0019 memory: 46355 loss: 0.1614 loss_ce: 0.1614 2023/02/24 08:39:31 - mmengine - INFO - Epoch(train) [14][1700/5047] lr: 3.1726e-05 eta: 7 days, 2:01:00 time: 0.9024 data_time: 0.0022 memory: 47209 loss: 0.1508 loss_ce: 0.1508 2023/02/24 08:40:57 - mmengine - INFO - Epoch(train) [14][1800/5047] lr: 3.1726e-05 eta: 7 days, 1:58:53 time: 0.8840 data_time: 0.0050 memory: 49334 loss: 0.1385 loss_ce: 0.1385 2023/02/24 08:42:24 - mmengine - INFO - Epoch(train) [14][1900/5047] lr: 3.1726e-05 eta: 7 days, 1:57:04 time: 0.8742 data_time: 0.0022 memory: 55562 loss: 0.1570 loss_ce: 0.1570 2023/02/24 08:43:52 - mmengine - INFO - Epoch(train) [14][2000/5047] lr: 3.1726e-05 eta: 7 days, 1:55:22 time: 0.8703 data_time: 0.0023 memory: 44342 loss: 0.1584 loss_ce: 0.1584 2023/02/24 08:45:19 - mmengine - INFO - Epoch(train) [14][2100/5047] lr: 3.1726e-05 eta: 7 days, 1:53:33 time: 0.8551 data_time: 0.0018 memory: 41122 loss: 0.1562 loss_ce: 0.1562 2023/02/24 08:46:47 - mmengine - INFO - Epoch(train) [14][2200/5047] lr: 3.1726e-05 eta: 7 days, 1:51:50 time: 0.8896 data_time: 0.0018 memory: 51719 loss: 0.1506 loss_ce: 0.1506 2023/02/24 08:48:13 - mmengine - INFO - Epoch(train) [14][2300/5047] lr: 3.1726e-05 eta: 7 days, 1:49:55 time: 0.8225 data_time: 0.0026 memory: 42024 loss: 0.1434 loss_ce: 0.1434 2023/02/24 08:49:30 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 08:49:40 - mmengine - INFO - Epoch(train) [14][2400/5047] lr: 3.1726e-05 eta: 7 days, 1:47:57 time: 0.8605 data_time: 0.0041 memory: 43289 loss: 0.1568 loss_ce: 0.1568 2023/02/24 08:51:09 - mmengine - INFO - Epoch(train) [14][2500/5047] lr: 3.1726e-05 eta: 7 days, 1:46:33 time: 0.9757 data_time: 0.0017 memory: 50244 loss: 0.1650 loss_ce: 0.1650 2023/02/24 08:52:37 - mmengine - INFO - Epoch(train) [14][2600/5047] lr: 3.1726e-05 eta: 7 days, 1:45:00 time: 0.8512 data_time: 0.0020 memory: 41724 loss: 0.1653 loss_ce: 0.1653 2023/02/24 08:54:06 - mmengine - INFO - Epoch(train) [14][2700/5047] lr: 3.1726e-05 eta: 7 days, 1:43:26 time: 0.9058 data_time: 0.0017 memory: 41724 loss: 0.1418 loss_ce: 0.1418 2023/02/24 08:55:33 - mmengine - INFO - Epoch(train) [14][2800/5047] lr: 3.1726e-05 eta: 7 days, 1:41:35 time: 0.8412 data_time: 0.0019 memory: 52127 loss: 0.1537 loss_ce: 0.1537 2023/02/24 08:57:01 - mmengine - INFO - Epoch(train) [14][2900/5047] lr: 3.1726e-05 eta: 7 days, 1:40:00 time: 0.8715 data_time: 0.0025 memory: 55562 loss: 0.1314 loss_ce: 0.1314 2023/02/24 08:58:26 - mmengine - INFO - Epoch(train) [14][3000/5047] lr: 3.1726e-05 eta: 7 days, 1:37:47 time: 0.8355 data_time: 0.0018 memory: 43114 loss: 0.1672 loss_ce: 0.1672 2023/02/24 08:59:52 - mmengine - INFO - Epoch(train) [14][3100/5047] lr: 3.1726e-05 eta: 7 days, 1:35:36 time: 0.8288 data_time: 0.0017 memory: 43685 loss: 0.1505 loss_ce: 0.1505 2023/02/24 09:01:18 - mmengine - INFO - Epoch(train) [14][3200/5047] lr: 3.1726e-05 eta: 7 days, 1:33:42 time: 0.8863 data_time: 0.0018 memory: 52543 loss: 0.1462 loss_ce: 0.1462 2023/02/24 09:02:46 - mmengine - INFO - Epoch(train) [14][3300/5047] lr: 3.1726e-05 eta: 7 days, 1:32:03 time: 0.8872 data_time: 0.0019 memory: 44840 loss: 0.1345 loss_ce: 0.1345 2023/02/24 09:04:06 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 09:04:16 - mmengine - INFO - Epoch(train) [14][3400/5047] lr: 3.1726e-05 eta: 7 days, 1:30:44 time: 0.8705 data_time: 0.0027 memory: 42772 loss: 0.1762 loss_ce: 0.1762 2023/02/24 09:05:43 - mmengine - INFO - Epoch(train) [14][3500/5047] lr: 3.1726e-05 eta: 7 days, 1:28:58 time: 0.8714 data_time: 0.0026 memory: 55562 loss: 0.1503 loss_ce: 0.1503 2023/02/24 09:07:12 - mmengine - INFO - Epoch(train) [14][3600/5047] lr: 3.1726e-05 eta: 7 days, 1:27:28 time: 0.8560 data_time: 0.0040 memory: 45643 loss: 0.1653 loss_ce: 0.1653 2023/02/24 09:08:40 - mmengine - INFO - Epoch(train) [14][3700/5047] lr: 3.1726e-05 eta: 7 days, 1:25:57 time: 0.8752 data_time: 0.0022 memory: 45302 loss: 0.1579 loss_ce: 0.1579 2023/02/24 09:10:05 - mmengine - INFO - Epoch(train) [14][3800/5047] lr: 3.1726e-05 eta: 7 days, 1:23:43 time: 0.8789 data_time: 0.0016 memory: 50906 loss: 0.1579 loss_ce: 0.1579 2023/02/24 09:11:33 - mmengine - INFO - Epoch(train) [14][3900/5047] lr: 3.1726e-05 eta: 7 days, 1:22:03 time: 0.8822 data_time: 0.0020 memory: 48133 loss: 0.1529 loss_ce: 0.1529 2023/02/24 09:13:00 - mmengine - INFO - Epoch(train) [14][4000/5047] lr: 3.1726e-05 eta: 7 days, 1:20:15 time: 0.8445 data_time: 0.0024 memory: 43289 loss: 0.1573 loss_ce: 0.1573 2023/02/24 09:14:28 - mmengine - INFO - Epoch(train) [14][4100/5047] lr: 3.1726e-05 eta: 7 days, 1:18:30 time: 0.8978 data_time: 0.0015 memory: 41308 loss: 0.1309 loss_ce: 0.1309 2023/02/24 09:15:52 - mmengine - INFO - Epoch(train) [14][4200/5047] lr: 3.1726e-05 eta: 7 days, 1:16:06 time: 0.8926 data_time: 0.0017 memory: 43526 loss: 0.1604 loss_ce: 0.1604 2023/02/24 09:17:18 - mmengine - INFO - Epoch(train) [14][4300/5047] lr: 3.1726e-05 eta: 7 days, 1:13:59 time: 0.8260 data_time: 0.0019 memory: 47665 loss: 0.1594 loss_ce: 0.1594 2023/02/24 09:18:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 09:18:44 - mmengine - INFO - Epoch(train) [14][4400/5047] lr: 3.1726e-05 eta: 7 days, 1:12:03 time: 0.8875 data_time: 0.0019 memory: 42024 loss: 0.1648 loss_ce: 0.1648 2023/02/24 09:20:12 - mmengine - INFO - Epoch(train) [14][4500/5047] lr: 3.1726e-05 eta: 7 days, 1:10:29 time: 0.8529 data_time: 0.0018 memory: 44617 loss: 0.1627 loss_ce: 0.1627 2023/02/24 09:21:40 - mmengine - INFO - Epoch(train) [14][4600/5047] lr: 3.1726e-05 eta: 7 days, 1:08:48 time: 0.8663 data_time: 0.0020 memory: 44477 loss: 0.1528 loss_ce: 0.1528 2023/02/24 09:23:08 - mmengine - INFO - Epoch(train) [14][4700/5047] lr: 3.1726e-05 eta: 7 days, 1:07:06 time: 0.9115 data_time: 0.0034 memory: 47866 loss: 0.1447 loss_ce: 0.1447 2023/02/24 09:24:36 - mmengine - INFO - Epoch(train) [14][4800/5047] lr: 3.1726e-05 eta: 7 days, 1:05:30 time: 0.8901 data_time: 0.0036 memory: 46713 loss: 0.1609 loss_ce: 0.1609 2023/02/24 09:26:04 - mmengine - INFO - Epoch(train) [14][4900/5047] lr: 3.1726e-05 eta: 7 days, 1:03:56 time: 0.8612 data_time: 0.0018 memory: 41701 loss: 0.1688 loss_ce: 0.1688 2023/02/24 09:27:30 - mmengine - INFO - Epoch(train) [14][5000/5047] lr: 3.1726e-05 eta: 7 days, 1:02:01 time: 0.9004 data_time: 0.0016 memory: 44705 loss: 0.1395 loss_ce: 0.1395 2023/02/24 09:28:11 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 09:28:11 - mmengine - INFO - Saving checkpoint at 14 epochs 2023/02/24 09:29:45 - mmengine - INFO - Epoch(train) [15][ 100/5047] lr: 3.1525e-05 eta: 7 days, 0:59:36 time: 0.8965 data_time: 0.0018 memory: 55562 loss: 0.1625 loss_ce: 0.1625 2023/02/24 09:31:14 - mmengine - INFO - Epoch(train) [15][ 200/5047] lr: 3.1525e-05 eta: 7 days, 0:58:12 time: 0.8626 data_time: 0.0024 memory: 42336 loss: 0.1658 loss_ce: 0.1658 2023/02/24 09:32:40 - mmengine - INFO - Epoch(train) [15][ 300/5047] lr: 3.1525e-05 eta: 7 days, 0:56:21 time: 0.8652 data_time: 0.0017 memory: 42398 loss: 0.1541 loss_ce: 0.1541 2023/02/24 09:33:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 09:34:07 - mmengine - INFO - Epoch(train) [15][ 400/5047] lr: 3.1525e-05 eta: 7 days, 0:54:26 time: 0.8623 data_time: 0.0018 memory: 43648 loss: 0.1521 loss_ce: 0.1521 2023/02/24 09:35:34 - mmengine - INFO - Epoch(train) [15][ 500/5047] lr: 3.1525e-05 eta: 7 days, 0:52:42 time: 0.8535 data_time: 0.0018 memory: 43108 loss: 0.1658 loss_ce: 0.1658 2023/02/24 09:37:02 - mmengine - INFO - Epoch(train) [15][ 600/5047] lr: 3.1525e-05 eta: 7 days, 0:51:06 time: 0.8530 data_time: 0.0024 memory: 42965 loss: 0.1483 loss_ce: 0.1483 2023/02/24 09:38:31 - mmengine - INFO - Epoch(train) [15][ 700/5047] lr: 3.1525e-05 eta: 7 days, 0:49:35 time: 0.8703 data_time: 0.0019 memory: 52881 loss: 0.1365 loss_ce: 0.1365 2023/02/24 09:39:58 - mmengine - INFO - Epoch(train) [15][ 800/5047] lr: 3.1525e-05 eta: 7 days, 0:47:50 time: 0.8674 data_time: 0.0021 memory: 55562 loss: 0.1607 loss_ce: 0.1607 2023/02/24 09:41:26 - mmengine - INFO - Epoch(train) [15][ 900/5047] lr: 3.1525e-05 eta: 7 days, 0:46:11 time: 0.8579 data_time: 0.0020 memory: 51564 loss: 0.1336 loss_ce: 0.1336 2023/02/24 09:42:54 - mmengine - INFO - Epoch(train) [15][1000/5047] lr: 3.1525e-05 eta: 7 days, 0:44:32 time: 0.8967 data_time: 0.0017 memory: 42336 loss: 0.1782 loss_ce: 0.1782 2023/02/24 09:44:22 - mmengine - INFO - Epoch(train) [15][1100/5047] lr: 3.1525e-05 eta: 7 days, 0:43:02 time: 0.8583 data_time: 0.0019 memory: 42965 loss: 0.1404 loss_ce: 0.1404 2023/02/24 09:45:50 - mmengine - INFO - Epoch(train) [15][1200/5047] lr: 3.1525e-05 eta: 7 days, 0:41:16 time: 0.8616 data_time: 0.0017 memory: 47813 loss: 0.1700 loss_ce: 0.1700 2023/02/24 09:47:15 - mmengine - INFO - Epoch(train) [15][1300/5047] lr: 3.1525e-05 eta: 7 days, 0:39:03 time: 0.8625 data_time: 0.0067 memory: 47619 loss: 0.1412 loss_ce: 0.1412 2023/02/24 09:47:53 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 09:48:44 - mmengine - INFO - Epoch(train) [15][1400/5047] lr: 3.1525e-05 eta: 7 days, 0:37:43 time: 0.8422 data_time: 0.0019 memory: 45879 loss: 0.1570 loss_ce: 0.1570 2023/02/24 09:50:13 - mmengine - INFO - Epoch(train) [15][1500/5047] lr: 3.1525e-05 eta: 7 days, 0:36:23 time: 0.8738 data_time: 0.0020 memory: 46713 loss: 0.1665 loss_ce: 0.1665 2023/02/24 09:51:40 - mmengine - INFO - Epoch(train) [15][1600/5047] lr: 3.1525e-05 eta: 7 days, 0:34:35 time: 0.8587 data_time: 0.0016 memory: 40942 loss: 0.1329 loss_ce: 0.1329 2023/02/24 09:53:07 - mmengine - INFO - Epoch(train) [15][1700/5047] lr: 3.1525e-05 eta: 7 days, 0:32:48 time: 0.8432 data_time: 0.0017 memory: 44036 loss: 0.1394 loss_ce: 0.1394 2023/02/24 09:54:34 - mmengine - INFO - Epoch(train) [15][1800/5047] lr: 3.1525e-05 eta: 7 days, 0:31:00 time: 0.8467 data_time: 0.0020 memory: 43289 loss: 0.1492 loss_ce: 0.1492 2023/02/24 09:56:02 - mmengine - INFO - Epoch(train) [15][1900/5047] lr: 3.1525e-05 eta: 7 days, 0:29:18 time: 0.8207 data_time: 0.0018 memory: 42284 loss: 0.1643 loss_ce: 0.1643 2023/02/24 09:57:28 - mmengine - INFO - Epoch(train) [15][2000/5047] lr: 3.1525e-05 eta: 7 days, 0:27:23 time: 0.8371 data_time: 0.0016 memory: 47148 loss: 0.1636 loss_ce: 0.1636 2023/02/24 09:58:57 - mmengine - INFO - Epoch(train) [15][2100/5047] lr: 3.1525e-05 eta: 7 days, 0:25:50 time: 0.8526 data_time: 0.0072 memory: 44722 loss: 0.1456 loss_ce: 0.1456 2023/02/24 10:00:21 - mmengine - INFO - Epoch(train) [15][2200/5047] lr: 3.1525e-05 eta: 7 days, 0:23:38 time: 0.8537 data_time: 0.0019 memory: 44617 loss: 0.1419 loss_ce: 0.1419 2023/02/24 10:01:48 - mmengine - INFO - Epoch(train) [15][2300/5047] lr: 3.1525e-05 eta: 7 days, 0:21:47 time: 0.8885 data_time: 0.0016 memory: 44565 loss: 0.1486 loss_ce: 0.1486 2023/02/24 10:02:26 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 10:03:16 - mmengine - INFO - Epoch(train) [15][2400/5047] lr: 3.1525e-05 eta: 7 days, 0:20:14 time: 0.8761 data_time: 0.0017 memory: 43613 loss: 0.1632 loss_ce: 0.1632 2023/02/24 10:04:45 - mmengine - INFO - Epoch(train) [15][2500/5047] lr: 3.1525e-05 eta: 7 days, 0:18:44 time: 0.8563 data_time: 0.0024 memory: 43001 loss: 0.1362 loss_ce: 0.1362 2023/02/24 10:06:10 - mmengine - INFO - Epoch(train) [15][2600/5047] lr: 3.1525e-05 eta: 7 days, 0:16:39 time: 0.8258 data_time: 0.0018 memory: 40535 loss: 0.1356 loss_ce: 0.1356 2023/02/24 10:07:38 - mmengine - INFO - Epoch(train) [15][2700/5047] lr: 3.1525e-05 eta: 7 days, 0:15:03 time: 0.8392 data_time: 0.0031 memory: 52974 loss: 0.1351 loss_ce: 0.1351 2023/02/24 10:09:04 - mmengine - INFO - Epoch(train) [15][2800/5047] lr: 3.1525e-05 eta: 7 days, 0:13:03 time: 0.8490 data_time: 0.0017 memory: 46964 loss: 0.1473 loss_ce: 0.1473 2023/02/24 10:10:32 - mmengine - INFO - Epoch(train) [15][2900/5047] lr: 3.1525e-05 eta: 7 days, 0:11:20 time: 0.8341 data_time: 0.0021 memory: 43289 loss: 0.1442 loss_ce: 0.1442 2023/02/24 10:11:58 - mmengine - INFO - Epoch(train) [15][3000/5047] lr: 3.1525e-05 eta: 7 days, 0:09:20 time: 0.8900 data_time: 0.0081 memory: 45849 loss: 0.1532 loss_ce: 0.1532 2023/02/24 10:13:25 - mmengine - INFO - Epoch(train) [15][3100/5047] lr: 3.1525e-05 eta: 7 days, 0:07:32 time: 0.8432 data_time: 0.0030 memory: 44496 loss: 0.1437 loss_ce: 0.1437 2023/02/24 10:14:51 - mmengine - INFO - Epoch(train) [15][3200/5047] lr: 3.1525e-05 eta: 7 days, 0:05:38 time: 0.8750 data_time: 0.0018 memory: 41175 loss: 0.1562 loss_ce: 0.1562 2023/02/24 10:16:17 - mmengine - INFO - Epoch(train) [15][3300/5047] lr: 3.1525e-05 eta: 7 days, 0:03:41 time: 0.8143 data_time: 0.0020 memory: 42649 loss: 0.1580 loss_ce: 0.1580 2023/02/24 10:16:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 10:17:45 - mmengine - INFO - Epoch(train) [15][3400/5047] lr: 3.1525e-05 eta: 7 days, 0:02:04 time: 0.8190 data_time: 0.0016 memory: 48493 loss: 0.1730 loss_ce: 0.1730 2023/02/24 10:19:12 - mmengine - INFO - Epoch(train) [15][3500/5047] lr: 3.1525e-05 eta: 7 days, 0:00:17 time: 0.8926 data_time: 0.0019 memory: 42885 loss: 0.1562 loss_ce: 0.1562 2023/02/24 10:20:39 - mmengine - INFO - Epoch(train) [15][3600/5047] lr: 3.1525e-05 eta: 6 days, 23:58:25 time: 0.8522 data_time: 0.0017 memory: 48892 loss: 0.1572 loss_ce: 0.1572 2023/02/24 10:22:06 - mmengine - INFO - Epoch(train) [15][3700/5047] lr: 3.1525e-05 eta: 6 days, 23:56:47 time: 0.9038 data_time: 0.0026 memory: 55562 loss: 0.1611 loss_ce: 0.1611 2023/02/24 10:23:36 - mmengine - INFO - Epoch(train) [15][3800/5047] lr: 3.1525e-05 eta: 6 days, 23:55:29 time: 0.8466 data_time: 0.0016 memory: 41122 loss: 0.1963 loss_ce: 0.1963 2023/02/24 10:25:04 - mmengine - INFO - Epoch(train) [15][3900/5047] lr: 3.1525e-05 eta: 6 days, 23:53:56 time: 0.8931 data_time: 0.0020 memory: 43253 loss: 0.1677 loss_ce: 0.1677 2023/02/24 10:26:31 - mmengine - INFO - Epoch(train) [15][4000/5047] lr: 3.1525e-05 eta: 6 days, 23:52:08 time: 0.8564 data_time: 0.0017 memory: 42309 loss: 0.1548 loss_ce: 0.1548 2023/02/24 10:27:57 - mmengine - INFO - Epoch(train) [15][4100/5047] lr: 3.1525e-05 eta: 6 days, 23:50:08 time: 0.8537 data_time: 0.0017 memory: 42965 loss: 0.1845 loss_ce: 0.1845 2023/02/24 10:29:24 - mmengine - INFO - Epoch(train) [15][4200/5047] lr: 3.1525e-05 eta: 6 days, 23:48:30 time: 0.8549 data_time: 0.0016 memory: 52881 loss: 0.1420 loss_ce: 0.1420 2023/02/24 10:30:52 - mmengine - INFO - Epoch(train) [15][4300/5047] lr: 3.1525e-05 eta: 6 days, 23:46:51 time: 0.8455 data_time: 0.0017 memory: 55296 loss: 0.1783 loss_ce: 0.1783 2023/02/24 10:31:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 10:32:18 - mmengine - INFO - Epoch(train) [15][4400/5047] lr: 3.1525e-05 eta: 6 days, 23:44:56 time: 0.8315 data_time: 0.0016 memory: 39960 loss: 0.1536 loss_ce: 0.1536 2023/02/24 10:33:45 - mmengine - INFO - Epoch(train) [15][4500/5047] lr: 3.1525e-05 eta: 6 days, 23:43:09 time: 0.8993 data_time: 0.0020 memory: 42649 loss: 0.1515 loss_ce: 0.1515 2023/02/24 10:35:11 - mmengine - INFO - Epoch(train) [15][4600/5047] lr: 3.1525e-05 eta: 6 days, 23:41:12 time: 0.8328 data_time: 0.0016 memory: 43221 loss: 0.1457 loss_ce: 0.1457 2023/02/24 10:36:39 - mmengine - INFO - Epoch(train) [15][4700/5047] lr: 3.1525e-05 eta: 6 days, 23:39:30 time: 0.8598 data_time: 0.0020 memory: 43744 loss: 0.1482 loss_ce: 0.1482 2023/02/24 10:38:07 - mmengine - INFO - Epoch(train) [15][4800/5047] lr: 3.1525e-05 eta: 6 days, 23:37:53 time: 0.8466 data_time: 0.0018 memory: 39702 loss: 0.1345 loss_ce: 0.1345 2023/02/24 10:39:32 - mmengine - INFO - Epoch(train) [15][4900/5047] lr: 3.1525e-05 eta: 6 days, 23:35:50 time: 0.8572 data_time: 0.0020 memory: 42465 loss: 0.1423 loss_ce: 0.1423 2023/02/24 10:41:01 - mmengine - INFO - Epoch(train) [15][5000/5047] lr: 3.1525e-05 eta: 6 days, 23:34:27 time: 0.8802 data_time: 0.0062 memory: 41161 loss: 0.1780 loss_ce: 0.1780 2023/02/24 10:41:41 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 10:41:41 - mmengine - INFO - Saving checkpoint at 15 epochs 2023/02/24 10:43:14 - mmengine - INFO - Epoch(train) [16][ 100/5047] lr: 3.1324e-05 eta: 6 days, 23:31:49 time: 0.9021 data_time: 0.0016 memory: 43289 loss: 0.1518 loss_ce: 0.1518 2023/02/24 10:44:40 - mmengine - INFO - Epoch(train) [16][ 200/5047] lr: 3.1324e-05 eta: 6 days, 23:29:56 time: 0.8592 data_time: 0.0019 memory: 49217 loss: 0.1542 loss_ce: 0.1542 2023/02/24 10:46:03 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 10:46:08 - mmengine - INFO - Epoch(train) [16][ 300/5047] lr: 3.1324e-05 eta: 6 days, 23:28:17 time: 0.8709 data_time: 0.0049 memory: 50443 loss: 0.1444 loss_ce: 0.1444 2023/02/24 10:47:35 - mmengine - INFO - Epoch(train) [16][ 400/5047] lr: 3.1324e-05 eta: 6 days, 23:26:36 time: 0.8919 data_time: 0.0042 memory: 44278 loss: 0.1502 loss_ce: 0.1502 2023/02/24 10:49:02 - mmengine - INFO - Epoch(train) [16][ 500/5047] lr: 3.1324e-05 eta: 6 days, 23:24:52 time: 0.8909 data_time: 0.0020 memory: 51267 loss: 0.1573 loss_ce: 0.1573 2023/02/24 10:50:28 - mmengine - INFO - Epoch(train) [16][ 600/5047] lr: 3.1324e-05 eta: 6 days, 23:22:48 time: 0.8038 data_time: 0.0017 memory: 54232 loss: 0.1461 loss_ce: 0.1461 2023/02/24 10:51:55 - mmengine - INFO - Epoch(train) [16][ 700/5047] lr: 3.1324e-05 eta: 6 days, 23:21:02 time: 0.8389 data_time: 0.0018 memory: 41786 loss: 0.1456 loss_ce: 0.1456 2023/02/24 10:53:22 - mmengine - INFO - Epoch(train) [16][ 800/5047] lr: 3.1324e-05 eta: 6 days, 23:19:24 time: 0.8923 data_time: 0.0031 memory: 46334 loss: 0.1546 loss_ce: 0.1546 2023/02/24 10:54:49 - mmengine - INFO - Epoch(train) [16][ 900/5047] lr: 3.1324e-05 eta: 6 days, 23:17:36 time: 0.8891 data_time: 0.0046 memory: 42187 loss: 0.1466 loss_ce: 0.1466 2023/02/24 10:56:18 - mmengine - INFO - Epoch(train) [16][1000/5047] lr: 3.1324e-05 eta: 6 days, 23:16:08 time: 0.8671 data_time: 0.0020 memory: 47134 loss: 0.1574 loss_ce: 0.1574 2023/02/24 10:57:44 - mmengine - INFO - Epoch(train) [16][1100/5047] lr: 3.1324e-05 eta: 6 days, 23:14:20 time: 0.8223 data_time: 0.0018 memory: 43947 loss: 0.1337 loss_ce: 0.1337 2023/02/24 10:59:11 - mmengine - INFO - Epoch(train) [16][1200/5047] lr: 3.1324e-05 eta: 6 days, 23:12:32 time: 0.9065 data_time: 0.0044 memory: 43289 loss: 0.1718 loss_ce: 0.1718 2023/02/24 11:00:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 11:00:38 - mmengine - INFO - Epoch(train) [16][1300/5047] lr: 3.1324e-05 eta: 6 days, 23:10:41 time: 0.8479 data_time: 0.0030 memory: 48188 loss: 0.1329 loss_ce: 0.1329 2023/02/24 11:02:06 - mmengine - INFO - Epoch(train) [16][1400/5047] lr: 3.1324e-05 eta: 6 days, 23:09:06 time: 0.8022 data_time: 0.0022 memory: 55562 loss: 0.1542 loss_ce: 0.1542 2023/02/24 11:03:33 - mmengine - INFO - Epoch(train) [16][1500/5047] lr: 3.1324e-05 eta: 6 days, 23:07:25 time: 0.8721 data_time: 0.0018 memory: 42649 loss: 0.1667 loss_ce: 0.1667 2023/02/24 11:04:58 - mmengine - INFO - Epoch(train) [16][1600/5047] lr: 3.1324e-05 eta: 6 days, 23:05:22 time: 0.8438 data_time: 0.0019 memory: 55562 loss: 0.1592 loss_ce: 0.1592 2023/02/24 11:06:24 - mmengine - INFO - Epoch(train) [16][1700/5047] lr: 3.1324e-05 eta: 6 days, 23:03:18 time: 0.8131 data_time: 0.0017 memory: 42718 loss: 0.1617 loss_ce: 0.1617 2023/02/24 11:07:50 - mmengine - INFO - Epoch(train) [16][1800/5047] lr: 3.1324e-05 eta: 6 days, 23:01:32 time: 0.8564 data_time: 0.0018 memory: 46794 loss: 0.1619 loss_ce: 0.1619 2023/02/24 11:09:18 - mmengine - INFO - Epoch(train) [16][1900/5047] lr: 3.1324e-05 eta: 6 days, 22:59:52 time: 0.8959 data_time: 0.0018 memory: 50505 loss: 0.1375 loss_ce: 0.1375 2023/02/24 11:10:45 - mmengine - INFO - Epoch(train) [16][2000/5047] lr: 3.1324e-05 eta: 6 days, 22:58:12 time: 0.8605 data_time: 0.0020 memory: 51637 loss: 0.1292 loss_ce: 0.1292 2023/02/24 11:12:13 - mmengine - INFO - Epoch(train) [16][2100/5047] lr: 3.1324e-05 eta: 6 days, 22:56:37 time: 0.8637 data_time: 0.0020 memory: 44313 loss: 0.1394 loss_ce: 0.1394 2023/02/24 11:13:41 - mmengine - INFO - Epoch(train) [16][2200/5047] lr: 3.1324e-05 eta: 6 days, 22:55:02 time: 0.8723 data_time: 0.0019 memory: 46054 loss: 0.1602 loss_ce: 0.1602 2023/02/24 11:15:03 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 11:15:07 - mmengine - INFO - Epoch(train) [16][2300/5047] lr: 3.1324e-05 eta: 6 days, 22:53:06 time: 0.8397 data_time: 0.0018 memory: 44338 loss: 0.1399 loss_ce: 0.1399 2023/02/24 11:16:35 - mmengine - INFO - Epoch(train) [16][2400/5047] lr: 3.1324e-05 eta: 6 days, 22:51:31 time: 0.8619 data_time: 0.0019 memory: 41724 loss: 0.1374 loss_ce: 0.1374 2023/02/24 11:18:02 - mmengine - INFO - Epoch(train) [16][2500/5047] lr: 3.1324e-05 eta: 6 days, 22:49:47 time: 0.8485 data_time: 0.0019 memory: 42965 loss: 0.1308 loss_ce: 0.1308 2023/02/24 11:19:31 - mmengine - INFO - Epoch(train) [16][2600/5047] lr: 3.1324e-05 eta: 6 days, 22:48:27 time: 0.9398 data_time: 0.0024 memory: 55562 loss: 0.1472 loss_ce: 0.1472 2023/02/24 11:20:57 - mmengine - INFO - Epoch(train) [16][2700/5047] lr: 3.1324e-05 eta: 6 days, 22:46:33 time: 0.8862 data_time: 0.0017 memory: 49295 loss: 0.1259 loss_ce: 0.1259 2023/02/24 11:22:24 - mmengine - INFO - Epoch(train) [16][2800/5047] lr: 3.1324e-05 eta: 6 days, 22:44:41 time: 0.8793 data_time: 0.0024 memory: 42336 loss: 0.1633 loss_ce: 0.1633 2023/02/24 11:23:50 - mmengine - INFO - Epoch(train) [16][2900/5047] lr: 3.1324e-05 eta: 6 days, 22:42:52 time: 0.9089 data_time: 0.0022 memory: 47813 loss: 0.1321 loss_ce: 0.1321 2023/02/24 11:25:16 - mmengine - INFO - Epoch(train) [16][3000/5047] lr: 3.1324e-05 eta: 6 days, 22:40:54 time: 0.8437 data_time: 0.0023 memory: 44554 loss: 0.1650 loss_ce: 0.1650 2023/02/24 11:26:41 - mmengine - INFO - Epoch(train) [16][3100/5047] lr: 3.1324e-05 eta: 6 days, 22:38:54 time: 0.8710 data_time: 0.0020 memory: 46770 loss: 0.1299 loss_ce: 0.1299 2023/02/24 11:28:08 - mmengine - INFO - Epoch(train) [16][3200/5047] lr: 3.1324e-05 eta: 6 days, 22:37:06 time: 0.8400 data_time: 0.0019 memory: 47813 loss: 0.1660 loss_ce: 0.1660 2023/02/24 11:29:31 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 11:29:35 - mmengine - INFO - Epoch(train) [16][3300/5047] lr: 3.1324e-05 eta: 6 days, 22:35:19 time: 0.8016 data_time: 0.0019 memory: 53387 loss: 0.1354 loss_ce: 0.1354 2023/02/24 11:31:02 - mmengine - INFO - Epoch(train) [16][3400/5047] lr: 3.1324e-05 eta: 6 days, 22:33:39 time: 0.8419 data_time: 0.0027 memory: 43289 loss: 0.1614 loss_ce: 0.1614 2023/02/24 11:32:30 - mmengine - INFO - Epoch(train) [16][3500/5047] lr: 3.1324e-05 eta: 6 days, 22:32:08 time: 0.8759 data_time: 0.0018 memory: 46719 loss: 0.1515 loss_ce: 0.1515 2023/02/24 11:33:57 - mmengine - INFO - Epoch(train) [16][3600/5047] lr: 3.1324e-05 eta: 6 days, 22:30:18 time: 0.9171 data_time: 0.0042 memory: 40825 loss: 0.1534 loss_ce: 0.1534 2023/02/24 11:35:23 - mmengine - INFO - Epoch(train) [16][3700/5047] lr: 3.1324e-05 eta: 6 days, 22:28:21 time: 0.8342 data_time: 0.0018 memory: 41987 loss: 0.1473 loss_ce: 0.1473 2023/02/24 11:36:50 - mmengine - INFO - Epoch(train) [16][3800/5047] lr: 3.1324e-05 eta: 6 days, 22:26:45 time: 0.8529 data_time: 0.0017 memory: 43252 loss: 0.1421 loss_ce: 0.1421 2023/02/24 11:38:18 - mmengine - INFO - Epoch(train) [16][3900/5047] lr: 3.1324e-05 eta: 6 days, 22:25:04 time: 0.8874 data_time: 0.0019 memory: 40535 loss: 0.1423 loss_ce: 0.1423 2023/02/24 11:39:46 - mmengine - INFO - Epoch(train) [16][4000/5047] lr: 3.1324e-05 eta: 6 days, 22:23:32 time: 0.9813 data_time: 0.0034 memory: 53387 loss: 0.1640 loss_ce: 0.1640 2023/02/24 11:41:13 - mmengine - INFO - Epoch(train) [16][4100/5047] lr: 3.1324e-05 eta: 6 days, 22:21:52 time: 0.8712 data_time: 0.0027 memory: 46730 loss: 0.1418 loss_ce: 0.1418 2023/02/24 11:42:39 - mmengine - INFO - Epoch(train) [16][4200/5047] lr: 3.1324e-05 eta: 6 days, 22:19:57 time: 0.8547 data_time: 0.0020 memory: 43289 loss: 0.1573 loss_ce: 0.1573 2023/02/24 11:44:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 11:44:09 - mmengine - INFO - Epoch(train) [16][4300/5047] lr: 3.1324e-05 eta: 6 days, 22:18:42 time: 0.8970 data_time: 0.0019 memory: 55562 loss: 0.1788 loss_ce: 0.1788 2023/02/24 11:45:37 - mmengine - INFO - Epoch(train) [16][4400/5047] lr: 3.1324e-05 eta: 6 days, 22:17:11 time: 0.8900 data_time: 0.0021 memory: 49219 loss: 0.1473 loss_ce: 0.1473 2023/02/24 11:47:02 - mmengine - INFO - Epoch(train) [16][4500/5047] lr: 3.1324e-05 eta: 6 days, 22:15:04 time: 0.8449 data_time: 0.0017 memory: 48210 loss: 0.1586 loss_ce: 0.1586 2023/02/24 11:48:28 - mmengine - INFO - Epoch(train) [16][4600/5047] lr: 3.1324e-05 eta: 6 days, 22:13:16 time: 0.8384 data_time: 0.0017 memory: 48035 loss: 0.1611 loss_ce: 0.1611 2023/02/24 11:49:57 - mmengine - INFO - Epoch(train) [16][4700/5047] lr: 3.1324e-05 eta: 6 days, 22:11:47 time: 0.8592 data_time: 0.0024 memory: 55562 loss: 0.1519 loss_ce: 0.1519 2023/02/24 11:51:23 - mmengine - INFO - Epoch(train) [16][4800/5047] lr: 3.1324e-05 eta: 6 days, 22:10:00 time: 0.8001 data_time: 0.0021 memory: 43808 loss: 0.1548 loss_ce: 0.1548 2023/02/24 11:52:50 - mmengine - INFO - Epoch(train) [16][4900/5047] lr: 3.1324e-05 eta: 6 days, 22:08:12 time: 0.8721 data_time: 0.0043 memory: 45643 loss: 0.1364 loss_ce: 0.1364 2023/02/24 11:54:15 - mmengine - INFO - Epoch(train) [16][5000/5047] lr: 3.1324e-05 eta: 6 days, 22:06:14 time: 0.8454 data_time: 0.0043 memory: 42024 loss: 0.1495 loss_ce: 0.1495 2023/02/24 11:54:57 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 11:54:57 - mmengine - INFO - Saving checkpoint at 16 epochs 2023/02/24 11:56:31 - mmengine - INFO - Epoch(train) [17][ 100/5047] lr: 3.1123e-05 eta: 6 days, 22:04:17 time: 0.8886 data_time: 0.0053 memory: 44278 loss: 0.1482 loss_ce: 0.1482 2023/02/24 11:57:58 - mmengine - INFO - Epoch(train) [17][ 200/5047] lr: 3.1123e-05 eta: 6 days, 22:02:27 time: 0.8567 data_time: 0.0024 memory: 40825 loss: 0.1554 loss_ce: 0.1554 2023/02/24 11:58:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 11:59:25 - mmengine - INFO - Epoch(train) [17][ 300/5047] lr: 3.1123e-05 eta: 6 days, 22:00:50 time: 0.8935 data_time: 0.0045 memory: 42535 loss: 0.1550 loss_ce: 0.1550 2023/02/24 12:00:54 - mmengine - INFO - Epoch(train) [17][ 400/5047] lr: 3.1123e-05 eta: 6 days, 21:59:23 time: 0.8397 data_time: 0.0025 memory: 55562 loss: 0.1410 loss_ce: 0.1410 2023/02/24 12:02:22 - mmengine - INFO - Epoch(train) [17][ 500/5047] lr: 3.1123e-05 eta: 6 days, 21:57:50 time: 0.8289 data_time: 0.0018 memory: 45302 loss: 0.1502 loss_ce: 0.1502 2023/02/24 12:03:51 - mmengine - INFO - Epoch(train) [17][ 600/5047] lr: 3.1123e-05 eta: 6 days, 21:56:31 time: 0.8857 data_time: 0.0018 memory: 43559 loss: 0.1431 loss_ce: 0.1431 2023/02/24 12:05:18 - mmengine - INFO - Epoch(train) [17][ 700/5047] lr: 3.1123e-05 eta: 6 days, 21:54:50 time: 0.9061 data_time: 0.0062 memory: 42715 loss: 0.1513 loss_ce: 0.1513 2023/02/24 12:06:45 - mmengine - INFO - Epoch(train) [17][ 800/5047] lr: 3.1123e-05 eta: 6 days, 21:53:06 time: 0.8759 data_time: 0.0026 memory: 47775 loss: 0.1542 loss_ce: 0.1542 2023/02/24 12:08:12 - mmengine - INFO - Epoch(train) [17][ 900/5047] lr: 3.1123e-05 eta: 6 days, 21:51:21 time: 0.8025 data_time: 0.0019 memory: 42651 loss: 0.1599 loss_ce: 0.1599 2023/02/24 12:09:39 - mmengine - INFO - Epoch(train) [17][1000/5047] lr: 3.1123e-05 eta: 6 days, 21:49:41 time: 0.8987 data_time: 0.0021 memory: 48565 loss: 0.1498 loss_ce: 0.1498 2023/02/24 12:11:08 - mmengine - INFO - Epoch(train) [17][1100/5047] lr: 3.1123e-05 eta: 6 days, 21:48:13 time: 0.8414 data_time: 0.0017 memory: 55114 loss: 0.1509 loss_ce: 0.1509 2023/02/24 12:12:33 - mmengine - INFO - Epoch(train) [17][1200/5047] lr: 3.1123e-05 eta: 6 days, 21:46:13 time: 0.8453 data_time: 0.0042 memory: 42705 loss: 0.1513 loss_ce: 0.1513 2023/02/24 12:13:15 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 12:14:01 - mmengine - INFO - Epoch(train) [17][1300/5047] lr: 3.1123e-05 eta: 6 days, 21:44:38 time: 0.8932 data_time: 0.0017 memory: 55562 loss: 0.1514 loss_ce: 0.1514 2023/02/24 12:15:30 - mmengine - INFO - Epoch(train) [17][1400/5047] lr: 3.1123e-05 eta: 6 days, 21:43:16 time: 0.8775 data_time: 0.0043 memory: 44617 loss: 0.1614 loss_ce: 0.1614 2023/02/24 12:16:57 - mmengine - INFO - Epoch(train) [17][1500/5047] lr: 3.1123e-05 eta: 6 days, 21:41:35 time: 0.8523 data_time: 0.0019 memory: 40901 loss: 0.1382 loss_ce: 0.1382 2023/02/24 12:18:24 - mmengine - INFO - Epoch(train) [17][1600/5047] lr: 3.1123e-05 eta: 6 days, 21:39:55 time: 0.8808 data_time: 0.0025 memory: 40825 loss: 0.1421 loss_ce: 0.1421 2023/02/24 12:19:49 - mmengine - INFO - Epoch(train) [17][1700/5047] lr: 3.1123e-05 eta: 6 days, 21:37:53 time: 0.8433 data_time: 0.0019 memory: 42336 loss: 0.1560 loss_ce: 0.1560 2023/02/24 12:21:17 - mmengine - INFO - Epoch(train) [17][1800/5047] lr: 3.1123e-05 eta: 6 days, 21:36:20 time: 0.8733 data_time: 0.0018 memory: 42649 loss: 0.1338 loss_ce: 0.1338 2023/02/24 12:22:46 - mmengine - INFO - Epoch(train) [17][1900/5047] lr: 3.1123e-05 eta: 6 days, 21:34:57 time: 0.8982 data_time: 0.0018 memory: 47982 loss: 0.1163 loss_ce: 0.1163 2023/02/24 12:24:11 - mmengine - INFO - Epoch(train) [17][2000/5047] lr: 3.1123e-05 eta: 6 days, 21:32:52 time: 0.8527 data_time: 0.0018 memory: 49334 loss: 0.1443 loss_ce: 0.1443 2023/02/24 12:25:38 - mmengine - INFO - Epoch(train) [17][2100/5047] lr: 3.1123e-05 eta: 6 days, 21:31:05 time: 0.9113 data_time: 0.0019 memory: 47958 loss: 0.1544 loss_ce: 0.1544 2023/02/24 12:27:04 - mmengine - INFO - Epoch(train) [17][2200/5047] lr: 3.1123e-05 eta: 6 days, 21:29:20 time: 0.8891 data_time: 0.0036 memory: 45302 loss: 0.1470 loss_ce: 0.1470 2023/02/24 12:27:46 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 12:28:32 - mmengine - INFO - Epoch(train) [17][2300/5047] lr: 3.1123e-05 eta: 6 days, 21:27:46 time: 0.8973 data_time: 0.0021 memory: 54072 loss: 0.1576 loss_ce: 0.1576 2023/02/24 12:30:00 - mmengine - INFO - Epoch(train) [17][2400/5047] lr: 3.1123e-05 eta: 6 days, 21:26:16 time: 0.8734 data_time: 0.0019 memory: 40241 loss: 0.1552 loss_ce: 0.1552 2023/02/24 12:31:26 - mmengine - INFO - Epoch(train) [17][2500/5047] lr: 3.1123e-05 eta: 6 days, 21:24:24 time: 0.8469 data_time: 0.0020 memory: 40820 loss: 0.1429 loss_ce: 0.1429 2023/02/24 12:32:53 - mmengine - INFO - Epoch(train) [17][2600/5047] lr: 3.1123e-05 eta: 6 days, 21:22:34 time: 0.8704 data_time: 0.0037 memory: 44617 loss: 0.1332 loss_ce: 0.1332 2023/02/24 12:34:20 - mmengine - INFO - Epoch(train) [17][2700/5047] lr: 3.1123e-05 eta: 6 days, 21:20:56 time: 0.8785 data_time: 0.0018 memory: 50106 loss: 0.1482 loss_ce: 0.1482 2023/02/24 12:35:47 - mmengine - INFO - Epoch(train) [17][2800/5047] lr: 3.1123e-05 eta: 6 days, 21:19:11 time: 0.8415 data_time: 0.0019 memory: 47037 loss: 0.1396 loss_ce: 0.1396 2023/02/24 12:37:13 - mmengine - INFO - Epoch(train) [17][2900/5047] lr: 3.1123e-05 eta: 6 days, 21:17:26 time: 0.8327 data_time: 0.0018 memory: 42649 loss: 0.1419 loss_ce: 0.1419 2023/02/24 12:38:41 - mmengine - INFO - Epoch(train) [17][3000/5047] lr: 3.1123e-05 eta: 6 days, 21:15:48 time: 0.8571 data_time: 0.0018 memory: 43846 loss: 0.1496 loss_ce: 0.1496 2023/02/24 12:40:07 - mmengine - INFO - Epoch(train) [17][3100/5047] lr: 3.1123e-05 eta: 6 days, 21:13:57 time: 0.8704 data_time: 0.0025 memory: 44956 loss: 0.1519 loss_ce: 0.1519 2023/02/24 12:41:33 - mmengine - INFO - Epoch(train) [17][3200/5047] lr: 3.1123e-05 eta: 6 days, 21:12:05 time: 0.8061 data_time: 0.0017 memory: 41724 loss: 0.1533 loss_ce: 0.1533 2023/02/24 12:42:14 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 12:43:00 - mmengine - INFO - Epoch(train) [17][3300/5047] lr: 3.1123e-05 eta: 6 days, 21:10:30 time: 0.8889 data_time: 0.0039 memory: 54205 loss: 0.1419 loss_ce: 0.1419 2023/02/24 12:44:28 - mmengine - INFO - Epoch(train) [17][3400/5047] lr: 3.1123e-05 eta: 6 days, 21:08:57 time: 0.9326 data_time: 0.0021 memory: 48534 loss: 0.1294 loss_ce: 0.1294 2023/02/24 12:45:54 - mmengine - INFO - Epoch(train) [17][3500/5047] lr: 3.1123e-05 eta: 6 days, 21:07:04 time: 0.8470 data_time: 0.0017 memory: 44956 loss: 0.1409 loss_ce: 0.1409 2023/02/24 12:47:21 - mmengine - INFO - Epoch(train) [17][3600/5047] lr: 3.1123e-05 eta: 6 days, 21:05:17 time: 0.9019 data_time: 0.0034 memory: 50349 loss: 0.1412 loss_ce: 0.1412 2023/02/24 12:48:47 - mmengine - INFO - Epoch(train) [17][3700/5047] lr: 3.1123e-05 eta: 6 days, 21:03:24 time: 0.8336 data_time: 0.0019 memory: 52816 loss: 0.1535 loss_ce: 0.1535 2023/02/24 12:50:13 - mmengine - INFO - Epoch(train) [17][3800/5047] lr: 3.1123e-05 eta: 6 days, 21:01:35 time: 0.8618 data_time: 0.0017 memory: 50505 loss: 0.1545 loss_ce: 0.1545 2023/02/24 12:51:40 - mmengine - INFO - Epoch(train) [17][3900/5047] lr: 3.1123e-05 eta: 6 days, 21:00:00 time: 0.8705 data_time: 0.0017 memory: 43476 loss: 0.1531 loss_ce: 0.1531 2023/02/24 12:53:06 - mmengine - INFO - Epoch(train) [17][4000/5047] lr: 3.1123e-05 eta: 6 days, 20:58:08 time: 0.8724 data_time: 0.0020 memory: 42965 loss: 0.1588 loss_ce: 0.1588 2023/02/24 12:54:31 - mmengine - INFO - Epoch(train) [17][4100/5047] lr: 3.1123e-05 eta: 6 days, 20:56:07 time: 0.8226 data_time: 0.0017 memory: 45302 loss: 0.1597 loss_ce: 0.1597 2023/02/24 12:55:57 - mmengine - INFO - Epoch(train) [17][4200/5047] lr: 3.1123e-05 eta: 6 days, 20:54:15 time: 0.8873 data_time: 0.0018 memory: 43947 loss: 0.1451 loss_ce: 0.1451 2023/02/24 12:56:41 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 12:57:26 - mmengine - INFO - Epoch(train) [17][4300/5047] lr: 3.1123e-05 eta: 6 days, 20:52:47 time: 0.8636 data_time: 0.0053 memory: 43289 loss: 0.1478 loss_ce: 0.1478 2023/02/24 12:58:51 - mmengine - INFO - Epoch(train) [17][4400/5047] lr: 3.1123e-05 eta: 6 days, 20:50:54 time: 0.9129 data_time: 0.0017 memory: 43289 loss: 0.1595 loss_ce: 0.1595 2023/02/24 13:00:20 - mmengine - INFO - Epoch(train) [17][4500/5047] lr: 3.1123e-05 eta: 6 days, 20:49:29 time: 0.9564 data_time: 0.0017 memory: 39681 loss: 0.1541 loss_ce: 0.1541 2023/02/24 13:01:46 - mmengine - INFO - Epoch(train) [17][4600/5047] lr: 3.1123e-05 eta: 6 days, 20:47:35 time: 0.8872 data_time: 0.0037 memory: 41780 loss: 0.1393 loss_ce: 0.1393 2023/02/24 13:03:11 - mmengine - INFO - Epoch(train) [17][4700/5047] lr: 3.1123e-05 eta: 6 days, 20:45:34 time: 0.8026 data_time: 0.0036 memory: 40825 loss: 0.1513 loss_ce: 0.1513 2023/02/24 13:04:37 - mmengine - INFO - Epoch(train) [17][4800/5047] lr: 3.1123e-05 eta: 6 days, 20:43:51 time: 0.8813 data_time: 0.0018 memory: 49216 loss: 0.1534 loss_ce: 0.1534 2023/02/24 13:06:07 - mmengine - INFO - Epoch(train) [17][4900/5047] lr: 3.1123e-05 eta: 6 days, 20:42:32 time: 1.0455 data_time: 0.0041 memory: 49334 loss: 0.1254 loss_ce: 0.1254 2023/02/24 13:07:34 - mmengine - INFO - Epoch(train) [17][5000/5047] lr: 3.1123e-05 eta: 6 days, 20:40:51 time: 0.8820 data_time: 0.0021 memory: 42649 loss: 0.1446 loss_ce: 0.1446 2023/02/24 13:08:14 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 13:08:14 - mmengine - INFO - Saving checkpoint at 17 epochs 2023/02/24 13:09:48 - mmengine - INFO - Epoch(train) [18][ 100/5047] lr: 3.0922e-05 eta: 6 days, 20:38:36 time: 0.8565 data_time: 0.0017 memory: 42336 loss: 0.1674 loss_ce: 0.1674 2023/02/24 13:11:15 - mmengine - INFO - Epoch(train) [18][ 200/5047] lr: 3.0922e-05 eta: 6 days, 20:36:54 time: 0.8720 data_time: 0.0018 memory: 50514 loss: 0.1277 loss_ce: 0.1277 2023/02/24 13:11:16 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 13:12:43 - mmengine - INFO - Epoch(train) [18][ 300/5047] lr: 3.0922e-05 eta: 6 days, 20:35:22 time: 0.8687 data_time: 0.0018 memory: 43613 loss: 0.1592 loss_ce: 0.1592 2023/02/24 13:14:09 - mmengine - INFO - Epoch(train) [18][ 400/5047] lr: 3.0922e-05 eta: 6 days, 20:33:32 time: 0.8728 data_time: 0.0018 memory: 55562 loss: 0.1599 loss_ce: 0.1599 2023/02/24 13:15:37 - mmengine - INFO - Epoch(train) [18][ 500/5047] lr: 3.0922e-05 eta: 6 days, 20:32:01 time: 0.8808 data_time: 0.0020 memory: 41585 loss: 0.1474 loss_ce: 0.1474 2023/02/24 13:17:04 - mmengine - INFO - Epoch(train) [18][ 600/5047] lr: 3.0922e-05 eta: 6 days, 20:30:28 time: 0.8380 data_time: 0.0041 memory: 42965 loss: 0.1428 loss_ce: 0.1428 2023/02/24 13:18:31 - mmengine - INFO - Epoch(train) [18][ 700/5047] lr: 3.0922e-05 eta: 6 days, 20:28:44 time: 0.8661 data_time: 0.0032 memory: 46711 loss: 0.1470 loss_ce: 0.1470 2023/02/24 13:19:58 - mmengine - INFO - Epoch(train) [18][ 800/5047] lr: 3.0922e-05 eta: 6 days, 20:27:03 time: 0.8802 data_time: 0.0032 memory: 42336 loss: 0.1542 loss_ce: 0.1542 2023/02/24 13:21:26 - mmengine - INFO - Epoch(train) [18][ 900/5047] lr: 3.0922e-05 eta: 6 days, 20:25:29 time: 0.8934 data_time: 0.0018 memory: 44593 loss: 0.1712 loss_ce: 0.1712 2023/02/24 13:22:51 - mmengine - INFO - Epoch(train) [18][1000/5047] lr: 3.0922e-05 eta: 6 days, 20:23:28 time: 0.8691 data_time: 0.0023 memory: 41122 loss: 0.1386 loss_ce: 0.1386 2023/02/24 13:24:17 - mmengine - INFO - Epoch(train) [18][1100/5047] lr: 3.0922e-05 eta: 6 days, 20:21:39 time: 0.8726 data_time: 0.0019 memory: 40825 loss: 0.1514 loss_ce: 0.1514 2023/02/24 13:25:44 - mmengine - INFO - Epoch(train) [18][1200/5047] lr: 3.0922e-05 eta: 6 days, 20:19:56 time: 0.8392 data_time: 0.0017 memory: 42960 loss: 0.1557 loss_ce: 0.1557 2023/02/24 13:25:44 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 13:27:13 - mmengine - INFO - Epoch(train) [18][1300/5047] lr: 3.0922e-05 eta: 6 days, 20:18:35 time: 0.9635 data_time: 0.0020 memory: 46355 loss: 0.1380 loss_ce: 0.1380 2023/02/24 13:28:41 - mmengine - INFO - Epoch(train) [18][1400/5047] lr: 3.0922e-05 eta: 6 days, 20:17:11 time: 0.8892 data_time: 0.0020 memory: 42333 loss: 0.1539 loss_ce: 0.1539 2023/02/24 13:30:08 - mmengine - INFO - Epoch(train) [18][1500/5047] lr: 3.0922e-05 eta: 6 days, 20:15:25 time: 0.8633 data_time: 0.0018 memory: 41913 loss: 0.1287 loss_ce: 0.1287 2023/02/24 13:31:33 - mmengine - INFO - Epoch(train) [18][1600/5047] lr: 3.0922e-05 eta: 6 days, 20:13:28 time: 0.7912 data_time: 0.0018 memory: 41724 loss: 0.1283 loss_ce: 0.1283 2023/02/24 13:32:59 - mmengine - INFO - Epoch(train) [18][1700/5047] lr: 3.0922e-05 eta: 6 days, 20:11:36 time: 0.8739 data_time: 0.0018 memory: 49334 loss: 0.1508 loss_ce: 0.1508 2023/02/24 13:34:24 - mmengine - INFO - Epoch(train) [18][1800/5047] lr: 3.0922e-05 eta: 6 days, 20:09:38 time: 0.8472 data_time: 0.0021 memory: 44294 loss: 0.1474 loss_ce: 0.1474 2023/02/24 13:35:50 - mmengine - INFO - Epoch(train) [18][1900/5047] lr: 3.0922e-05 eta: 6 days, 20:07:52 time: 0.8354 data_time: 0.0017 memory: 40627 loss: 0.1406 loss_ce: 0.1406 2023/02/24 13:37:17 - mmengine - INFO - Epoch(train) [18][2000/5047] lr: 3.0922e-05 eta: 6 days, 20:06:13 time: 0.8621 data_time: 0.0021 memory: 50349 loss: 0.1508 loss_ce: 0.1508 2023/02/24 13:38:45 - mmengine - INFO - Epoch(train) [18][2100/5047] lr: 3.0922e-05 eta: 6 days, 20:04:40 time: 0.8898 data_time: 0.0042 memory: 46005 loss: 0.1415 loss_ce: 0.1415 2023/02/24 13:40:11 - mmengine - INFO - Epoch(train) [18][2200/5047] lr: 3.0922e-05 eta: 6 days, 20:02:49 time: 0.8380 data_time: 0.0022 memory: 44956 loss: 0.1490 loss_ce: 0.1490 2023/02/24 13:40:12 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 13:41:36 - mmengine - INFO - Epoch(train) [18][2300/5047] lr: 3.0922e-05 eta: 6 days, 20:00:55 time: 0.8718 data_time: 0.0018 memory: 41419 loss: 0.1307 loss_ce: 0.1307 2023/02/24 13:43:03 - mmengine - INFO - Epoch(train) [18][2400/5047] lr: 3.0922e-05 eta: 6 days, 19:59:10 time: 0.8780 data_time: 0.0016 memory: 41326 loss: 0.1420 loss_ce: 0.1420 2023/02/24 13:44:29 - mmengine - INFO - Epoch(train) [18][2500/5047] lr: 3.0922e-05 eta: 6 days, 19:57:24 time: 0.8424 data_time: 0.0031 memory: 44539 loss: 0.1340 loss_ce: 0.1340 2023/02/24 13:45:57 - mmengine - INFO - Epoch(train) [18][2600/5047] lr: 3.0922e-05 eta: 6 days, 19:55:49 time: 0.9062 data_time: 0.0084 memory: 42024 loss: 0.1404 loss_ce: 0.1404 2023/02/24 13:47:24 - mmengine - INFO - Epoch(train) [18][2700/5047] lr: 3.0922e-05 eta: 6 days, 19:54:13 time: 0.8372 data_time: 0.0019 memory: 44617 loss: 0.1479 loss_ce: 0.1479 2023/02/24 13:48:51 - mmengine - INFO - Epoch(train) [18][2800/5047] lr: 3.0922e-05 eta: 6 days, 19:52:34 time: 0.8547 data_time: 0.0017 memory: 55562 loss: 0.1455 loss_ce: 0.1455 2023/02/24 13:50:19 - mmengine - INFO - Epoch(train) [18][2900/5047] lr: 3.0922e-05 eta: 6 days, 19:51:00 time: 0.8770 data_time: 0.0051 memory: 44617 loss: 0.1406 loss_ce: 0.1406 2023/02/24 13:51:44 - mmengine - INFO - Epoch(train) [18][3000/5047] lr: 3.0922e-05 eta: 6 days, 19:49:03 time: 0.8613 data_time: 0.0017 memory: 44617 loss: 0.1366 loss_ce: 0.1366 2023/02/24 13:53:12 - mmengine - INFO - Epoch(train) [18][3100/5047] lr: 3.0922e-05 eta: 6 days, 19:47:29 time: 0.8062 data_time: 0.0020 memory: 41724 loss: 0.1545 loss_ce: 0.1545 2023/02/24 13:54:36 - mmengine - INFO - Epoch(train) [18][3200/5047] lr: 3.0922e-05 eta: 6 days, 19:45:29 time: 0.8857 data_time: 0.0025 memory: 41115 loss: 0.1536 loss_ce: 0.1536 2023/02/24 13:54:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 13:56:04 - mmengine - INFO - Epoch(train) [18][3300/5047] lr: 3.0922e-05 eta: 6 days, 19:43:53 time: 0.8727 data_time: 0.0018 memory: 42368 loss: 0.1409 loss_ce: 0.1409 2023/02/24 13:57:30 - mmengine - INFO - Epoch(train) [18][3400/5047] lr: 3.0922e-05 eta: 6 days, 19:42:09 time: 0.8107 data_time: 0.0025 memory: 49144 loss: 0.1458 loss_ce: 0.1458 2023/02/24 13:59:00 - mmengine - INFO - Epoch(train) [18][3500/5047] lr: 3.0922e-05 eta: 6 days, 19:40:54 time: 0.8976 data_time: 0.0034 memory: 49409 loss: 0.1329 loss_ce: 0.1329 2023/02/24 14:00:29 - mmengine - INFO - Epoch(train) [18][3600/5047] lr: 3.0922e-05 eta: 6 days, 19:39:30 time: 0.9104 data_time: 0.0021 memory: 55562 loss: 0.1398 loss_ce: 0.1398 2023/02/24 14:01:57 - mmengine - INFO - Epoch(train) [18][3700/5047] lr: 3.0922e-05 eta: 6 days, 19:38:06 time: 0.8937 data_time: 0.0017 memory: 55114 loss: 0.1275 loss_ce: 0.1275 2023/02/24 14:03:26 - mmengine - INFO - Epoch(train) [18][3800/5047] lr: 3.0922e-05 eta: 6 days, 19:36:45 time: 0.8658 data_time: 0.0017 memory: 43203 loss: 0.1540 loss_ce: 0.1540 2023/02/24 14:04:54 - mmengine - INFO - Epoch(train) [18][3900/5047] lr: 3.0922e-05 eta: 6 days, 19:35:10 time: 0.8829 data_time: 0.0037 memory: 52775 loss: 0.1419 loss_ce: 0.1419 2023/02/24 14:06:22 - mmengine - INFO - Epoch(train) [18][4000/5047] lr: 3.0922e-05 eta: 6 days, 19:33:35 time: 0.8473 data_time: 0.0019 memory: 46898 loss: 0.1433 loss_ce: 0.1433 2023/02/24 14:07:48 - mmengine - INFO - Epoch(train) [18][4100/5047] lr: 3.0922e-05 eta: 6 days, 19:31:47 time: 0.8711 data_time: 0.0018 memory: 46841 loss: 0.1483 loss_ce: 0.1483 2023/02/24 14:09:15 - mmengine - INFO - Epoch(train) [18][4200/5047] lr: 3.0922e-05 eta: 6 days, 19:30:12 time: 0.8891 data_time: 0.0018 memory: 44338 loss: 0.1557 loss_ce: 0.1557 2023/02/24 14:09:16 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 14:10:42 - mmengine - INFO - Epoch(train) [18][4300/5047] lr: 3.0922e-05 eta: 6 days, 19:28:30 time: 0.8529 data_time: 0.0021 memory: 45643 loss: 0.1726 loss_ce: 0.1726 2023/02/24 14:12:08 - mmengine - INFO - Epoch(train) [18][4400/5047] lr: 3.0922e-05 eta: 6 days, 19:26:47 time: 0.8782 data_time: 0.0017 memory: 45302 loss: 0.1509 loss_ce: 0.1509 2023/02/24 14:13:35 - mmengine - INFO - Epoch(train) [18][4500/5047] lr: 3.0922e-05 eta: 6 days, 19:25:08 time: 0.9084 data_time: 0.0033 memory: 45471 loss: 0.1548 loss_ce: 0.1548 2023/02/24 14:15:02 - mmengine - INFO - Epoch(train) [18][4600/5047] lr: 3.0922e-05 eta: 6 days, 19:23:29 time: 0.8320 data_time: 0.0019 memory: 44705 loss: 0.1442 loss_ce: 0.1442 2023/02/24 14:16:28 - mmengine - INFO - Epoch(train) [18][4700/5047] lr: 3.0922e-05 eta: 6 days, 19:21:41 time: 0.8153 data_time: 0.0053 memory: 46005 loss: 0.1480 loss_ce: 0.1480 2023/02/24 14:17:56 - mmengine - INFO - Epoch(train) [18][4800/5047] lr: 3.0922e-05 eta: 6 days, 19:20:09 time: 0.8619 data_time: 0.0017 memory: 55562 loss: 0.1487 loss_ce: 0.1487 2023/02/24 14:19:24 - mmengine - INFO - Epoch(train) [18][4900/5047] lr: 3.0922e-05 eta: 6 days, 19:18:35 time: 0.8593 data_time: 0.0017 memory: 40180 loss: 0.1625 loss_ce: 0.1625 2023/02/24 14:20:51 - mmengine - INFO - Epoch(train) [18][5000/5047] lr: 3.0922e-05 eta: 6 days, 19:17:01 time: 0.8774 data_time: 0.0021 memory: 43324 loss: 0.1521 loss_ce: 0.1521 2023/02/24 14:21:31 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 14:21:31 - mmengine - INFO - Saving checkpoint at 18 epochs 2023/02/24 14:23:04 - mmengine - INFO - Epoch(train) [19][ 100/5047] lr: 3.0721e-05 eta: 6 days, 19:14:34 time: 0.8825 data_time: 0.0019 memory: 54242 loss: 0.1488 loss_ce: 0.1488 2023/02/24 14:23:50 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 14:24:29 - mmengine - INFO - Epoch(train) [19][ 200/5047] lr: 3.0721e-05 eta: 6 days, 19:12:40 time: 0.8600 data_time: 0.0023 memory: 41724 loss: 0.1384 loss_ce: 0.1384 2023/02/24 14:25:56 - mmengine - INFO - Epoch(train) [19][ 300/5047] lr: 3.0721e-05 eta: 6 days, 19:10:54 time: 0.8527 data_time: 0.0018 memory: 50594 loss: 0.1457 loss_ce: 0.1457 2023/02/24 14:27:23 - mmengine - INFO - Epoch(train) [19][ 400/5047] lr: 3.0721e-05 eta: 6 days, 19:09:19 time: 0.8623 data_time: 0.0018 memory: 55562 loss: 0.1602 loss_ce: 0.1602 2023/02/24 14:28:49 - mmengine - INFO - Epoch(train) [19][ 500/5047] lr: 3.0721e-05 eta: 6 days, 19:07:32 time: 0.8466 data_time: 0.0018 memory: 41724 loss: 0.1307 loss_ce: 0.1307 2023/02/24 14:30:16 - mmengine - INFO - Epoch(train) [19][ 600/5047] lr: 3.0721e-05 eta: 6 days, 19:05:53 time: 0.8715 data_time: 0.0017 memory: 50513 loss: 0.1627 loss_ce: 0.1627 2023/02/24 14:31:42 - mmengine - INFO - Epoch(train) [19][ 700/5047] lr: 3.0721e-05 eta: 6 days, 19:04:08 time: 0.8391 data_time: 0.0019 memory: 43289 loss: 0.1535 loss_ce: 0.1535 2023/02/24 14:33:11 - mmengine - INFO - Epoch(train) [19][ 800/5047] lr: 3.0721e-05 eta: 6 days, 19:02:45 time: 0.9066 data_time: 0.0042 memory: 55562 loss: 0.1243 loss_ce: 0.1243 2023/02/24 14:34:37 - mmengine - INFO - Epoch(train) [19][ 900/5047] lr: 3.0721e-05 eta: 6 days, 19:00:56 time: 0.9009 data_time: 0.0021 memory: 42965 loss: 0.1469 loss_ce: 0.1469 2023/02/24 14:36:03 - mmengine - INFO - Epoch(train) [19][1000/5047] lr: 3.0721e-05 eta: 6 days, 18:59:11 time: 0.8526 data_time: 0.0020 memory: 50906 loss: 0.1608 loss_ce: 0.1608 2023/02/24 14:37:32 - mmengine - INFO - Epoch(train) [19][1100/5047] lr: 3.0721e-05 eta: 6 days, 18:57:45 time: 0.8604 data_time: 0.0028 memory: 49378 loss: 0.1444 loss_ce: 0.1444 2023/02/24 14:38:19 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 14:39:00 - mmengine - INFO - Epoch(train) [19][1200/5047] lr: 3.0721e-05 eta: 6 days, 18:56:17 time: 0.8671 data_time: 0.0018 memory: 46005 loss: 0.1388 loss_ce: 0.1388 2023/02/24 14:40:29 - mmengine - INFO - Epoch(train) [19][1300/5047] lr: 3.0721e-05 eta: 6 days, 18:54:56 time: 0.8942 data_time: 0.0018 memory: 44631 loss: 0.1321 loss_ce: 0.1321 2023/02/24 14:41:57 - mmengine - INFO - Epoch(train) [19][1400/5047] lr: 3.0721e-05 eta: 6 days, 18:53:23 time: 0.8517 data_time: 0.0036 memory: 48869 loss: 0.1324 loss_ce: 0.1324 2023/02/24 14:43:24 - mmengine - INFO - Epoch(train) [19][1500/5047] lr: 3.0721e-05 eta: 6 days, 18:51:46 time: 0.8970 data_time: 0.0021 memory: 41122 loss: 0.1528 loss_ce: 0.1528 2023/02/24 14:44:51 - mmengine - INFO - Epoch(train) [19][1600/5047] lr: 3.0721e-05 eta: 6 days, 18:50:07 time: 0.8478 data_time: 0.0060 memory: 43947 loss: 0.1374 loss_ce: 0.1374 2023/02/24 14:46:18 - mmengine - INFO - Epoch(train) [19][1700/5047] lr: 3.0721e-05 eta: 6 days, 18:48:26 time: 0.8183 data_time: 0.0032 memory: 41122 loss: 0.1369 loss_ce: 0.1369 2023/02/24 14:47:43 - mmengine - INFO - Epoch(train) [19][1800/5047] lr: 3.0721e-05 eta: 6 days, 18:46:35 time: 0.8706 data_time: 0.0027 memory: 55562 loss: 0.1449 loss_ce: 0.1449 2023/02/24 14:49:12 - mmengine - INFO - Epoch(train) [19][1900/5047] lr: 3.0721e-05 eta: 6 days, 18:45:12 time: 0.8445 data_time: 0.0019 memory: 48812 loss: 0.1454 loss_ce: 0.1454 2023/02/24 14:50:40 - mmengine - INFO - Epoch(train) [19][2000/5047] lr: 3.0721e-05 eta: 6 days, 18:43:40 time: 0.9221 data_time: 0.0018 memory: 48189 loss: 0.1320 loss_ce: 0.1320 2023/02/24 14:52:08 - mmengine - INFO - Epoch(train) [19][2100/5047] lr: 3.0721e-05 eta: 6 days, 18:42:10 time: 0.8342 data_time: 0.0017 memory: 46554 loss: 0.1340 loss_ce: 0.1340 2023/02/24 14:52:53 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 14:53:33 - mmengine - INFO - Epoch(train) [19][2200/5047] lr: 3.0721e-05 eta: 6 days, 18:40:15 time: 0.8391 data_time: 0.0017 memory: 46355 loss: 0.1656 loss_ce: 0.1656 2023/02/24 14:55:01 - mmengine - INFO - Epoch(train) [19][2300/5047] lr: 3.0721e-05 eta: 6 days, 18:38:49 time: 0.8805 data_time: 0.0019 memory: 43289 loss: 0.1616 loss_ce: 0.1616 2023/02/24 14:56:28 - mmengine - INFO - Epoch(train) [19][2400/5047] lr: 3.0721e-05 eta: 6 days, 18:37:08 time: 0.8742 data_time: 0.0024 memory: 47070 loss: 0.1376 loss_ce: 0.1376 2023/02/24 14:57:54 - mmengine - INFO - Epoch(train) [19][2500/5047] lr: 3.0721e-05 eta: 6 days, 18:35:20 time: 0.8848 data_time: 0.0021 memory: 45643 loss: 0.1279 loss_ce: 0.1279 2023/02/24 14:59:20 - mmengine - INFO - Epoch(train) [19][2600/5047] lr: 3.0721e-05 eta: 6 days, 18:33:35 time: 0.8770 data_time: 0.0024 memory: 47447 loss: 0.1299 loss_ce: 0.1299 2023/02/24 15:00:48 - mmengine - INFO - Epoch(train) [19][2700/5047] lr: 3.0721e-05 eta: 6 days, 18:32:06 time: 0.8557 data_time: 0.0021 memory: 49568 loss: 0.1656 loss_ce: 0.1656 2023/02/24 15:02:14 - mmengine - INFO - Epoch(train) [19][2800/5047] lr: 3.0721e-05 eta: 6 days, 18:30:13 time: 0.8276 data_time: 0.0017 memory: 41724 loss: 0.1379 loss_ce: 0.1379 2023/02/24 15:03:40 - mmengine - INFO - Epoch(train) [19][2900/5047] lr: 3.0721e-05 eta: 6 days, 18:28:33 time: 0.8422 data_time: 0.0026 memory: 55562 loss: 0.1396 loss_ce: 0.1396 2023/02/24 15:05:08 - mmengine - INFO - Epoch(train) [19][3000/5047] lr: 3.0721e-05 eta: 6 days, 18:27:01 time: 0.8964 data_time: 0.0018 memory: 49471 loss: 0.1495 loss_ce: 0.1495 2023/02/24 15:06:35 - mmengine - INFO - Epoch(train) [19][3100/5047] lr: 3.0721e-05 eta: 6 days, 18:25:19 time: 0.8928 data_time: 0.0017 memory: 42336 loss: 0.1550 loss_ce: 0.1550 2023/02/24 15:07:22 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 15:08:01 - mmengine - INFO - Epoch(train) [19][3200/5047] lr: 3.0721e-05 eta: 6 days, 18:23:37 time: 0.8946 data_time: 0.0043 memory: 42336 loss: 0.1500 loss_ce: 0.1500 2023/02/24 15:09:26 - mmengine - INFO - Epoch(train) [19][3300/5047] lr: 3.0721e-05 eta: 6 days, 18:21:44 time: 0.8879 data_time: 0.0020 memory: 43289 loss: 0.1392 loss_ce: 0.1392 2023/02/24 15:10:54 - mmengine - INFO - Epoch(train) [19][3400/5047] lr: 3.0721e-05 eta: 6 days, 18:20:08 time: 0.8652 data_time: 0.0021 memory: 55562 loss: 0.1653 loss_ce: 0.1653 2023/02/24 15:12:20 - mmengine - INFO - Epoch(train) [19][3500/5047] lr: 3.0721e-05 eta: 6 days, 18:18:21 time: 0.8437 data_time: 0.0018 memory: 45200 loss: 0.1413 loss_ce: 0.1413 2023/02/24 15:13:46 - mmengine - INFO - Epoch(train) [19][3600/5047] lr: 3.0721e-05 eta: 6 days, 18:16:40 time: 0.7965 data_time: 0.0017 memory: 42965 loss: 0.1489 loss_ce: 0.1489 2023/02/24 15:15:12 - mmengine - INFO - Epoch(train) [19][3700/5047] lr: 3.0721e-05 eta: 6 days, 18:14:56 time: 0.9045 data_time: 0.0018 memory: 47037 loss: 0.1572 loss_ce: 0.1572 2023/02/24 15:16:40 - mmengine - INFO - Epoch(train) [19][3800/5047] lr: 3.0721e-05 eta: 6 days, 18:13:20 time: 0.8938 data_time: 0.0016 memory: 41552 loss: 0.1541 loss_ce: 0.1541 2023/02/24 15:18:06 - mmengine - INFO - Epoch(train) [19][3900/5047] lr: 3.0721e-05 eta: 6 days, 18:11:33 time: 0.8528 data_time: 0.0024 memory: 41724 loss: 0.1220 loss_ce: 0.1220 2023/02/24 15:19:32 - mmengine - INFO - Epoch(train) [19][4000/5047] lr: 3.0721e-05 eta: 6 days, 18:09:47 time: 0.8151 data_time: 0.0079 memory: 48146 loss: 0.1542 loss_ce: 0.1542 2023/02/24 15:21:00 - mmengine - INFO - Epoch(train) [19][4100/5047] lr: 3.0721e-05 eta: 6 days, 18:08:16 time: 0.8459 data_time: 0.0038 memory: 45230 loss: 0.1506 loss_ce: 0.1506 2023/02/24 15:21:47 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 15:22:27 - mmengine - INFO - Epoch(train) [19][4200/5047] lr: 3.0721e-05 eta: 6 days, 18:06:45 time: 0.9277 data_time: 0.0017 memory: 44154 loss: 0.1414 loss_ce: 0.1414 2023/02/24 15:23:55 - mmengine - INFO - Epoch(train) [19][4300/5047] lr: 3.0721e-05 eta: 6 days, 18:05:10 time: 0.8328 data_time: 0.0020 memory: 42488 loss: 0.1296 loss_ce: 0.1296 2023/02/24 15:25:23 - mmengine - INFO - Epoch(train) [19][4400/5047] lr: 3.0721e-05 eta: 6 days, 18:03:42 time: 0.8311 data_time: 0.0018 memory: 44617 loss: 0.1436 loss_ce: 0.1436 2023/02/24 15:26:49 - mmengine - INFO - Epoch(train) [19][4500/5047] lr: 3.0721e-05 eta: 6 days, 18:01:56 time: 0.8577 data_time: 0.0053 memory: 43409 loss: 0.1498 loss_ce: 0.1498 2023/02/24 15:28:17 - mmengine - INFO - Epoch(train) [19][4600/5047] lr: 3.0721e-05 eta: 6 days, 18:00:31 time: 0.8973 data_time: 0.0019 memory: 50446 loss: 0.1536 loss_ce: 0.1536 2023/02/24 15:29:45 - mmengine - INFO - Epoch(train) [19][4700/5047] lr: 3.0721e-05 eta: 6 days, 17:58:56 time: 0.8504 data_time: 0.0030 memory: 41308 loss: 0.1501 loss_ce: 0.1501 2023/02/24 15:31:14 - mmengine - INFO - Epoch(train) [19][4800/5047] lr: 3.0721e-05 eta: 6 days, 17:57:34 time: 0.9245 data_time: 0.0019 memory: 44705 loss: 0.1248 loss_ce: 0.1248 2023/02/24 15:32:40 - mmengine - INFO - Epoch(train) [19][4900/5047] lr: 3.0721e-05 eta: 6 days, 17:55:49 time: 0.8376 data_time: 0.0018 memory: 39960 loss: 0.1442 loss_ce: 0.1442 2023/02/24 15:34:05 - mmengine - INFO - Epoch(train) [19][5000/5047] lr: 3.0721e-05 eta: 6 days, 17:53:58 time: 0.8777 data_time: 0.0036 memory: 50106 loss: 0.1414 loss_ce: 0.1414 2023/02/24 15:34:47 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 15:34:47 - mmengine - INFO - Saving checkpoint at 19 epochs 2023/02/24 15:36:16 - mmengine - INFO - Epoch(train) [20][ 100/5047] lr: 3.0520e-05 eta: 6 days, 17:51:21 time: 0.8160 data_time: 0.0024 memory: 55562 loss: 0.1361 loss_ce: 0.1361 2023/02/24 15:36:22 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 15:37:44 - mmengine - INFO - Epoch(train) [20][ 200/5047] lr: 3.0520e-05 eta: 6 days, 17:49:49 time: 0.8427 data_time: 0.0035 memory: 47013 loss: 0.1327 loss_ce: 0.1327 2023/02/24 15:39:11 - mmengine - INFO - Epoch(train) [20][ 300/5047] lr: 3.0520e-05 eta: 6 days, 17:48:12 time: 0.9090 data_time: 0.0030 memory: 43289 loss: 0.1371 loss_ce: 0.1371 2023/02/24 15:40:38 - mmengine - INFO - Epoch(train) [20][ 400/5047] lr: 3.0520e-05 eta: 6 days, 17:46:39 time: 0.8560 data_time: 0.0018 memory: 45392 loss: 0.1476 loss_ce: 0.1476 2023/02/24 15:42:06 - mmengine - INFO - Epoch(train) [20][ 500/5047] lr: 3.0520e-05 eta: 6 days, 17:45:02 time: 0.8349 data_time: 0.0018 memory: 52369 loss: 0.1603 loss_ce: 0.1603 2023/02/24 15:43:32 - mmengine - INFO - Epoch(train) [20][ 600/5047] lr: 3.0520e-05 eta: 6 days, 17:43:19 time: 0.8639 data_time: 0.0018 memory: 41419 loss: 0.1501 loss_ce: 0.1501 2023/02/24 15:45:00 - mmengine - INFO - Epoch(train) [20][ 700/5047] lr: 3.0520e-05 eta: 6 days, 17:41:49 time: 0.8745 data_time: 0.0017 memory: 41724 loss: 0.1463 loss_ce: 0.1463 2023/02/24 15:46:27 - mmengine - INFO - Epoch(train) [20][ 800/5047] lr: 3.0520e-05 eta: 6 days, 17:40:11 time: 0.8922 data_time: 0.0018 memory: 45172 loss: 0.1269 loss_ce: 0.1269 2023/02/24 15:47:55 - mmengine - INFO - Epoch(train) [20][ 900/5047] lr: 3.0520e-05 eta: 6 days, 17:38:46 time: 0.8393 data_time: 0.0019 memory: 42701 loss: 0.1309 loss_ce: 0.1309 2023/02/24 15:49:22 - mmengine - INFO - Epoch(train) [20][1000/5047] lr: 3.0520e-05 eta: 6 days, 17:37:09 time: 0.8651 data_time: 0.0018 memory: 49378 loss: 0.1405 loss_ce: 0.1405 2023/02/24 15:50:49 - mmengine - INFO - Epoch(train) [20][1100/5047] lr: 3.0520e-05 eta: 6 days, 17:35:32 time: 0.8798 data_time: 0.0019 memory: 52543 loss: 0.1305 loss_ce: 0.1305 2023/02/24 15:50:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 15:52:16 - mmengine - INFO - Epoch(train) [20][1200/5047] lr: 3.0520e-05 eta: 6 days, 17:33:52 time: 0.8855 data_time: 0.0022 memory: 45549 loss: 0.1333 loss_ce: 0.1333 2023/02/24 15:53:42 - mmengine - INFO - Epoch(train) [20][1300/5047] lr: 3.0520e-05 eta: 6 days, 17:32:09 time: 0.8755 data_time: 0.0049 memory: 55562 loss: 0.1311 loss_ce: 0.1311 2023/02/24 15:55:08 - mmengine - INFO - Epoch(train) [20][1400/5047] lr: 3.0520e-05 eta: 6 days, 17:30:21 time: 0.9120 data_time: 0.0018 memory: 49334 loss: 0.1323 loss_ce: 0.1323 2023/02/24 15:56:36 - mmengine - INFO - Epoch(train) [20][1500/5047] lr: 3.0520e-05 eta: 6 days, 17:28:48 time: 0.8784 data_time: 0.0018 memory: 55562 loss: 0.1355 loss_ce: 0.1355 2023/02/24 15:58:02 - mmengine - INFO - Epoch(train) [20][1600/5047] lr: 3.0520e-05 eta: 6 days, 17:27:07 time: 0.8626 data_time: 0.0028 memory: 44648 loss: 0.1285 loss_ce: 0.1285 2023/02/24 15:59:29 - mmengine - INFO - Epoch(train) [20][1700/5047] lr: 3.0520e-05 eta: 6 days, 17:25:26 time: 0.8695 data_time: 0.0036 memory: 42319 loss: 0.1496 loss_ce: 0.1496 2023/02/24 16:00:55 - mmengine - INFO - Epoch(train) [20][1800/5047] lr: 3.0520e-05 eta: 6 days, 17:23:43 time: 0.8442 data_time: 0.0036 memory: 44956 loss: 0.1576 loss_ce: 0.1576 2023/02/24 16:02:23 - mmengine - INFO - Epoch(train) [20][1900/5047] lr: 3.0520e-05 eta: 6 days, 17:22:13 time: 0.8784 data_time: 0.0020 memory: 45643 loss: 0.1395 loss_ce: 0.1395 2023/02/24 16:03:50 - mmengine - INFO - Epoch(train) [20][2000/5047] lr: 3.0520e-05 eta: 6 days, 17:20:34 time: 0.9033 data_time: 0.0018 memory: 40535 loss: 0.1362 loss_ce: 0.1362 2023/02/24 16:05:17 - mmengine - INFO - Epoch(train) [20][2100/5047] lr: 3.0520e-05 eta: 6 days, 17:18:57 time: 0.8313 data_time: 0.0018 memory: 43947 loss: 0.1683 loss_ce: 0.1683 2023/02/24 16:05:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 16:06:43 - mmengine - INFO - Epoch(train) [20][2200/5047] lr: 3.0520e-05 eta: 6 days, 17:17:14 time: 0.8305 data_time: 0.0037 memory: 47447 loss: 0.1493 loss_ce: 0.1493 2023/02/24 16:08:10 - mmengine - INFO - Epoch(train) [20][2300/5047] lr: 3.0520e-05 eta: 6 days, 17:15:41 time: 0.8965 data_time: 0.0023 memory: 43289 loss: 0.1396 loss_ce: 0.1396 2023/02/24 16:09:36 - mmengine - INFO - Epoch(train) [20][2400/5047] lr: 3.0520e-05 eta: 6 days, 17:13:56 time: 0.8279 data_time: 0.0017 memory: 43947 loss: 0.1490 loss_ce: 0.1490 2023/02/24 16:11:03 - mmengine - INFO - Epoch(train) [20][2500/5047] lr: 3.0520e-05 eta: 6 days, 17:12:15 time: 0.8263 data_time: 0.0020 memory: 51734 loss: 0.1594 loss_ce: 0.1594 2023/02/24 16:12:31 - mmengine - INFO - Epoch(train) [20][2600/5047] lr: 3.0520e-05 eta: 6 days, 17:10:44 time: 0.8889 data_time: 0.0020 memory: 43296 loss: 0.1620 loss_ce: 0.1620 2023/02/24 16:13:59 - mmengine - INFO - Epoch(train) [20][2700/5047] lr: 3.0520e-05 eta: 6 days, 17:09:17 time: 0.8926 data_time: 0.0021 memory: 55562 loss: 0.1420 loss_ce: 0.1420 2023/02/24 16:15:25 - mmengine - INFO - Epoch(train) [20][2800/5047] lr: 3.0520e-05 eta: 6 days, 17:07:29 time: 0.8806 data_time: 0.0021 memory: 43613 loss: 0.1527 loss_ce: 0.1527 2023/02/24 16:16:51 - mmengine - INFO - Epoch(train) [20][2900/5047] lr: 3.0520e-05 eta: 6 days, 17:05:47 time: 0.8902 data_time: 0.0021 memory: 40825 loss: 0.1452 loss_ce: 0.1452 2023/02/24 16:18:20 - mmengine - INFO - Epoch(train) [20][3000/5047] lr: 3.0520e-05 eta: 6 days, 17:04:26 time: 0.8597 data_time: 0.0022 memory: 45137 loss: 0.1438 loss_ce: 0.1438 2023/02/24 16:19:47 - mmengine - INFO - Epoch(train) [20][3100/5047] lr: 3.0520e-05 eta: 6 days, 17:02:52 time: 0.9485 data_time: 0.0027 memory: 43613 loss: 0.1383 loss_ce: 0.1383 2023/02/24 16:19:53 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 16:21:16 - mmengine - INFO - Epoch(train) [20][3200/5047] lr: 3.0520e-05 eta: 6 days, 17:01:28 time: 0.8984 data_time: 0.0018 memory: 46009 loss: 0.1509 loss_ce: 0.1509 2023/02/24 16:22:43 - mmengine - INFO - Epoch(train) [20][3300/5047] lr: 3.0520e-05 eta: 6 days, 16:59:51 time: 0.9031 data_time: 0.0022 memory: 43587 loss: 0.1638 loss_ce: 0.1638 2023/02/24 16:24:12 - mmengine - INFO - Epoch(train) [20][3400/5047] lr: 3.0520e-05 eta: 6 days, 16:58:27 time: 0.8694 data_time: 0.0018 memory: 44617 loss: 0.1326 loss_ce: 0.1326 2023/02/24 16:25:38 - mmengine - INFO - Epoch(train) [20][3500/5047] lr: 3.0520e-05 eta: 6 days, 16:56:47 time: 0.8542 data_time: 0.0023 memory: 43613 loss: 0.1305 loss_ce: 0.1305 2023/02/24 16:27:05 - mmengine - INFO - Epoch(train) [20][3600/5047] lr: 3.0520e-05 eta: 6 days, 16:55:12 time: 0.8724 data_time: 0.0022 memory: 46867 loss: 0.1328 loss_ce: 0.1328 2023/02/24 16:28:35 - mmengine - INFO - Epoch(train) [20][3700/5047] lr: 3.0520e-05 eta: 6 days, 16:53:55 time: 0.9189 data_time: 0.0019 memory: 44617 loss: 0.1624 loss_ce: 0.1624 2023/02/24 16:30:00 - mmengine - INFO - Epoch(train) [20][3800/5047] lr: 3.0520e-05 eta: 6 days, 16:52:03 time: 0.8153 data_time: 0.0018 memory: 45400 loss: 0.1476 loss_ce: 0.1476 2023/02/24 16:31:26 - mmengine - INFO - Epoch(train) [20][3900/5047] lr: 3.0520e-05 eta: 6 days, 16:50:17 time: 0.8817 data_time: 0.0018 memory: 41724 loss: 0.1547 loss_ce: 0.1547 2023/02/24 16:32:51 - mmengine - INFO - Epoch(train) [20][4000/5047] lr: 3.0520e-05 eta: 6 days, 16:48:28 time: 0.8190 data_time: 0.0017 memory: 40535 loss: 0.1406 loss_ce: 0.1406 2023/02/24 16:34:18 - mmengine - INFO - Epoch(train) [20][4100/5047] lr: 3.0520e-05 eta: 6 days, 16:46:46 time: 0.9132 data_time: 0.0044 memory: 40535 loss: 0.1444 loss_ce: 0.1444 2023/02/24 16:34:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 16:35:45 - mmengine - INFO - Epoch(train) [20][4200/5047] lr: 3.0520e-05 eta: 6 days, 16:45:13 time: 0.8456 data_time: 0.0026 memory: 48883 loss: 0.1327 loss_ce: 0.1327 2023/02/24 16:37:12 - mmengine - INFO - Epoch(train) [20][4300/5047] lr: 3.0520e-05 eta: 6 days, 16:43:35 time: 0.8114 data_time: 0.0017 memory: 49378 loss: 0.1402 loss_ce: 0.1402 2023/02/24 16:38:38 - mmengine - INFO - Epoch(train) [20][4400/5047] lr: 3.0520e-05 eta: 6 days, 16:41:54 time: 0.8492 data_time: 0.0020 memory: 55562 loss: 0.1425 loss_ce: 0.1425 2023/02/24 16:40:05 - mmengine - INFO - Epoch(train) [20][4500/5047] lr: 3.0520e-05 eta: 6 days, 16:40:12 time: 0.9107 data_time: 0.0018 memory: 45986 loss: 0.1431 loss_ce: 0.1431 2023/02/24 16:41:32 - mmengine - INFO - Epoch(train) [20][4600/5047] lr: 3.0520e-05 eta: 6 days, 16:38:35 time: 0.8750 data_time: 0.0019 memory: 41254 loss: 0.1372 loss_ce: 0.1372 2023/02/24 16:43:00 - mmengine - INFO - Epoch(train) [20][4700/5047] lr: 3.0520e-05 eta: 6 days, 16:37:06 time: 0.9378 data_time: 0.0024 memory: 42310 loss: 0.1368 loss_ce: 0.1368 2023/02/24 16:44:27 - mmengine - INFO - Epoch(train) [20][4800/5047] lr: 3.0520e-05 eta: 6 days, 16:35:36 time: 0.8885 data_time: 0.0017 memory: 39960 loss: 0.1517 loss_ce: 0.1517 2023/02/24 16:45:55 - mmengine - INFO - Epoch(train) [20][4900/5047] lr: 3.0520e-05 eta: 6 days, 16:34:03 time: 0.8382 data_time: 0.0028 memory: 47447 loss: 0.1310 loss_ce: 0.1310 2023/02/24 16:47:22 - mmengine - INFO - Epoch(train) [20][5000/5047] lr: 3.0520e-05 eta: 6 days, 16:32:25 time: 0.8561 data_time: 0.0018 memory: 44198 loss: 0.1358 loss_ce: 0.1358 2023/02/24 16:48:02 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 16:48:02 - mmengine - INFO - Saving checkpoint at 20 epochs 2023/02/24 16:48:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 16:49:34 - mmengine - INFO - Epoch(train) [21][ 100/5047] lr: 3.0319e-05 eta: 6 days, 16:29:57 time: 0.8873 data_time: 0.0018 memory: 52789 loss: 0.1451 loss_ce: 0.1451 2023/02/24 16:51:01 - mmengine - INFO - Epoch(train) [21][ 200/5047] lr: 3.0319e-05 eta: 6 days, 16:28:22 time: 0.8660 data_time: 0.0019 memory: 41419 loss: 0.1459 loss_ce: 0.1459 2023/02/24 16:52:26 - mmengine - INFO - Epoch(train) [21][ 300/5047] lr: 3.0319e-05 eta: 6 days, 16:26:34 time: 0.8803 data_time: 0.0051 memory: 43112 loss: 0.1533 loss_ce: 0.1533 2023/02/24 16:53:53 - mmengine - INFO - Epoch(train) [21][ 400/5047] lr: 3.0319e-05 eta: 6 days, 16:24:58 time: 0.8775 data_time: 0.0027 memory: 45700 loss: 0.1493 loss_ce: 0.1493 2023/02/24 16:55:20 - mmengine - INFO - Epoch(train) [21][ 500/5047] lr: 3.0319e-05 eta: 6 days, 16:23:17 time: 0.8426 data_time: 0.0023 memory: 51308 loss: 0.1525 loss_ce: 0.1525 2023/02/24 16:56:46 - mmengine - INFO - Epoch(train) [21][ 600/5047] lr: 3.0319e-05 eta: 6 days, 16:21:38 time: 0.8659 data_time: 0.0018 memory: 46005 loss: 0.1390 loss_ce: 0.1390 2023/02/24 16:58:14 - mmengine - INFO - Epoch(train) [21][ 700/5047] lr: 3.0319e-05 eta: 6 days, 16:20:10 time: 0.9190 data_time: 0.0018 memory: 40241 loss: 0.1570 loss_ce: 0.1570 2023/02/24 16:59:43 - mmengine - INFO - Epoch(train) [21][ 800/5047] lr: 3.0319e-05 eta: 6 days, 16:18:45 time: 0.8801 data_time: 0.0048 memory: 43613 loss: 0.1607 loss_ce: 0.1607 2023/02/24 17:01:09 - mmengine - INFO - Epoch(train) [21][ 900/5047] lr: 3.0319e-05 eta: 6 days, 16:17:04 time: 0.8220 data_time: 0.0019 memory: 46005 loss: 0.1279 loss_ce: 0.1279 2023/02/24 17:02:37 - mmengine - INFO - Epoch(train) [21][1000/5047] lr: 3.0319e-05 eta: 6 days, 16:15:31 time: 0.8860 data_time: 0.0019 memory: 50143 loss: 0.1480 loss_ce: 0.1480 2023/02/24 17:03:29 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 17:04:03 - mmengine - INFO - Epoch(train) [21][1100/5047] lr: 3.0319e-05 eta: 6 days, 16:13:52 time: 0.8672 data_time: 0.0018 memory: 55297 loss: 0.1346 loss_ce: 0.1346 2023/02/24 17:05:32 - mmengine - INFO - Epoch(train) [21][1200/5047] lr: 3.0319e-05 eta: 6 days, 16:12:25 time: 0.9217 data_time: 0.0018 memory: 46478 loss: 0.1436 loss_ce: 0.1436 2023/02/24 17:06:58 - mmengine - INFO - Epoch(train) [21][1300/5047] lr: 3.0319e-05 eta: 6 days, 16:10:45 time: 0.8903 data_time: 0.0019 memory: 42463 loss: 0.1393 loss_ce: 0.1393 2023/02/24 17:08:27 - mmengine - INFO - Epoch(train) [21][1400/5047] lr: 3.0319e-05 eta: 6 days, 16:09:22 time: 0.8439 data_time: 0.0025 memory: 42336 loss: 0.1374 loss_ce: 0.1374 2023/02/24 17:09:54 - mmengine - INFO - Epoch(train) [21][1500/5047] lr: 3.0319e-05 eta: 6 days, 16:07:46 time: 0.8294 data_time: 0.0018 memory: 42336 loss: 0.1450 loss_ce: 0.1450 2023/02/24 17:11:23 - mmengine - INFO - Epoch(train) [21][1600/5047] lr: 3.0319e-05 eta: 6 days, 16:06:29 time: 0.8611 data_time: 0.0018 memory: 49175 loss: 0.1563 loss_ce: 0.1563 2023/02/24 17:12:50 - mmengine - INFO - Epoch(train) [21][1700/5047] lr: 3.0319e-05 eta: 6 days, 16:04:53 time: 0.8453 data_time: 0.0030 memory: 40535 loss: 0.1500 loss_ce: 0.1500 2023/02/24 17:14:17 - mmengine - INFO - Epoch(train) [21][1800/5047] lr: 3.0319e-05 eta: 6 days, 16:03:17 time: 0.8336 data_time: 0.0037 memory: 55562 loss: 0.1554 loss_ce: 0.1554 2023/02/24 17:15:44 - mmengine - INFO - Epoch(train) [21][1900/5047] lr: 3.0319e-05 eta: 6 days, 16:01:41 time: 0.8612 data_time: 0.0025 memory: 43160 loss: 0.1269 loss_ce: 0.1269 2023/02/24 17:17:10 - mmengine - INFO - Epoch(train) [21][2000/5047] lr: 3.0319e-05 eta: 6 days, 15:59:53 time: 0.8501 data_time: 0.0018 memory: 50106 loss: 0.1369 loss_ce: 0.1369 2023/02/24 17:18:01 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 17:18:35 - mmengine - INFO - Epoch(train) [21][2100/5047] lr: 3.0319e-05 eta: 6 days, 15:58:05 time: 0.9174 data_time: 0.0020 memory: 55562 loss: 0.1326 loss_ce: 0.1326 2023/02/24 17:20:03 - mmengine - INFO - Epoch(train) [21][2200/5047] lr: 3.0319e-05 eta: 6 days, 15:56:33 time: 0.8994 data_time: 0.0022 memory: 45302 loss: 0.1425 loss_ce: 0.1425 2023/02/24 17:21:30 - mmengine - INFO - Epoch(train) [21][2300/5047] lr: 3.0319e-05 eta: 6 days, 15:55:01 time: 0.9024 data_time: 0.0020 memory: 48188 loss: 0.1576 loss_ce: 0.1576 2023/02/24 17:22:58 - mmengine - INFO - Epoch(train) [21][2400/5047] lr: 3.0319e-05 eta: 6 days, 15:53:32 time: 0.9340 data_time: 0.0018 memory: 39960 loss: 0.1451 loss_ce: 0.1451 2023/02/24 17:24:26 - mmengine - INFO - Epoch(train) [21][2500/5047] lr: 3.0319e-05 eta: 6 days, 15:52:01 time: 0.8812 data_time: 0.0025 memory: 51755 loss: 0.1457 loss_ce: 0.1457 2023/02/24 17:25:52 - mmengine - INFO - Epoch(train) [21][2600/5047] lr: 3.0319e-05 eta: 6 days, 15:50:20 time: 0.8655 data_time: 0.0018 memory: 40093 loss: 0.1424 loss_ce: 0.1424 2023/02/24 17:27:19 - mmengine - INFO - Epoch(train) [21][2700/5047] lr: 3.0319e-05 eta: 6 days, 15:48:39 time: 0.8472 data_time: 0.0023 memory: 46964 loss: 0.1408 loss_ce: 0.1408 2023/02/24 17:28:47 - mmengine - INFO - Epoch(train) [21][2800/5047] lr: 3.0319e-05 eta: 6 days, 15:47:14 time: 0.8224 data_time: 0.0046 memory: 51308 loss: 0.1483 loss_ce: 0.1483 2023/02/24 17:30:15 - mmengine - INFO - Epoch(train) [21][2900/5047] lr: 3.0319e-05 eta: 6 days, 15:45:46 time: 0.8472 data_time: 0.0022 memory: 43289 loss: 0.1321 loss_ce: 0.1321 2023/02/24 17:31:42 - mmengine - INFO - Epoch(train) [21][3000/5047] lr: 3.0319e-05 eta: 6 days, 15:44:09 time: 0.8556 data_time: 0.0020 memory: 42024 loss: 0.1475 loss_ce: 0.1475 2023/02/24 17:32:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 17:33:10 - mmengine - INFO - Epoch(train) [21][3100/5047] lr: 3.0319e-05 eta: 6 days, 15:42:41 time: 0.9264 data_time: 0.0017 memory: 54072 loss: 0.1372 loss_ce: 0.1372 2023/02/24 17:34:37 - mmengine - INFO - Epoch(train) [21][3200/5047] lr: 3.0319e-05 eta: 6 days, 15:41:02 time: 0.8555 data_time: 0.0018 memory: 40825 loss: 0.1389 loss_ce: 0.1389 2023/02/24 17:36:03 - mmengine - INFO - Epoch(train) [21][3300/5047] lr: 3.0319e-05 eta: 6 days, 15:39:20 time: 0.8388 data_time: 0.0019 memory: 44886 loss: 0.1730 loss_ce: 0.1730 2023/02/24 17:37:30 - mmengine - INFO - Epoch(train) [21][3400/5047] lr: 3.0319e-05 eta: 6 days, 15:37:48 time: 0.9259 data_time: 0.0022 memory: 48055 loss: 0.1357 loss_ce: 0.1357 2023/02/24 17:38:58 - mmengine - INFO - Epoch(train) [21][3500/5047] lr: 3.0319e-05 eta: 6 days, 15:36:14 time: 0.8946 data_time: 0.0021 memory: 44956 loss: 0.1404 loss_ce: 0.1404 2023/02/24 17:40:25 - mmengine - INFO - Epoch(train) [21][3600/5047] lr: 3.0319e-05 eta: 6 days, 15:34:38 time: 0.8822 data_time: 0.0018 memory: 44648 loss: 0.1430 loss_ce: 0.1430 2023/02/24 17:41:52 - mmengine - INFO - Epoch(train) [21][3700/5047] lr: 3.0319e-05 eta: 6 days, 15:33:02 time: 0.9189 data_time: 0.0017 memory: 55562 loss: 0.1329 loss_ce: 0.1329 2023/02/24 17:43:17 - mmengine - INFO - Epoch(train) [21][3800/5047] lr: 3.0319e-05 eta: 6 days, 15:31:17 time: 0.8359 data_time: 0.0025 memory: 49334 loss: 0.1479 loss_ce: 0.1479 2023/02/24 17:44:45 - mmengine - INFO - Epoch(train) [21][3900/5047] lr: 3.0319e-05 eta: 6 days, 15:29:49 time: 0.8536 data_time: 0.0023 memory: 55562 loss: 0.1602 loss_ce: 0.1602 2023/02/24 17:46:13 - mmengine - INFO - Epoch(train) [21][4000/5047] lr: 3.0319e-05 eta: 6 days, 15:28:16 time: 0.8115 data_time: 0.0019 memory: 43289 loss: 0.1524 loss_ce: 0.1524 2023/02/24 17:47:05 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 17:47:41 - mmengine - INFO - Epoch(train) [21][4100/5047] lr: 3.0319e-05 eta: 6 days, 15:26:48 time: 0.9052 data_time: 0.0027 memory: 55562 loss: 0.1240 loss_ce: 0.1240 2023/02/24 17:49:07 - mmengine - INFO - Epoch(train) [21][4200/5047] lr: 3.0319e-05 eta: 6 days, 15:25:07 time: 0.8133 data_time: 0.0023 memory: 45643 loss: 0.1256 loss_ce: 0.1256 2023/02/24 17:50:33 - mmengine - INFO - Epoch(train) [21][4300/5047] lr: 3.0319e-05 eta: 6 days, 15:23:25 time: 0.8240 data_time: 0.0023 memory: 50520 loss: 0.1424 loss_ce: 0.1424 2023/02/24 17:52:01 - mmengine - INFO - Epoch(train) [21][4400/5047] lr: 3.0319e-05 eta: 6 days, 15:21:53 time: 0.8686 data_time: 0.0029 memory: 43706 loss: 0.1577 loss_ce: 0.1577 2023/02/24 17:53:27 - mmengine - INFO - Epoch(train) [21][4500/5047] lr: 3.0319e-05 eta: 6 days, 15:20:11 time: 0.8777 data_time: 0.0018 memory: 43947 loss: 0.1464 loss_ce: 0.1464 2023/02/24 17:54:53 - mmengine - INFO - Epoch(train) [21][4600/5047] lr: 3.0319e-05 eta: 6 days, 15:18:33 time: 0.8667 data_time: 0.0019 memory: 41724 loss: 0.1453 loss_ce: 0.1453 2023/02/24 17:56:22 - mmengine - INFO - Epoch(train) [21][4700/5047] lr: 3.0319e-05 eta: 6 days, 15:17:06 time: 0.8692 data_time: 0.0071 memory: 55562 loss: 0.1595 loss_ce: 0.1595 2023/02/24 17:57:47 - mmengine - INFO - Epoch(train) [21][4800/5047] lr: 3.0319e-05 eta: 6 days, 15:15:18 time: 0.8815 data_time: 0.0018 memory: 55562 loss: 0.1495 loss_ce: 0.1495 2023/02/24 17:59:15 - mmengine - INFO - Epoch(train) [21][4900/5047] lr: 3.0319e-05 eta: 6 days, 15:13:47 time: 0.9234 data_time: 0.0025 memory: 45760 loss: 0.1459 loss_ce: 0.1459 2023/02/24 18:00:42 - mmengine - INFO - Epoch(train) [21][5000/5047] lr: 3.0319e-05 eta: 6 days, 15:12:16 time: 0.8569 data_time: 0.0018 memory: 50420 loss: 0.1517 loss_ce: 0.1517 2023/02/24 18:01:22 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 18:01:22 - mmengine - INFO - Saving checkpoint at 21 epochs 2023/02/24 18:01:38 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 18:02:52 - mmengine - INFO - Epoch(train) [22][ 100/5047] lr: 3.0118e-05 eta: 6 days, 15:09:37 time: 0.8215 data_time: 0.0027 memory: 40535 loss: 0.1487 loss_ce: 0.1487 2023/02/24 18:04:19 - mmengine - INFO - Epoch(train) [22][ 200/5047] lr: 3.0118e-05 eta: 6 days, 15:07:56 time: 0.8418 data_time: 0.0019 memory: 42965 loss: 0.1402 loss_ce: 0.1402 2023/02/24 18:05:46 - mmengine - INFO - Epoch(train) [22][ 300/5047] lr: 3.0118e-05 eta: 6 days, 15:06:27 time: 0.8704 data_time: 0.0030 memory: 55389 loss: 0.1469 loss_ce: 0.1469 2023/02/24 18:07:14 - mmengine - INFO - Epoch(train) [22][ 400/5047] lr: 3.0118e-05 eta: 6 days, 15:04:57 time: 0.8603 data_time: 0.0018 memory: 47074 loss: 0.1261 loss_ce: 0.1261 2023/02/24 18:08:44 - mmengine - INFO - Epoch(train) [22][ 500/5047] lr: 3.0118e-05 eta: 6 days, 15:03:40 time: 0.9316 data_time: 0.0037 memory: 54242 loss: 0.1622 loss_ce: 0.1622 2023/02/24 18:10:11 - mmengine - INFO - Epoch(train) [22][ 600/5047] lr: 3.0118e-05 eta: 6 days, 15:02:08 time: 0.8585 data_time: 0.0019 memory: 43947 loss: 0.1383 loss_ce: 0.1383 2023/02/24 18:11:37 - mmengine - INFO - Epoch(train) [22][ 700/5047] lr: 3.0118e-05 eta: 6 days, 15:00:22 time: 0.8524 data_time: 0.0019 memory: 43613 loss: 0.1363 loss_ce: 0.1363 2023/02/24 18:13:02 - mmengine - INFO - Epoch(train) [22][ 800/5047] lr: 3.0118e-05 eta: 6 days, 14:58:37 time: 0.8794 data_time: 0.0036 memory: 43351 loss: 0.1442 loss_ce: 0.1442 2023/02/24 18:14:30 - mmengine - INFO - Epoch(train) [22][ 900/5047] lr: 3.0118e-05 eta: 6 days, 14:57:09 time: 0.9057 data_time: 0.0020 memory: 48188 loss: 0.1400 loss_ce: 0.1400 2023/02/24 18:15:57 - mmengine - INFO - Epoch(train) [22][1000/5047] lr: 3.0118e-05 eta: 6 days, 14:55:30 time: 0.8486 data_time: 0.0048 memory: 42965 loss: 0.1386 loss_ce: 0.1386 2023/02/24 18:16:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 18:17:24 - mmengine - INFO - Epoch(train) [22][1100/5047] lr: 3.0118e-05 eta: 6 days, 14:53:56 time: 0.8407 data_time: 0.0019 memory: 39960 loss: 0.1252 loss_ce: 0.1252 2023/02/24 18:18:50 - mmengine - INFO - Epoch(train) [22][1200/5047] lr: 3.0118e-05 eta: 6 days, 14:52:12 time: 0.8675 data_time: 0.0031 memory: 41047 loss: 0.1373 loss_ce: 0.1373 2023/02/24 18:20:16 - mmengine - INFO - Epoch(train) [22][1300/5047] lr: 3.0118e-05 eta: 6 days, 14:50:32 time: 0.9200 data_time: 0.0035 memory: 52543 loss: 0.1353 loss_ce: 0.1353 2023/02/24 18:21:43 - mmengine - INFO - Epoch(train) [22][1400/5047] lr: 3.0118e-05 eta: 6 days, 14:48:58 time: 0.8876 data_time: 0.0021 memory: 43947 loss: 0.1422 loss_ce: 0.1422 2023/02/24 18:23:10 - mmengine - INFO - Epoch(train) [22][1500/5047] lr: 3.0118e-05 eta: 6 days, 14:47:18 time: 0.8599 data_time: 0.0047 memory: 52606 loss: 0.1285 loss_ce: 0.1285 2023/02/24 18:24:37 - mmengine - INFO - Epoch(train) [22][1600/5047] lr: 3.0118e-05 eta: 6 days, 14:45:44 time: 0.8526 data_time: 0.0045 memory: 42965 loss: 0.1579 loss_ce: 0.1579 2023/02/24 18:26:05 - mmengine - INFO - Epoch(train) [22][1700/5047] lr: 3.0118e-05 eta: 6 days, 14:44:13 time: 0.8718 data_time: 0.0021 memory: 48188 loss: 0.1335 loss_ce: 0.1335 2023/02/24 18:27:32 - mmengine - INFO - Epoch(train) [22][1800/5047] lr: 3.0118e-05 eta: 6 days, 14:42:41 time: 0.9144 data_time: 0.0041 memory: 42756 loss: 0.1309 loss_ce: 0.1309 2023/02/24 18:28:58 - mmengine - INFO - Epoch(train) [22][1900/5047] lr: 3.0118e-05 eta: 6 days, 14:41:02 time: 0.8621 data_time: 0.0019 memory: 42336 loss: 0.1213 loss_ce: 0.1213 2023/02/24 18:30:24 - mmengine - INFO - Epoch(train) [22][2000/5047] lr: 3.0118e-05 eta: 6 days, 14:39:20 time: 0.8352 data_time: 0.0023 memory: 50906 loss: 0.1545 loss_ce: 0.1545 2023/02/24 18:30:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 18:31:51 - mmengine - INFO - Epoch(train) [22][2100/5047] lr: 3.0118e-05 eta: 6 days, 14:37:43 time: 0.9464 data_time: 0.0020 memory: 43289 loss: 0.1439 loss_ce: 0.1439 2023/02/24 18:33:17 - mmengine - INFO - Epoch(train) [22][2200/5047] lr: 3.0118e-05 eta: 6 days, 14:36:02 time: 0.8834 data_time: 0.0018 memory: 40825 loss: 0.1523 loss_ce: 0.1523 2023/02/24 18:34:44 - mmengine - INFO - Epoch(train) [22][2300/5047] lr: 3.0118e-05 eta: 6 days, 14:34:28 time: 0.8543 data_time: 0.0034 memory: 41419 loss: 0.1440 loss_ce: 0.1440 2023/02/24 18:36:11 - mmengine - INFO - Epoch(train) [22][2400/5047] lr: 3.0118e-05 eta: 6 days, 14:32:47 time: 0.8918 data_time: 0.0017 memory: 45302 loss: 0.1389 loss_ce: 0.1389 2023/02/24 18:37:38 - mmengine - INFO - Epoch(train) [22][2500/5047] lr: 3.0118e-05 eta: 6 days, 14:31:15 time: 0.8645 data_time: 0.0019 memory: 41724 loss: 0.1464 loss_ce: 0.1464 2023/02/24 18:39:05 - mmengine - INFO - Epoch(train) [22][2600/5047] lr: 3.0118e-05 eta: 6 days, 14:29:41 time: 0.8414 data_time: 0.0048 memory: 42965 loss: 0.1392 loss_ce: 0.1392 2023/02/24 18:40:33 - mmengine - INFO - Epoch(train) [22][2700/5047] lr: 3.0118e-05 eta: 6 days, 14:28:10 time: 0.8678 data_time: 0.0018 memory: 43263 loss: 0.1326 loss_ce: 0.1326 2023/02/24 18:42:00 - mmengine - INFO - Epoch(train) [22][2800/5047] lr: 3.0118e-05 eta: 6 days, 14:26:34 time: 0.8999 data_time: 0.0026 memory: 55562 loss: 0.1451 loss_ce: 0.1451 2023/02/24 18:43:25 - mmengine - INFO - Epoch(train) [22][2900/5047] lr: 3.0118e-05 eta: 6 days, 14:24:49 time: 0.8627 data_time: 0.0024 memory: 47447 loss: 0.1374 loss_ce: 0.1374 2023/02/24 18:44:53 - mmengine - INFO - Epoch(train) [22][3000/5047] lr: 3.0118e-05 eta: 6 days, 14:23:21 time: 0.9231 data_time: 0.0063 memory: 42965 loss: 0.1421 loss_ce: 0.1421 2023/02/24 18:45:03 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 18:46:18 - mmengine - INFO - Epoch(train) [22][3100/5047] lr: 3.0118e-05 eta: 6 days, 14:21:30 time: 0.8740 data_time: 0.0019 memory: 40825 loss: 0.1279 loss_ce: 0.1279 2023/02/24 18:47:44 - mmengine - INFO - Epoch(train) [22][3200/5047] lr: 3.0118e-05 eta: 6 days, 14:19:47 time: 0.8633 data_time: 0.0024 memory: 46772 loss: 0.1355 loss_ce: 0.1355 2023/02/24 18:49:10 - mmengine - INFO - Epoch(train) [22][3300/5047] lr: 3.0118e-05 eta: 6 days, 14:18:06 time: 0.8883 data_time: 0.0042 memory: 49188 loss: 0.1280 loss_ce: 0.1280 2023/02/24 18:50:35 - mmengine - INFO - Epoch(train) [22][3400/5047] lr: 3.0118e-05 eta: 6 days, 14:16:16 time: 0.8491 data_time: 0.0020 memory: 47106 loss: 0.1380 loss_ce: 0.1380 2023/02/24 18:52:03 - mmengine - INFO - Epoch(train) [22][3500/5047] lr: 3.0118e-05 eta: 6 days, 14:14:52 time: 0.9150 data_time: 0.0020 memory: 46355 loss: 0.1504 loss_ce: 0.1504 2023/02/24 18:53:30 - mmengine - INFO - Epoch(train) [22][3600/5047] lr: 3.0118e-05 eta: 6 days, 14:13:17 time: 0.8654 data_time: 0.0018 memory: 53044 loss: 0.1362 loss_ce: 0.1362 2023/02/24 18:54:57 - mmengine - INFO - Epoch(train) [22][3700/5047] lr: 3.0118e-05 eta: 6 days, 14:11:43 time: 0.8034 data_time: 0.0020 memory: 41589 loss: 0.1599 loss_ce: 0.1599 2023/02/24 18:56:25 - mmengine - INFO - Epoch(train) [22][3800/5047] lr: 3.0118e-05 eta: 6 days, 14:10:11 time: 0.8390 data_time: 0.0019 memory: 52817 loss: 0.1423 loss_ce: 0.1423 2023/02/24 18:57:52 - mmengine - INFO - Epoch(train) [22][3900/5047] lr: 3.0118e-05 eta: 6 days, 14:08:41 time: 0.8348 data_time: 0.0019 memory: 46964 loss: 0.1422 loss_ce: 0.1422 2023/02/24 18:59:19 - mmengine - INFO - Epoch(train) [22][4000/5047] lr: 3.0118e-05 eta: 6 days, 14:07:02 time: 0.8635 data_time: 0.0022 memory: 41724 loss: 0.1478 loss_ce: 0.1478 2023/02/24 18:59:30 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 19:00:46 - mmengine - INFO - Epoch(train) [22][4100/5047] lr: 3.0118e-05 eta: 6 days, 14:05:27 time: 0.8740 data_time: 0.0019 memory: 51308 loss: 0.1313 loss_ce: 0.1313 2023/02/24 19:02:13 - mmengine - INFO - Epoch(train) [22][4200/5047] lr: 3.0118e-05 eta: 6 days, 14:03:54 time: 0.8849 data_time: 0.0018 memory: 43346 loss: 0.1623 loss_ce: 0.1623 2023/02/24 19:03:41 - mmengine - INFO - Epoch(train) [22][4300/5047] lr: 3.0118e-05 eta: 6 days, 14:02:28 time: 0.8919 data_time: 0.0021 memory: 41609 loss: 0.1402 loss_ce: 0.1402 2023/02/24 19:05:08 - mmengine - INFO - Epoch(train) [22][4400/5047] lr: 3.0118e-05 eta: 6 days, 14:00:51 time: 0.9079 data_time: 0.0019 memory: 55562 loss: 0.1351 loss_ce: 0.1351 2023/02/24 19:06:33 - mmengine - INFO - Epoch(train) [22][4500/5047] lr: 3.0118e-05 eta: 6 days, 13:59:07 time: 0.8347 data_time: 0.0042 memory: 52791 loss: 0.1545 loss_ce: 0.1545 2023/02/24 19:08:00 - mmengine - INFO - Epoch(train) [22][4600/5047] lr: 3.0118e-05 eta: 6 days, 13:57:27 time: 0.8932 data_time: 0.0071 memory: 48188 loss: 0.1446 loss_ce: 0.1446 2023/02/24 19:09:27 - mmengine - INFO - Epoch(train) [22][4700/5047] lr: 3.0118e-05 eta: 6 days, 13:55:57 time: 0.8850 data_time: 0.0020 memory: 47447 loss: 0.1496 loss_ce: 0.1496 2023/02/24 19:10:54 - mmengine - INFO - Epoch(train) [22][4800/5047] lr: 3.0118e-05 eta: 6 days, 13:54:20 time: 0.8314 data_time: 0.0018 memory: 48133 loss: 0.1474 loss_ce: 0.1474 2023/02/24 19:12:21 - mmengine - INFO - Epoch(train) [22][4900/5047] lr: 3.0118e-05 eta: 6 days, 13:52:47 time: 0.8904 data_time: 0.0021 memory: 55562 loss: 0.1470 loss_ce: 0.1470 2023/02/24 19:13:50 - mmengine - INFO - Epoch(train) [22][5000/5047] lr: 3.0118e-05 eta: 6 days, 13:51:27 time: 0.8628 data_time: 0.0019 memory: 42399 loss: 0.1313 loss_ce: 0.1313 2023/02/24 19:14:02 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 19:14:31 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 19:14:31 - mmengine - INFO - Saving checkpoint at 22 epochs 2023/02/24 19:16:03 - mmengine - INFO - Epoch(train) [23][ 100/5047] lr: 2.9917e-05 eta: 6 days, 13:49:07 time: 0.9222 data_time: 0.0023 memory: 48565 loss: 0.1477 loss_ce: 0.1477 2023/02/24 19:17:31 - mmengine - INFO - Epoch(train) [23][ 200/5047] lr: 2.9917e-05 eta: 6 days, 13:47:39 time: 0.8956 data_time: 0.0020 memory: 46794 loss: 0.1356 loss_ce: 0.1356 2023/02/24 19:18:58 - mmengine - INFO - Epoch(train) [23][ 300/5047] lr: 2.9917e-05 eta: 6 days, 13:46:03 time: 0.8848 data_time: 0.0024 memory: 42024 loss: 0.1621 loss_ce: 0.1621 2023/02/24 19:20:23 - mmengine - INFO - Epoch(train) [23][ 400/5047] lr: 2.9917e-05 eta: 6 days, 13:44:17 time: 0.8654 data_time: 0.0037 memory: 44722 loss: 0.1638 loss_ce: 0.1638 2023/02/24 19:21:49 - mmengine - INFO - Epoch(train) [23][ 500/5047] lr: 2.9917e-05 eta: 6 days, 13:42:34 time: 0.8801 data_time: 0.0032 memory: 42649 loss: 0.1418 loss_ce: 0.1418 2023/02/24 19:23:15 - mmengine - INFO - Epoch(train) [23][ 600/5047] lr: 2.9917e-05 eta: 6 days, 13:40:57 time: 0.9050 data_time: 0.0021 memory: 51816 loss: 0.1459 loss_ce: 0.1459 2023/02/24 19:24:43 - mmengine - INFO - Epoch(train) [23][ 700/5047] lr: 2.9917e-05 eta: 6 days, 13:39:27 time: 0.8708 data_time: 0.0021 memory: 44632 loss: 0.1551 loss_ce: 0.1551 2023/02/24 19:26:09 - mmengine - INFO - Epoch(train) [23][ 800/5047] lr: 2.9917e-05 eta: 6 days, 13:37:48 time: 0.8595 data_time: 0.0062 memory: 40825 loss: 0.1321 loss_ce: 0.1321 2023/02/24 19:27:36 - mmengine - INFO - Epoch(train) [23][ 900/5047] lr: 2.9917e-05 eta: 6 days, 13:36:09 time: 0.8926 data_time: 0.0055 memory: 44278 loss: 0.1407 loss_ce: 0.1407 2023/02/24 19:28:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 19:29:03 - mmengine - INFO - Epoch(train) [23][1000/5047] lr: 2.9917e-05 eta: 6 days, 13:34:33 time: 0.8946 data_time: 0.0018 memory: 44956 loss: 0.1572 loss_ce: 0.1572 2023/02/24 19:30:29 - mmengine - INFO - Epoch(train) [23][1100/5047] lr: 2.9917e-05 eta: 6 days, 13:32:58 time: 0.8968 data_time: 0.0029 memory: 41219 loss: 0.1291 loss_ce: 0.1291 2023/02/24 19:31:57 - mmengine - INFO - Epoch(train) [23][1200/5047] lr: 2.9917e-05 eta: 6 days, 13:31:29 time: 0.9295 data_time: 0.0020 memory: 47813 loss: 0.1428 loss_ce: 0.1428 2023/02/24 19:33:24 - mmengine - INFO - Epoch(train) [23][1300/5047] lr: 2.9917e-05 eta: 6 days, 13:29:52 time: 0.8389 data_time: 0.0018 memory: 54229 loss: 0.1399 loss_ce: 0.1399 2023/02/24 19:34:50 - mmengine - INFO - Epoch(train) [23][1400/5047] lr: 2.9917e-05 eta: 6 days, 13:28:15 time: 0.8625 data_time: 0.0066 memory: 41952 loss: 0.1648 loss_ce: 0.1648 2023/02/24 19:36:17 - mmengine - INFO - Epoch(train) [23][1500/5047] lr: 2.9917e-05 eta: 6 days, 13:26:38 time: 0.8671 data_time: 0.0022 memory: 46948 loss: 0.1548 loss_ce: 0.1548 2023/02/24 19:37:43 - mmengine - INFO - Epoch(train) [23][1600/5047] lr: 2.9917e-05 eta: 6 days, 13:24:59 time: 0.9006 data_time: 0.0021 memory: 43559 loss: 0.1360 loss_ce: 0.1360 2023/02/24 19:39:11 - mmengine - INFO - Epoch(train) [23][1700/5047] lr: 2.9917e-05 eta: 6 days, 13:23:28 time: 0.8660 data_time: 0.0018 memory: 47813 loss: 0.1443 loss_ce: 0.1443 2023/02/24 19:40:37 - mmengine - INFO - Epoch(train) [23][1800/5047] lr: 2.9917e-05 eta: 6 days, 13:21:46 time: 0.8606 data_time: 0.0032 memory: 39960 loss: 0.1400 loss_ce: 0.1400 2023/02/24 19:42:04 - mmengine - INFO - Epoch(train) [23][1900/5047] lr: 2.9917e-05 eta: 6 days, 13:20:13 time: 0.8393 data_time: 0.0036 memory: 43317 loss: 0.1309 loss_ce: 0.1309 2023/02/24 19:43:01 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 19:43:30 - mmengine - INFO - Epoch(train) [23][2000/5047] lr: 2.9917e-05 eta: 6 days, 13:18:36 time: 0.8977 data_time: 0.0021 memory: 55562 loss: 0.1287 loss_ce: 0.1287 2023/02/24 19:44:57 - mmengine - INFO - Epoch(train) [23][2100/5047] lr: 2.9917e-05 eta: 6 days, 13:17:01 time: 0.8786 data_time: 0.0051 memory: 51112 loss: 0.1358 loss_ce: 0.1358 2023/02/24 19:46:24 - mmengine - INFO - Epoch(train) [23][2200/5047] lr: 2.9917e-05 eta: 6 days, 13:15:25 time: 0.8409 data_time: 0.0035 memory: 43289 loss: 0.1351 loss_ce: 0.1351 2023/02/24 19:47:51 - mmengine - INFO - Epoch(train) [23][2300/5047] lr: 2.9917e-05 eta: 6 days, 13:13:52 time: 0.8575 data_time: 0.0018 memory: 43289 loss: 0.1375 loss_ce: 0.1375 2023/02/24 19:49:18 - mmengine - INFO - Epoch(train) [23][2400/5047] lr: 2.9917e-05 eta: 6 days, 13:12:19 time: 0.8503 data_time: 0.0022 memory: 47074 loss: 0.1490 loss_ce: 0.1490 2023/02/24 19:50:45 - mmengine - INFO - Epoch(train) [23][2500/5047] lr: 2.9917e-05 eta: 6 days, 13:10:45 time: 0.8817 data_time: 0.0018 memory: 45623 loss: 0.1304 loss_ce: 0.1304 2023/02/24 19:52:12 - mmengine - INFO - Epoch(train) [23][2600/5047] lr: 2.9917e-05 eta: 6 days, 13:09:12 time: 0.8674 data_time: 0.0030 memory: 43289 loss: 0.1375 loss_ce: 0.1375 2023/02/24 19:53:39 - mmengine - INFO - Epoch(train) [23][2700/5047] lr: 2.9917e-05 eta: 6 days, 13:07:34 time: 0.8407 data_time: 0.0019 memory: 40969 loss: 0.1398 loss_ce: 0.1398 2023/02/24 19:55:03 - mmengine - INFO - Epoch(train) [23][2800/5047] lr: 2.9917e-05 eta: 6 days, 13:05:43 time: 0.8587 data_time: 0.0019 memory: 43613 loss: 0.1404 loss_ce: 0.1404 2023/02/24 19:56:31 - mmengine - INFO - Epoch(train) [23][2900/5047] lr: 2.9917e-05 eta: 6 days, 13:04:13 time: 0.8758 data_time: 0.0018 memory: 45033 loss: 0.1409 loss_ce: 0.1409 2023/02/24 19:57:29 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 19:57:59 - mmengine - INFO - Epoch(train) [23][3000/5047] lr: 2.9917e-05 eta: 6 days, 13:02:44 time: 0.8990 data_time: 0.0018 memory: 42649 loss: 0.1339 loss_ce: 0.1339 2023/02/24 19:59:26 - mmengine - INFO - Epoch(train) [23][3100/5047] lr: 2.9917e-05 eta: 6 days, 13:01:10 time: 0.8630 data_time: 0.0021 memory: 55389 loss: 0.1578 loss_ce: 0.1578 2023/02/24 20:00:52 - mmengine - INFO - Epoch(train) [23][3200/5047] lr: 2.9917e-05 eta: 6 days, 12:59:34 time: 0.8697 data_time: 0.0022 memory: 41419 loss: 0.1444 loss_ce: 0.1444 2023/02/24 20:02:17 - mmengine - INFO - Epoch(train) [23][3300/5047] lr: 2.9917e-05 eta: 6 days, 12:57:45 time: 0.8877 data_time: 0.0019 memory: 41724 loss: 0.1365 loss_ce: 0.1365 2023/02/24 20:03:44 - mmengine - INFO - Epoch(train) [23][3400/5047] lr: 2.9917e-05 eta: 6 days, 12:56:09 time: 0.8915 data_time: 0.0028 memory: 55562 loss: 0.1430 loss_ce: 0.1430 2023/02/24 20:05:11 - mmengine - INFO - Epoch(train) [23][3500/5047] lr: 2.9917e-05 eta: 6 days, 12:54:39 time: 0.8777 data_time: 0.0017 memory: 42592 loss: 0.1550 loss_ce: 0.1550 2023/02/24 20:06:37 - mmengine - INFO - Epoch(train) [23][3600/5047] lr: 2.9917e-05 eta: 6 days, 12:52:54 time: 0.8893 data_time: 0.0021 memory: 44279 loss: 0.1569 loss_ce: 0.1569 2023/02/24 20:08:04 - mmengine - INFO - Epoch(train) [23][3700/5047] lr: 2.9917e-05 eta: 6 days, 12:51:24 time: 0.8750 data_time: 0.0020 memory: 43613 loss: 0.1431 loss_ce: 0.1431 2023/02/24 20:09:31 - mmengine - INFO - Epoch(train) [23][3800/5047] lr: 2.9917e-05 eta: 6 days, 12:49:50 time: 0.8261 data_time: 0.0021 memory: 44536 loss: 0.1522 loss_ce: 0.1522 2023/02/24 20:10:57 - mmengine - INFO - Epoch(train) [23][3900/5047] lr: 2.9917e-05 eta: 6 days, 12:48:07 time: 0.8719 data_time: 0.0019 memory: 42166 loss: 0.1489 loss_ce: 0.1489 2023/02/24 20:11:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 20:12:24 - mmengine - INFO - Epoch(train) [23][4000/5047] lr: 2.9917e-05 eta: 6 days, 12:46:33 time: 0.8446 data_time: 0.0027 memory: 47447 loss: 0.1271 loss_ce: 0.1271 2023/02/24 20:13:53 - mmengine - INFO - Epoch(train) [23][4100/5047] lr: 2.9917e-05 eta: 6 days, 12:45:10 time: 0.9171 data_time: 0.0023 memory: 44956 loss: 0.1414 loss_ce: 0.1414 2023/02/24 20:15:19 - mmengine - INFO - Epoch(train) [23][4200/5047] lr: 2.9917e-05 eta: 6 days, 12:43:31 time: 0.8899 data_time: 0.0021 memory: 42024 loss: 0.1317 loss_ce: 0.1317 2023/02/24 20:16:45 - mmengine - INFO - Epoch(train) [23][4300/5047] lr: 2.9917e-05 eta: 6 days, 12:41:51 time: 0.8295 data_time: 0.0020 memory: 44617 loss: 0.1335 loss_ce: 0.1335 2023/02/24 20:18:11 - mmengine - INFO - Epoch(train) [23][4400/5047] lr: 2.9917e-05 eta: 6 days, 12:40:12 time: 0.8623 data_time: 0.0020 memory: 44632 loss: 0.1361 loss_ce: 0.1361 2023/02/24 20:19:39 - mmengine - INFO - Epoch(train) [23][4500/5047] lr: 2.9917e-05 eta: 6 days, 12:38:44 time: 0.8908 data_time: 0.0042 memory: 45302 loss: 0.1377 loss_ce: 0.1377 2023/02/24 20:21:07 - mmengine - INFO - Epoch(train) [23][4600/5047] lr: 2.9917e-05 eta: 6 days, 12:37:14 time: 0.8567 data_time: 0.0020 memory: 55562 loss: 0.1491 loss_ce: 0.1491 2023/02/24 20:22:33 - mmengine - INFO - Epoch(train) [23][4700/5047] lr: 2.9917e-05 eta: 6 days, 12:35:34 time: 0.8423 data_time: 0.0021 memory: 44956 loss: 0.1527 loss_ce: 0.1527 2023/02/24 20:23:59 - mmengine - INFO - Epoch(train) [23][4800/5047] lr: 2.9917e-05 eta: 6 days, 12:33:58 time: 0.8848 data_time: 0.0017 memory: 40825 loss: 0.1548 loss_ce: 0.1548 2023/02/24 20:25:28 - mmengine - INFO - Epoch(train) [23][4900/5047] lr: 2.9917e-05 eta: 6 days, 12:32:34 time: 0.8334 data_time: 0.0020 memory: 51795 loss: 0.1384 loss_ce: 0.1384 2023/02/24 20:26:25 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 20:26:54 - mmengine - INFO - Epoch(train) [23][5000/5047] lr: 2.9917e-05 eta: 6 days, 12:30:57 time: 0.8195 data_time: 0.0027 memory: 53387 loss: 0.1337 loss_ce: 0.1337 2023/02/24 20:27:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 20:27:34 - mmengine - INFO - Saving checkpoint at 23 epochs 2023/02/24 20:29:06 - mmengine - INFO - Epoch(train) [24][ 100/5047] lr: 2.9716e-05 eta: 6 days, 12:28:34 time: 0.9180 data_time: 0.0020 memory: 44278 loss: 0.1450 loss_ce: 0.1450 2023/02/24 20:30:34 - mmengine - INFO - Epoch(train) [24][ 200/5047] lr: 2.9716e-05 eta: 6 days, 12:27:02 time: 0.8794 data_time: 0.0019 memory: 42336 loss: 0.1308 loss_ce: 0.1308 2023/02/24 20:31:59 - mmengine - INFO - Epoch(train) [24][ 300/5047] lr: 2.9716e-05 eta: 6 days, 12:25:20 time: 0.8191 data_time: 0.0023 memory: 42649 loss: 0.1270 loss_ce: 0.1270 2023/02/24 20:33:26 - mmengine - INFO - Epoch(train) [24][ 400/5047] lr: 2.9716e-05 eta: 6 days, 12:23:46 time: 0.8842 data_time: 0.0018 memory: 43090 loss: 0.1291 loss_ce: 0.1291 2023/02/24 20:34:54 - mmengine - INFO - Epoch(train) [24][ 500/5047] lr: 2.9716e-05 eta: 6 days, 12:22:19 time: 0.9433 data_time: 0.0019 memory: 55562 loss: 0.1340 loss_ce: 0.1340 2023/02/24 20:36:23 - mmengine - INFO - Epoch(train) [24][ 600/5047] lr: 2.9716e-05 eta: 6 days, 12:20:59 time: 0.8859 data_time: 0.0018 memory: 51308 loss: 0.1159 loss_ce: 0.1159 2023/02/24 20:37:50 - mmengine - INFO - Epoch(train) [24][ 700/5047] lr: 2.9716e-05 eta: 6 days, 12:19:26 time: 0.8580 data_time: 0.0020 memory: 55562 loss: 0.1265 loss_ce: 0.1265 2023/02/24 20:39:18 - mmengine - INFO - Epoch(train) [24][ 800/5047] lr: 2.9716e-05 eta: 6 days, 12:17:55 time: 0.8743 data_time: 0.0025 memory: 44661 loss: 0.1378 loss_ce: 0.1378 2023/02/24 20:40:43 - mmengine - INFO - Epoch(train) [24][ 900/5047] lr: 2.9716e-05 eta: 6 days, 12:16:12 time: 0.8671 data_time: 0.0020 memory: 45090 loss: 0.1295 loss_ce: 0.1295 2023/02/24 20:41:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 20:42:11 - mmengine - INFO - Epoch(train) [24][1000/5047] lr: 2.9716e-05 eta: 6 days, 12:14:41 time: 0.8663 data_time: 0.0022 memory: 50347 loss: 0.1406 loss_ce: 0.1406 2023/02/24 20:43:38 - mmengine - INFO - Epoch(train) [24][1100/5047] lr: 2.9716e-05 eta: 6 days, 12:13:07 time: 0.8545 data_time: 0.0022 memory: 40825 loss: 0.1408 loss_ce: 0.1408 2023/02/24 20:45:05 - mmengine - INFO - Epoch(train) [24][1200/5047] lr: 2.9716e-05 eta: 6 days, 12:11:32 time: 0.8630 data_time: 0.0019 memory: 43289 loss: 0.1244 loss_ce: 0.1244 2023/02/24 20:46:30 - mmengine - INFO - Epoch(train) [24][1300/5047] lr: 2.9716e-05 eta: 6 days, 12:09:47 time: 0.8612 data_time: 0.0022 memory: 41215 loss: 0.1394 loss_ce: 0.1394 2023/02/24 20:47:58 - mmengine - INFO - Epoch(train) [24][1400/5047] lr: 2.9716e-05 eta: 6 days, 12:08:23 time: 0.9325 data_time: 0.0030 memory: 55562 loss: 0.1381 loss_ce: 0.1381 2023/02/24 20:49:26 - mmengine - INFO - Epoch(train) [24][1500/5047] lr: 2.9716e-05 eta: 6 days, 12:06:54 time: 0.8904 data_time: 0.0020 memory: 45302 loss: 0.1366 loss_ce: 0.1366 2023/02/24 20:50:53 - mmengine - INFO - Epoch(train) [24][1600/5047] lr: 2.9716e-05 eta: 6 days, 12:05:23 time: 0.8546 data_time: 0.0019 memory: 42336 loss: 0.1378 loss_ce: 0.1378 2023/02/24 20:52:20 - mmengine - INFO - Epoch(train) [24][1700/5047] lr: 2.9716e-05 eta: 6 days, 12:03:45 time: 0.8796 data_time: 0.0019 memory: 42965 loss: 0.1345 loss_ce: 0.1345 2023/02/24 20:53:46 - mmengine - INFO - Epoch(train) [24][1800/5047] lr: 2.9716e-05 eta: 6 days, 12:02:08 time: 0.8836 data_time: 0.0018 memory: 55562 loss: 0.1314 loss_ce: 0.1314 2023/02/24 20:55:15 - mmengine - INFO - Epoch(train) [24][1900/5047] lr: 2.9716e-05 eta: 6 days, 12:00:47 time: 0.9176 data_time: 0.0024 memory: 55562 loss: 0.1324 loss_ce: 0.1324 2023/02/24 20:55:32 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 20:56:42 - mmengine - INFO - Epoch(train) [24][2000/5047] lr: 2.9716e-05 eta: 6 days, 11:59:15 time: 0.9376 data_time: 0.0018 memory: 42024 loss: 0.1348 loss_ce: 0.1348 2023/02/24 20:58:09 - mmengine - INFO - Epoch(train) [24][2100/5047] lr: 2.9716e-05 eta: 6 days, 11:57:42 time: 0.8574 data_time: 0.0029 memory: 43091 loss: 0.1386 loss_ce: 0.1386 2023/02/24 20:59:35 - mmengine - INFO - Epoch(train) [24][2200/5047] lr: 2.9716e-05 eta: 6 days, 11:55:59 time: 0.8663 data_time: 0.0019 memory: 47813 loss: 0.1110 loss_ce: 0.1110 2023/02/24 21:01:01 - mmengine - INFO - Epoch(train) [24][2300/5047] lr: 2.9716e-05 eta: 6 days, 11:54:20 time: 0.8080 data_time: 0.0019 memory: 52569 loss: 0.1392 loss_ce: 0.1392 2023/02/24 21:02:29 - mmengine - INFO - Epoch(train) [24][2400/5047] lr: 2.9716e-05 eta: 6 days, 11:52:50 time: 0.8641 data_time: 0.0074 memory: 43585 loss: 0.1391 loss_ce: 0.1391 2023/02/24 21:03:55 - mmengine - INFO - Epoch(train) [24][2500/5047] lr: 2.9716e-05 eta: 6 days, 11:51:15 time: 0.8521 data_time: 0.0019 memory: 39126 loss: 0.1451 loss_ce: 0.1451 2023/02/24 21:05:23 - mmengine - INFO - Epoch(train) [24][2600/5047] lr: 2.9716e-05 eta: 6 days, 11:49:46 time: 0.9122 data_time: 0.0020 memory: 45876 loss: 0.1425 loss_ce: 0.1425 2023/02/24 21:06:51 - mmengine - INFO - Epoch(train) [24][2700/5047] lr: 2.9716e-05 eta: 6 days, 11:48:21 time: 0.8371 data_time: 0.0021 memory: 47074 loss: 0.1343 loss_ce: 0.1343 2023/02/24 21:08:18 - mmengine - INFO - Epoch(train) [24][2800/5047] lr: 2.9716e-05 eta: 6 days, 11:46:46 time: 0.8229 data_time: 0.0018 memory: 42628 loss: 0.1459 loss_ce: 0.1459 2023/02/24 21:09:44 - mmengine - INFO - Epoch(train) [24][2900/5047] lr: 2.9716e-05 eta: 6 days, 11:45:04 time: 0.8655 data_time: 0.0019 memory: 43613 loss: 0.1471 loss_ce: 0.1471 2023/02/24 21:10:01 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 21:11:12 - mmengine - INFO - Epoch(train) [24][3000/5047] lr: 2.9716e-05 eta: 6 days, 11:43:40 time: 0.8927 data_time: 0.0021 memory: 45302 loss: 0.1336 loss_ce: 0.1336 2023/02/24 21:12:39 - mmengine - INFO - Epoch(train) [24][3100/5047] lr: 2.9716e-05 eta: 6 days, 11:42:06 time: 0.8766 data_time: 0.0017 memory: 44465 loss: 0.1351 loss_ce: 0.1351 2023/02/24 21:14:06 - mmengine - INFO - Epoch(train) [24][3200/5047] lr: 2.9716e-05 eta: 6 days, 11:40:32 time: 0.8617 data_time: 0.0019 memory: 43947 loss: 0.1374 loss_ce: 0.1374 2023/02/24 21:15:33 - mmengine - INFO - Epoch(train) [24][3300/5047] lr: 2.9716e-05 eta: 6 days, 11:38:55 time: 0.8347 data_time: 0.0020 memory: 40241 loss: 0.1505 loss_ce: 0.1505 2023/02/24 21:16:58 - mmengine - INFO - Epoch(train) [24][3400/5047] lr: 2.9716e-05 eta: 6 days, 11:37:14 time: 0.8711 data_time: 0.0026 memory: 50514 loss: 0.1358 loss_ce: 0.1358 2023/02/24 21:18:26 - mmengine - INFO - Epoch(train) [24][3500/5047] lr: 2.9716e-05 eta: 6 days, 11:35:44 time: 0.8678 data_time: 0.0039 memory: 43289 loss: 0.1502 loss_ce: 0.1502 2023/02/24 21:19:54 - mmengine - INFO - Epoch(train) [24][3600/5047] lr: 2.9716e-05 eta: 6 days, 11:34:18 time: 0.8163 data_time: 0.0018 memory: 53025 loss: 0.1485 loss_ce: 0.1485 2023/02/24 21:21:22 - mmengine - INFO - Epoch(train) [24][3700/5047] lr: 2.9716e-05 eta: 6 days, 11:32:50 time: 0.8237 data_time: 0.0019 memory: 45708 loss: 0.1678 loss_ce: 0.1678 2023/02/24 21:22:48 - mmengine - INFO - Epoch(train) [24][3800/5047] lr: 2.9716e-05 eta: 6 days, 11:31:12 time: 0.8435 data_time: 0.0023 memory: 42644 loss: 0.1488 loss_ce: 0.1488 2023/02/24 21:24:14 - mmengine - INFO - Epoch(train) [24][3900/5047] lr: 2.9716e-05 eta: 6 days, 11:29:33 time: 0.8672 data_time: 0.0031 memory: 42965 loss: 0.1295 loss_ce: 0.1295 2023/02/24 21:24:31 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 21:25:53 - mmengine - INFO - Epoch(train) [24][4000/5047] lr: 2.9716e-05 eta: 6 days, 11:29:12 time: 0.9492 data_time: 0.0018 memory: 49851 loss: 0.1332 loss_ce: 0.1332 2023/02/24 21:27:23 - mmengine - INFO - Epoch(train) [24][4100/5047] lr: 2.9716e-05 eta: 6 days, 11:27:54 time: 0.8524 data_time: 0.0031 memory: 52863 loss: 0.1297 loss_ce: 0.1297 2023/02/24 21:28:49 - mmengine - INFO - Epoch(train) [24][4200/5047] lr: 2.9716e-05 eta: 6 days, 11:26:19 time: 0.8700 data_time: 0.0037 memory: 39960 loss: 0.1447 loss_ce: 0.1447 2023/02/24 21:30:17 - mmengine - INFO - Epoch(train) [24][4300/5047] lr: 2.9716e-05 eta: 6 days, 11:24:50 time: 0.8848 data_time: 0.0019 memory: 55562 loss: 0.1428 loss_ce: 0.1428 2023/02/24 21:31:45 - mmengine - INFO - Epoch(train) [24][4400/5047] lr: 2.9716e-05 eta: 6 days, 11:23:24 time: 0.8676 data_time: 0.0022 memory: 46504 loss: 0.1241 loss_ce: 0.1241 2023/02/24 21:33:12 - mmengine - INFO - Epoch(train) [24][4500/5047] lr: 2.9716e-05 eta: 6 days, 11:21:51 time: 0.8927 data_time: 0.0020 memory: 44661 loss: 0.1423 loss_ce: 0.1423 2023/02/24 21:34:39 - mmengine - INFO - Epoch(train) [24][4600/5047] lr: 2.9716e-05 eta: 6 days, 11:20:15 time: 0.8669 data_time: 0.0019 memory: 41347 loss: 0.1255 loss_ce: 0.1255 2023/02/24 21:36:05 - mmengine - INFO - Epoch(train) [24][4700/5047] lr: 2.9716e-05 eta: 6 days, 11:18:37 time: 0.9042 data_time: 0.0035 memory: 42477 loss: 0.1396 loss_ce: 0.1396 2023/02/24 21:37:34 - mmengine - INFO - Epoch(train) [24][4800/5047] lr: 2.9716e-05 eta: 6 days, 11:17:14 time: 0.8646 data_time: 0.0019 memory: 49334 loss: 0.1400 loss_ce: 0.1400 2023/02/24 21:39:00 - mmengine - INFO - Epoch(train) [24][4900/5047] lr: 2.9716e-05 eta: 6 days, 11:15:35 time: 0.8625 data_time: 0.0019 memory: 44956 loss: 0.1384 loss_ce: 0.1384 2023/02/24 21:39:16 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 21:40:27 - mmengine - INFO - Epoch(train) [24][5000/5047] lr: 2.9716e-05 eta: 6 days, 11:14:00 time: 0.8664 data_time: 0.0030 memory: 55562 loss: 0.1302 loss_ce: 0.1302 2023/02/24 21:41:06 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 21:41:06 - mmengine - INFO - Saving checkpoint at 24 epochs 2023/02/24 21:42:39 - mmengine - INFO - Epoch(train) [25][ 100/5047] lr: 2.9515e-05 eta: 6 days, 11:11:41 time: 0.8581 data_time: 0.0022 memory: 51288 loss: 0.1421 loss_ce: 0.1421 2023/02/24 21:44:07 - mmengine - INFO - Epoch(train) [25][ 200/5047] lr: 2.9515e-05 eta: 6 days, 11:10:14 time: 0.9004 data_time: 0.0020 memory: 43253 loss: 0.1283 loss_ce: 0.1283 2023/02/24 21:45:33 - mmengine - INFO - Epoch(train) [25][ 300/5047] lr: 2.9515e-05 eta: 6 days, 11:08:36 time: 0.8457 data_time: 0.0018 memory: 44617 loss: 0.1532 loss_ce: 0.1532 2023/02/24 21:47:02 - mmengine - INFO - Epoch(train) [25][ 400/5047] lr: 2.9515e-05 eta: 6 days, 11:07:12 time: 0.8883 data_time: 0.0021 memory: 43289 loss: 0.1495 loss_ce: 0.1495 2023/02/24 21:48:29 - mmengine - INFO - Epoch(train) [25][ 500/5047] lr: 2.9515e-05 eta: 6 days, 11:05:40 time: 0.8842 data_time: 0.0019 memory: 43249 loss: 0.1331 loss_ce: 0.1331 2023/02/24 21:49:55 - mmengine - INFO - Epoch(train) [25][ 600/5047] lr: 2.9515e-05 eta: 6 days, 11:04:00 time: 0.8917 data_time: 0.0025 memory: 41419 loss: 0.1320 loss_ce: 0.1320 2023/02/24 21:51:21 - mmengine - INFO - Epoch(train) [25][ 700/5047] lr: 2.9515e-05 eta: 6 days, 11:02:21 time: 0.8736 data_time: 0.0021 memory: 41419 loss: 0.1462 loss_ce: 0.1462 2023/02/24 21:52:48 - mmengine - INFO - Epoch(train) [25][ 800/5047] lr: 2.9515e-05 eta: 6 days, 11:00:46 time: 0.9356 data_time: 0.0018 memory: 42336 loss: 0.1359 loss_ce: 0.1359 2023/02/24 21:53:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 21:54:15 - mmengine - INFO - Epoch(train) [25][ 900/5047] lr: 2.9515e-05 eta: 6 days, 10:59:17 time: 0.8364 data_time: 0.0018 memory: 43947 loss: 0.1400 loss_ce: 0.1400 2023/02/24 21:55:47 - mmengine - INFO - Epoch(train) [25][1000/5047] lr: 2.9515e-05 eta: 6 days, 10:58:12 time: 0.9103 data_time: 0.0019 memory: 50137 loss: 0.1395 loss_ce: 0.1395 2023/02/24 21:57:13 - mmengine - INFO - Epoch(train) [25][1100/5047] lr: 2.9515e-05 eta: 6 days, 10:56:31 time: 0.8212 data_time: 0.0021 memory: 45643 loss: 0.1362 loss_ce: 0.1362 2023/02/24 21:58:40 - mmengine - INFO - Epoch(train) [25][1200/5047] lr: 2.9515e-05 eta: 6 days, 10:55:01 time: 0.8556 data_time: 0.0031 memory: 41122 loss: 0.1367 loss_ce: 0.1367 2023/02/24 22:00:10 - mmengine - INFO - Epoch(train) [25][1300/5047] lr: 2.9515e-05 eta: 6 days, 10:53:41 time: 0.8815 data_time: 0.0017 memory: 55562 loss: 0.1239 loss_ce: 0.1239 2023/02/24 22:01:36 - mmengine - INFO - Epoch(train) [25][1400/5047] lr: 2.9515e-05 eta: 6 days, 10:52:07 time: 0.8283 data_time: 0.0018 memory: 43289 loss: 0.1633 loss_ce: 0.1633 2023/02/24 22:03:03 - mmengine - INFO - Epoch(train) [25][1500/5047] lr: 2.9515e-05 eta: 6 days, 10:50:33 time: 0.8644 data_time: 0.0021 memory: 49547 loss: 0.1425 loss_ce: 0.1425 2023/02/24 22:04:31 - mmengine - INFO - Epoch(train) [25][1600/5047] lr: 2.9515e-05 eta: 6 days, 10:49:04 time: 0.8590 data_time: 0.0027 memory: 43613 loss: 0.1292 loss_ce: 0.1292 2023/02/24 22:05:58 - mmengine - INFO - Epoch(train) [25][1700/5047] lr: 2.9515e-05 eta: 6 days, 10:47:31 time: 0.9416 data_time: 0.0029 memory: 44617 loss: 0.1278 loss_ce: 0.1278 2023/02/24 22:07:26 - mmengine - INFO - Epoch(train) [25][1800/5047] lr: 2.9515e-05 eta: 6 days, 10:46:03 time: 0.8437 data_time: 0.0021 memory: 55392 loss: 0.1447 loss_ce: 0.1447 2023/02/24 22:08:30 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 22:08:54 - mmengine - INFO - Epoch(train) [25][1900/5047] lr: 2.9515e-05 eta: 6 days, 10:44:36 time: 0.8480 data_time: 0.0046 memory: 49171 loss: 0.1543 loss_ce: 0.1543 2023/02/24 22:10:22 - mmengine - INFO - Epoch(train) [25][2000/5047] lr: 2.9515e-05 eta: 6 days, 10:43:09 time: 0.8322 data_time: 0.0018 memory: 43289 loss: 0.1366 loss_ce: 0.1366 2023/02/24 22:11:50 - mmengine - INFO - Epoch(train) [25][2100/5047] lr: 2.9515e-05 eta: 6 days, 10:41:42 time: 0.9002 data_time: 0.0024 memory: 55562 loss: 0.1490 loss_ce: 0.1490 2023/02/24 22:13:19 - mmengine - INFO - Epoch(train) [25][2200/5047] lr: 2.9515e-05 eta: 6 days, 10:40:20 time: 0.8951 data_time: 0.0021 memory: 49293 loss: 0.1490 loss_ce: 0.1490 2023/02/24 22:14:46 - mmengine - INFO - Epoch(train) [25][2300/5047] lr: 2.9515e-05 eta: 6 days, 10:38:48 time: 0.9092 data_time: 0.0017 memory: 40048 loss: 0.1370 loss_ce: 0.1370 2023/02/24 22:16:13 - mmengine - INFO - Epoch(train) [25][2400/5047] lr: 2.9515e-05 eta: 6 days, 10:37:17 time: 0.8493 data_time: 0.0019 memory: 47696 loss: 0.1483 loss_ce: 0.1483 2023/02/24 22:17:42 - mmengine - INFO - Epoch(train) [25][2500/5047] lr: 2.9515e-05 eta: 6 days, 10:35:56 time: 0.8758 data_time: 0.0019 memory: 46673 loss: 0.1437 loss_ce: 0.1437 2023/02/24 22:19:09 - mmengine - INFO - Epoch(train) [25][2600/5047] lr: 2.9515e-05 eta: 6 days, 10:34:19 time: 0.8282 data_time: 0.0032 memory: 43947 loss: 0.1381 loss_ce: 0.1381 2023/02/24 22:20:35 - mmengine - INFO - Epoch(train) [25][2700/5047] lr: 2.9515e-05 eta: 6 days, 10:32:44 time: 0.8746 data_time: 0.0019 memory: 47613 loss: 0.1374 loss_ce: 0.1374 2023/02/24 22:22:02 - mmengine - INFO - Epoch(train) [25][2800/5047] lr: 2.9515e-05 eta: 6 days, 10:31:08 time: 0.9289 data_time: 0.0019 memory: 55468 loss: 0.1404 loss_ce: 0.1404 2023/02/24 22:23:05 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 22:23:29 - mmengine - INFO - Epoch(train) [25][2900/5047] lr: 2.9515e-05 eta: 6 days, 10:29:36 time: 0.9133 data_time: 0.0030 memory: 44613 loss: 0.1320 loss_ce: 0.1320 2023/02/24 22:24:57 - mmengine - INFO - Epoch(train) [25][3000/5047] lr: 2.9515e-05 eta: 6 days, 10:28:06 time: 0.8215 data_time: 0.0018 memory: 46005 loss: 0.1378 loss_ce: 0.1378 2023/02/24 22:26:24 - mmengine - INFO - Epoch(train) [25][3100/5047] lr: 2.9515e-05 eta: 6 days, 10:26:34 time: 0.8331 data_time: 0.0043 memory: 46005 loss: 0.1193 loss_ce: 0.1193 2023/02/24 22:27:51 - mmengine - INFO - Epoch(train) [25][3200/5047] lr: 2.9515e-05 eta: 6 days, 10:25:01 time: 0.9054 data_time: 0.0029 memory: 41196 loss: 0.1489 loss_ce: 0.1489 2023/02/24 22:29:19 - mmengine - INFO - Epoch(train) [25][3300/5047] lr: 2.9515e-05 eta: 6 days, 10:23:34 time: 0.8778 data_time: 0.0025 memory: 47077 loss: 0.1210 loss_ce: 0.1210 2023/02/24 22:30:46 - mmengine - INFO - Epoch(train) [25][3400/5047] lr: 2.9515e-05 eta: 6 days, 10:22:01 time: 0.8429 data_time: 0.0046 memory: 52543 loss: 0.1329 loss_ce: 0.1329 2023/02/24 22:32:20 - mmengine - INFO - Epoch(train) [25][3500/5047] lr: 2.9515e-05 eta: 6 days, 10:21:09 time: 1.0782 data_time: 0.0020 memory: 50106 loss: 0.1388 loss_ce: 0.1388 2023/02/24 22:34:04 - mmengine - INFO - Epoch(train) [25][3600/5047] lr: 2.9515e-05 eta: 6 days, 10:21:16 time: 0.8432 data_time: 0.0022 memory: 40825 loss: 0.1403 loss_ce: 0.1403 2023/02/24 22:35:34 - mmengine - INFO - Epoch(train) [25][3700/5047] lr: 2.9515e-05 eta: 6 days, 10:19:57 time: 0.8428 data_time: 0.0018 memory: 51719 loss: 0.1439 loss_ce: 0.1439 2023/02/24 22:37:01 - mmengine - INFO - Epoch(train) [25][3800/5047] lr: 2.9515e-05 eta: 6 days, 10:18:25 time: 0.9657 data_time: 0.0020 memory: 55562 loss: 0.1535 loss_ce: 0.1535 2023/02/24 22:38:02 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 22:38:26 - mmengine - INFO - Epoch(train) [25][3900/5047] lr: 2.9515e-05 eta: 6 days, 10:16:42 time: 0.8491 data_time: 0.0020 memory: 43613 loss: 0.1605 loss_ce: 0.1605 2023/02/24 22:39:52 - mmengine - INFO - Epoch(train) [25][4000/5047] lr: 2.9515e-05 eta: 6 days, 10:15:01 time: 0.8589 data_time: 0.0018 memory: 42825 loss: 0.1527 loss_ce: 0.1527 2023/02/24 22:41:19 - mmengine - INFO - Epoch(train) [25][4100/5047] lr: 2.9515e-05 eta: 6 days, 10:13:31 time: 0.9248 data_time: 0.0027 memory: 43429 loss: 0.1383 loss_ce: 0.1383 2023/02/24 22:42:47 - mmengine - INFO - Epoch(train) [25][4200/5047] lr: 2.9515e-05 eta: 6 days, 10:12:03 time: 0.8883 data_time: 0.0022 memory: 44956 loss: 0.1419 loss_ce: 0.1419 2023/02/24 22:44:14 - mmengine - INFO - Epoch(train) [25][4300/5047] lr: 2.9515e-05 eta: 6 days, 10:10:29 time: 0.9357 data_time: 0.0020 memory: 46355 loss: 0.1540 loss_ce: 0.1540 2023/02/24 22:45:40 - mmengine - INFO - Epoch(train) [25][4400/5047] lr: 2.9515e-05 eta: 6 days, 10:08:49 time: 0.8428 data_time: 0.0024 memory: 43947 loss: 0.1166 loss_ce: 0.1166 2023/02/24 22:47:07 - mmengine - INFO - Epoch(train) [25][4500/5047] lr: 2.9515e-05 eta: 6 days, 10:07:19 time: 0.8641 data_time: 0.0019 memory: 45815 loss: 0.1444 loss_ce: 0.1444 2023/02/24 22:48:34 - mmengine - INFO - Epoch(train) [25][4600/5047] lr: 2.9515e-05 eta: 6 days, 10:05:43 time: 0.9462 data_time: 0.0018 memory: 47074 loss: 0.1536 loss_ce: 0.1536 2023/02/24 22:49:59 - mmengine - INFO - Epoch(train) [25][4700/5047] lr: 2.9515e-05 eta: 6 days, 10:04:01 time: 0.8763 data_time: 0.0022 memory: 44185 loss: 0.1594 loss_ce: 0.1594 2023/02/24 22:51:23 - mmengine - INFO - Epoch(train) [25][4800/5047] lr: 2.9515e-05 eta: 6 days, 10:02:12 time: 0.8833 data_time: 0.0020 memory: 41008 loss: 0.1328 loss_ce: 0.1328 2023/02/24 22:52:31 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 22:52:58 - mmengine - INFO - Epoch(train) [25][4900/5047] lr: 2.9515e-05 eta: 6 days, 10:01:24 time: 0.8575 data_time: 0.0019 memory: 42794 loss: 0.1170 loss_ce: 0.1170 2023/02/24 22:54:24 - mmengine - INFO - Epoch(train) [25][5000/5047] lr: 2.9515e-05 eta: 6 days, 9:59:47 time: 0.8818 data_time: 0.0019 memory: 46355 loss: 0.1587 loss_ce: 0.1587 2023/02/24 22:55:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 22:55:04 - mmengine - INFO - Saving checkpoint at 25 epochs 2023/02/24 22:56:35 - mmengine - INFO - Epoch(train) [26][ 100/5047] lr: 2.9314e-05 eta: 6 days, 9:57:16 time: 0.8390 data_time: 0.0047 memory: 43613 loss: 0.1256 loss_ce: 0.1256 2023/02/24 22:58:05 - mmengine - INFO - Epoch(train) [26][ 200/5047] lr: 2.9314e-05 eta: 6 days, 9:56:00 time: 0.8120 data_time: 0.0045 memory: 42649 loss: 0.1309 loss_ce: 0.1309 2023/02/24 22:59:32 - mmengine - INFO - Epoch(train) [26][ 300/5047] lr: 2.9314e-05 eta: 6 days, 9:54:30 time: 0.8575 data_time: 0.0068 memory: 42965 loss: 0.1562 loss_ce: 0.1562 2023/02/24 23:01:00 - mmengine - INFO - Epoch(train) [26][ 400/5047] lr: 2.9314e-05 eta: 6 days, 9:52:59 time: 0.8780 data_time: 0.0022 memory: 46914 loss: 0.1248 loss_ce: 0.1248 2023/02/24 23:02:24 - mmengine - INFO - Epoch(train) [26][ 500/5047] lr: 2.9314e-05 eta: 6 days, 9:51:11 time: 0.8275 data_time: 0.0019 memory: 55562 loss: 0.1348 loss_ce: 0.1348 2023/02/24 23:03:51 - mmengine - INFO - Epoch(train) [26][ 600/5047] lr: 2.9314e-05 eta: 6 days, 9:49:38 time: 0.9145 data_time: 0.0022 memory: 40825 loss: 0.1272 loss_ce: 0.1272 2023/02/24 23:05:18 - mmengine - INFO - Epoch(train) [26][ 700/5047] lr: 2.9314e-05 eta: 6 days, 9:48:07 time: 0.8638 data_time: 0.0020 memory: 41419 loss: 0.1613 loss_ce: 0.1613 2023/02/24 23:06:44 - mmengine - INFO - Epoch(train) [26][ 800/5047] lr: 2.9314e-05 eta: 6 days, 9:46:30 time: 0.8713 data_time: 0.0035 memory: 44278 loss: 0.1417 loss_ce: 0.1417 2023/02/24 23:07:06 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 23:08:12 - mmengine - INFO - Epoch(train) [26][ 900/5047] lr: 2.9314e-05 eta: 6 days, 9:44:58 time: 0.8713 data_time: 0.0020 memory: 45018 loss: 0.1527 loss_ce: 0.1527 2023/02/24 23:09:38 - mmengine - INFO - Epoch(train) [26][1000/5047] lr: 2.9314e-05 eta: 6 days, 9:43:25 time: 0.8446 data_time: 0.0020 memory: 45037 loss: 0.1498 loss_ce: 0.1498 2023/02/24 23:11:05 - mmengine - INFO - Epoch(train) [26][1100/5047] lr: 2.9314e-05 eta: 6 days, 9:41:48 time: 0.8461 data_time: 0.0020 memory: 50446 loss: 0.1312 loss_ce: 0.1312 2023/02/24 23:12:32 - mmengine - INFO - Epoch(train) [26][1200/5047] lr: 2.9314e-05 eta: 6 days, 9:40:15 time: 0.8587 data_time: 0.0045 memory: 48906 loss: 0.1324 loss_ce: 0.1324 2023/02/24 23:13:59 - mmengine - INFO - Epoch(train) [26][1300/5047] lr: 2.9314e-05 eta: 6 days, 9:38:42 time: 0.8858 data_time: 0.0020 memory: 42795 loss: 0.1381 loss_ce: 0.1381 2023/02/24 23:15:25 - mmengine - INFO - Epoch(train) [26][1400/5047] lr: 2.9314e-05 eta: 6 days, 9:37:06 time: 0.8772 data_time: 0.0018 memory: 44617 loss: 0.1467 loss_ce: 0.1467 2023/02/24 23:16:53 - mmengine - INFO - Epoch(train) [26][1500/5047] lr: 2.9314e-05 eta: 6 days, 9:35:40 time: 0.8884 data_time: 0.0021 memory: 48123 loss: 0.1369 loss_ce: 0.1369 2023/02/24 23:18:19 - mmengine - INFO - Epoch(train) [26][1600/5047] lr: 2.9314e-05 eta: 6 days, 9:34:02 time: 0.8394 data_time: 0.0025 memory: 42649 loss: 0.1221 loss_ce: 0.1221 2023/02/24 23:19:48 - mmengine - INFO - Epoch(train) [26][1700/5047] lr: 2.9314e-05 eta: 6 days, 9:32:39 time: 0.8364 data_time: 0.0033 memory: 45931 loss: 0.1251 loss_ce: 0.1251 2023/02/24 23:21:15 - mmengine - INFO - Epoch(train) [26][1800/5047] lr: 2.9314e-05 eta: 6 days, 9:31:06 time: 0.8687 data_time: 0.0020 memory: 42328 loss: 0.1477 loss_ce: 0.1477 2023/02/24 23:21:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 23:22:44 - mmengine - INFO - Epoch(train) [26][1900/5047] lr: 2.9314e-05 eta: 6 days, 9:29:41 time: 0.8884 data_time: 0.0020 memory: 49217 loss: 0.1352 loss_ce: 0.1352 2023/02/24 23:24:10 - mmengine - INFO - Epoch(train) [26][2000/5047] lr: 2.9314e-05 eta: 6 days, 9:28:03 time: 0.8542 data_time: 0.0024 memory: 42336 loss: 0.1193 loss_ce: 0.1193 2023/02/24 23:25:39 - mmengine - INFO - Epoch(train) [26][2100/5047] lr: 2.9314e-05 eta: 6 days, 9:26:41 time: 0.8669 data_time: 0.0018 memory: 44202 loss: 0.1527 loss_ce: 0.1527 2023/02/24 23:27:07 - mmengine - INFO - Epoch(train) [26][2200/5047] lr: 2.9314e-05 eta: 6 days, 9:25:15 time: 0.8633 data_time: 0.0022 memory: 47892 loss: 0.1377 loss_ce: 0.1377 2023/02/24 23:28:35 - mmengine - INFO - Epoch(train) [26][2300/5047] lr: 2.9314e-05 eta: 6 days, 9:23:47 time: 0.8663 data_time: 0.0051 memory: 44956 loss: 0.1462 loss_ce: 0.1462 2023/02/24 23:30:02 - mmengine - INFO - Epoch(train) [26][2400/5047] lr: 2.9314e-05 eta: 6 days, 9:22:18 time: 0.8839 data_time: 0.0031 memory: 47813 loss: 0.1542 loss_ce: 0.1542 2023/02/24 23:31:29 - mmengine - INFO - Epoch(train) [26][2500/5047] lr: 2.9314e-05 eta: 6 days, 9:20:42 time: 0.8489 data_time: 0.0022 memory: 43613 loss: 0.1472 loss_ce: 0.1472 2023/02/24 23:32:55 - mmengine - INFO - Epoch(train) [26][2600/5047] lr: 2.9314e-05 eta: 6 days, 9:19:08 time: 0.8157 data_time: 0.0039 memory: 43054 loss: 0.1416 loss_ce: 0.1416 2023/02/24 23:34:21 - mmengine - INFO - Epoch(train) [26][2700/5047] lr: 2.9314e-05 eta: 6 days, 9:17:28 time: 0.8128 data_time: 0.0075 memory: 55562 loss: 0.1371 loss_ce: 0.1371 2023/02/24 23:35:48 - mmengine - INFO - Epoch(train) [26][2800/5047] lr: 2.9314e-05 eta: 6 days, 9:15:55 time: 0.8851 data_time: 0.0031 memory: 47813 loss: 0.1331 loss_ce: 0.1331 2023/02/24 23:36:10 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 23:37:16 - mmengine - INFO - Epoch(train) [26][2900/5047] lr: 2.9314e-05 eta: 6 days, 9:14:26 time: 0.8556 data_time: 0.0021 memory: 54072 loss: 0.1295 loss_ce: 0.1295 2023/02/24 23:38:44 - mmengine - INFO - Epoch(train) [26][3000/5047] lr: 2.9314e-05 eta: 6 days, 9:13:01 time: 0.8181 data_time: 0.0022 memory: 53634 loss: 0.1539 loss_ce: 0.1539 2023/02/24 23:40:11 - mmengine - INFO - Epoch(train) [26][3100/5047] lr: 2.9314e-05 eta: 6 days, 9:11:26 time: 0.8690 data_time: 0.0023 memory: 48517 loss: 0.1386 loss_ce: 0.1386 2023/02/24 23:41:38 - mmengine - INFO - Epoch(train) [26][3200/5047] lr: 2.9314e-05 eta: 6 days, 9:09:56 time: 0.9279 data_time: 0.0030 memory: 55562 loss: 0.1294 loss_ce: 0.1294 2023/02/24 23:43:04 - mmengine - INFO - Epoch(train) [26][3300/5047] lr: 2.9314e-05 eta: 6 days, 9:08:18 time: 0.8225 data_time: 0.0020 memory: 49216 loss: 0.1235 loss_ce: 0.1235 2023/02/24 23:44:31 - mmengine - INFO - Epoch(train) [26][3400/5047] lr: 2.9314e-05 eta: 6 days, 9:06:45 time: 0.8816 data_time: 0.0019 memory: 43947 loss: 0.1212 loss_ce: 0.1212 2023/02/24 23:45:58 - mmengine - INFO - Epoch(train) [26][3500/5047] lr: 2.9314e-05 eta: 6 days, 9:05:12 time: 0.8141 data_time: 0.0018 memory: 49715 loss: 0.1389 loss_ce: 0.1389 2023/02/24 23:47:24 - mmengine - INFO - Epoch(train) [26][3600/5047] lr: 2.9314e-05 eta: 6 days, 9:03:36 time: 0.8600 data_time: 0.0018 memory: 46258 loss: 0.1365 loss_ce: 0.1365 2023/02/24 23:48:51 - mmengine - INFO - Epoch(train) [26][3700/5047] lr: 2.9314e-05 eta: 6 days, 9:02:02 time: 0.9098 data_time: 0.0018 memory: 54603 loss: 0.1528 loss_ce: 0.1528 2023/02/24 23:50:20 - mmengine - INFO - Epoch(train) [26][3800/5047] lr: 2.9314e-05 eta: 6 days, 9:00:41 time: 0.8769 data_time: 0.0020 memory: 55562 loss: 0.1453 loss_ce: 0.1453 2023/02/24 23:50:41 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/24 23:51:46 - mmengine - INFO - Epoch(train) [26][3900/5047] lr: 2.9314e-05 eta: 6 days, 8:59:04 time: 0.8412 data_time: 0.0018 memory: 44970 loss: 0.1340 loss_ce: 0.1340 2023/02/24 23:53:13 - mmengine - INFO - Epoch(train) [26][4000/5047] lr: 2.9314e-05 eta: 6 days, 8:57:28 time: 0.8624 data_time: 0.0022 memory: 49193 loss: 0.1271 loss_ce: 0.1271 2023/02/24 23:54:40 - mmengine - INFO - Epoch(train) [26][4100/5047] lr: 2.9314e-05 eta: 6 days, 8:55:58 time: 0.8964 data_time: 0.0020 memory: 44617 loss: 0.1334 loss_ce: 0.1334 2023/02/24 23:56:07 - mmengine - INFO - Epoch(train) [26][4200/5047] lr: 2.9314e-05 eta: 6 days, 8:54:24 time: 0.8972 data_time: 0.0020 memory: 42336 loss: 0.1438 loss_ce: 0.1438 2023/02/24 23:57:33 - mmengine - INFO - Epoch(train) [26][4300/5047] lr: 2.9314e-05 eta: 6 days, 8:52:47 time: 0.8897 data_time: 0.0026 memory: 42649 loss: 0.1292 loss_ce: 0.1292 2023/02/24 23:58:59 - mmengine - INFO - Epoch(train) [26][4400/5047] lr: 2.9314e-05 eta: 6 days, 8:51:09 time: 0.9062 data_time: 0.0018 memory: 42648 loss: 0.1475 loss_ce: 0.1475 2023/02/25 00:00:26 - mmengine - INFO - Epoch(train) [26][4500/5047] lr: 2.9314e-05 eta: 6 days, 8:49:35 time: 0.8577 data_time: 0.0027 memory: 43613 loss: 0.1490 loss_ce: 0.1490 2023/02/25 00:01:52 - mmengine - INFO - Epoch(train) [26][4600/5047] lr: 2.9314e-05 eta: 6 days, 8:47:57 time: 0.9444 data_time: 0.0019 memory: 45426 loss: 0.1363 loss_ce: 0.1363 2023/02/25 00:03:19 - mmengine - INFO - Epoch(train) [26][4700/5047] lr: 2.9314e-05 eta: 6 days, 8:46:25 time: 0.8479 data_time: 0.0021 memory: 52739 loss: 0.1478 loss_ce: 0.1478 2023/02/25 00:04:46 - mmengine - INFO - Epoch(train) [26][4800/5047] lr: 2.9314e-05 eta: 6 days, 8:44:54 time: 0.8276 data_time: 0.0022 memory: 51792 loss: 0.1385 loss_ce: 0.1385 2023/02/25 00:05:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 00:06:14 - mmengine - INFO - Epoch(train) [26][4900/5047] lr: 2.9314e-05 eta: 6 days, 8:43:25 time: 0.8604 data_time: 0.0020 memory: 55559 loss: 0.1404 loss_ce: 0.1404 2023/02/25 00:07:41 - mmengine - INFO - Epoch(train) [26][5000/5047] lr: 2.9314e-05 eta: 6 days, 8:41:51 time: 0.8348 data_time: 0.0019 memory: 42474 loss: 0.1296 loss_ce: 0.1296 2023/02/25 00:08:22 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 00:08:22 - mmengine - INFO - Saving checkpoint at 26 epochs 2023/02/25 00:09:55 - mmengine - INFO - Epoch(train) [27][ 100/5047] lr: 2.9113e-05 eta: 6 days, 8:39:43 time: 0.9731 data_time: 0.0019 memory: 44477 loss: 0.1392 loss_ce: 0.1392 2023/02/25 00:11:22 - mmengine - INFO - Epoch(train) [27][ 200/5047] lr: 2.9113e-05 eta: 6 days, 8:38:11 time: 0.8697 data_time: 0.0019 memory: 42649 loss: 0.1305 loss_ce: 0.1305 2023/02/25 00:12:49 - mmengine - INFO - Epoch(train) [27][ 300/5047] lr: 2.9113e-05 eta: 6 days, 8:36:38 time: 0.9281 data_time: 0.0020 memory: 46449 loss: 0.1423 loss_ce: 0.1423 2023/02/25 00:14:17 - mmengine - INFO - Epoch(train) [27][ 400/5047] lr: 2.9113e-05 eta: 6 days, 8:35:10 time: 0.8948 data_time: 0.0030 memory: 54673 loss: 0.1350 loss_ce: 0.1350 2023/02/25 00:15:42 - mmengine - INFO - Epoch(train) [27][ 500/5047] lr: 2.9113e-05 eta: 6 days, 8:33:27 time: 0.8185 data_time: 0.0025 memory: 55562 loss: 0.1254 loss_ce: 0.1254 2023/02/25 00:17:07 - mmengine - INFO - Epoch(train) [27][ 600/5047] lr: 2.9113e-05 eta: 6 days, 8:31:47 time: 0.8476 data_time: 0.0021 memory: 45069 loss: 0.1349 loss_ce: 0.1349 2023/02/25 00:18:34 - mmengine - INFO - Epoch(train) [27][ 700/5047] lr: 2.9113e-05 eta: 6 days, 8:30:12 time: 0.8419 data_time: 0.0019 memory: 55562 loss: 0.1369 loss_ce: 0.1369 2023/02/25 00:19:42 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 00:20:01 - mmengine - INFO - Epoch(train) [27][ 800/5047] lr: 2.9113e-05 eta: 6 days, 8:28:41 time: 0.8647 data_time: 0.0064 memory: 48948 loss: 0.1390 loss_ce: 0.1390 2023/02/25 00:21:28 - mmengine - INFO - Epoch(train) [27][ 900/5047] lr: 2.9113e-05 eta: 6 days, 8:27:06 time: 0.8601 data_time: 0.0021 memory: 48030 loss: 0.1519 loss_ce: 0.1519 2023/02/25 00:22:54 - mmengine - INFO - Epoch(train) [27][1000/5047] lr: 2.9113e-05 eta: 6 days, 8:25:30 time: 0.8414 data_time: 0.0021 memory: 44278 loss: 0.1234 loss_ce: 0.1234 2023/02/25 00:24:21 - mmengine - INFO - Epoch(train) [27][1100/5047] lr: 2.9113e-05 eta: 6 days, 8:23:56 time: 0.8861 data_time: 0.0021 memory: 42649 loss: 0.1391 loss_ce: 0.1391 2023/02/25 00:25:47 - mmengine - INFO - Epoch(train) [27][1200/5047] lr: 2.9113e-05 eta: 6 days, 8:22:18 time: 0.8453 data_time: 0.0020 memory: 55562 loss: 0.1323 loss_ce: 0.1323 2023/02/25 00:27:13 - mmengine - INFO - Epoch(train) [27][1300/5047] lr: 2.9113e-05 eta: 6 days, 8:20:41 time: 0.8497 data_time: 0.0031 memory: 43947 loss: 0.1395 loss_ce: 0.1395 2023/02/25 00:28:38 - mmengine - INFO - Epoch(train) [27][1400/5047] lr: 2.9113e-05 eta: 6 days, 8:19:01 time: 0.9079 data_time: 0.0020 memory: 50106 loss: 0.1296 loss_ce: 0.1296 2023/02/25 00:30:06 - mmengine - INFO - Epoch(train) [27][1500/5047] lr: 2.9113e-05 eta: 6 days, 8:17:34 time: 0.8768 data_time: 0.0021 memory: 44108 loss: 0.1436 loss_ce: 0.1436 2023/02/25 00:31:33 - mmengine - INFO - Epoch(train) [27][1600/5047] lr: 2.9113e-05 eta: 6 days, 8:16:01 time: 0.8915 data_time: 0.0021 memory: 45689 loss: 0.1333 loss_ce: 0.1333 2023/02/25 00:33:00 - mmengine - INFO - Epoch(train) [27][1700/5047] lr: 2.9113e-05 eta: 6 days, 8:14:28 time: 0.8532 data_time: 0.0041 memory: 43947 loss: 0.1263 loss_ce: 0.1263 2023/02/25 00:34:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 00:34:27 - mmengine - INFO - Epoch(train) [27][1800/5047] lr: 2.9113e-05 eta: 6 days, 8:12:56 time: 0.8540 data_time: 0.0027 memory: 46133 loss: 0.1370 loss_ce: 0.1370 2023/02/25 00:35:54 - mmengine - INFO - Epoch(train) [27][1900/5047] lr: 2.9113e-05 eta: 6 days, 8:11:25 time: 0.9021 data_time: 0.0025 memory: 40535 loss: 0.1612 loss_ce: 0.1612 2023/02/25 00:37:20 - mmengine - INFO - Epoch(train) [27][2000/5047] lr: 2.9113e-05 eta: 6 days, 8:09:45 time: 0.9023 data_time: 0.0020 memory: 41942 loss: 0.1632 loss_ce: 0.1632 2023/02/25 00:38:48 - mmengine - INFO - Epoch(train) [27][2100/5047] lr: 2.9113e-05 eta: 6 days, 8:08:18 time: 0.8689 data_time: 0.0035 memory: 42965 loss: 0.1516 loss_ce: 0.1516 2023/02/25 00:40:15 - mmengine - INFO - Epoch(train) [27][2200/5047] lr: 2.9113e-05 eta: 6 days, 8:06:45 time: 0.8351 data_time: 0.0084 memory: 47719 loss: 0.1410 loss_ce: 0.1410 2023/02/25 00:41:42 - mmengine - INFO - Epoch(train) [27][2300/5047] lr: 2.9113e-05 eta: 6 days, 8:05:17 time: 0.8519 data_time: 0.0020 memory: 41419 loss: 0.1350 loss_ce: 0.1350 2023/02/25 00:43:09 - mmengine - INFO - Epoch(train) [27][2400/5047] lr: 2.9113e-05 eta: 6 days, 8:03:42 time: 0.8968 data_time: 0.0026 memory: 55562 loss: 0.1304 loss_ce: 0.1304 2023/02/25 00:44:37 - mmengine - INFO - Epoch(train) [27][2500/5047] lr: 2.9113e-05 eta: 6 days, 8:02:13 time: 0.8586 data_time: 0.0024 memory: 52956 loss: 0.1374 loss_ce: 0.1374 2023/02/25 00:46:03 - mmengine - INFO - Epoch(train) [27][2600/5047] lr: 2.9113e-05 eta: 6 days, 8:00:36 time: 0.8336 data_time: 0.0019 memory: 42024 loss: 0.1329 loss_ce: 0.1329 2023/02/25 00:47:28 - mmengine - INFO - Epoch(train) [27][2700/5047] lr: 2.9113e-05 eta: 6 days, 7:58:57 time: 0.8821 data_time: 0.0025 memory: 39960 loss: 0.1709 loss_ce: 0.1709 2023/02/25 00:48:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 00:48:59 - mmengine - INFO - Epoch(train) [27][2800/5047] lr: 2.9113e-05 eta: 6 days, 7:57:43 time: 0.9073 data_time: 0.0021 memory: 50608 loss: 0.1431 loss_ce: 0.1431 2023/02/25 00:50:25 - mmengine - INFO - Epoch(train) [27][2900/5047] lr: 2.9113e-05 eta: 6 days, 7:56:08 time: 0.8371 data_time: 0.0021 memory: 52964 loss: 0.1518 loss_ce: 0.1518 2023/02/25 00:51:52 - mmengine - INFO - Epoch(train) [27][3000/5047] lr: 2.9113e-05 eta: 6 days, 7:54:37 time: 0.8597 data_time: 0.0025 memory: 41927 loss: 0.1238 loss_ce: 0.1238 2023/02/25 00:53:20 - mmengine - INFO - Epoch(train) [27][3100/5047] lr: 2.9113e-05 eta: 6 days, 7:53:09 time: 0.8568 data_time: 0.0046 memory: 42965 loss: 0.1469 loss_ce: 0.1469 2023/02/25 00:54:47 - mmengine - INFO - Epoch(train) [27][3200/5047] lr: 2.9113e-05 eta: 6 days, 7:51:36 time: 0.9320 data_time: 0.0024 memory: 42965 loss: 0.1324 loss_ce: 0.1324 2023/02/25 00:56:14 - mmengine - INFO - Epoch(train) [27][3300/5047] lr: 2.9113e-05 eta: 6 days, 7:50:04 time: 0.8598 data_time: 0.0023 memory: 46005 loss: 0.1465 loss_ce: 0.1465 2023/02/25 00:57:42 - mmengine - INFO - Epoch(train) [27][3400/5047] lr: 2.9113e-05 eta: 6 days, 7:48:38 time: 0.8594 data_time: 0.0078 memory: 42336 loss: 0.1403 loss_ce: 0.1403 2023/02/25 00:59:10 - mmengine - INFO - Epoch(train) [27][3500/5047] lr: 2.9113e-05 eta: 6 days, 7:47:12 time: 0.8564 data_time: 0.0023 memory: 55114 loss: 0.1529 loss_ce: 0.1529 2023/02/25 01:00:39 - mmengine - INFO - Epoch(train) [27][3600/5047] lr: 2.9113e-05 eta: 6 days, 7:45:47 time: 0.9028 data_time: 0.0020 memory: 42024 loss: 0.1420 loss_ce: 0.1420 2023/02/25 01:02:06 - mmengine - INFO - Epoch(train) [27][3700/5047] lr: 2.9113e-05 eta: 6 days, 7:44:15 time: 0.8575 data_time: 0.0039 memory: 44950 loss: 0.1278 loss_ce: 0.1278 2023/02/25 01:03:14 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 01:03:33 - mmengine - INFO - Epoch(train) [27][3800/5047] lr: 2.9113e-05 eta: 6 days, 7:42:46 time: 0.8961 data_time: 0.0020 memory: 41998 loss: 0.1412 loss_ce: 0.1412 2023/02/25 01:05:01 - mmengine - INFO - Epoch(train) [27][3900/5047] lr: 2.9113e-05 eta: 6 days, 7:41:15 time: 0.8634 data_time: 0.0019 memory: 54242 loss: 0.1505 loss_ce: 0.1505 2023/02/25 01:06:26 - mmengine - INFO - Epoch(train) [27][4000/5047] lr: 2.9113e-05 eta: 6 days, 7:39:35 time: 0.8052 data_time: 0.0019 memory: 55562 loss: 0.1286 loss_ce: 0.1286 2023/02/25 01:07:52 - mmengine - INFO - Epoch(train) [27][4100/5047] lr: 2.9113e-05 eta: 6 days, 7:37:58 time: 0.8592 data_time: 0.0021 memory: 44617 loss: 0.1595 loss_ce: 0.1595 2023/02/25 01:09:19 - mmengine - INFO - Epoch(train) [27][4200/5047] lr: 2.9113e-05 eta: 6 days, 7:36:27 time: 0.8759 data_time: 0.0021 memory: 42273 loss: 0.1117 loss_ce: 0.1117 2023/02/25 01:10:46 - mmengine - INFO - Epoch(train) [27][4300/5047] lr: 2.9113e-05 eta: 6 days, 7:34:55 time: 0.8170 data_time: 0.0026 memory: 42624 loss: 0.1374 loss_ce: 0.1374 2023/02/25 01:12:14 - mmengine - INFO - Epoch(train) [27][4400/5047] lr: 2.9113e-05 eta: 6 days, 7:33:25 time: 0.8830 data_time: 0.0040 memory: 49407 loss: 0.1254 loss_ce: 0.1254 2023/02/25 01:13:40 - mmengine - INFO - Epoch(train) [27][4500/5047] lr: 2.9113e-05 eta: 6 days, 7:31:48 time: 0.8619 data_time: 0.0025 memory: 46713 loss: 0.1358 loss_ce: 0.1358 2023/02/25 01:15:06 - mmengine - INFO - Epoch(train) [27][4600/5047] lr: 2.9113e-05 eta: 6 days, 7:30:11 time: 0.8611 data_time: 0.0019 memory: 49168 loss: 0.1330 loss_ce: 0.1330 2023/02/25 01:16:32 - mmengine - INFO - Epoch(train) [27][4700/5047] lr: 2.9113e-05 eta: 6 days, 7:28:35 time: 0.8435 data_time: 0.0018 memory: 42311 loss: 0.1209 loss_ce: 0.1209 2023/02/25 01:17:40 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 01:18:00 - mmengine - INFO - Epoch(train) [27][4800/5047] lr: 2.9113e-05 eta: 6 days, 7:27:07 time: 0.8909 data_time: 0.0021 memory: 43613 loss: 0.1456 loss_ce: 0.1456 2023/02/25 01:19:28 - mmengine - INFO - Epoch(train) [27][4900/5047] lr: 2.9113e-05 eta: 6 days, 7:25:42 time: 0.8471 data_time: 0.0020 memory: 54673 loss: 0.1369 loss_ce: 0.1369 2023/02/25 01:20:54 - mmengine - INFO - Epoch(train) [27][5000/5047] lr: 2.9113e-05 eta: 6 days, 7:24:04 time: 0.8022 data_time: 0.0020 memory: 46355 loss: 0.1268 loss_ce: 0.1268 2023/02/25 01:21:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 01:21:34 - mmengine - INFO - Saving checkpoint at 27 epochs 2023/02/25 01:23:07 - mmengine - INFO - Epoch(train) [28][ 100/5047] lr: 2.8913e-05 eta: 6 days, 7:21:50 time: 0.9258 data_time: 0.0023 memory: 51719 loss: 0.1326 loss_ce: 0.1326 2023/02/25 01:24:34 - mmengine - INFO - Epoch(train) [28][ 200/5047] lr: 2.8913e-05 eta: 6 days, 7:20:20 time: 0.8508 data_time: 0.0103 memory: 41724 loss: 0.1338 loss_ce: 0.1338 2023/02/25 01:26:00 - mmengine - INFO - Epoch(train) [28][ 300/5047] lr: 2.8913e-05 eta: 6 days, 7:18:43 time: 0.8877 data_time: 0.0026 memory: 42649 loss: 0.1281 loss_ce: 0.1281 2023/02/25 01:27:28 - mmengine - INFO - Epoch(train) [28][ 400/5047] lr: 2.8913e-05 eta: 6 days, 7:17:13 time: 0.8380 data_time: 0.0023 memory: 43613 loss: 0.1383 loss_ce: 0.1383 2023/02/25 01:28:55 - mmengine - INFO - Epoch(train) [28][ 500/5047] lr: 2.8913e-05 eta: 6 days, 7:15:43 time: 0.8554 data_time: 0.0024 memory: 41419 loss: 0.1507 loss_ce: 0.1507 2023/02/25 01:30:23 - mmengine - INFO - Epoch(train) [28][ 600/5047] lr: 2.8913e-05 eta: 6 days, 7:14:17 time: 0.9131 data_time: 0.0019 memory: 55562 loss: 0.1351 loss_ce: 0.1351 2023/02/25 01:31:52 - mmengine - INFO - Epoch(train) [28][ 700/5047] lr: 2.8913e-05 eta: 6 days, 7:12:51 time: 0.8823 data_time: 0.0023 memory: 50505 loss: 0.1351 loss_ce: 0.1351 2023/02/25 01:32:19 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 01:33:20 - mmengine - INFO - Epoch(train) [28][ 800/5047] lr: 2.8913e-05 eta: 6 days, 7:11:24 time: 0.8486 data_time: 0.0026 memory: 47447 loss: 0.1538 loss_ce: 0.1538 2023/02/25 01:34:47 - mmengine - INFO - Epoch(train) [28][ 900/5047] lr: 2.8913e-05 eta: 6 days, 7:09:56 time: 0.8920 data_time: 0.0046 memory: 55562 loss: 0.1400 loss_ce: 0.1400 2023/02/25 01:36:15 - mmengine - INFO - Epoch(train) [28][1000/5047] lr: 2.8913e-05 eta: 6 days, 7:08:29 time: 0.8930 data_time: 0.0045 memory: 46964 loss: 0.1353 loss_ce: 0.1353 2023/02/25 01:37:41 - mmengine - INFO - Epoch(train) [28][1100/5047] lr: 2.8913e-05 eta: 6 days, 7:06:53 time: 0.9262 data_time: 0.0021 memory: 45689 loss: 0.1278 loss_ce: 0.1278 2023/02/25 01:39:09 - mmengine - INFO - Epoch(train) [28][1200/5047] lr: 2.8913e-05 eta: 6 days, 7:05:24 time: 0.8893 data_time: 0.0033 memory: 55562 loss: 0.1282 loss_ce: 0.1282 2023/02/25 01:40:36 - mmengine - INFO - Epoch(train) [28][1300/5047] lr: 2.8913e-05 eta: 6 days, 7:03:52 time: 0.8059 data_time: 0.0020 memory: 44278 loss: 0.1538 loss_ce: 0.1538 2023/02/25 01:42:02 - mmengine - INFO - Epoch(train) [28][1400/5047] lr: 2.8913e-05 eta: 6 days, 7:02:12 time: 0.8389 data_time: 0.0022 memory: 41724 loss: 0.1416 loss_ce: 0.1416 2023/02/25 01:43:28 - mmengine - INFO - Epoch(train) [28][1500/5047] lr: 2.8913e-05 eta: 6 days, 7:00:37 time: 0.8231 data_time: 0.0022 memory: 43493 loss: 0.1292 loss_ce: 0.1292 2023/02/25 01:44:54 - mmengine - INFO - Epoch(train) [28][1600/5047] lr: 2.8913e-05 eta: 6 days, 6:59:03 time: 0.8041 data_time: 0.0024 memory: 48948 loss: 0.1379 loss_ce: 0.1379 2023/02/25 01:46:22 - mmengine - INFO - Epoch(train) [28][1700/5047] lr: 2.8913e-05 eta: 6 days, 6:57:34 time: 0.8545 data_time: 0.0022 memory: 42445 loss: 0.1441 loss_ce: 0.1441 2023/02/25 01:46:49 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 01:47:49 - mmengine - INFO - Epoch(train) [28][1800/5047] lr: 2.8913e-05 eta: 6 days, 6:56:02 time: 0.9199 data_time: 0.0024 memory: 47074 loss: 0.1338 loss_ce: 0.1338 2023/02/25 01:49:16 - mmengine - INFO - Epoch(train) [28][1900/5047] lr: 2.8913e-05 eta: 6 days, 6:54:32 time: 0.8776 data_time: 0.0019 memory: 48565 loss: 0.1336 loss_ce: 0.1336 2023/02/25 01:50:43 - mmengine - INFO - Epoch(train) [28][2000/5047] lr: 2.8913e-05 eta: 6 days, 6:52:58 time: 0.8339 data_time: 0.0024 memory: 53270 loss: 0.1211 loss_ce: 0.1211 2023/02/25 01:52:09 - mmengine - INFO - Epoch(train) [28][2100/5047] lr: 2.8913e-05 eta: 6 days, 6:51:21 time: 0.8380 data_time: 0.0022 memory: 54135 loss: 0.1472 loss_ce: 0.1472 2023/02/25 01:53:37 - mmengine - INFO - Epoch(train) [28][2200/5047] lr: 2.8913e-05 eta: 6 days, 6:49:56 time: 0.9135 data_time: 0.0020 memory: 41419 loss: 0.1312 loss_ce: 0.1312 2023/02/25 01:55:03 - mmengine - INFO - Epoch(train) [28][2300/5047] lr: 2.8913e-05 eta: 6 days, 6:48:17 time: 0.8944 data_time: 0.0019 memory: 43289 loss: 0.1494 loss_ce: 0.1494 2023/02/25 01:56:29 - mmengine - INFO - Epoch(train) [28][2400/5047] lr: 2.8913e-05 eta: 6 days, 6:46:42 time: 0.8666 data_time: 0.0019 memory: 41696 loss: 0.1318 loss_ce: 0.1318 2023/02/25 01:57:55 - mmengine - INFO - Epoch(train) [28][2500/5047] lr: 2.8913e-05 eta: 6 days, 6:45:05 time: 0.8671 data_time: 0.0022 memory: 43289 loss: 0.1408 loss_ce: 0.1408 2023/02/25 01:59:22 - mmengine - INFO - Epoch(train) [28][2600/5047] lr: 2.8913e-05 eta: 6 days, 6:43:33 time: 0.8822 data_time: 0.0020 memory: 51734 loss: 0.1211 loss_ce: 0.1211 2023/02/25 02:00:49 - mmengine - INFO - Epoch(train) [28][2700/5047] lr: 2.8913e-05 eta: 6 days, 6:42:00 time: 0.8732 data_time: 0.0020 memory: 52964 loss: 0.1583 loss_ce: 0.1583 2023/02/25 02:01:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 02:02:16 - mmengine - INFO - Epoch(train) [28][2800/5047] lr: 2.8913e-05 eta: 6 days, 6:40:27 time: 0.8417 data_time: 0.0041 memory: 50640 loss: 0.1369 loss_ce: 0.1369 2023/02/25 02:03:43 - mmengine - INFO - Epoch(train) [28][2900/5047] lr: 2.8913e-05 eta: 6 days, 6:38:58 time: 0.8721 data_time: 0.0023 memory: 50518 loss: 0.1344 loss_ce: 0.1344 2023/02/25 02:05:09 - mmengine - INFO - Epoch(train) [28][3000/5047] lr: 2.8913e-05 eta: 6 days, 6:37:20 time: 0.8600 data_time: 0.0071 memory: 44617 loss: 0.1136 loss_ce: 0.1136 2023/02/25 02:06:34 - mmengine - INFO - Epoch(train) [28][3100/5047] lr: 2.8913e-05 eta: 6 days, 6:35:40 time: 0.9148 data_time: 0.0023 memory: 43289 loss: 0.1265 loss_ce: 0.1265 2023/02/25 02:08:01 - mmengine - INFO - Epoch(train) [28][3200/5047] lr: 2.8913e-05 eta: 6 days, 6:34:09 time: 0.8420 data_time: 0.0021 memory: 46713 loss: 0.1328 loss_ce: 0.1328 2023/02/25 02:09:29 - mmengine - INFO - Epoch(train) [28][3300/5047] lr: 2.8913e-05 eta: 6 days, 6:32:41 time: 0.8904 data_time: 0.0030 memory: 42965 loss: 0.1472 loss_ce: 0.1472 2023/02/25 02:10:57 - mmengine - INFO - Epoch(train) [28][3400/5047] lr: 2.8913e-05 eta: 6 days, 6:31:14 time: 0.8837 data_time: 0.0034 memory: 45643 loss: 0.1381 loss_ce: 0.1381 2023/02/25 02:12:23 - mmengine - INFO - Epoch(train) [28][3500/5047] lr: 2.8913e-05 eta: 6 days, 6:29:39 time: 0.8487 data_time: 0.0050 memory: 44617 loss: 0.1380 loss_ce: 0.1380 2023/02/25 02:13:49 - mmengine - INFO - Epoch(train) [28][3600/5047] lr: 2.8913e-05 eta: 6 days, 6:28:02 time: 0.8439 data_time: 0.0068 memory: 41122 loss: 0.1572 loss_ce: 0.1572 2023/02/25 02:15:18 - mmengine - INFO - Epoch(train) [28][3700/5047] lr: 2.8913e-05 eta: 6 days, 6:26:37 time: 0.8672 data_time: 0.0019 memory: 45785 loss: 0.1255 loss_ce: 0.1255 2023/02/25 02:15:45 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 02:16:45 - mmengine - INFO - Epoch(train) [28][3800/5047] lr: 2.8913e-05 eta: 6 days, 6:25:06 time: 0.8475 data_time: 0.0019 memory: 40966 loss: 0.1247 loss_ce: 0.1247 2023/02/25 02:18:13 - mmengine - INFO - Epoch(train) [28][3900/5047] lr: 2.8913e-05 eta: 6 days, 6:23:40 time: 0.8753 data_time: 0.0020 memory: 55298 loss: 0.1340 loss_ce: 0.1340 2023/02/25 02:19:41 - mmengine - INFO - Epoch(train) [28][4000/5047] lr: 2.8913e-05 eta: 6 days, 6:22:11 time: 0.8872 data_time: 0.0019 memory: 43852 loss: 0.1346 loss_ce: 0.1346 2023/02/25 02:21:08 - mmengine - INFO - Epoch(train) [28][4100/5047] lr: 2.8913e-05 eta: 6 days, 6:20:41 time: 0.8390 data_time: 0.0024 memory: 43289 loss: 0.1377 loss_ce: 0.1377 2023/02/25 02:22:36 - mmengine - INFO - Epoch(train) [28][4200/5047] lr: 2.8913e-05 eta: 6 days, 6:19:15 time: 0.8866 data_time: 0.0027 memory: 50668 loss: 0.1398 loss_ce: 0.1398 2023/02/25 02:24:04 - mmengine - INFO - Epoch(train) [28][4300/5047] lr: 2.8913e-05 eta: 6 days, 6:17:49 time: 0.9026 data_time: 0.0021 memory: 42336 loss: 0.1457 loss_ce: 0.1457 2023/02/25 02:25:30 - mmengine - INFO - Epoch(train) [28][4400/5047] lr: 2.8913e-05 eta: 6 days, 6:16:13 time: 0.7767 data_time: 0.0030 memory: 42437 loss: 0.1341 loss_ce: 0.1341 2023/02/25 02:26:57 - mmengine - INFO - Epoch(train) [28][4500/5047] lr: 2.8913e-05 eta: 6 days, 6:14:41 time: 0.8578 data_time: 0.0036 memory: 45302 loss: 0.1518 loss_ce: 0.1518 2023/02/25 02:28:24 - mmengine - INFO - Epoch(train) [28][4600/5047] lr: 2.8913e-05 eta: 6 days, 6:13:10 time: 0.8592 data_time: 0.0019 memory: 39960 loss: 0.1360 loss_ce: 0.1360 2023/02/25 02:29:51 - mmengine - INFO - Epoch(train) [28][4700/5047] lr: 2.8913e-05 eta: 6 days, 6:11:36 time: 0.8582 data_time: 0.0026 memory: 40535 loss: 0.1397 loss_ce: 0.1397 2023/02/25 02:30:19 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 02:31:18 - mmengine - INFO - Epoch(train) [28][4800/5047] lr: 2.8913e-05 eta: 6 days, 6:10:07 time: 0.8779 data_time: 0.0019 memory: 46713 loss: 0.1139 loss_ce: 0.1139 2023/02/25 02:32:43 - mmengine - INFO - Epoch(train) [28][4900/5047] lr: 2.8913e-05 eta: 6 days, 6:08:24 time: 0.8910 data_time: 0.0022 memory: 43289 loss: 0.1143 loss_ce: 0.1143 2023/02/25 02:34:10 - mmengine - INFO - Epoch(train) [28][5000/5047] lr: 2.8913e-05 eta: 6 days, 6:06:50 time: 0.8531 data_time: 0.0023 memory: 55562 loss: 0.1419 loss_ce: 0.1419 2023/02/25 02:34:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 02:34:52 - mmengine - INFO - Saving checkpoint at 28 epochs 2023/02/25 02:36:24 - mmengine - INFO - Epoch(train) [29][ 100/5047] lr: 2.8712e-05 eta: 6 days, 6:04:44 time: 0.8788 data_time: 0.0030 memory: 55562 loss: 0.1234 loss_ce: 0.1234 2023/02/25 02:37:52 - mmengine - INFO - Epoch(train) [29][ 200/5047] lr: 2.8712e-05 eta: 6 days, 6:03:16 time: 0.8587 data_time: 0.0020 memory: 46622 loss: 0.1446 loss_ce: 0.1446 2023/02/25 02:39:18 - mmengine - INFO - Epoch(train) [29][ 300/5047] lr: 2.8712e-05 eta: 6 days, 6:01:41 time: 0.8434 data_time: 0.0022 memory: 43289 loss: 0.1469 loss_ce: 0.1469 2023/02/25 02:40:47 - mmengine - INFO - Epoch(train) [29][ 400/5047] lr: 2.8712e-05 eta: 6 days, 6:00:16 time: 0.8857 data_time: 0.0033 memory: 55114 loss: 0.1300 loss_ce: 0.1300 2023/02/25 02:42:13 - mmengine - INFO - Epoch(train) [29][ 500/5047] lr: 2.8712e-05 eta: 6 days, 5:58:41 time: 0.8538 data_time: 0.0027 memory: 45643 loss: 0.1439 loss_ce: 0.1439 2023/02/25 02:43:39 - mmengine - INFO - Epoch(train) [29][ 600/5047] lr: 2.8712e-05 eta: 6 days, 5:57:06 time: 0.8369 data_time: 0.0051 memory: 44104 loss: 0.1246 loss_ce: 0.1246 2023/02/25 02:44:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 02:45:06 - mmengine - INFO - Epoch(train) [29][ 700/5047] lr: 2.8712e-05 eta: 6 days, 5:55:33 time: 0.8398 data_time: 0.0023 memory: 53025 loss: 0.1300 loss_ce: 0.1300 2023/02/25 02:46:32 - mmengine - INFO - Epoch(train) [29][ 800/5047] lr: 2.8712e-05 eta: 6 days, 5:53:58 time: 0.8912 data_time: 0.0019 memory: 40535 loss: 0.1395 loss_ce: 0.1395 2023/02/25 02:48:00 - mmengine - INFO - Epoch(train) [29][ 900/5047] lr: 2.8712e-05 eta: 6 days, 5:52:32 time: 0.8842 data_time: 0.0032 memory: 46853 loss: 0.1294 loss_ce: 0.1294 2023/02/25 02:49:28 - mmengine - INFO - Epoch(train) [29][1000/5047] lr: 2.8712e-05 eta: 6 days, 5:51:02 time: 0.8695 data_time: 0.0020 memory: 42649 loss: 0.1389 loss_ce: 0.1389 2023/02/25 02:50:54 - mmengine - INFO - Epoch(train) [29][1100/5047] lr: 2.8712e-05 eta: 6 days, 5:49:25 time: 0.8700 data_time: 0.0023 memory: 40825 loss: 0.1387 loss_ce: 0.1387 2023/02/25 02:52:22 - mmengine - INFO - Epoch(train) [29][1200/5047] lr: 2.8712e-05 eta: 6 days, 5:47:58 time: 0.8858 data_time: 0.0020 memory: 55562 loss: 0.1469 loss_ce: 0.1469 2023/02/25 02:53:48 - mmengine - INFO - Epoch(train) [29][1300/5047] lr: 2.8712e-05 eta: 6 days, 5:46:23 time: 0.8269 data_time: 0.0041 memory: 54072 loss: 0.1241 loss_ce: 0.1241 2023/02/25 02:55:14 - mmengine - INFO - Epoch(train) [29][1400/5047] lr: 2.8712e-05 eta: 6 days, 5:44:49 time: 0.8961 data_time: 0.0029 memory: 42336 loss: 0.1435 loss_ce: 0.1435 2023/02/25 02:56:41 - mmengine - INFO - Epoch(train) [29][1500/5047] lr: 2.8712e-05 eta: 6 days, 5:43:18 time: 0.8941 data_time: 0.0029 memory: 44278 loss: 0.1532 loss_ce: 0.1532 2023/02/25 02:58:09 - mmengine - INFO - Epoch(train) [29][1600/5047] lr: 2.8712e-05 eta: 6 days, 5:41:47 time: 0.9024 data_time: 0.0020 memory: 49151 loss: 0.1575 loss_ce: 0.1575 2023/02/25 02:59:20 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 02:59:34 - mmengine - INFO - Epoch(train) [29][1700/5047] lr: 2.8712e-05 eta: 6 days, 5:40:10 time: 0.8945 data_time: 0.0024 memory: 38476 loss: 0.1519 loss_ce: 0.1519 2023/02/25 03:01:02 - mmengine - INFO - Epoch(train) [29][1800/5047] lr: 2.8712e-05 eta: 6 days, 5:38:42 time: 0.9160 data_time: 0.0045 memory: 53902 loss: 0.1425 loss_ce: 0.1425 2023/02/25 03:02:31 - mmengine - INFO - Epoch(train) [29][1900/5047] lr: 2.8712e-05 eta: 6 days, 5:37:20 time: 0.9005 data_time: 0.0025 memory: 47447 loss: 0.1298 loss_ce: 0.1298 2023/02/25 03:03:59 - mmengine - INFO - Epoch(train) [29][2000/5047] lr: 2.8712e-05 eta: 6 days, 5:35:51 time: 0.8682 data_time: 0.0022 memory: 53809 loss: 0.1530 loss_ce: 0.1530 2023/02/25 03:05:27 - mmengine - INFO - Epoch(train) [29][2100/5047] lr: 2.8712e-05 eta: 6 days, 5:34:25 time: 0.9208 data_time: 0.0024 memory: 50608 loss: 0.1266 loss_ce: 0.1266 2023/02/25 03:06:53 - mmengine - INFO - Epoch(train) [29][2200/5047] lr: 2.8712e-05 eta: 6 days, 5:32:52 time: 0.8321 data_time: 0.0022 memory: 41601 loss: 0.1436 loss_ce: 0.1436 2023/02/25 03:08:19 - mmengine - INFO - Epoch(train) [29][2300/5047] lr: 2.8712e-05 eta: 6 days, 5:31:13 time: 0.8851 data_time: 0.0045 memory: 45302 loss: 0.1261 loss_ce: 0.1261 2023/02/25 03:09:45 - mmengine - INFO - Epoch(train) [29][2400/5047] lr: 2.8712e-05 eta: 6 days, 5:29:39 time: 0.8930 data_time: 0.0020 memory: 41122 loss: 0.1269 loss_ce: 0.1269 2023/02/25 03:11:14 - mmengine - INFO - Epoch(train) [29][2500/5047] lr: 2.8712e-05 eta: 6 days, 5:28:14 time: 0.9415 data_time: 0.0026 memory: 41000 loss: 0.1432 loss_ce: 0.1432 2023/02/25 03:12:41 - mmengine - INFO - Epoch(train) [29][2600/5047] lr: 2.8712e-05 eta: 6 days, 5:26:46 time: 0.8782 data_time: 0.0021 memory: 41419 loss: 0.1361 loss_ce: 0.1361 2023/02/25 03:13:56 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 03:14:10 - mmengine - INFO - Epoch(train) [29][2700/5047] lr: 2.8712e-05 eta: 6 days, 5:25:21 time: 0.8162 data_time: 0.0021 memory: 43289 loss: 0.1284 loss_ce: 0.1284 2023/02/25 03:15:37 - mmengine - INFO - Epoch(train) [29][2800/5047] lr: 2.8712e-05 eta: 6 days, 5:23:50 time: 0.8511 data_time: 0.0021 memory: 55114 loss: 0.1295 loss_ce: 0.1295 2023/02/25 03:17:03 - mmengine - INFO - Epoch(train) [29][2900/5047] lr: 2.8712e-05 eta: 6 days, 5:22:14 time: 0.8427 data_time: 0.0043 memory: 41658 loss: 0.1408 loss_ce: 0.1408 2023/02/25 03:18:30 - mmengine - INFO - Epoch(train) [29][3000/5047] lr: 2.8712e-05 eta: 6 days, 5:20:44 time: 0.9080 data_time: 0.0024 memory: 44440 loss: 0.1269 loss_ce: 0.1269 2023/02/25 03:19:59 - mmengine - INFO - Epoch(train) [29][3100/5047] lr: 2.8712e-05 eta: 6 days, 5:19:20 time: 0.9095 data_time: 0.0023 memory: 42822 loss: 0.1303 loss_ce: 0.1303 2023/02/25 03:21:27 - mmengine - INFO - Epoch(train) [29][3200/5047] lr: 2.8712e-05 eta: 6 days, 5:17:55 time: 0.9163 data_time: 0.0023 memory: 43613 loss: 0.1468 loss_ce: 0.1468 2023/02/25 03:22:56 - mmengine - INFO - Epoch(train) [29][3300/5047] lr: 2.8712e-05 eta: 6 days, 5:16:30 time: 0.8869 data_time: 0.0021 memory: 42024 loss: 0.1312 loss_ce: 0.1312 2023/02/25 03:24:24 - mmengine - INFO - Epoch(train) [29][3400/5047] lr: 2.8712e-05 eta: 6 days, 5:15:03 time: 0.8793 data_time: 0.0020 memory: 42965 loss: 0.1314 loss_ce: 0.1314 2023/02/25 03:25:52 - mmengine - INFO - Epoch(train) [29][3500/5047] lr: 2.8712e-05 eta: 6 days, 5:13:40 time: 0.9462 data_time: 0.0019 memory: 45200 loss: 0.1434 loss_ce: 0.1434 2023/02/25 03:27:21 - mmengine - INFO - Epoch(train) [29][3600/5047] lr: 2.8712e-05 eta: 6 days, 5:12:18 time: 0.8948 data_time: 0.0021 memory: 44632 loss: 0.1160 loss_ce: 0.1160 2023/02/25 03:28:35 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 03:28:49 - mmengine - INFO - Epoch(train) [29][3700/5047] lr: 2.8712e-05 eta: 6 days, 5:10:51 time: 0.8652 data_time: 0.0023 memory: 46355 loss: 0.1691 loss_ce: 0.1691 2023/02/25 03:30:16 - mmengine - INFO - Epoch(train) [29][3800/5047] lr: 2.8712e-05 eta: 6 days, 5:09:20 time: 0.9242 data_time: 0.0022 memory: 45302 loss: 0.1259 loss_ce: 0.1259 2023/02/25 03:31:45 - mmengine - INFO - Epoch(train) [29][3900/5047] lr: 2.8712e-05 eta: 6 days, 5:07:57 time: 0.9035 data_time: 0.0023 memory: 40690 loss: 0.1315 loss_ce: 0.1315 2023/02/25 03:33:11 - mmengine - INFO - Epoch(train) [29][4000/5047] lr: 2.8712e-05 eta: 6 days, 5:06:20 time: 0.8833 data_time: 0.0024 memory: 45479 loss: 0.1482 loss_ce: 0.1482 2023/02/25 03:34:37 - mmengine - INFO - Epoch(train) [29][4100/5047] lr: 2.8712e-05 eta: 6 days, 5:04:45 time: 0.8655 data_time: 0.0020 memory: 46835 loss: 0.1313 loss_ce: 0.1313 2023/02/25 03:36:03 - mmengine - INFO - Epoch(train) [29][4200/5047] lr: 2.8712e-05 eta: 6 days, 5:03:09 time: 0.8416 data_time: 0.0024 memory: 46966 loss: 0.1419 loss_ce: 0.1419 2023/02/25 03:37:31 - mmengine - INFO - Epoch(train) [29][4300/5047] lr: 2.8712e-05 eta: 6 days, 5:01:43 time: 0.9103 data_time: 0.0021 memory: 43289 loss: 0.1196 loss_ce: 0.1196 2023/02/25 03:38:58 - mmengine - INFO - Epoch(train) [29][4400/5047] lr: 2.8712e-05 eta: 6 days, 5:00:13 time: 0.8653 data_time: 0.0021 memory: 44617 loss: 0.1315 loss_ce: 0.1315 2023/02/25 03:40:26 - mmengine - INFO - Epoch(train) [29][4500/5047] lr: 2.8712e-05 eta: 6 days, 4:58:46 time: 0.9067 data_time: 0.0027 memory: 44956 loss: 0.1399 loss_ce: 0.1399 2023/02/25 03:41:53 - mmengine - INFO - Epoch(train) [29][4600/5047] lr: 2.8712e-05 eta: 6 days, 4:57:13 time: 0.8748 data_time: 0.0021 memory: 43613 loss: 0.1539 loss_ce: 0.1539 2023/02/25 03:43:07 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 03:43:21 - mmengine - INFO - Epoch(train) [29][4700/5047] lr: 2.8712e-05 eta: 6 days, 4:55:44 time: 0.8961 data_time: 0.0020 memory: 41419 loss: 0.1296 loss_ce: 0.1296 2023/02/25 03:44:47 - mmengine - INFO - Epoch(train) [29][4800/5047] lr: 2.8712e-05 eta: 6 days, 4:54:12 time: 0.8967 data_time: 0.0021 memory: 42336 loss: 0.1276 loss_ce: 0.1276 2023/02/25 03:46:15 - mmengine - INFO - Epoch(train) [29][4900/5047] lr: 2.8712e-05 eta: 6 days, 4:52:44 time: 0.8494 data_time: 0.0022 memory: 47447 loss: 0.1248 loss_ce: 0.1248 2023/02/25 03:47:43 - mmengine - INFO - Epoch(train) [29][5000/5047] lr: 2.8712e-05 eta: 6 days, 4:51:15 time: 0.8688 data_time: 0.0022 memory: 51815 loss: 0.1250 loss_ce: 0.1250 2023/02/25 03:48:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 03:48:24 - mmengine - INFO - Saving checkpoint at 29 epochs 2023/02/25 03:49:58 - mmengine - INFO - Epoch(train) [30][ 100/5047] lr: 2.8511e-05 eta: 6 days, 4:49:11 time: 0.8623 data_time: 0.0024 memory: 49312 loss: 0.1461 loss_ce: 0.1461 2023/02/25 03:51:25 - mmengine - INFO - Epoch(train) [30][ 200/5047] lr: 2.8511e-05 eta: 6 days, 4:47:39 time: 0.8922 data_time: 0.0020 memory: 41419 loss: 0.1298 loss_ce: 0.1298 2023/02/25 03:52:55 - mmengine - INFO - Epoch(train) [30][ 300/5047] lr: 2.8511e-05 eta: 6 days, 4:46:25 time: 0.9181 data_time: 0.0023 memory: 53044 loss: 0.1441 loss_ce: 0.1441 2023/02/25 03:54:24 - mmengine - INFO - Epoch(train) [30][ 400/5047] lr: 2.8511e-05 eta: 6 days, 4:45:00 time: 0.8731 data_time: 0.0020 memory: 46011 loss: 0.1140 loss_ce: 0.1140 2023/02/25 03:55:52 - mmengine - INFO - Epoch(train) [30][ 500/5047] lr: 2.8511e-05 eta: 6 days, 4:43:35 time: 0.8528 data_time: 0.0025 memory: 53387 loss: 0.1429 loss_ce: 0.1429 2023/02/25 03:57:20 - mmengine - INFO - Epoch(train) [30][ 600/5047] lr: 2.8511e-05 eta: 6 days, 4:42:09 time: 0.9059 data_time: 0.0025 memory: 44617 loss: 0.1462 loss_ce: 0.1462 2023/02/25 03:57:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 03:58:48 - mmengine - INFO - Epoch(train) [30][ 700/5047] lr: 2.8511e-05 eta: 6 days, 4:40:41 time: 0.8580 data_time: 0.0021 memory: 42024 loss: 0.1356 loss_ce: 0.1356 2023/02/25 04:00:15 - mmengine - INFO - Epoch(train) [30][ 800/5047] lr: 2.8511e-05 eta: 6 days, 4:39:08 time: 0.8451 data_time: 0.0021 memory: 44127 loss: 0.1333 loss_ce: 0.1333 2023/02/25 04:01:42 - mmengine - INFO - Epoch(train) [30][ 900/5047] lr: 2.8511e-05 eta: 6 days, 4:37:40 time: 0.9049 data_time: 0.0023 memory: 48948 loss: 0.1258 loss_ce: 0.1258 2023/02/25 04:03:11 - mmengine - INFO - Epoch(train) [30][1000/5047] lr: 2.8511e-05 eta: 6 days, 4:36:15 time: 0.8853 data_time: 0.0023 memory: 52964 loss: 0.1374 loss_ce: 0.1374 2023/02/25 04:04:39 - mmengine - INFO - Epoch(train) [30][1100/5047] lr: 2.8511e-05 eta: 6 days, 4:34:52 time: 0.9294 data_time: 0.0020 memory: 45783 loss: 0.1344 loss_ce: 0.1344 2023/02/25 04:06:07 - mmengine - INFO - Epoch(train) [30][1200/5047] lr: 2.8511e-05 eta: 6 days, 4:33:21 time: 0.8802 data_time: 0.0021 memory: 39960 loss: 0.1307 loss_ce: 0.1307 2023/02/25 04:07:34 - mmengine - INFO - Epoch(train) [30][1300/5047] lr: 2.8511e-05 eta: 6 days, 4:31:50 time: 0.8511 data_time: 0.0022 memory: 43030 loss: 0.1389 loss_ce: 0.1389 2023/02/25 04:09:00 - mmengine - INFO - Epoch(train) [30][1400/5047] lr: 2.8511e-05 eta: 6 days, 4:30:17 time: 0.8816 data_time: 0.0023 memory: 51932 loss: 0.1460 loss_ce: 0.1460 2023/02/25 04:10:28 - mmengine - INFO - Epoch(train) [30][1500/5047] lr: 2.8511e-05 eta: 6 days, 4:28:48 time: 0.8929 data_time: 0.0022 memory: 45302 loss: 0.1458 loss_ce: 0.1458 2023/02/25 04:11:56 - mmengine - INFO - Epoch(train) [30][1600/5047] lr: 2.8511e-05 eta: 6 days, 4:27:24 time: 0.8434 data_time: 0.0023 memory: 44278 loss: 0.0990 loss_ce: 0.0990 2023/02/25 04:12:29 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 04:13:26 - mmengine - INFO - Epoch(train) [30][1700/5047] lr: 2.8511e-05 eta: 6 days, 4:26:06 time: 0.8668 data_time: 0.0022 memory: 55562 loss: 0.1446 loss_ce: 0.1446 2023/02/25 04:14:55 - mmengine - INFO - Epoch(train) [30][1800/5047] lr: 2.8511e-05 eta: 6 days, 4:24:45 time: 0.9365 data_time: 0.0027 memory: 43947 loss: 0.1267 loss_ce: 0.1267 2023/02/25 04:16:25 - mmengine - INFO - Epoch(train) [30][1900/5047] lr: 2.8511e-05 eta: 6 days, 4:23:25 time: 0.9365 data_time: 0.0025 memory: 44268 loss: 0.1267 loss_ce: 0.1267 2023/02/25 04:17:53 - mmengine - INFO - Epoch(train) [30][2000/5047] lr: 2.8511e-05 eta: 6 days, 4:21:57 time: 0.8275 data_time: 0.0021 memory: 55562 loss: 0.1264 loss_ce: 0.1264 2023/02/25 04:19:21 - mmengine - INFO - Epoch(train) [30][2100/5047] lr: 2.8511e-05 eta: 6 days, 4:20:31 time: 0.9215 data_time: 0.0021 memory: 45571 loss: 0.1364 loss_ce: 0.1364 2023/02/25 04:20:49 - mmengine - INFO - Epoch(train) [30][2200/5047] lr: 2.8511e-05 eta: 6 days, 4:19:02 time: 0.8760 data_time: 0.0024 memory: 44617 loss: 0.1321 loss_ce: 0.1321 2023/02/25 04:22:16 - mmengine - INFO - Epoch(train) [30][2300/5047] lr: 2.8511e-05 eta: 6 days, 4:17:32 time: 0.8421 data_time: 0.0037 memory: 45353 loss: 0.1392 loss_ce: 0.1392 2023/02/25 04:23:44 - mmengine - INFO - Epoch(train) [30][2400/5047] lr: 2.8511e-05 eta: 6 days, 4:16:04 time: 0.8624 data_time: 0.0024 memory: 42794 loss: 0.1337 loss_ce: 0.1337 2023/02/25 04:25:10 - mmengine - INFO - Epoch(train) [30][2500/5047] lr: 2.8511e-05 eta: 6 days, 4:14:32 time: 0.8805 data_time: 0.0025 memory: 50906 loss: 0.1471 loss_ce: 0.1471 2023/02/25 04:26:38 - mmengine - INFO - Epoch(train) [30][2600/5047] lr: 2.8511e-05 eta: 6 days, 4:13:04 time: 0.8542 data_time: 0.0020 memory: 45785 loss: 0.1229 loss_ce: 0.1229 2023/02/25 04:27:10 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 04:28:06 - mmengine - INFO - Epoch(train) [30][2700/5047] lr: 2.8511e-05 eta: 6 days, 4:11:36 time: 0.8762 data_time: 0.0020 memory: 55562 loss: 0.1437 loss_ce: 0.1437 2023/02/25 04:29:35 - mmengine - INFO - Epoch(train) [30][2800/5047] lr: 2.8511e-05 eta: 6 days, 4:10:14 time: 0.9069 data_time: 0.0021 memory: 43222 loss: 0.1340 loss_ce: 0.1340 2023/02/25 04:31:03 - mmengine - INFO - Epoch(train) [30][2900/5047] lr: 2.8511e-05 eta: 6 days, 4:08:47 time: 0.8456 data_time: 0.0027 memory: 41122 loss: 0.1554 loss_ce: 0.1554 2023/02/25 04:32:30 - mmengine - INFO - Epoch(train) [30][3000/5047] lr: 2.8511e-05 eta: 6 days, 4:07:16 time: 0.8225 data_time: 0.0028 memory: 42010 loss: 0.1397 loss_ce: 0.1397 2023/02/25 04:33:58 - mmengine - INFO - Epoch(train) [30][3100/5047] lr: 2.8511e-05 eta: 6 days, 4:05:51 time: 0.8515 data_time: 0.0019 memory: 47632 loss: 0.1299 loss_ce: 0.1299 2023/02/25 04:35:26 - mmengine - INFO - Epoch(train) [30][3200/5047] lr: 2.8511e-05 eta: 6 days, 4:04:23 time: 0.9426 data_time: 0.0021 memory: 55562 loss: 0.1338 loss_ce: 0.1338 2023/02/25 04:36:53 - mmengine - INFO - Epoch(train) [30][3300/5047] lr: 2.8511e-05 eta: 6 days, 4:02:53 time: 0.8880 data_time: 0.0024 memory: 45302 loss: 0.1589 loss_ce: 0.1589 2023/02/25 04:38:21 - mmengine - INFO - Epoch(train) [30][3400/5047] lr: 2.8511e-05 eta: 6 days, 4:01:25 time: 0.8864 data_time: 0.0021 memory: 43965 loss: 0.1268 loss_ce: 0.1268 2023/02/25 04:39:49 - mmengine - INFO - Epoch(train) [30][3500/5047] lr: 2.8511e-05 eta: 6 days, 3:59:58 time: 0.9044 data_time: 0.0024 memory: 42336 loss: 0.1325 loss_ce: 0.1325 2023/02/25 04:41:17 - mmengine - INFO - Epoch(train) [30][3600/5047] lr: 2.8511e-05 eta: 6 days, 3:58:31 time: 0.8400 data_time: 0.0023 memory: 55562 loss: 0.1394 loss_ce: 0.1394 2023/02/25 04:41:49 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 04:42:44 - mmengine - INFO - Epoch(train) [30][3700/5047] lr: 2.8511e-05 eta: 6 days, 3:57:01 time: 0.8943 data_time: 0.0026 memory: 43613 loss: 0.1406 loss_ce: 0.1406 2023/02/25 04:44:11 - mmengine - INFO - Epoch(train) [30][3800/5047] lr: 2.8511e-05 eta: 6 days, 3:55:28 time: 0.8728 data_time: 0.0023 memory: 45727 loss: 0.1220 loss_ce: 0.1220 2023/02/25 04:45:38 - mmengine - INFO - Epoch(train) [30][3900/5047] lr: 2.8511e-05 eta: 6 days, 3:53:59 time: 0.8744 data_time: 0.0023 memory: 41419 loss: 0.1259 loss_ce: 0.1259 2023/02/25 04:47:05 - mmengine - INFO - Epoch(train) [30][4000/5047] lr: 2.8511e-05 eta: 6 days, 3:52:28 time: 0.8206 data_time: 0.0023 memory: 41122 loss: 0.1463 loss_ce: 0.1463 2023/02/25 04:48:33 - mmengine - INFO - Epoch(train) [30][4100/5047] lr: 2.8511e-05 eta: 6 days, 3:50:58 time: 0.8661 data_time: 0.0025 memory: 43289 loss: 0.1259 loss_ce: 0.1259 2023/02/25 04:50:01 - mmengine - INFO - Epoch(train) [30][4200/5047] lr: 2.8511e-05 eta: 6 days, 3:49:32 time: 0.8775 data_time: 0.0021 memory: 48188 loss: 0.1441 loss_ce: 0.1441 2023/02/25 04:51:28 - mmengine - INFO - Epoch(train) [30][4300/5047] lr: 2.8511e-05 eta: 6 days, 3:48:00 time: 0.8904 data_time: 0.0025 memory: 46355 loss: 0.1211 loss_ce: 0.1211 2023/02/25 04:52:57 - mmengine - INFO - Epoch(train) [30][4400/5047] lr: 2.8511e-05 eta: 6 days, 3:46:39 time: 0.8929 data_time: 0.0040 memory: 44617 loss: 0.1432 loss_ce: 0.1432 2023/02/25 04:54:26 - mmengine - INFO - Epoch(train) [30][4500/5047] lr: 2.8511e-05 eta: 6 days, 3:45:17 time: 0.9256 data_time: 0.0039 memory: 48188 loss: 0.1317 loss_ce: 0.1317 2023/02/25 04:55:53 - mmengine - INFO - Epoch(train) [30][4600/5047] lr: 2.8511e-05 eta: 6 days, 3:43:46 time: 0.8485 data_time: 0.0040 memory: 44220 loss: 0.1323 loss_ce: 0.1323 2023/02/25 04:56:25 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 04:57:21 - mmengine - INFO - Epoch(train) [30][4700/5047] lr: 2.8511e-05 eta: 6 days, 3:42:17 time: 0.9277 data_time: 0.0023 memory: 44787 loss: 0.1479 loss_ce: 0.1479 2023/02/25 04:58:48 - mmengine - INFO - Epoch(train) [30][4800/5047] lr: 2.8511e-05 eta: 6 days, 3:40:48 time: 0.8896 data_time: 0.0041 memory: 43947 loss: 0.1286 loss_ce: 0.1286 2023/02/25 05:00:17 - mmengine - INFO - Epoch(train) [30][4900/5047] lr: 2.8511e-05 eta: 6 days, 3:39:27 time: 0.9144 data_time: 0.0036 memory: 44539 loss: 0.1421 loss_ce: 0.1421 2023/02/25 05:01:46 - mmengine - INFO - Epoch(train) [30][5000/5047] lr: 2.8511e-05 eta: 6 days, 3:38:04 time: 0.8944 data_time: 0.0020 memory: 42965 loss: 0.1414 loss_ce: 0.1414 2023/02/25 05:02:26 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 05:02:26 - mmengine - INFO - Saving checkpoint at 30 epochs 2023/02/25 05:03:59 - mmengine - INFO - Epoch(train) [31][ 100/5047] lr: 2.8310e-05 eta: 6 days, 3:35:50 time: 0.8850 data_time: 0.0021 memory: 47447 loss: 0.1177 loss_ce: 0.1177 2023/02/25 05:05:28 - mmengine - INFO - Epoch(train) [31][ 200/5047] lr: 2.8310e-05 eta: 6 days, 3:34:27 time: 0.8933 data_time: 0.0022 memory: 47089 loss: 0.1297 loss_ce: 0.1297 2023/02/25 05:06:55 - mmengine - INFO - Epoch(train) [31][ 300/5047] lr: 2.8310e-05 eta: 6 days, 3:32:58 time: 0.9472 data_time: 0.0038 memory: 43001 loss: 0.1618 loss_ce: 0.1618 2023/02/25 05:08:22 - mmengine - INFO - Epoch(train) [31][ 400/5047] lr: 2.8310e-05 eta: 6 days, 3:31:27 time: 0.8374 data_time: 0.0022 memory: 47074 loss: 0.1429 loss_ce: 0.1429 2023/02/25 05:09:50 - mmengine - INFO - Epoch(train) [31][ 500/5047] lr: 2.8310e-05 eta: 6 days, 3:29:58 time: 0.8394 data_time: 0.0022 memory: 43613 loss: 0.1221 loss_ce: 0.1221 2023/02/25 05:11:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 05:11:17 - mmengine - INFO - Epoch(train) [31][ 600/5047] lr: 2.8310e-05 eta: 6 days, 3:28:26 time: 0.8459 data_time: 0.0055 memory: 42399 loss: 0.1258 loss_ce: 0.1258 2023/02/25 05:12:46 - mmengine - INFO - Epoch(train) [31][ 700/5047] lr: 2.8310e-05 eta: 6 days, 3:27:04 time: 0.8218 data_time: 0.0034 memory: 45010 loss: 0.1356 loss_ce: 0.1356 2023/02/25 05:14:13 - mmengine - INFO - Epoch(train) [31][ 800/5047] lr: 2.8310e-05 eta: 6 days, 3:25:35 time: 0.9120 data_time: 0.0028 memory: 50608 loss: 0.1252 loss_ce: 0.1252 2023/02/25 05:15:40 - mmengine - INFO - Epoch(train) [31][ 900/5047] lr: 2.8310e-05 eta: 6 days, 3:24:01 time: 0.8873 data_time: 0.0022 memory: 41086 loss: 0.1338 loss_ce: 0.1338 2023/02/25 05:17:08 - mmengine - INFO - Epoch(train) [31][1000/5047] lr: 2.8310e-05 eta: 6 days, 3:22:35 time: 0.8954 data_time: 0.0021 memory: 43553 loss: 0.1190 loss_ce: 0.1190 2023/02/25 05:18:37 - mmengine - INFO - Epoch(train) [31][1100/5047] lr: 2.8310e-05 eta: 6 days, 3:21:11 time: 0.8877 data_time: 0.0033 memory: 39126 loss: 0.1341 loss_ce: 0.1341 2023/02/25 05:20:04 - mmengine - INFO - Epoch(train) [31][1200/5047] lr: 2.8310e-05 eta: 6 days, 3:19:41 time: 0.8638 data_time: 0.0069 memory: 45280 loss: 0.1379 loss_ce: 0.1379 2023/02/25 05:21:30 - mmengine - INFO - Epoch(train) [31][1300/5047] lr: 2.8310e-05 eta: 6 days, 3:18:08 time: 0.8750 data_time: 0.0019 memory: 43947 loss: 0.1274 loss_ce: 0.1274 2023/02/25 05:22:58 - mmengine - INFO - Epoch(train) [31][1400/5047] lr: 2.8310e-05 eta: 6 days, 3:16:39 time: 0.8796 data_time: 0.0020 memory: 43140 loss: 0.1310 loss_ce: 0.1310 2023/02/25 05:24:25 - mmengine - INFO - Epoch(train) [31][1500/5047] lr: 2.8310e-05 eta: 6 days, 3:15:08 time: 0.8911 data_time: 0.0020 memory: 43613 loss: 0.1386 loss_ce: 0.1386 2023/02/25 05:25:43 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 05:25:52 - mmengine - INFO - Epoch(train) [31][1600/5047] lr: 2.8310e-05 eta: 6 days, 3:13:35 time: 0.8525 data_time: 0.0021 memory: 55562 loss: 0.1455 loss_ce: 0.1455 2023/02/25 05:27:19 - mmengine - INFO - Epoch(train) [31][1700/5047] lr: 2.8310e-05 eta: 6 days, 3:12:06 time: 0.8883 data_time: 0.0021 memory: 55562 loss: 0.1258 loss_ce: 0.1258 2023/02/25 05:28:44 - mmengine - INFO - Epoch(train) [31][1800/5047] lr: 2.8310e-05 eta: 6 days, 3:10:28 time: 0.8377 data_time: 0.0030 memory: 41724 loss: 0.1362 loss_ce: 0.1362 2023/02/25 05:30:13 - mmengine - INFO - Epoch(train) [31][1900/5047] lr: 2.8310e-05 eta: 6 days, 3:09:03 time: 0.8558 data_time: 0.0031 memory: 47813 loss: 0.1487 loss_ce: 0.1487 2023/02/25 05:31:41 - mmengine - INFO - Epoch(train) [31][2000/5047] lr: 2.8310e-05 eta: 6 days, 3:07:36 time: 0.9042 data_time: 0.0019 memory: 42965 loss: 0.1315 loss_ce: 0.1315 2023/02/25 05:33:08 - mmengine - INFO - Epoch(train) [31][2100/5047] lr: 2.8310e-05 eta: 6 days, 3:06:04 time: 0.8891 data_time: 0.0021 memory: 40535 loss: 0.1221 loss_ce: 0.1221 2023/02/25 05:34:35 - mmengine - INFO - Epoch(train) [31][2200/5047] lr: 2.8310e-05 eta: 6 days, 3:04:34 time: 0.8732 data_time: 0.0024 memory: 46106 loss: 0.1209 loss_ce: 0.1209 2023/02/25 05:36:02 - mmengine - INFO - Epoch(train) [31][2300/5047] lr: 2.8310e-05 eta: 6 days, 3:03:05 time: 0.8823 data_time: 0.0061 memory: 44617 loss: 0.1407 loss_ce: 0.1407 2023/02/25 05:37:31 - mmengine - INFO - Epoch(train) [31][2400/5047] lr: 2.8310e-05 eta: 6 days, 3:01:42 time: 0.8348 data_time: 0.0021 memory: 44617 loss: 0.1638 loss_ce: 0.1638 2023/02/25 05:38:59 - mmengine - INFO - Epoch(train) [31][2500/5047] lr: 2.8310e-05 eta: 6 days, 3:00:13 time: 0.9055 data_time: 0.0025 memory: 41724 loss: 0.1452 loss_ce: 0.1452 2023/02/25 05:40:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 05:40:26 - mmengine - INFO - Epoch(train) [31][2600/5047] lr: 2.8310e-05 eta: 6 days, 2:58:44 time: 0.8725 data_time: 0.0023 memory: 55562 loss: 0.1283 loss_ce: 0.1283 2023/02/25 05:41:53 - mmengine - INFO - Epoch(train) [31][2700/5047] lr: 2.8310e-05 eta: 6 days, 2:57:11 time: 0.8317 data_time: 0.0021 memory: 44278 loss: 0.1305 loss_ce: 0.1305 2023/02/25 05:43:20 - mmengine - INFO - Epoch(train) [31][2800/5047] lr: 2.8310e-05 eta: 6 days, 2:55:40 time: 0.8354 data_time: 0.0032 memory: 45715 loss: 0.1273 loss_ce: 0.1273 2023/02/25 05:44:46 - mmengine - INFO - Epoch(train) [31][2900/5047] lr: 2.8310e-05 eta: 6 days, 2:54:07 time: 0.8493 data_time: 0.0021 memory: 41515 loss: 0.1373 loss_ce: 0.1373 2023/02/25 05:46:14 - mmengine - INFO - Epoch(train) [31][3000/5047] lr: 2.8310e-05 eta: 6 days, 2:52:40 time: 0.9273 data_time: 0.0087 memory: 45815 loss: 0.1387 loss_ce: 0.1387 2023/02/25 05:47:41 - mmengine - INFO - Epoch(train) [31][3100/5047] lr: 2.8310e-05 eta: 6 days, 2:51:08 time: 0.8801 data_time: 0.0022 memory: 42336 loss: 0.1348 loss_ce: 0.1348 2023/02/25 05:49:09 - mmengine - INFO - Epoch(train) [31][3200/5047] lr: 2.8310e-05 eta: 6 days, 2:49:40 time: 0.8624 data_time: 0.0022 memory: 47813 loss: 0.1385 loss_ce: 0.1385 2023/02/25 05:50:37 - mmengine - INFO - Epoch(train) [31][3300/5047] lr: 2.8310e-05 eta: 6 days, 2:48:13 time: 0.9494 data_time: 0.0023 memory: 46460 loss: 0.1391 loss_ce: 0.1391 2023/02/25 05:52:06 - mmengine - INFO - Epoch(train) [31][3400/5047] lr: 2.8310e-05 eta: 6 days, 2:46:54 time: 0.9230 data_time: 0.0028 memory: 45831 loss: 0.1326 loss_ce: 0.1326 2023/02/25 05:53:34 - mmengine - INFO - Epoch(train) [31][3500/5047] lr: 2.8310e-05 eta: 6 days, 2:45:23 time: 0.8381 data_time: 0.0032 memory: 43432 loss: 0.1180 loss_ce: 0.1180 2023/02/25 05:54:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 05:55:02 - mmengine - INFO - Epoch(train) [31][3600/5047] lr: 2.8310e-05 eta: 6 days, 2:43:57 time: 0.9443 data_time: 0.0020 memory: 41724 loss: 0.1231 loss_ce: 0.1231 2023/02/25 05:56:28 - mmengine - INFO - Epoch(train) [31][3700/5047] lr: 2.8310e-05 eta: 6 days, 2:42:24 time: 0.8527 data_time: 0.0051 memory: 41724 loss: 0.1396 loss_ce: 0.1396 2023/02/25 05:57:56 - mmengine - INFO - Epoch(train) [31][3800/5047] lr: 2.8310e-05 eta: 6 days, 2:40:54 time: 0.9201 data_time: 0.0032 memory: 41597 loss: 0.1479 loss_ce: 0.1479 2023/02/25 05:59:23 - mmengine - INFO - Epoch(train) [31][3900/5047] lr: 2.8310e-05 eta: 6 days, 2:39:23 time: 0.8419 data_time: 0.0022 memory: 52517 loss: 0.1499 loss_ce: 0.1499 2023/02/25 06:00:51 - mmengine - INFO - Epoch(train) [31][4000/5047] lr: 2.8310e-05 eta: 6 days, 2:37:59 time: 0.9065 data_time: 0.0037 memory: 44956 loss: 0.1539 loss_ce: 0.1539 2023/02/25 06:02:19 - mmengine - INFO - Epoch(train) [31][4100/5047] lr: 2.8310e-05 eta: 6 days, 2:36:31 time: 0.8845 data_time: 0.0045 memory: 46116 loss: 0.1367 loss_ce: 0.1367 2023/02/25 06:03:47 - mmengine - INFO - Epoch(train) [31][4200/5047] lr: 2.8310e-05 eta: 6 days, 2:35:04 time: 0.8789 data_time: 0.0026 memory: 42649 loss: 0.1449 loss_ce: 0.1449 2023/02/25 06:05:16 - mmengine - INFO - Epoch(train) [31][4300/5047] lr: 2.8310e-05 eta: 6 days, 2:33:44 time: 0.8857 data_time: 0.0037 memory: 42849 loss: 0.1281 loss_ce: 0.1281 2023/02/25 06:06:45 - mmengine - INFO - Epoch(train) [31][4400/5047] lr: 2.8310e-05 eta: 6 days, 2:32:19 time: 0.8555 data_time: 0.0024 memory: 49168 loss: 0.1331 loss_ce: 0.1331 2023/02/25 06:08:12 - mmengine - INFO - Epoch(train) [31][4500/5047] lr: 2.8310e-05 eta: 6 days, 2:30:47 time: 0.8901 data_time: 0.0020 memory: 55562 loss: 0.1456 loss_ce: 0.1456 2023/02/25 06:09:32 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 06:09:41 - mmengine - INFO - Epoch(train) [31][4600/5047] lr: 2.8310e-05 eta: 6 days, 2:29:26 time: 0.9064 data_time: 0.0021 memory: 39960 loss: 0.1384 loss_ce: 0.1384 2023/02/25 06:11:08 - mmengine - INFO - Epoch(train) [31][4700/5047] lr: 2.8310e-05 eta: 6 days, 2:27:56 time: 0.8673 data_time: 0.0031 memory: 40241 loss: 0.1418 loss_ce: 0.1418 2023/02/25 06:12:35 - mmengine - INFO - Epoch(train) [31][4800/5047] lr: 2.8310e-05 eta: 6 days, 2:26:26 time: 0.8526 data_time: 0.0035 memory: 55562 loss: 0.1481 loss_ce: 0.1481 2023/02/25 06:14:01 - mmengine - INFO - Epoch(train) [31][4900/5047] lr: 2.8310e-05 eta: 6 days, 2:24:51 time: 0.8981 data_time: 0.0022 memory: 48948 loss: 0.1357 loss_ce: 0.1357 2023/02/25 06:15:29 - mmengine - INFO - Epoch(train) [31][5000/5047] lr: 2.8310e-05 eta: 6 days, 2:23:21 time: 0.8976 data_time: 0.0026 memory: 49188 loss: 0.1335 loss_ce: 0.1335 2023/02/25 06:16:09 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 06:16:09 - mmengine - INFO - Saving checkpoint at 31 epochs 2023/02/25 06:17:41 - mmengine - INFO - Epoch(train) [32][ 100/5047] lr: 2.8109e-05 eta: 6 days, 2:21:06 time: 0.8666 data_time: 0.0029 memory: 55562 loss: 0.1251 loss_ce: 0.1251 2023/02/25 06:19:09 - mmengine - INFO - Epoch(train) [32][ 200/5047] lr: 2.8109e-05 eta: 6 days, 2:19:38 time: 0.8882 data_time: 0.0050 memory: 42623 loss: 0.1309 loss_ce: 0.1309 2023/02/25 06:20:37 - mmengine - INFO - Epoch(train) [32][ 300/5047] lr: 2.8109e-05 eta: 6 days, 2:18:11 time: 0.8202 data_time: 0.0025 memory: 46355 loss: 0.1243 loss_ce: 0.1243 2023/02/25 06:22:04 - mmengine - INFO - Epoch(train) [32][ 400/5047] lr: 2.8109e-05 eta: 6 days, 2:16:41 time: 0.8865 data_time: 0.0023 memory: 51548 loss: 0.1318 loss_ce: 0.1318 2023/02/25 06:23:31 - mmengine - INFO - Epoch(train) [32][ 500/5047] lr: 2.8109e-05 eta: 6 days, 2:15:10 time: 0.8647 data_time: 0.0041 memory: 41323 loss: 0.1271 loss_ce: 0.1271 2023/02/25 06:24:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 06:24:57 - mmengine - INFO - Epoch(train) [32][ 600/5047] lr: 2.8109e-05 eta: 6 days, 2:13:35 time: 0.8979 data_time: 0.0022 memory: 42649 loss: 0.1277 loss_ce: 0.1277 2023/02/25 06:26:25 - mmengine - INFO - Epoch(train) [32][ 700/5047] lr: 2.8109e-05 eta: 6 days, 2:12:07 time: 0.8780 data_time: 0.0030 memory: 48774 loss: 0.1273 loss_ce: 0.1273 2023/02/25 06:27:54 - mmengine - INFO - Epoch(train) [32][ 800/5047] lr: 2.8109e-05 eta: 6 days, 2:10:43 time: 0.8748 data_time: 0.0019 memory: 43600 loss: 0.1418 loss_ce: 0.1418 2023/02/25 06:29:21 - mmengine - INFO - Epoch(train) [32][ 900/5047] lr: 2.8109e-05 eta: 6 days, 2:09:15 time: 0.8742 data_time: 0.0020 memory: 43585 loss: 0.1400 loss_ce: 0.1400 2023/02/25 06:30:49 - mmengine - INFO - Epoch(train) [32][1000/5047] lr: 2.8109e-05 eta: 6 days, 2:07:46 time: 0.8896 data_time: 0.0022 memory: 51693 loss: 0.1429 loss_ce: 0.1429 2023/02/25 06:32:17 - mmengine - INFO - Epoch(train) [32][1100/5047] lr: 2.8109e-05 eta: 6 days, 2:06:20 time: 0.9056 data_time: 0.0024 memory: 43289 loss: 0.1355 loss_ce: 0.1355 2023/02/25 06:33:43 - mmengine - INFO - Epoch(train) [32][1200/5047] lr: 2.8109e-05 eta: 6 days, 2:04:46 time: 0.8548 data_time: 0.0023 memory: 52543 loss: 0.1389 loss_ce: 0.1389 2023/02/25 06:35:12 - mmengine - INFO - Epoch(train) [32][1300/5047] lr: 2.8109e-05 eta: 6 days, 2:03:20 time: 0.8784 data_time: 0.0033 memory: 47958 loss: 0.1424 loss_ce: 0.1424 2023/02/25 06:36:39 - mmengine - INFO - Epoch(train) [32][1400/5047] lr: 2.8109e-05 eta: 6 days, 2:01:49 time: 0.8898 data_time: 0.0033 memory: 42965 loss: 0.1385 loss_ce: 0.1385 2023/02/25 06:38:05 - mmengine - INFO - Epoch(train) [32][1500/5047] lr: 2.8109e-05 eta: 6 days, 2:00:17 time: 0.8572 data_time: 0.0025 memory: 41122 loss: 0.1488 loss_ce: 0.1488 2023/02/25 06:38:43 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 06:39:34 - mmengine - INFO - Epoch(train) [32][1600/5047] lr: 2.8109e-05 eta: 6 days, 1:58:55 time: 0.8900 data_time: 0.0022 memory: 43613 loss: 0.1359 loss_ce: 0.1359 2023/02/25 06:41:02 - mmengine - INFO - Epoch(train) [32][1700/5047] lr: 2.8109e-05 eta: 6 days, 1:57:26 time: 0.8457 data_time: 0.0022 memory: 44617 loss: 0.1263 loss_ce: 0.1263 2023/02/25 06:42:30 - mmengine - INFO - Epoch(train) [32][1800/5047] lr: 2.8109e-05 eta: 6 days, 1:56:00 time: 0.8683 data_time: 0.0026 memory: 41122 loss: 0.1483 loss_ce: 0.1483 2023/02/25 06:43:57 - mmengine - INFO - Epoch(train) [32][1900/5047] lr: 2.8109e-05 eta: 6 days, 1:54:28 time: 0.9081 data_time: 0.0022 memory: 55562 loss: 0.1396 loss_ce: 0.1396 2023/02/25 06:45:24 - mmengine - INFO - Epoch(train) [32][2000/5047] lr: 2.8109e-05 eta: 6 days, 1:52:59 time: 0.8410 data_time: 0.0021 memory: 42965 loss: 0.1649 loss_ce: 0.1649 2023/02/25 06:46:52 - mmengine - INFO - Epoch(train) [32][2100/5047] lr: 2.8109e-05 eta: 6 days, 1:51:33 time: 0.8992 data_time: 0.0020 memory: 41419 loss: 0.1451 loss_ce: 0.1451 2023/02/25 06:48:21 - mmengine - INFO - Epoch(train) [32][2200/5047] lr: 2.8109e-05 eta: 6 days, 1:50:09 time: 0.8658 data_time: 0.0038 memory: 43289 loss: 0.1266 loss_ce: 0.1266 2023/02/25 06:49:49 - mmengine - INFO - Epoch(train) [32][2300/5047] lr: 2.8109e-05 eta: 6 days, 1:48:41 time: 0.8772 data_time: 0.0023 memory: 43289 loss: 0.1404 loss_ce: 0.1404 2023/02/25 06:51:17 - mmengine - INFO - Epoch(train) [32][2400/5047] lr: 2.8109e-05 eta: 6 days, 1:47:16 time: 0.8223 data_time: 0.0024 memory: 54303 loss: 0.1083 loss_ce: 0.1083 2023/02/25 06:52:44 - mmengine - INFO - Epoch(train) [32][2500/5047] lr: 2.8109e-05 eta: 6 days, 1:45:44 time: 0.8649 data_time: 0.0026 memory: 49144 loss: 0.1324 loss_ce: 0.1324 2023/02/25 06:53:21 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 06:54:12 - mmengine - INFO - Epoch(train) [32][2600/5047] lr: 2.8109e-05 eta: 6 days, 1:44:19 time: 0.8813 data_time: 0.0026 memory: 45973 loss: 0.1482 loss_ce: 0.1482 2023/02/25 06:55:40 - mmengine - INFO - Epoch(train) [32][2700/5047] lr: 2.8109e-05 eta: 6 days, 1:42:50 time: 0.8874 data_time: 0.0030 memory: 42965 loss: 0.1399 loss_ce: 0.1399 2023/02/25 06:57:07 - mmengine - INFO - Epoch(train) [32][2800/5047] lr: 2.8109e-05 eta: 6 days, 1:41:20 time: 0.8642 data_time: 0.0024 memory: 46275 loss: 0.1200 loss_ce: 0.1200 2023/02/25 06:58:35 - mmengine - INFO - Epoch(train) [32][2900/5047] lr: 2.8109e-05 eta: 6 days, 1:39:51 time: 0.8417 data_time: 0.0024 memory: 45302 loss: 0.1557 loss_ce: 0.1557 2023/02/25 07:00:03 - mmengine - INFO - Epoch(train) [32][3000/5047] lr: 2.8109e-05 eta: 6 days, 1:38:26 time: 0.8884 data_time: 0.0021 memory: 43289 loss: 0.1279 loss_ce: 0.1279 2023/02/25 07:01:32 - mmengine - INFO - Epoch(train) [32][3100/5047] lr: 2.8109e-05 eta: 6 days, 1:37:03 time: 0.8775 data_time: 0.0021 memory: 47813 loss: 0.1317 loss_ce: 0.1317 2023/02/25 07:03:01 - mmengine - INFO - Epoch(train) [32][3200/5047] lr: 2.8109e-05 eta: 6 days, 1:35:40 time: 0.8719 data_time: 0.0021 memory: 45851 loss: 0.1342 loss_ce: 0.1342 2023/02/25 07:04:28 - mmengine - INFO - Epoch(train) [32][3300/5047] lr: 2.8109e-05 eta: 6 days, 1:34:09 time: 0.9342 data_time: 0.0022 memory: 52083 loss: 0.1619 loss_ce: 0.1619 2023/02/25 07:05:56 - mmengine - INFO - Epoch(train) [32][3400/5047] lr: 2.8109e-05 eta: 6 days, 1:32:43 time: 0.9054 data_time: 0.0024 memory: 50513 loss: 0.1151 loss_ce: 0.1151 2023/02/25 07:07:25 - mmengine - INFO - Epoch(train) [32][3500/5047] lr: 2.8109e-05 eta: 6 days, 1:31:22 time: 0.8656 data_time: 0.0020 memory: 44587 loss: 0.1302 loss_ce: 0.1302 2023/02/25 07:08:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 07:08:53 - mmengine - INFO - Epoch(train) [32][3600/5047] lr: 2.8109e-05 eta: 6 days, 1:29:53 time: 0.8917 data_time: 0.0031 memory: 47293 loss: 0.1547 loss_ce: 0.1547 2023/02/25 07:10:20 - mmengine - INFO - Epoch(train) [32][3700/5047] lr: 2.8109e-05 eta: 6 days, 1:28:24 time: 0.8560 data_time: 0.0028 memory: 42498 loss: 0.1305 loss_ce: 0.1305 2023/02/25 07:11:48 - mmengine - INFO - Epoch(train) [32][3800/5047] lr: 2.8109e-05 eta: 6 days, 1:26:56 time: 0.8715 data_time: 0.0020 memory: 46794 loss: 0.1430 loss_ce: 0.1430 2023/02/25 07:13:15 - mmengine - INFO - Epoch(train) [32][3900/5047] lr: 2.8109e-05 eta: 6 days, 1:25:24 time: 0.8497 data_time: 0.0026 memory: 44720 loss: 0.1421 loss_ce: 0.1421 2023/02/25 07:14:42 - mmengine - INFO - Epoch(train) [32][4000/5047] lr: 2.8109e-05 eta: 6 days, 1:23:55 time: 0.8683 data_time: 0.0025 memory: 50758 loss: 0.1455 loss_ce: 0.1455 2023/02/25 07:16:12 - mmengine - INFO - Epoch(train) [32][4100/5047] lr: 2.8109e-05 eta: 6 days, 1:22:36 time: 0.8963 data_time: 0.0020 memory: 49235 loss: 0.1346 loss_ce: 0.1346 2023/02/25 07:17:37 - mmengine - INFO - Epoch(train) [32][4200/5047] lr: 2.8109e-05 eta: 6 days, 1:20:58 time: 0.8873 data_time: 0.0025 memory: 40000 loss: 0.1356 loss_ce: 0.1356 2023/02/25 07:19:06 - mmengine - INFO - Epoch(train) [32][4300/5047] lr: 2.8109e-05 eta: 6 days, 1:19:33 time: 0.9021 data_time: 0.0021 memory: 41724 loss: 0.1329 loss_ce: 0.1329 2023/02/25 07:20:33 - mmengine - INFO - Epoch(train) [32][4400/5047] lr: 2.8109e-05 eta: 6 days, 1:18:02 time: 0.8172 data_time: 0.0020 memory: 43947 loss: 0.1501 loss_ce: 0.1501 2023/02/25 07:22:00 - mmengine - INFO - Epoch(train) [32][4500/5047] lr: 2.8109e-05 eta: 6 days, 1:16:31 time: 0.9317 data_time: 0.0022 memory: 42783 loss: 0.1226 loss_ce: 0.1226 2023/02/25 07:22:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 07:23:27 - mmengine - INFO - Epoch(train) [32][4600/5047] lr: 2.8109e-05 eta: 6 days, 1:15:02 time: 0.8481 data_time: 0.0021 memory: 52792 loss: 0.1540 loss_ce: 0.1540 2023/02/25 07:24:55 - mmengine - INFO - Epoch(train) [32][4700/5047] lr: 2.8109e-05 eta: 6 days, 1:13:35 time: 0.8853 data_time: 0.0019 memory: 45200 loss: 0.1314 loss_ce: 0.1314 2023/02/25 07:26:21 - mmengine - INFO - Epoch(train) [32][4800/5047] lr: 2.8109e-05 eta: 6 days, 1:12:01 time: 0.8259 data_time: 0.0022 memory: 45643 loss: 0.1373 loss_ce: 0.1373 2023/02/25 07:27:48 - mmengine - INFO - Epoch(train) [32][4900/5047] lr: 2.8109e-05 eta: 6 days, 1:10:28 time: 0.8680 data_time: 0.0020 memory: 42649 loss: 0.1328 loss_ce: 0.1328 2023/02/25 07:29:16 - mmengine - INFO - Epoch(train) [32][5000/5047] lr: 2.8109e-05 eta: 6 days, 1:09:01 time: 0.8858 data_time: 0.0025 memory: 42336 loss: 0.1317 loss_ce: 0.1317 2023/02/25 07:29:57 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 07:29:57 - mmengine - INFO - Saving checkpoint at 32 epochs 2023/02/25 07:31:30 - mmengine - INFO - Epoch(train) [33][ 100/5047] lr: 2.7908e-05 eta: 6 days, 1:06:50 time: 0.8989 data_time: 0.0031 memory: 42336 loss: 0.1383 loss_ce: 0.1383 2023/02/25 07:32:57 - mmengine - INFO - Epoch(train) [33][ 200/5047] lr: 2.7908e-05 eta: 6 days, 1:05:22 time: 0.8549 data_time: 0.0022 memory: 42102 loss: 0.1378 loss_ce: 0.1378 2023/02/25 07:34:27 - mmengine - INFO - Epoch(train) [33][ 300/5047] lr: 2.7908e-05 eta: 6 days, 1:04:01 time: 0.9089 data_time: 0.0021 memory: 42707 loss: 0.1208 loss_ce: 0.1208 2023/02/25 07:35:54 - mmengine - INFO - Epoch(train) [33][ 400/5047] lr: 2.7908e-05 eta: 6 days, 1:02:31 time: 0.8145 data_time: 0.0022 memory: 48188 loss: 0.1212 loss_ce: 0.1212 2023/02/25 07:37:20 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 07:37:24 - mmengine - INFO - Epoch(train) [33][ 500/5047] lr: 2.7908e-05 eta: 6 days, 1:01:10 time: 0.9867 data_time: 0.0023 memory: 52817 loss: 0.1447 loss_ce: 0.1447 2023/02/25 07:38:52 - mmengine - INFO - Epoch(train) [33][ 600/5047] lr: 2.7908e-05 eta: 6 days, 0:59:44 time: 0.8623 data_time: 0.0022 memory: 40055 loss: 0.1273 loss_ce: 0.1273 2023/02/25 07:40:18 - mmengine - INFO - Epoch(train) [33][ 700/5047] lr: 2.7908e-05 eta: 6 days, 0:58:12 time: 0.8670 data_time: 0.0022 memory: 44278 loss: 0.1351 loss_ce: 0.1351 2023/02/25 07:41:46 - mmengine - INFO - Epoch(train) [33][ 800/5047] lr: 2.7908e-05 eta: 6 days, 0:56:42 time: 0.8614 data_time: 0.0022 memory: 43420 loss: 0.1222 loss_ce: 0.1222 2023/02/25 07:43:14 - mmengine - INFO - Epoch(train) [33][ 900/5047] lr: 2.7908e-05 eta: 6 days, 0:55:18 time: 0.9526 data_time: 0.0023 memory: 45302 loss: 0.1597 loss_ce: 0.1597 2023/02/25 07:44:43 - mmengine - INFO - Epoch(train) [33][1000/5047] lr: 2.7908e-05 eta: 6 days, 0:53:53 time: 0.8883 data_time: 0.0021 memory: 55393 loss: 0.1284 loss_ce: 0.1284 2023/02/25 07:46:10 - mmengine - INFO - Epoch(train) [33][1100/5047] lr: 2.7908e-05 eta: 6 days, 0:52:22 time: 0.8529 data_time: 0.0022 memory: 44956 loss: 0.1352 loss_ce: 0.1352 2023/02/25 07:47:37 - mmengine - INFO - Epoch(train) [33][1200/5047] lr: 2.7908e-05 eta: 6 days, 0:50:51 time: 0.9094 data_time: 0.0024 memory: 45104 loss: 0.1295 loss_ce: 0.1295 2023/02/25 07:49:04 - mmengine - INFO - Epoch(train) [33][1300/5047] lr: 2.7908e-05 eta: 6 days, 0:49:23 time: 0.8427 data_time: 0.0031 memory: 51308 loss: 0.1269 loss_ce: 0.1269 2023/02/25 07:50:32 - mmengine - INFO - Epoch(train) [33][1400/5047] lr: 2.7908e-05 eta: 6 days, 0:47:53 time: 0.8439 data_time: 0.0021 memory: 44956 loss: 0.1233 loss_ce: 0.1233 2023/02/25 07:51:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 07:51:58 - mmengine - INFO - Epoch(train) [33][1500/5047] lr: 2.7908e-05 eta: 6 days, 0:46:21 time: 0.8322 data_time: 0.0023 memory: 43782 loss: 0.1509 loss_ce: 0.1509 2023/02/25 07:53:25 - mmengine - INFO - Epoch(train) [33][1600/5047] lr: 2.7908e-05 eta: 6 days, 0:44:49 time: 0.8284 data_time: 0.0028 memory: 48148 loss: 0.1381 loss_ce: 0.1381 2023/02/25 07:54:54 - mmengine - INFO - Epoch(train) [33][1700/5047] lr: 2.7908e-05 eta: 6 days, 0:43:25 time: 0.8682 data_time: 0.0021 memory: 41122 loss: 0.1196 loss_ce: 0.1196 2023/02/25 07:56:37 - mmengine - INFO - Epoch(train) [33][1800/5047] lr: 2.7908e-05 eta: 6 days, 0:43:01 time: 0.8611 data_time: 0.0020 memory: 43289 loss: 0.1404 loss_ce: 0.1404 2023/02/25 07:58:04 - mmengine - INFO - Epoch(train) [33][1900/5047] lr: 2.7908e-05 eta: 6 days, 0:41:27 time: 0.8313 data_time: 0.0023 memory: 44631 loss: 0.1382 loss_ce: 0.1382 2023/02/25 07:59:31 - mmengine - INFO - Epoch(train) [33][2000/5047] lr: 2.7908e-05 eta: 6 days, 0:39:58 time: 0.9114 data_time: 0.0023 memory: 51734 loss: 0.1199 loss_ce: 0.1199 2023/02/25 08:01:00 - mmengine - INFO - Epoch(train) [33][2100/5047] lr: 2.7908e-05 eta: 6 days, 0:38:34 time: 0.8970 data_time: 0.0025 memory: 55562 loss: 0.1214 loss_ce: 0.1214 2023/02/25 08:02:27 - mmengine - INFO - Epoch(train) [33][2200/5047] lr: 2.7908e-05 eta: 6 days, 0:37:02 time: 0.9249 data_time: 0.0024 memory: 50103 loss: 0.1450 loss_ce: 0.1450 2023/02/25 08:03:54 - mmengine - INFO - Epoch(train) [33][2300/5047] lr: 2.7908e-05 eta: 6 days, 0:35:32 time: 0.8414 data_time: 0.0035 memory: 43791 loss: 0.1450 loss_ce: 0.1450 2023/02/25 08:05:23 - mmengine - INFO - Epoch(train) [33][2400/5047] lr: 2.7908e-05 eta: 6 days, 0:34:09 time: 0.9371 data_time: 0.0059 memory: 44533 loss: 0.1447 loss_ce: 0.1447 2023/02/25 08:06:46 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 08:06:50 - mmengine - INFO - Epoch(train) [33][2500/5047] lr: 2.7908e-05 eta: 6 days, 0:32:38 time: 0.8583 data_time: 0.0017 memory: 42649 loss: 0.1339 loss_ce: 0.1339 2023/02/25 08:08:17 - mmengine - INFO - Epoch(train) [33][2600/5047] lr: 2.7908e-05 eta: 6 days, 0:31:08 time: 0.8554 data_time: 0.0018 memory: 49715 loss: 0.1374 loss_ce: 0.1374 2023/02/25 08:09:44 - mmengine - INFO - Epoch(train) [33][2700/5047] lr: 2.7908e-05 eta: 6 days, 0:29:36 time: 0.8942 data_time: 0.0017 memory: 39336 loss: 0.1433 loss_ce: 0.1433 2023/02/25 08:11:12 - mmengine - INFO - Epoch(train) [33][2800/5047] lr: 2.7908e-05 eta: 6 days, 0:28:10 time: 0.8710 data_time: 0.0023 memory: 52520 loss: 0.1431 loss_ce: 0.1431 2023/02/25 08:12:41 - mmengine - INFO - Epoch(train) [33][2900/5047] lr: 2.7908e-05 eta: 6 days, 0:26:47 time: 0.8865 data_time: 0.0023 memory: 45182 loss: 0.1416 loss_ce: 0.1416 2023/02/25 08:14:07 - mmengine - INFO - Epoch(train) [33][3000/5047] lr: 2.7908e-05 eta: 6 days, 0:25:14 time: 0.8619 data_time: 0.0056 memory: 41122 loss: 0.1291 loss_ce: 0.1291 2023/02/25 08:15:34 - mmengine - INFO - Epoch(train) [33][3100/5047] lr: 2.7908e-05 eta: 6 days, 0:23:41 time: 0.8531 data_time: 0.0020 memory: 51585 loss: 0.1299 loss_ce: 0.1299 2023/02/25 08:17:01 - mmengine - INFO - Epoch(train) [33][3200/5047] lr: 2.7908e-05 eta: 6 days, 0:22:11 time: 0.8969 data_time: 0.0019 memory: 52955 loss: 0.1382 loss_ce: 0.1382 2023/02/25 08:18:29 - mmengine - INFO - Epoch(train) [33][3300/5047] lr: 2.7908e-05 eta: 6 days, 0:20:43 time: 0.8842 data_time: 0.0020 memory: 55562 loss: 0.1359 loss_ce: 0.1359 2023/02/25 08:19:56 - mmengine - INFO - Epoch(train) [33][3400/5047] lr: 2.7908e-05 eta: 6 days, 0:19:14 time: 0.8656 data_time: 0.0018 memory: 42024 loss: 0.1125 loss_ce: 0.1125 2023/02/25 08:21:20 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 08:21:24 - mmengine - INFO - Epoch(train) [33][3500/5047] lr: 2.7908e-05 eta: 6 days, 0:17:49 time: 0.9354 data_time: 0.0020 memory: 43420 loss: 0.1313 loss_ce: 0.1313 2023/02/25 08:22:51 - mmengine - INFO - Epoch(train) [33][3600/5047] lr: 2.7908e-05 eta: 6 days, 0:16:14 time: 0.8709 data_time: 0.0018 memory: 42486 loss: 0.1281 loss_ce: 0.1281 2023/02/25 08:24:18 - mmengine - INFO - Epoch(train) [33][3700/5047] lr: 2.7908e-05 eta: 6 days, 0:14:45 time: 0.8713 data_time: 0.0022 memory: 39410 loss: 0.1317 loss_ce: 0.1317 2023/02/25 08:25:45 - mmengine - INFO - Epoch(train) [33][3800/5047] lr: 2.7908e-05 eta: 6 days, 0:13:13 time: 0.8209 data_time: 0.0025 memory: 41122 loss: 0.1220 loss_ce: 0.1220 2023/02/25 08:27:10 - mmengine - INFO - Epoch(train) [33][3900/5047] lr: 2.7908e-05 eta: 6 days, 0:11:37 time: 0.8544 data_time: 0.0018 memory: 52964 loss: 0.1304 loss_ce: 0.1304 2023/02/25 08:28:38 - mmengine - INFO - Epoch(train) [33][4000/5047] lr: 2.7908e-05 eta: 6 days, 0:10:07 time: 0.8703 data_time: 0.0020 memory: 52127 loss: 0.1348 loss_ce: 0.1348 2023/02/25 08:30:04 - mmengine - INFO - Epoch(train) [33][4100/5047] lr: 2.7908e-05 eta: 6 days, 0:08:36 time: 0.8990 data_time: 0.0018 memory: 42649 loss: 0.1338 loss_ce: 0.1338 2023/02/25 08:31:30 - mmengine - INFO - Epoch(train) [33][4200/5047] lr: 2.7908e-05 eta: 6 days, 0:06:59 time: 0.8617 data_time: 0.0021 memory: 41122 loss: 0.1309 loss_ce: 0.1309 2023/02/25 08:32:57 - mmengine - INFO - Epoch(train) [33][4300/5047] lr: 2.7908e-05 eta: 6 days, 0:05:31 time: 0.8528 data_time: 0.0021 memory: 47813 loss: 0.1392 loss_ce: 0.1392 2023/02/25 08:34:23 - mmengine - INFO - Epoch(train) [33][4400/5047] lr: 2.7908e-05 eta: 6 days, 0:03:56 time: 0.8825 data_time: 0.0023 memory: 47447 loss: 0.1292 loss_ce: 0.1292 2023/02/25 08:35:48 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 08:35:52 - mmengine - INFO - Epoch(train) [33][4500/5047] lr: 2.7908e-05 eta: 6 days, 0:02:32 time: 0.9022 data_time: 0.0018 memory: 41122 loss: 0.1313 loss_ce: 0.1313 2023/02/25 08:37:21 - mmengine - INFO - Epoch(train) [33][4600/5047] lr: 2.7908e-05 eta: 6 days, 0:01:08 time: 0.8399 data_time: 0.0021 memory: 46713 loss: 0.1360 loss_ce: 0.1360 2023/02/25 08:38:47 - mmengine - INFO - Epoch(train) [33][4700/5047] lr: 2.7908e-05 eta: 5 days, 23:59:35 time: 0.8520 data_time: 0.0021 memory: 46854 loss: 0.1497 loss_ce: 0.1497 2023/02/25 08:40:13 - mmengine - INFO - Epoch(train) [33][4800/5047] lr: 2.7908e-05 eta: 5 days, 23:57:59 time: 0.8500 data_time: 0.0032 memory: 42816 loss: 0.1242 loss_ce: 0.1242 2023/02/25 08:41:39 - mmengine - INFO - Epoch(train) [33][4900/5047] lr: 2.7908e-05 eta: 5 days, 23:56:25 time: 0.8221 data_time: 0.0021 memory: 51719 loss: 0.1732 loss_ce: 0.1732 2023/02/25 08:43:05 - mmengine - INFO - Epoch(train) [33][5000/5047] lr: 2.7908e-05 eta: 5 days, 23:54:50 time: 0.8860 data_time: 0.0021 memory: 43403 loss: 0.1162 loss_ce: 0.1162 2023/02/25 08:43:45 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 08:43:45 - mmengine - INFO - Saving checkpoint at 33 epochs 2023/02/25 08:45:20 - mmengine - INFO - Epoch(train) [34][ 100/5047] lr: 2.7707e-05 eta: 5 days, 23:52:46 time: 0.9206 data_time: 0.0019 memory: 53021 loss: 0.1220 loss_ce: 0.1220 2023/02/25 08:46:47 - mmengine - INFO - Epoch(train) [34][ 200/5047] lr: 2.7707e-05 eta: 5 days, 23:51:15 time: 0.8173 data_time: 0.0019 memory: 42649 loss: 0.1259 loss_ce: 0.1259 2023/02/25 08:48:15 - mmengine - INFO - Epoch(train) [34][ 300/5047] lr: 2.7707e-05 eta: 5 days, 23:49:49 time: 0.8876 data_time: 0.0024 memory: 42965 loss: 0.1292 loss_ce: 0.1292 2023/02/25 08:49:41 - mmengine - INFO - Epoch(train) [34][ 400/5047] lr: 2.7707e-05 eta: 5 days, 23:48:12 time: 0.8342 data_time: 0.0022 memory: 43899 loss: 0.1244 loss_ce: 0.1244 2023/02/25 08:50:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 08:51:08 - mmengine - INFO - Epoch(train) [34][ 500/5047] lr: 2.7707e-05 eta: 5 days, 23:46:41 time: 0.8318 data_time: 0.0020 memory: 39960 loss: 0.1248 loss_ce: 0.1248 2023/02/25 08:52:35 - mmengine - INFO - Epoch(train) [34][ 600/5047] lr: 2.7707e-05 eta: 5 days, 23:45:12 time: 0.9242 data_time: 0.0088 memory: 42977 loss: 0.1290 loss_ce: 0.1290 2023/02/25 08:54:02 - mmengine - INFO - Epoch(train) [34][ 700/5047] lr: 2.7707e-05 eta: 5 days, 23:43:39 time: 0.8293 data_time: 0.0019 memory: 39960 loss: 0.1180 loss_ce: 0.1180 2023/02/25 08:55:31 - mmengine - INFO - Epoch(train) [34][ 800/5047] lr: 2.7707e-05 eta: 5 days, 23:42:18 time: 0.9351 data_time: 0.0069 memory: 55562 loss: 0.1050 loss_ce: 0.1050 2023/02/25 08:56:57 - mmengine - INFO - Epoch(train) [34][ 900/5047] lr: 2.7707e-05 eta: 5 days, 23:40:44 time: 0.8393 data_time: 0.0038 memory: 55562 loss: 0.1250 loss_ce: 0.1250 2023/02/25 08:58:26 - mmengine - INFO - Epoch(train) [34][1000/5047] lr: 2.7707e-05 eta: 5 days, 23:39:19 time: 0.9024 data_time: 0.0019 memory: 41254 loss: 0.1313 loss_ce: 0.1313 2023/02/25 08:59:54 - mmengine - INFO - Epoch(train) [34][1100/5047] lr: 2.7707e-05 eta: 5 days, 23:37:53 time: 0.8677 data_time: 0.0022 memory: 43947 loss: 0.1334 loss_ce: 0.1334 2023/02/25 09:01:22 - mmengine - INFO - Epoch(train) [34][1200/5047] lr: 2.7707e-05 eta: 5 days, 23:36:28 time: 0.9006 data_time: 0.0020 memory: 44617 loss: 0.1185 loss_ce: 0.1185 2023/02/25 09:02:49 - mmengine - INFO - Epoch(train) [34][1300/5047] lr: 2.7707e-05 eta: 5 days, 23:34:55 time: 0.8225 data_time: 0.0030 memory: 46080 loss: 0.1286 loss_ce: 0.1286 2023/02/25 09:04:17 - mmengine - INFO - Epoch(train) [34][1400/5047] lr: 2.7707e-05 eta: 5 days, 23:33:29 time: 0.9519 data_time: 0.0018 memory: 49709 loss: 0.1190 loss_ce: 0.1190 2023/02/25 09:05:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 09:05:44 - mmengine - INFO - Epoch(train) [34][1500/5047] lr: 2.7707e-05 eta: 5 days, 23:32:00 time: 0.8616 data_time: 0.0019 memory: 42024 loss: 0.1352 loss_ce: 0.1352 2023/02/25 09:07:14 - mmengine - INFO - Epoch(train) [34][1600/5047] lr: 2.7707e-05 eta: 5 days, 23:30:40 time: 0.9372 data_time: 0.0019 memory: 40825 loss: 0.1270 loss_ce: 0.1270 2023/02/25 09:08:42 - mmengine - INFO - Epoch(train) [34][1700/5047] lr: 2.7707e-05 eta: 5 days, 23:29:12 time: 0.8942 data_time: 0.0020 memory: 40535 loss: 0.1280 loss_ce: 0.1280 2023/02/25 09:10:10 - mmengine - INFO - Epoch(train) [34][1800/5047] lr: 2.7707e-05 eta: 5 days, 23:27:44 time: 0.9060 data_time: 0.0028 memory: 49373 loss: 0.1222 loss_ce: 0.1222 2023/02/25 09:11:37 - mmengine - INFO - Epoch(train) [34][1900/5047] lr: 2.7707e-05 eta: 5 days, 23:26:14 time: 0.8732 data_time: 0.0023 memory: 42336 loss: 0.1392 loss_ce: 0.1392 2023/02/25 09:13:05 - mmengine - INFO - Epoch(train) [34][2000/5047] lr: 2.7707e-05 eta: 5 days, 23:24:48 time: 0.8818 data_time: 0.0020 memory: 41044 loss: 0.1419 loss_ce: 0.1419 2023/02/25 09:14:30 - mmengine - INFO - Epoch(train) [34][2100/5047] lr: 2.7707e-05 eta: 5 days, 23:23:11 time: 0.8334 data_time: 0.0019 memory: 43312 loss: 0.1221 loss_ce: 0.1221 2023/02/25 09:15:57 - mmengine - INFO - Epoch(train) [34][2200/5047] lr: 2.7707e-05 eta: 5 days, 23:21:40 time: 0.8555 data_time: 0.0020 memory: 42649 loss: 0.1438 loss_ce: 0.1438 2023/02/25 09:17:24 - mmengine - INFO - Epoch(train) [34][2300/5047] lr: 2.7707e-05 eta: 5 days, 23:20:09 time: 0.9167 data_time: 0.0019 memory: 54876 loss: 0.1226 loss_ce: 0.1226 2023/02/25 09:18:51 - mmengine - INFO - Epoch(train) [34][2400/5047] lr: 2.7707e-05 eta: 5 days, 23:18:40 time: 0.8317 data_time: 0.0018 memory: 43429 loss: 0.1340 loss_ce: 0.1340 2023/02/25 09:19:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 09:20:20 - mmengine - INFO - Epoch(train) [34][2500/5047] lr: 2.7707e-05 eta: 5 days, 23:17:15 time: 0.8696 data_time: 0.0028 memory: 41956 loss: 0.1163 loss_ce: 0.1163 2023/02/25 09:21:47 - mmengine - INFO - Epoch(train) [34][2600/5047] lr: 2.7707e-05 eta: 5 days, 23:15:46 time: 0.8833 data_time: 0.0046 memory: 54673 loss: 0.1466 loss_ce: 0.1466 2023/02/25 09:23:14 - mmengine - INFO - Epoch(train) [34][2700/5047] lr: 2.7707e-05 eta: 5 days, 23:14:16 time: 0.8838 data_time: 0.0041 memory: 55114 loss: 0.1434 loss_ce: 0.1434 2023/02/25 09:24:41 - mmengine - INFO - Epoch(train) [34][2800/5047] lr: 2.7707e-05 eta: 5 days, 23:12:45 time: 0.8668 data_time: 0.0033 memory: 48390 loss: 0.1151 loss_ce: 0.1151 2023/02/25 09:26:10 - mmengine - INFO - Epoch(train) [34][2900/5047] lr: 2.7707e-05 eta: 5 days, 23:11:22 time: 0.9187 data_time: 0.0018 memory: 50797 loss: 0.1363 loss_ce: 0.1363 2023/02/25 09:27:36 - mmengine - INFO - Epoch(train) [34][3000/5047] lr: 2.7707e-05 eta: 5 days, 23:09:45 time: 0.8363 data_time: 0.0020 memory: 40241 loss: 0.1309 loss_ce: 0.1309 2023/02/25 09:29:02 - mmengine - INFO - Epoch(train) [34][3100/5047] lr: 2.7707e-05 eta: 5 days, 23:08:14 time: 0.8436 data_time: 0.0030 memory: 41956 loss: 0.1536 loss_ce: 0.1536 2023/02/25 09:30:28 - mmengine - INFO - Epoch(train) [34][3200/5047] lr: 2.7707e-05 eta: 5 days, 23:06:39 time: 0.8425 data_time: 0.0018 memory: 55562 loss: 0.1248 loss_ce: 0.1248 2023/02/25 09:31:55 - mmengine - INFO - Epoch(train) [34][3300/5047] lr: 2.7707e-05 eta: 5 days, 23:05:06 time: 0.8480 data_time: 0.0042 memory: 41724 loss: 0.1268 loss_ce: 0.1268 2023/02/25 09:33:22 - mmengine - INFO - Epoch(train) [34][3400/5047] lr: 2.7707e-05 eta: 5 days, 23:03:35 time: 0.8927 data_time: 0.0020 memory: 44617 loss: 0.1262 loss_ce: 0.1262 2023/02/25 09:34:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 09:34:47 - mmengine - INFO - Epoch(train) [34][3500/5047] lr: 2.7707e-05 eta: 5 days, 23:02:00 time: 0.8624 data_time: 0.0017 memory: 42336 loss: 0.1612 loss_ce: 0.1612 2023/02/25 09:36:15 - mmengine - INFO - Epoch(train) [34][3600/5047] lr: 2.7707e-05 eta: 5 days, 23:00:30 time: 0.9044 data_time: 0.0020 memory: 41724 loss: 0.1303 loss_ce: 0.1303 2023/02/25 09:37:41 - mmengine - INFO - Epoch(train) [34][3700/5047] lr: 2.7707e-05 eta: 5 days, 22:58:59 time: 0.9135 data_time: 0.0024 memory: 41724 loss: 0.1373 loss_ce: 0.1373 2023/02/25 09:39:10 - mmengine - INFO - Epoch(train) [34][3800/5047] lr: 2.7707e-05 eta: 5 days, 22:57:33 time: 0.8907 data_time: 0.0021 memory: 43056 loss: 0.1418 loss_ce: 0.1418 2023/02/25 09:40:39 - mmengine - INFO - Epoch(train) [34][3900/5047] lr: 2.7707e-05 eta: 5 days, 22:56:10 time: 0.8935 data_time: 0.0024 memory: 47813 loss: 0.1214 loss_ce: 0.1214 2023/02/25 09:42:05 - mmengine - INFO - Epoch(train) [34][4000/5047] lr: 2.7707e-05 eta: 5 days, 22:54:36 time: 0.8182 data_time: 0.0020 memory: 55562 loss: 0.1473 loss_ce: 0.1473 2023/02/25 09:43:32 - mmengine - INFO - Epoch(train) [34][4100/5047] lr: 2.7707e-05 eta: 5 days, 22:53:07 time: 0.8609 data_time: 0.0023 memory: 43854 loss: 0.1260 loss_ce: 0.1260 2023/02/25 09:45:00 - mmengine - INFO - Epoch(train) [34][4200/5047] lr: 2.7707e-05 eta: 5 days, 22:51:39 time: 0.8451 data_time: 0.0033 memory: 55562 loss: 0.1404 loss_ce: 0.1404 2023/02/25 09:46:28 - mmengine - INFO - Epoch(train) [34][4300/5047] lr: 2.7707e-05 eta: 5 days, 22:50:14 time: 0.8957 data_time: 0.0020 memory: 42119 loss: 0.1200 loss_ce: 0.1200 2023/02/25 09:47:54 - mmengine - INFO - Epoch(train) [34][4400/5047] lr: 2.7707e-05 eta: 5 days, 22:48:40 time: 0.8419 data_time: 0.0026 memory: 55389 loss: 0.1481 loss_ce: 0.1481 2023/02/25 09:48:38 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 09:49:21 - mmengine - INFO - Epoch(train) [34][4500/5047] lr: 2.7707e-05 eta: 5 days, 22:47:08 time: 0.9080 data_time: 0.0019 memory: 55562 loss: 0.1216 loss_ce: 0.1216 2023/02/25 09:50:47 - mmengine - INFO - Epoch(train) [34][4600/5047] lr: 2.7707e-05 eta: 5 days, 22:45:34 time: 0.9224 data_time: 0.0035 memory: 50514 loss: 0.1231 loss_ce: 0.1231 2023/02/25 09:52:13 - mmengine - INFO - Epoch(train) [34][4700/5047] lr: 2.7707e-05 eta: 5 days, 22:44:01 time: 0.8999 data_time: 0.0020 memory: 42458 loss: 0.1233 loss_ce: 0.1233 2023/02/25 09:53:39 - mmengine - INFO - Epoch(train) [34][4800/5047] lr: 2.7707e-05 eta: 5 days, 22:42:26 time: 0.9673 data_time: 0.0018 memory: 50906 loss: 0.1477 loss_ce: 0.1477 2023/02/25 09:55:07 - mmengine - INFO - Epoch(train) [34][4900/5047] lr: 2.7707e-05 eta: 5 days, 22:40:59 time: 0.8783 data_time: 0.0022 memory: 47628 loss: 0.1292 loss_ce: 0.1292 2023/02/25 09:56:33 - mmengine - INFO - Epoch(train) [34][5000/5047] lr: 2.7707e-05 eta: 5 days, 22:39:25 time: 0.8638 data_time: 0.0021 memory: 45426 loss: 0.1325 loss_ce: 0.1325 2023/02/25 09:57:15 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 09:57:15 - mmengine - INFO - Saving checkpoint at 34 epochs 2023/02/25 09:58:48 - mmengine - INFO - Epoch(train) [35][ 100/5047] lr: 2.7506e-05 eta: 5 days, 22:37:21 time: 0.8412 data_time: 0.0036 memory: 55562 loss: 0.1351 loss_ce: 0.1351 2023/02/25 10:00:16 - mmengine - INFO - Epoch(train) [35][ 200/5047] lr: 2.7506e-05 eta: 5 days, 22:35:53 time: 0.8828 data_time: 0.0044 memory: 41154 loss: 0.1439 loss_ce: 0.1439 2023/02/25 10:01:45 - mmengine - INFO - Epoch(train) [35][ 300/5047] lr: 2.7506e-05 eta: 5 days, 22:34:31 time: 0.8716 data_time: 0.0018 memory: 55562 loss: 0.1250 loss_ce: 0.1250 2023/02/25 10:03:12 - mmengine - INFO - Epoch(train) [35][ 400/5047] lr: 2.7506e-05 eta: 5 days, 22:33:00 time: 0.9316 data_time: 0.0023 memory: 44956 loss: 0.1432 loss_ce: 0.1432 2023/02/25 10:03:14 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 10:04:38 - mmengine - INFO - Epoch(train) [35][ 500/5047] lr: 2.7506e-05 eta: 5 days, 22:31:24 time: 0.8063 data_time: 0.0021 memory: 42649 loss: 0.1192 loss_ce: 0.1192 2023/02/25 10:06:04 - mmengine - INFO - Epoch(train) [35][ 600/5047] lr: 2.7506e-05 eta: 5 days, 22:29:50 time: 0.8533 data_time: 0.0017 memory: 42698 loss: 0.1336 loss_ce: 0.1336 2023/02/25 10:07:31 - mmengine - INFO - Epoch(train) [35][ 700/5047] lr: 2.7506e-05 eta: 5 days, 22:28:18 time: 0.8346 data_time: 0.0018 memory: 44278 loss: 0.1281 loss_ce: 0.1281 2023/02/25 10:08:58 - mmengine - INFO - Epoch(train) [35][ 800/5047] lr: 2.7506e-05 eta: 5 days, 22:26:49 time: 0.9163 data_time: 0.0019 memory: 54042 loss: 0.1285 loss_ce: 0.1285 2023/02/25 10:10:25 - mmengine - INFO - Epoch(train) [35][ 900/5047] lr: 2.7506e-05 eta: 5 days, 22:25:18 time: 0.8466 data_time: 0.0024 memory: 43289 loss: 0.1393 loss_ce: 0.1393 2023/02/25 10:11:51 - mmengine - INFO - Epoch(train) [35][1000/5047] lr: 2.7506e-05 eta: 5 days, 22:23:44 time: 0.9031 data_time: 0.0021 memory: 44760 loss: 0.1306 loss_ce: 0.1306 2023/02/25 10:13:18 - mmengine - INFO - Epoch(train) [35][1100/5047] lr: 2.7506e-05 eta: 5 days, 22:22:14 time: 0.9239 data_time: 0.0018 memory: 42024 loss: 0.1191 loss_ce: 0.1191 2023/02/25 10:14:45 - mmengine - INFO - Epoch(train) [35][1200/5047] lr: 2.7506e-05 eta: 5 days, 22:20:44 time: 0.8698 data_time: 0.0019 memory: 49171 loss: 0.1388 loss_ce: 0.1388 2023/02/25 10:16:12 - mmengine - INFO - Epoch(train) [35][1300/5047] lr: 2.7506e-05 eta: 5 days, 22:19:15 time: 0.8639 data_time: 0.0019 memory: 40825 loss: 0.1335 loss_ce: 0.1335 2023/02/25 10:17:40 - mmengine - INFO - Epoch(train) [35][1400/5047] lr: 2.7506e-05 eta: 5 days, 22:17:46 time: 0.8837 data_time: 0.0018 memory: 50414 loss: 0.1107 loss_ce: 0.1107 2023/02/25 10:17:41 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 10:19:06 - mmengine - INFO - Epoch(train) [35][1500/5047] lr: 2.7506e-05 eta: 5 days, 22:16:14 time: 0.8085 data_time: 0.0021 memory: 44041 loss: 0.1268 loss_ce: 0.1268 2023/02/25 10:20:33 - mmengine - INFO - Epoch(train) [35][1600/5047] lr: 2.7506e-05 eta: 5 days, 22:14:42 time: 0.8616 data_time: 0.0017 memory: 42965 loss: 0.1592 loss_ce: 0.1592 2023/02/25 10:22:01 - mmengine - INFO - Epoch(train) [35][1700/5047] lr: 2.7506e-05 eta: 5 days, 22:13:15 time: 0.8730 data_time: 0.0020 memory: 42965 loss: 0.1318 loss_ce: 0.1318 2023/02/25 10:23:27 - mmengine - INFO - Epoch(train) [35][1800/5047] lr: 2.7506e-05 eta: 5 days, 22:11:43 time: 0.8855 data_time: 0.0025 memory: 44617 loss: 0.1289 loss_ce: 0.1289 2023/02/25 10:24:54 - mmengine - INFO - Epoch(train) [35][1900/5047] lr: 2.7506e-05 eta: 5 days, 22:10:12 time: 0.8248 data_time: 0.0026 memory: 44053 loss: 0.1352 loss_ce: 0.1352 2023/02/25 10:26:22 - mmengine - INFO - Epoch(train) [35][2000/5047] lr: 2.7506e-05 eta: 5 days, 22:08:44 time: 0.8399 data_time: 0.0037 memory: 46156 loss: 0.1304 loss_ce: 0.1304 2023/02/25 10:27:48 - mmengine - INFO - Epoch(train) [35][2100/5047] lr: 2.7506e-05 eta: 5 days, 22:07:10 time: 0.9255 data_time: 0.0048 memory: 55562 loss: 0.1205 loss_ce: 0.1205 2023/02/25 10:29:15 - mmengine - INFO - Epoch(train) [35][2200/5047] lr: 2.7506e-05 eta: 5 days, 22:05:39 time: 0.8844 data_time: 0.0019 memory: 40825 loss: 0.1361 loss_ce: 0.1361 2023/02/25 10:30:41 - mmengine - INFO - Epoch(train) [35][2300/5047] lr: 2.7506e-05 eta: 5 days, 22:04:06 time: 0.8681 data_time: 0.0019 memory: 40825 loss: 0.1404 loss_ce: 0.1404 2023/02/25 10:32:08 - mmengine - INFO - Epoch(train) [35][2400/5047] lr: 2.7506e-05 eta: 5 days, 22:02:35 time: 0.7957 data_time: 0.0053 memory: 42305 loss: 0.1408 loss_ce: 0.1408 2023/02/25 10:32:10 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 10:33:36 - mmengine - INFO - Epoch(train) [35][2500/5047] lr: 2.7506e-05 eta: 5 days, 22:01:07 time: 0.8658 data_time: 0.0019 memory: 50514 loss: 0.1484 loss_ce: 0.1484 2023/02/25 10:35:03 - mmengine - INFO - Epoch(train) [35][2600/5047] lr: 2.7506e-05 eta: 5 days, 21:59:38 time: 0.8852 data_time: 0.0067 memory: 46521 loss: 0.1307 loss_ce: 0.1307 2023/02/25 10:36:31 - mmengine - INFO - Epoch(train) [35][2700/5047] lr: 2.7506e-05 eta: 5 days, 21:58:10 time: 0.8477 data_time: 0.0030 memory: 49146 loss: 0.1490 loss_ce: 0.1490 2023/02/25 10:37:57 - mmengine - INFO - Epoch(train) [35][2800/5047] lr: 2.7506e-05 eta: 5 days, 21:56:36 time: 0.7953 data_time: 0.0020 memory: 42336 loss: 0.1208 loss_ce: 0.1208 2023/02/25 10:39:22 - mmengine - INFO - Epoch(train) [35][2900/5047] lr: 2.7506e-05 eta: 5 days, 21:55:01 time: 0.8353 data_time: 0.0019 memory: 43324 loss: 0.1331 loss_ce: 0.1331 2023/02/25 10:40:50 - mmengine - INFO - Epoch(train) [35][3000/5047] lr: 2.7506e-05 eta: 5 days, 21:53:33 time: 0.8592 data_time: 0.0023 memory: 46713 loss: 0.1073 loss_ce: 0.1073 2023/02/25 10:42:18 - mmengine - INFO - Epoch(train) [35][3100/5047] lr: 2.7506e-05 eta: 5 days, 21:52:05 time: 0.8318 data_time: 0.0020 memory: 46005 loss: 0.1401 loss_ce: 0.1401 2023/02/25 10:43:45 - mmengine - INFO - Epoch(train) [35][3200/5047] lr: 2.7506e-05 eta: 5 days, 21:50:34 time: 0.8940 data_time: 0.0034 memory: 43589 loss: 0.1199 loss_ce: 0.1199 2023/02/25 10:45:12 - mmengine - INFO - Epoch(train) [35][3300/5047] lr: 2.7506e-05 eta: 5 days, 21:49:05 time: 0.8940 data_time: 0.0039 memory: 42024 loss: 0.1404 loss_ce: 0.1404 2023/02/25 10:46:39 - mmengine - INFO - Epoch(train) [35][3400/5047] lr: 2.7506e-05 eta: 5 days, 21:47:37 time: 0.8803 data_time: 0.0018 memory: 50505 loss: 0.1202 loss_ce: 0.1202 2023/02/25 10:46:41 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 10:48:07 - mmengine - INFO - Epoch(train) [35][3500/5047] lr: 2.7506e-05 eta: 5 days, 21:46:08 time: 0.8476 data_time: 0.0018 memory: 41724 loss: 0.1441 loss_ce: 0.1441 2023/02/25 10:49:34 - mmengine - INFO - Epoch(train) [35][3600/5047] lr: 2.7506e-05 eta: 5 days, 21:44:38 time: 0.8900 data_time: 0.0021 memory: 42965 loss: 0.1301 loss_ce: 0.1301 2023/02/25 10:51:02 - mmengine - INFO - Epoch(train) [35][3700/5047] lr: 2.7506e-05 eta: 5 days, 21:43:11 time: 0.8961 data_time: 0.0057 memory: 55562 loss: 0.1281 loss_ce: 0.1281 2023/02/25 10:52:29 - mmengine - INFO - Epoch(train) [35][3800/5047] lr: 2.7506e-05 eta: 5 days, 21:41:42 time: 0.8950 data_time: 0.0026 memory: 43416 loss: 0.1235 loss_ce: 0.1235 2023/02/25 10:53:58 - mmengine - INFO - Epoch(train) [35][3900/5047] lr: 2.7506e-05 eta: 5 days, 21:40:17 time: 0.9450 data_time: 0.0026 memory: 55562 loss: 0.1350 loss_ce: 0.1350 2023/02/25 10:55:25 - mmengine - INFO - Epoch(train) [35][4000/5047] lr: 2.7506e-05 eta: 5 days, 21:38:49 time: 0.8841 data_time: 0.0033 memory: 43809 loss: 0.1507 loss_ce: 0.1507 2023/02/25 10:56:54 - mmengine - INFO - Epoch(train) [35][4100/5047] lr: 2.7506e-05 eta: 5 days, 21:37:25 time: 0.8647 data_time: 0.0022 memory: 42336 loss: 0.1333 loss_ce: 0.1333 2023/02/25 10:58:20 - mmengine - INFO - Epoch(train) [35][4200/5047] lr: 2.7506e-05 eta: 5 days, 21:35:49 time: 0.8342 data_time: 0.0020 memory: 46713 loss: 0.1280 loss_ce: 0.1280 2023/02/25 10:59:46 - mmengine - INFO - Epoch(train) [35][4300/5047] lr: 2.7506e-05 eta: 5 days, 21:34:17 time: 0.8144 data_time: 0.0019 memory: 43289 loss: 0.1411 loss_ce: 0.1411 2023/02/25 11:01:13 - mmengine - INFO - Epoch(train) [35][4400/5047] lr: 2.7506e-05 eta: 5 days, 21:32:47 time: 0.8924 data_time: 0.0020 memory: 47527 loss: 0.1258 loss_ce: 0.1258 2023/02/25 11:01:15 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 11:02:42 - mmengine - INFO - Epoch(train) [35][4500/5047] lr: 2.7506e-05 eta: 5 days, 21:31:22 time: 0.8699 data_time: 0.0020 memory: 41724 loss: 0.1200 loss_ce: 0.1200 2023/02/25 11:04:09 - mmengine - INFO - Epoch(train) [35][4600/5047] lr: 2.7506e-05 eta: 5 days, 21:29:54 time: 0.8644 data_time: 0.0018 memory: 50443 loss: 0.1441 loss_ce: 0.1441 2023/02/25 11:05:38 - mmengine - INFO - Epoch(train) [35][4700/5047] lr: 2.7506e-05 eta: 5 days, 21:28:31 time: 0.9266 data_time: 0.0020 memory: 43346 loss: 0.1394 loss_ce: 0.1394 2023/02/25 11:07:05 - mmengine - INFO - Epoch(train) [35][4800/5047] lr: 2.7506e-05 eta: 5 days, 21:27:01 time: 0.8779 data_time: 0.0047 memory: 55562 loss: 0.1192 loss_ce: 0.1192 2023/02/25 11:08:32 - mmengine - INFO - Epoch(train) [35][4900/5047] lr: 2.7506e-05 eta: 5 days, 21:25:30 time: 0.8879 data_time: 0.0020 memory: 48149 loss: 0.1283 loss_ce: 0.1283 2023/02/25 11:09:58 - mmengine - INFO - Epoch(train) [35][5000/5047] lr: 2.7506e-05 eta: 5 days, 21:23:56 time: 0.8393 data_time: 0.0021 memory: 43613 loss: 0.1339 loss_ce: 0.1339 2023/02/25 11:10:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 11:10:39 - mmengine - INFO - Saving checkpoint at 35 epochs 2023/02/25 11:12:12 - mmengine - INFO - Epoch(train) [36][ 100/5047] lr: 2.7305e-05 eta: 5 days, 21:21:45 time: 0.9040 data_time: 0.0018 memory: 41419 loss: 0.1279 loss_ce: 0.1279 2023/02/25 11:13:40 - mmengine - INFO - Epoch(train) [36][ 200/5047] lr: 2.7305e-05 eta: 5 days, 21:20:18 time: 0.8896 data_time: 0.0021 memory: 43874 loss: 0.1310 loss_ce: 0.1310 2023/02/25 11:15:06 - mmengine - INFO - Epoch(train) [36][ 300/5047] lr: 2.7305e-05 eta: 5 days, 21:18:44 time: 0.8440 data_time: 0.0033 memory: 54303 loss: 0.1320 loss_ce: 0.1320 2023/02/25 11:15:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 11:16:34 - mmengine - INFO - Epoch(train) [36][ 400/5047] lr: 2.7305e-05 eta: 5 days, 21:17:17 time: 0.8833 data_time: 0.0019 memory: 55562 loss: 0.1388 loss_ce: 0.1388 2023/02/25 11:18:00 - mmengine - INFO - Epoch(train) [36][ 500/5047] lr: 2.7305e-05 eta: 5 days, 21:15:45 time: 0.8936 data_time: 0.0019 memory: 44278 loss: 0.1242 loss_ce: 0.1242 2023/02/25 11:19:28 - mmengine - INFO - Epoch(train) [36][ 600/5047] lr: 2.7305e-05 eta: 5 days, 21:14:18 time: 0.8349 data_time: 0.0018 memory: 43865 loss: 0.1307 loss_ce: 0.1307 2023/02/25 11:20:55 - mmengine - INFO - Epoch(train) [36][ 700/5047] lr: 2.7305e-05 eta: 5 days, 21:12:47 time: 0.8573 data_time: 0.0026 memory: 43947 loss: 0.1044 loss_ce: 0.1044 2023/02/25 11:22:21 - mmengine - INFO - Epoch(train) [36][ 800/5047] lr: 2.7305e-05 eta: 5 days, 21:11:14 time: 0.8269 data_time: 0.0020 memory: 54069 loss: 0.1299 loss_ce: 0.1299 2023/02/25 11:23:49 - mmengine - INFO - Epoch(train) [36][ 900/5047] lr: 2.7305e-05 eta: 5 days, 21:09:46 time: 0.8822 data_time: 0.0020 memory: 42965 loss: 0.1352 loss_ce: 0.1352 2023/02/25 11:25:15 - mmengine - INFO - Epoch(train) [36][1000/5047] lr: 2.7305e-05 eta: 5 days, 21:08:14 time: 0.9050 data_time: 0.0023 memory: 40421 loss: 0.1251 loss_ce: 0.1251 2023/02/25 11:26:42 - mmengine - INFO - Epoch(train) [36][1100/5047] lr: 2.7305e-05 eta: 5 days, 21:06:41 time: 0.8286 data_time: 0.0018 memory: 41419 loss: 0.1482 loss_ce: 0.1482 2023/02/25 11:28:08 - mmengine - INFO - Epoch(train) [36][1200/5047] lr: 2.7305e-05 eta: 5 days, 21:05:10 time: 0.8542 data_time: 0.0019 memory: 44565 loss: 0.1284 loss_ce: 0.1284 2023/02/25 11:29:34 - mmengine - INFO - Epoch(train) [36][1300/5047] lr: 2.7305e-05 eta: 5 days, 21:03:37 time: 0.8415 data_time: 0.0020 memory: 43289 loss: 0.1334 loss_ce: 0.1334 2023/02/25 11:30:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 11:31:03 - mmengine - INFO - Epoch(train) [36][1400/5047] lr: 2.7305e-05 eta: 5 days, 21:02:13 time: 0.8549 data_time: 0.0020 memory: 41257 loss: 0.1354 loss_ce: 0.1354 2023/02/25 11:32:29 - mmengine - INFO - Epoch(train) [36][1500/5047] lr: 2.7305e-05 eta: 5 days, 21:00:38 time: 0.8411 data_time: 0.0018 memory: 41838 loss: 0.1342 loss_ce: 0.1342 2023/02/25 11:33:54 - mmengine - INFO - Epoch(train) [36][1600/5047] lr: 2.7305e-05 eta: 5 days, 20:59:02 time: 0.8219 data_time: 0.0022 memory: 51044 loss: 0.1390 loss_ce: 0.1390 2023/02/25 11:35:21 - mmengine - INFO - Epoch(train) [36][1700/5047] lr: 2.7305e-05 eta: 5 days, 20:57:29 time: 0.8494 data_time: 0.0047 memory: 43947 loss: 0.1328 loss_ce: 0.1328 2023/02/25 11:36:50 - mmengine - INFO - Epoch(train) [36][1800/5047] lr: 2.7305e-05 eta: 5 days, 20:56:06 time: 0.8858 data_time: 0.0019 memory: 54045 loss: 0.1326 loss_ce: 0.1326 2023/02/25 11:38:16 - mmengine - INFO - Epoch(train) [36][1900/5047] lr: 2.7305e-05 eta: 5 days, 20:54:32 time: 0.8628 data_time: 0.0018 memory: 44722 loss: 0.1077 loss_ce: 0.1077 2023/02/25 11:39:42 - mmengine - INFO - Epoch(train) [36][2000/5047] lr: 2.7305e-05 eta: 5 days, 20:52:59 time: 0.9006 data_time: 0.0021 memory: 48948 loss: 0.1341 loss_ce: 0.1341 2023/02/25 11:41:11 - mmengine - INFO - Epoch(train) [36][2100/5047] lr: 2.7305e-05 eta: 5 days, 20:51:36 time: 0.8949 data_time: 0.0019 memory: 46966 loss: 0.1315 loss_ce: 0.1315 2023/02/25 11:42:38 - mmengine - INFO - Epoch(train) [36][2200/5047] lr: 2.7305e-05 eta: 5 days, 20:50:06 time: 0.8709 data_time: 0.0023 memory: 51308 loss: 0.1288 loss_ce: 0.1288 2023/02/25 11:44:06 - mmengine - INFO - Epoch(train) [36][2300/5047] lr: 2.7305e-05 eta: 5 days, 20:48:39 time: 0.8563 data_time: 0.0018 memory: 42463 loss: 0.1314 loss_ce: 0.1314 2023/02/25 11:44:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 11:45:32 - mmengine - INFO - Epoch(train) [36][2400/5047] lr: 2.7305e-05 eta: 5 days, 20:47:04 time: 0.8511 data_time: 0.0019 memory: 42336 loss: 0.1191 loss_ce: 0.1191 2023/02/25 11:46:59 - mmengine - INFO - Epoch(train) [36][2500/5047] lr: 2.7305e-05 eta: 5 days, 20:45:36 time: 0.8267 data_time: 0.0020 memory: 46966 loss: 0.1279 loss_ce: 0.1279 2023/02/25 11:48:26 - mmengine - INFO - Epoch(train) [36][2600/5047] lr: 2.7305e-05 eta: 5 days, 20:44:04 time: 0.8582 data_time: 0.0018 memory: 42309 loss: 0.1172 loss_ce: 0.1172 2023/02/25 11:49:53 - mmengine - INFO - Epoch(train) [36][2700/5047] lr: 2.7305e-05 eta: 5 days, 20:42:35 time: 0.8867 data_time: 0.0019 memory: 55298 loss: 0.1301 loss_ce: 0.1301 2023/02/25 11:51:21 - mmengine - INFO - Epoch(train) [36][2800/5047] lr: 2.7305e-05 eta: 5 days, 20:41:08 time: 0.8785 data_time: 0.0018 memory: 45643 loss: 0.1301 loss_ce: 0.1301 2023/02/25 11:52:48 - mmengine - INFO - Epoch(train) [36][2900/5047] lr: 2.7305e-05 eta: 5 days, 20:39:37 time: 0.8692 data_time: 0.0024 memory: 43613 loss: 0.1345 loss_ce: 0.1345 2023/02/25 11:54:14 - mmengine - INFO - Epoch(train) [36][3000/5047] lr: 2.7305e-05 eta: 5 days, 20:38:06 time: 0.8523 data_time: 0.0037 memory: 43613 loss: 0.1323 loss_ce: 0.1323 2023/02/25 11:55:41 - mmengine - INFO - Epoch(train) [36][3100/5047] lr: 2.7305e-05 eta: 5 days, 20:36:36 time: 0.8633 data_time: 0.0018 memory: 41419 loss: 0.1391 loss_ce: 0.1391 2023/02/25 11:57:09 - mmengine - INFO - Epoch(train) [36][3200/5047] lr: 2.7305e-05 eta: 5 days, 20:35:06 time: 0.8510 data_time: 0.0019 memory: 43339 loss: 0.1382 loss_ce: 0.1382 2023/02/25 11:58:34 - mmengine - INFO - Epoch(train) [36][3300/5047] lr: 2.7305e-05 eta: 5 days, 20:33:31 time: 0.8714 data_time: 0.0018 memory: 55562 loss: 0.1404 loss_ce: 0.1404 2023/02/25 11:59:22 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 12:00:01 - mmengine - INFO - Epoch(train) [36][3400/5047] lr: 2.7305e-05 eta: 5 days, 20:32:00 time: 0.8414 data_time: 0.0018 memory: 51719 loss: 0.1242 loss_ce: 0.1242 2023/02/25 12:01:29 - mmengine - INFO - Epoch(train) [36][3500/5047] lr: 2.7305e-05 eta: 5 days, 20:30:35 time: 0.8904 data_time: 0.0019 memory: 41757 loss: 0.1380 loss_ce: 0.1380 2023/02/25 12:02:56 - mmengine - INFO - Epoch(train) [36][3600/5047] lr: 2.7305e-05 eta: 5 days, 20:29:03 time: 0.9241 data_time: 0.0023 memory: 49334 loss: 0.1264 loss_ce: 0.1264 2023/02/25 12:04:23 - mmengine - INFO - Epoch(train) [36][3700/5047] lr: 2.7305e-05 eta: 5 days, 20:27:34 time: 0.8854 data_time: 0.0027 memory: 45643 loss: 0.1180 loss_ce: 0.1180 2023/02/25 12:05:49 - mmengine - INFO - Epoch(train) [36][3800/5047] lr: 2.7305e-05 eta: 5 days, 20:26:00 time: 0.8445 data_time: 0.0026 memory: 46005 loss: 0.1326 loss_ce: 0.1326 2023/02/25 12:07:17 - mmengine - INFO - Epoch(train) [36][3900/5047] lr: 2.7305e-05 eta: 5 days, 20:24:33 time: 0.9161 data_time: 0.0029 memory: 42399 loss: 0.1433 loss_ce: 0.1433 2023/02/25 12:08:42 - mmengine - INFO - Epoch(train) [36][4000/5047] lr: 2.7305e-05 eta: 5 days, 20:22:57 time: 0.8002 data_time: 0.0017 memory: 52057 loss: 0.1405 loss_ce: 0.1405 2023/02/25 12:10:09 - mmengine - INFO - Epoch(train) [36][4100/5047] lr: 2.7305e-05 eta: 5 days, 20:21:26 time: 0.8793 data_time: 0.0023 memory: 52127 loss: 0.1367 loss_ce: 0.1367 2023/02/25 12:11:39 - mmengine - INFO - Epoch(train) [36][4200/5047] lr: 2.7305e-05 eta: 5 days, 20:20:05 time: 0.8749 data_time: 0.0023 memory: 45688 loss: 0.1176 loss_ce: 0.1176 2023/02/25 12:13:06 - mmengine - INFO - Epoch(train) [36][4300/5047] lr: 2.7305e-05 eta: 5 days, 20:18:35 time: 0.8833 data_time: 0.0022 memory: 46439 loss: 0.1384 loss_ce: 0.1384 2023/02/25 12:13:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 12:14:33 - mmengine - INFO - Epoch(train) [36][4400/5047] lr: 2.7305e-05 eta: 5 days, 20:17:06 time: 0.8833 data_time: 0.0021 memory: 47074 loss: 0.1287 loss_ce: 0.1287 2023/02/25 12:15:59 - mmengine - INFO - Epoch(train) [36][4500/5047] lr: 2.7305e-05 eta: 5 days, 20:15:32 time: 0.8167 data_time: 0.0036 memory: 42465 loss: 0.1269 loss_ce: 0.1269 2023/02/25 12:17:25 - mmengine - INFO - Epoch(train) [36][4600/5047] lr: 2.7305e-05 eta: 5 days, 20:13:58 time: 0.8534 data_time: 0.0019 memory: 55562 loss: 0.1346 loss_ce: 0.1346 2023/02/25 12:18:52 - mmengine - INFO - Epoch(train) [36][4700/5047] lr: 2.7305e-05 eta: 5 days, 20:12:27 time: 0.8121 data_time: 0.0022 memory: 43613 loss: 0.1236 loss_ce: 0.1236 2023/02/25 12:20:20 - mmengine - INFO - Epoch(train) [36][4800/5047] lr: 2.7305e-05 eta: 5 days, 20:11:00 time: 0.9404 data_time: 0.0018 memory: 47813 loss: 0.1218 loss_ce: 0.1218 2023/02/25 12:21:45 - mmengine - INFO - Epoch(train) [36][4900/5047] lr: 2.7305e-05 eta: 5 days, 20:09:25 time: 0.8596 data_time: 0.0022 memory: 44956 loss: 0.1237 loss_ce: 0.1237 2023/02/25 12:23:12 - mmengine - INFO - Epoch(train) [36][5000/5047] lr: 2.7305e-05 eta: 5 days, 20:07:55 time: 0.8450 data_time: 0.0020 memory: 39793 loss: 0.1344 loss_ce: 0.1344 2023/02/25 12:23:51 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 12:23:51 - mmengine - INFO - Saving checkpoint at 36 epochs 2023/02/25 12:25:24 - mmengine - INFO - Epoch(train) [37][ 100/5047] lr: 2.7104e-05 eta: 5 days, 20:05:40 time: 0.8727 data_time: 0.0028 memory: 43613 loss: 0.1364 loss_ce: 0.1364 2023/02/25 12:26:51 - mmengine - INFO - Epoch(train) [37][ 200/5047] lr: 2.7104e-05 eta: 5 days, 20:04:09 time: 0.8416 data_time: 0.0023 memory: 50372 loss: 0.1364 loss_ce: 0.1364 2023/02/25 12:28:19 - mmengine - INFO - Epoch(train) [37][ 300/5047] lr: 2.7104e-05 eta: 5 days, 20:02:41 time: 0.8790 data_time: 0.0020 memory: 45604 loss: 0.1238 loss_ce: 0.1238 2023/02/25 12:28:26 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 12:29:45 - mmengine - INFO - Epoch(train) [37][ 400/5047] lr: 2.7104e-05 eta: 5 days, 20:01:09 time: 0.8418 data_time: 0.0020 memory: 48422 loss: 0.1253 loss_ce: 0.1253 2023/02/25 12:31:13 - mmengine - INFO - Epoch(train) [37][ 500/5047] lr: 2.7104e-05 eta: 5 days, 19:59:42 time: 0.9174 data_time: 0.0033 memory: 50607 loss: 0.1327 loss_ce: 0.1327 2023/02/25 12:32:39 - mmengine - INFO - Epoch(train) [37][ 600/5047] lr: 2.7104e-05 eta: 5 days, 19:58:11 time: 0.8067 data_time: 0.0018 memory: 43947 loss: 0.1267 loss_ce: 0.1267 2023/02/25 12:34:06 - mmengine - INFO - Epoch(train) [37][ 700/5047] lr: 2.7104e-05 eta: 5 days, 19:56:40 time: 0.9052 data_time: 0.0024 memory: 43289 loss: 0.1484 loss_ce: 0.1484 2023/02/25 12:35:32 - mmengine - INFO - Epoch(train) [37][ 800/5047] lr: 2.7104e-05 eta: 5 days, 19:55:07 time: 0.8710 data_time: 0.0020 memory: 51308 loss: 0.1200 loss_ce: 0.1200 2023/02/25 12:36:59 - mmengine - INFO - Epoch(train) [37][ 900/5047] lr: 2.7104e-05 eta: 5 days, 19:53:36 time: 0.8700 data_time: 0.0020 memory: 43893 loss: 0.1482 loss_ce: 0.1482 2023/02/25 12:38:25 - mmengine - INFO - Epoch(train) [37][1000/5047] lr: 2.7104e-05 eta: 5 days, 19:52:03 time: 0.8079 data_time: 0.0020 memory: 52956 loss: 0.1438 loss_ce: 0.1438 2023/02/25 12:39:54 - mmengine - INFO - Epoch(train) [37][1100/5047] lr: 2.7104e-05 eta: 5 days, 19:50:39 time: 0.8913 data_time: 0.0059 memory: 49715 loss: 0.1357 loss_ce: 0.1357 2023/02/25 12:41:20 - mmengine - INFO - Epoch(train) [37][1200/5047] lr: 2.7104e-05 eta: 5 days, 19:49:06 time: 0.8338 data_time: 0.0021 memory: 45931 loss: 0.1353 loss_ce: 0.1353 2023/02/25 12:42:49 - mmengine - INFO - Epoch(train) [37][1300/5047] lr: 2.7104e-05 eta: 5 days, 19:47:44 time: 0.8667 data_time: 0.0074 memory: 50255 loss: 0.1317 loss_ce: 0.1317 2023/02/25 12:42:57 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 12:44:17 - mmengine - INFO - Epoch(train) [37][1400/5047] lr: 2.7104e-05 eta: 5 days, 19:46:17 time: 0.8939 data_time: 0.0033 memory: 49168 loss: 0.1319 loss_ce: 0.1319 2023/02/25 12:45:46 - mmengine - INFO - Epoch(train) [37][1500/5047] lr: 2.7104e-05 eta: 5 days, 19:44:51 time: 0.8688 data_time: 0.0019 memory: 52543 loss: 0.1280 loss_ce: 0.1280 2023/02/25 12:47:13 - mmengine - INFO - Epoch(train) [37][1600/5047] lr: 2.7104e-05 eta: 5 days, 19:43:21 time: 0.8067 data_time: 0.0036 memory: 45035 loss: 0.1594 loss_ce: 0.1594 2023/02/25 12:48:41 - mmengine - INFO - Epoch(train) [37][1700/5047] lr: 2.7104e-05 eta: 5 days, 19:41:57 time: 0.8846 data_time: 0.0018 memory: 55562 loss: 0.1230 loss_ce: 0.1230 2023/02/25 12:50:08 - mmengine - INFO - Epoch(train) [37][1800/5047] lr: 2.7104e-05 eta: 5 days, 19:40:26 time: 0.8782 data_time: 0.0018 memory: 50311 loss: 0.1301 loss_ce: 0.1301 2023/02/25 12:51:35 - mmengine - INFO - Epoch(train) [37][1900/5047] lr: 2.7104e-05 eta: 5 days, 19:38:56 time: 0.8747 data_time: 0.0023 memory: 46713 loss: 0.1231 loss_ce: 0.1231 2023/02/25 12:53:04 - mmengine - INFO - Epoch(train) [37][2000/5047] lr: 2.7104e-05 eta: 5 days, 19:37:31 time: 0.9143 data_time: 0.0018 memory: 46355 loss: 0.1421 loss_ce: 0.1421 2023/02/25 12:54:32 - mmengine - INFO - Epoch(train) [37][2100/5047] lr: 2.7104e-05 eta: 5 days, 19:36:05 time: 0.8911 data_time: 0.0019 memory: 44278 loss: 0.1308 loss_ce: 0.1308 2023/02/25 12:56:00 - mmengine - INFO - Epoch(train) [37][2200/5047] lr: 2.7104e-05 eta: 5 days, 19:34:38 time: 0.8660 data_time: 0.0045 memory: 55562 loss: 0.1422 loss_ce: 0.1422 2023/02/25 12:57:26 - mmengine - INFO - Epoch(train) [37][2300/5047] lr: 2.7104e-05 eta: 5 days, 19:33:06 time: 0.8445 data_time: 0.0018 memory: 45660 loss: 0.1138 loss_ce: 0.1138 2023/02/25 12:57:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 12:58:54 - mmengine - INFO - Epoch(train) [37][2400/5047] lr: 2.7104e-05 eta: 5 days, 19:31:39 time: 0.8601 data_time: 0.0018 memory: 38873 loss: 0.1137 loss_ce: 0.1137 2023/02/25 13:00:19 - mmengine - INFO - Epoch(train) [37][2500/5047] lr: 2.7104e-05 eta: 5 days, 19:30:01 time: 0.8300 data_time: 0.0023 memory: 42024 loss: 0.1273 loss_ce: 0.1273 2023/02/25 13:01:47 - mmengine - INFO - Epoch(train) [37][2600/5047] lr: 2.7104e-05 eta: 5 days, 19:28:36 time: 0.9295 data_time: 0.0043 memory: 43884 loss: 0.1252 loss_ce: 0.1252 2023/02/25 13:03:14 - mmengine - INFO - Epoch(train) [37][2700/5047] lr: 2.7104e-05 eta: 5 days, 19:27:04 time: 0.9032 data_time: 0.0019 memory: 42074 loss: 0.1417 loss_ce: 0.1417 2023/02/25 13:04:41 - mmengine - INFO - Epoch(train) [37][2800/5047] lr: 2.7104e-05 eta: 5 days, 19:25:36 time: 0.8621 data_time: 0.0019 memory: 43613 loss: 0.1456 loss_ce: 0.1456 2023/02/25 13:06:08 - mmengine - INFO - Epoch(train) [37][2900/5047] lr: 2.7104e-05 eta: 5 days, 19:24:07 time: 0.8773 data_time: 0.0073 memory: 39960 loss: 0.1338 loss_ce: 0.1338 2023/02/25 13:07:36 - mmengine - INFO - Epoch(train) [37][3000/5047] lr: 2.7104e-05 eta: 5 days, 19:22:37 time: 0.9168 data_time: 0.0018 memory: 48565 loss: 0.1201 loss_ce: 0.1201 2023/02/25 13:09:04 - mmengine - INFO - Epoch(train) [37][3100/5047] lr: 2.7104e-05 eta: 5 days, 19:21:11 time: 0.8373 data_time: 0.0019 memory: 43289 loss: 0.1271 loss_ce: 0.1271 2023/02/25 13:10:31 - mmengine - INFO - Epoch(train) [37][3200/5047] lr: 2.7104e-05 eta: 5 days, 19:19:41 time: 0.8954 data_time: 0.0022 memory: 45643 loss: 0.1294 loss_ce: 0.1294 2023/02/25 13:11:58 - mmengine - INFO - Epoch(train) [37][3300/5047] lr: 2.7104e-05 eta: 5 days, 19:18:14 time: 0.8357 data_time: 0.0023 memory: 48822 loss: 0.1307 loss_ce: 0.1307 2023/02/25 13:12:06 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 13:13:25 - mmengine - INFO - Epoch(train) [37][3400/5047] lr: 2.7104e-05 eta: 5 days, 19:16:41 time: 0.8795 data_time: 0.0042 memory: 42534 loss: 0.1336 loss_ce: 0.1336 2023/02/25 13:14:52 - mmengine - INFO - Epoch(train) [37][3500/5047] lr: 2.7104e-05 eta: 5 days, 19:15:11 time: 0.8189 data_time: 0.0021 memory: 44671 loss: 0.1342 loss_ce: 0.1342 2023/02/25 13:16:18 - mmengine - INFO - Epoch(train) [37][3600/5047] lr: 2.7104e-05 eta: 5 days, 19:13:38 time: 0.8632 data_time: 0.0018 memory: 40535 loss: 0.1392 loss_ce: 0.1392 2023/02/25 13:17:46 - mmengine - INFO - Epoch(train) [37][3700/5047] lr: 2.7104e-05 eta: 5 days, 19:12:10 time: 0.9400 data_time: 0.0021 memory: 48948 loss: 0.1283 loss_ce: 0.1283 2023/02/25 13:19:12 - mmengine - INFO - Epoch(train) [37][3800/5047] lr: 2.7104e-05 eta: 5 days, 19:10:40 time: 0.8620 data_time: 0.0018 memory: 43289 loss: 0.1297 loss_ce: 0.1297 2023/02/25 13:20:39 - mmengine - INFO - Epoch(train) [37][3900/5047] lr: 2.7104e-05 eta: 5 days, 19:09:07 time: 0.8638 data_time: 0.0019 memory: 46853 loss: 0.1415 loss_ce: 0.1415 2023/02/25 13:22:06 - mmengine - INFO - Epoch(train) [37][4000/5047] lr: 2.7104e-05 eta: 5 days, 19:07:40 time: 0.8666 data_time: 0.0025 memory: 43947 loss: 0.1453 loss_ce: 0.1453 2023/02/25 13:23:34 - mmengine - INFO - Epoch(train) [37][4100/5047] lr: 2.7104e-05 eta: 5 days, 19:06:11 time: 0.9023 data_time: 0.0020 memory: 44134 loss: 0.1423 loss_ce: 0.1423 2023/02/25 13:25:01 - mmengine - INFO - Epoch(train) [37][4200/5047] lr: 2.7104e-05 eta: 5 days, 19:04:42 time: 0.9275 data_time: 0.0059 memory: 41793 loss: 0.1330 loss_ce: 0.1330 2023/02/25 13:26:26 - mmengine - INFO - Epoch(train) [37][4300/5047] lr: 2.7104e-05 eta: 5 days, 19:03:07 time: 0.8129 data_time: 0.0018 memory: 40469 loss: 0.1226 loss_ce: 0.1226 2023/02/25 13:26:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 13:27:52 - mmengine - INFO - Epoch(train) [37][4400/5047] lr: 2.7104e-05 eta: 5 days, 19:01:33 time: 0.8650 data_time: 0.0017 memory: 44658 loss: 0.1114 loss_ce: 0.1114 2023/02/25 13:29:20 - mmengine - INFO - Epoch(train) [37][4500/5047] lr: 2.7104e-05 eta: 5 days, 19:00:04 time: 0.8494 data_time: 0.0018 memory: 52964 loss: 0.1261 loss_ce: 0.1261 2023/02/25 13:30:46 - mmengine - INFO - Epoch(train) [37][4600/5047] lr: 2.7104e-05 eta: 5 days, 18:58:31 time: 0.7993 data_time: 0.0018 memory: 43289 loss: 0.1358 loss_ce: 0.1358 2023/02/25 13:32:12 - mmengine - INFO - Epoch(train) [37][4700/5047] lr: 2.7104e-05 eta: 5 days, 18:57:00 time: 0.8383 data_time: 0.0054 memory: 45478 loss: 0.1548 loss_ce: 0.1548 2023/02/25 13:33:37 - mmengine - INFO - Epoch(train) [37][4800/5047] lr: 2.7104e-05 eta: 5 days, 18:55:24 time: 0.8461 data_time: 0.0023 memory: 48133 loss: 0.1285 loss_ce: 0.1285 2023/02/25 13:35:03 - mmengine - INFO - Epoch(train) [37][4900/5047] lr: 2.7104e-05 eta: 5 days, 18:53:49 time: 0.9112 data_time: 0.0025 memory: 45302 loss: 0.1167 loss_ce: 0.1167 2023/02/25 13:36:30 - mmengine - INFO - Epoch(train) [37][5000/5047] lr: 2.7104e-05 eta: 5 days, 18:52:18 time: 0.8488 data_time: 0.0018 memory: 46966 loss: 0.1258 loss_ce: 0.1258 2023/02/25 13:37:11 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 13:37:11 - mmengine - INFO - Saving checkpoint at 37 epochs 2023/02/25 13:38:44 - mmengine - INFO - Epoch(train) [38][ 100/5047] lr: 2.6903e-05 eta: 5 days, 18:50:09 time: 0.8528 data_time: 0.0022 memory: 42024 loss: 0.1297 loss_ce: 0.1297 2023/02/25 13:40:12 - mmengine - INFO - Epoch(train) [38][ 200/5047] lr: 2.6903e-05 eta: 5 days, 18:48:44 time: 0.8909 data_time: 0.0020 memory: 41757 loss: 0.1375 loss_ce: 0.1375 2023/02/25 13:41:06 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 13:41:39 - mmengine - INFO - Epoch(train) [38][ 300/5047] lr: 2.6903e-05 eta: 5 days, 18:47:13 time: 0.8954 data_time: 0.0037 memory: 42336 loss: 0.1198 loss_ce: 0.1198 2023/02/25 13:43:06 - mmengine - INFO - Epoch(train) [38][ 400/5047] lr: 2.6903e-05 eta: 5 days, 18:45:45 time: 0.8556 data_time: 0.0017 memory: 38906 loss: 0.1202 loss_ce: 0.1202 2023/02/25 13:44:33 - mmengine - INFO - Epoch(train) [38][ 500/5047] lr: 2.6903e-05 eta: 5 days, 18:44:14 time: 0.8209 data_time: 0.0025 memory: 46355 loss: 0.1326 loss_ce: 0.1326 2023/02/25 13:46:01 - mmengine - INFO - Epoch(train) [38][ 600/5047] lr: 2.6903e-05 eta: 5 days, 18:42:47 time: 0.8745 data_time: 0.0026 memory: 45461 loss: 0.1245 loss_ce: 0.1245 2023/02/25 13:47:28 - mmengine - INFO - Epoch(train) [38][ 700/5047] lr: 2.6903e-05 eta: 5 days, 18:41:19 time: 0.9006 data_time: 0.0019 memory: 42326 loss: 0.1341 loss_ce: 0.1341 2023/02/25 13:48:55 - mmengine - INFO - Epoch(train) [38][ 800/5047] lr: 2.6903e-05 eta: 5 days, 18:39:46 time: 0.8974 data_time: 0.0028 memory: 41724 loss: 0.1297 loss_ce: 0.1297 2023/02/25 13:50:22 - mmengine - INFO - Epoch(train) [38][ 900/5047] lr: 2.6903e-05 eta: 5 days, 18:38:16 time: 0.8543 data_time: 0.0056 memory: 46005 loss: 0.1235 loss_ce: 0.1235 2023/02/25 13:51:47 - mmengine - INFO - Epoch(train) [38][1000/5047] lr: 2.6903e-05 eta: 5 days, 18:36:41 time: 0.8662 data_time: 0.0019 memory: 44632 loss: 0.1240 loss_ce: 0.1240 2023/02/25 13:53:13 - mmengine - INFO - Epoch(train) [38][1100/5047] lr: 2.6903e-05 eta: 5 days, 18:35:09 time: 0.9116 data_time: 0.0019 memory: 47447 loss: 0.1229 loss_ce: 0.1229 2023/02/25 13:54:41 - mmengine - INFO - Epoch(train) [38][1200/5047] lr: 2.6903e-05 eta: 5 days, 18:33:41 time: 0.9047 data_time: 0.0021 memory: 41122 loss: 0.1104 loss_ce: 0.1104 2023/02/25 13:55:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 13:56:09 - mmengine - INFO - Epoch(train) [38][1300/5047] lr: 2.6903e-05 eta: 5 days, 18:32:14 time: 0.8647 data_time: 0.0026 memory: 45760 loss: 0.1228 loss_ce: 0.1228 2023/02/25 13:57:37 - mmengine - INFO - Epoch(train) [38][1400/5047] lr: 2.6903e-05 eta: 5 days, 18:30:48 time: 0.9109 data_time: 0.0037 memory: 55562 loss: 0.1283 loss_ce: 0.1283 2023/02/25 13:59:05 - mmengine - INFO - Epoch(train) [38][1500/5047] lr: 2.6903e-05 eta: 5 days, 18:29:21 time: 0.9380 data_time: 0.0034 memory: 44278 loss: 0.1341 loss_ce: 0.1341 2023/02/25 14:00:32 - mmengine - INFO - Epoch(train) [38][1600/5047] lr: 2.6903e-05 eta: 5 days, 18:27:50 time: 0.8522 data_time: 0.0042 memory: 43289 loss: 0.1404 loss_ce: 0.1404 2023/02/25 14:02:00 - mmengine - INFO - Epoch(train) [38][1700/5047] lr: 2.6903e-05 eta: 5 days, 18:26:25 time: 0.8644 data_time: 0.0026 memory: 45879 loss: 0.1313 loss_ce: 0.1313 2023/02/25 14:03:28 - mmengine - INFO - Epoch(train) [38][1800/5047] lr: 2.6903e-05 eta: 5 days, 18:24:58 time: 0.8798 data_time: 0.0021 memory: 47447 loss: 0.1217 loss_ce: 0.1217 2023/02/25 14:04:54 - mmengine - INFO - Epoch(train) [38][1900/5047] lr: 2.6903e-05 eta: 5 days, 18:23:26 time: 0.8416 data_time: 0.0040 memory: 46982 loss: 0.1196 loss_ce: 0.1196 2023/02/25 14:06:21 - mmengine - INFO - Epoch(train) [38][2000/5047] lr: 2.6903e-05 eta: 5 days, 18:21:53 time: 0.8956 data_time: 0.0025 memory: 44278 loss: 0.1324 loss_ce: 0.1324 2023/02/25 14:07:48 - mmengine - INFO - Epoch(train) [38][2100/5047] lr: 2.6903e-05 eta: 5 days, 18:20:25 time: 0.8695 data_time: 0.0020 memory: 43289 loss: 0.1278 loss_ce: 0.1278 2023/02/25 14:09:17 - mmengine - INFO - Epoch(train) [38][2200/5047] lr: 2.6903e-05 eta: 5 days, 18:19:02 time: 0.9189 data_time: 0.0038 memory: 42336 loss: 0.1275 loss_ce: 0.1275 2023/02/25 14:10:10 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 14:10:44 - mmengine - INFO - Epoch(train) [38][2300/5047] lr: 2.6903e-05 eta: 5 days, 18:17:31 time: 0.8429 data_time: 0.0030 memory: 43624 loss: 0.1420 loss_ce: 0.1420 2023/02/25 14:12:12 - mmengine - INFO - Epoch(train) [38][2400/5047] lr: 2.6903e-05 eta: 5 days, 18:16:06 time: 0.8956 data_time: 0.0024 memory: 46382 loss: 0.1324 loss_ce: 0.1324 2023/02/25 14:13:38 - mmengine - INFO - Epoch(train) [38][2500/5047] lr: 2.6903e-05 eta: 5 days, 18:14:33 time: 0.8845 data_time: 0.0018 memory: 41186 loss: 0.1235 loss_ce: 0.1235 2023/02/25 14:15:08 - mmengine - INFO - Epoch(train) [38][2600/5047] lr: 2.6903e-05 eta: 5 days, 18:13:11 time: 0.8890 data_time: 0.0020 memory: 43289 loss: 0.1345 loss_ce: 0.1345 2023/02/25 14:16:35 - mmengine - INFO - Epoch(train) [38][2700/5047] lr: 2.6903e-05 eta: 5 days, 18:11:41 time: 0.8434 data_time: 0.0023 memory: 42024 loss: 0.1250 loss_ce: 0.1250 2023/02/25 14:18:01 - mmengine - INFO - Epoch(train) [38][2800/5047] lr: 2.6903e-05 eta: 5 days, 18:10:09 time: 0.8869 data_time: 0.0017 memory: 55562 loss: 0.1164 loss_ce: 0.1164 2023/02/25 14:19:30 - mmengine - INFO - Epoch(train) [38][2900/5047] lr: 2.6903e-05 eta: 5 days, 18:08:46 time: 0.8313 data_time: 0.0018 memory: 44956 loss: 0.1446 loss_ce: 0.1446 2023/02/25 14:20:57 - mmengine - INFO - Epoch(train) [38][3000/5047] lr: 2.6903e-05 eta: 5 days, 18:07:15 time: 0.8555 data_time: 0.0059 memory: 44215 loss: 0.1422 loss_ce: 0.1422 2023/02/25 14:22:22 - mmengine - INFO - Epoch(train) [38][3100/5047] lr: 2.6903e-05 eta: 5 days, 18:05:40 time: 0.9238 data_time: 0.0024 memory: 42965 loss: 0.1199 loss_ce: 0.1199 2023/02/25 14:23:50 - mmengine - INFO - Epoch(train) [38][3200/5047] lr: 2.6903e-05 eta: 5 days, 18:04:11 time: 0.8433 data_time: 0.0022 memory: 42965 loss: 0.1258 loss_ce: 0.1258 2023/02/25 14:24:43 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 14:25:17 - mmengine - INFO - Epoch(train) [38][3300/5047] lr: 2.6903e-05 eta: 5 days, 18:02:42 time: 0.8240 data_time: 0.0019 memory: 43289 loss: 0.1363 loss_ce: 0.1363 2023/02/25 14:26:42 - mmengine - INFO - Epoch(train) [38][3400/5047] lr: 2.6903e-05 eta: 5 days, 18:01:07 time: 0.8662 data_time: 0.0028 memory: 55562 loss: 0.1147 loss_ce: 0.1147 2023/02/25 14:28:10 - mmengine - INFO - Epoch(train) [38][3500/5047] lr: 2.6903e-05 eta: 5 days, 17:59:40 time: 0.8390 data_time: 0.0021 memory: 41419 loss: 0.1228 loss_ce: 0.1228 2023/02/25 14:29:36 - mmengine - INFO - Epoch(train) [38][3600/5047] lr: 2.6903e-05 eta: 5 days, 17:58:09 time: 0.8229 data_time: 0.0028 memory: 42819 loss: 0.1273 loss_ce: 0.1273 2023/02/25 14:31:05 - mmengine - INFO - Epoch(train) [38][3700/5047] lr: 2.6903e-05 eta: 5 days, 17:56:42 time: 0.8906 data_time: 0.0019 memory: 41219 loss: 0.1318 loss_ce: 0.1318 2023/02/25 14:32:33 - mmengine - INFO - Epoch(train) [38][3800/5047] lr: 2.6903e-05 eta: 5 days, 17:55:18 time: 0.8749 data_time: 0.0019 memory: 42024 loss: 0.1401 loss_ce: 0.1401 2023/02/25 14:34:02 - mmengine - INFO - Epoch(train) [38][3900/5047] lr: 2.6903e-05 eta: 5 days, 17:53:53 time: 0.8880 data_time: 0.0045 memory: 52361 loss: 0.1425 loss_ce: 0.1425 2023/02/25 14:35:30 - mmengine - INFO - Epoch(train) [38][4000/5047] lr: 2.6903e-05 eta: 5 days, 17:52:26 time: 0.8960 data_time: 0.0019 memory: 39681 loss: 0.1248 loss_ce: 0.1248 2023/02/25 14:36:56 - mmengine - INFO - Epoch(train) [38][4100/5047] lr: 2.6903e-05 eta: 5 days, 17:50:55 time: 0.8358 data_time: 0.0030 memory: 42965 loss: 0.1209 loss_ce: 0.1209 2023/02/25 14:38:23 - mmengine - INFO - Epoch(train) [38][4200/5047] lr: 2.6903e-05 eta: 5 days, 17:49:24 time: 0.8569 data_time: 0.0029 memory: 43613 loss: 0.1314 loss_ce: 0.1314 2023/02/25 14:39:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 14:39:50 - mmengine - INFO - Epoch(train) [38][4300/5047] lr: 2.6903e-05 eta: 5 days, 17:47:56 time: 0.8682 data_time: 0.0021 memory: 44278 loss: 0.1340 loss_ce: 0.1340 2023/02/25 14:41:18 - mmengine - INFO - Epoch(train) [38][4400/5047] lr: 2.6903e-05 eta: 5 days, 17:46:26 time: 0.8982 data_time: 0.0027 memory: 53572 loss: 0.1291 loss_ce: 0.1291 2023/02/25 14:42:45 - mmengine - INFO - Epoch(train) [38][4500/5047] lr: 2.6903e-05 eta: 5 days, 17:44:58 time: 0.8907 data_time: 0.0018 memory: 42336 loss: 0.1299 loss_ce: 0.1299 2023/02/25 14:44:12 - mmengine - INFO - Epoch(train) [38][4600/5047] lr: 2.6903e-05 eta: 5 days, 17:43:27 time: 0.8376 data_time: 0.0020 memory: 42336 loss: 0.1201 loss_ce: 0.1201 2023/02/25 14:45:39 - mmengine - INFO - Epoch(train) [38][4700/5047] lr: 2.6903e-05 eta: 5 days, 17:41:59 time: 0.9334 data_time: 0.0020 memory: 54073 loss: 0.1249 loss_ce: 0.1249 2023/02/25 14:47:06 - mmengine - INFO - Epoch(train) [38][4800/5047] lr: 2.6903e-05 eta: 5 days, 17:40:27 time: 0.8102 data_time: 0.0022 memory: 43947 loss: 0.1365 loss_ce: 0.1365 2023/02/25 14:48:31 - mmengine - INFO - Epoch(train) [38][4900/5047] lr: 2.6903e-05 eta: 5 days, 17:38:53 time: 0.8803 data_time: 0.0041 memory: 46752 loss: 0.1281 loss_ce: 0.1281 2023/02/25 14:49:56 - mmengine - INFO - Epoch(train) [38][5000/5047] lr: 2.6903e-05 eta: 5 days, 17:37:17 time: 0.8233 data_time: 0.0018 memory: 41122 loss: 0.1163 loss_ce: 0.1163 2023/02/25 14:50:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 14:50:36 - mmengine - INFO - Saving checkpoint at 38 epochs 2023/02/25 14:52:08 - mmengine - INFO - Epoch(train) [39][ 100/5047] lr: 2.6702e-05 eta: 5 days, 17:35:01 time: 0.8998 data_time: 0.0019 memory: 41137 loss: 0.1232 loss_ce: 0.1232 2023/02/25 14:53:35 - mmengine - INFO - Epoch(train) [39][ 200/5047] lr: 2.6702e-05 eta: 5 days, 17:33:31 time: 0.8862 data_time: 0.0021 memory: 51719 loss: 0.1441 loss_ce: 0.1441 2023/02/25 14:53:47 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 14:55:03 - mmengine - INFO - Epoch(train) [39][ 300/5047] lr: 2.6702e-05 eta: 5 days, 17:32:04 time: 0.8957 data_time: 0.0022 memory: 51968 loss: 0.1428 loss_ce: 0.1428 2023/02/25 14:56:29 - mmengine - INFO - Epoch(train) [39][ 400/5047] lr: 2.6702e-05 eta: 5 days, 17:30:33 time: 0.8600 data_time: 0.0022 memory: 46713 loss: 0.1248 loss_ce: 0.1248 2023/02/25 14:57:56 - mmengine - INFO - Epoch(train) [39][ 500/5047] lr: 2.6702e-05 eta: 5 days, 17:29:02 time: 0.8270 data_time: 0.0018 memory: 44705 loss: 0.1264 loss_ce: 0.1264 2023/02/25 14:59:23 - mmengine - INFO - Epoch(train) [39][ 600/5047] lr: 2.6702e-05 eta: 5 days, 17:27:33 time: 0.8527 data_time: 0.0017 memory: 41122 loss: 0.1335 loss_ce: 0.1335 2023/02/25 15:00:52 - mmengine - INFO - Epoch(train) [39][ 700/5047] lr: 2.6702e-05 eta: 5 days, 17:26:07 time: 0.9339 data_time: 0.0018 memory: 39960 loss: 0.1378 loss_ce: 0.1378 2023/02/25 15:02:21 - mmengine - INFO - Epoch(train) [39][ 800/5047] lr: 2.6702e-05 eta: 5 days, 17:24:45 time: 0.9062 data_time: 0.0042 memory: 46355 loss: 0.1261 loss_ce: 0.1261 2023/02/25 15:03:48 - mmengine - INFO - Epoch(train) [39][ 900/5047] lr: 2.6702e-05 eta: 5 days, 17:23:16 time: 0.8363 data_time: 0.0030 memory: 42984 loss: 0.1275 loss_ce: 0.1275 2023/02/25 15:05:15 - mmengine - INFO - Epoch(train) [39][1000/5047] lr: 2.6702e-05 eta: 5 days, 17:21:45 time: 0.8453 data_time: 0.0020 memory: 50589 loss: 0.1291 loss_ce: 0.1291 2023/02/25 15:06:43 - mmengine - INFO - Epoch(train) [39][1100/5047] lr: 2.6702e-05 eta: 5 days, 17:20:19 time: 0.9174 data_time: 0.0057 memory: 42965 loss: 0.1357 loss_ce: 0.1357 2023/02/25 15:08:10 - mmengine - INFO - Epoch(train) [39][1200/5047] lr: 2.6702e-05 eta: 5 days, 17:18:50 time: 0.9007 data_time: 0.0055 memory: 39398 loss: 0.1266 loss_ce: 0.1266 2023/02/25 15:08:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 15:09:38 - mmengine - INFO - Epoch(train) [39][1300/5047] lr: 2.6702e-05 eta: 5 days, 17:17:23 time: 0.9053 data_time: 0.0021 memory: 46951 loss: 0.1214 loss_ce: 0.1214 2023/02/25 15:11:06 - mmengine - INFO - Epoch(train) [39][1400/5047] lr: 2.6702e-05 eta: 5 days, 17:15:57 time: 0.8904 data_time: 0.0019 memory: 39960 loss: 0.1355 loss_ce: 0.1355 2023/02/25 15:12:32 - mmengine - INFO - Epoch(train) [39][1500/5047] lr: 2.6702e-05 eta: 5 days, 17:14:24 time: 0.8388 data_time: 0.0030 memory: 52611 loss: 0.1226 loss_ce: 0.1226 2023/02/25 15:13:57 - mmengine - INFO - Epoch(train) [39][1600/5047] lr: 2.6702e-05 eta: 5 days, 17:12:48 time: 0.8254 data_time: 0.0018 memory: 50906 loss: 0.1404 loss_ce: 0.1404 2023/02/25 15:15:25 - mmengine - INFO - Epoch(train) [39][1700/5047] lr: 2.6702e-05 eta: 5 days, 17:11:21 time: 0.8474 data_time: 0.0025 memory: 42649 loss: 0.1169 loss_ce: 0.1169 2023/02/25 15:16:54 - mmengine - INFO - Epoch(train) [39][1800/5047] lr: 2.6702e-05 eta: 5 days, 17:09:57 time: 0.8518 data_time: 0.0020 memory: 40535 loss: 0.1347 loss_ce: 0.1347 2023/02/25 15:18:21 - mmengine - INFO - Epoch(train) [39][1900/5047] lr: 2.6702e-05 eta: 5 days, 17:08:27 time: 0.8271 data_time: 0.0020 memory: 45643 loss: 0.1425 loss_ce: 0.1425 2023/02/25 15:19:46 - mmengine - INFO - Epoch(train) [39][2000/5047] lr: 2.6702e-05 eta: 5 days, 17:06:53 time: 0.8649 data_time: 0.0024 memory: 44785 loss: 0.1118 loss_ce: 0.1118 2023/02/25 15:21:13 - mmengine - INFO - Epoch(train) [39][2100/5047] lr: 2.6702e-05 eta: 5 days, 17:05:21 time: 0.8354 data_time: 0.0026 memory: 46713 loss: 0.1233 loss_ce: 0.1233 2023/02/25 15:22:40 - mmengine - INFO - Epoch(train) [39][2200/5047] lr: 2.6702e-05 eta: 5 days, 17:03:54 time: 0.8702 data_time: 0.0025 memory: 43947 loss: 0.1227 loss_ce: 0.1227 2023/02/25 15:22:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 15:24:06 - mmengine - INFO - Epoch(train) [39][2300/5047] lr: 2.6702e-05 eta: 5 days, 17:02:20 time: 0.8366 data_time: 0.0018 memory: 42336 loss: 0.1119 loss_ce: 0.1119 2023/02/25 15:25:34 - mmengine - INFO - Epoch(train) [39][2400/5047] lr: 2.6702e-05 eta: 5 days, 17:00:52 time: 0.8115 data_time: 0.0019 memory: 49171 loss: 0.1288 loss_ce: 0.1288 2023/02/25 15:27:01 - mmengine - INFO - Epoch(train) [39][2500/5047] lr: 2.6702e-05 eta: 5 days, 16:59:22 time: 0.9132 data_time: 0.0021 memory: 55562 loss: 0.1362 loss_ce: 0.1362 2023/02/25 15:28:28 - mmengine - INFO - Epoch(train) [39][2600/5047] lr: 2.6702e-05 eta: 5 days, 16:57:52 time: 0.8214 data_time: 0.0021 memory: 42158 loss: 0.1296 loss_ce: 0.1296 2023/02/25 15:29:55 - mmengine - INFO - Epoch(train) [39][2700/5047] lr: 2.6702e-05 eta: 5 days, 16:56:24 time: 0.9061 data_time: 0.0023 memory: 44956 loss: 0.1296 loss_ce: 0.1296 2023/02/25 15:31:22 - mmengine - INFO - Epoch(train) [39][2800/5047] lr: 2.6702e-05 eta: 5 days, 16:54:52 time: 0.8431 data_time: 0.0018 memory: 41395 loss: 0.1225 loss_ce: 0.1225 2023/02/25 15:32:46 - mmengine - INFO - Epoch(train) [39][2900/5047] lr: 2.6702e-05 eta: 5 days, 16:53:16 time: 0.8579 data_time: 0.0021 memory: 43613 loss: 0.1376 loss_ce: 0.1376 2023/02/25 15:34:13 - mmengine - INFO - Epoch(train) [39][3000/5047] lr: 2.6702e-05 eta: 5 days, 16:51:45 time: 0.9052 data_time: 0.0018 memory: 42190 loss: 0.1284 loss_ce: 0.1284 2023/02/25 15:35:39 - mmengine - INFO - Epoch(train) [39][3100/5047] lr: 2.6702e-05 eta: 5 days, 16:50:13 time: 0.8714 data_time: 0.0019 memory: 47037 loss: 0.1349 loss_ce: 0.1349 2023/02/25 15:37:07 - mmengine - INFO - Epoch(train) [39][3200/5047] lr: 2.6702e-05 eta: 5 days, 16:48:45 time: 0.8766 data_time: 0.0048 memory: 46853 loss: 0.1238 loss_ce: 0.1238 2023/02/25 15:37:19 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 15:38:34 - mmengine - INFO - Epoch(train) [39][3300/5047] lr: 2.6702e-05 eta: 5 days, 16:47:16 time: 0.8657 data_time: 0.0022 memory: 40825 loss: 0.1376 loss_ce: 0.1376 2023/02/25 15:40:01 - mmengine - INFO - Epoch(train) [39][3400/5047] lr: 2.6702e-05 eta: 5 days, 16:45:48 time: 0.8562 data_time: 0.0021 memory: 42024 loss: 0.1249 loss_ce: 0.1249 2023/02/25 15:41:28 - mmengine - INFO - Epoch(train) [39][3500/5047] lr: 2.6702e-05 eta: 5 days, 16:44:17 time: 0.8894 data_time: 0.0021 memory: 50106 loss: 0.1280 loss_ce: 0.1280 2023/02/25 15:42:55 - mmengine - INFO - Epoch(train) [39][3600/5047] lr: 2.6702e-05 eta: 5 days, 16:42:46 time: 0.8321 data_time: 0.0022 memory: 42649 loss: 0.1279 loss_ce: 0.1279 2023/02/25 15:44:22 - mmengine - INFO - Epoch(train) [39][3700/5047] lr: 2.6702e-05 eta: 5 days, 16:41:19 time: 0.8955 data_time: 0.0019 memory: 44956 loss: 0.1466 loss_ce: 0.1466 2023/02/25 15:45:50 - mmengine - INFO - Epoch(train) [39][3800/5047] lr: 2.6702e-05 eta: 5 days, 16:39:49 time: 0.8846 data_time: 0.0023 memory: 41630 loss: 0.1239 loss_ce: 0.1239 2023/02/25 15:47:16 - mmengine - INFO - Epoch(train) [39][3900/5047] lr: 2.6702e-05 eta: 5 days, 16:38:19 time: 0.8477 data_time: 0.0018 memory: 46278 loss: 0.1259 loss_ce: 0.1259 2023/02/25 15:48:44 - mmengine - INFO - Epoch(train) [39][4000/5047] lr: 2.6702e-05 eta: 5 days, 16:36:50 time: 0.9258 data_time: 0.0044 memory: 41175 loss: 0.1185 loss_ce: 0.1185 2023/02/25 15:50:12 - mmengine - INFO - Epoch(train) [39][4100/5047] lr: 2.6702e-05 eta: 5 days, 16:35:24 time: 0.9338 data_time: 0.0046 memory: 45643 loss: 0.1382 loss_ce: 0.1382 2023/02/25 15:51:39 - mmengine - INFO - Epoch(train) [39][4200/5047] lr: 2.6702e-05 eta: 5 days, 16:33:54 time: 0.8853 data_time: 0.0049 memory: 55562 loss: 0.1227 loss_ce: 0.1227 2023/02/25 15:51:51 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 15:53:05 - mmengine - INFO - Epoch(train) [39][4300/5047] lr: 2.6702e-05 eta: 5 days, 16:32:24 time: 0.9243 data_time: 0.0020 memory: 41724 loss: 0.1279 loss_ce: 0.1279 2023/02/25 15:54:33 - mmengine - INFO - Epoch(train) [39][4400/5047] lr: 2.6702e-05 eta: 5 days, 16:30:55 time: 0.8728 data_time: 0.0019 memory: 44617 loss: 0.1310 loss_ce: 0.1310 2023/02/25 15:56:00 - mmengine - INFO - Epoch(train) [39][4500/5047] lr: 2.6702e-05 eta: 5 days, 16:29:26 time: 0.9244 data_time: 0.0024 memory: 43613 loss: 0.1332 loss_ce: 0.1332 2023/02/25 15:57:27 - mmengine - INFO - Epoch(train) [39][4600/5047] lr: 2.6702e-05 eta: 5 days, 16:27:56 time: 0.9127 data_time: 0.0018 memory: 43947 loss: 0.1267 loss_ce: 0.1267 2023/02/25 15:58:53 - mmengine - INFO - Epoch(train) [39][4700/5047] lr: 2.6702e-05 eta: 5 days, 16:26:24 time: 0.8547 data_time: 0.0019 memory: 43779 loss: 0.1294 loss_ce: 0.1294 2023/02/25 16:00:21 - mmengine - INFO - Epoch(train) [39][4800/5047] lr: 2.6702e-05 eta: 5 days, 16:24:56 time: 0.9043 data_time: 0.0019 memory: 55298 loss: 0.1233 loss_ce: 0.1233 2023/02/25 16:01:47 - mmengine - INFO - Epoch(train) [39][4900/5047] lr: 2.6702e-05 eta: 5 days, 16:23:26 time: 0.8784 data_time: 0.0019 memory: 43613 loss: 0.1318 loss_ce: 0.1318 2023/02/25 16:03:16 - mmengine - INFO - Epoch(train) [39][5000/5047] lr: 2.6702e-05 eta: 5 days, 16:22:01 time: 0.8613 data_time: 0.0019 memory: 44617 loss: 0.1288 loss_ce: 0.1288 2023/02/25 16:03:56 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 16:03:56 - mmengine - INFO - Saving checkpoint at 39 epochs 2023/02/25 16:05:29 - mmengine - INFO - Epoch(train) [40][ 100/5047] lr: 2.6501e-05 eta: 5 days, 16:19:50 time: 0.8677 data_time: 0.0029 memory: 41427 loss: 0.1279 loss_ce: 0.1279 2023/02/25 16:06:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 16:06:56 - mmengine - INFO - Epoch(train) [40][ 200/5047] lr: 2.6501e-05 eta: 5 days, 16:18:19 time: 0.8925 data_time: 0.0044 memory: 44866 loss: 0.1188 loss_ce: 0.1188 2023/02/25 16:08:23 - mmengine - INFO - Epoch(train) [40][ 300/5047] lr: 2.6501e-05 eta: 5 days, 16:16:49 time: 0.8789 data_time: 0.0052 memory: 54647 loss: 0.1368 loss_ce: 0.1368 2023/02/25 16:09:52 - mmengine - INFO - Epoch(train) [40][ 400/5047] lr: 2.6501e-05 eta: 5 days, 16:15:26 time: 0.8968 data_time: 0.0021 memory: 42272 loss: 0.1237 loss_ce: 0.1237 2023/02/25 16:11:18 - mmengine - INFO - Epoch(train) [40][ 500/5047] lr: 2.6501e-05 eta: 5 days, 16:13:56 time: 0.8568 data_time: 0.0025 memory: 48948 loss: 0.1276 loss_ce: 0.1276 2023/02/25 16:12:45 - mmengine - INFO - Epoch(train) [40][ 600/5047] lr: 2.6501e-05 eta: 5 days, 16:12:24 time: 0.8672 data_time: 0.0018 memory: 45851 loss: 0.1389 loss_ce: 0.1389 2023/02/25 16:14:12 - mmengine - INFO - Epoch(train) [40][ 700/5047] lr: 2.6501e-05 eta: 5 days, 16:10:54 time: 0.8574 data_time: 0.0040 memory: 42336 loss: 0.1300 loss_ce: 0.1300 2023/02/25 16:15:38 - mmengine - INFO - Epoch(train) [40][ 800/5047] lr: 2.6501e-05 eta: 5 days, 16:09:23 time: 0.8975 data_time: 0.0035 memory: 43289 loss: 0.1119 loss_ce: 0.1119 2023/02/25 16:17:07 - mmengine - INFO - Epoch(train) [40][ 900/5047] lr: 2.6501e-05 eta: 5 days, 16:07:58 time: 0.8636 data_time: 0.0022 memory: 48210 loss: 0.1241 loss_ce: 0.1241 2023/02/25 16:18:34 - mmengine - INFO - Epoch(train) [40][1000/5047] lr: 2.6501e-05 eta: 5 days, 16:06:29 time: 0.9049 data_time: 0.0018 memory: 45813 loss: 0.1340 loss_ce: 0.1340 2023/02/25 16:20:01 - mmengine - INFO - Epoch(train) [40][1100/5047] lr: 2.6501e-05 eta: 5 days, 16:05:01 time: 0.9042 data_time: 0.0020 memory: 55562 loss: 0.1166 loss_ce: 0.1166 2023/02/25 16:21:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 16:21:29 - mmengine - INFO - Epoch(train) [40][1200/5047] lr: 2.6501e-05 eta: 5 days, 16:03:32 time: 0.8793 data_time: 0.0021 memory: 46005 loss: 0.1313 loss_ce: 0.1313 2023/02/25 16:22:57 - mmengine - INFO - Epoch(train) [40][1300/5047] lr: 2.6501e-05 eta: 5 days, 16:02:06 time: 0.8845 data_time: 0.0020 memory: 41419 loss: 0.1372 loss_ce: 0.1372 2023/02/25 16:24:24 - mmengine - INFO - Epoch(train) [40][1400/5047] lr: 2.6501e-05 eta: 5 days, 16:00:37 time: 0.8693 data_time: 0.0028 memory: 39681 loss: 0.1455 loss_ce: 0.1455 2023/02/25 16:25:52 - mmengine - INFO - Epoch(train) [40][1500/5047] lr: 2.6501e-05 eta: 5 days, 15:59:09 time: 0.8617 data_time: 0.0031 memory: 55562 loss: 0.1353 loss_ce: 0.1353 2023/02/25 16:27:18 - mmengine - INFO - Epoch(train) [40][1600/5047] lr: 2.6501e-05 eta: 5 days, 15:57:36 time: 0.8430 data_time: 0.0018 memory: 48398 loss: 0.1315 loss_ce: 0.1315 2023/02/25 16:28:44 - mmengine - INFO - Epoch(train) [40][1700/5047] lr: 2.6501e-05 eta: 5 days, 15:56:04 time: 0.8885 data_time: 0.0020 memory: 44956 loss: 0.1347 loss_ce: 0.1347 2023/02/25 16:30:10 - mmengine - INFO - Epoch(train) [40][1800/5047] lr: 2.6501e-05 eta: 5 days, 15:54:31 time: 0.8864 data_time: 0.0021 memory: 46005 loss: 0.1384 loss_ce: 0.1384 2023/02/25 16:31:38 - mmengine - INFO - Epoch(train) [40][1900/5047] lr: 2.6501e-05 eta: 5 days, 15:53:06 time: 0.8385 data_time: 0.0022 memory: 44956 loss: 0.1209 loss_ce: 0.1209 2023/02/25 16:33:04 - mmengine - INFO - Epoch(train) [40][2000/5047] lr: 2.6501e-05 eta: 5 days, 15:51:33 time: 0.8078 data_time: 0.0022 memory: 44956 loss: 0.1262 loss_ce: 0.1262 2023/02/25 16:34:30 - mmengine - INFO - Epoch(train) [40][2100/5047] lr: 2.6501e-05 eta: 5 days, 15:50:01 time: 0.8628 data_time: 0.0020 memory: 45291 loss: 0.1405 loss_ce: 0.1405 2023/02/25 16:35:29 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 16:35:58 - mmengine - INFO - Epoch(train) [40][2200/5047] lr: 2.6501e-05 eta: 5 days, 15:48:34 time: 0.8492 data_time: 0.0025 memory: 55562 loss: 0.1377 loss_ce: 0.1377 2023/02/25 16:37:26 - mmengine - INFO - Epoch(train) [40][2300/5047] lr: 2.6501e-05 eta: 5 days, 15:47:08 time: 0.9323 data_time: 0.0027 memory: 41419 loss: 0.1211 loss_ce: 0.1211 2023/02/25 16:38:55 - mmengine - INFO - Epoch(train) [40][2400/5047] lr: 2.6501e-05 eta: 5 days, 15:45:43 time: 0.8415 data_time: 0.0019 memory: 41445 loss: 0.1304 loss_ce: 0.1304 2023/02/25 16:40:21 - mmengine - INFO - Epoch(train) [40][2500/5047] lr: 2.6501e-05 eta: 5 days, 15:44:10 time: 0.8323 data_time: 0.0026 memory: 47074 loss: 0.1264 loss_ce: 0.1264 2023/02/25 16:41:47 - mmengine - INFO - Epoch(train) [40][2600/5047] lr: 2.6501e-05 eta: 5 days, 15:42:37 time: 0.8211 data_time: 0.0044 memory: 44617 loss: 0.1305 loss_ce: 0.1305 2023/02/25 16:43:14 - mmengine - INFO - Epoch(train) [40][2700/5047] lr: 2.6501e-05 eta: 5 days, 15:41:10 time: 0.9316 data_time: 0.0018 memory: 49378 loss: 0.1297 loss_ce: 0.1297 2023/02/25 16:44:45 - mmengine - INFO - Epoch(train) [40][2800/5047] lr: 2.6501e-05 eta: 5 days, 15:39:51 time: 0.9353 data_time: 0.0020 memory: 42336 loss: 0.1301 loss_ce: 0.1301 2023/02/25 16:46:11 - mmengine - INFO - Epoch(train) [40][2900/5047] lr: 2.6501e-05 eta: 5 days, 15:38:19 time: 0.8746 data_time: 0.0050 memory: 50419 loss: 0.1260 loss_ce: 0.1260 2023/02/25 16:47:36 - mmengine - INFO - Epoch(train) [40][3000/5047] lr: 2.6501e-05 eta: 5 days, 15:36:44 time: 0.8369 data_time: 0.0019 memory: 39928 loss: 0.1343 loss_ce: 0.1343 2023/02/25 16:49:04 - mmengine - INFO - Epoch(train) [40][3100/5047] lr: 2.6501e-05 eta: 5 days, 15:35:16 time: 0.8725 data_time: 0.0020 memory: 45302 loss: 0.1096 loss_ce: 0.1096 2023/02/25 16:50:01 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 16:50:29 - mmengine - INFO - Epoch(train) [40][3200/5047] lr: 2.6501e-05 eta: 5 days, 15:33:42 time: 0.8673 data_time: 0.0021 memory: 44865 loss: 0.1309 loss_ce: 0.1309 2023/02/25 16:51:56 - mmengine - INFO - Epoch(train) [40][3300/5047] lr: 2.6501e-05 eta: 5 days, 15:32:10 time: 0.8721 data_time: 0.0022 memory: 40825 loss: 0.1175 loss_ce: 0.1175 2023/02/25 16:53:22 - mmengine - INFO - Epoch(train) [40][3400/5047] lr: 2.6501e-05 eta: 5 days, 15:30:39 time: 0.8591 data_time: 0.0020 memory: 43613 loss: 0.1351 loss_ce: 0.1351 2023/02/25 16:54:50 - mmengine - INFO - Epoch(train) [40][3500/5047] lr: 2.6501e-05 eta: 5 days, 15:29:13 time: 0.8247 data_time: 0.0045 memory: 52881 loss: 0.1226 loss_ce: 0.1226 2023/02/25 16:56:17 - mmengine - INFO - Epoch(train) [40][3600/5047] lr: 2.6501e-05 eta: 5 days, 15:27:43 time: 0.8440 data_time: 0.0018 memory: 55562 loss: 0.1388 loss_ce: 0.1388 2023/02/25 16:57:43 - mmengine - INFO - Epoch(train) [40][3700/5047] lr: 2.6501e-05 eta: 5 days, 15:26:09 time: 0.8524 data_time: 0.0028 memory: 48719 loss: 0.1307 loss_ce: 0.1307 2023/02/25 16:59:10 - mmengine - INFO - Epoch(train) [40][3800/5047] lr: 2.6501e-05 eta: 5 days, 15:24:41 time: 0.8699 data_time: 0.0019 memory: 51719 loss: 0.1266 loss_ce: 0.1266 2023/02/25 17:00:38 - mmengine - INFO - Epoch(train) [40][3900/5047] lr: 2.6501e-05 eta: 5 days, 15:23:13 time: 0.9043 data_time: 0.0019 memory: 44278 loss: 0.1248 loss_ce: 0.1248 2023/02/25 17:02:04 - mmengine - INFO - Epoch(train) [40][4000/5047] lr: 2.6501e-05 eta: 5 days, 15:21:42 time: 0.8732 data_time: 0.0020 memory: 47982 loss: 0.1596 loss_ce: 0.1596 2023/02/25 17:03:29 - mmengine - INFO - Epoch(train) [40][4100/5047] lr: 2.6501e-05 eta: 5 days, 15:20:05 time: 0.8253 data_time: 0.0019 memory: 42649 loss: 0.1153 loss_ce: 0.1153 2023/02/25 17:04:28 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 17:04:56 - mmengine - INFO - Epoch(train) [40][4200/5047] lr: 2.6501e-05 eta: 5 days, 15:18:37 time: 0.8909 data_time: 0.0019 memory: 43154 loss: 0.1242 loss_ce: 0.1242 2023/02/25 17:06:22 - mmengine - INFO - Epoch(train) [40][4300/5047] lr: 2.6501e-05 eta: 5 days, 15:17:05 time: 0.8393 data_time: 0.0022 memory: 51308 loss: 0.1283 loss_ce: 0.1283 2023/02/25 17:07:49 - mmengine - INFO - Epoch(train) [40][4400/5047] lr: 2.6501e-05 eta: 5 days, 15:15:34 time: 0.8818 data_time: 0.0020 memory: 47813 loss: 0.1215 loss_ce: 0.1215 2023/02/25 17:09:15 - mmengine - INFO - Epoch(train) [40][4500/5047] lr: 2.6501e-05 eta: 5 days, 15:14:02 time: 0.8312 data_time: 0.0024 memory: 45813 loss: 0.1277 loss_ce: 0.1277 2023/02/25 17:10:43 - mmengine - INFO - Epoch(train) [40][4600/5047] lr: 2.6501e-05 eta: 5 days, 15:12:37 time: 0.8252 data_time: 0.0022 memory: 55487 loss: 0.1251 loss_ce: 0.1251 2023/02/25 17:12:10 - mmengine - INFO - Epoch(train) [40][4700/5047] lr: 2.6501e-05 eta: 5 days, 15:11:07 time: 0.8623 data_time: 0.0035 memory: 41724 loss: 0.1300 loss_ce: 0.1300 2023/02/25 17:13:36 - mmengine - INFO - Epoch(train) [40][4800/5047] lr: 2.6501e-05 eta: 5 days, 15:09:33 time: 0.9092 data_time: 0.0020 memory: 48981 loss: 0.1280 loss_ce: 0.1280 2023/02/25 17:15:02 - mmengine - INFO - Epoch(train) [40][4900/5047] lr: 2.6501e-05 eta: 5 days, 15:08:02 time: 0.8407 data_time: 0.0022 memory: 54072 loss: 0.1507 loss_ce: 0.1507 2023/02/25 17:16:31 - mmengine - INFO - Epoch(train) [40][5000/5047] lr: 2.6501e-05 eta: 5 days, 15:06:38 time: 0.8742 data_time: 0.0043 memory: 42336 loss: 0.1301 loss_ce: 0.1301 2023/02/25 17:17:12 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 17:17:12 - mmengine - INFO - Saving checkpoint at 40 epochs 2023/02/25 17:18:44 - mmengine - INFO - Epoch(train) [41][ 100/5047] lr: 2.6300e-05 eta: 5 days, 15:04:28 time: 0.8534 data_time: 0.0022 memory: 44716 loss: 0.1264 loss_ce: 0.1264 2023/02/25 17:19:02 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 17:20:11 - mmengine - INFO - Epoch(train) [41][ 200/5047] lr: 2.6300e-05 eta: 5 days, 15:02:57 time: 0.8084 data_time: 0.0019 memory: 40657 loss: 0.1261 loss_ce: 0.1261 2023/02/25 17:21:38 - mmengine - INFO - Epoch(train) [41][ 300/5047] lr: 2.6300e-05 eta: 5 days, 15:01:26 time: 0.9511 data_time: 0.0020 memory: 49714 loss: 0.1243 loss_ce: 0.1243 2023/02/25 17:23:05 - mmengine - INFO - Epoch(train) [41][ 400/5047] lr: 2.6300e-05 eta: 5 days, 14:59:58 time: 0.9077 data_time: 0.0018 memory: 40825 loss: 0.1275 loss_ce: 0.1275 2023/02/25 17:24:33 - mmengine - INFO - Epoch(train) [41][ 500/5047] lr: 2.6300e-05 eta: 5 days, 14:58:32 time: 0.8504 data_time: 0.0018 memory: 46355 loss: 0.1234 loss_ce: 0.1234 2023/02/25 17:25:59 - mmengine - INFO - Epoch(train) [41][ 600/5047] lr: 2.6300e-05 eta: 5 days, 14:57:00 time: 0.8293 data_time: 0.0020 memory: 42024 loss: 0.1288 loss_ce: 0.1288 2023/02/25 17:27:27 - mmengine - INFO - Epoch(train) [41][ 700/5047] lr: 2.6300e-05 eta: 5 days, 14:55:32 time: 0.8831 data_time: 0.0018 memory: 47171 loss: 0.1232 loss_ce: 0.1232 2023/02/25 17:28:54 - mmengine - INFO - Epoch(train) [41][ 800/5047] lr: 2.6300e-05 eta: 5 days, 14:54:04 time: 0.8291 data_time: 0.0035 memory: 44617 loss: 0.1464 loss_ce: 0.1464 2023/02/25 17:30:22 - mmengine - INFO - Epoch(train) [41][ 900/5047] lr: 2.6300e-05 eta: 5 days, 14:52:36 time: 0.8698 data_time: 0.0029 memory: 41724 loss: 0.1291 loss_ce: 0.1291 2023/02/25 17:31:50 - mmengine - INFO - Epoch(train) [41][1000/5047] lr: 2.6300e-05 eta: 5 days, 14:51:10 time: 0.8738 data_time: 0.0024 memory: 44956 loss: 0.1354 loss_ce: 0.1354 2023/02/25 17:33:16 - mmengine - INFO - Epoch(train) [41][1100/5047] lr: 2.6300e-05 eta: 5 days, 14:49:37 time: 0.8458 data_time: 0.0019 memory: 42336 loss: 0.1365 loss_ce: 0.1365 2023/02/25 17:33:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 17:34:43 - mmengine - INFO - Epoch(train) [41][1200/5047] lr: 2.6300e-05 eta: 5 days, 14:48:07 time: 0.8468 data_time: 0.0024 memory: 45643 loss: 0.1226 loss_ce: 0.1226 2023/02/25 17:36:09 - mmengine - INFO - Epoch(train) [41][1300/5047] lr: 2.6300e-05 eta: 5 days, 14:46:35 time: 0.8528 data_time: 0.0021 memory: 41419 loss: 0.1437 loss_ce: 0.1437 2023/02/25 17:37:34 - mmengine - INFO - Epoch(train) [41][1400/5047] lr: 2.6300e-05 eta: 5 days, 14:45:00 time: 0.8735 data_time: 0.0025 memory: 55114 loss: 0.1336 loss_ce: 0.1336 2023/02/25 17:39:00 - mmengine - INFO - Epoch(train) [41][1500/5047] lr: 2.6300e-05 eta: 5 days, 14:43:27 time: 0.8602 data_time: 0.0018 memory: 40825 loss: 0.1364 loss_ce: 0.1364 2023/02/25 17:40:27 - mmengine - INFO - Epoch(train) [41][1600/5047] lr: 2.6300e-05 eta: 5 days, 14:41:58 time: 0.8703 data_time: 0.0022 memory: 45834 loss: 0.1355 loss_ce: 0.1355 2023/02/25 17:41:52 - mmengine - INFO - Epoch(train) [41][1700/5047] lr: 2.6300e-05 eta: 5 days, 14:40:24 time: 0.8673 data_time: 0.0050 memory: 46713 loss: 0.1253 loss_ce: 0.1253 2023/02/25 17:43:20 - mmengine - INFO - Epoch(train) [41][1800/5047] lr: 2.6300e-05 eta: 5 days, 14:38:56 time: 0.8183 data_time: 0.0020 memory: 53640 loss: 0.1230 loss_ce: 0.1230 2023/02/25 17:44:47 - mmengine - INFO - Epoch(train) [41][1900/5047] lr: 2.6300e-05 eta: 5 days, 14:37:26 time: 0.8698 data_time: 0.0021 memory: 55562 loss: 0.1209 loss_ce: 0.1209 2023/02/25 17:46:13 - mmengine - INFO - Epoch(train) [41][2000/5047] lr: 2.6300e-05 eta: 5 days, 14:35:54 time: 0.8358 data_time: 0.0020 memory: 41724 loss: 0.1377 loss_ce: 0.1377 2023/02/25 17:47:38 - mmengine - INFO - Epoch(train) [41][2100/5047] lr: 2.6300e-05 eta: 5 days, 14:34:19 time: 0.8417 data_time: 0.0023 memory: 41020 loss: 0.1208 loss_ce: 0.1208 2023/02/25 17:47:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 17:49:05 - mmengine - INFO - Epoch(train) [41][2200/5047] lr: 2.6300e-05 eta: 5 days, 14:32:49 time: 0.8425 data_time: 0.0040 memory: 42649 loss: 0.1319 loss_ce: 0.1319 2023/02/25 17:50:30 - mmengine - INFO - Epoch(train) [41][2300/5047] lr: 2.6300e-05 eta: 5 days, 14:31:14 time: 0.8587 data_time: 0.0022 memory: 39126 loss: 0.1132 loss_ce: 0.1132 2023/02/25 17:51:56 - mmengine - INFO - Epoch(train) [41][2400/5047] lr: 2.6300e-05 eta: 5 days, 14:29:42 time: 0.8546 data_time: 0.0022 memory: 43289 loss: 0.1440 loss_ce: 0.1440 2023/02/25 17:53:23 - mmengine - INFO - Epoch(train) [41][2500/5047] lr: 2.6300e-05 eta: 5 days, 14:28:13 time: 0.9179 data_time: 0.0018 memory: 43768 loss: 0.1446 loss_ce: 0.1446 2023/02/25 17:54:49 - mmengine - INFO - Epoch(train) [41][2600/5047] lr: 2.6300e-05 eta: 5 days, 14:26:42 time: 0.8106 data_time: 0.0020 memory: 43947 loss: 0.1262 loss_ce: 0.1262 2023/02/25 17:56:17 - mmengine - INFO - Epoch(train) [41][2700/5047] lr: 2.6300e-05 eta: 5 days, 14:25:13 time: 0.9008 data_time: 0.0021 memory: 46827 loss: 0.1275 loss_ce: 0.1275 2023/02/25 17:57:43 - mmengine - INFO - Epoch(train) [41][2800/5047] lr: 2.6300e-05 eta: 5 days, 14:23:42 time: 0.8780 data_time: 0.0019 memory: 55562 loss: 0.1169 loss_ce: 0.1169 2023/02/25 17:59:10 - mmengine - INFO - Epoch(train) [41][2900/5047] lr: 2.6300e-05 eta: 5 days, 14:22:12 time: 0.8299 data_time: 0.0018 memory: 44223 loss: 0.1347 loss_ce: 0.1347 2023/02/25 18:00:36 - mmengine - INFO - Epoch(train) [41][3000/5047] lr: 2.6300e-05 eta: 5 days, 14:20:40 time: 0.9029 data_time: 0.0020 memory: 48188 loss: 0.1336 loss_ce: 0.1336 2023/02/25 18:02:03 - mmengine - INFO - Epoch(train) [41][3100/5047] lr: 2.6300e-05 eta: 5 days, 14:19:09 time: 0.8563 data_time: 0.0021 memory: 43324 loss: 0.1286 loss_ce: 0.1286 2023/02/25 18:02:20 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 18:03:28 - mmengine - INFO - Epoch(train) [41][3200/5047] lr: 2.6300e-05 eta: 5 days, 14:17:34 time: 0.8408 data_time: 0.0019 memory: 47638 loss: 0.1281 loss_ce: 0.1281 2023/02/25 18:04:53 - mmengine - INFO - Epoch(train) [41][3300/5047] lr: 2.6300e-05 eta: 5 days, 14:15:59 time: 0.8241 data_time: 0.0061 memory: 41782 loss: 0.1496 loss_ce: 0.1496 2023/02/25 18:06:20 - mmengine - INFO - Epoch(train) [41][3400/5047] lr: 2.6300e-05 eta: 5 days, 14:14:30 time: 0.8418 data_time: 0.0018 memory: 42024 loss: 0.1226 loss_ce: 0.1226 2023/02/25 18:07:46 - mmengine - INFO - Epoch(train) [41][3500/5047] lr: 2.6300e-05 eta: 5 days, 14:12:59 time: 0.8821 data_time: 0.0019 memory: 45662 loss: 0.1333 loss_ce: 0.1333 2023/02/25 18:09:14 - mmengine - INFO - Epoch(train) [41][3600/5047] lr: 2.6300e-05 eta: 5 days, 14:11:31 time: 0.8932 data_time: 0.0030 memory: 41724 loss: 0.1189 loss_ce: 0.1189 2023/02/25 18:10:40 - mmengine - INFO - Epoch(train) [41][3700/5047] lr: 2.6300e-05 eta: 5 days, 14:10:00 time: 0.8318 data_time: 0.0023 memory: 45643 loss: 0.1218 loss_ce: 0.1218 2023/02/25 18:12:06 - mmengine - INFO - Epoch(train) [41][3800/5047] lr: 2.6300e-05 eta: 5 days, 14:08:27 time: 0.8618 data_time: 0.0019 memory: 47447 loss: 0.1320 loss_ce: 0.1320 2023/02/25 18:13:33 - mmengine - INFO - Epoch(train) [41][3900/5047] lr: 2.6300e-05 eta: 5 days, 14:06:59 time: 0.9076 data_time: 0.0055 memory: 48684 loss: 0.1335 loss_ce: 0.1335 2023/02/25 18:15:00 - mmengine - INFO - Epoch(train) [41][4000/5047] lr: 2.6300e-05 eta: 5 days, 14:05:29 time: 0.8291 data_time: 0.0030 memory: 38103 loss: 0.1258 loss_ce: 0.1258 2023/02/25 18:16:27 - mmengine - INFO - Epoch(train) [41][4100/5047] lr: 2.6300e-05 eta: 5 days, 14:03:59 time: 0.8457 data_time: 0.0019 memory: 43289 loss: 0.1405 loss_ce: 0.1405 2023/02/25 18:16:45 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 18:17:54 - mmengine - INFO - Epoch(train) [41][4200/5047] lr: 2.6300e-05 eta: 5 days, 14:02:30 time: 0.8426 data_time: 0.0021 memory: 46853 loss: 0.1134 loss_ce: 0.1134 2023/02/25 18:19:19 - mmengine - INFO - Epoch(train) [41][4300/5047] lr: 2.6300e-05 eta: 5 days, 14:00:54 time: 0.8643 data_time: 0.0022 memory: 47738 loss: 0.1357 loss_ce: 0.1357 2023/02/25 18:20:46 - mmengine - INFO - Epoch(train) [41][4400/5047] lr: 2.6300e-05 eta: 5 days, 13:59:25 time: 0.8995 data_time: 0.0019 memory: 48096 loss: 0.1416 loss_ce: 0.1416 2023/02/25 18:22:12 - mmengine - INFO - Epoch(train) [41][4500/5047] lr: 2.6300e-05 eta: 5 days, 13:57:52 time: 0.8774 data_time: 0.0048 memory: 44998 loss: 0.1305 loss_ce: 0.1305 2023/02/25 18:23:37 - mmengine - INFO - Epoch(train) [41][4600/5047] lr: 2.6300e-05 eta: 5 days, 13:56:17 time: 0.8380 data_time: 0.0028 memory: 42239 loss: 0.1391 loss_ce: 0.1391 2023/02/25 18:25:05 - mmengine - INFO - Epoch(train) [41][4700/5047] lr: 2.6300e-05 eta: 5 days, 13:54:50 time: 0.9628 data_time: 0.0019 memory: 44956 loss: 0.1478 loss_ce: 0.1478 2023/02/25 18:26:31 - mmengine - INFO - Epoch(train) [41][4800/5047] lr: 2.6300e-05 eta: 5 days, 13:53:20 time: 0.8738 data_time: 0.0039 memory: 55114 loss: 0.1367 loss_ce: 0.1367 2023/02/25 18:27:58 - mmengine - INFO - Epoch(train) [41][4900/5047] lr: 2.6300e-05 eta: 5 days, 13:51:51 time: 0.8895 data_time: 0.0023 memory: 42649 loss: 0.1290 loss_ce: 0.1290 2023/02/25 18:29:27 - mmengine - INFO - Epoch(train) [41][5000/5047] lr: 2.6300e-05 eta: 5 days, 13:50:26 time: 0.9193 data_time: 0.0020 memory: 42596 loss: 0.1256 loss_ce: 0.1256 2023/02/25 18:30:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 18:30:08 - mmengine - INFO - Saving checkpoint at 41 epochs 2023/02/25 18:31:18 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 18:31:41 - mmengine - INFO - Epoch(train) [42][ 100/5047] lr: 2.6099e-05 eta: 5 days, 13:48:18 time: 0.8324 data_time: 0.0023 memory: 49312 loss: 0.1300 loss_ce: 0.1300 2023/02/25 18:33:09 - mmengine - INFO - Epoch(train) [42][ 200/5047] lr: 2.6099e-05 eta: 5 days, 13:46:51 time: 0.8805 data_time: 0.0023 memory: 55562 loss: 0.1381 loss_ce: 0.1381 2023/02/25 18:34:35 - mmengine - INFO - Epoch(train) [42][ 300/5047] lr: 2.6099e-05 eta: 5 days, 13:45:19 time: 0.8384 data_time: 0.0021 memory: 45643 loss: 0.1365 loss_ce: 0.1365 2023/02/25 18:36:00 - mmengine - INFO - Epoch(train) [42][ 400/5047] lr: 2.6099e-05 eta: 5 days, 13:43:44 time: 0.7898 data_time: 0.0020 memory: 40825 loss: 0.1417 loss_ce: 0.1417 2023/02/25 18:37:28 - mmengine - INFO - Epoch(train) [42][ 500/5047] lr: 2.6099e-05 eta: 5 days, 13:42:19 time: 0.8776 data_time: 0.0023 memory: 47074 loss: 0.1401 loss_ce: 0.1401 2023/02/25 18:38:55 - mmengine - INFO - Epoch(train) [42][ 600/5047] lr: 2.6099e-05 eta: 5 days, 13:40:48 time: 0.8140 data_time: 0.0022 memory: 47959 loss: 0.1018 loss_ce: 0.1018 2023/02/25 18:40:21 - mmengine - INFO - Epoch(train) [42][ 700/5047] lr: 2.6099e-05 eta: 5 days, 13:39:17 time: 0.8314 data_time: 0.0018 memory: 46435 loss: 0.1158 loss_ce: 0.1158 2023/02/25 18:41:47 - mmengine - INFO - Epoch(train) [42][ 800/5047] lr: 2.6099e-05 eta: 5 days, 13:37:45 time: 0.9160 data_time: 0.0027 memory: 40508 loss: 0.1288 loss_ce: 0.1288 2023/02/25 18:43:14 - mmengine - INFO - Epoch(train) [42][ 900/5047] lr: 2.6099e-05 eta: 5 days, 13:36:15 time: 0.8787 data_time: 0.0020 memory: 41095 loss: 0.1224 loss_ce: 0.1224 2023/02/25 18:44:41 - mmengine - INFO - Epoch(train) [42][1000/5047] lr: 2.6099e-05 eta: 5 days, 13:34:47 time: 0.8512 data_time: 0.0019 memory: 52127 loss: 0.1472 loss_ce: 0.1472 2023/02/25 18:45:44 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 18:46:07 - mmengine - INFO - Epoch(train) [42][1100/5047] lr: 2.6099e-05 eta: 5 days, 13:33:14 time: 0.8680 data_time: 0.0021 memory: 53021 loss: 0.1218 loss_ce: 0.1218 2023/02/25 18:47:33 - mmengine - INFO - Epoch(train) [42][1200/5047] lr: 2.6099e-05 eta: 5 days, 13:31:42 time: 0.8545 data_time: 0.0022 memory: 51817 loss: 0.1315 loss_ce: 0.1315 2023/02/25 18:49:00 - mmengine - INFO - Epoch(train) [42][1300/5047] lr: 2.6099e-05 eta: 5 days, 13:30:11 time: 0.8429 data_time: 0.0020 memory: 42965 loss: 0.1159 loss_ce: 0.1159 2023/02/25 18:50:27 - mmengine - INFO - Epoch(train) [42][1400/5047] lr: 2.6099e-05 eta: 5 days, 13:28:42 time: 0.8383 data_time: 0.0019 memory: 55562 loss: 0.1385 loss_ce: 0.1385 2023/02/25 18:51:53 - mmengine - INFO - Epoch(train) [42][1500/5047] lr: 2.6099e-05 eta: 5 days, 13:27:10 time: 0.8216 data_time: 0.0021 memory: 43947 loss: 0.1171 loss_ce: 0.1171 2023/02/25 18:53:19 - mmengine - INFO - Epoch(train) [42][1600/5047] lr: 2.6099e-05 eta: 5 days, 13:25:40 time: 0.8573 data_time: 0.0063 memory: 43826 loss: 0.1168 loss_ce: 0.1168 2023/02/25 18:54:45 - mmengine - INFO - Epoch(train) [42][1700/5047] lr: 2.6099e-05 eta: 5 days, 13:24:06 time: 0.8527 data_time: 0.0045 memory: 45200 loss: 0.1190 loss_ce: 0.1190 2023/02/25 18:56:11 - mmengine - INFO - Epoch(train) [42][1800/5047] lr: 2.6099e-05 eta: 5 days, 13:22:35 time: 0.8471 data_time: 0.0020 memory: 42712 loss: 0.1148 loss_ce: 0.1148 2023/02/25 18:57:37 - mmengine - INFO - Epoch(train) [42][1900/5047] lr: 2.6099e-05 eta: 5 days, 13:21:02 time: 0.8671 data_time: 0.0023 memory: 44617 loss: 0.1126 loss_ce: 0.1126 2023/02/25 18:59:03 - mmengine - INFO - Epoch(train) [42][2000/5047] lr: 2.6099e-05 eta: 5 days, 13:19:30 time: 0.8696 data_time: 0.0021 memory: 44802 loss: 0.1163 loss_ce: 0.1163 2023/02/25 19:00:07 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 19:00:30 - mmengine - INFO - Epoch(train) [42][2100/5047] lr: 2.6099e-05 eta: 5 days, 13:18:01 time: 0.8624 data_time: 0.0025 memory: 42233 loss: 0.1212 loss_ce: 0.1212 2023/02/25 19:01:57 - mmengine - INFO - Epoch(train) [42][2200/5047] lr: 2.6099e-05 eta: 5 days, 13:16:32 time: 0.8876 data_time: 0.0020 memory: 55562 loss: 0.1332 loss_ce: 0.1332 2023/02/25 19:03:23 - mmengine - INFO - Epoch(train) [42][2300/5047] lr: 2.6099e-05 eta: 5 days, 13:15:00 time: 0.8793 data_time: 0.0077 memory: 43289 loss: 0.1282 loss_ce: 0.1282 2023/02/25 19:04:49 - mmengine - INFO - Epoch(train) [42][2400/5047] lr: 2.6099e-05 eta: 5 days, 13:13:29 time: 0.8603 data_time: 0.0029 memory: 47952 loss: 0.1189 loss_ce: 0.1189 2023/02/25 19:06:16 - mmengine - INFO - Epoch(train) [42][2500/5047] lr: 2.6099e-05 eta: 5 days, 13:11:59 time: 0.8572 data_time: 0.0018 memory: 46271 loss: 0.1184 loss_ce: 0.1184 2023/02/25 19:07:44 - mmengine - INFO - Epoch(train) [42][2600/5047] lr: 2.6099e-05 eta: 5 days, 13:10:31 time: 0.8626 data_time: 0.0020 memory: 53044 loss: 0.1212 loss_ce: 0.1212 2023/02/25 19:09:07 - mmengine - INFO - Epoch(train) [42][2700/5047] lr: 2.6099e-05 eta: 5 days, 13:08:52 time: 0.7947 data_time: 0.0023 memory: 43115 loss: 0.1298 loss_ce: 0.1298 2023/02/25 19:10:34 - mmengine - INFO - Epoch(train) [42][2800/5047] lr: 2.6099e-05 eta: 5 days, 13:07:22 time: 0.8777 data_time: 0.0024 memory: 47813 loss: 0.1248 loss_ce: 0.1248 2023/02/25 19:12:07 - mmengine - INFO - Epoch(train) [42][2900/5047] lr: 2.6099e-05 eta: 5 days, 13:06:10 time: 0.8925 data_time: 0.0022 memory: 43289 loss: 0.1137 loss_ce: 0.1137 2023/02/25 19:13:34 - mmengine - INFO - Epoch(train) [42][3000/5047] lr: 2.6099e-05 eta: 5 days, 13:04:39 time: 0.8920 data_time: 0.0022 memory: 44617 loss: 0.1235 loss_ce: 0.1235 2023/02/25 19:14:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 19:15:00 - mmengine - INFO - Epoch(train) [42][3100/5047] lr: 2.6099e-05 eta: 5 days, 13:03:07 time: 0.8189 data_time: 0.0022 memory: 43897 loss: 0.1129 loss_ce: 0.1129 2023/02/25 19:16:38 - mmengine - INFO - Epoch(train) [42][3200/5047] lr: 2.6099e-05 eta: 5 days, 13:02:09 time: 0.8514 data_time: 0.0033 memory: 44843 loss: 0.1186 loss_ce: 0.1186 2023/02/25 19:18:04 - mmengine - INFO - Epoch(train) [42][3300/5047] lr: 2.6099e-05 eta: 5 days, 13:00:39 time: 0.8145 data_time: 0.0019 memory: 43613 loss: 0.1202 loss_ce: 0.1202 2023/02/25 19:19:31 - mmengine - INFO - Epoch(train) [42][3400/5047] lr: 2.6099e-05 eta: 5 days, 12:59:09 time: 0.8484 data_time: 0.0021 memory: 44830 loss: 0.1166 loss_ce: 0.1166 2023/02/25 19:21:08 - mmengine - INFO - Epoch(train) [42][3500/5047] lr: 2.6099e-05 eta: 5 days, 12:58:09 time: 0.8604 data_time: 0.0026 memory: 44607 loss: 0.1325 loss_ce: 0.1325 2023/02/25 19:22:35 - mmengine - INFO - Epoch(train) [42][3600/5047] lr: 2.6099e-05 eta: 5 days, 12:56:37 time: 0.8559 data_time: 0.0027 memory: 45639 loss: 0.1331 loss_ce: 0.1331 2023/02/25 19:24:01 - mmengine - INFO - Epoch(train) [42][3700/5047] lr: 2.6099e-05 eta: 5 days, 12:55:07 time: 0.8770 data_time: 0.0020 memory: 48114 loss: 0.1225 loss_ce: 0.1225 2023/02/25 19:25:36 - mmengine - INFO - Epoch(train) [42][3800/5047] lr: 2.6099e-05 eta: 5 days, 12:54:00 time: 0.9476 data_time: 0.0019 memory: 52127 loss: 0.1384 loss_ce: 0.1384 2023/02/25 19:27:04 - mmengine - INFO - Epoch(train) [42][3900/5047] lr: 2.6099e-05 eta: 5 days, 12:52:33 time: 0.8786 data_time: 0.0019 memory: 42336 loss: 0.1211 loss_ce: 0.1211 2023/02/25 19:28:32 - mmengine - INFO - Epoch(train) [42][4000/5047] lr: 2.6099e-05 eta: 5 days, 12:51:07 time: 0.8864 data_time: 0.0019 memory: 42336 loss: 0.1304 loss_ce: 0.1304 2023/02/25 19:29:47 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 19:30:10 - mmengine - INFO - Epoch(train) [42][4100/5047] lr: 2.6099e-05 eta: 5 days, 12:50:07 time: 0.8568 data_time: 0.0022 memory: 46774 loss: 0.1319 loss_ce: 0.1319 2023/02/25 19:31:38 - mmengine - INFO - Epoch(train) [42][4200/5047] lr: 2.6099e-05 eta: 5 days, 12:48:41 time: 0.8837 data_time: 0.0024 memory: 41419 loss: 0.1338 loss_ce: 0.1338 2023/02/25 19:33:08 - mmengine - INFO - Epoch(train) [42][4300/5047] lr: 2.6099e-05 eta: 5 days, 12:47:19 time: 1.0657 data_time: 0.0020 memory: 55562 loss: 0.1324 loss_ce: 0.1324 2023/02/25 19:34:45 - mmengine - INFO - Epoch(train) [42][4400/5047] lr: 2.6099e-05 eta: 5 days, 12:46:19 time: 0.9014 data_time: 0.0025 memory: 43420 loss: 0.1123 loss_ce: 0.1123 2023/02/25 19:36:12 - mmengine - INFO - Epoch(train) [42][4500/5047] lr: 2.6099e-05 eta: 5 days, 12:44:49 time: 0.8838 data_time: 0.0020 memory: 43289 loss: 0.1269 loss_ce: 0.1269 2023/02/25 19:37:38 - mmengine - INFO - Epoch(train) [42][4600/5047] lr: 2.6099e-05 eta: 5 days, 12:43:18 time: 0.8975 data_time: 0.0022 memory: 43435 loss: 0.1339 loss_ce: 0.1339 2023/02/25 19:39:05 - mmengine - INFO - Epoch(train) [42][4700/5047] lr: 2.6099e-05 eta: 5 days, 12:41:48 time: 0.8609 data_time: 0.0028 memory: 43289 loss: 0.1185 loss_ce: 0.1185 2023/02/25 19:40:33 - mmengine - INFO - Epoch(train) [42][4800/5047] lr: 2.6099e-05 eta: 5 days, 12:40:20 time: 0.8850 data_time: 0.0025 memory: 45643 loss: 0.1195 loss_ce: 0.1195 2023/02/25 19:41:59 - mmengine - INFO - Epoch(train) [42][4900/5047] lr: 2.6099e-05 eta: 5 days, 12:38:50 time: 0.8382 data_time: 0.0043 memory: 49334 loss: 0.1421 loss_ce: 0.1421 2023/02/25 19:43:27 - mmengine - INFO - Epoch(train) [42][5000/5047] lr: 2.6099e-05 eta: 5 days, 12:37:23 time: 0.8783 data_time: 0.0023 memory: 48146 loss: 0.1208 loss_ce: 0.1208 2023/02/25 19:44:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 19:44:08 - mmengine - INFO - Saving checkpoint at 42 epochs 2023/02/25 19:44:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 19:45:41 - mmengine - INFO - Epoch(train) [43][ 100/5047] lr: 2.5898e-05 eta: 5 days, 12:35:14 time: 0.8532 data_time: 0.0032 memory: 49409 loss: 0.1045 loss_ce: 0.1045 2023/02/25 19:47:09 - mmengine - INFO - Epoch(train) [43][ 200/5047] lr: 2.5898e-05 eta: 5 days, 12:33:49 time: 0.8557 data_time: 0.0033 memory: 44278 loss: 0.1242 loss_ce: 0.1242 2023/02/25 19:48:37 - mmengine - INFO - Epoch(train) [43][ 300/5047] lr: 2.5898e-05 eta: 5 days, 12:32:20 time: 0.8767 data_time: 0.0035 memory: 50106 loss: 0.1329 loss_ce: 0.1329 2023/02/25 19:50:04 - mmengine - INFO - Epoch(train) [43][ 400/5047] lr: 2.5898e-05 eta: 5 days, 12:30:51 time: 0.8703 data_time: 0.0021 memory: 52789 loss: 0.1229 loss_ce: 0.1229 2023/02/25 19:51:28 - mmengine - INFO - Epoch(train) [43][ 500/5047] lr: 2.5898e-05 eta: 5 days, 12:29:15 time: 0.8707 data_time: 0.0022 memory: 43611 loss: 0.1410 loss_ce: 0.1410 2023/02/25 19:52:56 - mmengine - INFO - Epoch(train) [43][ 600/5047] lr: 2.5898e-05 eta: 5 days, 12:27:47 time: 0.9301 data_time: 0.0020 memory: 44617 loss: 0.1286 loss_ce: 0.1286 2023/02/25 19:54:23 - mmengine - INFO - Epoch(train) [43][ 700/5047] lr: 2.5898e-05 eta: 5 days, 12:26:20 time: 0.8539 data_time: 0.0027 memory: 52743 loss: 0.1235 loss_ce: 0.1235 2023/02/25 19:55:52 - mmengine - INFO - Epoch(train) [43][ 800/5047] lr: 2.5898e-05 eta: 5 days, 12:24:55 time: 0.8963 data_time: 0.0020 memory: 42336 loss: 0.1353 loss_ce: 0.1353 2023/02/25 19:57:19 - mmengine - INFO - Epoch(train) [43][ 900/5047] lr: 2.5898e-05 eta: 5 days, 12:23:25 time: 0.8881 data_time: 0.0044 memory: 43289 loss: 0.1207 loss_ce: 0.1207 2023/02/25 19:58:45 - mmengine - INFO - Epoch(train) [43][1000/5047] lr: 2.5898e-05 eta: 5 days, 12:21:54 time: 0.7993 data_time: 0.0019 memory: 51762 loss: 0.1326 loss_ce: 0.1326 2023/02/25 19:59:07 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 20:00:11 - mmengine - INFO - Epoch(train) [43][1100/5047] lr: 2.5898e-05 eta: 5 days, 12:20:21 time: 0.8324 data_time: 0.0025 memory: 44848 loss: 0.1439 loss_ce: 0.1439 2023/02/25 20:01:37 - mmengine - INFO - Epoch(train) [43][1200/5047] lr: 2.5898e-05 eta: 5 days, 12:18:49 time: 0.8443 data_time: 0.0022 memory: 44278 loss: 0.1254 loss_ce: 0.1254 2023/02/25 20:03:03 - mmengine - INFO - Epoch(train) [43][1300/5047] lr: 2.5898e-05 eta: 5 days, 12:17:18 time: 0.8711 data_time: 0.0019 memory: 42896 loss: 0.1171 loss_ce: 0.1171 2023/02/25 20:04:31 - mmengine - INFO - Epoch(train) [43][1400/5047] lr: 2.5898e-05 eta: 5 days, 12:15:52 time: 0.8870 data_time: 0.0018 memory: 42024 loss: 0.1240 loss_ce: 0.1240 2023/02/25 20:05:57 - mmengine - INFO - Epoch(train) [43][1500/5047] lr: 2.5898e-05 eta: 5 days, 12:14:20 time: 0.8753 data_time: 0.0039 memory: 51561 loss: 0.1219 loss_ce: 0.1219 2023/02/25 20:07:24 - mmengine - INFO - Epoch(train) [43][1600/5047] lr: 2.5898e-05 eta: 5 days, 12:12:49 time: 0.8668 data_time: 0.0021 memory: 42649 loss: 0.1297 loss_ce: 0.1297 2023/02/25 20:08:51 - mmengine - INFO - Epoch(train) [43][1700/5047] lr: 2.5898e-05 eta: 5 days, 12:11:20 time: 0.9452 data_time: 0.0025 memory: 41217 loss: 0.1209 loss_ce: 0.1209 2023/02/25 20:10:17 - mmengine - INFO - Epoch(train) [43][1800/5047] lr: 2.5898e-05 eta: 5 days, 12:09:49 time: 0.9126 data_time: 0.0020 memory: 47963 loss: 0.1488 loss_ce: 0.1488 2023/02/25 20:11:44 - mmengine - INFO - Epoch(train) [43][1900/5047] lr: 2.5898e-05 eta: 5 days, 12:08:20 time: 0.8271 data_time: 0.0019 memory: 46005 loss: 0.1384 loss_ce: 0.1384 2023/02/25 20:13:12 - mmengine - INFO - Epoch(train) [43][2000/5047] lr: 2.5898e-05 eta: 5 days, 12:06:52 time: 0.8585 data_time: 0.0027 memory: 45302 loss: 0.1303 loss_ce: 0.1303 2023/02/25 20:13:35 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 20:14:38 - mmengine - INFO - Epoch(train) [43][2100/5047] lr: 2.5898e-05 eta: 5 days, 12:05:21 time: 0.8741 data_time: 0.0021 memory: 40633 loss: 0.1334 loss_ce: 0.1334 2023/02/25 20:16:05 - mmengine - INFO - Epoch(train) [43][2200/5047] lr: 2.5898e-05 eta: 5 days, 12:03:51 time: 0.9009 data_time: 0.0019 memory: 47736 loss: 0.1389 loss_ce: 0.1389 2023/02/25 20:17:30 - mmengine - INFO - Epoch(train) [43][2300/5047] lr: 2.5898e-05 eta: 5 days, 12:02:17 time: 0.8922 data_time: 0.0030 memory: 51734 loss: 0.1070 loss_ce: 0.1070 2023/02/25 20:18:56 - mmengine - INFO - Epoch(train) [43][2400/5047] lr: 2.5898e-05 eta: 5 days, 12:00:44 time: 0.8653 data_time: 0.0027 memory: 46713 loss: 0.1254 loss_ce: 0.1254 2023/02/25 20:20:23 - mmengine - INFO - Epoch(train) [43][2500/5047] lr: 2.5898e-05 eta: 5 days, 11:59:14 time: 0.9337 data_time: 0.0028 memory: 43289 loss: 0.1171 loss_ce: 0.1171 2023/02/25 20:21:48 - mmengine - INFO - Epoch(train) [43][2600/5047] lr: 2.5898e-05 eta: 5 days, 11:57:40 time: 0.8922 data_time: 0.0018 memory: 41724 loss: 0.1272 loss_ce: 0.1272 2023/02/25 20:23:14 - mmengine - INFO - Epoch(train) [43][2700/5047] lr: 2.5898e-05 eta: 5 days, 11:56:10 time: 0.8526 data_time: 0.0021 memory: 48146 loss: 0.1095 loss_ce: 0.1095 2023/02/25 20:24:42 - mmengine - INFO - Epoch(train) [43][2800/5047] lr: 2.5898e-05 eta: 5 days, 11:54:41 time: 0.8698 data_time: 0.0022 memory: 55562 loss: 0.1202 loss_ce: 0.1202 2023/02/25 20:26:07 - mmengine - INFO - Epoch(train) [43][2900/5047] lr: 2.5898e-05 eta: 5 days, 11:53:08 time: 0.8612 data_time: 0.0019 memory: 44722 loss: 0.1220 loss_ce: 0.1220 2023/02/25 20:27:36 - mmengine - INFO - Epoch(train) [43][3000/5047] lr: 2.5898e-05 eta: 5 days, 11:51:43 time: 0.8959 data_time: 0.0018 memory: 43289 loss: 0.1302 loss_ce: 0.1302 2023/02/25 20:27:59 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 20:29:03 - mmengine - INFO - Epoch(train) [43][3100/5047] lr: 2.5898e-05 eta: 5 days, 11:50:14 time: 0.8214 data_time: 0.0074 memory: 46794 loss: 0.1197 loss_ce: 0.1197 2023/02/25 20:30:28 - mmengine - INFO - Epoch(train) [43][3200/5047] lr: 2.5898e-05 eta: 5 days, 11:48:41 time: 0.8567 data_time: 0.0022 memory: 53809 loss: 0.1188 loss_ce: 0.1188 2023/02/25 20:31:55 - mmengine - INFO - Epoch(train) [43][3300/5047] lr: 2.5898e-05 eta: 5 days, 11:47:10 time: 0.8056 data_time: 0.0020 memory: 42024 loss: 0.1065 loss_ce: 0.1065 2023/02/25 20:33:22 - mmengine - INFO - Epoch(train) [43][3400/5047] lr: 2.5898e-05 eta: 5 days, 11:45:41 time: 0.8515 data_time: 0.0019 memory: 48948 loss: 0.1139 loss_ce: 0.1139 2023/02/25 20:34:48 - mmengine - INFO - Epoch(train) [43][3500/5047] lr: 2.5898e-05 eta: 5 days, 11:44:08 time: 0.9009 data_time: 0.0022 memory: 45643 loss: 0.1340 loss_ce: 0.1340 2023/02/25 20:36:15 - mmengine - INFO - Epoch(train) [43][3600/5047] lr: 2.5898e-05 eta: 5 days, 11:42:40 time: 0.8822 data_time: 0.0045 memory: 41724 loss: 0.1274 loss_ce: 0.1274 2023/02/25 20:37:42 - mmengine - INFO - Epoch(train) [43][3700/5047] lr: 2.5898e-05 eta: 5 days, 11:41:12 time: 0.8907 data_time: 0.0021 memory: 49712 loss: 0.1140 loss_ce: 0.1140 2023/02/25 20:39:09 - mmengine - INFO - Epoch(train) [43][3800/5047] lr: 2.5898e-05 eta: 5 days, 11:39:42 time: 0.8364 data_time: 0.0049 memory: 47074 loss: 0.1308 loss_ce: 0.1308 2023/02/25 20:40:37 - mmengine - INFO - Epoch(train) [43][3900/5047] lr: 2.5898e-05 eta: 5 days, 11:38:16 time: 0.9666 data_time: 0.0019 memory: 46713 loss: 0.1176 loss_ce: 0.1176 2023/02/25 20:42:04 - mmengine - INFO - Epoch(train) [43][4000/5047] lr: 2.5898e-05 eta: 5 days, 11:36:47 time: 0.8355 data_time: 0.0018 memory: 55562 loss: 0.1156 loss_ce: 0.1156 2023/02/25 20:42:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 20:43:30 - mmengine - INFO - Epoch(train) [43][4100/5047] lr: 2.5898e-05 eta: 5 days, 11:35:14 time: 0.8471 data_time: 0.0022 memory: 48094 loss: 0.1348 loss_ce: 0.1348 2023/02/25 20:44:55 - mmengine - INFO - Epoch(train) [43][4200/5047] lr: 2.5898e-05 eta: 5 days, 11:33:39 time: 0.8281 data_time: 0.0021 memory: 48215 loss: 0.1088 loss_ce: 0.1088 2023/02/25 20:46:21 - mmengine - INFO - Epoch(train) [43][4300/5047] lr: 2.5898e-05 eta: 5 days, 11:32:07 time: 0.8760 data_time: 0.0018 memory: 43289 loss: 0.1370 loss_ce: 0.1370 2023/02/25 20:47:47 - mmengine - INFO - Epoch(train) [43][4400/5047] lr: 2.5898e-05 eta: 5 days, 11:30:37 time: 0.8817 data_time: 0.0034 memory: 43218 loss: 0.1154 loss_ce: 0.1154 2023/02/25 20:49:14 - mmengine - INFO - Epoch(train) [43][4500/5047] lr: 2.5898e-05 eta: 5 days, 11:29:07 time: 0.8176 data_time: 0.0057 memory: 43010 loss: 0.1191 loss_ce: 0.1191 2023/02/25 20:50:41 - mmengine - INFO - Epoch(train) [43][4600/5047] lr: 2.5898e-05 eta: 5 days, 11:27:38 time: 0.8523 data_time: 0.0061 memory: 55562 loss: 0.1237 loss_ce: 0.1237 2023/02/25 20:52:07 - mmengine - INFO - Epoch(train) [43][4700/5047] lr: 2.5898e-05 eta: 5 days, 11:26:05 time: 0.8304 data_time: 0.0019 memory: 41724 loss: 0.1147 loss_ce: 0.1147 2023/02/25 20:53:33 - mmengine - INFO - Epoch(train) [43][4800/5047] lr: 2.5898e-05 eta: 5 days, 11:24:34 time: 0.8680 data_time: 0.0044 memory: 43289 loss: 0.1327 loss_ce: 0.1327 2023/02/25 20:55:00 - mmengine - INFO - Epoch(train) [43][4900/5047] lr: 2.5898e-05 eta: 5 days, 11:23:05 time: 0.8921 data_time: 0.0023 memory: 44565 loss: 0.1290 loss_ce: 0.1290 2023/02/25 20:56:27 - mmengine - INFO - Epoch(train) [43][5000/5047] lr: 2.5898e-05 eta: 5 days, 11:21:35 time: 0.8859 data_time: 0.0018 memory: 42336 loss: 0.1390 loss_ce: 0.1390 2023/02/25 20:56:50 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 20:57:07 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 20:57:07 - mmengine - INFO - Saving checkpoint at 43 epochs 2023/02/25 20:58:41 - mmengine - INFO - Epoch(train) [44][ 100/5047] lr: 2.5697e-05 eta: 5 days, 11:19:28 time: 0.9182 data_time: 0.0026 memory: 46772 loss: 0.1164 loss_ce: 0.1164 2023/02/25 21:00:08 - mmengine - INFO - Epoch(train) [44][ 200/5047] lr: 2.5697e-05 eta: 5 days, 11:17:58 time: 0.9035 data_time: 0.0026 memory: 45643 loss: 0.1222 loss_ce: 0.1222 2023/02/25 21:01:34 - mmengine - INFO - Epoch(train) [44][ 300/5047] lr: 2.5697e-05 eta: 5 days, 11:16:27 time: 0.8667 data_time: 0.0021 memory: 40825 loss: 0.1235 loss_ce: 0.1235 2023/02/25 21:03:01 - mmengine - INFO - Epoch(train) [44][ 400/5047] lr: 2.5697e-05 eta: 5 days, 11:14:57 time: 0.8790 data_time: 0.0020 memory: 47096 loss: 0.1227 loss_ce: 0.1227 2023/02/25 21:04:27 - mmengine - INFO - Epoch(train) [44][ 500/5047] lr: 2.5697e-05 eta: 5 days, 11:13:24 time: 0.8540 data_time: 0.0024 memory: 40825 loss: 0.1104 loss_ce: 0.1104 2023/02/25 21:05:53 - mmengine - INFO - Epoch(train) [44][ 600/5047] lr: 2.5697e-05 eta: 5 days, 11:11:53 time: 0.8346 data_time: 0.0019 memory: 39960 loss: 0.1360 loss_ce: 0.1360 2023/02/25 21:07:18 - mmengine - INFO - Epoch(train) [44][ 700/5047] lr: 2.5697e-05 eta: 5 days, 11:10:19 time: 0.8594 data_time: 0.0026 memory: 40825 loss: 0.1099 loss_ce: 0.1099 2023/02/25 21:08:46 - mmengine - INFO - Epoch(train) [44][ 800/5047] lr: 2.5697e-05 eta: 5 days, 11:08:53 time: 0.9051 data_time: 0.0019 memory: 46713 loss: 0.1196 loss_ce: 0.1196 2023/02/25 21:10:14 - mmengine - INFO - Epoch(train) [44][ 900/5047] lr: 2.5697e-05 eta: 5 days, 11:07:27 time: 0.8655 data_time: 0.0019 memory: 46713 loss: 0.1242 loss_ce: 0.1242 2023/02/25 21:11:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 21:11:43 - mmengine - INFO - Epoch(train) [44][1000/5047] lr: 2.5697e-05 eta: 5 days, 11:06:02 time: 0.8906 data_time: 0.0019 memory: 55562 loss: 0.1318 loss_ce: 0.1318 2023/02/25 21:13:10 - mmengine - INFO - Epoch(train) [44][1100/5047] lr: 2.5697e-05 eta: 5 days, 11:04:34 time: 0.9149 data_time: 0.0021 memory: 55562 loss: 0.1152 loss_ce: 0.1152 2023/02/25 21:14:36 - mmengine - INFO - Epoch(train) [44][1200/5047] lr: 2.5697e-05 eta: 5 days, 11:03:00 time: 0.8137 data_time: 0.0023 memory: 48565 loss: 0.1208 loss_ce: 0.1208 2023/02/25 21:16:02 - mmengine - INFO - Epoch(train) [44][1300/5047] lr: 2.5697e-05 eta: 5 days, 11:01:29 time: 0.8622 data_time: 0.0022 memory: 44617 loss: 0.1158 loss_ce: 0.1158 2023/02/25 21:17:29 - mmengine - INFO - Epoch(train) [44][1400/5047] lr: 2.5697e-05 eta: 5 days, 11:00:00 time: 0.8661 data_time: 0.0026 memory: 45945 loss: 0.1205 loss_ce: 0.1205 2023/02/25 21:18:55 - mmengine - INFO - Epoch(train) [44][1500/5047] lr: 2.5697e-05 eta: 5 days, 10:58:28 time: 0.8467 data_time: 0.0025 memory: 54232 loss: 0.1339 loss_ce: 0.1339 2023/02/25 21:20:20 - mmengine - INFO - Epoch(train) [44][1600/5047] lr: 2.5697e-05 eta: 5 days, 10:56:54 time: 0.8150 data_time: 0.0028 memory: 45302 loss: 0.1061 loss_ce: 0.1061 2023/02/25 21:21:47 - mmengine - INFO - Epoch(train) [44][1700/5047] lr: 2.5697e-05 eta: 5 days, 10:55:25 time: 0.9009 data_time: 0.0024 memory: 49684 loss: 0.1298 loss_ce: 0.1298 2023/02/25 21:23:15 - mmengine - INFO - Epoch(train) [44][1800/5047] lr: 2.5697e-05 eta: 5 days, 10:53:59 time: 0.9129 data_time: 0.0022 memory: 44278 loss: 0.1228 loss_ce: 0.1228 2023/02/25 21:24:42 - mmengine - INFO - Epoch(train) [44][1900/5047] lr: 2.5697e-05 eta: 5 days, 10:52:30 time: 0.8658 data_time: 0.0035 memory: 46355 loss: 0.1301 loss_ce: 0.1301 2023/02/25 21:25:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 21:26:10 - mmengine - INFO - Epoch(train) [44][2000/5047] lr: 2.5697e-05 eta: 5 days, 10:51:04 time: 0.8595 data_time: 0.0057 memory: 42007 loss: 0.1356 loss_ce: 0.1356 2023/02/25 21:27:34 - mmengine - INFO - Epoch(train) [44][2100/5047] lr: 2.5697e-05 eta: 5 days, 10:49:26 time: 0.8529 data_time: 0.0019 memory: 42336 loss: 0.1374 loss_ce: 0.1374 2023/02/25 21:28:59 - mmengine - INFO - Epoch(train) [44][2200/5047] lr: 2.5697e-05 eta: 5 days, 10:47:52 time: 0.8363 data_time: 0.0040 memory: 55562 loss: 0.1257 loss_ce: 0.1257 2023/02/25 21:30:24 - mmengine - INFO - Epoch(train) [44][2300/5047] lr: 2.5697e-05 eta: 5 days, 10:46:18 time: 0.8435 data_time: 0.0047 memory: 45972 loss: 0.1148 loss_ce: 0.1148 2023/02/25 21:31:50 - mmengine - INFO - Epoch(train) [44][2400/5047] lr: 2.5697e-05 eta: 5 days, 10:44:45 time: 0.8577 data_time: 0.0026 memory: 41537 loss: 0.1157 loss_ce: 0.1157 2023/02/25 21:33:16 - mmengine - INFO - Epoch(train) [44][2500/5047] lr: 2.5697e-05 eta: 5 days, 10:43:14 time: 0.8328 data_time: 0.0026 memory: 43289 loss: 0.1067 loss_ce: 0.1067 2023/02/25 21:34:43 - mmengine - INFO - Epoch(train) [44][2600/5047] lr: 2.5697e-05 eta: 5 days, 10:41:45 time: 0.8522 data_time: 0.0023 memory: 43521 loss: 0.1330 loss_ce: 0.1330 2023/02/25 21:36:10 - mmengine - INFO - Epoch(train) [44][2700/5047] lr: 2.5697e-05 eta: 5 days, 10:40:17 time: 0.8745 data_time: 0.0048 memory: 46713 loss: 0.1286 loss_ce: 0.1286 2023/02/25 21:37:35 - mmengine - INFO - Epoch(train) [44][2800/5047] lr: 2.5697e-05 eta: 5 days, 10:38:41 time: 0.8256 data_time: 0.0019 memory: 41996 loss: 0.1298 loss_ce: 0.1298 2023/02/25 21:39:01 - mmengine - INFO - Epoch(train) [44][2900/5047] lr: 2.5697e-05 eta: 5 days, 10:37:10 time: 0.8695 data_time: 0.0025 memory: 43346 loss: 0.1386 loss_ce: 0.1386 2023/02/25 21:40:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 21:40:26 - mmengine - INFO - Epoch(train) [44][3000/5047] lr: 2.5697e-05 eta: 5 days, 10:35:36 time: 0.8463 data_time: 0.0023 memory: 55562 loss: 0.1115 loss_ce: 0.1115 2023/02/25 21:41:53 - mmengine - INFO - Epoch(train) [44][3100/5047] lr: 2.5697e-05 eta: 5 days, 10:34:07 time: 0.8606 data_time: 0.0022 memory: 54242 loss: 0.1285 loss_ce: 0.1285 2023/02/25 21:43:21 - mmengine - INFO - Epoch(train) [44][3200/5047] lr: 2.5697e-05 eta: 5 days, 10:32:39 time: 0.9056 data_time: 0.0019 memory: 43289 loss: 0.1302 loss_ce: 0.1302 2023/02/25 21:44:47 - mmengine - INFO - Epoch(train) [44][3300/5047] lr: 2.5697e-05 eta: 5 days, 10:31:07 time: 0.8833 data_time: 0.0020 memory: 47813 loss: 0.1233 loss_ce: 0.1233 2023/02/25 21:46:13 - mmengine - INFO - Epoch(train) [44][3400/5047] lr: 2.5697e-05 eta: 5 days, 10:29:36 time: 0.8955 data_time: 0.0027 memory: 41419 loss: 0.1242 loss_ce: 0.1242 2023/02/25 21:47:40 - mmengine - INFO - Epoch(train) [44][3500/5047] lr: 2.5697e-05 eta: 5 days, 10:28:08 time: 0.9063 data_time: 0.0022 memory: 42336 loss: 0.1176 loss_ce: 0.1176 2023/02/25 21:49:06 - mmengine - INFO - Epoch(train) [44][3600/5047] lr: 2.5697e-05 eta: 5 days, 10:26:35 time: 0.8377 data_time: 0.0034 memory: 44617 loss: 0.1238 loss_ce: 0.1238 2023/02/25 21:50:32 - mmengine - INFO - Epoch(train) [44][3700/5047] lr: 2.5697e-05 eta: 5 days, 10:25:03 time: 0.8055 data_time: 0.0024 memory: 39681 loss: 0.1367 loss_ce: 0.1367 2023/02/25 21:51:58 - mmengine - INFO - Epoch(train) [44][3800/5047] lr: 2.5697e-05 eta: 5 days, 10:23:33 time: 0.8708 data_time: 0.0024 memory: 46713 loss: 0.1125 loss_ce: 0.1125 2023/02/25 21:53:25 - mmengine - INFO - Epoch(train) [44][3900/5047] lr: 2.5697e-05 eta: 5 days, 10:22:03 time: 0.8936 data_time: 0.0024 memory: 43289 loss: 0.1209 loss_ce: 0.1209 2023/02/25 21:54:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 21:54:52 - mmengine - INFO - Epoch(train) [44][4000/5047] lr: 2.5697e-05 eta: 5 days, 10:20:35 time: 0.8797 data_time: 0.0018 memory: 45770 loss: 0.1263 loss_ce: 0.1263 2023/02/25 21:56:20 - mmengine - INFO - Epoch(train) [44][4100/5047] lr: 2.5697e-05 eta: 5 days, 10:19:07 time: 0.8937 data_time: 0.0022 memory: 42876 loss: 0.1251 loss_ce: 0.1251 2023/02/25 21:57:47 - mmengine - INFO - Epoch(train) [44][4200/5047] lr: 2.5697e-05 eta: 5 days, 10:17:40 time: 0.8179 data_time: 0.0019 memory: 41115 loss: 0.1159 loss_ce: 0.1159 2023/02/25 21:59:14 - mmengine - INFO - Epoch(train) [44][4300/5047] lr: 2.5697e-05 eta: 5 days, 10:16:10 time: 0.8601 data_time: 0.0022 memory: 46713 loss: 0.1207 loss_ce: 0.1207 2023/02/25 22:00:42 - mmengine - INFO - Epoch(train) [44][4400/5047] lr: 2.5697e-05 eta: 5 days, 10:14:42 time: 0.8884 data_time: 0.0019 memory: 46713 loss: 0.1160 loss_ce: 0.1160 2023/02/25 22:02:07 - mmengine - INFO - Epoch(train) [44][4500/5047] lr: 2.5697e-05 eta: 5 days, 10:13:09 time: 0.8717 data_time: 0.0019 memory: 41724 loss: 0.1311 loss_ce: 0.1311 2023/02/25 22:03:32 - mmengine - INFO - Epoch(train) [44][4600/5047] lr: 2.5697e-05 eta: 5 days, 10:11:35 time: 0.8409 data_time: 0.0023 memory: 43289 loss: 0.1253 loss_ce: 0.1253 2023/02/25 22:04:57 - mmengine - INFO - Epoch(train) [44][4700/5047] lr: 2.5697e-05 eta: 5 days, 10:10:01 time: 0.8734 data_time: 0.0063 memory: 46770 loss: 0.1254 loss_ce: 0.1254 2023/02/25 22:06:26 - mmengine - INFO - Epoch(train) [44][4800/5047] lr: 2.5697e-05 eta: 5 days, 10:08:36 time: 0.8738 data_time: 0.0019 memory: 46287 loss: 0.1286 loss_ce: 0.1286 2023/02/25 22:07:53 - mmengine - INFO - Epoch(train) [44][4900/5047] lr: 2.5697e-05 eta: 5 days, 10:07:07 time: 0.8562 data_time: 0.0018 memory: 45851 loss: 0.1246 loss_ce: 0.1246 2023/02/25 22:09:02 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 22:09:21 - mmengine - INFO - Epoch(train) [44][5000/5047] lr: 2.5697e-05 eta: 5 days, 10:05:41 time: 0.8709 data_time: 0.0029 memory: 49409 loss: 0.1092 loss_ce: 0.1092 2023/02/25 22:10:02 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 22:10:02 - mmengine - INFO - Saving checkpoint at 44 epochs 2023/02/25 22:11:34 - mmengine - INFO - Epoch(train) [45][ 100/5047] lr: 2.5496e-05 eta: 5 days, 10:03:30 time: 0.9050 data_time: 0.0021 memory: 42336 loss: 0.1208 loss_ce: 0.1208 2023/02/25 22:13:00 - mmengine - INFO - Epoch(train) [45][ 200/5047] lr: 2.5496e-05 eta: 5 days, 10:02:00 time: 0.8167 data_time: 0.0020 memory: 47813 loss: 0.1203 loss_ce: 0.1203 2023/02/25 22:14:26 - mmengine - INFO - Epoch(train) [45][ 300/5047] lr: 2.5496e-05 eta: 5 days, 10:00:27 time: 0.8609 data_time: 0.0040 memory: 40825 loss: 0.1106 loss_ce: 0.1106 2023/02/25 22:15:53 - mmengine - INFO - Epoch(train) [45][ 400/5047] lr: 2.5496e-05 eta: 5 days, 9:58:58 time: 0.8848 data_time: 0.0022 memory: 52762 loss: 0.1376 loss_ce: 0.1376 2023/02/25 22:17:18 - mmengine - INFO - Epoch(train) [45][ 500/5047] lr: 2.5496e-05 eta: 5 days, 9:57:25 time: 0.8547 data_time: 0.0019 memory: 45302 loss: 0.1195 loss_ce: 0.1195 2023/02/25 22:18:45 - mmengine - INFO - Epoch(train) [45][ 600/5047] lr: 2.5496e-05 eta: 5 days, 9:55:56 time: 0.8825 data_time: 0.0024 memory: 42649 loss: 0.1317 loss_ce: 0.1317 2023/02/25 22:20:13 - mmengine - INFO - Epoch(train) [45][ 700/5047] lr: 2.5496e-05 eta: 5 days, 9:54:28 time: 0.8215 data_time: 0.0021 memory: 48565 loss: 0.1307 loss_ce: 0.1307 2023/02/25 22:21:40 - mmengine - INFO - Epoch(train) [45][ 800/5047] lr: 2.5496e-05 eta: 5 days, 9:53:00 time: 0.8870 data_time: 0.0021 memory: 40241 loss: 0.1281 loss_ce: 0.1281 2023/02/25 22:23:08 - mmengine - INFO - Epoch(train) [45][ 900/5047] lr: 2.5496e-05 eta: 5 days, 9:51:33 time: 0.8692 data_time: 0.0026 memory: 42956 loss: 0.1109 loss_ce: 0.1109 2023/02/25 22:23:35 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 22:24:34 - mmengine - INFO - Epoch(train) [45][1000/5047] lr: 2.5496e-05 eta: 5 days, 9:50:03 time: 0.7977 data_time: 0.0019 memory: 48195 loss: 0.1400 loss_ce: 0.1400 2023/02/25 22:26:00 - mmengine - INFO - Epoch(train) [45][1100/5047] lr: 2.5496e-05 eta: 5 days, 9:48:32 time: 0.8868 data_time: 0.0025 memory: 45878 loss: 0.1273 loss_ce: 0.1273 2023/02/25 22:27:27 - mmengine - INFO - Epoch(train) [45][1200/5047] lr: 2.5496e-05 eta: 5 days, 9:47:01 time: 0.9037 data_time: 0.0023 memory: 45643 loss: 0.1120 loss_ce: 0.1120 2023/02/25 22:28:55 - mmengine - INFO - Epoch(train) [45][1300/5047] lr: 2.5496e-05 eta: 5 days, 9:45:36 time: 0.8783 data_time: 0.0022 memory: 55562 loss: 0.1168 loss_ce: 0.1168 2023/02/25 22:30:23 - mmengine - INFO - Epoch(train) [45][1400/5047] lr: 2.5496e-05 eta: 5 days, 9:44:10 time: 0.8822 data_time: 0.0022 memory: 42965 loss: 0.1500 loss_ce: 0.1500 2023/02/25 22:31:50 - mmengine - INFO - Epoch(train) [45][1500/5047] lr: 2.5496e-05 eta: 5 days, 9:42:39 time: 0.8861 data_time: 0.0031 memory: 50607 loss: 0.1375 loss_ce: 0.1375 2023/02/25 22:33:17 - mmengine - INFO - Epoch(train) [45][1600/5047] lr: 2.5496e-05 eta: 5 days, 9:41:11 time: 0.8830 data_time: 0.0018 memory: 48471 loss: 0.1331 loss_ce: 0.1331 2023/02/25 22:34:45 - mmengine - INFO - Epoch(train) [45][1700/5047] lr: 2.5496e-05 eta: 5 days, 9:39:46 time: 0.8548 data_time: 0.0021 memory: 44278 loss: 0.1313 loss_ce: 0.1313 2023/02/25 22:36:13 - mmengine - INFO - Epoch(train) [45][1800/5047] lr: 2.5496e-05 eta: 5 days, 9:38:17 time: 0.9027 data_time: 0.0021 memory: 45217 loss: 0.1187 loss_ce: 0.1187 2023/02/25 22:37:40 - mmengine - INFO - Epoch(train) [45][1900/5047] lr: 2.5496e-05 eta: 5 days, 9:36:50 time: 0.8878 data_time: 0.0072 memory: 43613 loss: 0.1167 loss_ce: 0.1167 2023/02/25 22:38:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 22:39:07 - mmengine - INFO - Epoch(train) [45][2000/5047] lr: 2.5496e-05 eta: 5 days, 9:35:20 time: 0.8355 data_time: 0.0020 memory: 55562 loss: 0.1334 loss_ce: 0.1334 2023/02/25 22:40:33 - mmengine - INFO - Epoch(train) [45][2100/5047] lr: 2.5496e-05 eta: 5 days, 9:33:49 time: 0.8571 data_time: 0.0019 memory: 41484 loss: 0.1220 loss_ce: 0.1220 2023/02/25 22:41:59 - mmengine - INFO - Epoch(train) [45][2200/5047] lr: 2.5496e-05 eta: 5 days, 9:32:17 time: 0.8872 data_time: 0.0036 memory: 46355 loss: 0.1306 loss_ce: 0.1306 2023/02/25 22:43:27 - mmengine - INFO - Epoch(train) [45][2300/5047] lr: 2.5496e-05 eta: 5 days, 9:30:50 time: 0.8798 data_time: 0.0030 memory: 42024 loss: 0.1301 loss_ce: 0.1301 2023/02/25 22:44:53 - mmengine - INFO - Epoch(train) [45][2400/5047] lr: 2.5496e-05 eta: 5 days, 9:29:18 time: 0.8333 data_time: 0.0019 memory: 46713 loss: 0.1432 loss_ce: 0.1432 2023/02/25 22:46:18 - mmengine - INFO - Epoch(train) [45][2500/5047] lr: 2.5496e-05 eta: 5 days, 9:27:45 time: 0.8048 data_time: 0.0020 memory: 43947 loss: 0.1194 loss_ce: 0.1194 2023/02/25 22:47:44 - mmengine - INFO - Epoch(train) [45][2600/5047] lr: 2.5496e-05 eta: 5 days, 9:26:14 time: 0.8409 data_time: 0.0021 memory: 44587 loss: 0.1177 loss_ce: 0.1177 2023/02/25 22:49:11 - mmengine - INFO - Epoch(train) [45][2700/5047] lr: 2.5496e-05 eta: 5 days, 9:24:45 time: 0.8762 data_time: 0.0022 memory: 52127 loss: 0.1265 loss_ce: 0.1265 2023/02/25 22:50:37 - mmengine - INFO - Epoch(train) [45][2800/5047] lr: 2.5496e-05 eta: 5 days, 9:23:13 time: 0.9059 data_time: 0.0021 memory: 42024 loss: 0.1366 loss_ce: 0.1366 2023/02/25 22:52:02 - mmengine - INFO - Epoch(train) [45][2900/5047] lr: 2.5496e-05 eta: 5 days, 9:21:40 time: 0.8988 data_time: 0.0020 memory: 55562 loss: 0.1183 loss_ce: 0.1183 2023/02/25 22:52:30 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 22:53:28 - mmengine - INFO - Epoch(train) [45][3000/5047] lr: 2.5496e-05 eta: 5 days, 9:20:07 time: 0.8532 data_time: 0.0019 memory: 43584 loss: 0.1243 loss_ce: 0.1243 2023/02/25 22:54:56 - mmengine - INFO - Epoch(train) [45][3100/5047] lr: 2.5496e-05 eta: 5 days, 9:18:40 time: 0.8520 data_time: 0.0022 memory: 43420 loss: 0.1381 loss_ce: 0.1381 2023/02/25 22:56:24 - mmengine - INFO - Epoch(train) [45][3200/5047] lr: 2.5496e-05 eta: 5 days, 9:17:15 time: 0.8708 data_time: 0.0022 memory: 43090 loss: 0.1252 loss_ce: 0.1252 2023/02/25 22:57:52 - mmengine - INFO - Epoch(train) [45][3300/5047] lr: 2.5496e-05 eta: 5 days, 9:15:48 time: 0.8186 data_time: 0.0024 memory: 51422 loss: 0.1247 loss_ce: 0.1247 2023/02/25 22:59:18 - mmengine - INFO - Epoch(train) [45][3400/5047] lr: 2.5496e-05 eta: 5 days, 9:14:17 time: 0.8624 data_time: 0.0019 memory: 42024 loss: 0.1283 loss_ce: 0.1283 2023/02/25 23:00:45 - mmengine - INFO - Epoch(train) [45][3500/5047] lr: 2.5496e-05 eta: 5 days, 9:12:49 time: 0.8374 data_time: 0.0023 memory: 42336 loss: 0.1234 loss_ce: 0.1234 2023/02/25 23:02:11 - mmengine - INFO - Epoch(train) [45][3600/5047] lr: 2.5496e-05 eta: 5 days, 9:11:16 time: 0.8776 data_time: 0.0056 memory: 45643 loss: 0.1156 loss_ce: 0.1156 2023/02/25 23:03:37 - mmengine - INFO - Epoch(train) [45][3700/5047] lr: 2.5496e-05 eta: 5 days, 9:09:45 time: 0.8621 data_time: 0.0021 memory: 41419 loss: 0.1237 loss_ce: 0.1237 2023/02/25 23:05:04 - mmengine - INFO - Epoch(train) [45][3800/5047] lr: 2.5496e-05 eta: 5 days, 9:08:15 time: 0.8950 data_time: 0.0019 memory: 44582 loss: 0.1311 loss_ce: 0.1311 2023/02/25 23:06:29 - mmengine - INFO - Epoch(train) [45][3900/5047] lr: 2.5496e-05 eta: 5 days, 9:06:42 time: 0.8424 data_time: 0.0022 memory: 41527 loss: 0.1207 loss_ce: 0.1207 2023/02/25 23:06:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 23:07:55 - mmengine - INFO - Epoch(train) [45][4000/5047] lr: 2.5496e-05 eta: 5 days, 9:05:11 time: 0.8885 data_time: 0.0023 memory: 41017 loss: 0.1264 loss_ce: 0.1264 2023/02/25 23:09:20 - mmengine - INFO - Epoch(train) [45][4100/5047] lr: 2.5496e-05 eta: 5 days, 9:03:37 time: 0.8382 data_time: 0.0025 memory: 43289 loss: 0.1279 loss_ce: 0.1279 2023/02/25 23:10:46 - mmengine - INFO - Epoch(train) [45][4200/5047] lr: 2.5496e-05 eta: 5 days, 9:02:05 time: 0.8622 data_time: 0.0020 memory: 44617 loss: 0.1258 loss_ce: 0.1258 2023/02/25 23:12:12 - mmengine - INFO - Epoch(train) [45][4300/5047] lr: 2.5496e-05 eta: 5 days, 9:00:33 time: 0.8866 data_time: 0.0018 memory: 42336 loss: 0.1215 loss_ce: 0.1215 2023/02/25 23:13:40 - mmengine - INFO - Epoch(train) [45][4400/5047] lr: 2.5496e-05 eta: 5 days, 8:59:07 time: 0.8919 data_time: 0.0045 memory: 42336 loss: 0.1233 loss_ce: 0.1233 2023/02/25 23:15:08 - mmengine - INFO - Epoch(train) [45][4500/5047] lr: 2.5496e-05 eta: 5 days, 8:57:41 time: 0.8009 data_time: 0.0020 memory: 50541 loss: 0.1233 loss_ce: 0.1233 2023/02/25 23:16:36 - mmengine - INFO - Epoch(train) [45][4600/5047] lr: 2.5496e-05 eta: 5 days, 8:56:14 time: 0.8644 data_time: 0.0021 memory: 44215 loss: 0.1105 loss_ce: 0.1105 2023/02/25 23:18:03 - mmengine - INFO - Epoch(train) [45][4700/5047] lr: 2.5496e-05 eta: 5 days, 8:54:45 time: 0.8163 data_time: 0.0020 memory: 41612 loss: 0.1146 loss_ce: 0.1146 2023/02/25 23:19:30 - mmengine - INFO - Epoch(train) [45][4800/5047] lr: 2.5496e-05 eta: 5 days, 8:53:16 time: 0.8981 data_time: 0.0020 memory: 44956 loss: 0.1260 loss_ce: 0.1260 2023/02/25 23:20:58 - mmengine - INFO - Epoch(train) [45][4900/5047] lr: 2.5496e-05 eta: 5 days, 8:51:51 time: 0.8980 data_time: 0.0023 memory: 41724 loss: 0.1271 loss_ce: 0.1271 2023/02/25 23:21:26 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 23:22:26 - mmengine - INFO - Epoch(train) [45][5000/5047] lr: 2.5496e-05 eta: 5 days, 8:50:24 time: 0.8820 data_time: 0.0019 memory: 46355 loss: 0.1194 loss_ce: 0.1194 2023/02/25 23:23:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 23:23:08 - mmengine - INFO - Saving checkpoint at 45 epochs 2023/02/25 23:24:41 - mmengine - INFO - Epoch(train) [46][ 100/5047] lr: 2.5296e-05 eta: 5 days, 8:48:19 time: 0.8485 data_time: 0.0024 memory: 42913 loss: 0.1153 loss_ce: 0.1153 2023/02/25 23:26:08 - mmengine - INFO - Epoch(train) [46][ 200/5047] lr: 2.5296e-05 eta: 5 days, 8:46:48 time: 0.8865 data_time: 0.0024 memory: 44496 loss: 0.1348 loss_ce: 0.1348 2023/02/25 23:27:32 - mmengine - INFO - Epoch(train) [46][ 300/5047] lr: 2.5296e-05 eta: 5 days, 8:45:12 time: 0.8427 data_time: 0.0019 memory: 55562 loss: 0.1068 loss_ce: 0.1068 2023/02/25 23:29:00 - mmengine - INFO - Epoch(train) [46][ 400/5047] lr: 2.5296e-05 eta: 5 days, 8:43:47 time: 0.8423 data_time: 0.0019 memory: 55562 loss: 0.1328 loss_ce: 0.1328 2023/02/25 23:30:27 - mmengine - INFO - Epoch(train) [46][ 500/5047] lr: 2.5296e-05 eta: 5 days, 8:42:17 time: 0.8639 data_time: 0.0025 memory: 55562 loss: 0.1276 loss_ce: 0.1276 2023/02/25 23:31:53 - mmengine - INFO - Epoch(train) [46][ 600/5047] lr: 2.5296e-05 eta: 5 days, 8:40:45 time: 0.8524 data_time: 0.0025 memory: 51656 loss: 0.1131 loss_ce: 0.1131 2023/02/25 23:33:19 - mmengine - INFO - Epoch(train) [46][ 700/5047] lr: 2.5296e-05 eta: 5 days, 8:39:16 time: 0.8657 data_time: 0.0020 memory: 50518 loss: 0.1352 loss_ce: 0.1352 2023/02/25 23:34:45 - mmengine - INFO - Epoch(train) [46][ 800/5047] lr: 2.5296e-05 eta: 5 days, 8:37:44 time: 0.8421 data_time: 0.0026 memory: 48188 loss: 0.1292 loss_ce: 0.1292 2023/02/25 23:35:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 23:36:11 - mmengine - INFO - Epoch(train) [46][ 900/5047] lr: 2.5296e-05 eta: 5 days, 8:36:11 time: 0.8388 data_time: 0.0061 memory: 42336 loss: 0.1222 loss_ce: 0.1222 2023/02/25 23:37:35 - mmengine - INFO - Epoch(train) [46][1000/5047] lr: 2.5296e-05 eta: 5 days, 8:34:36 time: 0.8513 data_time: 0.0023 memory: 44658 loss: 0.1236 loss_ce: 0.1236 2023/02/25 23:39:01 - mmengine - INFO - Epoch(train) [46][1100/5047] lr: 2.5296e-05 eta: 5 days, 8:33:05 time: 0.8724 data_time: 0.0021 memory: 55562 loss: 0.1343 loss_ce: 0.1343 2023/02/25 23:40:28 - mmengine - INFO - Epoch(train) [46][1200/5047] lr: 2.5296e-05 eta: 5 days, 8:31:34 time: 0.8839 data_time: 0.0020 memory: 39681 loss: 0.1359 loss_ce: 0.1359 2023/02/25 23:41:55 - mmengine - INFO - Epoch(train) [46][1300/5047] lr: 2.5296e-05 eta: 5 days, 8:30:07 time: 0.8786 data_time: 0.0033 memory: 54303 loss: 0.1419 loss_ce: 0.1419 2023/02/25 23:43:25 - mmengine - INFO - Epoch(train) [46][1400/5047] lr: 2.5296e-05 eta: 5 days, 8:28:44 time: 0.8839 data_time: 0.0018 memory: 50670 loss: 0.1225 loss_ce: 0.1225 2023/02/25 23:44:51 - mmengine - INFO - Epoch(train) [46][1500/5047] lr: 2.5296e-05 eta: 5 days, 8:27:13 time: 0.8749 data_time: 0.0027 memory: 44238 loss: 0.1177 loss_ce: 0.1177 2023/02/25 23:46:18 - mmengine - INFO - Epoch(train) [46][1600/5047] lr: 2.5296e-05 eta: 5 days, 8:25:46 time: 0.8409 data_time: 0.0020 memory: 48055 loss: 0.1076 loss_ce: 0.1076 2023/02/25 23:47:47 - mmengine - INFO - Epoch(train) [46][1700/5047] lr: 2.5296e-05 eta: 5 days, 8:24:22 time: 0.8625 data_time: 0.0023 memory: 55562 loss: 0.1259 loss_ce: 0.1259 2023/02/25 23:49:11 - mmengine - INFO - Epoch(train) [46][1800/5047] lr: 2.5296e-05 eta: 5 days, 8:22:46 time: 0.8676 data_time: 0.0019 memory: 44617 loss: 0.1102 loss_ce: 0.1102 2023/02/25 23:50:25 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/25 23:50:39 - mmengine - INFO - Epoch(train) [46][1900/5047] lr: 2.5296e-05 eta: 5 days, 8:21:17 time: 0.8801 data_time: 0.0019 memory: 42024 loss: 0.1138 loss_ce: 0.1138 2023/02/25 23:52:06 - mmengine - INFO - Epoch(train) [46][2000/5047] lr: 2.5296e-05 eta: 5 days, 8:19:50 time: 0.8669 data_time: 0.0022 memory: 55114 loss: 0.1215 loss_ce: 0.1215 2023/02/25 23:53:34 - mmengine - INFO - Epoch(train) [46][2100/5047] lr: 2.5296e-05 eta: 5 days, 8:18:23 time: 0.8954 data_time: 0.0061 memory: 45545 loss: 0.1327 loss_ce: 0.1327 2023/02/25 23:55:01 - mmengine - INFO - Epoch(train) [46][2200/5047] lr: 2.5296e-05 eta: 5 days, 8:16:53 time: 0.8051 data_time: 0.0025 memory: 46005 loss: 0.1213 loss_ce: 0.1213 2023/02/25 23:56:28 - mmengine - INFO - Epoch(train) [46][2300/5047] lr: 2.5296e-05 eta: 5 days, 8:15:25 time: 0.8624 data_time: 0.0064 memory: 55114 loss: 0.1228 loss_ce: 0.1228 2023/02/25 23:57:55 - mmengine - INFO - Epoch(train) [46][2400/5047] lr: 2.5296e-05 eta: 5 days, 8:13:55 time: 0.8804 data_time: 0.0022 memory: 48796 loss: 0.1299 loss_ce: 0.1299 2023/02/25 23:59:20 - mmengine - INFO - Epoch(train) [46][2500/5047] lr: 2.5296e-05 eta: 5 days, 8:12:23 time: 0.8563 data_time: 0.0089 memory: 42932 loss: 0.1318 loss_ce: 0.1318 2023/02/26 00:00:46 - mmengine - INFO - Epoch(train) [46][2600/5047] lr: 2.5296e-05 eta: 5 days, 8:10:52 time: 0.8937 data_time: 0.0019 memory: 44956 loss: 0.0949 loss_ce: 0.0949 2023/02/26 00:02:14 - mmengine - INFO - Epoch(train) [46][2700/5047] lr: 2.5296e-05 eta: 5 days, 8:09:25 time: 0.8797 data_time: 0.0024 memory: 46005 loss: 0.1067 loss_ce: 0.1067 2023/02/26 00:03:43 - mmengine - INFO - Epoch(train) [46][2800/5047] lr: 2.5296e-05 eta: 5 days, 8:08:02 time: 0.8980 data_time: 0.0020 memory: 42476 loss: 0.1191 loss_ce: 0.1191 2023/02/26 00:04:57 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 00:05:10 - mmengine - INFO - Epoch(train) [46][2900/5047] lr: 2.5296e-05 eta: 5 days, 8:06:32 time: 0.8677 data_time: 0.0023 memory: 43020 loss: 0.1362 loss_ce: 0.1362 2023/02/26 00:06:38 - mmengine - INFO - Epoch(train) [46][3000/5047] lr: 2.5296e-05 eta: 5 days, 8:05:05 time: 0.8938 data_time: 0.0020 memory: 45118 loss: 0.1298 loss_ce: 0.1298 2023/02/26 00:08:05 - mmengine - INFO - Epoch(train) [46][3100/5047] lr: 2.5296e-05 eta: 5 days, 8:03:36 time: 0.8479 data_time: 0.0019 memory: 43613 loss: 0.1291 loss_ce: 0.1291 2023/02/26 00:09:31 - mmengine - INFO - Epoch(train) [46][3200/5047] lr: 2.5296e-05 eta: 5 days, 8:02:06 time: 0.8255 data_time: 0.0023 memory: 46658 loss: 0.1113 loss_ce: 0.1113 2023/02/26 00:10:59 - mmengine - INFO - Epoch(train) [46][3300/5047] lr: 2.5296e-05 eta: 5 days, 8:00:40 time: 0.9151 data_time: 0.0021 memory: 45642 loss: 0.1103 loss_ce: 0.1103 2023/02/26 00:12:27 - mmengine - INFO - Epoch(train) [46][3400/5047] lr: 2.5296e-05 eta: 5 days, 7:59:12 time: 0.8705 data_time: 0.0024 memory: 46355 loss: 0.1213 loss_ce: 0.1213 2023/02/26 00:13:53 - mmengine - INFO - Epoch(train) [46][3500/5047] lr: 2.5296e-05 eta: 5 days, 7:57:42 time: 0.8304 data_time: 0.0023 memory: 49217 loss: 0.1327 loss_ce: 0.1327 2023/02/26 00:15:18 - mmengine - INFO - Epoch(train) [46][3600/5047] lr: 2.5296e-05 eta: 5 days, 7:56:06 time: 0.7994 data_time: 0.0019 memory: 40147 loss: 0.1279 loss_ce: 0.1279 2023/02/26 00:16:45 - mmengine - INFO - Epoch(train) [46][3700/5047] lr: 2.5296e-05 eta: 5 days, 7:54:40 time: 0.8641 data_time: 0.0020 memory: 52974 loss: 0.1312 loss_ce: 0.1312 2023/02/26 00:18:12 - mmengine - INFO - Epoch(train) [46][3800/5047] lr: 2.5296e-05 eta: 5 days, 7:53:10 time: 0.8483 data_time: 0.0020 memory: 41419 loss: 0.1158 loss_ce: 0.1158 2023/02/26 00:19:26 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 00:19:39 - mmengine - INFO - Epoch(train) [46][3900/5047] lr: 2.5296e-05 eta: 5 days, 7:51:41 time: 0.8647 data_time: 0.0023 memory: 46794 loss: 0.1319 loss_ce: 0.1319 2023/02/26 00:21:05 - mmengine - INFO - Epoch(train) [46][4000/5047] lr: 2.5296e-05 eta: 5 days, 7:50:10 time: 0.8943 data_time: 0.0019 memory: 45621 loss: 0.1369 loss_ce: 0.1369 2023/02/26 00:22:31 - mmengine - INFO - Epoch(train) [46][4100/5047] lr: 2.5296e-05 eta: 5 days, 7:48:39 time: 0.8407 data_time: 0.0018 memory: 44278 loss: 0.1337 loss_ce: 0.1337 2023/02/26 00:23:58 - mmengine - INFO - Epoch(train) [46][4200/5047] lr: 2.5296e-05 eta: 5 days, 7:47:09 time: 0.9047 data_time: 0.0028 memory: 46637 loss: 0.1188 loss_ce: 0.1188 2023/02/26 00:25:26 - mmengine - INFO - Epoch(train) [46][4300/5047] lr: 2.5296e-05 eta: 5 days, 7:45:43 time: 0.8525 data_time: 0.0030 memory: 47037 loss: 0.1351 loss_ce: 0.1351 2023/02/26 00:26:54 - mmengine - INFO - Epoch(train) [46][4400/5047] lr: 2.5296e-05 eta: 5 days, 7:44:17 time: 0.8527 data_time: 0.0056 memory: 40352 loss: 0.1331 loss_ce: 0.1331 2023/02/26 00:28:22 - mmengine - INFO - Epoch(train) [46][4500/5047] lr: 2.5296e-05 eta: 5 days, 7:42:52 time: 0.8876 data_time: 0.0094 memory: 44617 loss: 0.1348 loss_ce: 0.1348 2023/02/26 00:29:50 - mmengine - INFO - Epoch(train) [46][4600/5047] lr: 2.5296e-05 eta: 5 days, 7:41:23 time: 0.8848 data_time: 0.0020 memory: 42336 loss: 0.1225 loss_ce: 0.1225 2023/02/26 00:31:18 - mmengine - INFO - Epoch(train) [46][4700/5047] lr: 2.5296e-05 eta: 5 days, 7:39:57 time: 0.8939 data_time: 0.0019 memory: 49117 loss: 0.1274 loss_ce: 0.1274 2023/02/26 00:32:46 - mmengine - INFO - Epoch(train) [46][4800/5047] lr: 2.5296e-05 eta: 5 days, 7:38:32 time: 0.8816 data_time: 0.0046 memory: 44661 loss: 0.1325 loss_ce: 0.1325 2023/02/26 00:34:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 00:34:13 - mmengine - INFO - Epoch(train) [46][4900/5047] lr: 2.5296e-05 eta: 5 days, 7:37:02 time: 0.8508 data_time: 0.0022 memory: 41419 loss: 0.1182 loss_ce: 0.1182 2023/02/26 00:35:40 - mmengine - INFO - Epoch(train) [46][5000/5047] lr: 2.5296e-05 eta: 5 days, 7:35:34 time: 0.8904 data_time: 0.0048 memory: 42443 loss: 0.1244 loss_ce: 0.1244 2023/02/26 00:36:21 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 00:36:21 - mmengine - INFO - Saving checkpoint at 46 epochs 2023/02/26 00:37:54 - mmengine - INFO - Epoch(train) [47][ 100/5047] lr: 2.5095e-05 eta: 5 days, 7:33:24 time: 0.8657 data_time: 0.0020 memory: 43854 loss: 0.1248 loss_ce: 0.1248 2023/02/26 00:39:21 - mmengine - INFO - Epoch(train) [47][ 200/5047] lr: 2.5095e-05 eta: 5 days, 7:31:56 time: 0.8850 data_time: 0.0022 memory: 55562 loss: 0.1235 loss_ce: 0.1235 2023/02/26 00:40:48 - mmengine - INFO - Epoch(train) [47][ 300/5047] lr: 2.5095e-05 eta: 5 days, 7:30:28 time: 0.9425 data_time: 0.0020 memory: 55562 loss: 0.1269 loss_ce: 0.1269 2023/02/26 00:42:14 - mmengine - INFO - Epoch(train) [47][ 400/5047] lr: 2.5095e-05 eta: 5 days, 7:28:56 time: 0.7950 data_time: 0.0024 memory: 55562 loss: 0.1223 loss_ce: 0.1223 2023/02/26 00:43:39 - mmengine - INFO - Epoch(train) [47][ 500/5047] lr: 2.5095e-05 eta: 5 days, 7:27:24 time: 0.8287 data_time: 0.0022 memory: 47689 loss: 0.1282 loss_ce: 0.1282 2023/02/26 00:45:06 - mmengine - INFO - Epoch(train) [47][ 600/5047] lr: 2.5095e-05 eta: 5 days, 7:25:54 time: 0.9209 data_time: 0.0025 memory: 54242 loss: 0.1162 loss_ce: 0.1162 2023/02/26 00:46:33 - mmengine - INFO - Epoch(train) [47][ 700/5047] lr: 2.5095e-05 eta: 5 days, 7:24:24 time: 0.8390 data_time: 0.0031 memory: 43350 loss: 0.1220 loss_ce: 0.1220 2023/02/26 00:47:59 - mmengine - INFO - Epoch(train) [47][ 800/5047] lr: 2.5095e-05 eta: 5 days, 7:22:55 time: 0.8760 data_time: 0.0029 memory: 42105 loss: 0.1315 loss_ce: 0.1315 2023/02/26 00:48:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 00:49:27 - mmengine - INFO - Epoch(train) [47][ 900/5047] lr: 2.5095e-05 eta: 5 days, 7:21:27 time: 0.8784 data_time: 0.0021 memory: 41419 loss: 0.1164 loss_ce: 0.1164 2023/02/26 00:50:54 - mmengine - INFO - Epoch(train) [47][1000/5047] lr: 2.5095e-05 eta: 5 days, 7:19:58 time: 0.8779 data_time: 0.0027 memory: 55562 loss: 0.1340 loss_ce: 0.1340 2023/02/26 00:52:22 - mmengine - INFO - Epoch(train) [47][1100/5047] lr: 2.5095e-05 eta: 5 days, 7:18:32 time: 0.8951 data_time: 0.0023 memory: 44386 loss: 0.1222 loss_ce: 0.1222 2023/02/26 00:53:49 - mmengine - INFO - Epoch(train) [47][1200/5047] lr: 2.5095e-05 eta: 5 days, 7:17:03 time: 0.8755 data_time: 0.0020 memory: 43613 loss: 0.1194 loss_ce: 0.1194 2023/02/26 00:55:15 - mmengine - INFO - Epoch(train) [47][1300/5047] lr: 2.5095e-05 eta: 5 days, 7:15:33 time: 0.8704 data_time: 0.0027 memory: 46713 loss: 0.1254 loss_ce: 0.1254 2023/02/26 00:56:42 - mmengine - INFO - Epoch(train) [47][1400/5047] lr: 2.5095e-05 eta: 5 days, 7:14:02 time: 0.8975 data_time: 0.0034 memory: 55562 loss: 0.1077 loss_ce: 0.1077 2023/02/26 00:58:08 - mmengine - INFO - Epoch(train) [47][1500/5047] lr: 2.5095e-05 eta: 5 days, 7:12:32 time: 0.8255 data_time: 0.0021 memory: 43348 loss: 0.1327 loss_ce: 0.1327 2023/02/26 00:59:36 - mmengine - INFO - Epoch(train) [47][1600/5047] lr: 2.5095e-05 eta: 5 days, 7:11:06 time: 0.9107 data_time: 0.0022 memory: 42596 loss: 0.1265 loss_ce: 0.1265 2023/02/26 01:01:02 - mmengine - INFO - Epoch(train) [47][1700/5047] lr: 2.5095e-05 eta: 5 days, 7:09:34 time: 0.8465 data_time: 0.0029 memory: 43947 loss: 0.1477 loss_ce: 0.1477 2023/02/26 01:02:30 - mmengine - INFO - Epoch(train) [47][1800/5047] lr: 2.5095e-05 eta: 5 days, 7:08:08 time: 0.9237 data_time: 0.0030 memory: 54242 loss: 0.1502 loss_ce: 0.1502 2023/02/26 01:03:02 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 01:03:55 - mmengine - INFO - Epoch(train) [47][1900/5047] lr: 2.5095e-05 eta: 5 days, 7:06:36 time: 0.8338 data_time: 0.0021 memory: 51637 loss: 0.1195 loss_ce: 0.1195 2023/02/26 01:05:22 - mmengine - INFO - Epoch(train) [47][2000/5047] lr: 2.5095e-05 eta: 5 days, 7:05:07 time: 0.8680 data_time: 0.0024 memory: 46637 loss: 0.1249 loss_ce: 0.1249 2023/02/26 01:06:49 - mmengine - INFO - Epoch(train) [47][2100/5047] lr: 2.5095e-05 eta: 5 days, 7:03:36 time: 0.8766 data_time: 0.0022 memory: 44592 loss: 0.1367 loss_ce: 0.1367 2023/02/26 01:08:14 - mmengine - INFO - Epoch(train) [47][2200/5047] lr: 2.5095e-05 eta: 5 days, 7:02:05 time: 0.8301 data_time: 0.0036 memory: 41775 loss: 0.1172 loss_ce: 0.1172 2023/02/26 01:09:40 - mmengine - INFO - Epoch(train) [47][2300/5047] lr: 2.5095e-05 eta: 5 days, 7:00:32 time: 0.8875 data_time: 0.0020 memory: 45199 loss: 0.1137 loss_ce: 0.1137 2023/02/26 01:11:07 - mmengine - INFO - Epoch(train) [47][2400/5047] lr: 2.5095e-05 eta: 5 days, 6:59:04 time: 0.8667 data_time: 0.0025 memory: 43585 loss: 0.1403 loss_ce: 0.1403 2023/02/26 01:12:35 - mmengine - INFO - Epoch(train) [47][2500/5047] lr: 2.5095e-05 eta: 5 days, 6:57:38 time: 0.8872 data_time: 0.0043 memory: 41630 loss: 0.1220 loss_ce: 0.1220 2023/02/26 01:14:03 - mmengine - INFO - Epoch(train) [47][2600/5047] lr: 2.5095e-05 eta: 5 days, 6:56:11 time: 0.8791 data_time: 0.0021 memory: 55562 loss: 0.1328 loss_ce: 0.1328 2023/02/26 01:15:29 - mmengine - INFO - Epoch(train) [47][2700/5047] lr: 2.5095e-05 eta: 5 days, 6:54:40 time: 0.8155 data_time: 0.0022 memory: 54116 loss: 0.1221 loss_ce: 0.1221 2023/02/26 01:16:56 - mmengine - INFO - Epoch(train) [47][2800/5047] lr: 2.5095e-05 eta: 5 days, 6:53:11 time: 0.8513 data_time: 0.0045 memory: 52955 loss: 0.1238 loss_ce: 0.1238 2023/02/26 01:17:30 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 01:18:25 - mmengine - INFO - Epoch(train) [47][2900/5047] lr: 2.5095e-05 eta: 5 days, 6:51:47 time: 0.9166 data_time: 0.0026 memory: 53809 loss: 0.1157 loss_ce: 0.1157 2023/02/26 01:19:53 - mmengine - INFO - Epoch(train) [47][3000/5047] lr: 2.5095e-05 eta: 5 days, 6:50:21 time: 0.8963 data_time: 0.0020 memory: 49518 loss: 0.1593 loss_ce: 0.1593 2023/02/26 01:21:19 - mmengine - INFO - Epoch(train) [47][3100/5047] lr: 2.5095e-05 eta: 5 days, 6:48:50 time: 0.9000 data_time: 0.0031 memory: 45302 loss: 0.1221 loss_ce: 0.1221 2023/02/26 01:22:47 - mmengine - INFO - Epoch(train) [47][3200/5047] lr: 2.5095e-05 eta: 5 days, 6:47:23 time: 0.8615 data_time: 0.0021 memory: 48565 loss: 0.1208 loss_ce: 0.1208 2023/02/26 01:24:14 - mmengine - INFO - Epoch(train) [47][3300/5047] lr: 2.5095e-05 eta: 5 days, 6:45:55 time: 0.8764 data_time: 0.0021 memory: 39960 loss: 0.1147 loss_ce: 0.1147 2023/02/26 01:25:43 - mmengine - INFO - Epoch(train) [47][3400/5047] lr: 2.5095e-05 eta: 5 days, 6:44:29 time: 0.8636 data_time: 0.0029 memory: 41143 loss: 0.1176 loss_ce: 0.1176 2023/02/26 01:27:09 - mmengine - INFO - Epoch(train) [47][3500/5047] lr: 2.5095e-05 eta: 5 days, 6:43:00 time: 0.8773 data_time: 0.0023 memory: 42149 loss: 0.1132 loss_ce: 0.1132 2023/02/26 01:28:37 - mmengine - INFO - Epoch(train) [47][3600/5047] lr: 2.5095e-05 eta: 5 days, 6:41:33 time: 0.8614 data_time: 0.0021 memory: 42719 loss: 0.1088 loss_ce: 0.1088 2023/02/26 01:30:04 - mmengine - INFO - Epoch(train) [47][3700/5047] lr: 2.5095e-05 eta: 5 days, 6:40:04 time: 0.8720 data_time: 0.0022 memory: 41832 loss: 0.1274 loss_ce: 0.1274 2023/02/26 01:31:34 - mmengine - INFO - Epoch(train) [47][3800/5047] lr: 2.5095e-05 eta: 5 days, 6:38:42 time: 0.8627 data_time: 0.0020 memory: 48188 loss: 0.1419 loss_ce: 0.1419 2023/02/26 01:32:07 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 01:33:01 - mmengine - INFO - Epoch(train) [47][3900/5047] lr: 2.5095e-05 eta: 5 days, 6:37:14 time: 0.8732 data_time: 0.0023 memory: 51308 loss: 0.1252 loss_ce: 0.1252 2023/02/26 01:34:30 - mmengine - INFO - Epoch(train) [47][4000/5047] lr: 2.5095e-05 eta: 5 days, 6:35:50 time: 0.8910 data_time: 0.0025 memory: 41677 loss: 0.1274 loss_ce: 0.1274 2023/02/26 01:35:56 - mmengine - INFO - Epoch(train) [47][4100/5047] lr: 2.5095e-05 eta: 5 days, 6:34:20 time: 0.9138 data_time: 0.0019 memory: 42928 loss: 0.1243 loss_ce: 0.1243 2023/02/26 01:37:23 - mmengine - INFO - Epoch(train) [47][4200/5047] lr: 2.5095e-05 eta: 5 days, 6:32:51 time: 0.8558 data_time: 0.0041 memory: 46915 loss: 0.1208 loss_ce: 0.1208 2023/02/26 01:38:50 - mmengine - INFO - Epoch(train) [47][4300/5047] lr: 2.5095e-05 eta: 5 days, 6:31:22 time: 0.8410 data_time: 0.0026 memory: 44956 loss: 0.1261 loss_ce: 0.1261 2023/02/26 01:40:16 - mmengine - INFO - Epoch(train) [47][4400/5047] lr: 2.5095e-05 eta: 5 days, 6:29:50 time: 0.8581 data_time: 0.0024 memory: 46005 loss: 0.1281 loss_ce: 0.1281 2023/02/26 01:41:42 - mmengine - INFO - Epoch(train) [47][4500/5047] lr: 2.5095e-05 eta: 5 days, 6:28:20 time: 0.8597 data_time: 0.0021 memory: 43947 loss: 0.1254 loss_ce: 0.1254 2023/02/26 01:43:10 - mmengine - INFO - Epoch(train) [47][4600/5047] lr: 2.5095e-05 eta: 5 days, 6:26:52 time: 0.8491 data_time: 0.0059 memory: 45634 loss: 0.1102 loss_ce: 0.1102 2023/02/26 01:44:38 - mmengine - INFO - Epoch(train) [47][4700/5047] lr: 2.5095e-05 eta: 5 days, 6:25:26 time: 0.8212 data_time: 0.0021 memory: 49715 loss: 0.1189 loss_ce: 0.1189 2023/02/26 01:46:07 - mmengine - INFO - Epoch(train) [47][4800/5047] lr: 2.5095e-05 eta: 5 days, 6:24:01 time: 0.8953 data_time: 0.0029 memory: 43289 loss: 0.1259 loss_ce: 0.1259 2023/02/26 01:46:40 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 01:47:34 - mmengine - INFO - Epoch(train) [47][4900/5047] lr: 2.5095e-05 eta: 5 days, 6:22:34 time: 0.8885 data_time: 0.0040 memory: 42336 loss: 0.1394 loss_ce: 0.1394 2023/02/26 01:49:02 - mmengine - INFO - Epoch(train) [47][5000/5047] lr: 2.5095e-05 eta: 5 days, 6:21:06 time: 0.8969 data_time: 0.0026 memory: 48209 loss: 0.1142 loss_ce: 0.1142 2023/02/26 01:49:42 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 01:49:42 - mmengine - INFO - Saving checkpoint at 47 epochs 2023/02/26 01:51:16 - mmengine - INFO - Epoch(train) [48][ 100/5047] lr: 2.4894e-05 eta: 5 days, 6:18:58 time: 0.8729 data_time: 0.0026 memory: 43153 loss: 0.1251 loss_ce: 0.1251 2023/02/26 01:52:41 - mmengine - INFO - Epoch(train) [48][ 200/5047] lr: 2.4894e-05 eta: 5 days, 6:17:26 time: 0.8874 data_time: 0.0020 memory: 42309 loss: 0.1393 loss_ce: 0.1393 2023/02/26 01:54:07 - mmengine - INFO - Epoch(train) [48][ 300/5047] lr: 2.4894e-05 eta: 5 days, 6:15:55 time: 0.8421 data_time: 0.0020 memory: 41753 loss: 0.1157 loss_ce: 0.1157 2023/02/26 01:55:35 - mmengine - INFO - Epoch(train) [48][ 400/5047] lr: 2.4894e-05 eta: 5 days, 6:14:27 time: 0.8892 data_time: 0.0027 memory: 55562 loss: 0.1158 loss_ce: 0.1158 2023/02/26 01:57:02 - mmengine - INFO - Epoch(train) [48][ 500/5047] lr: 2.4894e-05 eta: 5 days, 6:12:59 time: 0.8647 data_time: 0.0020 memory: 49334 loss: 0.1322 loss_ce: 0.1322 2023/02/26 01:58:30 - mmengine - INFO - Epoch(train) [48][ 600/5047] lr: 2.4894e-05 eta: 5 days, 6:11:32 time: 0.8479 data_time: 0.0024 memory: 41724 loss: 0.1310 loss_ce: 0.1310 2023/02/26 01:59:57 - mmengine - INFO - Epoch(train) [48][ 700/5047] lr: 2.4894e-05 eta: 5 days, 6:10:04 time: 0.8612 data_time: 0.0024 memory: 44956 loss: 0.1271 loss_ce: 0.1271 2023/02/26 02:01:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 02:01:25 - mmengine - INFO - Epoch(train) [48][ 800/5047] lr: 2.4894e-05 eta: 5 days, 6:08:38 time: 0.8595 data_time: 0.0030 memory: 47813 loss: 0.1137 loss_ce: 0.1137 2023/02/26 02:02:52 - mmengine - INFO - Epoch(train) [48][ 900/5047] lr: 2.4894e-05 eta: 5 days, 6:07:09 time: 0.8584 data_time: 0.0082 memory: 44278 loss: 0.1434 loss_ce: 0.1434 2023/02/26 02:04:19 - mmengine - INFO - Epoch(train) [48][1000/5047] lr: 2.4894e-05 eta: 5 days, 6:05:40 time: 0.8323 data_time: 0.0028 memory: 55562 loss: 0.1275 loss_ce: 0.1275 2023/02/26 02:05:45 - mmengine - INFO - Epoch(train) [48][1100/5047] lr: 2.4894e-05 eta: 5 days, 6:04:09 time: 0.8551 data_time: 0.0076 memory: 42649 loss: 0.1201 loss_ce: 0.1201 2023/02/26 02:07:13 - mmengine - INFO - Epoch(train) [48][1200/5047] lr: 2.4894e-05 eta: 5 days, 6:02:42 time: 0.8779 data_time: 0.0020 memory: 51792 loss: 0.1289 loss_ce: 0.1289 2023/02/26 02:08:40 - mmengine - INFO - Epoch(train) [48][1300/5047] lr: 2.4894e-05 eta: 5 days, 6:01:15 time: 0.8808 data_time: 0.0026 memory: 42649 loss: 0.1143 loss_ce: 0.1143 2023/02/26 02:10:07 - mmengine - INFO - Epoch(train) [48][1400/5047] lr: 2.4894e-05 eta: 5 days, 5:59:46 time: 0.8714 data_time: 0.0020 memory: 54072 loss: 0.1290 loss_ce: 0.1290 2023/02/26 02:11:34 - mmengine - INFO - Epoch(train) [48][1500/5047] lr: 2.4894e-05 eta: 5 days, 5:58:17 time: 0.8607 data_time: 0.0023 memory: 43613 loss: 0.1214 loss_ce: 0.1214 2023/02/26 02:13:01 - mmengine - INFO - Epoch(train) [48][1600/5047] lr: 2.4894e-05 eta: 5 days, 5:56:48 time: 0.8809 data_time: 0.0019 memory: 39660 loss: 0.1250 loss_ce: 0.1250 2023/02/26 02:14:27 - mmengine - INFO - Epoch(train) [48][1700/5047] lr: 2.4894e-05 eta: 5 days, 5:55:17 time: 0.8725 data_time: 0.0022 memory: 55537 loss: 0.1351 loss_ce: 0.1351 2023/02/26 02:15:45 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 02:15:53 - mmengine - INFO - Epoch(train) [48][1800/5047] lr: 2.4894e-05 eta: 5 days, 5:53:47 time: 0.8767 data_time: 0.0030 memory: 49616 loss: 0.1240 loss_ce: 0.1240 2023/02/26 02:17:18 - mmengine - INFO - Epoch(train) [48][1900/5047] lr: 2.4894e-05 eta: 5 days, 5:52:13 time: 0.8675 data_time: 0.0021 memory: 43404 loss: 0.1232 loss_ce: 0.1232 2023/02/26 02:18:45 - mmengine - INFO - Epoch(train) [48][2000/5047] lr: 2.4894e-05 eta: 5 days, 5:50:44 time: 0.8659 data_time: 0.0054 memory: 43440 loss: 0.1191 loss_ce: 0.1191 2023/02/26 02:20:10 - mmengine - INFO - Epoch(train) [48][2100/5047] lr: 2.4894e-05 eta: 5 days, 5:49:11 time: 0.8065 data_time: 0.0023 memory: 43288 loss: 0.1312 loss_ce: 0.1312 2023/02/26 02:21:37 - mmengine - INFO - Epoch(train) [48][2200/5047] lr: 2.4894e-05 eta: 5 days, 5:47:42 time: 0.8776 data_time: 0.0032 memory: 55562 loss: 0.1531 loss_ce: 0.1531 2023/02/26 02:23:04 - mmengine - INFO - Epoch(train) [48][2300/5047] lr: 2.4894e-05 eta: 5 days, 5:46:14 time: 0.8160 data_time: 0.0023 memory: 42307 loss: 0.1216 loss_ce: 0.1216 2023/02/26 02:24:30 - mmengine - INFO - Epoch(train) [48][2400/5047] lr: 2.4894e-05 eta: 5 days, 5:44:43 time: 0.8703 data_time: 0.0020 memory: 55562 loss: 0.1326 loss_ce: 0.1326 2023/02/26 02:25:58 - mmengine - INFO - Epoch(train) [48][2500/5047] lr: 2.4894e-05 eta: 5 days, 5:43:17 time: 0.8842 data_time: 0.0046 memory: 43947 loss: 0.1323 loss_ce: 0.1323 2023/02/26 02:27:24 - mmengine - INFO - Epoch(train) [48][2600/5047] lr: 2.4894e-05 eta: 5 days, 5:41:45 time: 0.8490 data_time: 0.0045 memory: 42336 loss: 0.1238 loss_ce: 0.1238 2023/02/26 02:28:50 - mmengine - INFO - Epoch(train) [48][2700/5047] lr: 2.4894e-05 eta: 5 days, 5:40:13 time: 0.8762 data_time: 0.0025 memory: 42965 loss: 0.1484 loss_ce: 0.1484 2023/02/26 02:30:10 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 02:30:18 - mmengine - INFO - Epoch(train) [48][2800/5047] lr: 2.4894e-05 eta: 5 days, 5:38:47 time: 0.8559 data_time: 0.0022 memory: 42965 loss: 0.1199 loss_ce: 0.1199 2023/02/26 02:31:43 - mmengine - INFO - Epoch(train) [48][2900/5047] lr: 2.4894e-05 eta: 5 days, 5:37:15 time: 0.8504 data_time: 0.0022 memory: 40825 loss: 0.1319 loss_ce: 0.1319 2023/02/26 02:33:09 - mmengine - INFO - Epoch(train) [48][3000/5047] lr: 2.4894e-05 eta: 5 days, 5:35:43 time: 0.8842 data_time: 0.0031 memory: 42965 loss: 0.1293 loss_ce: 0.1293 2023/02/26 02:34:35 - mmengine - INFO - Epoch(train) [48][3100/5047] lr: 2.4894e-05 eta: 5 days, 5:34:13 time: 0.8427 data_time: 0.0058 memory: 55562 loss: 0.1339 loss_ce: 0.1339 2023/02/26 02:36:02 - mmengine - INFO - Epoch(train) [48][3200/5047] lr: 2.4894e-05 eta: 5 days, 5:32:42 time: 0.8664 data_time: 0.0023 memory: 41724 loss: 0.1365 loss_ce: 0.1365 2023/02/26 02:37:29 - mmengine - INFO - Epoch(train) [48][3300/5047] lr: 2.4894e-05 eta: 5 days, 5:31:16 time: 0.9044 data_time: 0.0046 memory: 52964 loss: 0.1310 loss_ce: 0.1310 2023/02/26 02:38:56 - mmengine - INFO - Epoch(train) [48][3400/5047] lr: 2.4894e-05 eta: 5 days, 5:29:46 time: 0.8640 data_time: 0.0028 memory: 43947 loss: 0.1205 loss_ce: 0.1205 2023/02/26 02:40:23 - mmengine - INFO - Epoch(train) [48][3500/5047] lr: 2.4894e-05 eta: 5 days, 5:28:16 time: 0.8769 data_time: 0.0032 memory: 50505 loss: 0.1237 loss_ce: 0.1237 2023/02/26 02:41:50 - mmengine - INFO - Epoch(train) [48][3600/5047] lr: 2.4894e-05 eta: 5 days, 5:26:49 time: 0.8859 data_time: 0.0026 memory: 45711 loss: 0.1354 loss_ce: 0.1354 2023/02/26 02:43:17 - mmengine - INFO - Epoch(train) [48][3700/5047] lr: 2.4894e-05 eta: 5 days, 5:25:19 time: 0.8428 data_time: 0.0027 memory: 44659 loss: 0.1318 loss_ce: 0.1318 2023/02/26 02:44:35 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 02:44:43 - mmengine - INFO - Epoch(train) [48][3800/5047] lr: 2.4894e-05 eta: 5 days, 5:23:49 time: 0.8623 data_time: 0.0022 memory: 44216 loss: 0.1361 loss_ce: 0.1361 2023/02/26 02:46:10 - mmengine - INFO - Epoch(train) [48][3900/5047] lr: 2.4894e-05 eta: 5 days, 5:22:20 time: 0.8606 data_time: 0.0031 memory: 49099 loss: 0.1261 loss_ce: 0.1261 2023/02/26 02:47:36 - mmengine - INFO - Epoch(train) [48][4000/5047] lr: 2.4894e-05 eta: 5 days, 5:20:50 time: 0.8408 data_time: 0.0025 memory: 41521 loss: 0.1151 loss_ce: 0.1151 2023/02/26 02:49:03 - mmengine - INFO - Epoch(train) [48][4100/5047] lr: 2.4894e-05 eta: 5 days, 5:19:22 time: 0.8352 data_time: 0.0025 memory: 45871 loss: 0.1250 loss_ce: 0.1250 2023/02/26 02:50:31 - mmengine - INFO - Epoch(train) [48][4200/5047] lr: 2.4894e-05 eta: 5 days, 5:17:54 time: 0.8766 data_time: 0.0023 memory: 42336 loss: 0.1311 loss_ce: 0.1311 2023/02/26 02:51:58 - mmengine - INFO - Epoch(train) [48][4300/5047] lr: 2.4894e-05 eta: 5 days, 5:16:25 time: 0.8772 data_time: 0.0022 memory: 42579 loss: 0.1398 loss_ce: 0.1398 2023/02/26 02:53:23 - mmengine - INFO - Epoch(train) [48][4400/5047] lr: 2.4894e-05 eta: 5 days, 5:14:53 time: 0.8422 data_time: 0.0019 memory: 52964 loss: 0.1130 loss_ce: 0.1130 2023/02/26 02:54:52 - mmengine - INFO - Epoch(train) [48][4500/5047] lr: 2.4894e-05 eta: 5 days, 5:13:29 time: 0.9425 data_time: 0.0080 memory: 40499 loss: 0.1202 loss_ce: 0.1202 2023/02/26 02:56:19 - mmengine - INFO - Epoch(train) [48][4600/5047] lr: 2.4894e-05 eta: 5 days, 5:12:01 time: 0.8337 data_time: 0.0021 memory: 41724 loss: 0.1360 loss_ce: 0.1360 2023/02/26 02:57:47 - mmengine - INFO - Epoch(train) [48][4700/5047] lr: 2.4894e-05 eta: 5 days, 5:10:33 time: 0.8676 data_time: 0.0019 memory: 44278 loss: 0.1158 loss_ce: 0.1158 2023/02/26 02:59:05 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 02:59:13 - mmengine - INFO - Epoch(train) [48][4800/5047] lr: 2.4894e-05 eta: 5 days, 5:09:03 time: 0.8748 data_time: 0.0023 memory: 52955 loss: 0.1119 loss_ce: 0.1119 2023/02/26 03:00:42 - mmengine - INFO - Epoch(train) [48][4900/5047] lr: 2.4894e-05 eta: 5 days, 5:07:38 time: 0.9059 data_time: 0.0021 memory: 52882 loss: 0.1212 loss_ce: 0.1212 2023/02/26 03:02:09 - mmengine - INFO - Epoch(train) [48][5000/5047] lr: 2.4894e-05 eta: 5 days, 5:06:10 time: 0.8568 data_time: 0.0033 memory: 44956 loss: 0.1291 loss_ce: 0.1291 2023/02/26 03:02:50 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 03:02:50 - mmengine - INFO - Saving checkpoint at 48 epochs 2023/02/26 03:04:22 - mmengine - INFO - Epoch(train) [49][ 100/5047] lr: 2.4693e-05 eta: 5 days, 5:03:58 time: 0.8874 data_time: 0.0021 memory: 43420 loss: 0.1147 loss_ce: 0.1147 2023/02/26 03:05:47 - mmengine - INFO - Epoch(train) [49][ 200/5047] lr: 2.4693e-05 eta: 5 days, 5:02:25 time: 0.8625 data_time: 0.0021 memory: 48815 loss: 0.1293 loss_ce: 0.1293 2023/02/26 03:07:14 - mmengine - INFO - Epoch(train) [49][ 300/5047] lr: 2.4693e-05 eta: 5 days, 5:00:58 time: 0.8760 data_time: 0.0023 memory: 46713 loss: 0.1310 loss_ce: 0.1310 2023/02/26 03:08:40 - mmengine - INFO - Epoch(train) [49][ 400/5047] lr: 2.4693e-05 eta: 5 days, 4:59:26 time: 0.8994 data_time: 0.0047 memory: 40674 loss: 0.1107 loss_ce: 0.1107 2023/02/26 03:10:06 - mmengine - INFO - Epoch(train) [49][ 500/5047] lr: 2.4693e-05 eta: 5 days, 4:57:57 time: 0.8635 data_time: 0.0020 memory: 54072 loss: 0.1352 loss_ce: 0.1352 2023/02/26 03:11:35 - mmengine - INFO - Epoch(train) [49][ 600/5047] lr: 2.4693e-05 eta: 5 days, 4:56:31 time: 0.8583 data_time: 0.0020 memory: 43289 loss: 0.1417 loss_ce: 0.1417 2023/02/26 03:13:01 - mmengine - INFO - Epoch(train) [49][ 700/5047] lr: 2.4693e-05 eta: 5 days, 4:55:00 time: 0.8814 data_time: 0.0041 memory: 44453 loss: 0.1192 loss_ce: 0.1192 2023/02/26 03:13:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 03:14:29 - mmengine - INFO - Epoch(train) [49][ 800/5047] lr: 2.4693e-05 eta: 5 days, 4:53:34 time: 0.8921 data_time: 0.0032 memory: 52964 loss: 0.1180 loss_ce: 0.1180 2023/02/26 03:15:54 - mmengine - INFO - Epoch(train) [49][ 900/5047] lr: 2.4693e-05 eta: 5 days, 4:52:01 time: 0.8366 data_time: 0.0029 memory: 44278 loss: 0.1229 loss_ce: 0.1229 2023/02/26 03:17:19 - mmengine - INFO - Epoch(train) [49][1000/5047] lr: 2.4693e-05 eta: 5 days, 4:50:28 time: 0.8876 data_time: 0.0023 memory: 41108 loss: 0.1217 loss_ce: 0.1217 2023/02/26 03:18:46 - mmengine - INFO - Epoch(train) [49][1100/5047] lr: 2.4693e-05 eta: 5 days, 4:49:00 time: 0.9122 data_time: 0.0021 memory: 48948 loss: 0.1233 loss_ce: 0.1233 2023/02/26 03:20:14 - mmengine - INFO - Epoch(train) [49][1200/5047] lr: 2.4693e-05 eta: 5 days, 4:47:33 time: 0.8446 data_time: 0.0025 memory: 46783 loss: 0.1194 loss_ce: 0.1194 2023/02/26 03:21:40 - mmengine - INFO - Epoch(train) [49][1300/5047] lr: 2.4693e-05 eta: 5 days, 4:46:03 time: 0.8560 data_time: 0.0024 memory: 48948 loss: 0.1153 loss_ce: 0.1153 2023/02/26 03:23:05 - mmengine - INFO - Epoch(train) [49][1400/5047] lr: 2.4693e-05 eta: 5 days, 4:44:29 time: 0.8284 data_time: 0.0020 memory: 55562 loss: 0.1313 loss_ce: 0.1313 2023/02/26 03:24:32 - mmengine - INFO - Epoch(train) [49][1500/5047] lr: 2.4693e-05 eta: 5 days, 4:43:01 time: 0.9124 data_time: 0.0022 memory: 43546 loss: 0.1406 loss_ce: 0.1406 2023/02/26 03:25:58 - mmengine - INFO - Epoch(train) [49][1600/5047] lr: 2.4693e-05 eta: 5 days, 4:41:29 time: 0.8375 data_time: 0.0022 memory: 41419 loss: 0.1188 loss_ce: 0.1188 2023/02/26 03:27:25 - mmengine - INFO - Epoch(train) [49][1700/5047] lr: 2.4693e-05 eta: 5 days, 4:40:01 time: 0.8720 data_time: 0.0024 memory: 45563 loss: 0.1256 loss_ce: 0.1256 2023/02/26 03:28:02 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 03:28:51 - mmengine - INFO - Epoch(train) [49][1800/5047] lr: 2.4693e-05 eta: 5 days, 4:38:29 time: 0.8650 data_time: 0.0024 memory: 47673 loss: 0.1288 loss_ce: 0.1288 2023/02/26 03:30:19 - mmengine - INFO - Epoch(train) [49][1900/5047] lr: 2.4693e-05 eta: 5 days, 4:37:03 time: 0.9468 data_time: 0.0020 memory: 40535 loss: 0.1239 loss_ce: 0.1239 2023/02/26 03:31:44 - mmengine - INFO - Epoch(train) [49][2000/5047] lr: 2.4693e-05 eta: 5 days, 4:35:31 time: 0.7832 data_time: 0.0093 memory: 43613 loss: 0.1138 loss_ce: 0.1138 2023/02/26 03:33:11 - mmengine - INFO - Epoch(train) [49][2100/5047] lr: 2.4693e-05 eta: 5 days, 4:34:01 time: 0.9131 data_time: 0.0033 memory: 43947 loss: 0.1367 loss_ce: 0.1367 2023/02/26 03:34:36 - mmengine - INFO - Epoch(train) [49][2200/5047] lr: 2.4693e-05 eta: 5 days, 4:32:30 time: 0.8816 data_time: 0.0020 memory: 45206 loss: 0.1433 loss_ce: 0.1433 2023/02/26 03:36:04 - mmengine - INFO - Epoch(train) [49][2300/5047] lr: 2.4693e-05 eta: 5 days, 4:31:03 time: 0.8913 data_time: 0.0022 memory: 55562 loss: 0.1153 loss_ce: 0.1153 2023/02/26 03:37:32 - mmengine - INFO - Epoch(train) [49][2400/5047] lr: 2.4693e-05 eta: 5 days, 4:29:35 time: 0.9015 data_time: 0.0023 memory: 44617 loss: 0.1441 loss_ce: 0.1441 2023/02/26 03:38:57 - mmengine - INFO - Epoch(train) [49][2500/5047] lr: 2.4693e-05 eta: 5 days, 4:28:02 time: 0.9029 data_time: 0.0026 memory: 45643 loss: 0.1246 loss_ce: 0.1246 2023/02/26 03:40:21 - mmengine - INFO - Epoch(train) [49][2600/5047] lr: 2.4693e-05 eta: 5 days, 4:26:27 time: 0.8667 data_time: 0.0023 memory: 49171 loss: 0.1408 loss_ce: 0.1408 2023/02/26 03:41:47 - mmengine - INFO - Epoch(train) [49][2700/5047] lr: 2.4693e-05 eta: 5 days, 4:24:57 time: 0.8873 data_time: 0.0048 memory: 40900 loss: 0.1110 loss_ce: 0.1110 2023/02/26 03:42:26 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 03:43:15 - mmengine - INFO - Epoch(train) [49][2800/5047] lr: 2.4693e-05 eta: 5 days, 4:23:30 time: 0.8541 data_time: 0.0020 memory: 51816 loss: 0.1248 loss_ce: 0.1248 2023/02/26 03:44:41 - mmengine - INFO - Epoch(train) [49][2900/5047] lr: 2.4693e-05 eta: 5 days, 4:21:59 time: 0.8658 data_time: 0.0021 memory: 49407 loss: 0.1188 loss_ce: 0.1188 2023/02/26 03:46:06 - mmengine - INFO - Epoch(train) [49][3000/5047] lr: 2.4693e-05 eta: 5 days, 4:20:27 time: 0.8748 data_time: 0.0020 memory: 44185 loss: 0.1207 loss_ce: 0.1207 2023/02/26 03:47:33 - mmengine - INFO - Epoch(train) [49][3100/5047] lr: 2.4693e-05 eta: 5 days, 4:18:58 time: 0.8968 data_time: 0.0021 memory: 48146 loss: 0.1247 loss_ce: 0.1247 2023/02/26 03:48:59 - mmengine - INFO - Epoch(train) [49][3200/5047] lr: 2.4693e-05 eta: 5 days, 4:17:27 time: 0.8643 data_time: 0.0024 memory: 48565 loss: 0.1417 loss_ce: 0.1417 2023/02/26 03:50:24 - mmengine - INFO - Epoch(train) [49][3300/5047] lr: 2.4693e-05 eta: 5 days, 4:15:54 time: 0.8177 data_time: 0.0022 memory: 38772 loss: 0.1228 loss_ce: 0.1228 2023/02/26 03:51:50 - mmengine - INFO - Epoch(train) [49][3400/5047] lr: 2.4693e-05 eta: 5 days, 4:14:24 time: 0.8347 data_time: 0.0041 memory: 42984 loss: 0.1187 loss_ce: 0.1187 2023/02/26 03:53:17 - mmengine - INFO - Epoch(train) [49][3500/5047] lr: 2.4693e-05 eta: 5 days, 4:12:56 time: 0.8555 data_time: 0.0084 memory: 51561 loss: 0.1192 loss_ce: 0.1192 2023/02/26 03:54:43 - mmengine - INFO - Epoch(train) [49][3600/5047] lr: 2.4693e-05 eta: 5 days, 4:11:24 time: 0.8707 data_time: 0.0020 memory: 46005 loss: 0.1248 loss_ce: 0.1248 2023/02/26 03:56:11 - mmengine - INFO - Epoch(train) [49][3700/5047] lr: 2.4693e-05 eta: 5 days, 4:09:57 time: 0.8577 data_time: 0.0027 memory: 43557 loss: 0.1174 loss_ce: 0.1174 2023/02/26 03:56:49 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 03:57:38 - mmengine - INFO - Epoch(train) [49][3800/5047] lr: 2.4693e-05 eta: 5 days, 4:08:30 time: 0.8711 data_time: 0.0025 memory: 43409 loss: 0.1210 loss_ce: 0.1210 2023/02/26 03:59:04 - mmengine - INFO - Epoch(train) [49][3900/5047] lr: 2.4693e-05 eta: 5 days, 4:06:59 time: 0.8079 data_time: 0.0020 memory: 53310 loss: 0.1269 loss_ce: 0.1269 2023/02/26 04:00:31 - mmengine - INFO - Epoch(train) [49][4000/5047] lr: 2.4693e-05 eta: 5 days, 4:05:30 time: 0.8746 data_time: 0.0048 memory: 40241 loss: 0.1266 loss_ce: 0.1266 2023/02/26 04:01:59 - mmengine - INFO - Epoch(train) [49][4100/5047] lr: 2.4693e-05 eta: 5 days, 4:04:03 time: 0.8801 data_time: 0.0025 memory: 46614 loss: 0.1253 loss_ce: 0.1253 2023/02/26 04:03:25 - mmengine - INFO - Epoch(train) [49][4200/5047] lr: 2.4693e-05 eta: 5 days, 4:02:33 time: 0.8241 data_time: 0.0022 memory: 52543 loss: 0.1247 loss_ce: 0.1247 2023/02/26 04:04:52 - mmengine - INFO - Epoch(train) [49][4300/5047] lr: 2.4693e-05 eta: 5 days, 4:01:03 time: 0.8165 data_time: 0.0055 memory: 45643 loss: 0.1509 loss_ce: 0.1509 2023/02/26 04:06:20 - mmengine - INFO - Epoch(train) [49][4400/5047] lr: 2.4693e-05 eta: 5 days, 3:59:37 time: 0.8546 data_time: 0.0023 memory: 55562 loss: 0.1189 loss_ce: 0.1189 2023/02/26 04:07:46 - mmengine - INFO - Epoch(train) [49][4500/5047] lr: 2.4693e-05 eta: 5 days, 3:58:07 time: 0.8704 data_time: 0.0028 memory: 41122 loss: 0.1263 loss_ce: 0.1263 2023/02/26 04:09:12 - mmengine - INFO - Epoch(train) [49][4600/5047] lr: 2.4693e-05 eta: 5 days, 3:56:35 time: 0.8447 data_time: 0.0022 memory: 43613 loss: 0.1107 loss_ce: 0.1107 2023/02/26 04:10:37 - mmengine - INFO - Epoch(train) [49][4700/5047] lr: 2.4693e-05 eta: 5 days, 3:55:04 time: 0.8312 data_time: 0.0021 memory: 47534 loss: 0.1172 loss_ce: 0.1172 2023/02/26 04:11:15 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 04:12:05 - mmengine - INFO - Epoch(train) [49][4800/5047] lr: 2.4693e-05 eta: 5 days, 3:53:37 time: 0.9089 data_time: 0.0026 memory: 44477 loss: 0.1224 loss_ce: 0.1224 2023/02/26 04:13:30 - mmengine - INFO - Epoch(train) [49][4900/5047] lr: 2.4693e-05 eta: 5 days, 3:52:05 time: 0.8716 data_time: 0.0021 memory: 43287 loss: 0.1329 loss_ce: 0.1329 2023/02/26 04:14:57 - mmengine - INFO - Epoch(train) [49][5000/5047] lr: 2.4693e-05 eta: 5 days, 3:50:36 time: 0.8752 data_time: 0.0021 memory: 42024 loss: 0.1155 loss_ce: 0.1155 2023/02/26 04:15:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 04:15:37 - mmengine - INFO - Saving checkpoint at 49 epochs 2023/02/26 04:17:09 - mmengine - INFO - Epoch(train) [50][ 100/5047] lr: 2.4492e-05 eta: 5 days, 3:48:23 time: 0.8533 data_time: 0.0041 memory: 42006 loss: 0.1247 loss_ce: 0.1247 2023/02/26 04:18:35 - mmengine - INFO - Epoch(train) [50][ 200/5047] lr: 2.4492e-05 eta: 5 days, 3:46:53 time: 0.8531 data_time: 0.0023 memory: 41122 loss: 0.1148 loss_ce: 0.1148 2023/02/26 04:20:01 - mmengine - INFO - Epoch(train) [50][ 300/5047] lr: 2.4492e-05 eta: 5 days, 3:45:22 time: 0.9117 data_time: 0.0024 memory: 50906 loss: 0.1412 loss_ce: 0.1412 2023/02/26 04:21:26 - mmengine - INFO - Epoch(train) [50][ 400/5047] lr: 2.4492e-05 eta: 5 days, 3:43:50 time: 0.8314 data_time: 0.0033 memory: 47062 loss: 0.1160 loss_ce: 0.1160 2023/02/26 04:22:52 - mmengine - INFO - Epoch(train) [50][ 500/5047] lr: 2.4492e-05 eta: 5 days, 3:42:19 time: 0.7933 data_time: 0.0028 memory: 43289 loss: 0.1260 loss_ce: 0.1260 2023/02/26 04:24:19 - mmengine - INFO - Epoch(train) [50][ 600/5047] lr: 2.4492e-05 eta: 5 days, 3:40:49 time: 0.8466 data_time: 0.0023 memory: 43271 loss: 0.1287 loss_ce: 0.1287 2023/02/26 04:25:42 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 04:25:45 - mmengine - INFO - Epoch(train) [50][ 700/5047] lr: 2.4492e-05 eta: 5 days, 3:39:18 time: 0.8276 data_time: 0.0019 memory: 43613 loss: 0.1486 loss_ce: 0.1486 2023/02/26 04:27:11 - mmengine - INFO - Epoch(train) [50][ 800/5047] lr: 2.4492e-05 eta: 5 days, 3:37:49 time: 0.8696 data_time: 0.0021 memory: 45302 loss: 0.1180 loss_ce: 0.1180 2023/02/26 04:28:39 - mmengine - INFO - Epoch(train) [50][ 900/5047] lr: 2.4492e-05 eta: 5 days, 3:36:21 time: 0.8199 data_time: 0.0023 memory: 46005 loss: 0.1033 loss_ce: 0.1033 2023/02/26 04:30:06 - mmengine - INFO - Epoch(train) [50][1000/5047] lr: 2.4492e-05 eta: 5 days, 3:34:54 time: 0.8481 data_time: 0.0022 memory: 49958 loss: 0.1199 loss_ce: 0.1199 2023/02/26 04:31:34 - mmengine - INFO - Epoch(train) [50][1100/5047] lr: 2.4492e-05 eta: 5 days, 3:33:27 time: 0.8535 data_time: 0.0020 memory: 45681 loss: 0.1259 loss_ce: 0.1259 2023/02/26 04:32:59 - mmengine - INFO - Epoch(train) [50][1200/5047] lr: 2.4492e-05 eta: 5 days, 3:31:55 time: 0.8324 data_time: 0.0023 memory: 48188 loss: 0.1304 loss_ce: 0.1304 2023/02/26 04:34:26 - mmengine - INFO - Epoch(train) [50][1300/5047] lr: 2.4492e-05 eta: 5 days, 3:30:26 time: 0.9026 data_time: 0.0090 memory: 46875 loss: 0.1174 loss_ce: 0.1174 2023/02/26 04:35:56 - mmengine - INFO - Epoch(train) [50][1400/5047] lr: 2.4492e-05 eta: 5 days, 3:29:04 time: 0.8774 data_time: 0.0046 memory: 47037 loss: 0.1124 loss_ce: 0.1124 2023/02/26 04:37:22 - mmengine - INFO - Epoch(train) [50][1500/5047] lr: 2.4492e-05 eta: 5 days, 3:27:33 time: 0.8580 data_time: 0.0023 memory: 39398 loss: 0.1191 loss_ce: 0.1191 2023/02/26 04:38:49 - mmengine - INFO - Epoch(train) [50][1600/5047] lr: 2.4492e-05 eta: 5 days, 3:26:03 time: 0.8422 data_time: 0.0049 memory: 49312 loss: 0.1229 loss_ce: 0.1229 2023/02/26 04:40:14 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 04:40:17 - mmengine - INFO - Epoch(train) [50][1700/5047] lr: 2.4492e-05 eta: 5 days, 3:24:37 time: 0.8723 data_time: 0.0026 memory: 44617 loss: 0.1294 loss_ce: 0.1294 2023/02/26 04:41:45 - mmengine - INFO - Epoch(train) [50][1800/5047] lr: 2.4492e-05 eta: 5 days, 3:23:11 time: 0.8982 data_time: 0.0020 memory: 45302 loss: 0.1258 loss_ce: 0.1258 2023/02/26 04:43:10 - mmengine - INFO - Epoch(train) [50][1900/5047] lr: 2.4492e-05 eta: 5 days, 3:21:40 time: 0.8701 data_time: 0.0020 memory: 49590 loss: 0.1295 loss_ce: 0.1295 2023/02/26 04:44:38 - mmengine - INFO - Epoch(train) [50][2000/5047] lr: 2.4492e-05 eta: 5 days, 3:20:13 time: 0.8971 data_time: 0.0021 memory: 42105 loss: 0.1107 loss_ce: 0.1107 2023/02/26 04:46:06 - mmengine - INFO - Epoch(train) [50][2100/5047] lr: 2.4492e-05 eta: 5 days, 3:18:46 time: 0.8667 data_time: 0.0022 memory: 50505 loss: 0.1243 loss_ce: 0.1243 2023/02/26 04:47:33 - mmengine - INFO - Epoch(train) [50][2200/5047] lr: 2.4492e-05 eta: 5 days, 3:17:18 time: 0.8894 data_time: 0.0040 memory: 45643 loss: 0.1251 loss_ce: 0.1251 2023/02/26 04:49:00 - mmengine - INFO - Epoch(train) [50][2300/5047] lr: 2.4492e-05 eta: 5 days, 3:15:49 time: 0.8677 data_time: 0.0023 memory: 43477 loss: 0.1096 loss_ce: 0.1096 2023/02/26 04:50:29 - mmengine - INFO - Epoch(train) [50][2400/5047] lr: 2.4492e-05 eta: 5 days, 3:14:24 time: 0.9274 data_time: 0.0024 memory: 45216 loss: 0.1234 loss_ce: 0.1234 2023/02/26 04:51:54 - mmengine - INFO - Epoch(train) [50][2500/5047] lr: 2.4492e-05 eta: 5 days, 3:12:52 time: 0.8268 data_time: 0.0020 memory: 51299 loss: 0.1246 loss_ce: 0.1246 2023/02/26 04:53:21 - mmengine - INFO - Epoch(train) [50][2600/5047] lr: 2.4492e-05 eta: 5 days, 3:11:24 time: 0.8872 data_time: 0.0021 memory: 46130 loss: 0.1372 loss_ce: 0.1372 2023/02/26 04:54:46 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 04:54:48 - mmengine - INFO - Epoch(train) [50][2700/5047] lr: 2.4492e-05 eta: 5 days, 3:09:56 time: 0.8515 data_time: 0.0022 memory: 46005 loss: 0.1301 loss_ce: 0.1301 2023/02/26 04:56:17 - mmengine - INFO - Epoch(train) [50][2800/5047] lr: 2.4492e-05 eta: 5 days, 3:08:30 time: 0.8925 data_time: 0.0023 memory: 42683 loss: 0.1187 loss_ce: 0.1187 2023/02/26 04:57:44 - mmengine - INFO - Epoch(train) [50][2900/5047] lr: 2.4492e-05 eta: 5 days, 3:07:03 time: 0.8298 data_time: 0.0024 memory: 43289 loss: 0.1311 loss_ce: 0.1311 2023/02/26 04:59:10 - mmengine - INFO - Epoch(train) [50][3000/5047] lr: 2.4492e-05 eta: 5 days, 3:05:31 time: 0.8376 data_time: 0.0022 memory: 43613 loss: 0.1292 loss_ce: 0.1292 2023/02/26 05:00:38 - mmengine - INFO - Epoch(train) [50][3100/5047] lr: 2.4492e-05 eta: 5 days, 3:04:04 time: 0.8202 data_time: 0.0062 memory: 41724 loss: 0.1210 loss_ce: 0.1210 2023/02/26 05:02:04 - mmengine - INFO - Epoch(train) [50][3200/5047] lr: 2.4492e-05 eta: 5 days, 3:02:35 time: 0.8276 data_time: 0.0037 memory: 43613 loss: 0.1140 loss_ce: 0.1140 2023/02/26 05:03:30 - mmengine - INFO - Epoch(train) [50][3300/5047] lr: 2.4492e-05 eta: 5 days, 3:01:04 time: 0.8303 data_time: 0.0026 memory: 40396 loss: 0.1248 loss_ce: 0.1248 2023/02/26 05:04:57 - mmengine - INFO - Epoch(train) [50][3400/5047] lr: 2.4492e-05 eta: 5 days, 2:59:37 time: 0.8520 data_time: 0.0025 memory: 43947 loss: 0.1281 loss_ce: 0.1281 2023/02/26 05:06:24 - mmengine - INFO - Epoch(train) [50][3500/5047] lr: 2.4492e-05 eta: 5 days, 2:58:07 time: 0.8182 data_time: 0.0023 memory: 51586 loss: 0.1273 loss_ce: 0.1273 2023/02/26 05:07:48 - mmengine - INFO - Epoch(train) [50][3600/5047] lr: 2.4492e-05 eta: 5 days, 2:56:33 time: 0.8765 data_time: 0.0022 memory: 42965 loss: 0.1239 loss_ce: 0.1239 2023/02/26 05:09:14 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 05:09:17 - mmengine - INFO - Epoch(train) [50][3700/5047] lr: 2.4492e-05 eta: 5 days, 2:55:07 time: 0.8676 data_time: 0.0046 memory: 46794 loss: 0.1186 loss_ce: 0.1186 2023/02/26 05:10:44 - mmengine - INFO - Epoch(train) [50][3800/5047] lr: 2.4492e-05 eta: 5 days, 2:53:39 time: 0.9062 data_time: 0.0022 memory: 51619 loss: 0.1153 loss_ce: 0.1153 2023/02/26 05:12:10 - mmengine - INFO - Epoch(train) [50][3900/5047] lr: 2.4492e-05 eta: 5 days, 2:52:08 time: 0.8834 data_time: 0.0021 memory: 42264 loss: 0.1301 loss_ce: 0.1301 2023/02/26 05:13:40 - mmengine - INFO - Epoch(train) [50][4000/5047] lr: 2.4492e-05 eta: 5 days, 2:50:47 time: 0.8847 data_time: 0.0022 memory: 44539 loss: 0.1171 loss_ce: 0.1171 2023/02/26 05:15:06 - mmengine - INFO - Epoch(train) [50][4100/5047] lr: 2.4492e-05 eta: 5 days, 2:49:16 time: 0.8231 data_time: 0.0028 memory: 48035 loss: 0.1159 loss_ce: 0.1159 2023/02/26 05:16:32 - mmengine - INFO - Epoch(train) [50][4200/5047] lr: 2.4492e-05 eta: 5 days, 2:47:45 time: 0.8540 data_time: 0.0019 memory: 44500 loss: 0.1223 loss_ce: 0.1223 2023/02/26 05:18:05 - mmengine - INFO - Epoch(train) [50][4300/5047] lr: 2.4492e-05 eta: 5 days, 2:46:30 time: 0.8134 data_time: 0.0019 memory: 51308 loss: 0.1379 loss_ce: 0.1379 2023/02/26 05:19:32 - mmengine - INFO - Epoch(train) [50][4400/5047] lr: 2.4492e-05 eta: 5 days, 2:45:02 time: 0.8994 data_time: 0.0021 memory: 42269 loss: 0.1436 loss_ce: 0.1436 2023/02/26 05:21:00 - mmengine - INFO - Epoch(train) [50][4500/5047] lr: 2.4492e-05 eta: 5 days, 2:43:35 time: 0.8682 data_time: 0.0046 memory: 54232 loss: 0.1335 loss_ce: 0.1335 2023/02/26 05:22:26 - mmengine - INFO - Epoch(train) [50][4600/5047] lr: 2.4492e-05 eta: 5 days, 2:42:05 time: 0.9510 data_time: 0.0052 memory: 48565 loss: 0.1154 loss_ce: 0.1154 2023/02/26 05:23:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 05:23:54 - mmengine - INFO - Epoch(train) [50][4700/5047] lr: 2.4492e-05 eta: 5 days, 2:40:38 time: 0.8278 data_time: 0.0023 memory: 43757 loss: 0.1369 loss_ce: 0.1369 2023/02/26 05:25:20 - mmengine - INFO - Epoch(train) [50][4800/5047] lr: 2.4492e-05 eta: 5 days, 2:39:09 time: 0.9301 data_time: 0.0028 memory: 46342 loss: 0.1129 loss_ce: 0.1129 2023/02/26 05:26:47 - mmengine - INFO - Epoch(train) [50][4900/5047] lr: 2.4492e-05 eta: 5 days, 2:37:39 time: 0.9506 data_time: 0.0034 memory: 46503 loss: 0.1152 loss_ce: 0.1152 2023/02/26 05:28:13 - mmengine - INFO - Epoch(train) [50][5000/5047] lr: 2.4492e-05 eta: 5 days, 2:36:09 time: 0.8471 data_time: 0.0024 memory: 55562 loss: 0.1216 loss_ce: 0.1216 2023/02/26 05:28:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 05:28:55 - mmengine - INFO - Saving checkpoint at 50 epochs 2023/02/26 05:30:27 - mmengine - INFO - Epoch(train) [51][ 100/5047] lr: 2.4291e-05 eta: 5 days, 2:34:00 time: 0.7958 data_time: 0.0027 memory: 53387 loss: 0.1231 loss_ce: 0.1231 2023/02/26 05:31:52 - mmengine - INFO - Epoch(train) [51][ 200/5047] lr: 2.4291e-05 eta: 5 days, 2:32:29 time: 0.8985 data_time: 0.0021 memory: 55562 loss: 0.1194 loss_ce: 0.1194 2023/02/26 05:33:18 - mmengine - INFO - Epoch(train) [51][ 300/5047] lr: 2.4291e-05 eta: 5 days, 2:30:57 time: 0.8337 data_time: 0.0025 memory: 47963 loss: 0.1223 loss_ce: 0.1223 2023/02/26 05:34:46 - mmengine - INFO - Epoch(train) [51][ 400/5047] lr: 2.4291e-05 eta: 5 days, 2:29:30 time: 0.8808 data_time: 0.0092 memory: 42269 loss: 0.1343 loss_ce: 0.1343 2023/02/26 05:36:13 - mmengine - INFO - Epoch(train) [51][ 500/5047] lr: 2.4291e-05 eta: 5 days, 2:28:02 time: 0.8711 data_time: 0.0020 memory: 41998 loss: 0.1264 loss_ce: 0.1264 2023/02/26 05:37:41 - mmengine - INFO - Epoch(train) [51][ 600/5047] lr: 2.4291e-05 eta: 5 days, 2:26:35 time: 0.9325 data_time: 0.0023 memory: 46005 loss: 0.1196 loss_ce: 0.1196 2023/02/26 05:38:25 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 05:39:08 - mmengine - INFO - Epoch(train) [51][ 700/5047] lr: 2.4291e-05 eta: 5 days, 2:25:08 time: 0.8611 data_time: 0.0021 memory: 47910 loss: 0.1249 loss_ce: 0.1249 2023/02/26 05:40:35 - mmengine - INFO - Epoch(train) [51][ 800/5047] lr: 2.4291e-05 eta: 5 days, 2:23:39 time: 0.8330 data_time: 0.0031 memory: 48188 loss: 0.1395 loss_ce: 0.1395 2023/02/26 05:42:00 - mmengine - INFO - Epoch(train) [51][ 900/5047] lr: 2.4291e-05 eta: 5 days, 2:22:07 time: 0.8210 data_time: 0.0029 memory: 42965 loss: 0.1333 loss_ce: 0.1333 2023/02/26 05:43:27 - mmengine - INFO - Epoch(train) [51][1000/5047] lr: 2.4291e-05 eta: 5 days, 2:20:37 time: 0.8922 data_time: 0.0025 memory: 46770 loss: 0.1152 loss_ce: 0.1152 2023/02/26 05:44:54 - mmengine - INFO - Epoch(train) [51][1100/5047] lr: 2.4291e-05 eta: 5 days, 2:19:09 time: 0.8637 data_time: 0.0027 memory: 40001 loss: 0.1308 loss_ce: 0.1308 2023/02/26 05:46:21 - mmengine - INFO - Epoch(train) [51][1200/5047] lr: 2.4291e-05 eta: 5 days, 2:17:40 time: 0.8825 data_time: 0.0027 memory: 42590 loss: 0.1251 loss_ce: 0.1251 2023/02/26 05:47:46 - mmengine - INFO - Epoch(train) [51][1300/5047] lr: 2.4291e-05 eta: 5 days, 2:16:08 time: 0.8582 data_time: 0.0024 memory: 47983 loss: 0.1132 loss_ce: 0.1132 2023/02/26 05:49:13 - mmengine - INFO - Epoch(train) [51][1400/5047] lr: 2.4291e-05 eta: 5 days, 2:14:41 time: 0.9055 data_time: 0.0020 memory: 50343 loss: 0.1253 loss_ce: 0.1253 2023/02/26 05:50:41 - mmengine - INFO - Epoch(train) [51][1500/5047] lr: 2.4291e-05 eta: 5 days, 2:13:14 time: 0.8932 data_time: 0.0021 memory: 55562 loss: 0.1165 loss_ce: 0.1165 2023/02/26 05:52:08 - mmengine - INFO - Epoch(train) [51][1600/5047] lr: 2.4291e-05 eta: 5 days, 2:11:46 time: 0.8915 data_time: 0.0026 memory: 41419 loss: 0.1159 loss_ce: 0.1159 2023/02/26 05:52:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 05:53:36 - mmengine - INFO - Epoch(train) [51][1700/5047] lr: 2.4291e-05 eta: 5 days, 2:10:19 time: 0.8665 data_time: 0.0029 memory: 42499 loss: 0.1160 loss_ce: 0.1160 2023/02/26 05:55:04 - mmengine - INFO - Epoch(train) [51][1800/5047] lr: 2.4291e-05 eta: 5 days, 2:08:52 time: 0.9242 data_time: 0.0023 memory: 49407 loss: 0.1102 loss_ce: 0.1102 2023/02/26 05:56:30 - mmengine - INFO - Epoch(train) [51][1900/5047] lr: 2.4291e-05 eta: 5 days, 2:07:22 time: 0.8924 data_time: 0.0021 memory: 50906 loss: 0.1165 loss_ce: 0.1165 2023/02/26 05:57:56 - mmengine - INFO - Epoch(train) [51][2000/5047] lr: 2.4291e-05 eta: 5 days, 2:05:50 time: 0.8762 data_time: 0.0022 memory: 42649 loss: 0.1243 loss_ce: 0.1243 2023/02/26 05:59:22 - mmengine - INFO - Epoch(train) [51][2100/5047] lr: 2.4291e-05 eta: 5 days, 2:04:20 time: 0.8619 data_time: 0.0023 memory: 46713 loss: 0.1271 loss_ce: 0.1271 2023/02/26 06:00:50 - mmengine - INFO - Epoch(train) [51][2200/5047] lr: 2.4291e-05 eta: 5 days, 2:02:55 time: 0.8689 data_time: 0.0019 memory: 45280 loss: 0.1367 loss_ce: 0.1367 2023/02/26 06:02:18 - mmengine - INFO - Epoch(train) [51][2300/5047] lr: 2.4291e-05 eta: 5 days, 2:01:27 time: 0.8883 data_time: 0.0044 memory: 50347 loss: 0.1247 loss_ce: 0.1247 2023/02/26 06:03:43 - mmengine - INFO - Epoch(train) [51][2400/5047] lr: 2.4291e-05 eta: 5 days, 1:59:55 time: 0.8442 data_time: 0.0134 memory: 44956 loss: 0.1174 loss_ce: 0.1174 2023/02/26 06:05:11 - mmengine - INFO - Epoch(train) [51][2500/5047] lr: 2.4291e-05 eta: 5 days, 1:58:29 time: 0.9010 data_time: 0.0019 memory: 55562 loss: 0.1299 loss_ce: 0.1299 2023/02/26 06:06:39 - mmengine - INFO - Epoch(train) [51][2600/5047] lr: 2.4291e-05 eta: 5 days, 1:57:02 time: 0.9406 data_time: 0.0030 memory: 55562 loss: 0.1090 loss_ce: 0.1090 2023/02/26 06:07:22 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 06:08:03 - mmengine - INFO - Epoch(train) [51][2700/5047] lr: 2.4291e-05 eta: 5 days, 1:55:28 time: 0.9112 data_time: 0.0041 memory: 42875 loss: 0.1184 loss_ce: 0.1184 2023/02/26 06:09:30 - mmengine - INFO - Epoch(train) [51][2800/5047] lr: 2.4291e-05 eta: 5 days, 1:53:59 time: 0.8840 data_time: 0.0072 memory: 42293 loss: 0.1281 loss_ce: 0.1281 2023/02/26 06:10:55 - mmengine - INFO - Epoch(train) [51][2900/5047] lr: 2.4291e-05 eta: 5 days, 1:52:28 time: 0.8650 data_time: 0.0022 memory: 42024 loss: 0.1228 loss_ce: 0.1228 2023/02/26 06:12:21 - mmengine - INFO - Epoch(train) [51][3000/5047] lr: 2.4291e-05 eta: 5 days, 1:50:57 time: 0.8621 data_time: 0.0023 memory: 55562 loss: 0.1283 loss_ce: 0.1283 2023/02/26 06:13:48 - mmengine - INFO - Epoch(train) [51][3100/5047] lr: 2.4291e-05 eta: 5 days, 1:49:29 time: 0.8018 data_time: 0.0031 memory: 42649 loss: 0.1243 loss_ce: 0.1243 2023/02/26 06:15:16 - mmengine - INFO - Epoch(train) [51][3200/5047] lr: 2.4291e-05 eta: 5 days, 1:48:01 time: 0.8606 data_time: 0.0022 memory: 41613 loss: 0.1223 loss_ce: 0.1223 2023/02/26 06:16:42 - mmengine - INFO - Epoch(train) [51][3300/5047] lr: 2.4291e-05 eta: 5 days, 1:46:30 time: 0.8562 data_time: 0.0062 memory: 42649 loss: 0.1390 loss_ce: 0.1390 2023/02/26 06:18:10 - mmengine - INFO - Epoch(train) [51][3400/5047] lr: 2.4291e-05 eta: 5 days, 1:45:04 time: 0.8882 data_time: 0.0020 memory: 51734 loss: 0.1185 loss_ce: 0.1185 2023/02/26 06:19:36 - mmengine - INFO - Epoch(train) [51][3500/5047] lr: 2.4291e-05 eta: 5 days, 1:43:34 time: 0.8956 data_time: 0.0022 memory: 49170 loss: 0.1095 loss_ce: 0.1095 2023/02/26 06:21:01 - mmengine - INFO - Epoch(train) [51][3600/5047] lr: 2.4291e-05 eta: 5 days, 1:42:01 time: 0.8718 data_time: 0.0022 memory: 51707 loss: 0.1224 loss_ce: 0.1224 2023/02/26 06:21:44 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 06:22:28 - mmengine - INFO - Epoch(train) [51][3700/5047] lr: 2.4291e-05 eta: 5 days, 1:40:34 time: 0.8538 data_time: 0.0020 memory: 55562 loss: 0.1253 loss_ce: 0.1253 2023/02/26 06:23:55 - mmengine - INFO - Epoch(train) [51][3800/5047] lr: 2.4291e-05 eta: 5 days, 1:39:05 time: 0.8613 data_time: 0.0042 memory: 55562 loss: 0.1321 loss_ce: 0.1321 2023/02/26 06:25:22 - mmengine - INFO - Epoch(train) [51][3900/5047] lr: 2.4291e-05 eta: 5 days, 1:37:37 time: 0.8601 data_time: 0.0032 memory: 50505 loss: 0.1379 loss_ce: 0.1379 2023/02/26 06:26:48 - mmengine - INFO - Epoch(train) [51][4000/5047] lr: 2.4291e-05 eta: 5 days, 1:36:06 time: 0.8692 data_time: 0.0022 memory: 41419 loss: 0.1227 loss_ce: 0.1227 2023/02/26 06:28:14 - mmengine - INFO - Epoch(train) [51][4100/5047] lr: 2.4291e-05 eta: 5 days, 1:34:35 time: 0.8534 data_time: 0.0021 memory: 48327 loss: 0.1344 loss_ce: 0.1344 2023/02/26 06:29:40 - mmengine - INFO - Epoch(train) [51][4200/5047] lr: 2.4291e-05 eta: 5 days, 1:33:04 time: 0.9123 data_time: 0.0020 memory: 45294 loss: 0.1181 loss_ce: 0.1181 2023/02/26 06:31:06 - mmengine - INFO - Epoch(train) [51][4300/5047] lr: 2.4291e-05 eta: 5 days, 1:31:34 time: 0.8814 data_time: 0.0027 memory: 43947 loss: 0.1540 loss_ce: 0.1540 2023/02/26 06:32:33 - mmengine - INFO - Epoch(train) [51][4400/5047] lr: 2.4291e-05 eta: 5 days, 1:30:06 time: 0.8245 data_time: 0.0024 memory: 42293 loss: 0.1169 loss_ce: 0.1169 2023/02/26 06:33:59 - mmengine - INFO - Epoch(train) [51][4500/5047] lr: 2.4291e-05 eta: 5 days, 1:28:35 time: 0.8817 data_time: 0.0020 memory: 49620 loss: 0.1241 loss_ce: 0.1241 2023/02/26 06:35:27 - mmengine - INFO - Epoch(train) [51][4600/5047] lr: 2.4291e-05 eta: 5 days, 1:27:08 time: 0.9132 data_time: 0.0019 memory: 41927 loss: 0.1361 loss_ce: 0.1361 2023/02/26 06:36:09 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 06:36:52 - mmengine - INFO - Epoch(train) [51][4700/5047] lr: 2.4291e-05 eta: 5 days, 1:25:36 time: 0.8436 data_time: 0.0021 memory: 43001 loss: 0.1165 loss_ce: 0.1165 2023/02/26 06:38:18 - mmengine - INFO - Epoch(train) [51][4800/5047] lr: 2.4291e-05 eta: 5 days, 1:24:07 time: 0.8613 data_time: 0.0029 memory: 42418 loss: 0.1061 loss_ce: 0.1061 2023/02/26 06:39:46 - mmengine - INFO - Epoch(train) [51][4900/5047] lr: 2.4291e-05 eta: 5 days, 1:22:39 time: 0.8805 data_time: 0.0020 memory: 43613 loss: 0.1298 loss_ce: 0.1298 2023/02/26 06:41:13 - mmengine - INFO - Epoch(train) [51][5000/5047] lr: 2.4291e-05 eta: 5 days, 1:21:11 time: 0.8652 data_time: 0.0023 memory: 42099 loss: 0.1177 loss_ce: 0.1177 2023/02/26 06:41:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 06:41:54 - mmengine - INFO - Saving checkpoint at 51 epochs 2023/02/26 06:43:27 - mmengine - INFO - Epoch(train) [52][ 100/5047] lr: 2.4090e-05 eta: 5 days, 1:19:04 time: 0.8903 data_time: 0.0019 memory: 55562 loss: 0.1141 loss_ce: 0.1141 2023/02/26 06:44:53 - mmengine - INFO - Epoch(train) [52][ 200/5047] lr: 2.4090e-05 eta: 5 days, 1:17:35 time: 0.8871 data_time: 0.0024 memory: 42336 loss: 0.1267 loss_ce: 0.1267 2023/02/26 06:46:19 - mmengine - INFO - Epoch(train) [52][ 300/5047] lr: 2.4090e-05 eta: 5 days, 1:16:04 time: 0.8950 data_time: 0.0020 memory: 43289 loss: 0.1275 loss_ce: 0.1275 2023/02/26 06:47:46 - mmengine - INFO - Epoch(train) [52][ 400/5047] lr: 2.4090e-05 eta: 5 days, 1:14:34 time: 0.8698 data_time: 0.0033 memory: 48053 loss: 0.1076 loss_ce: 0.1076 2023/02/26 06:49:12 - mmengine - INFO - Epoch(train) [52][ 500/5047] lr: 2.4090e-05 eta: 5 days, 1:13:04 time: 0.8636 data_time: 0.0023 memory: 43159 loss: 0.1317 loss_ce: 0.1317 2023/02/26 06:50:36 - mmengine - INFO - Epoch(train) [52][ 600/5047] lr: 2.4090e-05 eta: 5 days, 1:11:31 time: 0.8557 data_time: 0.0040 memory: 42551 loss: 0.1208 loss_ce: 0.1208 2023/02/26 06:50:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 06:52:02 - mmengine - INFO - Epoch(train) [52][ 700/5047] lr: 2.4090e-05 eta: 5 days, 1:10:00 time: 0.8710 data_time: 0.0023 memory: 49173 loss: 0.1213 loss_ce: 0.1213 2023/02/26 06:53:29 - mmengine - INFO - Epoch(train) [52][ 800/5047] lr: 2.4090e-05 eta: 5 days, 1:08:31 time: 0.8839 data_time: 0.0025 memory: 54277 loss: 0.1244 loss_ce: 0.1244 2023/02/26 06:54:54 - mmengine - INFO - Epoch(train) [52][ 900/5047] lr: 2.4090e-05 eta: 5 days, 1:06:59 time: 0.8418 data_time: 0.0023 memory: 42649 loss: 0.1043 loss_ce: 0.1043 2023/02/26 06:56:22 - mmengine - INFO - Epoch(train) [52][1000/5047] lr: 2.4090e-05 eta: 5 days, 1:05:32 time: 0.8464 data_time: 0.0031 memory: 55562 loss: 0.1219 loss_ce: 0.1219 2023/02/26 06:57:48 - mmengine - INFO - Epoch(train) [52][1100/5047] lr: 2.4090e-05 eta: 5 days, 1:04:02 time: 0.8357 data_time: 0.0021 memory: 44956 loss: 0.1087 loss_ce: 0.1087 2023/02/26 06:59:16 - mmengine - INFO - Epoch(train) [52][1200/5047] lr: 2.4090e-05 eta: 5 days, 1:02:35 time: 0.9175 data_time: 0.0034 memory: 49553 loss: 0.1319 loss_ce: 0.1319 2023/02/26 07:00:43 - mmengine - INFO - Epoch(train) [52][1300/5047] lr: 2.4090e-05 eta: 5 days, 1:01:07 time: 0.9150 data_time: 0.0020 memory: 41419 loss: 0.1257 loss_ce: 0.1257 2023/02/26 07:02:10 - mmengine - INFO - Epoch(train) [52][1400/5047] lr: 2.4090e-05 eta: 5 days, 0:59:38 time: 0.8607 data_time: 0.0022 memory: 40825 loss: 0.1316 loss_ce: 0.1316 2023/02/26 07:03:36 - mmengine - INFO - Epoch(train) [52][1500/5047] lr: 2.4090e-05 eta: 5 days, 0:58:09 time: 0.8331 data_time: 0.0021 memory: 43613 loss: 0.1240 loss_ce: 0.1240 2023/02/26 07:05:03 - mmengine - INFO - Epoch(train) [52][1600/5047] lr: 2.4090e-05 eta: 5 days, 0:56:39 time: 0.8522 data_time: 0.0021 memory: 42965 loss: 0.1209 loss_ce: 0.1209 2023/02/26 07:05:05 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 07:06:28 - mmengine - INFO - Epoch(train) [52][1700/5047] lr: 2.4090e-05 eta: 5 days, 0:55:07 time: 0.8585 data_time: 0.0025 memory: 40241 loss: 0.1393 loss_ce: 0.1393 2023/02/26 07:07:54 - mmengine - INFO - Epoch(train) [52][1800/5047] lr: 2.4090e-05 eta: 5 days, 0:53:37 time: 0.8692 data_time: 0.0024 memory: 55562 loss: 0.1253 loss_ce: 0.1253 2023/02/26 07:09:20 - mmengine - INFO - Epoch(train) [52][1900/5047] lr: 2.4090e-05 eta: 5 days, 0:52:06 time: 0.8941 data_time: 0.0048 memory: 41419 loss: 0.1260 loss_ce: 0.1260 2023/02/26 07:10:46 - mmengine - INFO - Epoch(train) [52][2000/5047] lr: 2.4090e-05 eta: 5 days, 0:50:35 time: 0.8069 data_time: 0.0083 memory: 40241 loss: 0.1119 loss_ce: 0.1119 2023/02/26 07:12:13 - mmengine - INFO - Epoch(train) [52][2100/5047] lr: 2.4090e-05 eta: 5 days, 0:49:07 time: 0.8757 data_time: 0.0024 memory: 42649 loss: 0.1420 loss_ce: 0.1420 2023/02/26 07:13:37 - mmengine - INFO - Epoch(train) [52][2200/5047] lr: 2.4090e-05 eta: 5 days, 0:47:34 time: 0.8450 data_time: 0.0022 memory: 44617 loss: 0.1117 loss_ce: 0.1117 2023/02/26 07:15:02 - mmengine - INFO - Epoch(train) [52][2300/5047] lr: 2.4090e-05 eta: 5 days, 0:46:02 time: 0.8505 data_time: 0.0027 memory: 49378 loss: 0.1180 loss_ce: 0.1180 2023/02/26 07:16:30 - mmengine - INFO - Epoch(train) [52][2400/5047] lr: 2.4090e-05 eta: 5 days, 0:44:35 time: 0.9042 data_time: 0.0021 memory: 51328 loss: 0.1203 loss_ce: 0.1203 2023/02/26 07:17:55 - mmengine - INFO - Epoch(train) [52][2500/5047] lr: 2.4090e-05 eta: 5 days, 0:43:02 time: 0.8654 data_time: 0.0020 memory: 46355 loss: 0.1133 loss_ce: 0.1133 2023/02/26 07:19:22 - mmengine - INFO - Epoch(train) [52][2600/5047] lr: 2.4090e-05 eta: 5 days, 0:41:34 time: 0.8584 data_time: 0.0022 memory: 50906 loss: 0.1273 loss_ce: 0.1273 2023/02/26 07:19:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 07:20:49 - mmengine - INFO - Epoch(train) [52][2700/5047] lr: 2.4090e-05 eta: 5 days, 0:40:06 time: 0.9014 data_time: 0.0022 memory: 55562 loss: 0.1330 loss_ce: 0.1330 2023/02/26 07:22:16 - mmengine - INFO - Epoch(train) [52][2800/5047] lr: 2.4090e-05 eta: 5 days, 0:38:38 time: 0.8652 data_time: 0.0020 memory: 44956 loss: 0.1228 loss_ce: 0.1228 2023/02/26 07:23:42 - mmengine - INFO - Epoch(train) [52][2900/5047] lr: 2.4090e-05 eta: 5 days, 0:37:07 time: 0.8393 data_time: 0.0022 memory: 42649 loss: 0.1193 loss_ce: 0.1193 2023/02/26 07:25:08 - mmengine - INFO - Epoch(train) [52][3000/5047] lr: 2.4090e-05 eta: 5 days, 0:35:36 time: 0.8789 data_time: 0.0024 memory: 42743 loss: 0.1267 loss_ce: 0.1267 2023/02/26 07:26:35 - mmengine - INFO - Epoch(train) [52][3100/5047] lr: 2.4090e-05 eta: 5 days, 0:34:08 time: 0.8419 data_time: 0.0020 memory: 41419 loss: 0.1067 loss_ce: 0.1067 2023/02/26 07:28:00 - mmengine - INFO - Epoch(train) [52][3200/5047] lr: 2.4090e-05 eta: 5 days, 0:32:36 time: 0.8405 data_time: 0.0025 memory: 48565 loss: 0.1200 loss_ce: 0.1200 2023/02/26 07:29:27 - mmengine - INFO - Epoch(train) [52][3300/5047] lr: 2.4090e-05 eta: 5 days, 0:31:08 time: 0.8496 data_time: 0.0025 memory: 55562 loss: 0.1230 loss_ce: 0.1230 2023/02/26 07:30:52 - mmengine - INFO - Epoch(train) [52][3400/5047] lr: 2.4090e-05 eta: 5 days, 0:29:34 time: 0.8766 data_time: 0.0038 memory: 46474 loss: 0.1336 loss_ce: 0.1336 2023/02/26 07:32:17 - mmengine - INFO - Epoch(train) [52][3500/5047] lr: 2.4090e-05 eta: 5 days, 0:28:02 time: 0.8572 data_time: 0.0037 memory: 46005 loss: 0.1229 loss_ce: 0.1229 2023/02/26 07:33:43 - mmengine - INFO - Epoch(train) [52][3600/5047] lr: 2.4090e-05 eta: 5 days, 0:26:33 time: 0.8468 data_time: 0.0040 memory: 42743 loss: 0.1118 loss_ce: 0.1118 2023/02/26 07:33:46 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 07:35:10 - mmengine - INFO - Epoch(train) [52][3700/5047] lr: 2.4090e-05 eta: 5 days, 0:25:04 time: 0.8577 data_time: 0.0053 memory: 45708 loss: 0.1274 loss_ce: 0.1274 2023/02/26 07:36:37 - mmengine - INFO - Epoch(train) [52][3800/5047] lr: 2.4090e-05 eta: 5 days, 0:23:36 time: 0.8803 data_time: 0.0020 memory: 42024 loss: 0.1110 loss_ce: 0.1110 2023/02/26 07:38:02 - mmengine - INFO - Epoch(train) [52][3900/5047] lr: 2.4090e-05 eta: 5 days, 0:22:03 time: 0.8499 data_time: 0.0035 memory: 55562 loss: 0.1252 loss_ce: 0.1252 2023/02/26 07:39:29 - mmengine - INFO - Epoch(train) [52][4000/5047] lr: 2.4090e-05 eta: 5 days, 0:20:34 time: 0.8679 data_time: 0.0023 memory: 45302 loss: 0.1245 loss_ce: 0.1245 2023/02/26 07:40:56 - mmengine - INFO - Epoch(train) [52][4100/5047] lr: 2.4090e-05 eta: 5 days, 0:19:06 time: 0.8988 data_time: 0.0024 memory: 50443 loss: 0.1217 loss_ce: 0.1217 2023/02/26 07:42:23 - mmengine - INFO - Epoch(train) [52][4200/5047] lr: 2.4090e-05 eta: 5 days, 0:17:37 time: 0.8915 data_time: 0.0021 memory: 53387 loss: 0.1199 loss_ce: 0.1199 2023/02/26 07:43:49 - mmengine - INFO - Epoch(train) [52][4300/5047] lr: 2.4090e-05 eta: 5 days, 0:16:08 time: 0.8662 data_time: 0.0028 memory: 55468 loss: 0.1294 loss_ce: 0.1294 2023/02/26 07:45:15 - mmengine - INFO - Epoch(train) [52][4400/5047] lr: 2.4090e-05 eta: 5 days, 0:14:38 time: 0.8973 data_time: 0.0020 memory: 42211 loss: 0.1264 loss_ce: 0.1264 2023/02/26 07:46:41 - mmengine - INFO - Epoch(train) [52][4500/5047] lr: 2.4090e-05 eta: 5 days, 0:13:08 time: 0.8676 data_time: 0.0036 memory: 46005 loss: 0.1168 loss_ce: 0.1168 2023/02/26 07:48:10 - mmengine - INFO - Epoch(train) [52][4600/5047] lr: 2.4090e-05 eta: 5 days, 0:11:42 time: 0.8421 data_time: 0.0031 memory: 50059 loss: 0.1209 loss_ce: 0.1209 2023/02/26 07:48:12 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 07:49:36 - mmengine - INFO - Epoch(train) [52][4700/5047] lr: 2.4090e-05 eta: 5 days, 0:10:12 time: 0.8445 data_time: 0.0020 memory: 42649 loss: 0.1151 loss_ce: 0.1151 2023/02/26 07:51:02 - mmengine - INFO - Epoch(train) [52][4800/5047] lr: 2.4090e-05 eta: 5 days, 0:08:42 time: 0.9172 data_time: 0.0025 memory: 52816 loss: 0.1341 loss_ce: 0.1341 2023/02/26 07:52:30 - mmengine - INFO - Epoch(train) [52][4900/5047] lr: 2.4090e-05 eta: 5 days, 0:07:15 time: 0.9115 data_time: 0.0025 memory: 43558 loss: 0.1236 loss_ce: 0.1236 2023/02/26 07:53:56 - mmengine - INFO - Epoch(train) [52][5000/5047] lr: 2.4090e-05 eta: 5 days, 0:05:46 time: 0.8576 data_time: 0.0024 memory: 50272 loss: 0.1069 loss_ce: 0.1069 2023/02/26 07:54:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 07:54:36 - mmengine - INFO - Saving checkpoint at 52 epochs 2023/02/26 07:56:08 - mmengine - INFO - Epoch(train) [53][ 100/5047] lr: 2.3889e-05 eta: 5 days, 0:03:34 time: 0.8901 data_time: 0.0020 memory: 44877 loss: 0.1328 loss_ce: 0.1328 2023/02/26 07:57:35 - mmengine - INFO - Epoch(train) [53][ 200/5047] lr: 2.3889e-05 eta: 5 days, 0:02:05 time: 0.8211 data_time: 0.0024 memory: 50446 loss: 0.1188 loss_ce: 0.1188 2023/02/26 07:59:01 - mmengine - INFO - Epoch(train) [53][ 300/5047] lr: 2.3889e-05 eta: 5 days, 0:00:36 time: 0.8696 data_time: 0.0022 memory: 43476 loss: 0.1169 loss_ce: 0.1169 2023/02/26 08:00:28 - mmengine - INFO - Epoch(train) [53][ 400/5047] lr: 2.3889e-05 eta: 4 days, 23:59:08 time: 0.8627 data_time: 0.0021 memory: 41724 loss: 0.1094 loss_ce: 0.1094 2023/02/26 08:01:55 - mmengine - INFO - Epoch(train) [53][ 500/5047] lr: 2.3889e-05 eta: 4 days, 23:57:38 time: 0.8755 data_time: 0.0021 memory: 43947 loss: 0.1265 loss_ce: 0.1265 2023/02/26 08:02:42 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 08:03:20 - mmengine - INFO - Epoch(train) [53][ 600/5047] lr: 2.3889e-05 eta: 4 days, 23:56:06 time: 0.8409 data_time: 0.0032 memory: 44617 loss: 0.1420 loss_ce: 0.1420 2023/02/26 08:04:45 - mmengine - INFO - Epoch(train) [53][ 700/5047] lr: 2.3889e-05 eta: 4 days, 23:54:34 time: 0.8691 data_time: 0.0025 memory: 49334 loss: 0.1133 loss_ce: 0.1133 2023/02/26 08:06:12 - mmengine - INFO - Epoch(train) [53][ 800/5047] lr: 2.3889e-05 eta: 4 days, 23:53:06 time: 0.9251 data_time: 0.0019 memory: 45494 loss: 0.1286 loss_ce: 0.1286 2023/02/26 08:07:37 - mmengine - INFO - Epoch(train) [53][ 900/5047] lr: 2.3889e-05 eta: 4 days, 23:51:33 time: 0.8895 data_time: 0.0022 memory: 46355 loss: 0.1316 loss_ce: 0.1316 2023/02/26 08:09:03 - mmengine - INFO - Epoch(train) [53][1000/5047] lr: 2.3889e-05 eta: 4 days, 23:50:02 time: 0.8304 data_time: 0.0019 memory: 45850 loss: 0.1333 loss_ce: 0.1333 2023/02/26 08:10:30 - mmengine - INFO - Epoch(train) [53][1100/5047] lr: 2.3889e-05 eta: 4 days, 23:48:35 time: 0.9081 data_time: 0.0021 memory: 42965 loss: 0.1164 loss_ce: 0.1164 2023/02/26 08:11:57 - mmengine - INFO - Epoch(train) [53][1200/5047] lr: 2.3889e-05 eta: 4 days, 23:47:07 time: 0.8869 data_time: 0.0020 memory: 45603 loss: 0.1451 loss_ce: 0.1451 2023/02/26 08:13:24 - mmengine - INFO - Epoch(train) [53][1300/5047] lr: 2.3889e-05 eta: 4 days, 23:45:38 time: 0.8247 data_time: 0.0051 memory: 44278 loss: 0.1180 loss_ce: 0.1180 2023/02/26 08:14:50 - mmengine - INFO - Epoch(train) [53][1400/5047] lr: 2.3889e-05 eta: 4 days, 23:44:09 time: 0.8798 data_time: 0.0029 memory: 48053 loss: 0.1226 loss_ce: 0.1226 2023/02/26 08:16:19 - mmengine - INFO - Epoch(train) [53][1500/5047] lr: 2.3889e-05 eta: 4 days, 23:42:43 time: 0.8303 data_time: 0.0021 memory: 44925 loss: 0.1186 loss_ce: 0.1186 2023/02/26 08:17:07 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 08:17:45 - mmengine - INFO - Epoch(train) [53][1600/5047] lr: 2.3889e-05 eta: 4 days, 23:41:15 time: 0.8812 data_time: 0.0036 memory: 55114 loss: 0.1049 loss_ce: 0.1049 2023/02/26 08:19:13 - mmengine - INFO - Epoch(train) [53][1700/5047] lr: 2.3889e-05 eta: 4 days, 23:39:47 time: 0.8657 data_time: 0.0018 memory: 44563 loss: 0.1450 loss_ce: 0.1450 2023/02/26 08:20:40 - mmengine - INFO - Epoch(train) [53][1800/5047] lr: 2.3889e-05 eta: 4 days, 23:38:20 time: 0.9641 data_time: 0.0021 memory: 44617 loss: 0.1138 loss_ce: 0.1138 2023/02/26 08:22:05 - mmengine - INFO - Epoch(train) [53][1900/5047] lr: 2.3889e-05 eta: 4 days, 23:36:48 time: 0.8590 data_time: 0.0018 memory: 42371 loss: 0.1304 loss_ce: 0.1304 2023/02/26 08:23:31 - mmengine - INFO - Epoch(train) [53][2000/5047] lr: 2.3889e-05 eta: 4 days, 23:35:18 time: 0.8748 data_time: 0.0023 memory: 55562 loss: 0.1353 loss_ce: 0.1353 2023/02/26 08:24:57 - mmengine - INFO - Epoch(train) [53][2100/5047] lr: 2.3889e-05 eta: 4 days, 23:33:47 time: 0.8506 data_time: 0.0035 memory: 42168 loss: 0.1095 loss_ce: 0.1095 2023/02/26 08:26:22 - mmengine - INFO - Epoch(train) [53][2200/5047] lr: 2.3889e-05 eta: 4 days, 23:32:15 time: 0.8270 data_time: 0.0089 memory: 40241 loss: 0.1203 loss_ce: 0.1203 2023/02/26 08:27:49 - mmengine - INFO - Epoch(train) [53][2300/5047] lr: 2.3889e-05 eta: 4 days, 23:30:46 time: 0.8872 data_time: 0.0020 memory: 38073 loss: 0.1286 loss_ce: 0.1286 2023/02/26 08:29:17 - mmengine - INFO - Epoch(train) [53][2400/5047] lr: 2.3889e-05 eta: 4 days, 23:29:19 time: 0.8643 data_time: 0.0019 memory: 54116 loss: 0.1133 loss_ce: 0.1133 2023/02/26 08:30:44 - mmengine - INFO - Epoch(train) [53][2500/5047] lr: 2.3889e-05 eta: 4 days, 23:27:51 time: 0.9055 data_time: 0.0019 memory: 50349 loss: 0.1219 loss_ce: 0.1219 2023/02/26 08:31:30 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 08:32:09 - mmengine - INFO - Epoch(train) [53][2600/5047] lr: 2.3889e-05 eta: 4 days, 23:26:20 time: 0.8809 data_time: 0.0037 memory: 43414 loss: 0.1212 loss_ce: 0.1212 2023/02/26 08:33:38 - mmengine - INFO - Epoch(train) [53][2700/5047] lr: 2.3889e-05 eta: 4 days, 23:24:56 time: 0.9179 data_time: 0.0019 memory: 44617 loss: 0.1168 loss_ce: 0.1168 2023/02/26 08:35:04 - mmengine - INFO - Epoch(train) [53][2800/5047] lr: 2.3889e-05 eta: 4 days, 23:23:26 time: 0.8766 data_time: 0.0030 memory: 47575 loss: 0.1346 loss_ce: 0.1346 2023/02/26 08:36:28 - mmengine - INFO - Epoch(train) [53][2900/5047] lr: 2.3889e-05 eta: 4 days, 23:21:51 time: 0.8515 data_time: 0.0021 memory: 49715 loss: 0.1264 loss_ce: 0.1264 2023/02/26 08:37:53 - mmengine - INFO - Epoch(train) [53][3000/5047] lr: 2.3889e-05 eta: 4 days, 23:20:19 time: 0.8714 data_time: 0.0019 memory: 41122 loss: 0.1175 loss_ce: 0.1175 2023/02/26 08:39:22 - mmengine - INFO - Epoch(train) [53][3100/5047] lr: 2.3889e-05 eta: 4 days, 23:18:54 time: 0.8681 data_time: 0.0024 memory: 43947 loss: 0.1463 loss_ce: 0.1463 2023/02/26 08:40:48 - mmengine - INFO - Epoch(train) [53][3200/5047] lr: 2.3889e-05 eta: 4 days, 23:17:25 time: 0.8949 data_time: 0.0021 memory: 55562 loss: 0.1161 loss_ce: 0.1161 2023/02/26 08:42:14 - mmengine - INFO - Epoch(train) [53][3300/5047] lr: 2.3889e-05 eta: 4 days, 23:15:53 time: 0.9044 data_time: 0.0022 memory: 55562 loss: 0.1146 loss_ce: 0.1146 2023/02/26 08:43:40 - mmengine - INFO - Epoch(train) [53][3400/5047] lr: 2.3889e-05 eta: 4 days, 23:14:23 time: 0.8615 data_time: 0.0020 memory: 55562 loss: 0.1129 loss_ce: 0.1129 2023/02/26 08:45:06 - mmengine - INFO - Epoch(train) [53][3500/5047] lr: 2.3889e-05 eta: 4 days, 23:12:53 time: 0.8586 data_time: 0.0023 memory: 55562 loss: 0.1236 loss_ce: 0.1236 2023/02/26 08:45:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 08:46:32 - mmengine - INFO - Epoch(train) [53][3600/5047] lr: 2.3889e-05 eta: 4 days, 23:11:23 time: 0.8121 data_time: 0.0023 memory: 39960 loss: 0.1163 loss_ce: 0.1163 2023/02/26 08:47:58 - mmengine - INFO - Epoch(train) [53][3700/5047] lr: 2.3889e-05 eta: 4 days, 23:09:52 time: 0.9301 data_time: 0.0022 memory: 43613 loss: 0.1417 loss_ce: 0.1417 2023/02/26 08:49:23 - mmengine - INFO - Epoch(train) [53][3800/5047] lr: 2.3889e-05 eta: 4 days, 23:08:21 time: 0.8739 data_time: 0.0056 memory: 43613 loss: 0.1305 loss_ce: 0.1305 2023/02/26 08:50:51 - mmengine - INFO - Epoch(train) [53][3900/5047] lr: 2.3889e-05 eta: 4 days, 23:06:54 time: 0.9053 data_time: 0.0019 memory: 44617 loss: 0.1086 loss_ce: 0.1086 2023/02/26 08:52:16 - mmengine - INFO - Epoch(train) [53][4000/5047] lr: 2.3889e-05 eta: 4 days, 23:05:23 time: 0.9168 data_time: 0.0019 memory: 44407 loss: 0.1341 loss_ce: 0.1341 2023/02/26 08:53:42 - mmengine - INFO - Epoch(train) [53][4100/5047] lr: 2.3889e-05 eta: 4 days, 23:03:53 time: 0.8396 data_time: 0.0021 memory: 42913 loss: 0.1301 loss_ce: 0.1301 2023/02/26 08:55:09 - mmengine - INFO - Epoch(train) [53][4200/5047] lr: 2.3889e-05 eta: 4 days, 23:02:24 time: 0.8573 data_time: 0.0020 memory: 45478 loss: 0.1199 loss_ce: 0.1199 2023/02/26 08:56:35 - mmengine - INFO - Epoch(train) [53][4300/5047] lr: 2.3889e-05 eta: 4 days, 23:00:55 time: 0.8731 data_time: 0.0020 memory: 44537 loss: 0.1170 loss_ce: 0.1170 2023/02/26 08:58:02 - mmengine - INFO - Epoch(train) [53][4400/5047] lr: 2.3889e-05 eta: 4 days, 22:59:25 time: 0.8491 data_time: 0.0026 memory: 47989 loss: 0.1177 loss_ce: 0.1177 2023/02/26 08:59:28 - mmengine - INFO - Epoch(train) [53][4500/5047] lr: 2.3889e-05 eta: 4 days, 22:57:56 time: 0.9046 data_time: 0.0026 memory: 43289 loss: 0.1167 loss_ce: 0.1167 2023/02/26 09:00:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 09:00:54 - mmengine - INFO - Epoch(train) [53][4600/5047] lr: 2.3889e-05 eta: 4 days, 22:56:26 time: 0.8338 data_time: 0.0022 memory: 40182 loss: 0.1258 loss_ce: 0.1258 2023/02/26 09:02:23 - mmengine - INFO - Epoch(train) [53][4700/5047] lr: 2.3889e-05 eta: 4 days, 22:55:01 time: 0.8509 data_time: 0.0021 memory: 42233 loss: 0.1394 loss_ce: 0.1394 2023/02/26 09:03:48 - mmengine - INFO - Epoch(train) [53][4800/5047] lr: 2.3889e-05 eta: 4 days, 22:53:30 time: 0.8999 data_time: 0.0034 memory: 45640 loss: 0.1224 loss_ce: 0.1224 2023/02/26 09:05:15 - mmengine - INFO - Epoch(train) [53][4900/5047] lr: 2.3889e-05 eta: 4 days, 22:52:01 time: 0.8982 data_time: 0.0021 memory: 43613 loss: 0.1208 loss_ce: 0.1208 2023/02/26 09:06:43 - mmengine - INFO - Epoch(train) [53][5000/5047] lr: 2.3889e-05 eta: 4 days, 22:50:35 time: 0.8623 data_time: 0.0019 memory: 43947 loss: 0.1281 loss_ce: 0.1281 2023/02/26 09:07:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 09:07:24 - mmengine - INFO - Saving checkpoint at 53 epochs 2023/02/26 09:08:55 - mmengine - INFO - Epoch(train) [54][ 100/5047] lr: 2.3688e-05 eta: 4 days, 22:48:22 time: 0.8833 data_time: 0.0020 memory: 45302 loss: 0.1285 loss_ce: 0.1285 2023/02/26 09:10:20 - mmengine - INFO - Epoch(train) [54][ 200/5047] lr: 2.3688e-05 eta: 4 days, 22:46:51 time: 0.8393 data_time: 0.0019 memory: 41171 loss: 0.1290 loss_ce: 0.1290 2023/02/26 09:11:46 - mmengine - INFO - Epoch(train) [54][ 300/5047] lr: 2.3688e-05 eta: 4 days, 22:45:21 time: 0.8252 data_time: 0.0070 memory: 44433 loss: 0.1433 loss_ce: 0.1433 2023/02/26 09:13:13 - mmengine - INFO - Epoch(train) [54][ 400/5047] lr: 2.3688e-05 eta: 4 days, 22:43:52 time: 0.8377 data_time: 0.0020 memory: 43613 loss: 0.1310 loss_ce: 0.1310 2023/02/26 09:14:40 - mmengine - INFO - Epoch(train) [54][ 500/5047] lr: 2.3688e-05 eta: 4 days, 22:42:25 time: 0.8835 data_time: 0.0019 memory: 39960 loss: 0.1084 loss_ce: 0.1084 2023/02/26 09:14:48 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 09:16:07 - mmengine - INFO - Epoch(train) [54][ 600/5047] lr: 2.3688e-05 eta: 4 days, 22:40:56 time: 0.8813 data_time: 0.0023 memory: 46851 loss: 0.1302 loss_ce: 0.1302 2023/02/26 09:17:34 - mmengine - INFO - Epoch(train) [54][ 700/5047] lr: 2.3688e-05 eta: 4 days, 22:39:28 time: 0.8750 data_time: 0.0019 memory: 43289 loss: 0.1147 loss_ce: 0.1147 2023/02/26 09:19:01 - mmengine - INFO - Epoch(train) [54][ 800/5047] lr: 2.3688e-05 eta: 4 days, 22:38:00 time: 0.9130 data_time: 0.0022 memory: 39193 loss: 0.0992 loss_ce: 0.0992 2023/02/26 09:20:25 - mmengine - INFO - Epoch(train) [54][ 900/5047] lr: 2.3688e-05 eta: 4 days, 22:36:27 time: 0.8936 data_time: 0.0019 memory: 40735 loss: 0.1230 loss_ce: 0.1230 2023/02/26 09:21:51 - mmengine - INFO - Epoch(train) [54][1000/5047] lr: 2.3688e-05 eta: 4 days, 22:34:55 time: 0.8583 data_time: 0.0044 memory: 55323 loss: 0.1085 loss_ce: 0.1085 2023/02/26 09:23:17 - mmengine - INFO - Epoch(train) [54][1100/5047] lr: 2.3688e-05 eta: 4 days, 22:33:26 time: 0.8765 data_time: 0.0022 memory: 41419 loss: 0.1027 loss_ce: 0.1027 2023/02/26 09:24:44 - mmengine - INFO - Epoch(train) [54][1200/5047] lr: 2.3688e-05 eta: 4 days, 22:31:57 time: 0.8768 data_time: 0.0023 memory: 44278 loss: 0.1145 loss_ce: 0.1145 2023/02/26 09:26:10 - mmengine - INFO - Epoch(train) [54][1300/5047] lr: 2.3688e-05 eta: 4 days, 22:30:27 time: 0.8265 data_time: 0.0021 memory: 44440 loss: 0.1246 loss_ce: 0.1246 2023/02/26 09:27:37 - mmengine - INFO - Epoch(train) [54][1400/5047] lr: 2.3688e-05 eta: 4 days, 22:28:59 time: 0.8660 data_time: 0.0021 memory: 49146 loss: 0.1259 loss_ce: 0.1259 2023/02/26 09:29:05 - mmengine - INFO - Epoch(train) [54][1500/5047] lr: 2.3688e-05 eta: 4 days, 22:27:32 time: 0.9090 data_time: 0.0022 memory: 41393 loss: 0.1226 loss_ce: 0.1226 2023/02/26 09:29:12 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 09:30:32 - mmengine - INFO - Epoch(train) [54][1600/5047] lr: 2.3688e-05 eta: 4 days, 22:26:05 time: 0.8758 data_time: 0.0020 memory: 42969 loss: 0.1155 loss_ce: 0.1155 2023/02/26 09:31:59 - mmengine - INFO - Epoch(train) [54][1700/5047] lr: 2.3688e-05 eta: 4 days, 22:24:36 time: 0.8140 data_time: 0.0018 memory: 42885 loss: 0.1139 loss_ce: 0.1139 2023/02/26 09:33:26 - mmengine - INFO - Epoch(train) [54][1800/5047] lr: 2.3688e-05 eta: 4 days, 22:23:08 time: 0.9130 data_time: 0.0022 memory: 44278 loss: 0.1222 loss_ce: 0.1222 2023/02/26 09:34:52 - mmengine - INFO - Epoch(train) [54][1900/5047] lr: 2.3688e-05 eta: 4 days, 22:21:38 time: 0.8522 data_time: 0.0066 memory: 43289 loss: 0.1329 loss_ce: 0.1329 2023/02/26 09:36:20 - mmengine - INFO - Epoch(train) [54][2000/5047] lr: 2.3688e-05 eta: 4 days, 22:20:11 time: 0.8280 data_time: 0.0023 memory: 46879 loss: 0.1145 loss_ce: 0.1145 2023/02/26 09:37:46 - mmengine - INFO - Epoch(train) [54][2100/5047] lr: 2.3688e-05 eta: 4 days, 22:18:42 time: 0.8227 data_time: 0.0026 memory: 46966 loss: 0.1317 loss_ce: 0.1317 2023/02/26 09:39:13 - mmengine - INFO - Epoch(train) [54][2200/5047] lr: 2.3688e-05 eta: 4 days, 22:17:14 time: 0.8616 data_time: 0.0023 memory: 48188 loss: 0.1185 loss_ce: 0.1185 2023/02/26 09:40:41 - mmengine - INFO - Epoch(train) [54][2300/5047] lr: 2.3688e-05 eta: 4 days, 22:15:47 time: 0.8705 data_time: 0.0021 memory: 44255 loss: 0.1299 loss_ce: 0.1299 2023/02/26 09:42:08 - mmengine - INFO - Epoch(train) [54][2400/5047] lr: 2.3688e-05 eta: 4 days, 22:14:18 time: 0.9054 data_time: 0.0059 memory: 43613 loss: 0.1152 loss_ce: 0.1152 2023/02/26 09:43:36 - mmengine - INFO - Epoch(train) [54][2500/5047] lr: 2.3688e-05 eta: 4 days, 22:12:52 time: 0.8604 data_time: 0.0019 memory: 43947 loss: 0.1185 loss_ce: 0.1185 2023/02/26 09:43:43 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 09:45:02 - mmengine - INFO - Epoch(train) [54][2600/5047] lr: 2.3688e-05 eta: 4 days, 22:11:24 time: 0.8808 data_time: 0.0021 memory: 49378 loss: 0.1317 loss_ce: 0.1317 2023/02/26 09:46:29 - mmengine - INFO - Epoch(train) [54][2700/5047] lr: 2.3688e-05 eta: 4 days, 22:09:55 time: 0.8606 data_time: 0.0019 memory: 46988 loss: 0.1263 loss_ce: 0.1263 2023/02/26 09:47:55 - mmengine - INFO - Epoch(train) [54][2800/5047] lr: 2.3688e-05 eta: 4 days, 22:08:25 time: 0.8754 data_time: 0.0020 memory: 49715 loss: 0.1277 loss_ce: 0.1277 2023/02/26 09:49:23 - mmengine - INFO - Epoch(train) [54][2900/5047] lr: 2.3688e-05 eta: 4 days, 22:06:58 time: 0.9177 data_time: 0.0019 memory: 39398 loss: 0.1185 loss_ce: 0.1185 2023/02/26 09:50:50 - mmengine - INFO - Epoch(train) [54][3000/5047] lr: 2.3688e-05 eta: 4 days, 22:05:29 time: 0.8727 data_time: 0.0019 memory: 41122 loss: 0.1168 loss_ce: 0.1168 2023/02/26 09:52:15 - mmengine - INFO - Epoch(train) [54][3100/5047] lr: 2.3688e-05 eta: 4 days, 22:03:58 time: 0.8577 data_time: 0.0020 memory: 39960 loss: 0.1294 loss_ce: 0.1294 2023/02/26 09:53:42 - mmengine - INFO - Epoch(train) [54][3200/5047] lr: 2.3688e-05 eta: 4 days, 22:02:30 time: 0.8619 data_time: 0.0018 memory: 44410 loss: 0.1406 loss_ce: 0.1406 2023/02/26 09:55:09 - mmengine - INFO - Epoch(train) [54][3300/5047] lr: 2.3688e-05 eta: 4 days, 22:01:02 time: 0.8210 data_time: 0.0029 memory: 49715 loss: 0.1146 loss_ce: 0.1146 2023/02/26 10:01:24 - mmengine - INFO - Epoch(train) [54][3400/5047] lr: 2.3688e-05 eta: 4 days, 22:08:41 time: 29.7181 data_time: 0.0022 memory: 41122 loss: 0.1228 loss_ce: 0.1228 2023/02/26 10:02:50 - mmengine - INFO - Epoch(train) [54][3500/5047] lr: 2.3688e-05 eta: 4 days, 22:07:11 time: 0.8297 data_time: 0.0028 memory: 40535 loss: 0.1339 loss_ce: 0.1339 2023/02/26 10:02:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 10:04:16 - mmengine - INFO - Epoch(train) [54][3600/5047] lr: 2.3688e-05 eta: 4 days, 22:05:41 time: 0.8478 data_time: 0.0033 memory: 45643 loss: 0.1247 loss_ce: 0.1247 2023/02/26 10:05:41 - mmengine - INFO - Epoch(train) [54][3700/5047] lr: 2.3688e-05 eta: 4 days, 22:04:09 time: 0.8984 data_time: 0.0019 memory: 42649 loss: 0.1141 loss_ce: 0.1141 2023/02/26 10:07:06 - mmengine - INFO - Epoch(train) [54][3800/5047] lr: 2.3688e-05 eta: 4 days, 22:02:35 time: 0.8441 data_time: 0.0021 memory: 43289 loss: 0.1349 loss_ce: 0.1349 2023/02/26 10:08:31 - mmengine - INFO - Epoch(train) [54][3900/5047] lr: 2.3688e-05 eta: 4 days, 22:01:04 time: 0.8699 data_time: 0.0020 memory: 44956 loss: 0.1433 loss_ce: 0.1433 2023/02/26 10:09:56 - mmengine - INFO - Epoch(train) [54][4000/5047] lr: 2.3688e-05 eta: 4 days, 21:59:32 time: 0.8853 data_time: 0.0031 memory: 42336 loss: 0.1292 loss_ce: 0.1292 2023/02/26 10:11:22 - mmengine - INFO - Epoch(train) [54][4100/5047] lr: 2.3688e-05 eta: 4 days, 21:58:01 time: 0.8197 data_time: 0.0024 memory: 45643 loss: 0.1369 loss_ce: 0.1369 2023/02/26 10:12:49 - mmengine - INFO - Epoch(train) [54][4200/5047] lr: 2.3688e-05 eta: 4 days, 21:56:32 time: 0.8416 data_time: 0.0021 memory: 45621 loss: 0.1166 loss_ce: 0.1166 2023/02/26 10:14:16 - mmengine - INFO - Epoch(train) [54][4300/5047] lr: 2.3688e-05 eta: 4 days, 21:55:04 time: 0.8486 data_time: 0.0021 memory: 55562 loss: 0.1230 loss_ce: 0.1230 2023/02/26 10:15:43 - mmengine - INFO - Epoch(train) [54][4400/5047] lr: 2.3688e-05 eta: 4 days, 21:53:36 time: 0.7919 data_time: 0.0019 memory: 47011 loss: 0.1192 loss_ce: 0.1192 2023/02/26 10:17:11 - mmengine - INFO - Epoch(train) [54][4500/5047] lr: 2.3688e-05 eta: 4 days, 21:52:09 time: 0.8565 data_time: 0.0020 memory: 44278 loss: 0.1318 loss_ce: 0.1318 2023/02/26 10:17:19 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 10:18:38 - mmengine - INFO - Epoch(train) [54][4600/5047] lr: 2.3688e-05 eta: 4 days, 21:50:41 time: 0.8434 data_time: 0.0019 memory: 43947 loss: 0.1205 loss_ce: 0.1205 2023/02/26 10:20:04 - mmengine - INFO - Epoch(train) [54][4700/5047] lr: 2.3688e-05 eta: 4 days, 21:49:10 time: 0.9088 data_time: 0.0021 memory: 50505 loss: 0.1039 loss_ce: 0.1039 2023/02/26 10:21:30 - mmengine - INFO - Epoch(train) [54][4800/5047] lr: 2.3688e-05 eta: 4 days, 21:47:39 time: 0.8709 data_time: 0.0020 memory: 47071 loss: 0.1424 loss_ce: 0.1424 2023/02/26 10:22:55 - mmengine - INFO - Epoch(train) [54][4900/5047] lr: 2.3688e-05 eta: 4 days, 21:46:08 time: 0.8823 data_time: 0.0019 memory: 45638 loss: 0.1225 loss_ce: 0.1225 2023/02/26 10:24:22 - mmengine - INFO - Epoch(train) [54][5000/5047] lr: 2.3688e-05 eta: 4 days, 21:44:38 time: 0.8238 data_time: 0.0029 memory: 40241 loss: 0.1380 loss_ce: 0.1380 2023/02/26 10:25:02 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 10:25:02 - mmengine - INFO - Saving checkpoint at 54 epochs 2023/02/26 10:26:33 - mmengine - INFO - Epoch(train) [55][ 100/5047] lr: 2.3487e-05 eta: 4 days, 21:42:26 time: 0.8335 data_time: 0.0020 memory: 46869 loss: 0.1150 loss_ce: 0.1150 2023/02/26 10:28:01 - mmengine - INFO - Epoch(train) [55][ 200/5047] lr: 2.3487e-05 eta: 4 days, 21:40:58 time: 0.9582 data_time: 0.0020 memory: 54232 loss: 0.1171 loss_ce: 0.1171 2023/02/26 10:29:26 - mmengine - INFO - Epoch(train) [55][ 300/5047] lr: 2.3487e-05 eta: 4 days, 21:39:26 time: 0.8448 data_time: 0.0021 memory: 47813 loss: 0.1284 loss_ce: 0.1284 2023/02/26 10:30:51 - mmengine - INFO - Epoch(train) [55][ 400/5047] lr: 2.3487e-05 eta: 4 days, 21:37:54 time: 0.8620 data_time: 0.0020 memory: 40535 loss: 0.1198 loss_ce: 0.1198 2023/02/26 10:31:45 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 10:32:18 - mmengine - INFO - Epoch(train) [55][ 500/5047] lr: 2.3487e-05 eta: 4 days, 21:36:26 time: 0.8952 data_time: 0.0023 memory: 42024 loss: 0.1409 loss_ce: 0.1409 2023/02/26 10:33:44 - mmengine - INFO - Epoch(train) [55][ 600/5047] lr: 2.3487e-05 eta: 4 days, 21:34:56 time: 0.8769 data_time: 0.0022 memory: 45302 loss: 0.1251 loss_ce: 0.1251 2023/02/26 10:35:19 - mmengine - INFO - Epoch(train) [55][ 700/5047] lr: 2.3487e-05 eta: 4 days, 21:33:42 time: 0.8836 data_time: 0.0019 memory: 38593 loss: 0.1189 loss_ce: 0.1189 2023/02/26 10:36:45 - mmengine - INFO - Epoch(train) [55][ 800/5047] lr: 2.3487e-05 eta: 4 days, 21:32:12 time: 0.8342 data_time: 0.0020 memory: 41530 loss: 0.1252 loss_ce: 0.1252 2023/02/26 10:38:10 - mmengine - INFO - Epoch(train) [55][ 900/5047] lr: 2.3487e-05 eta: 4 days, 21:30:40 time: 0.8434 data_time: 0.0022 memory: 43446 loss: 0.1149 loss_ce: 0.1149 2023/02/26 10:39:36 - mmengine - INFO - Epoch(train) [55][1000/5047] lr: 2.3487e-05 eta: 4 days, 21:29:10 time: 0.8639 data_time: 0.0021 memory: 43854 loss: 0.1193 loss_ce: 0.1193 2023/02/26 10:41:03 - mmengine - INFO - Epoch(train) [55][1100/5047] lr: 2.3487e-05 eta: 4 days, 21:27:41 time: 0.8608 data_time: 0.0021 memory: 55562 loss: 0.1116 loss_ce: 0.1116 2023/02/26 10:42:30 - mmengine - INFO - Epoch(train) [55][1200/5047] lr: 2.3487e-05 eta: 4 days, 21:26:13 time: 0.8503 data_time: 0.0019 memory: 42965 loss: 0.1190 loss_ce: 0.1190 2023/02/26 10:44:03 - mmengine - INFO - Epoch(train) [55][1300/5047] lr: 2.3487e-05 eta: 4 days, 21:24:55 time: 0.9562 data_time: 0.0020 memory: 44587 loss: 0.1143 loss_ce: 0.1143 2023/02/26 10:45:28 - mmengine - INFO - Epoch(train) [55][1400/5047] lr: 2.3487e-05 eta: 4 days, 21:23:23 time: 0.8764 data_time: 0.0020 memory: 43944 loss: 0.1225 loss_ce: 0.1225 2023/02/26 10:46:22 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 10:46:55 - mmengine - INFO - Epoch(train) [55][1500/5047] lr: 2.3487e-05 eta: 4 days, 21:21:54 time: 0.9388 data_time: 0.0019 memory: 54242 loss: 0.1126 loss_ce: 0.1126 2023/02/26 10:48:20 - mmengine - INFO - Epoch(train) [55][1600/5047] lr: 2.3487e-05 eta: 4 days, 21:20:22 time: 0.8401 data_time: 0.0018 memory: 44956 loss: 0.1131 loss_ce: 0.1131 2023/02/26 10:49:46 - mmengine - INFO - Epoch(train) [55][1700/5047] lr: 2.3487e-05 eta: 4 days, 21:18:51 time: 0.8545 data_time: 0.0019 memory: 43947 loss: 0.1098 loss_ce: 0.1098 2023/02/26 10:51:12 - mmengine - INFO - Epoch(train) [55][1800/5047] lr: 2.3487e-05 eta: 4 days, 21:17:22 time: 0.9421 data_time: 0.0020 memory: 43001 loss: 0.1245 loss_ce: 0.1245 2023/02/26 10:52:37 - mmengine - INFO - Epoch(train) [55][1900/5047] lr: 2.3487e-05 eta: 4 days, 21:15:50 time: 0.8400 data_time: 0.0034 memory: 42799 loss: 0.1286 loss_ce: 0.1286 2023/02/26 10:54:02 - mmengine - INFO - Epoch(train) [55][2000/5047] lr: 2.3487e-05 eta: 4 days, 21:14:18 time: 0.8423 data_time: 0.0024 memory: 54137 loss: 0.1155 loss_ce: 0.1155 2023/02/26 10:55:29 - mmengine - INFO - Epoch(train) [55][2100/5047] lr: 2.3487e-05 eta: 4 days, 21:12:49 time: 0.8837 data_time: 0.0018 memory: 49715 loss: 0.1283 loss_ce: 0.1283 2023/02/26 10:56:55 - mmengine - INFO - Epoch(train) [55][2200/5047] lr: 2.3487e-05 eta: 4 days, 21:11:18 time: 0.8895 data_time: 0.0031 memory: 45549 loss: 0.1248 loss_ce: 0.1248 2023/02/26 10:58:21 - mmengine - INFO - Epoch(train) [55][2300/5047] lr: 2.3487e-05 eta: 4 days, 21:09:49 time: 0.8467 data_time: 0.0041 memory: 40508 loss: 0.1264 loss_ce: 0.1264 2023/02/26 10:59:46 - mmengine - INFO - Epoch(train) [55][2400/5047] lr: 2.3487e-05 eta: 4 days, 21:08:17 time: 0.8180 data_time: 0.0027 memory: 43289 loss: 0.1238 loss_ce: 0.1238 2023/02/26 11:00:40 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 11:01:13 - mmengine - INFO - Epoch(train) [55][2500/5047] lr: 2.3487e-05 eta: 4 days, 21:06:47 time: 0.8030 data_time: 0.0056 memory: 45731 loss: 0.1171 loss_ce: 0.1171 2023/02/26 11:02:40 - mmengine - INFO - Epoch(train) [55][2600/5047] lr: 2.3487e-05 eta: 4 days, 21:05:19 time: 0.8746 data_time: 0.0021 memory: 50349 loss: 0.1161 loss_ce: 0.1161 2023/02/26 11:04:06 - mmengine - INFO - Epoch(train) [55][2700/5047] lr: 2.3487e-05 eta: 4 days, 21:03:48 time: 0.8458 data_time: 0.0020 memory: 42977 loss: 0.1206 loss_ce: 0.1206 2023/02/26 11:05:32 - mmengine - INFO - Epoch(train) [55][2800/5047] lr: 2.3487e-05 eta: 4 days, 21:02:18 time: 0.8590 data_time: 0.0043 memory: 42494 loss: 0.1407 loss_ce: 0.1407 2023/02/26 11:06:57 - mmengine - INFO - Epoch(train) [55][2900/5047] lr: 2.3487e-05 eta: 4 days, 21:00:47 time: 0.8495 data_time: 0.0046 memory: 42336 loss: 0.1104 loss_ce: 0.1104 2023/02/26 11:08:24 - mmengine - INFO - Epoch(train) [55][3000/5047] lr: 2.3487e-05 eta: 4 days, 20:59:19 time: 0.8835 data_time: 0.0021 memory: 41724 loss: 0.1136 loss_ce: 0.1136 2023/02/26 11:09:51 - mmengine - INFO - Epoch(train) [55][3100/5047] lr: 2.3487e-05 eta: 4 days, 20:57:51 time: 0.8357 data_time: 0.0027 memory: 47447 loss: 0.1044 loss_ce: 0.1044 2023/02/26 11:11:16 - mmengine - INFO - Epoch(train) [55][3200/5047] lr: 2.3487e-05 eta: 4 days, 20:56:17 time: 0.8192 data_time: 0.0021 memory: 43253 loss: 0.1047 loss_ce: 0.1047 2023/02/26 11:12:42 - mmengine - INFO - Epoch(train) [55][3300/5047] lr: 2.3487e-05 eta: 4 days, 20:54:48 time: 0.8146 data_time: 0.0023 memory: 45104 loss: 0.1159 loss_ce: 0.1159 2023/02/26 11:14:07 - mmengine - INFO - Epoch(train) [55][3400/5047] lr: 2.3487e-05 eta: 4 days, 20:53:16 time: 0.8666 data_time: 0.0019 memory: 45440 loss: 0.1200 loss_ce: 0.1200 2023/02/26 11:15:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 11:15:32 - mmengine - INFO - Epoch(train) [55][3500/5047] lr: 2.3487e-05 eta: 4 days, 20:51:43 time: 0.8195 data_time: 0.0048 memory: 40878 loss: 0.1311 loss_ce: 0.1311 2023/02/26 11:16:58 - mmengine - INFO - Epoch(train) [55][3600/5047] lr: 2.3487e-05 eta: 4 days, 20:50:14 time: 0.9064 data_time: 0.0057 memory: 44278 loss: 0.1165 loss_ce: 0.1165 2023/02/26 11:18:24 - mmengine - INFO - Epoch(train) [55][3700/5047] lr: 2.3487e-05 eta: 4 days, 20:48:43 time: 0.8405 data_time: 0.0019 memory: 44956 loss: 0.1276 loss_ce: 0.1276 2023/02/26 11:19:49 - mmengine - INFO - Epoch(train) [55][3800/5047] lr: 2.3487e-05 eta: 4 days, 20:47:11 time: 0.8757 data_time: 0.0020 memory: 41724 loss: 0.1253 loss_ce: 0.1253 2023/02/26 11:21:22 - mmengine - INFO - Epoch(train) [55][3900/5047] lr: 2.3487e-05 eta: 4 days, 20:45:53 time: 0.8730 data_time: 0.0032 memory: 55562 loss: 0.1205 loss_ce: 0.1205 2023/02/26 11:22:47 - mmengine - INFO - Epoch(train) [55][4000/5047] lr: 2.3487e-05 eta: 4 days, 20:44:22 time: 0.8111 data_time: 0.0023 memory: 42336 loss: 0.1261 loss_ce: 0.1261 2023/02/26 11:24:16 - mmengine - INFO - Epoch(train) [55][4100/5047] lr: 2.3487e-05 eta: 4 days, 20:42:58 time: 0.8907 data_time: 0.0044 memory: 53791 loss: 0.1191 loss_ce: 0.1191 2023/02/26 11:25:43 - mmengine - INFO - Epoch(train) [55][4200/5047] lr: 2.3487e-05 eta: 4 days, 20:41:29 time: 0.8955 data_time: 0.0023 memory: 45064 loss: 0.1355 loss_ce: 0.1355 2023/02/26 11:27:11 - mmengine - INFO - Epoch(train) [55][4300/5047] lr: 2.3487e-05 eta: 4 days, 20:40:02 time: 0.8975 data_time: 0.0022 memory: 47813 loss: 0.1106 loss_ce: 0.1106 2023/02/26 11:28:38 - mmengine - INFO - Epoch(train) [55][4400/5047] lr: 2.3487e-05 eta: 4 days, 20:38:33 time: 0.8529 data_time: 0.0022 memory: 45129 loss: 0.1246 loss_ce: 0.1246 2023/02/26 11:29:31 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 11:30:03 - mmengine - INFO - Epoch(train) [55][4500/5047] lr: 2.3487e-05 eta: 4 days, 20:37:02 time: 0.8939 data_time: 0.0022 memory: 43348 loss: 0.1289 loss_ce: 0.1289 2023/02/26 11:31:29 - mmengine - INFO - Epoch(train) [55][4600/5047] lr: 2.3487e-05 eta: 4 days, 20:35:32 time: 0.8840 data_time: 0.0023 memory: 46005 loss: 0.1249 loss_ce: 0.1249 2023/02/26 11:32:57 - mmengine - INFO - Epoch(train) [55][4700/5047] lr: 2.3487e-05 eta: 4 days, 20:34:04 time: 0.8863 data_time: 0.0020 memory: 42024 loss: 0.1275 loss_ce: 0.1275 2023/02/26 11:34:23 - mmengine - INFO - Epoch(train) [55][4800/5047] lr: 2.3487e-05 eta: 4 days, 20:32:34 time: 0.9367 data_time: 0.0021 memory: 44005 loss: 0.1337 loss_ce: 0.1337 2023/02/26 11:35:50 - mmengine - INFO - Epoch(train) [55][4900/5047] lr: 2.3487e-05 eta: 4 days, 20:31:06 time: 0.8913 data_time: 0.0019 memory: 55562 loss: 0.1351 loss_ce: 0.1351 2023/02/26 11:37:15 - mmengine - INFO - Epoch(train) [55][5000/5047] lr: 2.3487e-05 eta: 4 days, 20:29:33 time: 0.8259 data_time: 0.0023 memory: 43557 loss: 0.1210 loss_ce: 0.1210 2023/02/26 11:37:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 11:37:55 - mmengine - INFO - Saving checkpoint at 55 epochs 2023/02/26 11:39:25 - mmengine - INFO - Epoch(train) [56][ 100/5047] lr: 2.3286e-05 eta: 4 days, 20:27:19 time: 0.8705 data_time: 0.0025 memory: 43613 loss: 0.1224 loss_ce: 0.1224 2023/02/26 11:40:50 - mmengine - INFO - Epoch(train) [56][ 200/5047] lr: 2.3286e-05 eta: 4 days, 20:25:48 time: 0.8604 data_time: 0.0020 memory: 44278 loss: 0.1114 loss_ce: 0.1114 2023/02/26 11:42:17 - mmengine - INFO - Epoch(train) [56][ 300/5047] lr: 2.3286e-05 eta: 4 days, 20:24:18 time: 0.8685 data_time: 0.0020 memory: 55562 loss: 0.1087 loss_ce: 0.1087 2023/02/26 11:43:44 - mmengine - INFO - Epoch(train) [56][ 400/5047] lr: 2.3286e-05 eta: 4 days, 20:22:49 time: 0.8149 data_time: 0.0021 memory: 40263 loss: 0.1293 loss_ce: 0.1293 2023/02/26 11:43:56 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 11:45:10 - mmengine - INFO - Epoch(train) [56][ 500/5047] lr: 2.3286e-05 eta: 4 days, 20:21:19 time: 0.8674 data_time: 0.0041 memory: 42649 loss: 0.1292 loss_ce: 0.1292 2023/02/26 11:46:37 - mmengine - INFO - Epoch(train) [56][ 600/5047] lr: 2.3286e-05 eta: 4 days, 20:19:52 time: 0.8985 data_time: 0.0025 memory: 48210 loss: 0.1085 loss_ce: 0.1085 2023/02/26 11:48:05 - mmengine - INFO - Epoch(train) [56][ 700/5047] lr: 2.3286e-05 eta: 4 days, 20:18:25 time: 0.9574 data_time: 0.0021 memory: 40535 loss: 0.1185 loss_ce: 0.1185 2023/02/26 11:49:30 - mmengine - INFO - Epoch(train) [56][ 800/5047] lr: 2.3286e-05 eta: 4 days, 20:16:54 time: 0.8453 data_time: 0.0022 memory: 43613 loss: 0.1107 loss_ce: 0.1107 2023/02/26 11:50:57 - mmengine - INFO - Epoch(train) [56][ 900/5047] lr: 2.3286e-05 eta: 4 days, 20:15:25 time: 0.9056 data_time: 0.0029 memory: 51761 loss: 0.1286 loss_ce: 0.1286 2023/02/26 11:52:23 - mmengine - INFO - Epoch(train) [56][1000/5047] lr: 2.3286e-05 eta: 4 days, 20:13:54 time: 0.8594 data_time: 0.0019 memory: 44278 loss: 0.1226 loss_ce: 0.1226 2023/02/26 11:53:50 - mmengine - INFO - Epoch(train) [56][1100/5047] lr: 2.3286e-05 eta: 4 days, 20:12:26 time: 0.9117 data_time: 0.0020 memory: 55562 loss: 0.1307 loss_ce: 0.1307 2023/02/26 11:55:17 - mmengine - INFO - Epoch(train) [56][1200/5047] lr: 2.3286e-05 eta: 4 days, 20:10:58 time: 0.8624 data_time: 0.0018 memory: 48215 loss: 0.1209 loss_ce: 0.1209 2023/02/26 11:56:43 - mmengine - INFO - Epoch(train) [56][1300/5047] lr: 2.3286e-05 eta: 4 days, 20:09:29 time: 0.8519 data_time: 0.0019 memory: 43613 loss: 0.1278 loss_ce: 0.1278 2023/02/26 11:58:09 - mmengine - INFO - Epoch(train) [56][1400/5047] lr: 2.3286e-05 eta: 4 days, 20:07:57 time: 0.9101 data_time: 0.0020 memory: 46005 loss: 0.1211 loss_ce: 0.1211 2023/02/26 11:58:21 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 11:59:34 - mmengine - INFO - Epoch(train) [56][1500/5047] lr: 2.3286e-05 eta: 4 days, 20:06:25 time: 0.9027 data_time: 0.0032 memory: 48030 loss: 0.1260 loss_ce: 0.1260 2023/02/26 12:01:00 - mmengine - INFO - Epoch(train) [56][1600/5047] lr: 2.3286e-05 eta: 4 days, 20:04:56 time: 0.9039 data_time: 0.0022 memory: 55562 loss: 0.1204 loss_ce: 0.1204 2023/02/26 12:02:27 - mmengine - INFO - Epoch(train) [56][1700/5047] lr: 2.3286e-05 eta: 4 days, 20:03:28 time: 0.8656 data_time: 0.0034 memory: 42649 loss: 0.1077 loss_ce: 0.1077 2023/02/26 12:03:54 - mmengine - INFO - Epoch(train) [56][1800/5047] lr: 2.3286e-05 eta: 4 days, 20:01:58 time: 0.8756 data_time: 0.0022 memory: 53043 loss: 0.1205 loss_ce: 0.1205 2023/02/26 12:05:21 - mmengine - INFO - Epoch(train) [56][1900/5047] lr: 2.3286e-05 eta: 4 days, 20:00:30 time: 0.8610 data_time: 0.0020 memory: 42102 loss: 0.1314 loss_ce: 0.1314 2023/02/26 12:06:48 - mmengine - INFO - Epoch(train) [56][2000/5047] lr: 2.3286e-05 eta: 4 days, 19:59:02 time: 0.8167 data_time: 0.0020 memory: 42819 loss: 0.1301 loss_ce: 0.1301 2023/02/26 12:08:14 - mmengine - INFO - Epoch(train) [56][2100/5047] lr: 2.3286e-05 eta: 4 days, 19:57:31 time: 0.8182 data_time: 0.0020 memory: 42649 loss: 0.1256 loss_ce: 0.1256 2023/02/26 12:09:39 - mmengine - INFO - Epoch(train) [56][2200/5047] lr: 2.3286e-05 eta: 4 days, 19:56:01 time: 0.8850 data_time: 0.0021 memory: 45514 loss: 0.1233 loss_ce: 0.1233 2023/02/26 12:11:06 - mmengine - INFO - Epoch(train) [56][2300/5047] lr: 2.3286e-05 eta: 4 days, 19:54:32 time: 0.8726 data_time: 0.0024 memory: 43613 loss: 0.1224 loss_ce: 0.1224 2023/02/26 12:12:33 - mmengine - INFO - Epoch(train) [56][2400/5047] lr: 2.3286e-05 eta: 4 days, 19:53:03 time: 0.8680 data_time: 0.0020 memory: 42024 loss: 0.1212 loss_ce: 0.1212 2023/02/26 12:12:45 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 12:13:59 - mmengine - INFO - Epoch(train) [56][2500/5047] lr: 2.3286e-05 eta: 4 days, 19:51:33 time: 0.8999 data_time: 0.0059 memory: 43947 loss: 0.1206 loss_ce: 0.1206 2023/02/26 12:15:25 - mmengine - INFO - Epoch(train) [56][2600/5047] lr: 2.3286e-05 eta: 4 days, 19:50:03 time: 0.8635 data_time: 0.0022 memory: 47504 loss: 0.1207 loss_ce: 0.1207 2023/02/26 12:16:50 - mmengine - INFO - Epoch(train) [56][2700/5047] lr: 2.3286e-05 eta: 4 days, 19:48:32 time: 0.8396 data_time: 0.0027 memory: 42024 loss: 0.1214 loss_ce: 0.1214 2023/02/26 12:18:18 - mmengine - INFO - Epoch(train) [56][2800/5047] lr: 2.3286e-05 eta: 4 days, 19:47:05 time: 0.8788 data_time: 0.0040 memory: 46713 loss: 0.1190 loss_ce: 0.1190 2023/02/26 12:19:45 - mmengine - INFO - Epoch(train) [56][2900/5047] lr: 2.3286e-05 eta: 4 days, 19:45:36 time: 0.8558 data_time: 0.0026 memory: 54232 loss: 0.1209 loss_ce: 0.1209 2023/02/26 12:21:13 - mmengine - INFO - Epoch(train) [56][3000/5047] lr: 2.3286e-05 eta: 4 days, 19:44:09 time: 0.8764 data_time: 0.0026 memory: 55562 loss: 0.1318 loss_ce: 0.1318 2023/02/26 12:22:41 - mmengine - INFO - Epoch(train) [56][3100/5047] lr: 2.3286e-05 eta: 4 days, 19:42:43 time: 0.8687 data_time: 0.0018 memory: 55562 loss: 0.1176 loss_ce: 0.1176 2023/02/26 12:24:07 - mmengine - INFO - Epoch(train) [56][3200/5047] lr: 2.3286e-05 eta: 4 days, 19:41:12 time: 0.8879 data_time: 0.0019 memory: 55562 loss: 0.1434 loss_ce: 0.1434 2023/02/26 12:25:32 - mmengine - INFO - Epoch(train) [56][3300/5047] lr: 2.3286e-05 eta: 4 days, 19:39:42 time: 0.8482 data_time: 0.0046 memory: 41419 loss: 0.1290 loss_ce: 0.1290 2023/02/26 12:26:58 - mmengine - INFO - Epoch(train) [56][3400/5047] lr: 2.3286e-05 eta: 4 days, 19:38:10 time: 0.7981 data_time: 0.0022 memory: 43852 loss: 0.1337 loss_ce: 0.1337 2023/02/26 12:27:10 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 12:28:25 - mmengine - INFO - Epoch(train) [56][3500/5047] lr: 2.3286e-05 eta: 4 days, 19:36:42 time: 0.8723 data_time: 0.0019 memory: 48188 loss: 0.1154 loss_ce: 0.1154 2023/02/26 12:29:50 - mmengine - INFO - Epoch(train) [56][3600/5047] lr: 2.3286e-05 eta: 4 days, 19:35:11 time: 0.8691 data_time: 0.0023 memory: 43091 loss: 0.1231 loss_ce: 0.1231 2023/02/26 12:31:16 - mmengine - INFO - Epoch(train) [56][3700/5047] lr: 2.3286e-05 eta: 4 days, 19:33:42 time: 0.8494 data_time: 0.0018 memory: 44346 loss: 0.1300 loss_ce: 0.1300 2023/02/26 12:32:42 - mmengine - INFO - Epoch(train) [56][3800/5047] lr: 2.3286e-05 eta: 4 days, 19:32:11 time: 0.8258 data_time: 0.0037 memory: 43947 loss: 0.1242 loss_ce: 0.1242 2023/02/26 12:34:08 - mmengine - INFO - Epoch(train) [56][3900/5047] lr: 2.3286e-05 eta: 4 days, 19:30:41 time: 0.8671 data_time: 0.0019 memory: 51739 loss: 0.1171 loss_ce: 0.1171 2023/02/26 12:35:34 - mmengine - INFO - Epoch(train) [56][4000/5047] lr: 2.3286e-05 eta: 4 days, 19:29:10 time: 0.8797 data_time: 0.0020 memory: 44969 loss: 0.1229 loss_ce: 0.1229 2023/02/26 12:36:59 - mmengine - INFO - Epoch(train) [56][4100/5047] lr: 2.3286e-05 eta: 4 days, 19:27:38 time: 0.8147 data_time: 0.0028 memory: 42599 loss: 0.1349 loss_ce: 0.1349 2023/02/26 12:38:25 - mmengine - INFO - Epoch(train) [56][4200/5047] lr: 2.3286e-05 eta: 4 days, 19:26:09 time: 0.9029 data_time: 0.0020 memory: 50443 loss: 0.1148 loss_ce: 0.1148 2023/02/26 12:39:52 - mmengine - INFO - Epoch(train) [56][4300/5047] lr: 2.3286e-05 eta: 4 days, 19:24:40 time: 0.8953 data_time: 0.0020 memory: 51308 loss: 0.1235 loss_ce: 0.1235 2023/02/26 12:41:18 - mmengine - INFO - Epoch(train) [56][4400/5047] lr: 2.3286e-05 eta: 4 days, 19:23:11 time: 0.8518 data_time: 0.0021 memory: 52953 loss: 0.1347 loss_ce: 0.1347 2023/02/26 12:41:31 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 12:42:43 - mmengine - INFO - Epoch(train) [56][4500/5047] lr: 2.3286e-05 eta: 4 days, 19:21:38 time: 0.8354 data_time: 0.0020 memory: 48215 loss: 0.1211 loss_ce: 0.1211 2023/02/26 12:44:10 - mmengine - INFO - Epoch(train) [56][4600/5047] lr: 2.3286e-05 eta: 4 days, 19:20:10 time: 0.9032 data_time: 0.0027 memory: 40942 loss: 0.1132 loss_ce: 0.1132 2023/02/26 12:45:36 - mmengine - INFO - Epoch(train) [56][4700/5047] lr: 2.3286e-05 eta: 4 days, 19:18:40 time: 0.8562 data_time: 0.0031 memory: 41624 loss: 0.1419 loss_ce: 0.1419 2023/02/26 12:47:03 - mmengine - INFO - Epoch(train) [56][4800/5047] lr: 2.3286e-05 eta: 4 days, 19:17:12 time: 0.8844 data_time: 0.0018 memory: 49409 loss: 0.1232 loss_ce: 0.1232 2023/02/26 12:48:29 - mmengine - INFO - Epoch(train) [56][4900/5047] lr: 2.3286e-05 eta: 4 days, 19:15:42 time: 0.9036 data_time: 0.0019 memory: 55562 loss: 0.1392 loss_ce: 0.1392 2023/02/26 12:49:57 - mmengine - INFO - Epoch(train) [56][5000/5047] lr: 2.3286e-05 eta: 4 days, 19:14:14 time: 0.8757 data_time: 0.0040 memory: 42965 loss: 0.1315 loss_ce: 0.1315 2023/02/26 12:50:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 12:50:37 - mmengine - INFO - Saving checkpoint at 56 epochs 2023/02/26 12:52:11 - mmengine - INFO - Epoch(train) [57][ 100/5047] lr: 2.3085e-05 eta: 4 days, 19:12:07 time: 0.8800 data_time: 0.0025 memory: 42024 loss: 0.1180 loss_ce: 0.1180 2023/02/26 12:53:37 - mmengine - INFO - Epoch(train) [57][ 200/5047] lr: 2.3085e-05 eta: 4 days, 19:10:37 time: 0.8179 data_time: 0.0020 memory: 40825 loss: 0.1095 loss_ce: 0.1095 2023/02/26 12:55:03 - mmengine - INFO - Epoch(train) [57][ 300/5047] lr: 2.3085e-05 eta: 4 days, 19:09:08 time: 0.9101 data_time: 0.0019 memory: 55562 loss: 0.1289 loss_ce: 0.1289 2023/02/26 12:56:02 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 12:56:29 - mmengine - INFO - Epoch(train) [57][ 400/5047] lr: 2.3085e-05 eta: 4 days, 19:07:37 time: 0.8907 data_time: 0.0022 memory: 42328 loss: 0.1231 loss_ce: 0.1231 2023/02/26 12:57:54 - mmengine - INFO - Epoch(train) [57][ 500/5047] lr: 2.3085e-05 eta: 4 days, 19:06:06 time: 0.8938 data_time: 0.0028 memory: 50443 loss: 0.1211 loss_ce: 0.1211 2023/02/26 12:59:19 - mmengine - INFO - Epoch(train) [57][ 600/5047] lr: 2.3085e-05 eta: 4 days, 19:04:33 time: 0.8246 data_time: 0.0020 memory: 41828 loss: 0.1268 loss_ce: 0.1268 2023/02/26 13:00:45 - mmengine - INFO - Epoch(train) [57][ 700/5047] lr: 2.3085e-05 eta: 4 days, 19:03:03 time: 0.8897 data_time: 0.0019 memory: 47463 loss: 0.1134 loss_ce: 0.1134 2023/02/26 13:02:11 - mmengine - INFO - Epoch(train) [57][ 800/5047] lr: 2.3085e-05 eta: 4 days, 19:01:33 time: 0.8830 data_time: 0.0025 memory: 47447 loss: 0.1159 loss_ce: 0.1159 2023/02/26 13:03:36 - mmengine - INFO - Epoch(train) [57][ 900/5047] lr: 2.3085e-05 eta: 4 days, 19:00:02 time: 0.8473 data_time: 0.0019 memory: 52791 loss: 0.1123 loss_ce: 0.1123 2023/02/26 13:05:02 - mmengine - INFO - Epoch(train) [57][1000/5047] lr: 2.3085e-05 eta: 4 days, 18:58:32 time: 0.8516 data_time: 0.0022 memory: 43289 loss: 0.1298 loss_ce: 0.1298 2023/02/26 13:06:27 - mmengine - INFO - Epoch(train) [57][1100/5047] lr: 2.3085e-05 eta: 4 days, 18:57:00 time: 0.8451 data_time: 0.0023 memory: 43947 loss: 0.1367 loss_ce: 0.1367 2023/02/26 13:07:53 - mmengine - INFO - Epoch(train) [57][1200/5047] lr: 2.3085e-05 eta: 4 days, 18:55:30 time: 0.8718 data_time: 0.0022 memory: 41630 loss: 0.1166 loss_ce: 0.1166 2023/02/26 13:09:20 - mmengine - INFO - Epoch(train) [57][1300/5047] lr: 2.3085e-05 eta: 4 days, 18:54:01 time: 0.8402 data_time: 0.0021 memory: 42649 loss: 0.1136 loss_ce: 0.1136 2023/02/26 13:10:18 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 13:10:45 - mmengine - INFO - Epoch(train) [57][1400/5047] lr: 2.3085e-05 eta: 4 days, 18:52:30 time: 0.8741 data_time: 0.0019 memory: 41724 loss: 0.1370 loss_ce: 0.1370 2023/02/26 13:12:11 - mmengine - INFO - Epoch(train) [57][1500/5047] lr: 2.3085e-05 eta: 4 days, 18:51:00 time: 0.8696 data_time: 0.0083 memory: 43557 loss: 0.1318 loss_ce: 0.1318 2023/02/26 13:13:36 - mmengine - INFO - Epoch(train) [57][1600/5047] lr: 2.3085e-05 eta: 4 days, 18:49:28 time: 0.8779 data_time: 0.0034 memory: 43289 loss: 0.1100 loss_ce: 0.1100 2023/02/26 13:15:01 - mmengine - INFO - Epoch(train) [57][1700/5047] lr: 2.3085e-05 eta: 4 days, 18:47:57 time: 0.8783 data_time: 0.0028 memory: 55562 loss: 0.1224 loss_ce: 0.1224 2023/02/26 13:16:28 - mmengine - INFO - Epoch(train) [57][1800/5047] lr: 2.3085e-05 eta: 4 days, 18:46:27 time: 0.8333 data_time: 0.0020 memory: 44565 loss: 0.1269 loss_ce: 0.1269 2023/02/26 13:17:53 - mmengine - INFO - Epoch(train) [57][1900/5047] lr: 2.3085e-05 eta: 4 days, 18:44:56 time: 0.8668 data_time: 0.0024 memory: 55562 loss: 0.1266 loss_ce: 0.1266 2023/02/26 13:19:19 - mmengine - INFO - Epoch(train) [57][2000/5047] lr: 2.3085e-05 eta: 4 days, 18:43:26 time: 0.8817 data_time: 0.0020 memory: 44541 loss: 0.1067 loss_ce: 0.1067 2023/02/26 13:20:44 - mmengine - INFO - Epoch(train) [57][2100/5047] lr: 2.3085e-05 eta: 4 days, 18:41:54 time: 0.8414 data_time: 0.0022 memory: 51637 loss: 0.1081 loss_ce: 0.1081 2023/02/26 13:22:10 - mmengine - INFO - Epoch(train) [57][2200/5047] lr: 2.3085e-05 eta: 4 days, 18:40:24 time: 0.9091 data_time: 0.0020 memory: 41444 loss: 0.1287 loss_ce: 0.1287 2023/02/26 13:23:34 - mmengine - INFO - Epoch(train) [57][2300/5047] lr: 2.3085e-05 eta: 4 days, 18:38:51 time: 0.8528 data_time: 0.0020 memory: 45643 loss: 0.1210 loss_ce: 0.1210 2023/02/26 13:24:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 13:25:01 - mmengine - INFO - Epoch(train) [57][2400/5047] lr: 2.3085e-05 eta: 4 days, 18:37:23 time: 0.8338 data_time: 0.0024 memory: 47813 loss: 0.1283 loss_ce: 0.1283 2023/02/26 13:26:27 - mmengine - INFO - Epoch(train) [57][2500/5047] lr: 2.3085e-05 eta: 4 days, 18:35:53 time: 0.8649 data_time: 0.0020 memory: 48187 loss: 0.1288 loss_ce: 0.1288 2023/02/26 13:27:54 - mmengine - INFO - Epoch(train) [57][2600/5047] lr: 2.3085e-05 eta: 4 days, 18:34:25 time: 0.8949 data_time: 0.0019 memory: 43289 loss: 0.1096 loss_ce: 0.1096 2023/02/26 13:29:21 - mmengine - INFO - Epoch(train) [57][2700/5047] lr: 2.3085e-05 eta: 4 days, 18:32:57 time: 0.8530 data_time: 0.0019 memory: 42051 loss: 0.1120 loss_ce: 0.1120 2023/02/26 13:30:49 - mmengine - INFO - Epoch(train) [57][2800/5047] lr: 2.3085e-05 eta: 4 days, 18:31:29 time: 0.8783 data_time: 0.0020 memory: 39960 loss: 0.1203 loss_ce: 0.1203 2023/02/26 13:32:14 - mmengine - INFO - Epoch(train) [57][2900/5047] lr: 2.3085e-05 eta: 4 days, 18:29:58 time: 0.8620 data_time: 0.0022 memory: 43586 loss: 0.1183 loss_ce: 0.1183 2023/02/26 13:33:40 - mmengine - INFO - Epoch(train) [57][3000/5047] lr: 2.3085e-05 eta: 4 days, 18:28:28 time: 0.8402 data_time: 0.0021 memory: 44477 loss: 0.1309 loss_ce: 0.1309 2023/02/26 13:35:05 - mmengine - INFO - Epoch(train) [57][3100/5047] lr: 2.3085e-05 eta: 4 days, 18:26:57 time: 0.8358 data_time: 0.0022 memory: 42649 loss: 0.1136 loss_ce: 0.1136 2023/02/26 13:36:30 - mmengine - INFO - Epoch(train) [57][3200/5047] lr: 2.3085e-05 eta: 4 days, 18:25:25 time: 0.8828 data_time: 0.0020 memory: 39960 loss: 0.1161 loss_ce: 0.1161 2023/02/26 13:37:57 - mmengine - INFO - Epoch(train) [57][3300/5047] lr: 2.3085e-05 eta: 4 days, 18:23:56 time: 0.8443 data_time: 0.0021 memory: 50446 loss: 0.1257 loss_ce: 0.1257 2023/02/26 13:38:56 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 13:39:24 - mmengine - INFO - Epoch(train) [57][3400/5047] lr: 2.3085e-05 eta: 4 days, 18:22:29 time: 0.8864 data_time: 0.0020 memory: 49715 loss: 0.1196 loss_ce: 0.1196 2023/02/26 13:40:51 - mmengine - INFO - Epoch(train) [57][3500/5047] lr: 2.3085e-05 eta: 4 days, 18:20:59 time: 0.7938 data_time: 0.0021 memory: 47074 loss: 0.1322 loss_ce: 0.1322 2023/02/26 13:42:19 - mmengine - INFO - Epoch(train) [57][3600/5047] lr: 2.3085e-05 eta: 4 days, 18:19:34 time: 0.9126 data_time: 0.0021 memory: 42508 loss: 0.1225 loss_ce: 0.1225 2023/02/26 13:43:45 - mmengine - INFO - Epoch(train) [57][3700/5047] lr: 2.3085e-05 eta: 4 days, 18:18:04 time: 0.8749 data_time: 0.0019 memory: 43263 loss: 0.1002 loss_ce: 0.1002 2023/02/26 13:45:11 - mmengine - INFO - Epoch(train) [57][3800/5047] lr: 2.3085e-05 eta: 4 days, 18:16:33 time: 0.8440 data_time: 0.0024 memory: 41832 loss: 0.1203 loss_ce: 0.1203 2023/02/26 13:46:38 - mmengine - INFO - Epoch(train) [57][3900/5047] lr: 2.3085e-05 eta: 4 days, 18:15:05 time: 0.8697 data_time: 0.0031 memory: 45302 loss: 0.1109 loss_ce: 0.1109 2023/02/26 13:48:04 - mmengine - INFO - Epoch(train) [57][4000/5047] lr: 2.3085e-05 eta: 4 days, 18:13:36 time: 0.8407 data_time: 0.0024 memory: 49715 loss: 0.1265 loss_ce: 0.1265 2023/02/26 13:49:30 - mmengine - INFO - Epoch(train) [57][4100/5047] lr: 2.3085e-05 eta: 4 days, 18:12:05 time: 0.8797 data_time: 0.0021 memory: 48210 loss: 0.1179 loss_ce: 0.1179 2023/02/26 13:50:56 - mmengine - INFO - Epoch(train) [57][4200/5047] lr: 2.3085e-05 eta: 4 days, 18:10:35 time: 0.8639 data_time: 0.0021 memory: 49144 loss: 0.1266 loss_ce: 0.1266 2023/02/26 13:52:22 - mmengine - INFO - Epoch(train) [57][4300/5047] lr: 2.3085e-05 eta: 4 days, 18:09:06 time: 0.9156 data_time: 0.0019 memory: 50417 loss: 0.1222 loss_ce: 0.1222 2023/02/26 13:53:20 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 13:53:48 - mmengine - INFO - Epoch(train) [57][4400/5047] lr: 2.3085e-05 eta: 4 days, 18:07:35 time: 0.8365 data_time: 0.0034 memory: 45643 loss: 0.1129 loss_ce: 0.1129 2023/02/26 13:55:15 - mmengine - INFO - Epoch(train) [57][4500/5047] lr: 2.3085e-05 eta: 4 days, 18:06:07 time: 0.8692 data_time: 0.0020 memory: 43613 loss: 0.1298 loss_ce: 0.1298 2023/02/26 13:56:39 - mmengine - INFO - Epoch(train) [57][4600/5047] lr: 2.3085e-05 eta: 4 days, 18:04:34 time: 0.8719 data_time: 0.0024 memory: 52964 loss: 0.1269 loss_ce: 0.1269 2023/02/26 13:58:07 - mmengine - INFO - Epoch(train) [57][4700/5047] lr: 2.3085e-05 eta: 4 days, 18:03:08 time: 0.8785 data_time: 0.0030 memory: 45732 loss: 0.1129 loss_ce: 0.1129 2023/02/26 13:59:33 - mmengine - INFO - Epoch(train) [57][4800/5047] lr: 2.3085e-05 eta: 4 days, 18:01:39 time: 0.8336 data_time: 0.0021 memory: 45027 loss: 0.1184 loss_ce: 0.1184 2023/02/26 14:01:00 - mmengine - INFO - Epoch(train) [57][4900/5047] lr: 2.3085e-05 eta: 4 days, 18:00:09 time: 0.8641 data_time: 0.0019 memory: 44722 loss: 0.1301 loss_ce: 0.1301 2023/02/26 14:02:26 - mmengine - INFO - Epoch(train) [57][5000/5047] lr: 2.3085e-05 eta: 4 days, 17:58:39 time: 0.9091 data_time: 0.0023 memory: 42024 loss: 0.1419 loss_ce: 0.1419 2023/02/26 14:03:07 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 14:03:07 - mmengine - INFO - Saving checkpoint at 57 epochs 2023/02/26 14:04:40 - mmengine - INFO - Epoch(train) [58][ 100/5047] lr: 2.2884e-05 eta: 4 days, 17:56:30 time: 0.8682 data_time: 0.0077 memory: 44278 loss: 0.1301 loss_ce: 0.1301 2023/02/26 14:06:05 - mmengine - INFO - Epoch(train) [58][ 200/5047] lr: 2.2884e-05 eta: 4 days, 17:54:59 time: 0.8158 data_time: 0.0022 memory: 44237 loss: 0.1268 loss_ce: 0.1268 2023/02/26 14:07:32 - mmengine - INFO - Epoch(train) [58][ 300/5047] lr: 2.2884e-05 eta: 4 days, 17:53:31 time: 0.8982 data_time: 0.0026 memory: 50164 loss: 0.1148 loss_ce: 0.1148 2023/02/26 14:07:50 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 14:08:58 - mmengine - INFO - Epoch(train) [58][ 400/5047] lr: 2.2884e-05 eta: 4 days, 17:52:01 time: 0.8096 data_time: 0.0021 memory: 43289 loss: 0.1104 loss_ce: 0.1104 2023/02/26 14:10:23 - mmengine - INFO - Epoch(train) [58][ 500/5047] lr: 2.2884e-05 eta: 4 days, 17:50:29 time: 0.8680 data_time: 0.0020 memory: 46713 loss: 0.1193 loss_ce: 0.1193 2023/02/26 14:11:48 - mmengine - INFO - Epoch(train) [58][ 600/5047] lr: 2.2884e-05 eta: 4 days, 17:48:58 time: 0.8179 data_time: 0.0020 memory: 42649 loss: 0.1161 loss_ce: 0.1161 2023/02/26 14:13:14 - mmengine - INFO - Epoch(train) [58][ 700/5047] lr: 2.2884e-05 eta: 4 days, 17:47:28 time: 0.8368 data_time: 0.0028 memory: 43613 loss: 0.1149 loss_ce: 0.1149 2023/02/26 14:14:41 - mmengine - INFO - Epoch(train) [58][ 800/5047] lr: 2.2884e-05 eta: 4 days, 17:45:59 time: 0.8373 data_time: 0.0022 memory: 46070 loss: 0.1522 loss_ce: 0.1522 2023/02/26 14:16:06 - mmengine - INFO - Epoch(train) [58][ 900/5047] lr: 2.2884e-05 eta: 4 days, 17:44:27 time: 0.9124 data_time: 0.0020 memory: 48188 loss: 0.1188 loss_ce: 0.1188 2023/02/26 14:17:31 - mmengine - INFO - Epoch(train) [58][1000/5047] lr: 2.2884e-05 eta: 4 days, 17:42:56 time: 0.8356 data_time: 0.0021 memory: 52705 loss: 0.1357 loss_ce: 0.1357 2023/02/26 14:18:58 - mmengine - INFO - Epoch(train) [58][1100/5047] lr: 2.2884e-05 eta: 4 days, 17:41:27 time: 0.8491 data_time: 0.0023 memory: 43613 loss: 0.1192 loss_ce: 0.1192 2023/02/26 14:20:24 - mmengine - INFO - Epoch(train) [58][1200/5047] lr: 2.2884e-05 eta: 4 days, 17:39:58 time: 0.8268 data_time: 0.0019 memory: 49915 loss: 0.1339 loss_ce: 0.1339 2023/02/26 14:21:50 - mmengine - INFO - Epoch(train) [58][1300/5047] lr: 2.2884e-05 eta: 4 days, 17:38:28 time: 0.8442 data_time: 0.0024 memory: 39509 loss: 0.1164 loss_ce: 0.1164 2023/02/26 14:22:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 14:23:17 - mmengine - INFO - Epoch(train) [58][1400/5047] lr: 2.2884e-05 eta: 4 days, 17:36:59 time: 0.8301 data_time: 0.0022 memory: 48133 loss: 0.1133 loss_ce: 0.1133 2023/02/26 14:24:44 - mmengine - INFO - Epoch(train) [58][1500/5047] lr: 2.2884e-05 eta: 4 days, 17:35:32 time: 0.8529 data_time: 0.0024 memory: 44956 loss: 0.1177 loss_ce: 0.1177 2023/02/26 14:26:12 - mmengine - INFO - Epoch(train) [58][1600/5047] lr: 2.2884e-05 eta: 4 days, 17:34:04 time: 0.8592 data_time: 0.0021 memory: 55562 loss: 0.1371 loss_ce: 0.1371 2023/02/26 14:27:37 - mmengine - INFO - Epoch(train) [58][1700/5047] lr: 2.2884e-05 eta: 4 days, 17:32:33 time: 0.8594 data_time: 0.0021 memory: 44278 loss: 0.1263 loss_ce: 0.1263 2023/02/26 14:29:02 - mmengine - INFO - Epoch(train) [58][1800/5047] lr: 2.2884e-05 eta: 4 days, 17:31:02 time: 0.8244 data_time: 0.0020 memory: 50097 loss: 0.1302 loss_ce: 0.1302 2023/02/26 14:30:30 - mmengine - INFO - Epoch(train) [58][1900/5047] lr: 2.2884e-05 eta: 4 days, 17:29:35 time: 0.8732 data_time: 0.0024 memory: 42649 loss: 0.1210 loss_ce: 0.1210 2023/02/26 14:31:56 - mmengine - INFO - Epoch(train) [58][2000/5047] lr: 2.2884e-05 eta: 4 days, 17:28:05 time: 0.9000 data_time: 0.0026 memory: 44345 loss: 0.1198 loss_ce: 0.1198 2023/02/26 14:33:21 - mmengine - INFO - Epoch(train) [58][2100/5047] lr: 2.2884e-05 eta: 4 days, 17:26:35 time: 0.8359 data_time: 0.0023 memory: 42649 loss: 0.1100 loss_ce: 0.1100 2023/02/26 14:34:49 - mmengine - INFO - Epoch(train) [58][2200/5047] lr: 2.2884e-05 eta: 4 days, 17:25:07 time: 0.8484 data_time: 0.0023 memory: 44617 loss: 0.1289 loss_ce: 0.1289 2023/02/26 14:36:15 - mmengine - INFO - Epoch(train) [58][2300/5047] lr: 2.2884e-05 eta: 4 days, 17:23:37 time: 0.8128 data_time: 0.0025 memory: 44906 loss: 0.1325 loss_ce: 0.1325 2023/02/26 14:36:32 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 14:37:41 - mmengine - INFO - Epoch(train) [58][2400/5047] lr: 2.2884e-05 eta: 4 days, 17:22:07 time: 0.9557 data_time: 0.0025 memory: 49184 loss: 0.1194 loss_ce: 0.1194 2023/02/26 14:39:06 - mmengine - INFO - Epoch(train) [58][2500/5047] lr: 2.2884e-05 eta: 4 days, 17:20:37 time: 0.8158 data_time: 0.0021 memory: 44956 loss: 0.1262 loss_ce: 0.1262 2023/02/26 14:40:30 - mmengine - INFO - Epoch(train) [58][2600/5047] lr: 2.2884e-05 eta: 4 days, 17:19:03 time: 0.8466 data_time: 0.0021 memory: 50906 loss: 0.1261 loss_ce: 0.1261 2023/02/26 14:41:55 - mmengine - INFO - Epoch(train) [58][2700/5047] lr: 2.2884e-05 eta: 4 days, 17:17:32 time: 0.8607 data_time: 0.0019 memory: 51308 loss: 0.1355 loss_ce: 0.1355 2023/02/26 14:43:22 - mmengine - INFO - Epoch(train) [58][2800/5047] lr: 2.2884e-05 eta: 4 days, 17:16:03 time: 0.8540 data_time: 0.0024 memory: 43289 loss: 0.1114 loss_ce: 0.1114 2023/02/26 14:44:49 - mmengine - INFO - Epoch(train) [58][2900/5047] lr: 2.2884e-05 eta: 4 days, 17:14:35 time: 0.8515 data_time: 0.0023 memory: 48070 loss: 0.1215 loss_ce: 0.1215 2023/02/26 14:46:14 - mmengine - INFO - Epoch(train) [58][3000/5047] lr: 2.2884e-05 eta: 4 days, 17:13:04 time: 0.8675 data_time: 0.0038 memory: 50752 loss: 0.1178 loss_ce: 0.1178 2023/02/26 14:47:41 - mmengine - INFO - Epoch(train) [58][3100/5047] lr: 2.2884e-05 eta: 4 days, 17:11:35 time: 0.9365 data_time: 0.0027 memory: 41514 loss: 0.1184 loss_ce: 0.1184 2023/02/26 14:49:06 - mmengine - INFO - Epoch(train) [58][3200/5047] lr: 2.2884e-05 eta: 4 days, 17:10:04 time: 0.8397 data_time: 0.0019 memory: 50490 loss: 0.1216 loss_ce: 0.1216 2023/02/26 14:50:31 - mmengine - INFO - Epoch(train) [58][3300/5047] lr: 2.2884e-05 eta: 4 days, 17:08:34 time: 0.8017 data_time: 0.0023 memory: 41729 loss: 0.1313 loss_ce: 0.1313 2023/02/26 14:50:49 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 14:51:56 - mmengine - INFO - Epoch(train) [58][3400/5047] lr: 2.2884e-05 eta: 4 days, 17:07:02 time: 0.8801 data_time: 0.0020 memory: 44570 loss: 0.1315 loss_ce: 0.1315 2023/02/26 14:53:23 - mmengine - INFO - Epoch(train) [58][3500/5047] lr: 2.2884e-05 eta: 4 days, 17:05:32 time: 0.8436 data_time: 0.0024 memory: 41446 loss: 0.1247 loss_ce: 0.1247 2023/02/26 14:54:48 - mmengine - INFO - Epoch(train) [58][3600/5047] lr: 2.2884e-05 eta: 4 days, 17:04:02 time: 0.8383 data_time: 0.0018 memory: 42024 loss: 0.1175 loss_ce: 0.1175 2023/02/26 14:56:14 - mmengine - INFO - Epoch(train) [58][3700/5047] lr: 2.2884e-05 eta: 4 days, 17:02:32 time: 0.8544 data_time: 0.0028 memory: 40224 loss: 0.1290 loss_ce: 0.1290 2023/02/26 14:57:38 - mmengine - INFO - Epoch(train) [58][3800/5047] lr: 2.2884e-05 eta: 4 days, 17:00:59 time: 0.8331 data_time: 0.0020 memory: 39625 loss: 0.1245 loss_ce: 0.1245 2023/02/26 14:59:04 - mmengine - INFO - Epoch(train) [58][3900/5047] lr: 2.2884e-05 eta: 4 days, 16:59:28 time: 0.8413 data_time: 0.0021 memory: 44956 loss: 0.1331 loss_ce: 0.1331 2023/02/26 15:00:30 - mmengine - INFO - Epoch(train) [58][4000/5047] lr: 2.2884e-05 eta: 4 days, 16:57:59 time: 0.8412 data_time: 0.0025 memory: 42336 loss: 0.1404 loss_ce: 0.1404 2023/02/26 15:01:56 - mmengine - INFO - Epoch(train) [58][4100/5047] lr: 2.2884e-05 eta: 4 days, 16:56:30 time: 0.8627 data_time: 0.0020 memory: 44275 loss: 0.1208 loss_ce: 0.1208 2023/02/26 15:03:23 - mmengine - INFO - Epoch(train) [58][4200/5047] lr: 2.2884e-05 eta: 4 days, 16:55:01 time: 0.8454 data_time: 0.0019 memory: 44025 loss: 0.1209 loss_ce: 0.1209 2023/02/26 15:04:49 - mmengine - INFO - Epoch(train) [58][4300/5047] lr: 2.2884e-05 eta: 4 days, 16:53:32 time: 0.8458 data_time: 0.0020 memory: 51600 loss: 0.1172 loss_ce: 0.1172 2023/02/26 15:05:07 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 15:06:15 - mmengine - INFO - Epoch(train) [58][4400/5047] lr: 2.2884e-05 eta: 4 days, 16:52:02 time: 0.8670 data_time: 0.0019 memory: 50505 loss: 0.1170 loss_ce: 0.1170 2023/02/26 15:07:41 - mmengine - INFO - Epoch(train) [58][4500/5047] lr: 2.2884e-05 eta: 4 days, 16:50:32 time: 0.8864 data_time: 0.0020 memory: 46355 loss: 0.1263 loss_ce: 0.1263 2023/02/26 15:09:07 - mmengine - INFO - Epoch(train) [58][4600/5047] lr: 2.2884e-05 eta: 4 days, 16:49:03 time: 0.8635 data_time: 0.0033 memory: 44617 loss: 0.1252 loss_ce: 0.1252 2023/02/26 15:10:34 - mmengine - INFO - Epoch(train) [58][4700/5047] lr: 2.2884e-05 eta: 4 days, 16:47:34 time: 0.8940 data_time: 0.0047 memory: 47963 loss: 0.1045 loss_ce: 0.1045 2023/02/26 15:12:02 - mmengine - INFO - Epoch(train) [58][4800/5047] lr: 2.2884e-05 eta: 4 days, 16:46:07 time: 0.8690 data_time: 0.0025 memory: 43776 loss: 0.1202 loss_ce: 0.1202 2023/02/26 15:13:28 - mmengine - INFO - Epoch(train) [58][4900/5047] lr: 2.2884e-05 eta: 4 days, 16:44:37 time: 0.8523 data_time: 0.0029 memory: 41479 loss: 0.1435 loss_ce: 0.1435 2023/02/26 15:14:55 - mmengine - INFO - Epoch(train) [58][5000/5047] lr: 2.2884e-05 eta: 4 days, 16:43:09 time: 0.8777 data_time: 0.0058 memory: 50443 loss: 0.1049 loss_ce: 0.1049 2023/02/26 15:15:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 15:15:34 - mmengine - INFO - Saving checkpoint at 58 epochs 2023/02/26 15:17:08 - mmengine - INFO - Epoch(train) [59][ 100/5047] lr: 2.2683e-05 eta: 4 days, 16:40:57 time: 0.8812 data_time: 0.0022 memory: 49147 loss: 0.1184 loss_ce: 0.1184 2023/02/26 15:18:32 - mmengine - INFO - Epoch(train) [59][ 200/5047] lr: 2.2683e-05 eta: 4 days, 16:39:25 time: 0.8763 data_time: 0.0035 memory: 40627 loss: 0.1262 loss_ce: 0.1262 2023/02/26 15:19:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 15:19:58 - mmengine - INFO - Epoch(train) [59][ 300/5047] lr: 2.2683e-05 eta: 4 days, 16:37:54 time: 0.8454 data_time: 0.0021 memory: 43947 loss: 0.1363 loss_ce: 0.1363 2023/02/26 15:21:24 - mmengine - INFO - Epoch(train) [59][ 400/5047] lr: 2.2683e-05 eta: 4 days, 16:36:25 time: 0.8803 data_time: 0.0025 memory: 55327 loss: 0.1170 loss_ce: 0.1170 2023/02/26 15:22:48 - mmengine - INFO - Epoch(train) [59][ 500/5047] lr: 2.2683e-05 eta: 4 days, 16:34:52 time: 0.8610 data_time: 0.0021 memory: 42329 loss: 0.1153 loss_ce: 0.1153 2023/02/26 15:24:15 - mmengine - INFO - Epoch(train) [59][ 600/5047] lr: 2.2683e-05 eta: 4 days, 16:33:24 time: 0.8785 data_time: 0.0019 memory: 44498 loss: 0.1048 loss_ce: 0.1048 2023/02/26 15:25:40 - mmengine - INFO - Epoch(train) [59][ 700/5047] lr: 2.2683e-05 eta: 4 days, 16:31:52 time: 0.8577 data_time: 0.0030 memory: 41190 loss: 0.1309 loss_ce: 0.1309 2023/02/26 15:27:06 - mmengine - INFO - Epoch(train) [59][ 800/5047] lr: 2.2683e-05 eta: 4 days, 16:30:22 time: 0.8479 data_time: 0.0021 memory: 44781 loss: 0.1188 loss_ce: 0.1188 2023/02/26 15:28:30 - mmengine - INFO - Epoch(train) [59][ 900/5047] lr: 2.2683e-05 eta: 4 days, 16:28:50 time: 0.8346 data_time: 0.0020 memory: 48006 loss: 0.1356 loss_ce: 0.1356 2023/02/26 15:29:55 - mmengine - INFO - Epoch(train) [59][1000/5047] lr: 2.2683e-05 eta: 4 days, 16:27:19 time: 0.8345 data_time: 0.0057 memory: 42024 loss: 0.1146 loss_ce: 0.1146 2023/02/26 15:31:23 - mmengine - INFO - Epoch(train) [59][1100/5047] lr: 2.2683e-05 eta: 4 days, 16:25:51 time: 0.8630 data_time: 0.0051 memory: 40825 loss: 0.1218 loss_ce: 0.1218 2023/02/26 15:32:49 - mmengine - INFO - Epoch(train) [59][1200/5047] lr: 2.2683e-05 eta: 4 days, 16:24:22 time: 0.8723 data_time: 0.0021 memory: 43091 loss: 0.1146 loss_ce: 0.1146 2023/02/26 15:33:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 15:34:16 - mmengine - INFO - Epoch(train) [59][1300/5047] lr: 2.2683e-05 eta: 4 days, 16:22:54 time: 0.8480 data_time: 0.0020 memory: 42880 loss: 0.1260 loss_ce: 0.1260 2023/02/26 15:35:41 - mmengine - INFO - Epoch(train) [59][1400/5047] lr: 2.2683e-05 eta: 4 days, 16:21:23 time: 0.8106 data_time: 0.0028 memory: 42898 loss: 0.1226 loss_ce: 0.1226 2023/02/26 15:37:08 - mmengine - INFO - Epoch(train) [59][1500/5047] lr: 2.2683e-05 eta: 4 days, 16:19:54 time: 0.8823 data_time: 0.0019 memory: 55562 loss: 0.1164 loss_ce: 0.1164 2023/02/26 15:38:33 - mmengine - INFO - Epoch(train) [59][1600/5047] lr: 2.2683e-05 eta: 4 days, 16:18:23 time: 0.8613 data_time: 0.0055 memory: 53044 loss: 0.1219 loss_ce: 0.1219 2023/02/26 15:39:57 - mmengine - INFO - Epoch(train) [59][1700/5047] lr: 2.2683e-05 eta: 4 days, 16:16:50 time: 0.8471 data_time: 0.0019 memory: 42024 loss: 0.1180 loss_ce: 0.1180 2023/02/26 15:41:23 - mmengine - INFO - Epoch(train) [59][1800/5047] lr: 2.2683e-05 eta: 4 days, 16:15:21 time: 0.8597 data_time: 0.0021 memory: 43068 loss: 0.1395 loss_ce: 0.1395 2023/02/26 15:42:51 - mmengine - INFO - Epoch(train) [59][1900/5047] lr: 2.2683e-05 eta: 4 days, 16:13:54 time: 0.8393 data_time: 0.0022 memory: 42649 loss: 0.1210 loss_ce: 0.1210 2023/02/26 15:44:18 - mmengine - INFO - Epoch(train) [59][2000/5047] lr: 2.2683e-05 eta: 4 days, 16:12:26 time: 0.8436 data_time: 0.0020 memory: 42557 loss: 0.1281 loss_ce: 0.1281 2023/02/26 15:45:44 - mmengine - INFO - Epoch(train) [59][2100/5047] lr: 2.2683e-05 eta: 4 days, 16:10:56 time: 0.8540 data_time: 0.0033 memory: 41696 loss: 0.1154 loss_ce: 0.1154 2023/02/26 15:47:10 - mmengine - INFO - Epoch(train) [59][2200/5047] lr: 2.2683e-05 eta: 4 days, 16:09:27 time: 0.8588 data_time: 0.0020 memory: 42336 loss: 0.1108 loss_ce: 0.1108 2023/02/26 15:48:15 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 15:48:37 - mmengine - INFO - Epoch(train) [59][2300/5047] lr: 2.2683e-05 eta: 4 days, 16:07:58 time: 0.8657 data_time: 0.0031 memory: 40825 loss: 0.1301 loss_ce: 0.1301 2023/02/26 15:50:04 - mmengine - INFO - Epoch(train) [59][2400/5047] lr: 2.2683e-05 eta: 4 days, 16:06:31 time: 0.8907 data_time: 0.0020 memory: 41122 loss: 0.1230 loss_ce: 0.1230 2023/02/26 15:51:31 - mmengine - INFO - Epoch(train) [59][2500/5047] lr: 2.2683e-05 eta: 4 days, 16:05:02 time: 0.8310 data_time: 0.0023 memory: 43947 loss: 0.1245 loss_ce: 0.1245 2023/02/26 15:52:57 - mmengine - INFO - Epoch(train) [59][2600/5047] lr: 2.2683e-05 eta: 4 days, 16:03:33 time: 0.9061 data_time: 0.0057 memory: 52127 loss: 0.1281 loss_ce: 0.1281 2023/02/26 15:54:22 - mmengine - INFO - Epoch(train) [59][2700/5047] lr: 2.2683e-05 eta: 4 days, 16:02:02 time: 0.9108 data_time: 0.0029 memory: 51483 loss: 0.1130 loss_ce: 0.1130 2023/02/26 15:55:49 - mmengine - INFO - Epoch(train) [59][2800/5047] lr: 2.2683e-05 eta: 4 days, 16:00:33 time: 0.8939 data_time: 0.0025 memory: 47737 loss: 0.1290 loss_ce: 0.1290 2023/02/26 15:57:16 - mmengine - INFO - Epoch(train) [59][2900/5047] lr: 2.2683e-05 eta: 4 days, 15:59:06 time: 0.8631 data_time: 0.0045 memory: 49144 loss: 0.1132 loss_ce: 0.1132 2023/02/26 15:58:42 - mmengine - INFO - Epoch(train) [59][3000/5047] lr: 2.2683e-05 eta: 4 days, 15:57:35 time: 0.8722 data_time: 0.0021 memory: 53044 loss: 0.1156 loss_ce: 0.1156 2023/02/26 16:00:07 - mmengine - INFO - Epoch(train) [59][3100/5047] lr: 2.2683e-05 eta: 4 days, 15:56:04 time: 0.8774 data_time: 0.0033 memory: 47813 loss: 0.1135 loss_ce: 0.1135 2023/02/26 16:01:32 - mmengine - INFO - Epoch(train) [59][3200/5047] lr: 2.2683e-05 eta: 4 days, 15:54:33 time: 0.8495 data_time: 0.0021 memory: 43403 loss: 0.1195 loss_ce: 0.1195 2023/02/26 16:02:35 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 16:02:58 - mmengine - INFO - Epoch(train) [59][3300/5047] lr: 2.2683e-05 eta: 4 days, 15:53:03 time: 0.8424 data_time: 0.0019 memory: 49072 loss: 0.1259 loss_ce: 0.1259 2023/02/26 16:04:24 - mmengine - INFO - Epoch(train) [59][3400/5047] lr: 2.2683e-05 eta: 4 days, 15:51:33 time: 0.8683 data_time: 0.0019 memory: 44278 loss: 0.1131 loss_ce: 0.1131 2023/02/26 16:05:49 - mmengine - INFO - Epoch(train) [59][3500/5047] lr: 2.2683e-05 eta: 4 days, 15:50:02 time: 0.8339 data_time: 0.0020 memory: 43433 loss: 0.1104 loss_ce: 0.1104 2023/02/26 16:07:15 - mmengine - INFO - Epoch(train) [59][3600/5047] lr: 2.2683e-05 eta: 4 days, 15:48:32 time: 0.8385 data_time: 0.0021 memory: 51640 loss: 0.1239 loss_ce: 0.1239 2023/02/26 16:08:41 - mmengine - INFO - Epoch(train) [59][3700/5047] lr: 2.2683e-05 eta: 4 days, 15:47:02 time: 0.8976 data_time: 0.0019 memory: 43511 loss: 0.1138 loss_ce: 0.1138 2023/02/26 16:10:07 - mmengine - INFO - Epoch(train) [59][3800/5047] lr: 2.2683e-05 eta: 4 days, 15:45:33 time: 0.8585 data_time: 0.0026 memory: 45643 loss: 0.1269 loss_ce: 0.1269 2023/02/26 16:11:33 - mmengine - INFO - Epoch(train) [59][3900/5047] lr: 2.2683e-05 eta: 4 days, 15:44:04 time: 0.8540 data_time: 0.0021 memory: 38341 loss: 0.1166 loss_ce: 0.1166 2023/02/26 16:12:59 - mmengine - INFO - Epoch(train) [59][4000/5047] lr: 2.2683e-05 eta: 4 days, 15:42:34 time: 0.8745 data_time: 0.0023 memory: 44482 loss: 0.1256 loss_ce: 0.1256 2023/02/26 16:14:25 - mmengine - INFO - Epoch(train) [59][4100/5047] lr: 2.2683e-05 eta: 4 days, 15:41:05 time: 0.8640 data_time: 0.0019 memory: 43289 loss: 0.1195 loss_ce: 0.1195 2023/02/26 16:15:52 - mmengine - INFO - Epoch(train) [59][4200/5047] lr: 2.2683e-05 eta: 4 days, 15:39:35 time: 0.8978 data_time: 0.0021 memory: 48053 loss: 0.1238 loss_ce: 0.1238 2023/02/26 16:16:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 16:17:17 - mmengine - INFO - Epoch(train) [59][4300/5047] lr: 2.2683e-05 eta: 4 days, 15:38:05 time: 0.8480 data_time: 0.0082 memory: 41757 loss: 0.1374 loss_ce: 0.1374 2023/02/26 16:18:45 - mmengine - INFO - Epoch(train) [59][4400/5047] lr: 2.2683e-05 eta: 4 days, 15:36:38 time: 0.8507 data_time: 0.0020 memory: 42707 loss: 0.1092 loss_ce: 0.1092 2023/02/26 16:20:10 - mmengine - INFO - Epoch(train) [59][4500/5047] lr: 2.2683e-05 eta: 4 days, 15:35:08 time: 0.8726 data_time: 0.0052 memory: 43613 loss: 0.1227 loss_ce: 0.1227 2023/02/26 16:21:37 - mmengine - INFO - Epoch(train) [59][4600/5047] lr: 2.2683e-05 eta: 4 days, 15:33:38 time: 0.8496 data_time: 0.0024 memory: 54072 loss: 0.1211 loss_ce: 0.1211 2023/02/26 16:23:02 - mmengine - INFO - Epoch(train) [59][4700/5047] lr: 2.2683e-05 eta: 4 days, 15:32:08 time: 0.8428 data_time: 0.0020 memory: 51586 loss: 0.1212 loss_ce: 0.1212 2023/02/26 16:24:27 - mmengine - INFO - Epoch(train) [59][4800/5047] lr: 2.2683e-05 eta: 4 days, 15:30:37 time: 0.7958 data_time: 0.0019 memory: 51792 loss: 0.1193 loss_ce: 0.1193 2023/02/26 16:25:54 - mmengine - INFO - Epoch(train) [59][4900/5047] lr: 2.2683e-05 eta: 4 days, 15:29:08 time: 0.8988 data_time: 0.0020 memory: 49715 loss: 0.1106 loss_ce: 0.1106 2023/02/26 16:27:20 - mmengine - INFO - Epoch(train) [59][5000/5047] lr: 2.2683e-05 eta: 4 days, 15:27:39 time: 0.8401 data_time: 0.0020 memory: 44278 loss: 0.1264 loss_ce: 0.1264 2023/02/26 16:28:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 16:28:00 - mmengine - INFO - Saving checkpoint at 59 epochs 2023/02/26 16:29:34 - mmengine - INFO - Epoch(train) [60][ 100/5047] lr: 2.2482e-05 eta: 4 days, 15:25:30 time: 0.8972 data_time: 0.0023 memory: 48464 loss: 0.1081 loss_ce: 0.1081 2023/02/26 16:31:00 - mmengine - INFO - Epoch(train) [60][ 200/5047] lr: 2.2482e-05 eta: 4 days, 15:24:01 time: 0.8683 data_time: 0.0020 memory: 55562 loss: 0.1300 loss_ce: 0.1300 2023/02/26 16:31:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 16:32:25 - mmengine - INFO - Epoch(train) [60][ 300/5047] lr: 2.2482e-05 eta: 4 days, 15:22:29 time: 0.8572 data_time: 0.0040 memory: 44617 loss: 0.1143 loss_ce: 0.1143 2023/02/26 16:33:51 - mmengine - INFO - Epoch(train) [60][ 400/5047] lr: 2.2482e-05 eta: 4 days, 15:20:59 time: 0.8490 data_time: 0.0021 memory: 44610 loss: 0.1269 loss_ce: 0.1269 2023/02/26 16:35:17 - mmengine - INFO - Epoch(train) [60][ 500/5047] lr: 2.2482e-05 eta: 4 days, 15:19:30 time: 0.8324 data_time: 0.0034 memory: 42898 loss: 0.1295 loss_ce: 0.1295 2023/02/26 16:36:41 - mmengine - INFO - Epoch(train) [60][ 600/5047] lr: 2.2482e-05 eta: 4 days, 15:17:57 time: 0.8627 data_time: 0.0019 memory: 42006 loss: 0.1245 loss_ce: 0.1245 2023/02/26 16:38:06 - mmengine - INFO - Epoch(train) [60][ 700/5047] lr: 2.2482e-05 eta: 4 days, 15:16:26 time: 0.8426 data_time: 0.0020 memory: 50349 loss: 0.1099 loss_ce: 0.1099 2023/02/26 16:39:32 - mmengine - INFO - Epoch(train) [60][ 800/5047] lr: 2.2482e-05 eta: 4 days, 15:14:56 time: 0.8256 data_time: 0.0024 memory: 42649 loss: 0.1233 loss_ce: 0.1233 2023/02/26 16:40:59 - mmengine - INFO - Epoch(train) [60][ 900/5047] lr: 2.2482e-05 eta: 4 days, 15:13:28 time: 0.8904 data_time: 0.0021 memory: 40825 loss: 0.1090 loss_ce: 0.1090 2023/02/26 16:42:24 - mmengine - INFO - Epoch(train) [60][1000/5047] lr: 2.2482e-05 eta: 4 days, 15:11:57 time: 0.8192 data_time: 0.0021 memory: 45850 loss: 0.1188 loss_ce: 0.1188 2023/02/26 16:43:52 - mmengine - INFO - Epoch(train) [60][1100/5047] lr: 2.2482e-05 eta: 4 days, 15:10:30 time: 0.8552 data_time: 0.0020 memory: 55562 loss: 0.1111 loss_ce: 0.1111 2023/02/26 16:45:18 - mmengine - INFO - Epoch(train) [60][1200/5047] lr: 2.2482e-05 eta: 4 days, 15:09:00 time: 0.8729 data_time: 0.0035 memory: 46713 loss: 0.1062 loss_ce: 0.1062 2023/02/26 16:45:41 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 16:46:44 - mmengine - INFO - Epoch(train) [60][1300/5047] lr: 2.2482e-05 eta: 4 days, 15:07:32 time: 0.8330 data_time: 0.0053 memory: 55562 loss: 0.1120 loss_ce: 0.1120 2023/02/26 16:48:10 - mmengine - INFO - Epoch(train) [60][1400/5047] lr: 2.2482e-05 eta: 4 days, 15:06:01 time: 0.8281 data_time: 0.0020 memory: 43559 loss: 0.1165 loss_ce: 0.1165 2023/02/26 16:49:37 - mmengine - INFO - Epoch(train) [60][1500/5047] lr: 2.2482e-05 eta: 4 days, 15:04:34 time: 0.8655 data_time: 0.0020 memory: 42336 loss: 0.1125 loss_ce: 0.1125 2023/02/26 16:51:01 - mmengine - INFO - Epoch(train) [60][1600/5047] lr: 2.2482e-05 eta: 4 days, 15:03:00 time: 0.8163 data_time: 0.0021 memory: 41961 loss: 0.1100 loss_ce: 0.1100 2023/02/26 16:52:25 - mmengine - INFO - Epoch(train) [60][1700/5047] lr: 2.2482e-05 eta: 4 days, 15:01:28 time: 0.8150 data_time: 0.0022 memory: 40352 loss: 0.1283 loss_ce: 0.1283 2023/02/26 16:53:52 - mmengine - INFO - Epoch(train) [60][1800/5047] lr: 2.2482e-05 eta: 4 days, 14:59:59 time: 0.8295 data_time: 0.0019 memory: 50589 loss: 0.1254 loss_ce: 0.1254 2023/02/26 16:55:18 - mmengine - INFO - Epoch(train) [60][1900/5047] lr: 2.2482e-05 eta: 4 days, 14:58:30 time: 0.8977 data_time: 0.0020 memory: 48021 loss: 0.1191 loss_ce: 0.1191 2023/02/26 16:56:45 - mmengine - INFO - Epoch(train) [60][2000/5047] lr: 2.2482e-05 eta: 4 days, 14:57:02 time: 0.8587 data_time: 0.0019 memory: 43287 loss: 0.1253 loss_ce: 0.1253 2023/02/26 16:58:11 - mmengine - INFO - Epoch(train) [60][2100/5047] lr: 2.2482e-05 eta: 4 days, 14:55:33 time: 0.9118 data_time: 0.0020 memory: 39960 loss: 0.1227 loss_ce: 0.1227 2023/02/26 16:59:36 - mmengine - INFO - Epoch(train) [60][2200/5047] lr: 2.2482e-05 eta: 4 days, 14:54:02 time: 0.8591 data_time: 0.0025 memory: 41724 loss: 0.1230 loss_ce: 0.1230 2023/02/26 17:00:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 17:01:05 - mmengine - INFO - Epoch(train) [60][2300/5047] lr: 2.2482e-05 eta: 4 days, 14:52:36 time: 0.8767 data_time: 0.0019 memory: 48055 loss: 0.1149 loss_ce: 0.1149 2023/02/26 17:02:29 - mmengine - INFO - Epoch(train) [60][2400/5047] lr: 2.2482e-05 eta: 4 days, 14:51:05 time: 0.8390 data_time: 0.0033 memory: 41419 loss: 0.1194 loss_ce: 0.1194 2023/02/26 17:03:58 - mmengine - INFO - Epoch(train) [60][2500/5047] lr: 2.2482e-05 eta: 4 days, 14:49:39 time: 0.8963 data_time: 0.0021 memory: 44409 loss: 0.1100 loss_ce: 0.1100 2023/02/26 17:05:24 - mmengine - INFO - Epoch(train) [60][2600/5047] lr: 2.2482e-05 eta: 4 days, 14:48:10 time: 0.8707 data_time: 0.0038 memory: 47074 loss: 0.1210 loss_ce: 0.1210 2023/02/26 17:06:50 - mmengine - INFO - Epoch(train) [60][2700/5047] lr: 2.2482e-05 eta: 4 days, 14:46:40 time: 0.8115 data_time: 0.0024 memory: 44278 loss: 0.1171 loss_ce: 0.1171 2023/02/26 17:08:15 - mmengine - INFO - Epoch(train) [60][2800/5047] lr: 2.2482e-05 eta: 4 days, 14:45:08 time: 0.8278 data_time: 0.0023 memory: 43115 loss: 0.1138 loss_ce: 0.1138 2023/02/26 17:09:40 - mmengine - INFO - Epoch(train) [60][2900/5047] lr: 2.2482e-05 eta: 4 days, 14:43:38 time: 0.8080 data_time: 0.0023 memory: 43947 loss: 0.1241 loss_ce: 0.1241 2023/02/26 17:11:05 - mmengine - INFO - Epoch(train) [60][3000/5047] lr: 2.2482e-05 eta: 4 days, 14:42:07 time: 0.8733 data_time: 0.0041 memory: 47074 loss: 0.1248 loss_ce: 0.1248 2023/02/26 17:12:30 - mmengine - INFO - Epoch(train) [60][3100/5047] lr: 2.2482e-05 eta: 4 days, 14:40:35 time: 0.8273 data_time: 0.0021 memory: 44502 loss: 0.1088 loss_ce: 0.1088 2023/02/26 17:13:55 - mmengine - INFO - Epoch(train) [60][3200/5047] lr: 2.2482e-05 eta: 4 days, 14:39:05 time: 0.9058 data_time: 0.0020 memory: 43287 loss: 0.1245 loss_ce: 0.1245 2023/02/26 17:14:19 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 17:15:23 - mmengine - INFO - Epoch(train) [60][3300/5047] lr: 2.2482e-05 eta: 4 days, 14:37:38 time: 0.8277 data_time: 0.0019 memory: 55562 loss: 0.1205 loss_ce: 0.1205 2023/02/26 17:16:49 - mmengine - INFO - Epoch(train) [60][3400/5047] lr: 2.2482e-05 eta: 4 days, 14:36:08 time: 0.9045 data_time: 0.0020 memory: 40717 loss: 0.1142 loss_ce: 0.1142 2023/02/26 17:18:14 - mmengine - INFO - Epoch(train) [60][3500/5047] lr: 2.2482e-05 eta: 4 days, 14:34:37 time: 0.8547 data_time: 0.0059 memory: 48188 loss: 0.1406 loss_ce: 0.1406 2023/02/26 17:19:39 - mmengine - INFO - Epoch(train) [60][3600/5047] lr: 2.2482e-05 eta: 4 days, 14:33:07 time: 0.8375 data_time: 0.0019 memory: 42154 loss: 0.1135 loss_ce: 0.1135 2023/02/26 17:21:07 - mmengine - INFO - Epoch(train) [60][3700/5047] lr: 2.2482e-05 eta: 4 days, 14:31:39 time: 0.8475 data_time: 0.0025 memory: 39681 loss: 0.1319 loss_ce: 0.1319 2023/02/26 17:22:32 - mmengine - INFO - Epoch(train) [60][3800/5047] lr: 2.2482e-05 eta: 4 days, 14:30:08 time: 0.8721 data_time: 0.0028 memory: 41724 loss: 0.1301 loss_ce: 0.1301 2023/02/26 17:23:58 - mmengine - INFO - Epoch(train) [60][3900/5047] lr: 2.2482e-05 eta: 4 days, 14:28:40 time: 0.8248 data_time: 0.0038 memory: 42649 loss: 0.1221 loss_ce: 0.1221 2023/02/26 17:34:01 - mmengine - INFO - Epoch(train) [60][4000/5047] lr: 2.2482e-05 eta: 4 days, 14:40:50 time: 0.8237 data_time: 0.0022 memory: 43613 loss: 0.0962 loss_ce: 0.0962 2023/02/26 17:35:26 - mmengine - INFO - Epoch(train) [60][4100/5047] lr: 2.2482e-05 eta: 4 days, 14:39:20 time: 0.8274 data_time: 0.0020 memory: 41419 loss: 0.1154 loss_ce: 0.1154 2023/02/26 17:36:53 - mmengine - INFO - Epoch(train) [60][4200/5047] lr: 2.2482e-05 eta: 4 days, 14:37:51 time: 0.8720 data_time: 0.0021 memory: 55562 loss: 0.1212 loss_ce: 0.1212 2023/02/26 17:37:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 17:38:21 - mmengine - INFO - Epoch(train) [60][4300/5047] lr: 2.2482e-05 eta: 4 days, 14:36:24 time: 0.8928 data_time: 0.0020 memory: 55562 loss: 0.1169 loss_ce: 0.1169 2023/02/26 17:39:46 - mmengine - INFO - Epoch(train) [60][4400/5047] lr: 2.2482e-05 eta: 4 days, 14:34:53 time: 0.8564 data_time: 0.0023 memory: 51563 loss: 0.1167 loss_ce: 0.1167 2023/02/26 17:41:12 - mmengine - INFO - Epoch(train) [60][4500/5047] lr: 2.2482e-05 eta: 4 days, 14:33:23 time: 0.8506 data_time: 0.0025 memory: 43613 loss: 0.1103 loss_ce: 0.1103 2023/02/26 17:42:38 - mmengine - INFO - Epoch(train) [60][4600/5047] lr: 2.2482e-05 eta: 4 days, 14:31:52 time: 0.8801 data_time: 0.0033 memory: 49171 loss: 0.1206 loss_ce: 0.1206 2023/02/26 17:44:04 - mmengine - INFO - Epoch(train) [60][4700/5047] lr: 2.2482e-05 eta: 4 days, 14:30:23 time: 0.8292 data_time: 0.0021 memory: 46474 loss: 0.1276 loss_ce: 0.1276 2023/02/26 17:45:30 - mmengine - INFO - Epoch(train) [60][4800/5047] lr: 2.2482e-05 eta: 4 days, 14:28:53 time: 0.8855 data_time: 0.0020 memory: 44377 loss: 0.1206 loss_ce: 0.1206 2023/02/26 17:46:56 - mmengine - INFO - Epoch(train) [60][4900/5047] lr: 2.2482e-05 eta: 4 days, 14:27:22 time: 0.8519 data_time: 0.0020 memory: 41122 loss: 0.1252 loss_ce: 0.1252 2023/02/26 17:48:23 - mmengine - INFO - Epoch(train) [60][5000/5047] lr: 2.2482e-05 eta: 4 days, 14:25:54 time: 0.8673 data_time: 0.0019 memory: 44496 loss: 0.1104 loss_ce: 0.1104 2023/02/26 17:49:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 17:49:04 - mmengine - INFO - Saving checkpoint at 60 epochs 2023/02/26 17:50:37 - mmengine - INFO - Epoch(train) [61][ 100/5047] lr: 2.2281e-05 eta: 4 days, 14:23:43 time: 0.9291 data_time: 0.0022 memory: 48188 loss: 0.1226 loss_ce: 0.1226 2023/02/26 17:51:45 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 17:52:02 - mmengine - INFO - Epoch(train) [61][ 200/5047] lr: 2.2281e-05 eta: 4 days, 14:22:12 time: 0.8390 data_time: 0.0074 memory: 41724 loss: 0.1232 loss_ce: 0.1232 2023/02/26 17:53:27 - mmengine - INFO - Epoch(train) [61][ 300/5047] lr: 2.2281e-05 eta: 4 days, 14:20:40 time: 0.8816 data_time: 0.0023 memory: 45827 loss: 0.1176 loss_ce: 0.1176 2023/02/26 17:54:53 - mmengine - INFO - Epoch(train) [61][ 400/5047] lr: 2.2281e-05 eta: 4 days, 14:19:11 time: 0.8636 data_time: 0.0025 memory: 41347 loss: 0.1232 loss_ce: 0.1232 2023/02/26 17:56:19 - mmengine - INFO - Epoch(train) [61][ 500/5047] lr: 2.2281e-05 eta: 4 days, 14:17:41 time: 0.8962 data_time: 0.0024 memory: 43947 loss: 0.1274 loss_ce: 0.1274 2023/02/26 17:57:45 - mmengine - INFO - Epoch(train) [61][ 600/5047] lr: 2.2281e-05 eta: 4 days, 14:16:11 time: 0.7975 data_time: 0.0027 memory: 44956 loss: 0.1167 loss_ce: 0.1167 2023/02/26 17:59:12 - mmengine - INFO - Epoch(train) [61][ 700/5047] lr: 2.2281e-05 eta: 4 days, 14:14:43 time: 0.8335 data_time: 0.0021 memory: 41724 loss: 0.1147 loss_ce: 0.1147 2023/02/26 18:00:40 - mmengine - INFO - Epoch(train) [61][ 800/5047] lr: 2.2281e-05 eta: 4 days, 14:13:15 time: 0.8649 data_time: 0.0021 memory: 39559 loss: 0.1347 loss_ce: 0.1347 2023/02/26 18:02:08 - mmengine - INFO - Epoch(train) [61][ 900/5047] lr: 2.2281e-05 eta: 4 days, 14:11:49 time: 0.8850 data_time: 0.0055 memory: 42336 loss: 0.1126 loss_ce: 0.1126 2023/02/26 18:03:34 - mmengine - INFO - Epoch(train) [61][1000/5047] lr: 2.2281e-05 eta: 4 days, 14:10:19 time: 0.8279 data_time: 0.0047 memory: 45974 loss: 0.1147 loss_ce: 0.1147 2023/02/26 18:04:59 - mmengine - INFO - Epoch(train) [61][1100/5047] lr: 2.2281e-05 eta: 4 days, 14:08:47 time: 0.9067 data_time: 0.0021 memory: 47074 loss: 0.1067 loss_ce: 0.1067 2023/02/26 18:06:10 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 18:06:27 - mmengine - INFO - Epoch(train) [61][1200/5047] lr: 2.2281e-05 eta: 4 days, 14:07:20 time: 0.8385 data_time: 0.0057 memory: 43375 loss: 0.1150 loss_ce: 0.1150 2023/02/26 18:07:53 - mmengine - INFO - Epoch(train) [61][1300/5047] lr: 2.2281e-05 eta: 4 days, 14:05:50 time: 0.8573 data_time: 0.0024 memory: 40535 loss: 0.1322 loss_ce: 0.1322 2023/02/26 18:09:18 - mmengine - INFO - Epoch(train) [61][1400/5047] lr: 2.2281e-05 eta: 4 days, 14:04:19 time: 0.8838 data_time: 0.0023 memory: 42649 loss: 0.1349 loss_ce: 0.1349 2023/02/26 18:10:44 - mmengine - INFO - Epoch(train) [61][1500/5047] lr: 2.2281e-05 eta: 4 days, 14:02:49 time: 0.8455 data_time: 0.0032 memory: 40825 loss: 0.1301 loss_ce: 0.1301 2023/02/26 18:12:09 - mmengine - INFO - Epoch(train) [61][1600/5047] lr: 2.2281e-05 eta: 4 days, 14:01:18 time: 0.8256 data_time: 0.0058 memory: 42336 loss: 0.1202 loss_ce: 0.1202 2023/02/26 18:13:36 - mmengine - INFO - Epoch(train) [61][1700/5047] lr: 2.2281e-05 eta: 4 days, 13:59:49 time: 0.8636 data_time: 0.0022 memory: 50732 loss: 0.1222 loss_ce: 0.1222 2023/02/26 18:15:03 - mmengine - INFO - Epoch(train) [61][1800/5047] lr: 2.2281e-05 eta: 4 days, 13:58:21 time: 0.8466 data_time: 0.0022 memory: 47447 loss: 0.1131 loss_ce: 0.1131 2023/02/26 18:16:29 - mmengine - INFO - Epoch(train) [61][1900/5047] lr: 2.2281e-05 eta: 4 days, 13:56:51 time: 0.9034 data_time: 0.0028 memory: 55487 loss: 0.1243 loss_ce: 0.1243 2023/02/26 18:17:55 - mmengine - INFO - Epoch(train) [61][2000/5047] lr: 2.2281e-05 eta: 4 days, 13:55:22 time: 0.9018 data_time: 0.0019 memory: 43289 loss: 0.1091 loss_ce: 0.1091 2023/02/26 18:19:21 - mmengine - INFO - Epoch(train) [61][2100/5047] lr: 2.2281e-05 eta: 4 days, 13:53:51 time: 0.8537 data_time: 0.0021 memory: 41724 loss: 0.1190 loss_ce: 0.1190 2023/02/26 18:20:28 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 18:20:45 - mmengine - INFO - Epoch(train) [61][2200/5047] lr: 2.2281e-05 eta: 4 days, 13:52:18 time: 0.8539 data_time: 0.0020 memory: 45329 loss: 0.1236 loss_ce: 0.1236 2023/02/26 18:22:11 - mmengine - INFO - Epoch(train) [61][2300/5047] lr: 2.2281e-05 eta: 4 days, 13:50:48 time: 0.8680 data_time: 0.0021 memory: 49922 loss: 0.1340 loss_ce: 0.1340 2023/02/26 18:23:37 - mmengine - INFO - Epoch(train) [61][2400/5047] lr: 2.2281e-05 eta: 4 days, 13:49:19 time: 0.8831 data_time: 0.0021 memory: 45643 loss: 0.1200 loss_ce: 0.1200 2023/02/26 18:25:04 - mmengine - INFO - Epoch(train) [61][2500/5047] lr: 2.2281e-05 eta: 4 days, 13:47:49 time: 0.8548 data_time: 0.0025 memory: 43613 loss: 0.1192 loss_ce: 0.1192 2023/02/26 18:26:29 - mmengine - INFO - Epoch(train) [61][2600/5047] lr: 2.2281e-05 eta: 4 days, 13:46:18 time: 0.8285 data_time: 0.0020 memory: 40457 loss: 0.1173 loss_ce: 0.1173 2023/02/26 18:27:56 - mmengine - INFO - Epoch(train) [61][2700/5047] lr: 2.2281e-05 eta: 4 days, 13:44:50 time: 0.8708 data_time: 0.0022 memory: 42745 loss: 0.1233 loss_ce: 0.1233 2023/02/26 18:29:22 - mmengine - INFO - Epoch(train) [61][2800/5047] lr: 2.2281e-05 eta: 4 days, 13:43:21 time: 0.8402 data_time: 0.0035 memory: 46474 loss: 0.1155 loss_ce: 0.1155 2023/02/26 18:30:49 - mmengine - INFO - Epoch(train) [61][2900/5047] lr: 2.2281e-05 eta: 4 days, 13:41:52 time: 0.8875 data_time: 0.0024 memory: 42965 loss: 0.1082 loss_ce: 0.1082 2023/02/26 18:32:15 - mmengine - INFO - Epoch(train) [61][3000/5047] lr: 2.2281e-05 eta: 4 days, 13:40:22 time: 0.8396 data_time: 0.0022 memory: 46355 loss: 0.1143 loss_ce: 0.1143 2023/02/26 18:33:42 - mmengine - INFO - Epoch(train) [61][3100/5047] lr: 2.2281e-05 eta: 4 days, 13:38:53 time: 0.9457 data_time: 0.0024 memory: 46772 loss: 0.1263 loss_ce: 0.1263 2023/02/26 18:34:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 18:35:09 - mmengine - INFO - Epoch(train) [61][3200/5047] lr: 2.2281e-05 eta: 4 days, 13:37:25 time: 0.8863 data_time: 0.0021 memory: 48676 loss: 0.1104 loss_ce: 0.1104 2023/02/26 18:36:35 - mmengine - INFO - Epoch(train) [61][3300/5047] lr: 2.2281e-05 eta: 4 days, 13:35:55 time: 0.8498 data_time: 0.0035 memory: 40535 loss: 0.1198 loss_ce: 0.1198 2023/02/26 18:38:03 - mmengine - INFO - Epoch(train) [61][3400/5047] lr: 2.2281e-05 eta: 4 days, 13:34:29 time: 0.8385 data_time: 0.0022 memory: 43919 loss: 0.1215 loss_ce: 0.1215 2023/02/26 18:39:30 - mmengine - INFO - Epoch(train) [61][3500/5047] lr: 2.2281e-05 eta: 4 days, 13:33:00 time: 0.9465 data_time: 0.0029 memory: 43585 loss: 0.1255 loss_ce: 0.1255 2023/02/26 18:40:58 - mmengine - INFO - Epoch(train) [61][3600/5047] lr: 2.2281e-05 eta: 4 days, 13:31:32 time: 0.9330 data_time: 0.0019 memory: 44478 loss: 0.1108 loss_ce: 0.1108 2023/02/26 18:42:22 - mmengine - INFO - Epoch(train) [61][3700/5047] lr: 2.2281e-05 eta: 4 days, 13:30:01 time: 0.8250 data_time: 0.0021 memory: 42116 loss: 0.1175 loss_ce: 0.1175 2023/02/26 18:43:48 - mmengine - INFO - Epoch(train) [61][3800/5047] lr: 2.2281e-05 eta: 4 days, 13:28:31 time: 0.8560 data_time: 0.0024 memory: 52964 loss: 0.1213 loss_ce: 0.1213 2023/02/26 18:45:14 - mmengine - INFO - Epoch(train) [61][3900/5047] lr: 2.2281e-05 eta: 4 days, 13:27:00 time: 0.8067 data_time: 0.0025 memory: 41154 loss: 0.1290 loss_ce: 0.1290 2023/02/26 18:46:40 - mmengine - INFO - Epoch(train) [61][4000/5047] lr: 2.2281e-05 eta: 4 days, 13:25:30 time: 0.8229 data_time: 0.0019 memory: 55562 loss: 0.1251 loss_ce: 0.1251 2023/02/26 18:48:07 - mmengine - INFO - Epoch(train) [61][4100/5047] lr: 2.2281e-05 eta: 4 days, 13:24:02 time: 0.8697 data_time: 0.0025 memory: 47813 loss: 0.1128 loss_ce: 0.1128 2023/02/26 18:49:16 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 18:49:33 - mmengine - INFO - Epoch(train) [61][4200/5047] lr: 2.2281e-05 eta: 4 days, 13:22:33 time: 0.8724 data_time: 0.0041 memory: 49378 loss: 0.1225 loss_ce: 0.1225 2023/02/26 18:50:58 - mmengine - INFO - Epoch(train) [61][4300/5047] lr: 2.2281e-05 eta: 4 days, 13:21:01 time: 0.8178 data_time: 0.0020 memory: 42649 loss: 0.1449 loss_ce: 0.1449 2023/02/26 18:52:22 - mmengine - INFO - Epoch(train) [61][4400/5047] lr: 2.2281e-05 eta: 4 days, 13:19:28 time: 0.8054 data_time: 0.0044 memory: 42965 loss: 0.1190 loss_ce: 0.1190 2023/02/26 18:53:48 - mmengine - INFO - Epoch(train) [61][4500/5047] lr: 2.2281e-05 eta: 4 days, 13:17:57 time: 0.8198 data_time: 0.0029 memory: 50370 loss: 0.1143 loss_ce: 0.1143 2023/02/26 18:55:13 - mmengine - INFO - Epoch(train) [61][4600/5047] lr: 2.2281e-05 eta: 4 days, 13:16:27 time: 0.8297 data_time: 0.0026 memory: 44339 loss: 0.1234 loss_ce: 0.1234 2023/02/26 18:56:40 - mmengine - INFO - Epoch(train) [61][4700/5047] lr: 2.2281e-05 eta: 4 days, 13:14:59 time: 0.8530 data_time: 0.0022 memory: 46005 loss: 0.1328 loss_ce: 0.1328 2023/02/26 18:58:08 - mmengine - INFO - Epoch(train) [61][4800/5047] lr: 2.2281e-05 eta: 4 days, 13:13:31 time: 0.8979 data_time: 0.0022 memory: 45551 loss: 0.1206 loss_ce: 0.1206 2023/02/26 18:59:34 - mmengine - INFO - Epoch(train) [61][4900/5047] lr: 2.2281e-05 eta: 4 days, 13:12:02 time: 0.9010 data_time: 0.0019 memory: 51731 loss: 0.1238 loss_ce: 0.1238 2023/02/26 19:01:00 - mmengine - INFO - Epoch(train) [61][5000/5047] lr: 2.2281e-05 eta: 4 days, 13:10:33 time: 0.8787 data_time: 0.0021 memory: 46355 loss: 0.1342 loss_ce: 0.1342 2023/02/26 19:01:41 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 19:01:41 - mmengine - INFO - Saving checkpoint at 61 epochs 2023/02/26 19:03:12 - mmengine - INFO - Epoch(train) [62][ 100/5047] lr: 2.2080e-05 eta: 4 days, 13:08:20 time: 0.8669 data_time: 0.0020 memory: 46324 loss: 0.1134 loss_ce: 0.1134 2023/02/26 19:03:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 19:04:36 - mmengine - INFO - Epoch(train) [62][ 200/5047] lr: 2.2080e-05 eta: 4 days, 13:06:48 time: 0.8855 data_time: 0.0020 memory: 44617 loss: 0.1190 loss_ce: 0.1190 2023/02/26 19:06:02 - mmengine - INFO - Epoch(train) [62][ 300/5047] lr: 2.2080e-05 eta: 4 days, 13:05:18 time: 0.8891 data_time: 0.0026 memory: 45376 loss: 0.1261 loss_ce: 0.1261 2023/02/26 19:07:27 - mmengine - INFO - Epoch(train) [62][ 400/5047] lr: 2.2080e-05 eta: 4 days, 13:03:47 time: 0.8918 data_time: 0.0027 memory: 49715 loss: 0.1187 loss_ce: 0.1187 2023/02/26 19:08:52 - mmengine - INFO - Epoch(train) [62][ 500/5047] lr: 2.2080e-05 eta: 4 days, 13:02:16 time: 0.8395 data_time: 0.0020 memory: 48459 loss: 0.1427 loss_ce: 0.1427 2023/02/26 19:10:16 - mmengine - INFO - Epoch(train) [62][ 600/5047] lr: 2.2080e-05 eta: 4 days, 13:00:43 time: 0.8529 data_time: 0.0020 memory: 44823 loss: 0.1108 loss_ce: 0.1108 2023/02/26 19:11:43 - mmengine - INFO - Epoch(train) [62][ 700/5047] lr: 2.2080e-05 eta: 4 days, 12:59:14 time: 0.8631 data_time: 0.0021 memory: 40241 loss: 0.1096 loss_ce: 0.1096 2023/02/26 19:13:08 - mmengine - INFO - Epoch(train) [62][ 800/5047] lr: 2.2080e-05 eta: 4 days, 12:57:42 time: 0.8107 data_time: 0.0078 memory: 43222 loss: 0.1196 loss_ce: 0.1196 2023/02/26 19:14:32 - mmengine - INFO - Epoch(train) [62][ 900/5047] lr: 2.2080e-05 eta: 4 days, 12:56:10 time: 0.8497 data_time: 0.0019 memory: 41419 loss: 0.1435 loss_ce: 0.1435 2023/02/26 19:15:59 - mmengine - INFO - Epoch(train) [62][1000/5047] lr: 2.2080e-05 eta: 4 days, 12:54:41 time: 0.8480 data_time: 0.0020 memory: 51719 loss: 0.1273 loss_ce: 0.1273 2023/02/26 19:17:25 - mmengine - INFO - Epoch(train) [62][1100/5047] lr: 2.2080e-05 eta: 4 days, 12:53:11 time: 0.9065 data_time: 0.0024 memory: 44617 loss: 0.1339 loss_ce: 0.1339 2023/02/26 19:17:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 19:18:50 - mmengine - INFO - Epoch(train) [62][1200/5047] lr: 2.2080e-05 eta: 4 days, 12:51:41 time: 0.8804 data_time: 0.0026 memory: 55562 loss: 0.1103 loss_ce: 0.1103 2023/02/26 19:20:15 - mmengine - INFO - Epoch(train) [62][1300/5047] lr: 2.2080e-05 eta: 4 days, 12:50:09 time: 0.8873 data_time: 0.0021 memory: 41093 loss: 0.1137 loss_ce: 0.1137 2023/02/26 19:21:40 - mmengine - INFO - Epoch(train) [62][1400/5047] lr: 2.2080e-05 eta: 4 days, 12:48:38 time: 0.8420 data_time: 0.0023 memory: 41724 loss: 0.1009 loss_ce: 0.1009 2023/02/26 19:23:06 - mmengine - INFO - Epoch(train) [62][1500/5047] lr: 2.2080e-05 eta: 4 days, 12:47:08 time: 0.8963 data_time: 0.0074 memory: 48130 loss: 0.1102 loss_ce: 0.1102 2023/02/26 19:24:33 - mmengine - INFO - Epoch(train) [62][1600/5047] lr: 2.2080e-05 eta: 4 days, 12:45:40 time: 0.8572 data_time: 0.0036 memory: 43219 loss: 0.1247 loss_ce: 0.1247 2023/02/26 19:25:58 - mmengine - INFO - Epoch(train) [62][1700/5047] lr: 2.2080e-05 eta: 4 days, 12:44:08 time: 0.8413 data_time: 0.0023 memory: 55562 loss: 0.1356 loss_ce: 0.1356 2023/02/26 19:27:26 - mmengine - INFO - Epoch(train) [62][1800/5047] lr: 2.2080e-05 eta: 4 days, 12:42:41 time: 0.8071 data_time: 0.0024 memory: 47813 loss: 0.1146 loss_ce: 0.1146 2023/02/26 19:28:53 - mmengine - INFO - Epoch(train) [62][1900/5047] lr: 2.2080e-05 eta: 4 days, 12:41:13 time: 0.8470 data_time: 0.0021 memory: 54042 loss: 0.1220 loss_ce: 0.1220 2023/02/26 19:30:18 - mmengine - INFO - Epoch(train) [62][2000/5047] lr: 2.2080e-05 eta: 4 days, 12:39:42 time: 0.8606 data_time: 0.0021 memory: 44278 loss: 0.1041 loss_ce: 0.1041 2023/02/26 19:31:45 - mmengine - INFO - Epoch(train) [62][2100/5047] lr: 2.2080e-05 eta: 4 days, 12:38:14 time: 0.8959 data_time: 0.0023 memory: 55559 loss: 0.1153 loss_ce: 0.1153 2023/02/26 19:32:15 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 19:33:13 - mmengine - INFO - Epoch(train) [62][2200/5047] lr: 2.2080e-05 eta: 4 days, 12:36:47 time: 0.8467 data_time: 0.0021 memory: 41345 loss: 0.1216 loss_ce: 0.1216 2023/02/26 19:34:37 - mmengine - INFO - Epoch(train) [62][2300/5047] lr: 2.2080e-05 eta: 4 days, 12:35:15 time: 0.7904 data_time: 0.0020 memory: 49715 loss: 0.1072 loss_ce: 0.1072 2023/02/26 19:36:03 - mmengine - INFO - Epoch(train) [62][2400/5047] lr: 2.2080e-05 eta: 4 days, 12:33:44 time: 0.8529 data_time: 0.0021 memory: 41122 loss: 0.1091 loss_ce: 0.1091 2023/02/26 19:37:28 - mmengine - INFO - Epoch(train) [62][2500/5047] lr: 2.2080e-05 eta: 4 days, 12:32:14 time: 0.8545 data_time: 0.0028 memory: 50419 loss: 0.1119 loss_ce: 0.1119 2023/02/26 19:38:54 - mmengine - INFO - Epoch(train) [62][2600/5047] lr: 2.2080e-05 eta: 4 days, 12:30:44 time: 0.8443 data_time: 0.0020 memory: 47728 loss: 0.1209 loss_ce: 0.1209 2023/02/26 19:40:21 - mmengine - INFO - Epoch(train) [62][2700/5047] lr: 2.2080e-05 eta: 4 days, 12:29:15 time: 0.8202 data_time: 0.0021 memory: 52127 loss: 0.1088 loss_ce: 0.1088 2023/02/26 19:41:47 - mmengine - INFO - Epoch(train) [62][2800/5047] lr: 2.2080e-05 eta: 4 days, 12:27:46 time: 0.8641 data_time: 0.0025 memory: 50355 loss: 0.1202 loss_ce: 0.1202 2023/02/26 19:43:13 - mmengine - INFO - Epoch(train) [62][2900/5047] lr: 2.2080e-05 eta: 4 days, 12:26:15 time: 0.8348 data_time: 0.0026 memory: 50906 loss: 0.1222 loss_ce: 0.1222 2023/02/26 19:44:38 - mmengine - INFO - Epoch(train) [62][3000/5047] lr: 2.2080e-05 eta: 4 days, 12:24:45 time: 0.8403 data_time: 0.0020 memory: 46477 loss: 0.1167 loss_ce: 0.1167 2023/02/26 19:46:04 - mmengine - INFO - Epoch(train) [62][3100/5047] lr: 2.2080e-05 eta: 4 days, 12:23:15 time: 0.8816 data_time: 0.0030 memory: 48948 loss: 0.1170 loss_ce: 0.1170 2023/02/26 19:46:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 19:47:32 - mmengine - INFO - Epoch(train) [62][3200/5047] lr: 2.2080e-05 eta: 4 days, 12:21:48 time: 0.9111 data_time: 0.0083 memory: 45003 loss: 0.1184 loss_ce: 0.1184 2023/02/26 19:49:00 - mmengine - INFO - Epoch(train) [62][3300/5047] lr: 2.2080e-05 eta: 4 days, 12:20:21 time: 0.8202 data_time: 0.0024 memory: 41122 loss: 0.1191 loss_ce: 0.1191 2023/02/26 19:50:26 - mmengine - INFO - Epoch(train) [62][3400/5047] lr: 2.2080e-05 eta: 4 days, 12:18:52 time: 0.8677 data_time: 0.0039 memory: 50106 loss: 0.1085 loss_ce: 0.1085 2023/02/26 19:51:51 - mmengine - INFO - Epoch(train) [62][3500/5047] lr: 2.2080e-05 eta: 4 days, 12:17:20 time: 0.7842 data_time: 0.0031 memory: 43613 loss: 0.1159 loss_ce: 0.1159 2023/02/26 19:53:16 - mmengine - INFO - Epoch(train) [62][3600/5047] lr: 2.2080e-05 eta: 4 days, 12:15:48 time: 0.7767 data_time: 0.0021 memory: 43947 loss: 0.1116 loss_ce: 0.1116 2023/02/26 19:54:42 - mmengine - INFO - Epoch(train) [62][3700/5047] lr: 2.2080e-05 eta: 4 days, 12:14:19 time: 0.8730 data_time: 0.0024 memory: 49488 loss: 0.1286 loss_ce: 0.1286 2023/02/26 19:56:08 - mmengine - INFO - Epoch(train) [62][3800/5047] lr: 2.2080e-05 eta: 4 days, 12:12:50 time: 0.8842 data_time: 0.0048 memory: 50505 loss: 0.1142 loss_ce: 0.1142 2023/02/26 19:57:33 - mmengine - INFO - Epoch(train) [62][3900/5047] lr: 2.2080e-05 eta: 4 days, 12:11:18 time: 0.8070 data_time: 0.0019 memory: 43952 loss: 0.1140 loss_ce: 0.1140 2023/02/26 19:58:59 - mmengine - INFO - Epoch(train) [62][4000/5047] lr: 2.2080e-05 eta: 4 days, 12:09:48 time: 0.8797 data_time: 0.0030 memory: 44716 loss: 0.1026 loss_ce: 0.1026 2023/02/26 20:00:24 - mmengine - INFO - Epoch(train) [62][4100/5047] lr: 2.2080e-05 eta: 4 days, 12:08:17 time: 0.8250 data_time: 0.0036 memory: 48734 loss: 0.1068 loss_ce: 0.1068 2023/02/26 20:00:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 20:01:50 - mmengine - INFO - Epoch(train) [62][4200/5047] lr: 2.2080e-05 eta: 4 days, 12:06:47 time: 0.8778 data_time: 0.0044 memory: 41122 loss: 0.1298 loss_ce: 0.1298 2023/02/26 20:03:15 - mmengine - INFO - Epoch(train) [62][4300/5047] lr: 2.2080e-05 eta: 4 days, 12:05:17 time: 0.8833 data_time: 0.0023 memory: 51818 loss: 0.1227 loss_ce: 0.1227 2023/02/26 20:04:41 - mmengine - INFO - Epoch(train) [62][4400/5047] lr: 2.2080e-05 eta: 4 days, 12:03:46 time: 0.8341 data_time: 0.0023 memory: 41419 loss: 0.1318 loss_ce: 0.1318 2023/02/26 20:06:05 - mmengine - INFO - Epoch(train) [62][4500/5047] lr: 2.2080e-05 eta: 4 days, 12:02:14 time: 0.8447 data_time: 0.0028 memory: 51597 loss: 0.1230 loss_ce: 0.1230 2023/02/26 20:07:32 - mmengine - INFO - Epoch(train) [62][4600/5047] lr: 2.2080e-05 eta: 4 days, 12:00:45 time: 0.8619 data_time: 0.0021 memory: 46005 loss: 0.1249 loss_ce: 0.1249 2023/02/26 20:08:58 - mmengine - INFO - Epoch(train) [62][4700/5047] lr: 2.2080e-05 eta: 4 days, 11:59:15 time: 0.8758 data_time: 0.0029 memory: 42649 loss: 0.1232 loss_ce: 0.1232 2023/02/26 20:10:24 - mmengine - INFO - Epoch(train) [62][4800/5047] lr: 2.2080e-05 eta: 4 days, 11:57:47 time: 0.8797 data_time: 0.0019 memory: 46713 loss: 0.1183 loss_ce: 0.1183 2023/02/26 20:11:51 - mmengine - INFO - Epoch(train) [62][4900/5047] lr: 2.2080e-05 eta: 4 days, 11:56:18 time: 0.8245 data_time: 0.0021 memory: 44617 loss: 0.1293 loss_ce: 0.1293 2023/02/26 20:13:18 - mmengine - INFO - Epoch(train) [62][5000/5047] lr: 2.2080e-05 eta: 4 days, 11:54:50 time: 0.8768 data_time: 0.0021 memory: 44705 loss: 0.1143 loss_ce: 0.1143 2023/02/26 20:13:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 20:13:58 - mmengine - INFO - Saving checkpoint at 62 epochs 2023/02/26 20:15:20 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 20:15:33 - mmengine - INFO - Epoch(train) [63][ 100/5047] lr: 2.1879e-05 eta: 4 days, 11:52:40 time: 0.8814 data_time: 0.0020 memory: 46167 loss: 0.1247 loss_ce: 0.1247 2023/02/26 20:16:59 - mmengine - INFO - Epoch(train) [63][ 200/5047] lr: 2.1879e-05 eta: 4 days, 11:51:10 time: 0.8476 data_time: 0.0019 memory: 55562 loss: 0.1216 loss_ce: 0.1216 2023/02/26 20:18:25 - mmengine - INFO - Epoch(train) [63][ 300/5047] lr: 2.1879e-05 eta: 4 days, 11:49:40 time: 0.8676 data_time: 0.0024 memory: 45377 loss: 0.1146 loss_ce: 0.1146 2023/02/26 20:19:50 - mmengine - INFO - Epoch(train) [63][ 400/5047] lr: 2.1879e-05 eta: 4 days, 11:48:10 time: 0.8389 data_time: 0.0021 memory: 42402 loss: 0.1251 loss_ce: 0.1251 2023/02/26 20:21:17 - mmengine - INFO - Epoch(train) [63][ 500/5047] lr: 2.1879e-05 eta: 4 days, 11:46:42 time: 0.8564 data_time: 0.0031 memory: 42965 loss: 0.1234 loss_ce: 0.1234 2023/02/26 20:22:44 - mmengine - INFO - Epoch(train) [63][ 600/5047] lr: 2.1879e-05 eta: 4 days, 11:45:14 time: 0.8778 data_time: 0.0020 memory: 43491 loss: 0.1165 loss_ce: 0.1165 2023/02/26 20:24:10 - mmengine - INFO - Epoch(train) [63][ 700/5047] lr: 2.1879e-05 eta: 4 days, 11:43:43 time: 0.8805 data_time: 0.0021 memory: 41419 loss: 0.1255 loss_ce: 0.1255 2023/02/26 20:25:35 - mmengine - INFO - Epoch(train) [63][ 800/5047] lr: 2.1879e-05 eta: 4 days, 11:42:12 time: 0.8316 data_time: 0.0027 memory: 45302 loss: 0.1243 loss_ce: 0.1243 2023/02/26 20:27:00 - mmengine - INFO - Epoch(train) [63][ 900/5047] lr: 2.1879e-05 eta: 4 days, 11:40:41 time: 0.8559 data_time: 0.0048 memory: 40797 loss: 0.1105 loss_ce: 0.1105 2023/02/26 20:28:27 - mmengine - INFO - Epoch(train) [63][1000/5047] lr: 2.1879e-05 eta: 4 days, 11:39:12 time: 0.8550 data_time: 0.0021 memory: 43289 loss: 0.1105 loss_ce: 0.1105 2023/02/26 20:29:41 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 20:29:53 - mmengine - INFO - Epoch(train) [63][1100/5047] lr: 2.1879e-05 eta: 4 days, 11:37:43 time: 0.8592 data_time: 0.0025 memory: 42024 loss: 0.1233 loss_ce: 0.1233 2023/02/26 20:31:18 - mmengine - INFO - Epoch(train) [63][1200/5047] lr: 2.1879e-05 eta: 4 days, 11:36:12 time: 0.8856 data_time: 0.0025 memory: 42336 loss: 0.1140 loss_ce: 0.1140 2023/02/26 20:32:46 - mmengine - INFO - Epoch(train) [63][1300/5047] lr: 2.1879e-05 eta: 4 days, 11:34:45 time: 0.8558 data_time: 0.0022 memory: 48323 loss: 0.1093 loss_ce: 0.1093 2023/02/26 20:34:12 - mmengine - INFO - Epoch(train) [63][1400/5047] lr: 2.1879e-05 eta: 4 days, 11:33:16 time: 0.9056 data_time: 0.0021 memory: 44409 loss: 0.1240 loss_ce: 0.1240 2023/02/26 20:35:38 - mmengine - INFO - Epoch(train) [63][1500/5047] lr: 2.1879e-05 eta: 4 days, 11:31:46 time: 0.8573 data_time: 0.0022 memory: 44840 loss: 0.1258 loss_ce: 0.1258 2023/02/26 20:37:07 - mmengine - INFO - Epoch(train) [63][1600/5047] lr: 2.1879e-05 eta: 4 days, 11:30:20 time: 0.8659 data_time: 0.0022 memory: 47447 loss: 0.1110 loss_ce: 0.1110 2023/02/26 20:38:33 - mmengine - INFO - Epoch(train) [63][1700/5047] lr: 2.1879e-05 eta: 4 days, 11:28:51 time: 0.7955 data_time: 0.0023 memory: 52976 loss: 0.1084 loss_ce: 0.1084 2023/02/26 20:39:59 - mmengine - INFO - Epoch(train) [63][1800/5047] lr: 2.1879e-05 eta: 4 days, 11:27:21 time: 0.8791 data_time: 0.0022 memory: 43947 loss: 0.1146 loss_ce: 0.1146 2023/02/26 20:41:24 - mmengine - INFO - Epoch(train) [63][1900/5047] lr: 2.1879e-05 eta: 4 days, 11:25:50 time: 0.8614 data_time: 0.0020 memory: 55562 loss: 0.1198 loss_ce: 0.1198 2023/02/26 20:42:50 - mmengine - INFO - Epoch(train) [63][2000/5047] lr: 2.1879e-05 eta: 4 days, 11:24:20 time: 0.8044 data_time: 0.0029 memory: 47348 loss: 0.1182 loss_ce: 0.1182 2023/02/26 20:44:06 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 20:44:18 - mmengine - INFO - Epoch(train) [63][2100/5047] lr: 2.1879e-05 eta: 4 days, 11:22:53 time: 0.8519 data_time: 0.0021 memory: 42447 loss: 0.1122 loss_ce: 0.1122 2023/02/26 20:45:43 - mmengine - INFO - Epoch(train) [63][2200/5047] lr: 2.1879e-05 eta: 4 days, 11:21:23 time: 0.8754 data_time: 0.0020 memory: 43947 loss: 0.1189 loss_ce: 0.1189 2023/02/26 20:47:10 - mmengine - INFO - Epoch(train) [63][2300/5047] lr: 2.1879e-05 eta: 4 days, 11:19:54 time: 0.8606 data_time: 0.0026 memory: 48539 loss: 0.1214 loss_ce: 0.1214 2023/02/26 20:48:38 - mmengine - INFO - Epoch(train) [63][2400/5047] lr: 2.1879e-05 eta: 4 days, 11:18:28 time: 0.8261 data_time: 0.0031 memory: 50906 loss: 0.1205 loss_ce: 0.1205 2023/02/26 20:50:04 - mmengine - INFO - Epoch(train) [63][2500/5047] lr: 2.1879e-05 eta: 4 days, 11:16:58 time: 0.8208 data_time: 0.0020 memory: 48894 loss: 0.1233 loss_ce: 0.1233 2023/02/26 20:51:30 - mmengine - INFO - Epoch(train) [63][2600/5047] lr: 2.1879e-05 eta: 4 days, 11:15:28 time: 0.8808 data_time: 0.0029 memory: 43249 loss: 0.1064 loss_ce: 0.1064 2023/02/26 20:52:57 - mmengine - INFO - Epoch(train) [63][2700/5047] lr: 2.1879e-05 eta: 4 days, 11:14:00 time: 0.8941 data_time: 0.0026 memory: 54303 loss: 0.1095 loss_ce: 0.1095 2023/02/26 20:54:25 - mmengine - INFO - Epoch(train) [63][2800/5047] lr: 2.1879e-05 eta: 4 days, 11:12:34 time: 0.8555 data_time: 0.0022 memory: 49715 loss: 0.1234 loss_ce: 0.1234 2023/02/26 20:55:52 - mmengine - INFO - Epoch(train) [63][2900/5047] lr: 2.1879e-05 eta: 4 days, 11:11:05 time: 0.8578 data_time: 0.0025 memory: 45623 loss: 0.1300 loss_ce: 0.1300 2023/02/26 20:57:18 - mmengine - INFO - Epoch(train) [63][3000/5047] lr: 2.1879e-05 eta: 4 days, 11:09:36 time: 0.8573 data_time: 0.0022 memory: 45302 loss: 0.1128 loss_ce: 0.1128 2023/02/26 20:58:32 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 20:58:44 - mmengine - INFO - Epoch(train) [63][3100/5047] lr: 2.1879e-05 eta: 4 days, 11:08:06 time: 0.8497 data_time: 0.0043 memory: 42270 loss: 0.1274 loss_ce: 0.1274 2023/02/26 21:00:09 - mmengine - INFO - Epoch(train) [63][3200/5047] lr: 2.1879e-05 eta: 4 days, 11:06:35 time: 0.8335 data_time: 0.0020 memory: 52964 loss: 0.1124 loss_ce: 0.1124 2023/02/26 21:01:35 - mmengine - INFO - Epoch(train) [63][3300/5047] lr: 2.1879e-05 eta: 4 days, 11:05:06 time: 0.8396 data_time: 0.0020 memory: 55562 loss: 0.1253 loss_ce: 0.1253 2023/02/26 21:03:02 - mmengine - INFO - Epoch(train) [63][3400/5047] lr: 2.1879e-05 eta: 4 days, 11:03:38 time: 0.8612 data_time: 0.0023 memory: 47013 loss: 0.1231 loss_ce: 0.1231 2023/02/26 21:04:30 - mmengine - INFO - Epoch(train) [63][3500/5047] lr: 2.1879e-05 eta: 4 days, 11:02:10 time: 0.8981 data_time: 0.0020 memory: 44756 loss: 0.1067 loss_ce: 0.1067 2023/02/26 21:05:54 - mmengine - INFO - Epoch(train) [63][3600/5047] lr: 2.1879e-05 eta: 4 days, 11:00:38 time: 0.8889 data_time: 0.0059 memory: 41630 loss: 0.1209 loss_ce: 0.1209 2023/02/26 21:07:20 - mmengine - INFO - Epoch(train) [63][3700/5047] lr: 2.1879e-05 eta: 4 days, 10:59:08 time: 0.8427 data_time: 0.0020 memory: 46278 loss: 0.1171 loss_ce: 0.1171 2023/02/26 21:08:44 - mmengine - INFO - Epoch(train) [63][3800/5047] lr: 2.1879e-05 eta: 4 days, 10:57:36 time: 0.7996 data_time: 0.0023 memory: 44192 loss: 0.1035 loss_ce: 0.1035 2023/02/26 21:10:11 - mmengine - INFO - Epoch(train) [63][3900/5047] lr: 2.1879e-05 eta: 4 days, 10:56:08 time: 0.8836 data_time: 0.0020 memory: 49127 loss: 0.1169 loss_ce: 0.1169 2023/02/26 21:11:37 - mmengine - INFO - Epoch(train) [63][4000/5047] lr: 2.1879e-05 eta: 4 days, 10:54:38 time: 0.8698 data_time: 0.0020 memory: 55559 loss: 0.1077 loss_ce: 0.1077 2023/02/26 21:12:51 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 21:13:03 - mmengine - INFO - Epoch(train) [63][4100/5047] lr: 2.1879e-05 eta: 4 days, 10:53:08 time: 0.8359 data_time: 0.0023 memory: 42336 loss: 0.1228 loss_ce: 0.1228 2023/02/26 21:14:28 - mmengine - INFO - Epoch(train) [63][4200/5047] lr: 2.1879e-05 eta: 4 days, 10:51:37 time: 0.8049 data_time: 0.0026 memory: 44537 loss: 0.1213 loss_ce: 0.1213 2023/02/26 21:15:53 - mmengine - INFO - Epoch(train) [63][4300/5047] lr: 2.1879e-05 eta: 4 days, 10:50:06 time: 0.8876 data_time: 0.0019 memory: 41724 loss: 0.1380 loss_ce: 0.1380 2023/02/26 21:17:20 - mmengine - INFO - Epoch(train) [63][4400/5047] lr: 2.1879e-05 eta: 4 days, 10:48:38 time: 0.8513 data_time: 0.0051 memory: 46876 loss: 0.1235 loss_ce: 0.1235 2023/02/26 21:18:46 - mmengine - INFO - Epoch(train) [63][4500/5047] lr: 2.1879e-05 eta: 4 days, 10:47:08 time: 0.8419 data_time: 0.0021 memory: 43289 loss: 0.1110 loss_ce: 0.1110 2023/02/26 21:20:13 - mmengine - INFO - Epoch(train) [63][4600/5047] lr: 2.1879e-05 eta: 4 days, 10:45:41 time: 0.9210 data_time: 0.0023 memory: 42965 loss: 0.0992 loss_ce: 0.0992 2023/02/26 21:21:40 - mmengine - INFO - Epoch(train) [63][4700/5047] lr: 2.1879e-05 eta: 4 days, 10:44:12 time: 0.8360 data_time: 0.0030 memory: 50370 loss: 0.1182 loss_ce: 0.1182 2023/02/26 21:23:06 - mmengine - INFO - Epoch(train) [63][4800/5047] lr: 2.1879e-05 eta: 4 days, 10:42:42 time: 0.8347 data_time: 0.0022 memory: 39960 loss: 0.1078 loss_ce: 0.1078 2023/02/26 21:24:31 - mmengine - INFO - Epoch(train) [63][4900/5047] lr: 2.1879e-05 eta: 4 days, 10:41:11 time: 0.8871 data_time: 0.0020 memory: 44956 loss: 0.1115 loss_ce: 0.1115 2023/02/26 21:25:57 - mmengine - INFO - Epoch(train) [63][5000/5047] lr: 2.1879e-05 eta: 4 days, 10:39:42 time: 0.8752 data_time: 0.0020 memory: 45733 loss: 0.1239 loss_ce: 0.1239 2023/02/26 21:26:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 21:26:37 - mmengine - INFO - Saving checkpoint at 63 epochs 2023/02/26 21:27:19 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 21:28:13 - mmengine - INFO - Epoch(train) [64][ 100/5047] lr: 2.1678e-05 eta: 4 days, 10:37:33 time: 0.8858 data_time: 0.0020 memory: 47318 loss: 0.1215 loss_ce: 0.1215 2023/02/26 21:29:39 - mmengine - INFO - Epoch(train) [64][ 200/5047] lr: 2.1678e-05 eta: 4 days, 10:36:04 time: 0.8483 data_time: 0.0066 memory: 42336 loss: 0.1229 loss_ce: 0.1229 2023/02/26 21:31:06 - mmengine - INFO - Epoch(train) [64][ 300/5047] lr: 2.1678e-05 eta: 4 days, 10:34:36 time: 0.9110 data_time: 0.0020 memory: 44496 loss: 0.1122 loss_ce: 0.1122 2023/02/26 21:32:31 - mmengine - INFO - Epoch(train) [64][ 400/5047] lr: 2.1678e-05 eta: 4 days, 10:33:05 time: 0.9250 data_time: 0.0038 memory: 39960 loss: 0.1259 loss_ce: 0.1259 2023/02/26 21:33:59 - mmengine - INFO - Epoch(train) [64][ 500/5047] lr: 2.1678e-05 eta: 4 days, 10:31:38 time: 0.8559 data_time: 0.0030 memory: 42649 loss: 0.1197 loss_ce: 0.1197 2023/02/26 21:35:25 - mmengine - INFO - Epoch(train) [64][ 600/5047] lr: 2.1678e-05 eta: 4 days, 10:30:08 time: 0.8678 data_time: 0.0027 memory: 40535 loss: 0.1147 loss_ce: 0.1147 2023/02/26 21:36:51 - mmengine - INFO - Epoch(train) [64][ 700/5047] lr: 2.1678e-05 eta: 4 days, 10:28:39 time: 0.8300 data_time: 0.0117 memory: 45787 loss: 0.1331 loss_ce: 0.1331 2023/02/26 21:38:18 - mmengine - INFO - Epoch(train) [64][ 800/5047] lr: 2.1678e-05 eta: 4 days, 10:27:12 time: 0.8835 data_time: 0.0026 memory: 41843 loss: 0.1376 loss_ce: 0.1376 2023/02/26 21:39:44 - mmengine - INFO - Epoch(train) [64][ 900/5047] lr: 2.1678e-05 eta: 4 days, 10:25:42 time: 0.8349 data_time: 0.0023 memory: 41519 loss: 0.1275 loss_ce: 0.1275 2023/02/26 21:41:10 - mmengine - INFO - Epoch(train) [64][1000/5047] lr: 2.1678e-05 eta: 4 days, 10:24:11 time: 0.8162 data_time: 0.0024 memory: 48398 loss: 0.1145 loss_ce: 0.1145 2023/02/26 21:41:43 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 21:42:35 - mmengine - INFO - Epoch(train) [64][1100/5047] lr: 2.1678e-05 eta: 4 days, 10:22:41 time: 0.8534 data_time: 0.0022 memory: 43947 loss: 0.1135 loss_ce: 0.1135 2023/02/26 21:44:01 - mmengine - INFO - Epoch(train) [64][1200/5047] lr: 2.1678e-05 eta: 4 days, 10:21:11 time: 0.8116 data_time: 0.0026 memory: 41724 loss: 0.1258 loss_ce: 0.1258 2023/02/26 21:45:29 - mmengine - INFO - Epoch(train) [64][1300/5047] lr: 2.1678e-05 eta: 4 days, 10:19:45 time: 0.8914 data_time: 0.0022 memory: 49170 loss: 0.1309 loss_ce: 0.1309 2023/02/26 21:46:55 - mmengine - INFO - Epoch(train) [64][1400/5047] lr: 2.1678e-05 eta: 4 days, 10:18:16 time: 0.8649 data_time: 0.0028 memory: 49334 loss: 0.1255 loss_ce: 0.1255 2023/02/26 21:48:22 - mmengine - INFO - Epoch(train) [64][1500/5047] lr: 2.1678e-05 eta: 4 days, 10:16:47 time: 0.8990 data_time: 0.0020 memory: 42336 loss: 0.1181 loss_ce: 0.1181 2023/02/26 21:49:49 - mmengine - INFO - Epoch(train) [64][1600/5047] lr: 2.1678e-05 eta: 4 days, 10:15:19 time: 0.8465 data_time: 0.0034 memory: 41122 loss: 0.1143 loss_ce: 0.1143 2023/02/26 21:51:17 - mmengine - INFO - Epoch(train) [64][1700/5047] lr: 2.1678e-05 eta: 4 days, 10:13:52 time: 0.9009 data_time: 0.0021 memory: 43744 loss: 0.1213 loss_ce: 0.1213 2023/02/26 21:52:42 - mmengine - INFO - Epoch(train) [64][1800/5047] lr: 2.1678e-05 eta: 4 days, 10:12:21 time: 0.8785 data_time: 0.0023 memory: 41766 loss: 0.1240 loss_ce: 0.1240 2023/02/26 21:54:07 - mmengine - INFO - Epoch(train) [64][1900/5047] lr: 2.1678e-05 eta: 4 days, 10:10:49 time: 0.8600 data_time: 0.0023 memory: 50540 loss: 0.1124 loss_ce: 0.1124 2023/02/26 21:55:33 - mmengine - INFO - Epoch(train) [64][2000/5047] lr: 2.1678e-05 eta: 4 days, 10:09:20 time: 0.8690 data_time: 0.0030 memory: 52964 loss: 0.1110 loss_ce: 0.1110 2023/02/26 21:56:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 21:56:58 - mmengine - INFO - Epoch(train) [64][2100/5047] lr: 2.1678e-05 eta: 4 days, 10:07:49 time: 0.7959 data_time: 0.0069 memory: 42336 loss: 0.1237 loss_ce: 0.1237 2023/02/26 21:58:25 - mmengine - INFO - Epoch(train) [64][2200/5047] lr: 2.1678e-05 eta: 4 days, 10:06:21 time: 0.8312 data_time: 0.0021 memory: 50505 loss: 0.1241 loss_ce: 0.1241 2023/02/26 21:59:49 - mmengine - INFO - Epoch(train) [64][2300/5047] lr: 2.1678e-05 eta: 4 days, 10:04:48 time: 0.8830 data_time: 0.0052 memory: 40522 loss: 0.1158 loss_ce: 0.1158 2023/02/26 22:01:15 - mmengine - INFO - Epoch(train) [64][2400/5047] lr: 2.1678e-05 eta: 4 days, 10:03:19 time: 0.8792 data_time: 0.0020 memory: 44617 loss: 0.1242 loss_ce: 0.1242 2023/02/26 22:02:41 - mmengine - INFO - Epoch(train) [64][2500/5047] lr: 2.1678e-05 eta: 4 days, 10:01:50 time: 0.8713 data_time: 0.0021 memory: 44631 loss: 0.1118 loss_ce: 0.1118 2023/02/26 22:04:08 - mmengine - INFO - Epoch(train) [64][2600/5047] lr: 2.1678e-05 eta: 4 days, 10:00:22 time: 0.8209 data_time: 0.0021 memory: 52282 loss: 0.1112 loss_ce: 0.1112 2023/02/26 22:05:34 - mmengine - INFO - Epoch(train) [64][2700/5047] lr: 2.1678e-05 eta: 4 days, 9:58:52 time: 0.8634 data_time: 0.0022 memory: 43613 loss: 0.1245 loss_ce: 0.1245 2023/02/26 22:07:00 - mmengine - INFO - Epoch(train) [64][2800/5047] lr: 2.1678e-05 eta: 4 days, 9:57:23 time: 0.8445 data_time: 0.0021 memory: 42649 loss: 0.1254 loss_ce: 0.1254 2023/02/26 22:08:29 - mmengine - INFO - Epoch(train) [64][2900/5047] lr: 2.1678e-05 eta: 4 days, 9:55:57 time: 0.9042 data_time: 0.0021 memory: 41419 loss: 0.1272 loss_ce: 0.1272 2023/02/26 22:09:55 - mmengine - INFO - Epoch(train) [64][3000/5047] lr: 2.1678e-05 eta: 4 days, 9:54:27 time: 0.8515 data_time: 0.0021 memory: 42718 loss: 0.1191 loss_ce: 0.1191 2023/02/26 22:10:28 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 22:11:20 - mmengine - INFO - Epoch(train) [64][3100/5047] lr: 2.1678e-05 eta: 4 days, 9:52:56 time: 0.9021 data_time: 0.0025 memory: 45217 loss: 0.1130 loss_ce: 0.1130 2023/02/26 22:12:45 - mmengine - INFO - Epoch(train) [64][3200/5047] lr: 2.1678e-05 eta: 4 days, 9:51:26 time: 0.8337 data_time: 0.0020 memory: 47447 loss: 0.1018 loss_ce: 0.1018 2023/02/26 22:14:12 - mmengine - INFO - Epoch(train) [64][3300/5047] lr: 2.1678e-05 eta: 4 days, 9:49:58 time: 0.8888 data_time: 0.0027 memory: 44632 loss: 0.1000 loss_ce: 0.1000 2023/02/26 22:15:39 - mmengine - INFO - Epoch(train) [64][3400/5047] lr: 2.1678e-05 eta: 4 days, 9:48:29 time: 0.8655 data_time: 0.0020 memory: 44956 loss: 0.1173 loss_ce: 0.1173 2023/02/26 22:17:06 - mmengine - INFO - Epoch(train) [64][3500/5047] lr: 2.1678e-05 eta: 4 days, 9:47:01 time: 0.8722 data_time: 0.0020 memory: 45643 loss: 0.1162 loss_ce: 0.1162 2023/02/26 22:18:34 - mmengine - INFO - Epoch(train) [64][3600/5047] lr: 2.1678e-05 eta: 4 days, 9:45:35 time: 0.9415 data_time: 0.0021 memory: 55562 loss: 0.1066 loss_ce: 0.1066 2023/02/26 22:19:59 - mmengine - INFO - Epoch(train) [64][3700/5047] lr: 2.1678e-05 eta: 4 days, 9:44:04 time: 0.8489 data_time: 0.0023 memory: 42649 loss: 0.1332 loss_ce: 0.1332 2023/02/26 22:21:27 - mmengine - INFO - Epoch(train) [64][3800/5047] lr: 2.1678e-05 eta: 4 days, 9:42:38 time: 0.8633 data_time: 0.0027 memory: 44250 loss: 0.1228 loss_ce: 0.1228 2023/02/26 22:22:54 - mmengine - INFO - Epoch(train) [64][3900/5047] lr: 2.1678e-05 eta: 4 days, 9:41:10 time: 0.9326 data_time: 0.0032 memory: 40825 loss: 0.1176 loss_ce: 0.1176 2023/02/26 22:24:21 - mmengine - INFO - Epoch(train) [64][4000/5047] lr: 2.1678e-05 eta: 4 days, 9:39:41 time: 0.8271 data_time: 0.0044 memory: 43613 loss: 0.1024 loss_ce: 0.1024 2023/02/26 22:24:56 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 22:25:48 - mmengine - INFO - Epoch(train) [64][4100/5047] lr: 2.1678e-05 eta: 4 days, 9:38:13 time: 0.8457 data_time: 0.0027 memory: 42649 loss: 0.1270 loss_ce: 0.1270 2023/02/26 22:27:14 - mmengine - INFO - Epoch(train) [64][4200/5047] lr: 2.1678e-05 eta: 4 days, 9:36:43 time: 0.8387 data_time: 0.0021 memory: 40825 loss: 0.1294 loss_ce: 0.1294 2023/02/26 22:28:41 - mmengine - INFO - Epoch(train) [64][4300/5047] lr: 2.1678e-05 eta: 4 days, 9:35:15 time: 0.9621 data_time: 0.0022 memory: 41942 loss: 0.1320 loss_ce: 0.1320 2023/02/26 22:30:10 - mmengine - INFO - Epoch(train) [64][4400/5047] lr: 2.1678e-05 eta: 4 days, 9:33:51 time: 0.8903 data_time: 0.0022 memory: 39681 loss: 0.1089 loss_ce: 0.1089 2023/02/26 22:31:36 - mmengine - INFO - Epoch(train) [64][4500/5047] lr: 2.1678e-05 eta: 4 days, 9:32:21 time: 0.8299 data_time: 0.0023 memory: 47003 loss: 0.1126 loss_ce: 0.1126 2023/02/26 22:33:00 - mmengine - INFO - Epoch(train) [64][4600/5047] lr: 2.1678e-05 eta: 4 days, 9:30:48 time: 0.8308 data_time: 0.0032 memory: 37333 loss: 0.1140 loss_ce: 0.1140 2023/02/26 22:34:25 - mmengine - INFO - Epoch(train) [64][4700/5047] lr: 2.1678e-05 eta: 4 days, 9:29:18 time: 0.8580 data_time: 0.0022 memory: 50368 loss: 0.1104 loss_ce: 0.1104 2023/02/26 22:35:51 - mmengine - INFO - Epoch(train) [64][4800/5047] lr: 2.1678e-05 eta: 4 days, 9:27:48 time: 0.7975 data_time: 0.0020 memory: 41419 loss: 0.1273 loss_ce: 0.1273 2023/02/26 22:37:17 - mmengine - INFO - Epoch(train) [64][4900/5047] lr: 2.1678e-05 eta: 4 days, 9:26:18 time: 0.8475 data_time: 0.0021 memory: 43947 loss: 0.1237 loss_ce: 0.1237 2023/02/26 22:38:43 - mmengine - INFO - Epoch(train) [64][5000/5047] lr: 2.1678e-05 eta: 4 days, 9:24:49 time: 0.8610 data_time: 0.0038 memory: 47905 loss: 0.1234 loss_ce: 0.1234 2023/02/26 22:39:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 22:39:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 22:39:24 - mmengine - INFO - Saving checkpoint at 64 epochs 2023/02/26 22:40:55 - mmengine - INFO - Epoch(train) [65][ 100/5047] lr: 2.1478e-05 eta: 4 days, 9:22:35 time: 0.8983 data_time: 0.0048 memory: 55562 loss: 0.1197 loss_ce: 0.1197 2023/02/26 22:42:20 - mmengine - INFO - Epoch(train) [65][ 200/5047] lr: 2.1478e-05 eta: 4 days, 9:21:05 time: 0.8340 data_time: 0.0026 memory: 51578 loss: 0.1275 loss_ce: 0.1275 2023/02/26 22:43:45 - mmengine - INFO - Epoch(train) [65][ 300/5047] lr: 2.1478e-05 eta: 4 days, 9:19:33 time: 0.9206 data_time: 0.0023 memory: 51443 loss: 0.1221 loss_ce: 0.1221 2023/02/26 22:45:10 - mmengine - INFO - Epoch(train) [65][ 400/5047] lr: 2.1478e-05 eta: 4 days, 9:18:03 time: 0.8516 data_time: 0.0021 memory: 48210 loss: 0.1283 loss_ce: 0.1283 2023/02/26 22:46:35 - mmengine - INFO - Epoch(train) [65][ 500/5047] lr: 2.1478e-05 eta: 4 days, 9:16:33 time: 0.8832 data_time: 0.0021 memory: 45277 loss: 0.1221 loss_ce: 0.1221 2023/02/26 22:48:03 - mmengine - INFO - Epoch(train) [65][ 600/5047] lr: 2.1478e-05 eta: 4 days, 9:15:05 time: 0.8626 data_time: 0.0044 memory: 55562 loss: 0.1320 loss_ce: 0.1320 2023/02/26 22:49:30 - mmengine - INFO - Epoch(train) [65][ 700/5047] lr: 2.1478e-05 eta: 4 days, 9:13:38 time: 0.8349 data_time: 0.0023 memory: 45787 loss: 0.1145 loss_ce: 0.1145 2023/02/26 22:50:57 - mmengine - INFO - Epoch(train) [65][ 800/5047] lr: 2.1478e-05 eta: 4 days, 9:12:09 time: 0.8697 data_time: 0.0021 memory: 40535 loss: 0.1193 loss_ce: 0.1193 2023/02/26 22:52:23 - mmengine - INFO - Epoch(train) [65][ 900/5047] lr: 2.1478e-05 eta: 4 days, 9:10:39 time: 0.8647 data_time: 0.0021 memory: 47074 loss: 0.1234 loss_ce: 0.1234 2023/02/26 22:53:42 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 22:53:49 - mmengine - INFO - Epoch(train) [65][1000/5047] lr: 2.1478e-05 eta: 4 days, 9:09:11 time: 0.8524 data_time: 0.0025 memory: 38894 loss: 0.1270 loss_ce: 0.1270 2023/02/26 22:55:16 - mmengine - INFO - Epoch(train) [65][1100/5047] lr: 2.1478e-05 eta: 4 days, 9:07:42 time: 0.8925 data_time: 0.0026 memory: 42965 loss: 0.1136 loss_ce: 0.1136 2023/02/26 22:56:41 - mmengine - INFO - Epoch(train) [65][1200/5047] lr: 2.1478e-05 eta: 4 days, 9:06:12 time: 0.8791 data_time: 0.0022 memory: 40825 loss: 0.1047 loss_ce: 0.1047 2023/02/26 22:58:07 - mmengine - INFO - Epoch(train) [65][1300/5047] lr: 2.1478e-05 eta: 4 days, 9:04:42 time: 0.9036 data_time: 0.0022 memory: 41419 loss: 0.1091 loss_ce: 0.1091 2023/02/26 22:59:34 - mmengine - INFO - Epoch(train) [65][1400/5047] lr: 2.1478e-05 eta: 4 days, 9:03:14 time: 0.8736 data_time: 0.0031 memory: 44448 loss: 0.1146 loss_ce: 0.1146 2023/02/26 23:01:00 - mmengine - INFO - Epoch(train) [65][1500/5047] lr: 2.1478e-05 eta: 4 days, 9:01:45 time: 0.8390 data_time: 0.0022 memory: 42327 loss: 0.1012 loss_ce: 0.1012 2023/02/26 23:02:26 - mmengine - INFO - Epoch(train) [65][1600/5047] lr: 2.1478e-05 eta: 4 days, 9:00:15 time: 0.8182 data_time: 0.0032 memory: 43947 loss: 0.1191 loss_ce: 0.1191 2023/02/26 23:03:52 - mmengine - INFO - Epoch(train) [65][1700/5047] lr: 2.1478e-05 eta: 4 days, 8:58:45 time: 0.8488 data_time: 0.0020 memory: 44722 loss: 0.1129 loss_ce: 0.1129 2023/02/26 23:05:17 - mmengine - INFO - Epoch(train) [65][1800/5047] lr: 2.1478e-05 eta: 4 days, 8:57:14 time: 0.8347 data_time: 0.0021 memory: 42024 loss: 0.1201 loss_ce: 0.1201 2023/02/26 23:06:41 - mmengine - INFO - Epoch(train) [65][1900/5047] lr: 2.1478e-05 eta: 4 days, 8:55:43 time: 0.8611 data_time: 0.0032 memory: 41724 loss: 0.1265 loss_ce: 0.1265 2023/02/26 23:07:59 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 23:08:05 - mmengine - INFO - Epoch(train) [65][2000/5047] lr: 2.1478e-05 eta: 4 days, 8:54:11 time: 0.8335 data_time: 0.0034 memory: 41419 loss: 0.1301 loss_ce: 0.1301 2023/02/26 23:09:30 - mmengine - INFO - Epoch(train) [65][2100/5047] lr: 2.1478e-05 eta: 4 days, 8:52:40 time: 0.8442 data_time: 0.0023 memory: 48929 loss: 0.1230 loss_ce: 0.1230 2023/02/26 23:10:56 - mmengine - INFO - Epoch(train) [65][2200/5047] lr: 2.1478e-05 eta: 4 days, 8:51:10 time: 0.8669 data_time: 0.0023 memory: 40535 loss: 0.1182 loss_ce: 0.1182 2023/02/26 23:12:21 - mmengine - INFO - Epoch(train) [65][2300/5047] lr: 2.1478e-05 eta: 4 days, 8:49:39 time: 0.8734 data_time: 0.0028 memory: 44787 loss: 0.1195 loss_ce: 0.1195 2023/02/26 23:13:47 - mmengine - INFO - Epoch(train) [65][2400/5047] lr: 2.1478e-05 eta: 4 days, 8:48:09 time: 0.8247 data_time: 0.0020 memory: 49241 loss: 0.1120 loss_ce: 0.1120 2023/02/26 23:15:12 - mmengine - INFO - Epoch(train) [65][2500/5047] lr: 2.1478e-05 eta: 4 days, 8:46:39 time: 0.8854 data_time: 0.0021 memory: 42022 loss: 0.1041 loss_ce: 0.1041 2023/02/26 23:16:37 - mmengine - INFO - Epoch(train) [65][2600/5047] lr: 2.1478e-05 eta: 4 days, 8:45:07 time: 0.8171 data_time: 0.0024 memory: 44278 loss: 0.1189 loss_ce: 0.1189 2023/02/26 23:18:01 - mmengine - INFO - Epoch(train) [65][2700/5047] lr: 2.1478e-05 eta: 4 days, 8:43:35 time: 0.8516 data_time: 0.0023 memory: 55562 loss: 0.1222 loss_ce: 0.1222 2023/02/26 23:19:25 - mmengine - INFO - Epoch(train) [65][2800/5047] lr: 2.1478e-05 eta: 4 days, 8:42:04 time: 0.8699 data_time: 0.0019 memory: 55562 loss: 0.1119 loss_ce: 0.1119 2023/02/26 23:20:51 - mmengine - INFO - Epoch(train) [65][2900/5047] lr: 2.1478e-05 eta: 4 days, 8:40:35 time: 0.8586 data_time: 0.0020 memory: 40535 loss: 0.1221 loss_ce: 0.1221 2023/02/26 23:22:11 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 23:22:17 - mmengine - INFO - Epoch(train) [65][3000/5047] lr: 2.1478e-05 eta: 4 days, 8:39:05 time: 0.8048 data_time: 0.0021 memory: 42336 loss: 0.1299 loss_ce: 0.1299 2023/02/26 23:23:43 - mmengine - INFO - Epoch(train) [65][3100/5047] lr: 2.1478e-05 eta: 4 days, 8:37:35 time: 0.8565 data_time: 0.0020 memory: 41122 loss: 0.1200 loss_ce: 0.1200 2023/02/26 23:25:11 - mmengine - INFO - Epoch(train) [65][3200/5047] lr: 2.1478e-05 eta: 4 days, 8:36:09 time: 0.8563 data_time: 0.0022 memory: 43919 loss: 0.1229 loss_ce: 0.1229 2023/02/26 23:26:38 - mmengine - INFO - Epoch(train) [65][3300/5047] lr: 2.1478e-05 eta: 4 days, 8:34:41 time: 0.9410 data_time: 0.0020 memory: 55537 loss: 0.1136 loss_ce: 0.1136 2023/02/26 23:28:04 - mmengine - INFO - Epoch(train) [65][3400/5047] lr: 2.1478e-05 eta: 4 days, 8:33:11 time: 0.7865 data_time: 0.0023 memory: 39398 loss: 0.1207 loss_ce: 0.1207 2023/02/26 23:29:30 - mmengine - INFO - Epoch(train) [65][3500/5047] lr: 2.1478e-05 eta: 4 days, 8:31:42 time: 0.8952 data_time: 0.0021 memory: 46182 loss: 0.1283 loss_ce: 0.1283 2023/02/26 23:30:58 - mmengine - INFO - Epoch(train) [65][3600/5047] lr: 2.1478e-05 eta: 4 days, 8:30:15 time: 0.8465 data_time: 0.0020 memory: 53809 loss: 0.1204 loss_ce: 0.1204 2023/02/26 23:32:22 - mmengine - INFO - Epoch(train) [65][3700/5047] lr: 2.1478e-05 eta: 4 days, 8:28:43 time: 0.8414 data_time: 0.0020 memory: 43222 loss: 0.1338 loss_ce: 0.1338 2023/02/26 23:33:48 - mmengine - INFO - Epoch(train) [65][3800/5047] lr: 2.1478e-05 eta: 4 days, 8:27:14 time: 0.9032 data_time: 0.0023 memory: 41724 loss: 0.1214 loss_ce: 0.1214 2023/02/26 23:35:15 - mmengine - INFO - Epoch(train) [65][3900/5047] lr: 2.1478e-05 eta: 4 days, 8:25:45 time: 0.9496 data_time: 0.0023 memory: 42336 loss: 0.1261 loss_ce: 0.1261 2023/02/26 23:36:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 23:36:40 - mmengine - INFO - Epoch(train) [65][4000/5047] lr: 2.1478e-05 eta: 4 days, 8:24:15 time: 0.8368 data_time: 0.0023 memory: 40691 loss: 0.1117 loss_ce: 0.1117 2023/02/26 23:38:07 - mmengine - INFO - Epoch(train) [65][4100/5047] lr: 2.1478e-05 eta: 4 days, 8:22:47 time: 0.9117 data_time: 0.0021 memory: 38243 loss: 0.1176 loss_ce: 0.1176 2023/02/26 23:39:33 - mmengine - INFO - Epoch(train) [65][4200/5047] lr: 2.1478e-05 eta: 4 days, 8:21:18 time: 0.8670 data_time: 0.0026 memory: 47074 loss: 0.1117 loss_ce: 0.1117 2023/02/26 23:40:58 - mmengine - INFO - Epoch(train) [65][4300/5047] lr: 2.1478e-05 eta: 4 days, 8:19:46 time: 0.8307 data_time: 0.0020 memory: 42965 loss: 0.1181 loss_ce: 0.1181 2023/02/26 23:42:25 - mmengine - INFO - Epoch(train) [65][4400/5047] lr: 2.1478e-05 eta: 4 days, 8:18:18 time: 0.8574 data_time: 0.0021 memory: 44079 loss: 0.1161 loss_ce: 0.1161 2023/02/26 23:43:51 - mmengine - INFO - Epoch(train) [65][4500/5047] lr: 2.1478e-05 eta: 4 days, 8:16:49 time: 0.8373 data_time: 0.0021 memory: 47813 loss: 0.1215 loss_ce: 0.1215 2023/02/26 23:45:18 - mmengine - INFO - Epoch(train) [65][4600/5047] lr: 2.1478e-05 eta: 4 days, 8:15:22 time: 0.8652 data_time: 0.0046 memory: 41556 loss: 0.1191 loss_ce: 0.1191 2023/02/26 23:46:45 - mmengine - INFO - Epoch(train) [65][4700/5047] lr: 2.1478e-05 eta: 4 days, 8:13:53 time: 0.8446 data_time: 0.0021 memory: 43585 loss: 0.1201 loss_ce: 0.1201 2023/02/26 23:48:11 - mmengine - INFO - Epoch(train) [65][4800/5047] lr: 2.1478e-05 eta: 4 days, 8:12:24 time: 0.8997 data_time: 0.0022 memory: 55562 loss: 0.1193 loss_ce: 0.1193 2023/02/26 23:49:37 - mmengine - INFO - Epoch(train) [65][4900/5047] lr: 2.1478e-05 eta: 4 days, 8:10:55 time: 0.8670 data_time: 0.0030 memory: 47293 loss: 0.1268 loss_ce: 0.1268 2023/02/26 23:50:57 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 23:51:04 - mmengine - INFO - Epoch(train) [65][5000/5047] lr: 2.1478e-05 eta: 4 days, 8:09:26 time: 0.8461 data_time: 0.0021 memory: 45643 loss: 0.1275 loss_ce: 0.1275 2023/02/26 23:51:43 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/26 23:51:43 - mmengine - INFO - Saving checkpoint at 65 epochs 2023/02/26 23:53:17 - mmengine - INFO - Epoch(train) [66][ 100/5047] lr: 2.1277e-05 eta: 4 days, 8:07:14 time: 0.8766 data_time: 0.0022 memory: 42336 loss: 0.1132 loss_ce: 0.1132 2023/02/26 23:54:43 - mmengine - INFO - Epoch(train) [66][ 200/5047] lr: 2.1277e-05 eta: 4 days, 8:05:45 time: 0.8663 data_time: 0.0020 memory: 52127 loss: 0.1239 loss_ce: 0.1239 2023/02/26 23:56:09 - mmengine - INFO - Epoch(train) [66][ 300/5047] lr: 2.1277e-05 eta: 4 days, 8:04:17 time: 0.8807 data_time: 0.0058 memory: 46713 loss: 0.1227 loss_ce: 0.1227 2023/02/26 23:57:36 - mmengine - INFO - Epoch(train) [66][ 400/5047] lr: 2.1277e-05 eta: 4 days, 8:02:49 time: 0.8710 data_time: 0.0020 memory: 50505 loss: 0.1270 loss_ce: 0.1270 2023/02/26 23:59:02 - mmengine - INFO - Epoch(train) [66][ 500/5047] lr: 2.1277e-05 eta: 4 days, 8:01:19 time: 0.8332 data_time: 0.0021 memory: 51281 loss: 0.1096 loss_ce: 0.1096 2023/02/27 00:00:28 - mmengine - INFO - Epoch(train) [66][ 600/5047] lr: 2.1277e-05 eta: 4 days, 7:59:50 time: 0.8252 data_time: 0.0020 memory: 40540 loss: 0.1188 loss_ce: 0.1188 2023/02/27 00:01:55 - mmengine - INFO - Epoch(train) [66][ 700/5047] lr: 2.1277e-05 eta: 4 days, 7:58:21 time: 0.9325 data_time: 0.0019 memory: 43613 loss: 0.1126 loss_ce: 0.1126 2023/02/27 00:03:22 - mmengine - INFO - Epoch(train) [66][ 800/5047] lr: 2.1277e-05 eta: 4 days, 7:56:54 time: 0.8674 data_time: 0.0020 memory: 41161 loss: 0.1241 loss_ce: 0.1241 2023/02/27 00:04:47 - mmengine - INFO - Epoch(train) [66][ 900/5047] lr: 2.1277e-05 eta: 4 days, 7:55:23 time: 0.9032 data_time: 0.0020 memory: 46460 loss: 0.1103 loss_ce: 0.1103 2023/02/27 00:05:26 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 00:06:13 - mmengine - INFO - Epoch(train) [66][1000/5047] lr: 2.1277e-05 eta: 4 days, 7:53:54 time: 0.8503 data_time: 0.0019 memory: 42336 loss: 0.1263 loss_ce: 0.1263 2023/02/27 00:07:41 - mmengine - INFO - Epoch(train) [66][1100/5047] lr: 2.1277e-05 eta: 4 days, 7:52:27 time: 0.8976 data_time: 0.0019 memory: 42941 loss: 0.1190 loss_ce: 0.1190 2023/02/27 00:09:06 - mmengine - INFO - Epoch(train) [66][1200/5047] lr: 2.1277e-05 eta: 4 days, 7:50:56 time: 0.8719 data_time: 0.0024 memory: 40241 loss: 0.1170 loss_ce: 0.1170 2023/02/27 00:10:33 - mmengine - INFO - Epoch(train) [66][1300/5047] lr: 2.1277e-05 eta: 4 days, 7:49:28 time: 0.8815 data_time: 0.0028 memory: 48188 loss: 0.1169 loss_ce: 0.1169 2023/02/27 00:12:01 - mmengine - INFO - Epoch(train) [66][1400/5047] lr: 2.1277e-05 eta: 4 days, 7:48:01 time: 0.8444 data_time: 0.0046 memory: 44617 loss: 0.1279 loss_ce: 0.1279 2023/02/27 00:13:24 - mmengine - INFO - Epoch(train) [66][1500/5047] lr: 2.1277e-05 eta: 4 days, 7:46:27 time: 0.8375 data_time: 0.0026 memory: 40352 loss: 0.1123 loss_ce: 0.1123 2023/02/27 00:14:51 - mmengine - INFO - Epoch(train) [66][1600/5047] lr: 2.1277e-05 eta: 4 days, 7:44:59 time: 0.9119 data_time: 0.0020 memory: 45985 loss: 0.1282 loss_ce: 0.1282 2023/02/27 00:16:18 - mmengine - INFO - Epoch(train) [66][1700/5047] lr: 2.1277e-05 eta: 4 days, 7:43:32 time: 0.8810 data_time: 0.0020 memory: 51308 loss: 0.1205 loss_ce: 0.1205 2023/02/27 00:17:46 - mmengine - INFO - Epoch(train) [66][1800/5047] lr: 2.1277e-05 eta: 4 days, 7:42:05 time: 0.8547 data_time: 0.0056 memory: 45302 loss: 0.1169 loss_ce: 0.1169 2023/02/27 00:19:14 - mmengine - INFO - Epoch(train) [66][1900/5047] lr: 2.1277e-05 eta: 4 days, 7:40:38 time: 0.9132 data_time: 0.0020 memory: 47813 loss: 0.1266 loss_ce: 0.1266 2023/02/27 00:19:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 00:20:39 - mmengine - INFO - Epoch(train) [66][2000/5047] lr: 2.1277e-05 eta: 4 days, 7:39:08 time: 0.8762 data_time: 0.0020 memory: 44971 loss: 0.1150 loss_ce: 0.1150 2023/02/27 00:22:05 - mmengine - INFO - Epoch(train) [66][2100/5047] lr: 2.1277e-05 eta: 4 days, 7:37:38 time: 0.8533 data_time: 0.0020 memory: 51795 loss: 0.1188 loss_ce: 0.1188 2023/02/27 00:23:31 - mmengine - INFO - Epoch(train) [66][2200/5047] lr: 2.1277e-05 eta: 4 days, 7:36:09 time: 0.8269 data_time: 0.0035 memory: 55562 loss: 0.1260 loss_ce: 0.1260 2023/02/27 00:24:58 - mmengine - INFO - Epoch(train) [66][2300/5047] lr: 2.1277e-05 eta: 4 days, 7:34:41 time: 0.8822 data_time: 0.0030 memory: 55562 loss: 0.1143 loss_ce: 0.1143 2023/02/27 00:26:24 - mmengine - INFO - Epoch(train) [66][2400/5047] lr: 2.1277e-05 eta: 4 days, 7:33:11 time: 0.8730 data_time: 0.0019 memory: 39126 loss: 0.1149 loss_ce: 0.1149 2023/02/27 00:27:51 - mmengine - INFO - Epoch(train) [66][2500/5047] lr: 2.1277e-05 eta: 4 days, 7:31:45 time: 0.8651 data_time: 0.0021 memory: 40535 loss: 0.1287 loss_ce: 0.1287 2023/02/27 00:29:19 - mmengine - INFO - Epoch(train) [66][2600/5047] lr: 2.1277e-05 eta: 4 days, 7:30:17 time: 0.8423 data_time: 0.0022 memory: 55562 loss: 0.1243 loss_ce: 0.1243 2023/02/27 00:30:42 - mmengine - INFO - Epoch(train) [66][2700/5047] lr: 2.1277e-05 eta: 4 days, 7:28:44 time: 0.7940 data_time: 0.0021 memory: 45289 loss: 0.1148 loss_ce: 0.1148 2023/02/27 00:32:09 - mmengine - INFO - Epoch(train) [66][2800/5047] lr: 2.1277e-05 eta: 4 days, 7:27:17 time: 0.9026 data_time: 0.0022 memory: 41419 loss: 0.1179 loss_ce: 0.1179 2023/02/27 00:33:36 - mmengine - INFO - Epoch(train) [66][2900/5047] lr: 2.1277e-05 eta: 4 days, 7:25:48 time: 0.8714 data_time: 0.0049 memory: 55562 loss: 0.1094 loss_ce: 0.1094 2023/02/27 00:34:15 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 00:35:02 - mmengine - INFO - Epoch(train) [66][3000/5047] lr: 2.1277e-05 eta: 4 days, 7:24:19 time: 0.8689 data_time: 0.0021 memory: 44621 loss: 0.1209 loss_ce: 0.1209 2023/02/27 00:36:28 - mmengine - INFO - Epoch(train) [66][3100/5047] lr: 2.1277e-05 eta: 4 days, 7:22:50 time: 0.8302 data_time: 0.0052 memory: 55562 loss: 0.1117 loss_ce: 0.1117 2023/02/27 00:37:53 - mmengine - INFO - Epoch(train) [66][3200/5047] lr: 2.1277e-05 eta: 4 days, 7:21:19 time: 0.8691 data_time: 0.0031 memory: 42772 loss: 0.1231 loss_ce: 0.1231 2023/02/27 00:39:19 - mmengine - INFO - Epoch(train) [66][3300/5047] lr: 2.1277e-05 eta: 4 days, 7:19:49 time: 0.8082 data_time: 0.0019 memory: 42628 loss: 0.1202 loss_ce: 0.1202 2023/02/27 00:40:46 - mmengine - INFO - Epoch(train) [66][3400/5047] lr: 2.1277e-05 eta: 4 days, 7:18:22 time: 0.9064 data_time: 0.0020 memory: 43420 loss: 0.1171 loss_ce: 0.1171 2023/02/27 00:42:13 - mmengine - INFO - Epoch(train) [66][3500/5047] lr: 2.1277e-05 eta: 4 days, 7:16:54 time: 0.8455 data_time: 0.0061 memory: 48891 loss: 0.1277 loss_ce: 0.1277 2023/02/27 00:43:38 - mmengine - INFO - Epoch(train) [66][3600/5047] lr: 2.1277e-05 eta: 4 days, 7:15:24 time: 0.8195 data_time: 0.0022 memory: 41097 loss: 0.1269 loss_ce: 0.1269 2023/02/27 00:45:05 - mmengine - INFO - Epoch(train) [66][3700/5047] lr: 2.1277e-05 eta: 4 days, 7:13:55 time: 0.8878 data_time: 0.0061 memory: 41193 loss: 0.1087 loss_ce: 0.1087 2023/02/27 00:46:28 - mmengine - INFO - Epoch(train) [66][3800/5047] lr: 2.1277e-05 eta: 4 days, 7:12:22 time: 0.8244 data_time: 0.0021 memory: 43440 loss: 0.1038 loss_ce: 0.1038 2023/02/27 00:47:55 - mmengine - INFO - Epoch(train) [66][3900/5047] lr: 2.1277e-05 eta: 4 days, 7:10:54 time: 0.8435 data_time: 0.0023 memory: 41122 loss: 0.1218 loss_ce: 0.1218 2023/02/27 00:48:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 00:49:22 - mmengine - INFO - Epoch(train) [66][4000/5047] lr: 2.1277e-05 eta: 4 days, 7:09:25 time: 0.9045 data_time: 0.0022 memory: 44617 loss: 0.1233 loss_ce: 0.1233 2023/02/27 00:50:47 - mmengine - INFO - Epoch(train) [66][4100/5047] lr: 2.1277e-05 eta: 4 days, 7:07:55 time: 0.8836 data_time: 0.0064 memory: 55562 loss: 0.1112 loss_ce: 0.1112 2023/02/27 00:52:13 - mmengine - INFO - Epoch(train) [66][4200/5047] lr: 2.1277e-05 eta: 4 days, 7:06:26 time: 0.8483 data_time: 0.0020 memory: 46007 loss: 0.1113 loss_ce: 0.1113 2023/02/27 00:53:38 - mmengine - INFO - Epoch(train) [66][4300/5047] lr: 2.1277e-05 eta: 4 days, 7:04:55 time: 0.8652 data_time: 0.0020 memory: 43557 loss: 0.1271 loss_ce: 0.1271 2023/02/27 00:55:05 - mmengine - INFO - Epoch(train) [66][4400/5047] lr: 2.1277e-05 eta: 4 days, 7:03:27 time: 0.8893 data_time: 0.0023 memory: 53809 loss: 0.1217 loss_ce: 0.1217 2023/02/27 01:05:01 - mmengine - INFO - Epoch(train) [66][4500/5047] lr: 2.1277e-05 eta: 4 days, 7:13:20 time: 0.8424 data_time: 0.0031 memory: 47906 loss: 0.1172 loss_ce: 0.1172 2023/02/27 01:06:28 - mmengine - INFO - Epoch(train) [66][4600/5047] lr: 2.1277e-05 eta: 4 days, 7:11:51 time: 0.8624 data_time: 0.0027 memory: 44617 loss: 0.1191 loss_ce: 0.1191 2023/02/27 01:07:55 - mmengine - INFO - Epoch(train) [66][4700/5047] lr: 2.1277e-05 eta: 4 days, 7:10:23 time: 0.9310 data_time: 0.0061 memory: 48301 loss: 0.1000 loss_ce: 0.1000 2023/02/27 01:09:22 - mmengine - INFO - Epoch(train) [66][4800/5047] lr: 2.1277e-05 eta: 4 days, 7:08:54 time: 0.8615 data_time: 0.0021 memory: 52543 loss: 0.1025 loss_ce: 0.1025 2023/02/27 01:10:49 - mmengine - INFO - Epoch(train) [66][4900/5047] lr: 2.1277e-05 eta: 4 days, 7:07:27 time: 0.8811 data_time: 0.0021 memory: 55559 loss: 0.1295 loss_ce: 0.1295 2023/02/27 01:11:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 01:12:14 - mmengine - INFO - Epoch(train) [66][5000/5047] lr: 2.1277e-05 eta: 4 days, 7:05:56 time: 0.8677 data_time: 0.0020 memory: 48565 loss: 0.1299 loss_ce: 0.1299 2023/02/27 01:12:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 01:12:55 - mmengine - INFO - Saving checkpoint at 66 epochs 2023/02/27 01:14:25 - mmengine - INFO - Epoch(train) [67][ 100/5047] lr: 2.1076e-05 eta: 4 days, 7:03:43 time: 0.8310 data_time: 0.0028 memory: 55562 loss: 0.1120 loss_ce: 0.1120 2023/02/27 01:15:52 - mmengine - INFO - Epoch(train) [67][ 200/5047] lr: 2.1076e-05 eta: 4 days, 7:02:15 time: 0.9179 data_time: 0.0020 memory: 44412 loss: 0.1147 loss_ce: 0.1147 2023/02/27 01:17:18 - mmengine - INFO - Epoch(train) [67][ 300/5047] lr: 2.1076e-05 eta: 4 days, 7:00:45 time: 0.8337 data_time: 0.0033 memory: 42024 loss: 0.1186 loss_ce: 0.1186 2023/02/27 01:18:43 - mmengine - INFO - Epoch(train) [67][ 400/5047] lr: 2.1076e-05 eta: 4 days, 6:59:14 time: 0.8548 data_time: 0.0021 memory: 41419 loss: 0.1068 loss_ce: 0.1068 2023/02/27 01:20:11 - mmengine - INFO - Epoch(train) [67][ 500/5047] lr: 2.1076e-05 eta: 4 days, 6:57:46 time: 0.8559 data_time: 0.0021 memory: 49303 loss: 0.1157 loss_ce: 0.1157 2023/02/27 01:21:36 - mmengine - INFO - Epoch(train) [67][ 600/5047] lr: 2.1076e-05 eta: 4 days, 6:56:15 time: 0.8418 data_time: 0.0024 memory: 43613 loss: 0.1087 loss_ce: 0.1087 2023/02/27 01:23:00 - mmengine - INFO - Epoch(train) [67][ 700/5047] lr: 2.1076e-05 eta: 4 days, 6:54:44 time: 0.8332 data_time: 0.0023 memory: 41161 loss: 0.1286 loss_ce: 0.1286 2023/02/27 01:24:28 - mmengine - INFO - Epoch(train) [67][ 800/5047] lr: 2.1076e-05 eta: 4 days, 6:53:17 time: 0.8129 data_time: 0.0021 memory: 44617 loss: 0.1265 loss_ce: 0.1265 2023/02/27 01:25:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 01:25:56 - mmengine - INFO - Epoch(train) [67][ 900/5047] lr: 2.1076e-05 eta: 4 days, 6:51:50 time: 0.8579 data_time: 0.0022 memory: 43420 loss: 0.1169 loss_ce: 0.1169 2023/02/27 01:27:25 - mmengine - INFO - Epoch(train) [67][1000/5047] lr: 2.1076e-05 eta: 4 days, 6:50:24 time: 0.8874 data_time: 0.0025 memory: 55562 loss: 0.1277 loss_ce: 0.1277 2023/02/27 01:28:50 - mmengine - INFO - Epoch(train) [67][1100/5047] lr: 2.1076e-05 eta: 4 days, 6:48:53 time: 0.8772 data_time: 0.0020 memory: 47813 loss: 0.1215 loss_ce: 0.1215 2023/02/27 01:30:19 - mmengine - INFO - Epoch(train) [67][1200/5047] lr: 2.1076e-05 eta: 4 days, 6:47:27 time: 1.0277 data_time: 0.0020 memory: 43748 loss: 0.1069 loss_ce: 0.1069 2023/02/27 01:31:45 - mmengine - INFO - Epoch(train) [67][1300/5047] lr: 2.1076e-05 eta: 4 days, 6:45:57 time: 0.8776 data_time: 0.0021 memory: 52891 loss: 0.1175 loss_ce: 0.1175 2023/02/27 01:33:11 - mmengine - INFO - Epoch(train) [67][1400/5047] lr: 2.1076e-05 eta: 4 days, 6:44:28 time: 0.8668 data_time: 0.0020 memory: 42648 loss: 0.1311 loss_ce: 0.1311 2023/02/27 01:34:37 - mmengine - INFO - Epoch(train) [67][1500/5047] lr: 2.1076e-05 eta: 4 days, 6:42:59 time: 0.8250 data_time: 0.0023 memory: 42592 loss: 0.1235 loss_ce: 0.1235 2023/02/27 01:36:03 - mmengine - INFO - Epoch(train) [67][1600/5047] lr: 2.1076e-05 eta: 4 days, 6:41:29 time: 0.8486 data_time: 0.0027 memory: 44374 loss: 0.1107 loss_ce: 0.1107 2023/02/27 01:37:29 - mmengine - INFO - Epoch(train) [67][1700/5047] lr: 2.1076e-05 eta: 4 days, 6:40:00 time: 0.8771 data_time: 0.0021 memory: 39681 loss: 0.1209 loss_ce: 0.1209 2023/02/27 01:38:57 - mmengine - INFO - Epoch(train) [67][1800/5047] lr: 2.1076e-05 eta: 4 days, 6:38:32 time: 0.8658 data_time: 0.0025 memory: 49240 loss: 0.1088 loss_ce: 0.1088 2023/02/27 01:40:21 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 01:40:22 - mmengine - INFO - Epoch(train) [67][1900/5047] lr: 2.1076e-05 eta: 4 days, 6:37:02 time: 0.9132 data_time: 0.0020 memory: 38563 loss: 0.1289 loss_ce: 0.1289 2023/02/27 01:41:49 - mmengine - INFO - Epoch(train) [67][2000/5047] lr: 2.1076e-05 eta: 4 days, 6:35:33 time: 0.8728 data_time: 0.0022 memory: 42336 loss: 0.1112 loss_ce: 0.1112 2023/02/27 01:43:16 - mmengine - INFO - Epoch(train) [67][2100/5047] lr: 2.1076e-05 eta: 4 days, 6:34:05 time: 0.8681 data_time: 0.0028 memory: 44131 loss: 0.1163 loss_ce: 0.1163 2023/02/27 01:44:42 - mmengine - INFO - Epoch(train) [67][2200/5047] lr: 2.1076e-05 eta: 4 days, 6:32:35 time: 0.8216 data_time: 0.0020 memory: 42293 loss: 0.1211 loss_ce: 0.1211 2023/02/27 01:46:07 - mmengine - INFO - Epoch(train) [67][2300/5047] lr: 2.1076e-05 eta: 4 days, 6:31:05 time: 0.8454 data_time: 0.0021 memory: 43613 loss: 0.1067 loss_ce: 0.1067 2023/02/27 01:47:34 - mmengine - INFO - Epoch(train) [67][2400/5047] lr: 2.1076e-05 eta: 4 days, 6:29:37 time: 0.8537 data_time: 0.0021 memory: 52964 loss: 0.1137 loss_ce: 0.1137 2023/02/27 01:49:00 - mmengine - INFO - Epoch(train) [67][2500/5047] lr: 2.1076e-05 eta: 4 days, 6:28:06 time: 0.8429 data_time: 0.0020 memory: 44956 loss: 0.1277 loss_ce: 0.1277 2023/02/27 01:50:26 - mmengine - INFO - Epoch(train) [67][2600/5047] lr: 2.1076e-05 eta: 4 days, 6:26:37 time: 0.8598 data_time: 0.0021 memory: 41419 loss: 0.1100 loss_ce: 0.1100 2023/02/27 01:51:51 - mmengine - INFO - Epoch(train) [67][2700/5047] lr: 2.1076e-05 eta: 4 days, 6:25:06 time: 0.8685 data_time: 0.0022 memory: 46713 loss: 0.1368 loss_ce: 0.1368 2023/02/27 01:53:14 - mmengine - INFO - Epoch(train) [67][2800/5047] lr: 2.1076e-05 eta: 4 days, 6:23:33 time: 0.8235 data_time: 0.0021 memory: 40241 loss: 0.1108 loss_ce: 0.1108 2023/02/27 01:54:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 01:54:40 - mmengine - INFO - Epoch(train) [67][2900/5047] lr: 2.1076e-05 eta: 4 days, 6:22:03 time: 0.9641 data_time: 0.0032 memory: 43613 loss: 0.1278 loss_ce: 0.1278 2023/02/27 01:56:05 - mmengine - INFO - Epoch(train) [67][3000/5047] lr: 2.1076e-05 eta: 4 days, 6:20:32 time: 0.8374 data_time: 0.0020 memory: 40241 loss: 0.1218 loss_ce: 0.1218 2023/02/27 01:57:31 - mmengine - INFO - Epoch(train) [67][3100/5047] lr: 2.1076e-05 eta: 4 days, 6:19:02 time: 0.8454 data_time: 0.0022 memory: 40859 loss: 0.1157 loss_ce: 0.1157 2023/02/27 01:58:57 - mmengine - INFO - Epoch(train) [67][3200/5047] lr: 2.1076e-05 eta: 4 days, 6:17:32 time: 0.9145 data_time: 0.0024 memory: 45302 loss: 0.1288 loss_ce: 0.1288 2023/02/27 02:00:24 - mmengine - INFO - Epoch(train) [67][3300/5047] lr: 2.1076e-05 eta: 4 days, 6:16:04 time: 0.8550 data_time: 0.0025 memory: 44617 loss: 0.1183 loss_ce: 0.1183 2023/02/27 02:01:51 - mmengine - INFO - Epoch(train) [67][3400/5047] lr: 2.1076e-05 eta: 4 days, 6:14:36 time: 0.9037 data_time: 0.0022 memory: 44539 loss: 0.1259 loss_ce: 0.1259 2023/02/27 02:03:18 - mmengine - INFO - Epoch(train) [67][3500/5047] lr: 2.1076e-05 eta: 4 days, 6:13:08 time: 0.8645 data_time: 0.0024 memory: 42336 loss: 0.1240 loss_ce: 0.1240 2023/02/27 02:04:43 - mmengine - INFO - Epoch(train) [67][3600/5047] lr: 2.1076e-05 eta: 4 days, 6:11:38 time: 0.8281 data_time: 0.0026 memory: 43068 loss: 0.1045 loss_ce: 0.1045 2023/02/27 02:06:10 - mmengine - INFO - Epoch(train) [67][3700/5047] lr: 2.1076e-05 eta: 4 days, 6:10:08 time: 0.8301 data_time: 0.0082 memory: 46005 loss: 0.1077 loss_ce: 0.1077 2023/02/27 02:07:36 - mmengine - INFO - Epoch(train) [67][3800/5047] lr: 2.1076e-05 eta: 4 days, 6:08:39 time: 0.8773 data_time: 0.0021 memory: 42336 loss: 0.1080 loss_ce: 0.1080 2023/02/27 02:09:01 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 02:09:02 - mmengine - INFO - Epoch(train) [67][3900/5047] lr: 2.1076e-05 eta: 4 days, 6:07:10 time: 0.8946 data_time: 0.0063 memory: 42154 loss: 0.1197 loss_ce: 0.1197 2023/02/27 02:10:28 - mmengine - INFO - Epoch(train) [67][4000/5047] lr: 2.1076e-05 eta: 4 days, 6:05:40 time: 0.8404 data_time: 0.0022 memory: 43613 loss: 0.1099 loss_ce: 0.1099 2023/02/27 02:11:56 - mmengine - INFO - Epoch(train) [67][4100/5047] lr: 2.1076e-05 eta: 4 days, 6:04:13 time: 0.8358 data_time: 0.0021 memory: 44590 loss: 0.1286 loss_ce: 0.1286 2023/02/27 02:13:22 - mmengine - INFO - Epoch(train) [67][4200/5047] lr: 2.1076e-05 eta: 4 days, 6:02:44 time: 0.8832 data_time: 0.0021 memory: 53211 loss: 0.1354 loss_ce: 0.1354 2023/02/27 02:14:49 - mmengine - INFO - Epoch(train) [67][4300/5047] lr: 2.1076e-05 eta: 4 days, 6:01:15 time: 0.8546 data_time: 0.0022 memory: 41419 loss: 0.1232 loss_ce: 0.1232 2023/02/27 02:16:14 - mmengine - INFO - Epoch(train) [67][4400/5047] lr: 2.1076e-05 eta: 4 days, 5:59:45 time: 0.8375 data_time: 0.0046 memory: 43491 loss: 0.1191 loss_ce: 0.1191 2023/02/27 02:17:39 - mmengine - INFO - Epoch(train) [67][4500/5047] lr: 2.1076e-05 eta: 4 days, 5:58:14 time: 0.8091 data_time: 0.0023 memory: 43491 loss: 0.1185 loss_ce: 0.1185 2023/02/27 02:19:06 - mmengine - INFO - Epoch(train) [67][4600/5047] lr: 2.1076e-05 eta: 4 days, 5:56:45 time: 0.9014 data_time: 0.0020 memory: 43289 loss: 0.1124 loss_ce: 0.1124 2023/02/27 02:20:32 - mmengine - INFO - Epoch(train) [67][4700/5047] lr: 2.1076e-05 eta: 4 days, 5:55:15 time: 0.8450 data_time: 0.0030 memory: 48278 loss: 0.1261 loss_ce: 0.1261 2023/02/27 02:21:56 - mmengine - INFO - Epoch(train) [67][4800/5047] lr: 2.1076e-05 eta: 4 days, 5:53:44 time: 0.8375 data_time: 0.0019 memory: 41122 loss: 0.1373 loss_ce: 0.1373 2023/02/27 02:23:22 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 02:23:24 - mmengine - INFO - Epoch(train) [67][4900/5047] lr: 2.1076e-05 eta: 4 days, 5:52:16 time: 0.9076 data_time: 0.0022 memory: 42024 loss: 0.1254 loss_ce: 0.1254 2023/02/27 02:24:49 - mmengine - INFO - Epoch(train) [67][5000/5047] lr: 2.1076e-05 eta: 4 days, 5:50:46 time: 0.8450 data_time: 0.0019 memory: 45060 loss: 0.1080 loss_ce: 0.1080 2023/02/27 02:25:28 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 02:25:28 - mmengine - INFO - Saving checkpoint at 67 epochs 2023/02/27 02:26:58 - mmengine - INFO - Epoch(train) [68][ 100/5047] lr: 2.0875e-05 eta: 4 days, 5:48:31 time: 0.8202 data_time: 0.0021 memory: 43947 loss: 0.1195 loss_ce: 0.1195 2023/02/27 02:28:25 - mmengine - INFO - Epoch(train) [68][ 200/5047] lr: 2.0875e-05 eta: 4 days, 5:47:03 time: 0.9480 data_time: 0.0058 memory: 50505 loss: 0.1175 loss_ce: 0.1175 2023/02/27 02:29:53 - mmengine - INFO - Epoch(train) [68][ 300/5047] lr: 2.0875e-05 eta: 4 days, 5:45:35 time: 0.8857 data_time: 0.0022 memory: 45688 loss: 0.1071 loss_ce: 0.1071 2023/02/27 02:31:17 - mmengine - INFO - Epoch(train) [68][ 400/5047] lr: 2.0875e-05 eta: 4 days, 5:44:03 time: 0.8327 data_time: 0.0022 memory: 41624 loss: 0.1243 loss_ce: 0.1243 2023/02/27 02:32:44 - mmengine - INFO - Epoch(train) [68][ 500/5047] lr: 2.0875e-05 eta: 4 days, 5:42:35 time: 0.8098 data_time: 0.0023 memory: 42649 loss: 0.1125 loss_ce: 0.1125 2023/02/27 02:34:09 - mmengine - INFO - Epoch(train) [68][ 600/5047] lr: 2.0875e-05 eta: 4 days, 5:41:04 time: 0.9355 data_time: 0.0021 memory: 40241 loss: 0.1342 loss_ce: 0.1342 2023/02/27 02:35:35 - mmengine - INFO - Epoch(train) [68][ 700/5047] lr: 2.0875e-05 eta: 4 days, 5:39:35 time: 0.8979 data_time: 0.0021 memory: 50505 loss: 0.1258 loss_ce: 0.1258 2023/02/27 02:37:01 - mmengine - INFO - Epoch(train) [68][ 800/5047] lr: 2.0875e-05 eta: 4 days, 5:38:06 time: 0.8390 data_time: 0.0027 memory: 41419 loss: 0.1012 loss_ce: 0.1012 2023/02/27 02:37:45 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 02:38:27 - mmengine - INFO - Epoch(train) [68][ 900/5047] lr: 2.0875e-05 eta: 4 days, 5:36:36 time: 0.8315 data_time: 0.0025 memory: 41419 loss: 0.1087 loss_ce: 0.1087 2023/02/27 02:39:54 - mmengine - INFO - Epoch(train) [68][1000/5047] lr: 2.0875e-05 eta: 4 days, 5:35:08 time: 0.8207 data_time: 0.0021 memory: 46277 loss: 0.1373 loss_ce: 0.1373 2023/02/27 02:41:20 - mmengine - INFO - Epoch(train) [68][1100/5047] lr: 2.0875e-05 eta: 4 days, 5:33:38 time: 0.8961 data_time: 0.0028 memory: 41724 loss: 0.1039 loss_ce: 0.1039 2023/02/27 02:42:46 - mmengine - INFO - Epoch(train) [68][1200/5047] lr: 2.0875e-05 eta: 4 days, 5:32:09 time: 0.8180 data_time: 0.0023 memory: 38593 loss: 0.1151 loss_ce: 0.1151 2023/02/27 02:44:13 - mmengine - INFO - Epoch(train) [68][1300/5047] lr: 2.0875e-05 eta: 4 days, 5:30:40 time: 0.8555 data_time: 0.0024 memory: 47074 loss: 0.1206 loss_ce: 0.1206 2023/02/27 02:45:40 - mmengine - INFO - Epoch(train) [68][1400/5047] lr: 2.0875e-05 eta: 4 days, 5:29:12 time: 0.8585 data_time: 0.0024 memory: 44278 loss: 0.1220 loss_ce: 0.1220 2023/02/27 02:47:08 - mmengine - INFO - Epoch(train) [68][1500/5047] lr: 2.0875e-05 eta: 4 days, 5:27:45 time: 0.9233 data_time: 0.0022 memory: 49257 loss: 0.1089 loss_ce: 0.1089 2023/02/27 02:48:34 - mmengine - INFO - Epoch(train) [68][1600/5047] lr: 2.0875e-05 eta: 4 days, 5:26:16 time: 0.8499 data_time: 0.0024 memory: 47074 loss: 0.1236 loss_ce: 0.1236 2023/02/27 02:50:01 - mmengine - INFO - Epoch(train) [68][1700/5047] lr: 2.0875e-05 eta: 4 days, 5:24:48 time: 0.8634 data_time: 0.0023 memory: 55562 loss: 0.1073 loss_ce: 0.1073 2023/02/27 02:51:28 - mmengine - INFO - Epoch(train) [68][1800/5047] lr: 2.0875e-05 eta: 4 days, 5:23:19 time: 0.8502 data_time: 0.0033 memory: 52978 loss: 0.1172 loss_ce: 0.1172 2023/02/27 02:52:11 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 02:52:54 - mmengine - INFO - Epoch(train) [68][1900/5047] lr: 2.0875e-05 eta: 4 days, 5:21:49 time: 0.8979 data_time: 0.0020 memory: 48188 loss: 0.1070 loss_ce: 0.1070 2023/02/27 02:54:20 - mmengine - INFO - Epoch(train) [68][2000/5047] lr: 2.0875e-05 eta: 4 days, 5:20:20 time: 0.8306 data_time: 0.0030 memory: 40092 loss: 0.1049 loss_ce: 0.1049 2023/02/27 02:55:45 - mmengine - INFO - Epoch(train) [68][2100/5047] lr: 2.0875e-05 eta: 4 days, 5:18:50 time: 0.8996 data_time: 0.0023 memory: 46657 loss: 0.1112 loss_ce: 0.1112 2023/02/27 02:57:13 - mmengine - INFO - Epoch(train) [68][2200/5047] lr: 2.0875e-05 eta: 4 days, 5:17:22 time: 0.9296 data_time: 0.0024 memory: 43048 loss: 0.1203 loss_ce: 0.1203 2023/02/27 02:58:40 - mmengine - INFO - Epoch(train) [68][2300/5047] lr: 2.0875e-05 eta: 4 days, 5:15:54 time: 0.8564 data_time: 0.0034 memory: 47813 loss: 0.1238 loss_ce: 0.1238 2023/02/27 03:00:05 - mmengine - INFO - Epoch(train) [68][2400/5047] lr: 2.0875e-05 eta: 4 days, 5:14:24 time: 0.8448 data_time: 0.0020 memory: 42373 loss: 0.1306 loss_ce: 0.1306 2023/02/27 03:05:36 - mmengine - INFO - Epoch(train) [68][2500/5047] lr: 2.0875e-05 eta: 4 days, 5:18:08 time: 0.8456 data_time: 0.0021 memory: 41794 loss: 0.1139 loss_ce: 0.1139 2023/02/27 03:07:03 - mmengine - INFO - Epoch(train) [68][2600/5047] lr: 2.0875e-05 eta: 4 days, 5:16:39 time: 0.8778 data_time: 0.0019 memory: 43485 loss: 0.1193 loss_ce: 0.1193 2023/02/27 03:08:30 - mmengine - INFO - Epoch(train) [68][2700/5047] lr: 2.0875e-05 eta: 4 days, 5:15:10 time: 0.8762 data_time: 0.0021 memory: 40676 loss: 0.1268 loss_ce: 0.1268 2023/02/27 03:09:57 - mmengine - INFO - Epoch(train) [68][2800/5047] lr: 2.0875e-05 eta: 4 days, 5:13:42 time: 0.8419 data_time: 0.0023 memory: 47556 loss: 0.1361 loss_ce: 0.1361 2023/02/27 03:10:41 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 03:11:23 - mmengine - INFO - Epoch(train) [68][2900/5047] lr: 2.0875e-05 eta: 4 days, 5:12:13 time: 0.8796 data_time: 0.0021 memory: 45230 loss: 0.1275 loss_ce: 0.1275 2023/02/27 03:12:50 - mmengine - INFO - Epoch(train) [68][3000/5047] lr: 2.0875e-05 eta: 4 days, 5:10:43 time: 0.8215 data_time: 0.0024 memory: 51561 loss: 0.1099 loss_ce: 0.1099 2023/02/27 03:14:17 - mmengine - INFO - Epoch(train) [68][3100/5047] lr: 2.0875e-05 eta: 4 days, 5:09:16 time: 0.8786 data_time: 0.0021 memory: 41143 loss: 0.1191 loss_ce: 0.1191 2023/02/27 03:15:42 - mmengine - INFO - Epoch(train) [68][3200/5047] lr: 2.0875e-05 eta: 4 days, 5:07:45 time: 0.8462 data_time: 0.0020 memory: 46355 loss: 0.1390 loss_ce: 0.1390 2023/02/27 03:17:09 - mmengine - INFO - Epoch(train) [68][3300/5047] lr: 2.0875e-05 eta: 4 days, 5:06:17 time: 0.8867 data_time: 0.0048 memory: 43613 loss: 0.1237 loss_ce: 0.1237 2023/02/27 03:18:35 - mmengine - INFO - Epoch(train) [68][3400/5047] lr: 2.0875e-05 eta: 4 days, 5:04:46 time: 0.8799 data_time: 0.0021 memory: 45570 loss: 0.1332 loss_ce: 0.1332 2023/02/27 03:20:01 - mmengine - INFO - Epoch(train) [68][3500/5047] lr: 2.0875e-05 eta: 4 days, 5:03:17 time: 0.8861 data_time: 0.0021 memory: 41724 loss: 0.1028 loss_ce: 0.1028 2023/02/27 03:21:28 - mmengine - INFO - Epoch(train) [68][3600/5047] lr: 2.0875e-05 eta: 4 days, 5:01:49 time: 0.8859 data_time: 0.0054 memory: 42336 loss: 0.1219 loss_ce: 0.1219 2023/02/27 03:22:53 - mmengine - INFO - Epoch(train) [68][3700/5047] lr: 2.0875e-05 eta: 4 days, 5:00:18 time: 0.9053 data_time: 0.0021 memory: 45640 loss: 0.1160 loss_ce: 0.1160 2023/02/27 03:24:21 - mmengine - INFO - Epoch(train) [68][3800/5047] lr: 2.0875e-05 eta: 4 days, 4:58:50 time: 0.8155 data_time: 0.0024 memory: 46355 loss: 0.1147 loss_ce: 0.1147 2023/02/27 03:25:05 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 03:25:46 - mmengine - INFO - Epoch(train) [68][3900/5047] lr: 2.0875e-05 eta: 4 days, 4:57:20 time: 0.8421 data_time: 0.0041 memory: 42336 loss: 0.1153 loss_ce: 0.1153 2023/02/27 03:27:16 - mmengine - INFO - Epoch(train) [68][4000/5047] lr: 2.0875e-05 eta: 4 days, 4:55:55 time: 0.8537 data_time: 0.0019 memory: 46355 loss: 0.1126 loss_ce: 0.1126 2023/02/27 03:28:40 - mmengine - INFO - Epoch(train) [68][4100/5047] lr: 2.0875e-05 eta: 4 days, 4:54:22 time: 0.8281 data_time: 0.0021 memory: 55021 loss: 0.1226 loss_ce: 0.1226 2023/02/27 03:30:06 - mmengine - INFO - Epoch(train) [68][4200/5047] lr: 2.0875e-05 eta: 4 days, 4:52:53 time: 0.8884 data_time: 0.0019 memory: 45815 loss: 0.1079 loss_ce: 0.1079 2023/02/27 03:31:31 - mmengine - INFO - Epoch(train) [68][4300/5047] lr: 2.0875e-05 eta: 4 days, 4:51:22 time: 0.8882 data_time: 0.0023 memory: 47238 loss: 0.1140 loss_ce: 0.1140 2023/02/27 03:32:57 - mmengine - INFO - Epoch(train) [68][4400/5047] lr: 2.0875e-05 eta: 4 days, 4:49:53 time: 0.8511 data_time: 0.0049 memory: 45720 loss: 0.1090 loss_ce: 0.1090 2023/02/27 03:34:23 - mmengine - INFO - Epoch(train) [68][4500/5047] lr: 2.0875e-05 eta: 4 days, 4:48:23 time: 0.8269 data_time: 0.0020 memory: 41703 loss: 0.1257 loss_ce: 0.1257 2023/02/27 03:35:50 - mmengine - INFO - Epoch(train) [68][4600/5047] lr: 2.0875e-05 eta: 4 days, 4:46:55 time: 0.9169 data_time: 0.0027 memory: 42965 loss: 0.1109 loss_ce: 0.1109 2023/02/27 03:37:18 - mmengine - INFO - Epoch(train) [68][4700/5047] lr: 2.0875e-05 eta: 4 days, 4:45:27 time: 0.8930 data_time: 0.0021 memory: 53387 loss: 0.1221 loss_ce: 0.1221 2023/02/27 03:38:44 - mmengine - INFO - Epoch(train) [68][4800/5047] lr: 2.0875e-05 eta: 4 days, 4:43:57 time: 0.8748 data_time: 0.0021 memory: 45643 loss: 0.1133 loss_ce: 0.1133 2023/02/27 03:39:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 03:40:10 - mmengine - INFO - Epoch(train) [68][4900/5047] lr: 2.0875e-05 eta: 4 days, 4:42:29 time: 0.8822 data_time: 0.0053 memory: 39681 loss: 0.1235 loss_ce: 0.1235 2023/02/27 03:41:36 - mmengine - INFO - Epoch(train) [68][5000/5047] lr: 2.0875e-05 eta: 4 days, 4:40:59 time: 0.8435 data_time: 0.0023 memory: 41996 loss: 0.1248 loss_ce: 0.1248 2023/02/27 03:42:16 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 03:42:16 - mmengine - INFO - Saving checkpoint at 68 epochs 2023/02/27 03:43:51 - mmengine - INFO - Epoch(train) [69][ 100/5047] lr: 2.0674e-05 eta: 4 days, 4:38:49 time: 0.8120 data_time: 0.0020 memory: 50758 loss: 0.1178 loss_ce: 0.1178 2023/02/27 03:45:15 - mmengine - INFO - Epoch(train) [69][ 200/5047] lr: 2.0674e-05 eta: 4 days, 4:37:18 time: 0.8520 data_time: 0.0020 memory: 44617 loss: 0.1255 loss_ce: 0.1255 2023/02/27 03:46:42 - mmengine - INFO - Epoch(train) [69][ 300/5047] lr: 2.0674e-05 eta: 4 days, 4:35:49 time: 0.8579 data_time: 0.0021 memory: 49481 loss: 0.1016 loss_ce: 0.1016 2023/02/27 03:48:09 - mmengine - INFO - Epoch(train) [69][ 400/5047] lr: 2.0674e-05 eta: 4 days, 4:34:20 time: 0.8791 data_time: 0.0022 memory: 43773 loss: 0.1010 loss_ce: 0.1010 2023/02/27 03:49:34 - mmengine - INFO - Epoch(train) [69][ 500/5047] lr: 2.0674e-05 eta: 4 days, 4:32:49 time: 0.8232 data_time: 0.0021 memory: 54072 loss: 0.1300 loss_ce: 0.1300 2023/02/27 03:51:00 - mmengine - INFO - Epoch(train) [69][ 600/5047] lr: 2.0674e-05 eta: 4 days, 4:31:20 time: 0.8772 data_time: 0.0024 memory: 40882 loss: 0.1101 loss_ce: 0.1101 2023/02/27 03:52:26 - mmengine - INFO - Epoch(train) [69][ 700/5047] lr: 2.0674e-05 eta: 4 days, 4:29:50 time: 0.8507 data_time: 0.0020 memory: 41724 loss: 0.1205 loss_ce: 0.1205 2023/02/27 03:53:50 - mmengine - INFO - Epoch(train) [69][ 800/5047] lr: 2.0674e-05 eta: 4 days, 4:28:19 time: 0.8229 data_time: 0.0028 memory: 45643 loss: 0.1334 loss_ce: 0.1334 2023/02/27 03:53:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 03:55:17 - mmengine - INFO - Epoch(train) [69][ 900/5047] lr: 2.0674e-05 eta: 4 days, 4:26:50 time: 0.8533 data_time: 0.0021 memory: 39278 loss: 0.1124 loss_ce: 0.1124 2023/02/27 03:56:41 - mmengine - INFO - Epoch(train) [69][1000/5047] lr: 2.0674e-05 eta: 4 days, 4:25:19 time: 0.8147 data_time: 0.0025 memory: 52964 loss: 0.1094 loss_ce: 0.1094 2023/02/27 03:58:07 - mmengine - INFO - Epoch(train) [69][1100/5047] lr: 2.0674e-05 eta: 4 days, 4:23:48 time: 0.8605 data_time: 0.0021 memory: 46966 loss: 0.1033 loss_ce: 0.1033 2023/02/27 03:59:34 - mmengine - INFO - Epoch(train) [69][1200/5047] lr: 2.0674e-05 eta: 4 days, 4:22:19 time: 0.8593 data_time: 0.0021 memory: 42024 loss: 0.1181 loss_ce: 0.1181 2023/02/27 04:00:59 - mmengine - INFO - Epoch(train) [69][1300/5047] lr: 2.0674e-05 eta: 4 days, 4:20:50 time: 0.8179 data_time: 0.0023 memory: 41425 loss: 0.1325 loss_ce: 0.1325 2023/02/27 04:02:28 - mmengine - INFO - Epoch(train) [69][1400/5047] lr: 2.0674e-05 eta: 4 days, 4:19:23 time: 0.8829 data_time: 0.0022 memory: 42443 loss: 0.1135 loss_ce: 0.1135 2023/02/27 04:03:56 - mmengine - INFO - Epoch(train) [69][1500/5047] lr: 2.0674e-05 eta: 4 days, 4:17:56 time: 0.8347 data_time: 0.0023 memory: 42024 loss: 0.1124 loss_ce: 0.1124 2023/02/27 04:05:23 - mmengine - INFO - Epoch(train) [69][1600/5047] lr: 2.0674e-05 eta: 4 days, 4:16:28 time: 0.8920 data_time: 0.0023 memory: 47247 loss: 0.1199 loss_ce: 0.1199 2023/02/27 04:06:49 - mmengine - INFO - Epoch(train) [69][1700/5047] lr: 2.0674e-05 eta: 4 days, 4:14:58 time: 0.8599 data_time: 0.0022 memory: 43289 loss: 0.1067 loss_ce: 0.1067 2023/02/27 04:08:14 - mmengine - INFO - Epoch(train) [69][1800/5047] lr: 2.0674e-05 eta: 4 days, 4:13:28 time: 0.8467 data_time: 0.0025 memory: 42875 loss: 0.1210 loss_ce: 0.1210 2023/02/27 04:08:18 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 04:09:42 - mmengine - INFO - Epoch(train) [69][1900/5047] lr: 2.0674e-05 eta: 4 days, 4:12:01 time: 0.9311 data_time: 0.0020 memory: 48030 loss: 0.1183 loss_ce: 0.1183 2023/02/27 04:11:08 - mmengine - INFO - Epoch(train) [69][2000/5047] lr: 2.0674e-05 eta: 4 days, 4:10:31 time: 0.8339 data_time: 0.0021 memory: 45716 loss: 0.1227 loss_ce: 0.1227 2023/02/27 04:12:36 - mmengine - INFO - Epoch(train) [69][2100/5047] lr: 2.0674e-05 eta: 4 days, 4:09:04 time: 0.8610 data_time: 0.0028 memory: 42336 loss: 0.1098 loss_ce: 0.1098 2023/02/27 04:14:01 - mmengine - INFO - Epoch(train) [69][2200/5047] lr: 2.0674e-05 eta: 4 days, 4:07:33 time: 0.8500 data_time: 0.0060 memory: 44278 loss: 0.1113 loss_ce: 0.1113 2023/02/27 04:15:25 - mmengine - INFO - Epoch(train) [69][2300/5047] lr: 2.0674e-05 eta: 4 days, 4:06:01 time: 0.8278 data_time: 0.0022 memory: 52543 loss: 0.1089 loss_ce: 0.1089 2023/02/27 04:16:51 - mmengine - INFO - Epoch(train) [69][2400/5047] lr: 2.0674e-05 eta: 4 days, 4:04:32 time: 0.8454 data_time: 0.0021 memory: 55535 loss: 0.1153 loss_ce: 0.1153 2023/02/27 04:18:19 - mmengine - INFO - Epoch(train) [69][2500/5047] lr: 2.0674e-05 eta: 4 days, 4:03:04 time: 0.8464 data_time: 0.0022 memory: 47447 loss: 0.1280 loss_ce: 0.1280 2023/02/27 04:19:44 - mmengine - INFO - Epoch(train) [69][2600/5047] lr: 2.0674e-05 eta: 4 days, 4:01:34 time: 0.8488 data_time: 0.0022 memory: 44008 loss: 0.1086 loss_ce: 0.1086 2023/02/27 04:21:11 - mmengine - INFO - Epoch(train) [69][2700/5047] lr: 2.0674e-05 eta: 4 days, 4:00:05 time: 0.8606 data_time: 0.0034 memory: 42965 loss: 0.1117 loss_ce: 0.1117 2023/02/27 04:22:37 - mmengine - INFO - Epoch(train) [69][2800/5047] lr: 2.0674e-05 eta: 4 days, 3:58:36 time: 0.8732 data_time: 0.0021 memory: 44956 loss: 0.1154 loss_ce: 0.1154 2023/02/27 04:22:41 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 04:24:04 - mmengine - INFO - Epoch(train) [69][2900/5047] lr: 2.0674e-05 eta: 4 days, 3:57:08 time: 0.8556 data_time: 0.0025 memory: 43947 loss: 0.1211 loss_ce: 0.1211 2023/02/27 04:25:31 - mmengine - INFO - Epoch(train) [69][3000/5047] lr: 2.0674e-05 eta: 4 days, 3:55:38 time: 0.8489 data_time: 0.0024 memory: 55562 loss: 0.1146 loss_ce: 0.1146 2023/02/27 04:26:57 - mmengine - INFO - Epoch(train) [69][3100/5047] lr: 2.0674e-05 eta: 4 days, 3:54:09 time: 0.8940 data_time: 0.0022 memory: 41419 loss: 0.1132 loss_ce: 0.1132 2023/02/27 04:28:22 - mmengine - INFO - Epoch(train) [69][3200/5047] lr: 2.0674e-05 eta: 4 days, 3:52:39 time: 0.8649 data_time: 0.0023 memory: 41122 loss: 0.1222 loss_ce: 0.1222 2023/02/27 04:29:48 - mmengine - INFO - Epoch(train) [69][3300/5047] lr: 2.0674e-05 eta: 4 days, 3:51:08 time: 0.8843 data_time: 0.0021 memory: 43289 loss: 0.1149 loss_ce: 0.1149 2023/02/27 04:31:13 - mmengine - INFO - Epoch(train) [69][3400/5047] lr: 2.0674e-05 eta: 4 days, 3:49:38 time: 0.8936 data_time: 0.0024 memory: 41834 loss: 0.1233 loss_ce: 0.1233 2023/02/27 04:32:40 - mmengine - INFO - Epoch(train) [69][3500/5047] lr: 2.0674e-05 eta: 4 days, 3:48:10 time: 0.8720 data_time: 0.0020 memory: 44956 loss: 0.1090 loss_ce: 0.1090 2023/02/27 04:34:08 - mmengine - INFO - Epoch(train) [69][3600/5047] lr: 2.0674e-05 eta: 4 days, 3:46:42 time: 0.8893 data_time: 0.0022 memory: 44278 loss: 0.1184 loss_ce: 0.1184 2023/02/27 04:35:34 - mmengine - INFO - Epoch(train) [69][3700/5047] lr: 2.0674e-05 eta: 4 days, 3:45:13 time: 0.8665 data_time: 0.0020 memory: 46951 loss: 0.1138 loss_ce: 0.1138 2023/02/27 04:36:59 - mmengine - INFO - Epoch(train) [69][3800/5047] lr: 2.0674e-05 eta: 4 days, 3:43:43 time: 0.8823 data_time: 0.0020 memory: 48565 loss: 0.1232 loss_ce: 0.1232 2023/02/27 04:37:03 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 04:38:27 - mmengine - INFO - Epoch(train) [69][3900/5047] lr: 2.0674e-05 eta: 4 days, 3:42:15 time: 0.8865 data_time: 0.0024 memory: 46854 loss: 0.1069 loss_ce: 0.1069 2023/02/27 04:39:54 - mmengine - INFO - Epoch(train) [69][4000/5047] lr: 2.0674e-05 eta: 4 days, 3:40:47 time: 0.9248 data_time: 0.0024 memory: 44956 loss: 0.1172 loss_ce: 0.1172 2023/02/27 04:41:20 - mmengine - INFO - Epoch(train) [69][4100/5047] lr: 2.0674e-05 eta: 4 days, 3:39:17 time: 0.8653 data_time: 0.0022 memory: 45642 loss: 0.1164 loss_ce: 0.1164 2023/02/27 04:42:46 - mmengine - INFO - Epoch(train) [69][4200/5047] lr: 2.0674e-05 eta: 4 days, 3:37:48 time: 0.8620 data_time: 0.0036 memory: 43709 loss: 0.1334 loss_ce: 0.1334 2023/02/27 04:44:12 - mmengine - INFO - Epoch(train) [69][4300/5047] lr: 2.0674e-05 eta: 4 days, 3:36:18 time: 0.8728 data_time: 0.0027 memory: 43289 loss: 0.1194 loss_ce: 0.1194 2023/02/27 04:45:39 - mmengine - INFO - Epoch(train) [69][4400/5047] lr: 2.0674e-05 eta: 4 days, 3:34:50 time: 0.8622 data_time: 0.0020 memory: 48146 loss: 0.1239 loss_ce: 0.1239 2023/02/27 04:47:06 - mmengine - INFO - Epoch(train) [69][4500/5047] lr: 2.0674e-05 eta: 4 days, 3:33:22 time: 0.9330 data_time: 0.0034 memory: 42233 loss: 0.1253 loss_ce: 0.1253 2023/02/27 04:48:32 - mmengine - INFO - Epoch(train) [69][4600/5047] lr: 2.0674e-05 eta: 4 days, 3:31:52 time: 0.8540 data_time: 0.0022 memory: 43613 loss: 0.1133 loss_ce: 0.1133 2023/02/27 04:49:58 - mmengine - INFO - Epoch(train) [69][4700/5047] lr: 2.0674e-05 eta: 4 days, 3:30:23 time: 0.8986 data_time: 0.0024 memory: 45985 loss: 0.1073 loss_ce: 0.1073 2023/02/27 04:51:24 - mmengine - INFO - Epoch(train) [69][4800/5047] lr: 2.0674e-05 eta: 4 days, 3:28:53 time: 0.8683 data_time: 0.0041 memory: 43947 loss: 0.1243 loss_ce: 0.1243 2023/02/27 04:51:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 04:52:51 - mmengine - INFO - Epoch(train) [69][4900/5047] lr: 2.0674e-05 eta: 4 days, 3:27:24 time: 0.8333 data_time: 0.0026 memory: 46454 loss: 0.1187 loss_ce: 0.1187 2023/02/27 04:54:17 - mmengine - INFO - Epoch(train) [69][5000/5047] lr: 2.0674e-05 eta: 4 days, 3:25:55 time: 0.8611 data_time: 0.0022 memory: 55562 loss: 0.1132 loss_ce: 0.1132 2023/02/27 04:54:56 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 04:54:56 - mmengine - INFO - Saving checkpoint at 69 epochs 2023/02/27 04:56:28 - mmengine - INFO - Epoch(train) [70][ 100/5047] lr: 2.0473e-05 eta: 4 days, 3:23:43 time: 0.8215 data_time: 0.0023 memory: 44278 loss: 0.1262 loss_ce: 0.1262 2023/02/27 04:57:53 - mmengine - INFO - Epoch(train) [70][ 200/5047] lr: 2.0473e-05 eta: 4 days, 3:22:13 time: 0.8309 data_time: 0.0022 memory: 51637 loss: 0.1025 loss_ce: 0.1025 2023/02/27 04:59:20 - mmengine - INFO - Epoch(train) [70][ 300/5047] lr: 2.0473e-05 eta: 4 days, 3:20:45 time: 0.8262 data_time: 0.0024 memory: 43613 loss: 0.1136 loss_ce: 0.1136 2023/02/27 05:00:46 - mmengine - INFO - Epoch(train) [70][ 400/5047] lr: 2.0473e-05 eta: 4 days, 3:19:15 time: 0.8311 data_time: 0.0021 memory: 53387 loss: 0.1141 loss_ce: 0.1141 2023/02/27 05:02:11 - mmengine - INFO - Epoch(train) [70][ 500/5047] lr: 2.0473e-05 eta: 4 days, 3:17:45 time: 0.8203 data_time: 0.0022 memory: 44278 loss: 0.1227 loss_ce: 0.1227 2023/02/27 05:03:37 - mmengine - INFO - Epoch(train) [70][ 600/5047] lr: 2.0473e-05 eta: 4 days, 3:16:15 time: 0.8265 data_time: 0.0022 memory: 47074 loss: 0.1220 loss_ce: 0.1220 2023/02/27 05:05:03 - mmengine - INFO - Epoch(train) [70][ 700/5047] lr: 2.0473e-05 eta: 4 days, 3:14:45 time: 0.8667 data_time: 0.0021 memory: 47309 loss: 0.1310 loss_ce: 0.1310 2023/02/27 05:05:53 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 05:06:31 - mmengine - INFO - Epoch(train) [70][ 800/5047] lr: 2.0473e-05 eta: 4 days, 3:13:18 time: 0.8852 data_time: 0.0021 memory: 44478 loss: 0.1352 loss_ce: 0.1352 2023/02/27 05:07:55 - mmengine - INFO - Epoch(train) [70][ 900/5047] lr: 2.0473e-05 eta: 4 days, 3:11:47 time: 0.8780 data_time: 0.0021 memory: 44956 loss: 0.1174 loss_ce: 0.1174 2023/02/27 05:09:22 - mmengine - INFO - Epoch(train) [70][1000/5047] lr: 2.0473e-05 eta: 4 days, 3:10:18 time: 0.8921 data_time: 0.0030 memory: 51647 loss: 0.1128 loss_ce: 0.1128 2023/02/27 05:10:48 - mmengine - INFO - Epoch(train) [70][1100/5047] lr: 2.0473e-05 eta: 4 days, 3:08:48 time: 0.8267 data_time: 0.0020 memory: 47086 loss: 0.1229 loss_ce: 0.1229 2023/02/27 05:12:14 - mmengine - INFO - Epoch(train) [70][1200/5047] lr: 2.0473e-05 eta: 4 days, 3:07:19 time: 0.8531 data_time: 0.0026 memory: 54303 loss: 0.0954 loss_ce: 0.0954 2023/02/27 05:13:39 - mmengine - INFO - Epoch(train) [70][1300/5047] lr: 2.0473e-05 eta: 4 days, 3:05:49 time: 0.8481 data_time: 0.0021 memory: 52863 loss: 0.1118 loss_ce: 0.1118 2023/02/27 05:15:05 - mmengine - INFO - Epoch(train) [70][1400/5047] lr: 2.0473e-05 eta: 4 days, 3:04:18 time: 0.8585 data_time: 0.0023 memory: 52964 loss: 0.1298 loss_ce: 0.1298 2023/02/27 05:16:31 - mmengine - INFO - Epoch(train) [70][1500/5047] lr: 2.0473e-05 eta: 4 days, 3:02:49 time: 0.8740 data_time: 0.0022 memory: 44330 loss: 0.1065 loss_ce: 0.1065 2023/02/27 05:17:57 - mmengine - INFO - Epoch(train) [70][1600/5047] lr: 2.0473e-05 eta: 4 days, 3:01:20 time: 0.8431 data_time: 0.0020 memory: 43420 loss: 0.1234 loss_ce: 0.1234 2023/02/27 05:19:21 - mmengine - INFO - Epoch(train) [70][1700/5047] lr: 2.0473e-05 eta: 4 days, 2:59:48 time: 0.8202 data_time: 0.0026 memory: 39166 loss: 0.1253 loss_ce: 0.1253 2023/02/27 05:20:12 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 05:20:48 - mmengine - INFO - Epoch(train) [70][1800/5047] lr: 2.0473e-05 eta: 4 days, 2:58:20 time: 0.8299 data_time: 0.0026 memory: 46992 loss: 0.1179 loss_ce: 0.1179 2023/02/27 05:22:16 - mmengine - INFO - Epoch(train) [70][1900/5047] lr: 2.0473e-05 eta: 4 days, 2:56:52 time: 0.8303 data_time: 0.0023 memory: 44956 loss: 0.1293 loss_ce: 0.1293 2023/02/27 05:23:41 - mmengine - INFO - Epoch(train) [70][2000/5047] lr: 2.0473e-05 eta: 4 days, 2:55:22 time: 0.8477 data_time: 0.0021 memory: 46355 loss: 0.1307 loss_ce: 0.1307 2023/02/27 05:25:08 - mmengine - INFO - Epoch(train) [70][2100/5047] lr: 2.0473e-05 eta: 4 days, 2:53:53 time: 0.8896 data_time: 0.0020 memory: 42735 loss: 0.1137 loss_ce: 0.1137 2023/02/27 05:26:33 - mmengine - INFO - Epoch(train) [70][2200/5047] lr: 2.0473e-05 eta: 4 days, 2:52:23 time: 0.8621 data_time: 0.0029 memory: 42336 loss: 0.1214 loss_ce: 0.1214 2023/02/27 05:28:01 - mmengine - INFO - Epoch(train) [70][2300/5047] lr: 2.0473e-05 eta: 4 days, 2:50:56 time: 0.9144 data_time: 0.0028 memory: 50106 loss: 0.1137 loss_ce: 0.1137 2023/02/27 05:29:26 - mmengine - INFO - Epoch(train) [70][2400/5047] lr: 2.0473e-05 eta: 4 days, 2:49:25 time: 0.8246 data_time: 0.0021 memory: 44617 loss: 0.1206 loss_ce: 0.1206 2023/02/27 05:30:54 - mmengine - INFO - Epoch(train) [70][2500/5047] lr: 2.0473e-05 eta: 4 days, 2:47:58 time: 0.8100 data_time: 0.0022 memory: 43289 loss: 0.1159 loss_ce: 0.1159 2023/02/27 05:32:18 - mmengine - INFO - Epoch(train) [70][2600/5047] lr: 2.0473e-05 eta: 4 days, 2:46:27 time: 0.8529 data_time: 0.0024 memory: 48565 loss: 0.1182 loss_ce: 0.1182 2023/02/27 05:33:45 - mmengine - INFO - Epoch(train) [70][2700/5047] lr: 2.0473e-05 eta: 4 days, 2:44:57 time: 0.7931 data_time: 0.0026 memory: 38862 loss: 0.1206 loss_ce: 0.1206 2023/02/27 05:34:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 05:35:12 - mmengine - INFO - Epoch(train) [70][2800/5047] lr: 2.0473e-05 eta: 4 days, 2:43:30 time: 0.8885 data_time: 0.0051 memory: 47813 loss: 0.1199 loss_ce: 0.1199 2023/02/27 05:36:39 - mmengine - INFO - Epoch(train) [70][2900/5047] lr: 2.0473e-05 eta: 4 days, 2:42:01 time: 0.8379 data_time: 0.0022 memory: 52964 loss: 0.1208 loss_ce: 0.1208 2023/02/27 05:38:04 - mmengine - INFO - Epoch(train) [70][3000/5047] lr: 2.0473e-05 eta: 4 days, 2:40:30 time: 0.7991 data_time: 0.0024 memory: 44632 loss: 0.1160 loss_ce: 0.1160 2023/02/27 05:39:29 - mmengine - INFO - Epoch(train) [70][3100/5047] lr: 2.0473e-05 eta: 4 days, 2:39:01 time: 0.8367 data_time: 0.0025 memory: 40825 loss: 0.1008 loss_ce: 0.1008 2023/02/27 05:40:56 - mmengine - INFO - Epoch(train) [70][3200/5047] lr: 2.0473e-05 eta: 4 days, 2:37:32 time: 0.9101 data_time: 0.0020 memory: 55562 loss: 0.1125 loss_ce: 0.1125 2023/02/27 05:42:23 - mmengine - INFO - Epoch(train) [70][3300/5047] lr: 2.0473e-05 eta: 4 days, 2:36:03 time: 0.8526 data_time: 0.0023 memory: 42336 loss: 0.1298 loss_ce: 0.1298 2023/02/27 05:43:50 - mmengine - INFO - Epoch(train) [70][3400/5047] lr: 2.0473e-05 eta: 4 days, 2:34:35 time: 0.8961 data_time: 0.0023 memory: 55562 loss: 0.1205 loss_ce: 0.1205 2023/02/27 05:45:16 - mmengine - INFO - Epoch(train) [70][3500/5047] lr: 2.0473e-05 eta: 4 days, 2:33:06 time: 0.8551 data_time: 0.0024 memory: 50106 loss: 0.1219 loss_ce: 0.1219 2023/02/27 05:46:41 - mmengine - INFO - Epoch(train) [70][3600/5047] lr: 2.0473e-05 eta: 4 days, 2:31:36 time: 0.9302 data_time: 0.0020 memory: 46005 loss: 0.1023 loss_ce: 0.1023 2023/02/27 05:48:07 - mmengine - INFO - Epoch(train) [70][3700/5047] lr: 2.0473e-05 eta: 4 days, 2:30:05 time: 0.8416 data_time: 0.0021 memory: 41122 loss: 0.1154 loss_ce: 0.1154 2023/02/27 05:48:56 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 05:49:34 - mmengine - INFO - Epoch(train) [70][3800/5047] lr: 2.0473e-05 eta: 4 days, 2:28:38 time: 0.8376 data_time: 0.0024 memory: 49312 loss: 0.1405 loss_ce: 0.1405 2023/02/27 05:51:00 - mmengine - INFO - Epoch(train) [70][3900/5047] lr: 2.0473e-05 eta: 4 days, 2:27:09 time: 0.8711 data_time: 0.0027 memory: 47447 loss: 0.1173 loss_ce: 0.1173 2023/02/27 05:52:27 - mmengine - INFO - Epoch(train) [70][4000/5047] lr: 2.0473e-05 eta: 4 days, 2:25:39 time: 0.9003 data_time: 0.0038 memory: 44192 loss: 0.1266 loss_ce: 0.1266 2023/02/27 05:53:53 - mmengine - INFO - Epoch(train) [70][4100/5047] lr: 2.0473e-05 eta: 4 days, 2:24:10 time: 0.8420 data_time: 0.0041 memory: 50540 loss: 0.1188 loss_ce: 0.1188 2023/02/27 05:55:19 - mmengine - INFO - Epoch(train) [70][4200/5047] lr: 2.0473e-05 eta: 4 days, 2:22:41 time: 0.8269 data_time: 0.0023 memory: 42699 loss: 0.1079 loss_ce: 0.1079 2023/02/27 05:56:44 - mmengine - INFO - Epoch(train) [70][4300/5047] lr: 2.0473e-05 eta: 4 days, 2:21:11 time: 0.8320 data_time: 0.0029 memory: 41161 loss: 0.1114 loss_ce: 0.1114 2023/02/27 05:58:08 - mmengine - INFO - Epoch(train) [70][4400/5047] lr: 2.0473e-05 eta: 4 days, 2:19:39 time: 0.8518 data_time: 0.0023 memory: 41516 loss: 0.1325 loss_ce: 0.1325 2023/02/27 05:59:33 - mmengine - INFO - Epoch(train) [70][4500/5047] lr: 2.0473e-05 eta: 4 days, 2:18:08 time: 0.8769 data_time: 0.0024 memory: 42024 loss: 0.1179 loss_ce: 0.1179 2023/02/27 06:00:59 - mmengine - INFO - Epoch(train) [70][4600/5047] lr: 2.0473e-05 eta: 4 days, 2:16:39 time: 0.8977 data_time: 0.0043 memory: 43348 loss: 0.1164 loss_ce: 0.1164 2023/02/27 06:02:26 - mmengine - INFO - Epoch(train) [70][4700/5047] lr: 2.0473e-05 eta: 4 days, 2:15:10 time: 0.8551 data_time: 0.0022 memory: 45128 loss: 0.1124 loss_ce: 0.1124 2023/02/27 06:03:15 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 06:03:52 - mmengine - INFO - Epoch(train) [70][4800/5047] lr: 2.0473e-05 eta: 4 days, 2:13:41 time: 0.8230 data_time: 0.0022 memory: 49715 loss: 0.1115 loss_ce: 0.1115 2023/02/27 06:05:18 - mmengine - INFO - Epoch(train) [70][4900/5047] lr: 2.0473e-05 eta: 4 days, 2:12:11 time: 0.8952 data_time: 0.0023 memory: 55562 loss: 0.1202 loss_ce: 0.1202 2023/02/27 06:06:44 - mmengine - INFO - Epoch(train) [70][5000/5047] lr: 2.0473e-05 eta: 4 days, 2:10:42 time: 0.8401 data_time: 0.0022 memory: 42336 loss: 0.1255 loss_ce: 0.1255 2023/02/27 06:07:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 06:07:23 - mmengine - INFO - Saving checkpoint at 70 epochs 2023/02/27 06:08:53 - mmengine - INFO - Epoch(train) [71][ 100/5047] lr: 2.0272e-05 eta: 4 days, 2:08:28 time: 0.8315 data_time: 0.0022 memory: 44754 loss: 0.1057 loss_ce: 0.1057 2023/02/27 06:10:19 - mmengine - INFO - Epoch(train) [71][ 200/5047] lr: 2.0272e-05 eta: 4 days, 2:06:58 time: 0.8058 data_time: 0.0038 memory: 46076 loss: 0.1131 loss_ce: 0.1131 2023/02/27 06:11:45 - mmengine - INFO - Epoch(train) [71][ 300/5047] lr: 2.0272e-05 eta: 4 days, 2:05:29 time: 0.8378 data_time: 0.0023 memory: 48857 loss: 0.1225 loss_ce: 0.1225 2023/02/27 06:13:10 - mmengine - INFO - Epoch(train) [71][ 400/5047] lr: 2.0272e-05 eta: 4 days, 2:03:59 time: 0.8364 data_time: 0.0020 memory: 43289 loss: 0.1149 loss_ce: 0.1149 2023/02/27 06:14:37 - mmengine - INFO - Epoch(train) [71][ 500/5047] lr: 2.0272e-05 eta: 4 days, 2:02:30 time: 0.9166 data_time: 0.0025 memory: 43348 loss: 0.1282 loss_ce: 0.1282 2023/02/27 06:16:04 - mmengine - INFO - Epoch(train) [71][ 600/5047] lr: 2.0272e-05 eta: 4 days, 2:01:01 time: 0.8676 data_time: 0.0021 memory: 45095 loss: 0.1095 loss_ce: 0.1095 2023/02/27 06:17:30 - mmengine - INFO - Epoch(train) [71][ 700/5047] lr: 2.0272e-05 eta: 4 days, 1:59:33 time: 0.9320 data_time: 0.0021 memory: 51792 loss: 0.1113 loss_ce: 0.1113 2023/02/27 06:17:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 06:18:58 - mmengine - INFO - Epoch(train) [71][ 800/5047] lr: 2.0272e-05 eta: 4 days, 1:58:05 time: 0.8399 data_time: 0.0022 memory: 52792 loss: 0.1245 loss_ce: 0.1245 2023/02/27 06:20:22 - mmengine - INFO - Epoch(train) [71][ 900/5047] lr: 2.0272e-05 eta: 4 days, 1:56:34 time: 0.8404 data_time: 0.0021 memory: 44966 loss: 0.1220 loss_ce: 0.1220 2023/02/27 06:21:48 - mmengine - INFO - Epoch(train) [71][1000/5047] lr: 2.0272e-05 eta: 4 days, 1:55:04 time: 0.8513 data_time: 0.0049 memory: 47639 loss: 0.1129 loss_ce: 0.1129 2023/02/27 06:23:15 - mmengine - INFO - Epoch(train) [71][1100/5047] lr: 2.0272e-05 eta: 4 days, 1:53:36 time: 0.8707 data_time: 0.0022 memory: 55562 loss: 0.1178 loss_ce: 0.1178 2023/02/27 06:24:42 - mmengine - INFO - Epoch(train) [71][1200/5047] lr: 2.0272e-05 eta: 4 days, 1:52:07 time: 0.8362 data_time: 0.0025 memory: 41965 loss: 0.1041 loss_ce: 0.1041 2023/02/27 06:26:08 - mmengine - INFO - Epoch(train) [71][1300/5047] lr: 2.0272e-05 eta: 4 days, 1:50:38 time: 0.8496 data_time: 0.0100 memory: 44956 loss: 0.1195 loss_ce: 0.1195 2023/02/27 06:27:34 - mmengine - INFO - Epoch(train) [71][1400/5047] lr: 2.0272e-05 eta: 4 days, 1:49:09 time: 0.8925 data_time: 0.0020 memory: 43613 loss: 0.1064 loss_ce: 0.1064 2023/02/27 06:29:01 - mmengine - INFO - Epoch(train) [71][1500/5047] lr: 2.0272e-05 eta: 4 days, 1:47:40 time: 0.8003 data_time: 0.0020 memory: 55468 loss: 0.1037 loss_ce: 0.1037 2023/02/27 06:30:28 - mmengine - INFO - Epoch(train) [71][1600/5047] lr: 2.0272e-05 eta: 4 days, 1:46:13 time: 0.8597 data_time: 0.0065 memory: 55562 loss: 0.1171 loss_ce: 0.1171 2023/02/27 06:31:55 - mmengine - INFO - Epoch(train) [71][1700/5047] lr: 2.0272e-05 eta: 4 days, 1:44:44 time: 0.8096 data_time: 0.0052 memory: 43052 loss: 0.1212 loss_ce: 0.1212 2023/02/27 06:32:03 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 06:33:21 - mmengine - INFO - Epoch(train) [71][1800/5047] lr: 2.0272e-05 eta: 4 days, 1:43:15 time: 0.8657 data_time: 0.0027 memory: 46504 loss: 0.1286 loss_ce: 0.1286 2023/02/27 06:34:45 - mmengine - INFO - Epoch(train) [71][1900/5047] lr: 2.0272e-05 eta: 4 days, 1:41:43 time: 0.8601 data_time: 0.0025 memory: 44721 loss: 0.1354 loss_ce: 0.1354 2023/02/27 06:36:09 - mmengine - INFO - Epoch(train) [71][2000/5047] lr: 2.0272e-05 eta: 4 days, 1:40:11 time: 0.8139 data_time: 0.0021 memory: 40825 loss: 0.1142 loss_ce: 0.1142 2023/02/27 06:37:35 - mmengine - INFO - Epoch(train) [71][2100/5047] lr: 2.0272e-05 eta: 4 days, 1:38:42 time: 0.8402 data_time: 0.0020 memory: 43613 loss: 0.1200 loss_ce: 0.1200 2023/02/27 06:39:02 - mmengine - INFO - Epoch(train) [71][2200/5047] lr: 2.0272e-05 eta: 4 days, 1:37:14 time: 0.8839 data_time: 0.0022 memory: 42072 loss: 0.1214 loss_ce: 0.1214 2023/02/27 06:40:26 - mmengine - INFO - Epoch(train) [71][2300/5047] lr: 2.0272e-05 eta: 4 days, 1:35:43 time: 0.8665 data_time: 0.0022 memory: 41417 loss: 0.1168 loss_ce: 0.1168 2023/02/27 06:41:53 - mmengine - INFO - Epoch(train) [71][2400/5047] lr: 2.0272e-05 eta: 4 days, 1:34:14 time: 0.8518 data_time: 0.0021 memory: 54242 loss: 0.1115 loss_ce: 0.1115 2023/02/27 06:43:19 - mmengine - INFO - Epoch(train) [71][2500/5047] lr: 2.0272e-05 eta: 4 days, 1:32:45 time: 0.8352 data_time: 0.0023 memory: 53975 loss: 0.1253 loss_ce: 0.1253 2023/02/27 06:44:45 - mmengine - INFO - Epoch(train) [71][2600/5047] lr: 2.0272e-05 eta: 4 days, 1:31:16 time: 0.8489 data_time: 0.0022 memory: 41196 loss: 0.1380 loss_ce: 0.1380 2023/02/27 06:46:11 - mmengine - INFO - Epoch(train) [71][2700/5047] lr: 2.0272e-05 eta: 4 days, 1:29:46 time: 0.8447 data_time: 0.0022 memory: 47627 loss: 0.1075 loss_ce: 0.1075 2023/02/27 06:46:20 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 06:47:39 - mmengine - INFO - Epoch(train) [71][2800/5047] lr: 2.0272e-05 eta: 4 days, 1:28:19 time: 0.8808 data_time: 0.0021 memory: 50514 loss: 0.1343 loss_ce: 0.1343 2023/02/27 06:49:05 - mmengine - INFO - Epoch(train) [71][2900/5047] lr: 2.0272e-05 eta: 4 days, 1:26:49 time: 0.8730 data_time: 0.0022 memory: 43289 loss: 0.1088 loss_ce: 0.1088 2023/02/27 06:50:29 - mmengine - INFO - Epoch(train) [71][3000/5047] lr: 2.0272e-05 eta: 4 days, 1:25:18 time: 0.8384 data_time: 0.0020 memory: 43613 loss: 0.1318 loss_ce: 0.1318 2023/02/27 06:51:54 - mmengine - INFO - Epoch(train) [71][3100/5047] lr: 2.0272e-05 eta: 4 days, 1:23:47 time: 0.8162 data_time: 0.0035 memory: 46005 loss: 0.1230 loss_ce: 0.1230 2023/02/27 06:53:19 - mmengine - INFO - Epoch(train) [71][3200/5047] lr: 2.0272e-05 eta: 4 days, 1:22:16 time: 0.8786 data_time: 0.0023 memory: 55562 loss: 0.1002 loss_ce: 0.1002 2023/02/27 06:54:45 - mmengine - INFO - Epoch(train) [71][3300/5047] lr: 2.0272e-05 eta: 4 days, 1:20:48 time: 0.8885 data_time: 0.0023 memory: 53809 loss: 0.1324 loss_ce: 0.1324 2023/02/27 06:56:12 - mmengine - INFO - Epoch(train) [71][3400/5047] lr: 2.0272e-05 eta: 4 days, 1:19:20 time: 0.8685 data_time: 0.0050 memory: 42475 loss: 0.1114 loss_ce: 0.1114 2023/02/27 06:57:37 - mmengine - INFO - Epoch(train) [71][3500/5047] lr: 2.0272e-05 eta: 4 days, 1:17:48 time: 0.8676 data_time: 0.0023 memory: 42372 loss: 0.1206 loss_ce: 0.1206 2023/02/27 06:59:02 - mmengine - INFO - Epoch(train) [71][3600/5047] lr: 2.0272e-05 eta: 4 days, 1:16:19 time: 0.9019 data_time: 0.0020 memory: 41419 loss: 0.1170 loss_ce: 0.1170 2023/02/27 07:00:28 - mmengine - INFO - Epoch(train) [71][3700/5047] lr: 2.0272e-05 eta: 4 days, 1:14:49 time: 0.8832 data_time: 0.0029 memory: 44330 loss: 0.1230 loss_ce: 0.1230 2023/02/27 07:00:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 07:01:54 - mmengine - INFO - Epoch(train) [71][3800/5047] lr: 2.0272e-05 eta: 4 days, 1:13:19 time: 0.8623 data_time: 0.0021 memory: 47140 loss: 0.1334 loss_ce: 0.1334 2023/02/27 07:03:19 - mmengine - INFO - Epoch(train) [71][3900/5047] lr: 2.0272e-05 eta: 4 days, 1:11:49 time: 0.8737 data_time: 0.0021 memory: 45302 loss: 0.1092 loss_ce: 0.1092 2023/02/27 07:04:46 - mmengine - INFO - Epoch(train) [71][4000/5047] lr: 2.0272e-05 eta: 4 days, 1:10:21 time: 0.8583 data_time: 0.0021 memory: 53809 loss: 0.1141 loss_ce: 0.1141 2023/02/27 07:06:12 - mmengine - INFO - Epoch(train) [71][4100/5047] lr: 2.0272e-05 eta: 4 days, 1:08:51 time: 0.8401 data_time: 0.0025 memory: 47813 loss: 0.1171 loss_ce: 0.1171 2023/02/27 07:07:38 - mmengine - INFO - Epoch(train) [71][4200/5047] lr: 2.0272e-05 eta: 4 days, 1:07:22 time: 0.8747 data_time: 0.0021 memory: 47813 loss: 0.1158 loss_ce: 0.1158 2023/02/27 07:09:04 - mmengine - INFO - Epoch(train) [71][4300/5047] lr: 2.0272e-05 eta: 4 days, 1:05:53 time: 0.8609 data_time: 0.0022 memory: 42336 loss: 0.1140 loss_ce: 0.1140 2023/02/27 07:10:32 - mmengine - INFO - Epoch(train) [71][4400/5047] lr: 2.0272e-05 eta: 4 days, 1:04:26 time: 0.8807 data_time: 0.0025 memory: 52964 loss: 0.1174 loss_ce: 0.1174 2023/02/27 07:11:58 - mmengine - INFO - Epoch(train) [71][4500/5047] lr: 2.0272e-05 eta: 4 days, 1:02:56 time: 0.8292 data_time: 0.0021 memory: 55562 loss: 0.1408 loss_ce: 0.1408 2023/02/27 07:13:26 - mmengine - INFO - Epoch(train) [71][4600/5047] lr: 2.0272e-05 eta: 4 days, 1:01:29 time: 0.8361 data_time: 0.0019 memory: 46951 loss: 0.1258 loss_ce: 0.1258 2023/02/27 07:14:50 - mmengine - INFO - Epoch(train) [71][4700/5047] lr: 2.0272e-05 eta: 4 days, 0:59:58 time: 0.9166 data_time: 0.0021 memory: 47074 loss: 0.1114 loss_ce: 0.1114 2023/02/27 07:14:59 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 07:16:17 - mmengine - INFO - Epoch(train) [71][4800/5047] lr: 2.0272e-05 eta: 4 days, 0:58:30 time: 0.8742 data_time: 0.0059 memory: 44278 loss: 0.1219 loss_ce: 0.1219 2023/02/27 07:17:45 - mmengine - INFO - Epoch(train) [71][4900/5047] lr: 2.0272e-05 eta: 4 days, 0:57:03 time: 0.8640 data_time: 0.0022 memory: 41069 loss: 0.1164 loss_ce: 0.1164 2023/02/27 07:19:12 - mmengine - INFO - Epoch(train) [71][5000/5047] lr: 2.0272e-05 eta: 4 days, 0:55:35 time: 0.9050 data_time: 0.0021 memory: 50906 loss: 0.1207 loss_ce: 0.1207 2023/02/27 07:19:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 07:19:52 - mmengine - INFO - Saving checkpoint at 71 epochs 2023/02/27 07:21:22 - mmengine - INFO - Epoch(train) [72][ 100/5047] lr: 2.0071e-05 eta: 4 days, 0:53:21 time: 0.8420 data_time: 0.0021 memory: 45320 loss: 0.1074 loss_ce: 0.1074 2023/02/27 07:22:48 - mmengine - INFO - Epoch(train) [72][ 200/5047] lr: 2.0071e-05 eta: 4 days, 0:51:52 time: 0.8283 data_time: 0.0020 memory: 53809 loss: 0.1272 loss_ce: 0.1272 2023/02/27 07:24:13 - mmengine - INFO - Epoch(train) [72][ 300/5047] lr: 2.0071e-05 eta: 4 days, 0:50:22 time: 0.8396 data_time: 0.0025 memory: 42348 loss: 0.1095 loss_ce: 0.1095 2023/02/27 07:25:40 - mmengine - INFO - Epoch(train) [72][ 400/5047] lr: 2.0071e-05 eta: 4 days, 0:48:54 time: 0.8165 data_time: 0.0023 memory: 46794 loss: 0.1041 loss_ce: 0.1041 2023/02/27 07:27:05 - mmengine - INFO - Epoch(train) [72][ 500/5047] lr: 2.0071e-05 eta: 4 days, 0:47:24 time: 0.8284 data_time: 0.0051 memory: 44971 loss: 0.1149 loss_ce: 0.1149 2023/02/27 07:28:32 - mmengine - INFO - Epoch(train) [72][ 600/5047] lr: 2.0071e-05 eta: 4 days, 0:45:56 time: 0.8634 data_time: 0.0021 memory: 55114 loss: 0.1175 loss_ce: 0.1175 2023/02/27 07:29:28 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 07:30:00 - mmengine - INFO - Epoch(train) [72][ 700/5047] lr: 2.0071e-05 eta: 4 days, 0:44:28 time: 0.9086 data_time: 0.0028 memory: 42965 loss: 0.1225 loss_ce: 0.1225 2023/02/27 07:31:26 - mmengine - INFO - Epoch(train) [72][ 800/5047] lr: 2.0071e-05 eta: 4 days, 0:42:58 time: 0.7970 data_time: 0.0020 memory: 41122 loss: 0.1290 loss_ce: 0.1290 2023/02/27 07:32:54 - mmengine - INFO - Epoch(train) [72][ 900/5047] lr: 2.0071e-05 eta: 4 days, 0:41:32 time: 0.9258 data_time: 0.0023 memory: 45643 loss: 0.1067 loss_ce: 0.1067 2023/02/27 07:34:20 - mmengine - INFO - Epoch(train) [72][1000/5047] lr: 2.0071e-05 eta: 4 days, 0:40:02 time: 0.8858 data_time: 0.0023 memory: 41257 loss: 0.1048 loss_ce: 0.1048 2023/02/27 07:35:45 - mmengine - INFO - Epoch(train) [72][1100/5047] lr: 2.0071e-05 eta: 4 days, 0:38:33 time: 0.8406 data_time: 0.0078 memory: 43289 loss: 0.1072 loss_ce: 0.1072 2023/02/27 07:37:12 - mmengine - INFO - Epoch(train) [72][1200/5047] lr: 2.0071e-05 eta: 4 days, 0:37:04 time: 0.8668 data_time: 0.0023 memory: 41122 loss: 0.1152 loss_ce: 0.1152 2023/02/27 07:38:39 - mmengine - INFO - Epoch(train) [72][1300/5047] lr: 2.0071e-05 eta: 4 days, 0:35:36 time: 0.8835 data_time: 0.0021 memory: 46379 loss: 0.1051 loss_ce: 0.1051 2023/02/27 07:40:06 - mmengine - INFO - Epoch(train) [72][1400/5047] lr: 2.0071e-05 eta: 4 days, 0:34:07 time: 0.8558 data_time: 0.0022 memory: 55485 loss: 0.1249 loss_ce: 0.1249 2023/02/27 07:41:34 - mmengine - INFO - Epoch(train) [72][1500/5047] lr: 2.0071e-05 eta: 4 days, 0:32:40 time: 0.9018 data_time: 0.0025 memory: 46563 loss: 0.1027 loss_ce: 0.1027 2023/02/27 07:42:59 - mmengine - INFO - Epoch(train) [72][1600/5047] lr: 2.0071e-05 eta: 4 days, 0:31:11 time: 0.8410 data_time: 0.0021 memory: 53021 loss: 0.1171 loss_ce: 0.1171 2023/02/27 07:43:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 07:44:25 - mmengine - INFO - Epoch(train) [72][1700/5047] lr: 2.0071e-05 eta: 4 days, 0:29:41 time: 0.8589 data_time: 0.0023 memory: 42024 loss: 0.1225 loss_ce: 0.1225 2023/02/27 07:45:50 - mmengine - INFO - Epoch(train) [72][1800/5047] lr: 2.0071e-05 eta: 4 days, 0:28:10 time: 0.8257 data_time: 0.0024 memory: 43289 loss: 0.1099 loss_ce: 0.1099 2023/02/27 07:47:13 - mmengine - INFO - Epoch(train) [72][1900/5047] lr: 2.0071e-05 eta: 4 days, 0:26:38 time: 0.8184 data_time: 0.0030 memory: 45302 loss: 0.1217 loss_ce: 0.1217 2023/02/27 07:48:38 - mmengine - INFO - Epoch(train) [72][2000/5047] lr: 2.0071e-05 eta: 4 days, 0:25:08 time: 0.8389 data_time: 0.0025 memory: 55562 loss: 0.1220 loss_ce: 0.1220 2023/02/27 07:50:05 - mmengine - INFO - Epoch(train) [72][2100/5047] lr: 2.0071e-05 eta: 4 days, 0:23:40 time: 0.8733 data_time: 0.0020 memory: 44781 loss: 0.1147 loss_ce: 0.1147 2023/02/27 07:51:31 - mmengine - INFO - Epoch(train) [72][2200/5047] lr: 2.0071e-05 eta: 4 days, 0:22:10 time: 0.8998 data_time: 0.0025 memory: 52541 loss: 0.1227 loss_ce: 0.1227 2023/02/27 07:52:57 - mmengine - INFO - Epoch(train) [72][2300/5047] lr: 2.0071e-05 eta: 4 days, 0:20:41 time: 0.8502 data_time: 0.0022 memory: 44587 loss: 0.1210 loss_ce: 0.1210 2023/02/27 07:54:23 - mmengine - INFO - Epoch(train) [72][2400/5047] lr: 2.0071e-05 eta: 4 days, 0:19:12 time: 0.8483 data_time: 0.0028 memory: 52022 loss: 0.1109 loss_ce: 0.1109 2023/02/27 07:55:51 - mmengine - INFO - Epoch(train) [72][2500/5047] lr: 2.0071e-05 eta: 4 days, 0:17:44 time: 0.8838 data_time: 0.0047 memory: 41122 loss: 0.1101 loss_ce: 0.1101 2023/02/27 07:57:16 - mmengine - INFO - Epoch(train) [72][2600/5047] lr: 2.0071e-05 eta: 4 days, 0:16:14 time: 0.8280 data_time: 0.0022 memory: 54242 loss: 0.1224 loss_ce: 0.1224 2023/02/27 07:58:10 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 07:58:42 - mmengine - INFO - Epoch(train) [72][2700/5047] lr: 2.0071e-05 eta: 4 days, 0:14:45 time: 0.8476 data_time: 0.0027 memory: 48055 loss: 0.1287 loss_ce: 0.1287 2023/02/27 08:00:08 - mmengine - INFO - Epoch(train) [72][2800/5047] lr: 2.0071e-05 eta: 4 days, 0:13:15 time: 0.8843 data_time: 0.0024 memory: 43721 loss: 0.1080 loss_ce: 0.1080 2023/02/27 08:01:33 - mmengine - INFO - Epoch(train) [72][2900/5047] lr: 2.0071e-05 eta: 4 days, 0:11:45 time: 0.8970 data_time: 0.0020 memory: 44851 loss: 0.1069 loss_ce: 0.1069 2023/02/27 08:02:59 - mmengine - INFO - Epoch(train) [72][3000/5047] lr: 2.0071e-05 eta: 4 days, 0:10:16 time: 0.8600 data_time: 0.0024 memory: 42649 loss: 0.1309 loss_ce: 0.1309 2023/02/27 08:04:25 - mmengine - INFO - Epoch(train) [72][3100/5047] lr: 2.0071e-05 eta: 4 days, 0:08:47 time: 0.8402 data_time: 0.0025 memory: 44616 loss: 0.1264 loss_ce: 0.1264 2023/02/27 08:05:52 - mmengine - INFO - Epoch(train) [72][3200/5047] lr: 2.0071e-05 eta: 4 days, 0:07:19 time: 0.8576 data_time: 0.0020 memory: 44585 loss: 0.1177 loss_ce: 0.1177 2023/02/27 08:07:20 - mmengine - INFO - Epoch(train) [72][3300/5047] lr: 2.0071e-05 eta: 4 days, 0:05:51 time: 0.8503 data_time: 0.0057 memory: 47695 loss: 0.1185 loss_ce: 0.1185 2023/02/27 08:08:46 - mmengine - INFO - Epoch(train) [72][3400/5047] lr: 2.0071e-05 eta: 4 days, 0:04:22 time: 0.8151 data_time: 0.0021 memory: 43613 loss: 0.1125 loss_ce: 0.1125 2023/02/27 08:10:12 - mmengine - INFO - Epoch(train) [72][3500/5047] lr: 2.0071e-05 eta: 4 days, 0:02:54 time: 0.9086 data_time: 0.0023 memory: 44663 loss: 0.1242 loss_ce: 0.1242 2023/02/27 08:11:40 - mmengine - INFO - Epoch(train) [72][3600/5047] lr: 2.0071e-05 eta: 4 days, 0:01:26 time: 0.8288 data_time: 0.0022 memory: 41122 loss: 0.1076 loss_ce: 0.1076 2023/02/27 08:12:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 08:13:05 - mmengine - INFO - Epoch(train) [72][3700/5047] lr: 2.0071e-05 eta: 3 days, 23:59:56 time: 0.8217 data_time: 0.0029 memory: 55562 loss: 0.1158 loss_ce: 0.1158 2023/02/27 08:14:31 - mmengine - INFO - Epoch(train) [72][3800/5047] lr: 2.0071e-05 eta: 3 days, 23:58:26 time: 0.8100 data_time: 0.0025 memory: 49618 loss: 0.1086 loss_ce: 0.1086 2023/02/27 08:15:57 - mmengine - INFO - Epoch(train) [72][3900/5047] lr: 2.0071e-05 eta: 3 days, 23:56:57 time: 0.8601 data_time: 0.0045 memory: 55562 loss: 0.1057 loss_ce: 0.1057 2023/02/27 08:17:26 - mmengine - INFO - Epoch(train) [72][4000/5047] lr: 2.0071e-05 eta: 3 days, 23:55:31 time: 0.9146 data_time: 0.0022 memory: 42024 loss: 0.1050 loss_ce: 0.1050 2023/02/27 08:18:52 - mmengine - INFO - Epoch(train) [72][4100/5047] lr: 2.0071e-05 eta: 3 days, 23:54:03 time: 0.8642 data_time: 0.0025 memory: 54135 loss: 0.1085 loss_ce: 0.1085 2023/02/27 08:20:19 - mmengine - INFO - Epoch(train) [72][4200/5047] lr: 2.0071e-05 eta: 3 days, 23:52:34 time: 0.8909 data_time: 0.0020 memory: 55562 loss: 0.1114 loss_ce: 0.1114 2023/02/27 08:21:46 - mmengine - INFO - Epoch(train) [72][4300/5047] lr: 2.0071e-05 eta: 3 days, 23:51:06 time: 0.9017 data_time: 0.0042 memory: 40535 loss: 0.1299 loss_ce: 0.1299 2023/02/27 08:23:12 - mmengine - INFO - Epoch(train) [72][4400/5047] lr: 2.0071e-05 eta: 3 days, 23:49:36 time: 0.8195 data_time: 0.0020 memory: 45984 loss: 0.1260 loss_ce: 0.1260 2023/02/27 08:24:40 - mmengine - INFO - Epoch(train) [72][4500/5047] lr: 2.0071e-05 eta: 3 days, 23:48:10 time: 0.9060 data_time: 0.0019 memory: 48129 loss: 0.1127 loss_ce: 0.1127 2023/02/27 08:26:07 - mmengine - INFO - Epoch(train) [72][4600/5047] lr: 2.0071e-05 eta: 3 days, 23:46:41 time: 0.8693 data_time: 0.0022 memory: 51686 loss: 0.1254 loss_ce: 0.1254 2023/02/27 08:26:59 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 08:27:32 - mmengine - INFO - Epoch(train) [72][4700/5047] lr: 2.0071e-05 eta: 3 days, 23:45:11 time: 0.8226 data_time: 0.0021 memory: 45585 loss: 0.1227 loss_ce: 0.1227 2023/02/27 08:34:13 - mmengine - INFO - Epoch(train) [72][4800/5047] lr: 2.0071e-05 eta: 3 days, 23:49:39 time: 0.8209 data_time: 0.0022 memory: 42399 loss: 0.1124 loss_ce: 0.1124 2023/02/27 08:35:41 - mmengine - INFO - Epoch(train) [72][4900/5047] lr: 2.0071e-05 eta: 3 days, 23:48:11 time: 0.9132 data_time: 0.0022 memory: 41724 loss: 0.1144 loss_ce: 0.1144 2023/02/27 08:37:07 - mmengine - INFO - Epoch(train) [72][5000/5047] lr: 2.0071e-05 eta: 3 days, 23:46:42 time: 0.8369 data_time: 0.0024 memory: 48892 loss: 0.1044 loss_ce: 0.1044 2023/02/27 08:37:48 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 08:37:48 - mmengine - INFO - Saving checkpoint at 72 epochs 2023/02/27 08:39:21 - mmengine - INFO - Epoch(train) [73][ 100/5047] lr: 1.9870e-05 eta: 3 days, 23:44:33 time: 0.8967 data_time: 0.0057 memory: 43055 loss: 0.1253 loss_ce: 0.1253 2023/02/27 08:40:48 - mmengine - INFO - Epoch(train) [73][ 200/5047] lr: 1.9870e-05 eta: 3 days, 23:43:04 time: 0.8437 data_time: 0.0033 memory: 42649 loss: 0.1197 loss_ce: 0.1197 2023/02/27 08:42:12 - mmengine - INFO - Epoch(train) [73][ 300/5047] lr: 1.9870e-05 eta: 3 days, 23:41:32 time: 0.8305 data_time: 0.0022 memory: 44956 loss: 0.1179 loss_ce: 0.1179 2023/02/27 08:43:37 - mmengine - INFO - Epoch(train) [73][ 400/5047] lr: 1.9870e-05 eta: 3 days, 23:40:02 time: 0.8294 data_time: 0.0025 memory: 43947 loss: 0.1108 loss_ce: 0.1108 2023/02/27 08:45:02 - mmengine - INFO - Epoch(train) [73][ 500/5047] lr: 1.9870e-05 eta: 3 days, 23:38:31 time: 0.9160 data_time: 0.0022 memory: 50369 loss: 0.1252 loss_ce: 0.1252 2023/02/27 08:46:29 - mmengine - INFO - Epoch(train) [73][ 600/5047] lr: 1.9870e-05 eta: 3 days, 23:37:03 time: 0.8780 data_time: 0.0023 memory: 43289 loss: 0.1074 loss_ce: 0.1074 2023/02/27 08:46:43 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 08:47:55 - mmengine - INFO - Epoch(train) [73][ 700/5047] lr: 1.9870e-05 eta: 3 days, 23:35:34 time: 0.8633 data_time: 0.0020 memory: 42024 loss: 0.1116 loss_ce: 0.1116 2023/02/27 08:49:21 - mmengine - INFO - Epoch(train) [73][ 800/5047] lr: 1.9870e-05 eta: 3 days, 23:34:04 time: 0.8645 data_time: 0.0021 memory: 45643 loss: 0.1141 loss_ce: 0.1141 2023/02/27 08:50:47 - mmengine - INFO - Epoch(train) [73][ 900/5047] lr: 1.9870e-05 eta: 3 days, 23:32:35 time: 0.8784 data_time: 0.0028 memory: 46323 loss: 0.1078 loss_ce: 0.1078 2023/02/27 08:52:13 - mmengine - INFO - Epoch(train) [73][1000/5047] lr: 1.9870e-05 eta: 3 days, 23:31:06 time: 0.8598 data_time: 0.0022 memory: 50906 loss: 0.1319 loss_ce: 0.1319 2023/02/27 08:53:38 - mmengine - INFO - Epoch(train) [73][1100/5047] lr: 1.9870e-05 eta: 3 days, 23:29:36 time: 0.8604 data_time: 0.0026 memory: 49715 loss: 0.1169 loss_ce: 0.1169 2023/02/27 08:55:05 - mmengine - INFO - Epoch(train) [73][1200/5047] lr: 1.9870e-05 eta: 3 days, 23:28:07 time: 0.8645 data_time: 0.0020 memory: 43947 loss: 0.1152 loss_ce: 0.1152 2023/02/27 08:56:32 - mmengine - INFO - Epoch(train) [73][1300/5047] lr: 1.9870e-05 eta: 3 days, 23:26:39 time: 0.8920 data_time: 0.0023 memory: 43348 loss: 0.1251 loss_ce: 0.1251 2023/02/27 08:57:59 - mmengine - INFO - Epoch(train) [73][1400/5047] lr: 1.9870e-05 eta: 3 days, 23:25:11 time: 0.8469 data_time: 0.0024 memory: 42336 loss: 0.1318 loss_ce: 0.1318 2023/02/27 08:59:26 - mmengine - INFO - Epoch(train) [73][1500/5047] lr: 1.9870e-05 eta: 3 days, 23:23:42 time: 0.8440 data_time: 0.0020 memory: 47447 loss: 0.1193 loss_ce: 0.1193 2023/02/27 09:00:52 - mmengine - INFO - Epoch(train) [73][1600/5047] lr: 1.9870e-05 eta: 3 days, 23:22:12 time: 0.8731 data_time: 0.0022 memory: 50608 loss: 0.1167 loss_ce: 0.1167 2023/02/27 09:01:05 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 09:02:19 - mmengine - INFO - Epoch(train) [73][1700/5047] lr: 1.9870e-05 eta: 3 days, 23:20:44 time: 0.8633 data_time: 0.0019 memory: 42649 loss: 0.1251 loss_ce: 0.1251 2023/02/27 09:03:46 - mmengine - INFO - Epoch(train) [73][1800/5047] lr: 1.9870e-05 eta: 3 days, 23:19:16 time: 0.8725 data_time: 0.0022 memory: 43550 loss: 0.1195 loss_ce: 0.1195 2023/02/27 09:05:11 - mmengine - INFO - Epoch(train) [73][1900/5047] lr: 1.9870e-05 eta: 3 days, 23:17:46 time: 0.8339 data_time: 0.0023 memory: 41419 loss: 0.1092 loss_ce: 0.1092 2023/02/27 09:06:37 - mmengine - INFO - Epoch(train) [73][2000/5047] lr: 1.9870e-05 eta: 3 days, 23:16:16 time: 0.8828 data_time: 0.0020 memory: 55562 loss: 0.1198 loss_ce: 0.1198 2023/02/27 09:08:04 - mmengine - INFO - Epoch(train) [73][2100/5047] lr: 1.9870e-05 eta: 3 days, 23:14:47 time: 0.8602 data_time: 0.0033 memory: 49334 loss: 0.1142 loss_ce: 0.1142 2023/02/27 09:09:29 - mmengine - INFO - Epoch(train) [73][2200/5047] lr: 1.9870e-05 eta: 3 days, 23:13:17 time: 0.8939 data_time: 0.0023 memory: 43780 loss: 0.1368 loss_ce: 0.1368 2023/02/27 09:15:32 - mmengine - INFO - Epoch(train) [73][2300/5047] lr: 1.9870e-05 eta: 3 days, 23:16:57 time: 0.8376 data_time: 0.0022 memory: 51795 loss: 0.1288 loss_ce: 0.1288 2023/02/27 09:17:33 - mmengine - INFO - Epoch(train) [73][2400/5047] lr: 1.9870e-05 eta: 3 days, 23:16:06 time: 0.8740 data_time: 0.0023 memory: 42336 loss: 0.0995 loss_ce: 0.0995 2023/02/27 09:18:58 - mmengine - INFO - Epoch(train) [73][2500/5047] lr: 1.9870e-05 eta: 3 days, 23:14:36 time: 0.8317 data_time: 0.0024 memory: 43289 loss: 0.1191 loss_ce: 0.1191 2023/02/27 09:20:23 - mmengine - INFO - Epoch(train) [73][2600/5047] lr: 1.9870e-05 eta: 3 days, 23:13:06 time: 0.8517 data_time: 0.0048 memory: 49715 loss: 0.1072 loss_ce: 0.1072 2023/02/27 09:20:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 09:21:48 - mmengine - INFO - Epoch(train) [73][2700/5047] lr: 1.9870e-05 eta: 3 days, 23:11:35 time: 0.8622 data_time: 0.0022 memory: 43043 loss: 0.1079 loss_ce: 0.1079 2023/02/27 09:23:13 - mmengine - INFO - Epoch(train) [73][2800/5047] lr: 1.9870e-05 eta: 3 days, 23:10:04 time: 0.7892 data_time: 0.0021 memory: 38084 loss: 0.1165 loss_ce: 0.1165 2023/02/27 09:24:40 - mmengine - INFO - Epoch(train) [73][2900/5047] lr: 1.9870e-05 eta: 3 days, 23:08:35 time: 0.8746 data_time: 0.0023 memory: 55562 loss: 0.1236 loss_ce: 0.1236 2023/02/27 09:26:06 - mmengine - INFO - Epoch(train) [73][3000/5047] lr: 1.9870e-05 eta: 3 days, 23:07:06 time: 0.8361 data_time: 0.0021 memory: 50417 loss: 0.1112 loss_ce: 0.1112 2023/02/27 09:27:34 - mmengine - INFO - Epoch(train) [73][3100/5047] lr: 1.9870e-05 eta: 3 days, 23:05:39 time: 0.8897 data_time: 0.0022 memory: 47074 loss: 0.1063 loss_ce: 0.1063 2023/02/27 09:29:00 - mmengine - INFO - Epoch(train) [73][3200/5047] lr: 1.9870e-05 eta: 3 days, 23:04:09 time: 0.8500 data_time: 0.0032 memory: 46507 loss: 0.1230 loss_ce: 0.1230 2023/02/27 09:30:29 - mmengine - INFO - Epoch(train) [73][3300/5047] lr: 1.9870e-05 eta: 3 days, 23:02:43 time: 0.8668 data_time: 0.0026 memory: 47074 loss: 0.1173 loss_ce: 0.1173 2023/02/27 09:31:55 - mmengine - INFO - Epoch(train) [73][3400/5047] lr: 1.9870e-05 eta: 3 days, 23:01:14 time: 0.8732 data_time: 0.0020 memory: 55562 loss: 0.1112 loss_ce: 0.1112 2023/02/27 09:33:21 - mmengine - INFO - Epoch(train) [73][3500/5047] lr: 1.9870e-05 eta: 3 days, 22:59:44 time: 0.8075 data_time: 0.0021 memory: 51279 loss: 0.1179 loss_ce: 0.1179 2023/02/27 09:34:49 - mmengine - INFO - Epoch(train) [73][3600/5047] lr: 1.9870e-05 eta: 3 days, 22:58:16 time: 0.8473 data_time: 0.0021 memory: 42024 loss: 0.1170 loss_ce: 0.1170 2023/02/27 09:35:03 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 09:36:15 - mmengine - INFO - Epoch(train) [73][3700/5047] lr: 1.9870e-05 eta: 3 days, 22:56:47 time: 0.8891 data_time: 0.0022 memory: 47037 loss: 0.1086 loss_ce: 0.1086 2023/02/27 09:37:42 - mmengine - INFO - Epoch(train) [73][3800/5047] lr: 1.9870e-05 eta: 3 days, 22:55:19 time: 0.8956 data_time: 0.0024 memory: 55297 loss: 0.1203 loss_ce: 0.1203 2023/02/27 09:39:10 - mmengine - INFO - Epoch(train) [73][3900/5047] lr: 1.9870e-05 eta: 3 days, 22:53:51 time: 0.9011 data_time: 0.0028 memory: 42024 loss: 0.1124 loss_ce: 0.1124 2023/02/27 09:40:35 - mmengine - INFO - Epoch(train) [73][4000/5047] lr: 1.9870e-05 eta: 3 days, 22:52:21 time: 0.8314 data_time: 0.0024 memory: 43613 loss: 0.1187 loss_ce: 0.1187 2023/02/27 09:42:03 - mmengine - INFO - Epoch(train) [73][4100/5047] lr: 1.9870e-05 eta: 3 days, 22:50:53 time: 0.8747 data_time: 0.0023 memory: 41122 loss: 0.0987 loss_ce: 0.0987 2023/02/27 09:43:28 - mmengine - INFO - Epoch(train) [73][4200/5047] lr: 1.9870e-05 eta: 3 days, 22:49:23 time: 0.8285 data_time: 0.0030 memory: 41122 loss: 0.1186 loss_ce: 0.1186 2023/02/27 09:44:52 - mmengine - INFO - Epoch(train) [73][4300/5047] lr: 1.9870e-05 eta: 3 days, 22:47:51 time: 0.8581 data_time: 0.0021 memory: 41724 loss: 0.1038 loss_ce: 0.1038 2023/02/27 09:46:18 - mmengine - INFO - Epoch(train) [73][4400/5047] lr: 1.9870e-05 eta: 3 days, 22:46:22 time: 0.9059 data_time: 0.0021 memory: 44524 loss: 0.1283 loss_ce: 0.1283 2023/02/27 09:47:43 - mmengine - INFO - Epoch(train) [73][4500/5047] lr: 1.9870e-05 eta: 3 days, 22:44:51 time: 0.8630 data_time: 0.0022 memory: 55562 loss: 0.1201 loss_ce: 0.1201 2023/02/27 09:49:10 - mmengine - INFO - Epoch(train) [73][4600/5047] lr: 1.9870e-05 eta: 3 days, 22:43:22 time: 0.8910 data_time: 0.0021 memory: 48187 loss: 0.1083 loss_ce: 0.1083 2023/02/27 09:49:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 09:50:36 - mmengine - INFO - Epoch(train) [73][4700/5047] lr: 1.9870e-05 eta: 3 days, 22:41:53 time: 0.8451 data_time: 0.0027 memory: 43289 loss: 0.1175 loss_ce: 0.1175 2023/02/27 09:52:01 - mmengine - INFO - Epoch(train) [73][4800/5047] lr: 1.9870e-05 eta: 3 days, 22:40:23 time: 0.8470 data_time: 0.0032 memory: 48565 loss: 0.1109 loss_ce: 0.1109 2023/02/27 09:53:28 - mmengine - INFO - Epoch(train) [73][4900/5047] lr: 1.9870e-05 eta: 3 days, 22:38:54 time: 0.8388 data_time: 0.0026 memory: 45302 loss: 0.1185 loss_ce: 0.1185 2023/02/27 09:54:54 - mmengine - INFO - Epoch(train) [73][5000/5047] lr: 1.9870e-05 eta: 3 days, 22:37:24 time: 0.9107 data_time: 0.0047 memory: 44954 loss: 0.1068 loss_ce: 0.1068 2023/02/27 09:55:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 09:55:33 - mmengine - INFO - Saving checkpoint at 73 epochs 2023/02/27 09:57:05 - mmengine - INFO - Epoch(train) [74][ 100/5047] lr: 1.9669e-05 eta: 3 days, 22:35:12 time: 0.8548 data_time: 0.0020 memory: 45813 loss: 0.1247 loss_ce: 0.1247 2023/02/27 09:58:30 - mmengine - INFO - Epoch(train) [74][ 200/5047] lr: 1.9669e-05 eta: 3 days, 22:33:41 time: 0.8896 data_time: 0.0021 memory: 51580 loss: 0.1297 loss_ce: 0.1297 2023/02/27 09:59:57 - mmengine - INFO - Epoch(train) [74][ 300/5047] lr: 1.9669e-05 eta: 3 days, 22:32:13 time: 0.8997 data_time: 0.0024 memory: 53387 loss: 0.0986 loss_ce: 0.0986 2023/02/27 10:01:23 - mmengine - INFO - Epoch(train) [74][ 400/5047] lr: 1.9669e-05 eta: 3 days, 22:30:43 time: 0.8411 data_time: 0.0022 memory: 41956 loss: 0.1101 loss_ce: 0.1101 2023/02/27 10:02:50 - mmengine - INFO - Epoch(train) [74][ 500/5047] lr: 1.9669e-05 eta: 3 days, 22:29:15 time: 0.8352 data_time: 0.0022 memory: 47077 loss: 0.1184 loss_ce: 0.1184 2023/02/27 10:03:50 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 10:04:16 - mmengine - INFO - Epoch(train) [74][ 600/5047] lr: 1.9669e-05 eta: 3 days, 22:27:46 time: 0.8830 data_time: 0.0022 memory: 50589 loss: 0.1122 loss_ce: 0.1122 2023/02/27 10:05:41 - mmengine - INFO - Epoch(train) [74][ 700/5047] lr: 1.9669e-05 eta: 3 days, 22:26:15 time: 0.8129 data_time: 0.0024 memory: 41419 loss: 0.1137 loss_ce: 0.1137 2023/02/27 10:07:08 - mmengine - INFO - Epoch(train) [74][ 800/5047] lr: 1.9669e-05 eta: 3 days, 22:24:46 time: 0.9363 data_time: 0.0023 memory: 43403 loss: 0.1150 loss_ce: 0.1150 2023/02/27 10:08:34 - mmengine - INFO - Epoch(train) [74][ 900/5047] lr: 1.9669e-05 eta: 3 days, 22:23:17 time: 0.8765 data_time: 0.0024 memory: 39627 loss: 0.1185 loss_ce: 0.1185 2023/02/27 10:09:58 - mmengine - INFO - Epoch(train) [74][1000/5047] lr: 1.9669e-05 eta: 3 days, 22:21:46 time: 0.8560 data_time: 0.0032 memory: 39960 loss: 0.1189 loss_ce: 0.1189 2023/02/27 10:11:23 - mmengine - INFO - Epoch(train) [74][1100/5047] lr: 1.9669e-05 eta: 3 days, 22:20:15 time: 0.8759 data_time: 0.0031 memory: 48188 loss: 0.1083 loss_ce: 0.1083 2023/02/27 10:12:49 - mmengine - INFO - Epoch(train) [74][1200/5047] lr: 1.9669e-05 eta: 3 days, 22:18:45 time: 0.8230 data_time: 0.0025 memory: 42336 loss: 0.1163 loss_ce: 0.1163 2023/02/27 10:14:14 - mmengine - INFO - Epoch(train) [74][1300/5047] lr: 1.9669e-05 eta: 3 days, 22:17:14 time: 0.8378 data_time: 0.0021 memory: 55562 loss: 0.1365 loss_ce: 0.1365 2023/02/27 10:15:40 - mmengine - INFO - Epoch(train) [74][1400/5047] lr: 1.9669e-05 eta: 3 days, 22:15:46 time: 0.8132 data_time: 0.0024 memory: 40825 loss: 0.1263 loss_ce: 0.1263 2023/02/27 10:17:08 - mmengine - INFO - Epoch(train) [74][1500/5047] lr: 1.9669e-05 eta: 3 days, 22:14:18 time: 0.8764 data_time: 0.0023 memory: 44617 loss: 0.1193 loss_ce: 0.1193 2023/02/27 10:18:06 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 10:18:32 - mmengine - INFO - Epoch(train) [74][1600/5047] lr: 1.9669e-05 eta: 3 days, 22:12:47 time: 0.8658 data_time: 0.0048 memory: 52791 loss: 0.1211 loss_ce: 0.1211 2023/02/27 10:19:57 - mmengine - INFO - Epoch(train) [74][1700/5047] lr: 1.9669e-05 eta: 3 days, 22:11:16 time: 0.8556 data_time: 0.0022 memory: 42649 loss: 0.1204 loss_ce: 0.1204 2023/02/27 10:21:23 - mmengine - INFO - Epoch(train) [74][1800/5047] lr: 1.9669e-05 eta: 3 days, 22:09:46 time: 0.8545 data_time: 0.0027 memory: 41961 loss: 0.1317 loss_ce: 0.1317 2023/02/27 10:22:50 - mmengine - INFO - Epoch(train) [74][1900/5047] lr: 1.9669e-05 eta: 3 days, 22:08:18 time: 0.8153 data_time: 0.0022 memory: 55562 loss: 0.1247 loss_ce: 0.1247 2023/02/27 10:24:15 - mmengine - INFO - Epoch(train) [74][2000/5047] lr: 1.9669e-05 eta: 3 days, 22:06:48 time: 0.8650 data_time: 0.0022 memory: 44617 loss: 0.1202 loss_ce: 0.1202 2023/02/27 10:25:40 - mmengine - INFO - Epoch(train) [74][2100/5047] lr: 1.9669e-05 eta: 3 days, 22:05:17 time: 0.8610 data_time: 0.0069 memory: 41512 loss: 0.1056 loss_ce: 0.1056 2023/02/27 10:27:06 - mmengine - INFO - Epoch(train) [74][2200/5047] lr: 1.9669e-05 eta: 3 days, 22:03:48 time: 0.8732 data_time: 0.0025 memory: 42332 loss: 0.1171 loss_ce: 0.1171 2023/02/27 10:28:32 - mmengine - INFO - Epoch(train) [74][2300/5047] lr: 1.9669e-05 eta: 3 days, 22:02:18 time: 0.8868 data_time: 0.0026 memory: 49257 loss: 0.1257 loss_ce: 0.1257 2023/02/27 10:29:59 - mmengine - INFO - Epoch(train) [74][2400/5047] lr: 1.9669e-05 eta: 3 days, 22:00:50 time: 0.9201 data_time: 0.0030 memory: 44278 loss: 0.1194 loss_ce: 0.1194 2023/02/27 10:31:25 - mmengine - INFO - Epoch(train) [74][2500/5047] lr: 1.9669e-05 eta: 3 days, 21:59:20 time: 0.8824 data_time: 0.0022 memory: 44278 loss: 0.1106 loss_ce: 0.1106 2023/02/27 10:32:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 10:32:51 - mmengine - INFO - Epoch(train) [74][2600/5047] lr: 1.9669e-05 eta: 3 days, 21:57:51 time: 0.8856 data_time: 0.0024 memory: 49715 loss: 0.1062 loss_ce: 0.1062 2023/02/27 10:34:17 - mmengine - INFO - Epoch(train) [74][2700/5047] lr: 1.9669e-05 eta: 3 days, 21:56:21 time: 0.8669 data_time: 0.0020 memory: 42336 loss: 0.1234 loss_ce: 0.1234 2023/02/27 10:35:43 - mmengine - INFO - Epoch(train) [74][2800/5047] lr: 1.9669e-05 eta: 3 days, 21:54:52 time: 0.8902 data_time: 0.0023 memory: 46005 loss: 0.1095 loss_ce: 0.1095 2023/02/27 10:37:10 - mmengine - INFO - Epoch(train) [74][2900/5047] lr: 1.9669e-05 eta: 3 days, 21:53:23 time: 0.8640 data_time: 0.0021 memory: 45733 loss: 0.1074 loss_ce: 0.1074 2023/02/27 10:38:35 - mmengine - INFO - Epoch(train) [74][3000/5047] lr: 1.9669e-05 eta: 3 days, 21:51:53 time: 0.8266 data_time: 0.0026 memory: 43400 loss: 0.1292 loss_ce: 0.1292 2023/02/27 10:40:00 - mmengine - INFO - Epoch(train) [74][3100/5047] lr: 1.9669e-05 eta: 3 days, 21:50:22 time: 0.8237 data_time: 0.0029 memory: 45302 loss: 0.1116 loss_ce: 0.1116 2023/02/27 10:41:27 - mmengine - INFO - Epoch(train) [74][3200/5047] lr: 1.9669e-05 eta: 3 days, 21:48:54 time: 0.8601 data_time: 0.0023 memory: 55364 loss: 0.1263 loss_ce: 0.1263 2023/02/27 10:42:52 - mmengine - INFO - Epoch(train) [74][3300/5047] lr: 1.9669e-05 eta: 3 days, 21:47:23 time: 0.8640 data_time: 0.0020 memory: 41919 loss: 0.1117 loss_ce: 0.1117 2023/02/27 10:44:18 - mmengine - INFO - Epoch(train) [74][3400/5047] lr: 1.9669e-05 eta: 3 days, 21:45:53 time: 0.8378 data_time: 0.0023 memory: 43947 loss: 0.1218 loss_ce: 0.1218 2023/02/27 10:45:44 - mmengine - INFO - Epoch(train) [74][3500/5047] lr: 1.9669e-05 eta: 3 days, 21:44:25 time: 0.9717 data_time: 0.0022 memory: 50475 loss: 0.1171 loss_ce: 0.1171 2023/02/27 10:46:45 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 10:47:10 - mmengine - INFO - Epoch(train) [74][3600/5047] lr: 1.9669e-05 eta: 3 days, 21:42:55 time: 0.8457 data_time: 0.0021 memory: 48188 loss: 0.0990 loss_ce: 0.0990 2023/02/27 10:48:37 - mmengine - INFO - Epoch(train) [74][3700/5047] lr: 1.9669e-05 eta: 3 days, 21:41:27 time: 0.8689 data_time: 0.0022 memory: 47958 loss: 0.1085 loss_ce: 0.1085 2023/02/27 10:50:05 - mmengine - INFO - Epoch(train) [74][3800/5047] lr: 1.9669e-05 eta: 3 days, 21:39:59 time: 0.8705 data_time: 0.0027 memory: 46278 loss: 0.1188 loss_ce: 0.1188 2023/02/27 10:51:30 - mmengine - INFO - Epoch(train) [74][3900/5047] lr: 1.9669e-05 eta: 3 days, 21:38:29 time: 0.8265 data_time: 0.0022 memory: 46005 loss: 0.1231 loss_ce: 0.1231 2023/02/27 10:52:54 - mmengine - INFO - Epoch(train) [74][4000/5047] lr: 1.9669e-05 eta: 3 days, 21:36:57 time: 0.8634 data_time: 0.0020 memory: 41419 loss: 0.1139 loss_ce: 0.1139 2023/02/27 10:54:19 - mmengine - INFO - Epoch(train) [74][4100/5047] lr: 1.9669e-05 eta: 3 days, 21:35:27 time: 0.8575 data_time: 0.0022 memory: 49144 loss: 0.1021 loss_ce: 0.1021 2023/02/27 10:55:44 - mmengine - INFO - Epoch(train) [74][4200/5047] lr: 1.9669e-05 eta: 3 days, 21:33:56 time: 0.8401 data_time: 0.0027 memory: 44592 loss: 0.0978 loss_ce: 0.0978 2023/02/27 10:57:09 - mmengine - INFO - Epoch(train) [74][4300/5047] lr: 1.9669e-05 eta: 3 days, 21:32:25 time: 0.8907 data_time: 0.0023 memory: 41419 loss: 0.1200 loss_ce: 0.1200 2023/02/27 10:58:34 - mmengine - INFO - Epoch(train) [74][4400/5047] lr: 1.9669e-05 eta: 3 days, 21:30:56 time: 0.8646 data_time: 0.0042 memory: 44872 loss: 0.1116 loss_ce: 0.1116 2023/02/27 10:59:59 - mmengine - INFO - Epoch(train) [74][4500/5047] lr: 1.9669e-05 eta: 3 days, 21:29:25 time: 0.8769 data_time: 0.0031 memory: 40032 loss: 0.1170 loss_ce: 0.1170 2023/02/27 11:01:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 11:01:25 - mmengine - INFO - Epoch(train) [74][4600/5047] lr: 1.9669e-05 eta: 3 days, 21:27:56 time: 0.8389 data_time: 0.0024 memory: 44778 loss: 0.1346 loss_ce: 0.1346 2023/02/27 11:02:50 - mmengine - INFO - Epoch(train) [74][4700/5047] lr: 1.9669e-05 eta: 3 days, 21:26:25 time: 0.8474 data_time: 0.0023 memory: 43947 loss: 0.1215 loss_ce: 0.1215 2023/02/27 11:04:15 - mmengine - INFO - Epoch(train) [74][4800/5047] lr: 1.9669e-05 eta: 3 days, 21:24:54 time: 0.8603 data_time: 0.0024 memory: 41419 loss: 0.1359 loss_ce: 0.1359 2023/02/27 11:05:43 - mmengine - INFO - Epoch(train) [74][4900/5047] lr: 1.9669e-05 eta: 3 days, 21:23:27 time: 0.8844 data_time: 0.0021 memory: 46713 loss: 0.1164 loss_ce: 0.1164 2023/02/27 11:07:06 - mmengine - INFO - Epoch(train) [74][5000/5047] lr: 1.9669e-05 eta: 3 days, 21:21:55 time: 0.8552 data_time: 0.0038 memory: 42707 loss: 0.1214 loss_ce: 0.1214 2023/02/27 11:07:47 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 11:07:47 - mmengine - INFO - Saving checkpoint at 74 epochs 2023/02/27 11:09:19 - mmengine - INFO - Epoch(train) [75][ 100/5047] lr: 1.9468e-05 eta: 3 days, 21:19:44 time: 0.8681 data_time: 0.0023 memory: 44956 loss: 0.1119 loss_ce: 0.1119 2023/02/27 11:10:45 - mmengine - INFO - Epoch(train) [75][ 200/5047] lr: 1.9468e-05 eta: 3 days, 21:18:14 time: 0.8885 data_time: 0.0024 memory: 49326 loss: 0.1265 loss_ce: 0.1265 2023/02/27 11:12:13 - mmengine - INFO - Epoch(train) [75][ 300/5047] lr: 1.9468e-05 eta: 3 days, 21:16:47 time: 0.8800 data_time: 0.0021 memory: 52127 loss: 0.1255 loss_ce: 0.1255 2023/02/27 11:13:39 - mmengine - INFO - Epoch(train) [75][ 400/5047] lr: 1.9468e-05 eta: 3 days, 21:15:17 time: 0.8275 data_time: 0.0022 memory: 42649 loss: 0.1191 loss_ce: 0.1191 2023/02/27 11:15:05 - mmengine - INFO - Epoch(train) [75][ 500/5047] lr: 1.9468e-05 eta: 3 days, 21:13:48 time: 0.8351 data_time: 0.0033 memory: 41724 loss: 0.1230 loss_ce: 0.1230 2023/02/27 11:15:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 11:16:30 - mmengine - INFO - Epoch(train) [75][ 600/5047] lr: 1.9468e-05 eta: 3 days, 21:12:18 time: 0.8729 data_time: 0.0023 memory: 41724 loss: 0.1289 loss_ce: 0.1289 2023/02/27 11:17:55 - mmengine - INFO - Epoch(train) [75][ 700/5047] lr: 1.9468e-05 eta: 3 days, 21:10:48 time: 0.8669 data_time: 0.0021 memory: 43506 loss: 0.1031 loss_ce: 0.1031 2023/02/27 11:19:21 - mmengine - INFO - Epoch(train) [75][ 800/5047] lr: 1.9468e-05 eta: 3 days, 21:09:18 time: 0.8156 data_time: 0.0030 memory: 42744 loss: 0.1093 loss_ce: 0.1093 2023/02/27 11:20:46 - mmengine - INFO - Epoch(train) [75][ 900/5047] lr: 1.9468e-05 eta: 3 days, 21:07:47 time: 0.8794 data_time: 0.0024 memory: 48565 loss: 0.1276 loss_ce: 0.1276 2023/02/27 11:22:12 - mmengine - INFO - Epoch(train) [75][1000/5047] lr: 1.9468e-05 eta: 3 days, 21:06:18 time: 0.8466 data_time: 0.0021 memory: 41122 loss: 0.1052 loss_ce: 0.1052 2023/02/27 11:23:38 - mmengine - INFO - Epoch(train) [75][1100/5047] lr: 1.9468e-05 eta: 3 days, 21:04:48 time: 0.8027 data_time: 0.0019 memory: 43947 loss: 0.1283 loss_ce: 0.1283 2023/02/27 11:25:02 - mmengine - INFO - Epoch(train) [75][1200/5047] lr: 1.9468e-05 eta: 3 days, 21:03:18 time: 0.8241 data_time: 0.0020 memory: 46005 loss: 0.1173 loss_ce: 0.1173 2023/02/27 11:26:28 - mmengine - INFO - Epoch(train) [75][1300/5047] lr: 1.9468e-05 eta: 3 days, 21:01:48 time: 0.8606 data_time: 0.0023 memory: 44410 loss: 0.1160 loss_ce: 0.1160 2023/02/27 11:27:54 - mmengine - INFO - Epoch(train) [75][1400/5047] lr: 1.9468e-05 eta: 3 days, 21:00:19 time: 0.8801 data_time: 0.0020 memory: 43249 loss: 0.0959 loss_ce: 0.0959 2023/02/27 11:29:21 - mmengine - INFO - Epoch(train) [75][1500/5047] lr: 1.9468e-05 eta: 3 days, 20:58:50 time: 0.8622 data_time: 0.0023 memory: 41419 loss: 0.0923 loss_ce: 0.0923 2023/02/27 11:29:40 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 11:30:47 - mmengine - INFO - Epoch(train) [75][1600/5047] lr: 1.9468e-05 eta: 3 days, 20:57:20 time: 0.8418 data_time: 0.0022 memory: 53124 loss: 0.1043 loss_ce: 0.1043 2023/02/27 11:32:15 - mmengine - INFO - Epoch(train) [75][1700/5047] lr: 1.9468e-05 eta: 3 days, 20:55:53 time: 0.8847 data_time: 0.0026 memory: 53954 loss: 0.1254 loss_ce: 0.1254 2023/02/27 11:33:40 - mmengine - INFO - Epoch(train) [75][1800/5047] lr: 1.9468e-05 eta: 3 days, 20:54:23 time: 0.8404 data_time: 0.0034 memory: 55468 loss: 0.1069 loss_ce: 0.1069 2023/02/27 11:35:06 - mmengine - INFO - Epoch(train) [75][1900/5047] lr: 1.9468e-05 eta: 3 days, 20:52:53 time: 0.8173 data_time: 0.0029 memory: 44956 loss: 0.0985 loss_ce: 0.0985 2023/02/27 11:36:33 - mmengine - INFO - Epoch(train) [75][2000/5047] lr: 1.9468e-05 eta: 3 days, 20:51:25 time: 0.8732 data_time: 0.0027 memory: 43613 loss: 0.1236 loss_ce: 0.1236 2023/02/27 11:37:59 - mmengine - INFO - Epoch(train) [75][2100/5047] lr: 1.9468e-05 eta: 3 days, 20:49:56 time: 0.8734 data_time: 0.0020 memory: 46074 loss: 0.1216 loss_ce: 0.1216 2023/02/27 11:39:24 - mmengine - INFO - Epoch(train) [75][2200/5047] lr: 1.9468e-05 eta: 3 days, 20:48:26 time: 0.8302 data_time: 0.0021 memory: 42336 loss: 0.1201 loss_ce: 0.1201 2023/02/27 11:40:48 - mmengine - INFO - Epoch(train) [75][2300/5047] lr: 1.9468e-05 eta: 3 days, 20:46:54 time: 0.8675 data_time: 0.0025 memory: 45837 loss: 0.1053 loss_ce: 0.1053 2023/02/27 11:42:13 - mmengine - INFO - Epoch(train) [75][2400/5047] lr: 1.9468e-05 eta: 3 days, 20:45:24 time: 0.8439 data_time: 0.0022 memory: 43899 loss: 0.1064 loss_ce: 0.1064 2023/02/27 11:43:37 - mmengine - INFO - Epoch(train) [75][2500/5047] lr: 1.9468e-05 eta: 3 days, 20:43:53 time: 0.8386 data_time: 0.0023 memory: 40825 loss: 0.1216 loss_ce: 0.1216 2023/02/27 11:43:56 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 11:45:03 - mmengine - INFO - Epoch(train) [75][2600/5047] lr: 1.9468e-05 eta: 3 days, 20:42:23 time: 0.9309 data_time: 0.0048 memory: 44617 loss: 0.1049 loss_ce: 0.1049 2023/02/27 11:46:29 - mmengine - INFO - Epoch(train) [75][2700/5047] lr: 1.9468e-05 eta: 3 days, 20:40:53 time: 0.8443 data_time: 0.0035 memory: 47447 loss: 0.1129 loss_ce: 0.1129 2023/02/27 11:47:56 - mmengine - INFO - Epoch(train) [75][2800/5047] lr: 1.9468e-05 eta: 3 days, 20:39:25 time: 0.8898 data_time: 0.0022 memory: 41095 loss: 0.1206 loss_ce: 0.1206 2023/02/27 11:49:21 - mmengine - INFO - Epoch(train) [75][2900/5047] lr: 1.9468e-05 eta: 3 days, 20:37:55 time: 0.8407 data_time: 0.0021 memory: 42024 loss: 0.1167 loss_ce: 0.1167 2023/02/27 11:50:47 - mmengine - INFO - Epoch(train) [75][3000/5047] lr: 1.9468e-05 eta: 3 days, 20:36:26 time: 0.8039 data_time: 0.0021 memory: 50592 loss: 0.1206 loss_ce: 0.1206 2023/02/27 11:52:16 - mmengine - INFO - Epoch(train) [75][3100/5047] lr: 1.9468e-05 eta: 3 days, 20:34:59 time: 0.8147 data_time: 0.0061 memory: 55562 loss: 0.1160 loss_ce: 0.1160 2023/02/27 11:53:40 - mmengine - INFO - Epoch(train) [75][3200/5047] lr: 1.9468e-05 eta: 3 days, 20:33:28 time: 0.8203 data_time: 0.0076 memory: 55562 loss: 0.1257 loss_ce: 0.1257 2023/02/27 11:55:06 - mmengine - INFO - Epoch(train) [75][3300/5047] lr: 1.9468e-05 eta: 3 days, 20:31:58 time: 0.8410 data_time: 0.0021 memory: 41095 loss: 0.1138 loss_ce: 0.1138 2023/02/27 11:56:30 - mmengine - INFO - Epoch(train) [75][3400/5047] lr: 1.9468e-05 eta: 3 days, 20:30:26 time: 0.7780 data_time: 0.0056 memory: 46355 loss: 0.1244 loss_ce: 0.1244 2023/02/27 11:57:56 - mmengine - INFO - Epoch(train) [75][3500/5047] lr: 1.9468e-05 eta: 3 days, 20:28:58 time: 0.8588 data_time: 0.0024 memory: 44189 loss: 0.1039 loss_ce: 0.1039 2023/02/27 11:58:15 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 11:59:21 - mmengine - INFO - Epoch(train) [75][3600/5047] lr: 1.9468e-05 eta: 3 days, 20:27:27 time: 0.8221 data_time: 0.0053 memory: 51815 loss: 0.1042 loss_ce: 0.1042 2023/02/27 12:00:47 - mmengine - INFO - Epoch(train) [75][3700/5047] lr: 1.9468e-05 eta: 3 days, 20:25:58 time: 0.8383 data_time: 0.0023 memory: 43508 loss: 0.1076 loss_ce: 0.1076 2023/02/27 12:02:13 - mmengine - INFO - Epoch(train) [75][3800/5047] lr: 1.9468e-05 eta: 3 days, 20:24:29 time: 0.8511 data_time: 0.0021 memory: 52976 loss: 0.1352 loss_ce: 0.1352 2023/02/27 12:03:38 - mmengine - INFO - Epoch(train) [75][3900/5047] lr: 1.9468e-05 eta: 3 days, 20:22:59 time: 0.8210 data_time: 0.0020 memory: 51755 loss: 0.1199 loss_ce: 0.1199 2023/02/27 12:05:05 - mmengine - INFO - Epoch(train) [75][4000/5047] lr: 1.9468e-05 eta: 3 days, 20:21:29 time: 0.8643 data_time: 0.0023 memory: 45101 loss: 0.1277 loss_ce: 0.1277 2023/02/27 12:06:30 - mmengine - INFO - Epoch(train) [75][4100/5047] lr: 1.9468e-05 eta: 3 days, 20:19:59 time: 0.8618 data_time: 0.0025 memory: 42901 loss: 0.1114 loss_ce: 0.1114 2023/02/27 12:07:54 - mmengine - INFO - Epoch(train) [75][4200/5047] lr: 1.9468e-05 eta: 3 days, 20:18:28 time: 0.8685 data_time: 0.0027 memory: 45302 loss: 0.0983 loss_ce: 0.0983 2023/02/27 12:09:21 - mmengine - INFO - Epoch(train) [75][4300/5047] lr: 1.9468e-05 eta: 3 days, 20:17:00 time: 0.8744 data_time: 0.0052 memory: 53387 loss: 0.1103 loss_ce: 0.1103 2023/02/27 12:10:48 - mmengine - INFO - Epoch(train) [75][4400/5047] lr: 1.9468e-05 eta: 3 days, 20:15:31 time: 0.8740 data_time: 0.0022 memory: 45955 loss: 0.1217 loss_ce: 0.1217 2023/02/27 12:12:13 - mmengine - INFO - Epoch(train) [75][4500/5047] lr: 1.9468e-05 eta: 3 days, 20:14:01 time: 0.8183 data_time: 0.0078 memory: 47077 loss: 0.1148 loss_ce: 0.1148 2023/02/27 12:12:32 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 12:13:39 - mmengine - INFO - Epoch(train) [75][4600/5047] lr: 1.9468e-05 eta: 3 days, 20:12:31 time: 0.8680 data_time: 0.0025 memory: 43289 loss: 0.1351 loss_ce: 0.1351 2023/02/27 12:15:05 - mmengine - INFO - Epoch(train) [75][4700/5047] lr: 1.9468e-05 eta: 3 days, 20:11:03 time: 0.8284 data_time: 0.0026 memory: 46982 loss: 0.1420 loss_ce: 0.1420 2023/02/27 12:16:32 - mmengine - INFO - Epoch(train) [75][4800/5047] lr: 1.9468e-05 eta: 3 days, 20:09:34 time: 0.8815 data_time: 0.0022 memory: 43289 loss: 0.1177 loss_ce: 0.1177 2023/02/27 12:17:58 - mmengine - INFO - Epoch(train) [75][4900/5047] lr: 1.9468e-05 eta: 3 days, 20:08:05 time: 0.8272 data_time: 0.0024 memory: 42670 loss: 0.1077 loss_ce: 0.1077 2023/02/27 12:19:23 - mmengine - INFO - Epoch(train) [75][5000/5047] lr: 1.9468e-05 eta: 3 days, 20:06:34 time: 0.8426 data_time: 0.0025 memory: 50505 loss: 0.1242 loss_ce: 0.1242 2023/02/27 12:20:03 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 12:20:03 - mmengine - INFO - Saving checkpoint at 75 epochs 2023/02/27 12:21:35 - mmengine - INFO - Epoch(train) [76][ 100/5047] lr: 1.9267e-05 eta: 3 days, 20:04:23 time: 0.8846 data_time: 0.0021 memory: 55562 loss: 0.1228 loss_ce: 0.1228 2023/02/27 12:23:02 - mmengine - INFO - Epoch(train) [76][ 200/5047] lr: 1.9267e-05 eta: 3 days, 20:02:54 time: 0.8484 data_time: 0.0026 memory: 42649 loss: 0.1124 loss_ce: 0.1124 2023/02/27 12:24:26 - mmengine - INFO - Epoch(train) [76][ 300/5047] lr: 1.9267e-05 eta: 3 days, 20:01:23 time: 0.8419 data_time: 0.0021 memory: 42024 loss: 0.1026 loss_ce: 0.1026 2023/02/27 12:25:52 - mmengine - INFO - Epoch(train) [76][ 400/5047] lr: 1.9267e-05 eta: 3 days, 19:59:55 time: 0.8097 data_time: 0.0047 memory: 40877 loss: 0.1071 loss_ce: 0.1071 2023/02/27 12:26:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 12:27:19 - mmengine - INFO - Epoch(train) [76][ 500/5047] lr: 1.9267e-05 eta: 3 days, 19:58:26 time: 0.8588 data_time: 0.0022 memory: 41419 loss: 0.1358 loss_ce: 0.1358 2023/02/27 12:28:44 - mmengine - INFO - Epoch(train) [76][ 600/5047] lr: 1.9267e-05 eta: 3 days, 19:56:55 time: 0.8609 data_time: 0.0022 memory: 44617 loss: 0.1169 loss_ce: 0.1169 2023/02/27 12:30:09 - mmengine - INFO - Epoch(train) [76][ 700/5047] lr: 1.9267e-05 eta: 3 days, 19:55:25 time: 0.8656 data_time: 0.0061 memory: 47247 loss: 0.1179 loss_ce: 0.1179 2023/02/27 12:31:34 - mmengine - INFO - Epoch(train) [76][ 800/5047] lr: 1.9267e-05 eta: 3 days, 19:53:55 time: 0.8776 data_time: 0.0023 memory: 50106 loss: 0.1048 loss_ce: 0.1048 2023/02/27 12:33:01 - mmengine - INFO - Epoch(train) [76][ 900/5047] lr: 1.9267e-05 eta: 3 days, 19:52:26 time: 0.8121 data_time: 0.0023 memory: 38998 loss: 0.1058 loss_ce: 0.1058 2023/02/27 12:34:29 - mmengine - INFO - Epoch(train) [76][1000/5047] lr: 1.9267e-05 eta: 3 days, 19:50:59 time: 0.8768 data_time: 0.0023 memory: 43613 loss: 0.1305 loss_ce: 0.1305 2023/02/27 12:35:57 - mmengine - INFO - Epoch(train) [76][1100/5047] lr: 1.9267e-05 eta: 3 days, 19:49:32 time: 0.8517 data_time: 0.0031 memory: 44956 loss: 0.1190 loss_ce: 0.1190 2023/02/27 12:37:23 - mmengine - INFO - Epoch(train) [76][1200/5047] lr: 1.9267e-05 eta: 3 days, 19:48:02 time: 0.8346 data_time: 0.0022 memory: 44661 loss: 0.1120 loss_ce: 0.1120 2023/02/27 12:38:47 - mmengine - INFO - Epoch(train) [76][1300/5047] lr: 1.9267e-05 eta: 3 days, 19:46:31 time: 0.8477 data_time: 0.0027 memory: 43613 loss: 0.1347 loss_ce: 0.1347 2023/02/27 12:40:13 - mmengine - INFO - Epoch(train) [76][1400/5047] lr: 1.9267e-05 eta: 3 days, 19:45:03 time: 0.8572 data_time: 0.0023 memory: 49180 loss: 0.1217 loss_ce: 0.1217 2023/02/27 12:41:18 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 12:41:40 - mmengine - INFO - Epoch(train) [76][1500/5047] lr: 1.9267e-05 eta: 3 days, 19:43:34 time: 0.8583 data_time: 0.0021 memory: 42965 loss: 0.1122 loss_ce: 0.1122 2023/02/27 12:43:07 - mmengine - INFO - Epoch(train) [76][1600/5047] lr: 1.9267e-05 eta: 3 days, 19:42:06 time: 0.8368 data_time: 0.0030 memory: 42965 loss: 0.1162 loss_ce: 0.1162 2023/02/27 12:44:32 - mmengine - INFO - Epoch(train) [76][1700/5047] lr: 1.9267e-05 eta: 3 days, 19:40:35 time: 0.8407 data_time: 0.0065 memory: 45622 loss: 0.1080 loss_ce: 0.1080 2023/02/27 12:45:58 - mmengine - INFO - Epoch(train) [76][1800/5047] lr: 1.9267e-05 eta: 3 days, 19:39:06 time: 0.8622 data_time: 0.0023 memory: 43947 loss: 0.1072 loss_ce: 0.1072 2023/02/27 12:47:24 - mmengine - INFO - Epoch(train) [76][1900/5047] lr: 1.9267e-05 eta: 3 days, 19:37:37 time: 0.8329 data_time: 0.0022 memory: 41142 loss: 0.1050 loss_ce: 0.1050 2023/02/27 12:48:50 - mmengine - INFO - Epoch(train) [76][2000/5047] lr: 1.9267e-05 eta: 3 days, 19:36:07 time: 0.8414 data_time: 0.0033 memory: 44105 loss: 0.1242 loss_ce: 0.1242 2023/02/27 12:50:15 - mmengine - INFO - Epoch(train) [76][2100/5047] lr: 1.9267e-05 eta: 3 days, 19:34:38 time: 0.8185 data_time: 0.0021 memory: 55562 loss: 0.1078 loss_ce: 0.1078 2023/02/27 12:51:45 - mmengine - INFO - Epoch(train) [76][2200/5047] lr: 1.9267e-05 eta: 3 days, 19:33:12 time: 0.8321 data_time: 0.0028 memory: 43613 loss: 0.1299 loss_ce: 0.1299 2023/02/27 12:53:11 - mmengine - INFO - Epoch(train) [76][2300/5047] lr: 1.9267e-05 eta: 3 days, 19:31:43 time: 0.9276 data_time: 0.0023 memory: 41724 loss: 0.1141 loss_ce: 0.1141 2023/02/27 12:54:38 - mmengine - INFO - Epoch(train) [76][2400/5047] lr: 1.9267e-05 eta: 3 days, 19:30:15 time: 0.8506 data_time: 0.0021 memory: 39960 loss: 0.1208 loss_ce: 0.1208 2023/02/27 12:55:43 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 12:56:05 - mmengine - INFO - Epoch(train) [76][2500/5047] lr: 1.9267e-05 eta: 3 days, 19:28:46 time: 0.9076 data_time: 0.0023 memory: 42999 loss: 0.1131 loss_ce: 0.1131 2023/02/27 12:57:32 - mmengine - INFO - Epoch(train) [76][2600/5047] lr: 1.9267e-05 eta: 3 days, 19:27:18 time: 0.8613 data_time: 0.0024 memory: 42336 loss: 0.1243 loss_ce: 0.1243 2023/02/27 12:58:58 - mmengine - INFO - Epoch(train) [76][2700/5047] lr: 1.9267e-05 eta: 3 days, 19:25:49 time: 0.8527 data_time: 0.0025 memory: 47447 loss: 0.1179 loss_ce: 0.1179 2023/02/27 13:00:22 - mmengine - INFO - Epoch(train) [76][2800/5047] lr: 1.9267e-05 eta: 3 days, 19:24:18 time: 0.8184 data_time: 0.0034 memory: 41724 loss: 0.1035 loss_ce: 0.1035 2023/02/27 13:01:48 - mmengine - INFO - Epoch(train) [76][2900/5047] lr: 1.9267e-05 eta: 3 days, 19:22:48 time: 0.8151 data_time: 0.0023 memory: 41122 loss: 0.1051 loss_ce: 0.1051 2023/02/27 13:03:14 - mmengine - INFO - Epoch(train) [76][3000/5047] lr: 1.9267e-05 eta: 3 days, 19:21:19 time: 0.8709 data_time: 0.0022 memory: 42965 loss: 0.1189 loss_ce: 0.1189 2023/02/27 13:04:42 - mmengine - INFO - Epoch(train) [76][3100/5047] lr: 1.9267e-05 eta: 3 days, 19:19:52 time: 0.8877 data_time: 0.0025 memory: 41419 loss: 0.1169 loss_ce: 0.1169 2023/02/27 13:06:06 - mmengine - INFO - Epoch(train) [76][3200/5047] lr: 1.9267e-05 eta: 3 days, 19:18:20 time: 0.8543 data_time: 0.0051 memory: 47982 loss: 0.1159 loss_ce: 0.1159 2023/02/27 13:07:32 - mmengine - INFO - Epoch(train) [76][3300/5047] lr: 1.9267e-05 eta: 3 days, 19:16:51 time: 0.8534 data_time: 0.0061 memory: 44617 loss: 0.1143 loss_ce: 0.1143 2023/02/27 13:08:59 - mmengine - INFO - Epoch(train) [76][3400/5047] lr: 1.9267e-05 eta: 3 days, 19:15:24 time: 0.8734 data_time: 0.0023 memory: 43646 loss: 0.1180 loss_ce: 0.1180 2023/02/27 13:10:02 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 13:10:24 - mmengine - INFO - Epoch(train) [76][3500/5047] lr: 1.9267e-05 eta: 3 days, 19:13:53 time: 0.8488 data_time: 0.0021 memory: 42772 loss: 0.1061 loss_ce: 0.1061 2023/02/27 13:11:47 - mmengine - INFO - Epoch(train) [76][3600/5047] lr: 1.9267e-05 eta: 3 days, 19:12:21 time: 0.8234 data_time: 0.0028 memory: 41122 loss: 0.1335 loss_ce: 0.1335 2023/02/27 13:13:14 - mmengine - INFO - Epoch(train) [76][3700/5047] lr: 1.9267e-05 eta: 3 days, 19:10:53 time: 0.8398 data_time: 0.0068 memory: 53809 loss: 0.1175 loss_ce: 0.1175 2023/02/27 13:14:41 - mmengine - INFO - Epoch(train) [76][3800/5047] lr: 1.9267e-05 eta: 3 days, 19:09:24 time: 0.8867 data_time: 0.0024 memory: 55562 loss: 0.1014 loss_ce: 0.1014 2023/02/27 13:16:07 - mmengine - INFO - Epoch(train) [76][3900/5047] lr: 1.9267e-05 eta: 3 days, 19:07:54 time: 0.8706 data_time: 0.0020 memory: 55562 loss: 0.1281 loss_ce: 0.1281 2023/02/27 13:17:34 - mmengine - INFO - Epoch(train) [76][4000/5047] lr: 1.9267e-05 eta: 3 days, 19:06:26 time: 0.8246 data_time: 0.0024 memory: 49715 loss: 0.1170 loss_ce: 0.1170 2023/02/27 13:18:59 - mmengine - INFO - Epoch(train) [76][4100/5047] lr: 1.9267e-05 eta: 3 days, 19:04:57 time: 0.7971 data_time: 0.0033 memory: 44948 loss: 0.1188 loss_ce: 0.1188 2023/02/27 13:20:26 - mmengine - INFO - Epoch(train) [76][4200/5047] lr: 1.9267e-05 eta: 3 days, 19:03:28 time: 0.8605 data_time: 0.0025 memory: 49715 loss: 0.1173 loss_ce: 0.1173 2023/02/27 13:21:53 - mmengine - INFO - Epoch(train) [76][4300/5047] lr: 1.9267e-05 eta: 3 days, 19:02:00 time: 0.8669 data_time: 0.0044 memory: 44739 loss: 0.1107 loss_ce: 0.1107 2023/02/27 13:23:18 - mmengine - INFO - Epoch(train) [76][4400/5047] lr: 1.9267e-05 eta: 3 days, 19:00:30 time: 0.8511 data_time: 0.0028 memory: 42336 loss: 0.1036 loss_ce: 0.1036 2023/02/27 13:24:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 13:24:45 - mmengine - INFO - Epoch(train) [76][4500/5047] lr: 1.9267e-05 eta: 3 days, 18:59:02 time: 0.8959 data_time: 0.0048 memory: 43613 loss: 0.1083 loss_ce: 0.1083 2023/02/27 13:26:12 - mmengine - INFO - Epoch(train) [76][4600/5047] lr: 1.9267e-05 eta: 3 days, 18:57:33 time: 0.8629 data_time: 0.0022 memory: 40658 loss: 0.1095 loss_ce: 0.1095 2023/02/27 13:27:38 - mmengine - INFO - Epoch(train) [76][4700/5047] lr: 1.9267e-05 eta: 3 days, 18:56:04 time: 0.8629 data_time: 0.0023 memory: 45291 loss: 0.1172 loss_ce: 0.1172 2023/02/27 13:29:05 - mmengine - INFO - Epoch(train) [76][4800/5047] lr: 1.9267e-05 eta: 3 days, 18:54:35 time: 0.9189 data_time: 0.0024 memory: 55562 loss: 0.1168 loss_ce: 0.1168 2023/02/27 13:30:30 - mmengine - INFO - Epoch(train) [76][4900/5047] lr: 1.9267e-05 eta: 3 days, 18:53:06 time: 0.8337 data_time: 0.0027 memory: 42024 loss: 0.1142 loss_ce: 0.1142 2023/02/27 13:31:56 - mmengine - INFO - Epoch(train) [76][5000/5047] lr: 1.9267e-05 eta: 3 days, 18:51:37 time: 0.8180 data_time: 0.0036 memory: 51792 loss: 0.1377 loss_ce: 0.1377 2023/02/27 13:32:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 13:32:36 - mmengine - INFO - Saving checkpoint at 76 epochs 2023/02/27 13:34:07 - mmengine - INFO - Epoch(train) [77][ 100/5047] lr: 1.9066e-05 eta: 3 days, 18:49:24 time: 0.8597 data_time: 0.0025 memory: 51237 loss: 0.0909 loss_ce: 0.0909 2023/02/27 13:35:32 - mmengine - INFO - Epoch(train) [77][ 200/5047] lr: 1.9066e-05 eta: 3 days, 18:47:55 time: 0.8357 data_time: 0.0021 memory: 55323 loss: 0.1253 loss_ce: 0.1253 2023/02/27 13:37:00 - mmengine - INFO - Epoch(train) [77][ 300/5047] lr: 1.9066e-05 eta: 3 days, 18:46:27 time: 0.8692 data_time: 0.0024 memory: 43613 loss: 0.1203 loss_ce: 0.1203 2023/02/27 13:38:26 - mmengine - INFO - Epoch(train) [77][ 400/5047] lr: 1.9066e-05 eta: 3 days, 18:44:58 time: 0.8654 data_time: 0.0022 memory: 43289 loss: 0.1137 loss_ce: 0.1137 2023/02/27 13:38:50 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 13:39:53 - mmengine - INFO - Epoch(train) [77][ 500/5047] lr: 1.9066e-05 eta: 3 days, 18:43:29 time: 0.8296 data_time: 0.0024 memory: 41701 loss: 0.1244 loss_ce: 0.1244 2023/02/27 13:41:18 - mmengine - INFO - Epoch(train) [77][ 600/5047] lr: 1.9066e-05 eta: 3 days, 18:42:00 time: 0.8486 data_time: 0.0022 memory: 43493 loss: 0.0932 loss_ce: 0.0932 2023/02/27 13:42:45 - mmengine - INFO - Epoch(train) [77][ 700/5047] lr: 1.9066e-05 eta: 3 days, 18:40:31 time: 0.8113 data_time: 0.0023 memory: 50505 loss: 0.1222 loss_ce: 0.1222 2023/02/27 13:44:12 - mmengine - INFO - Epoch(train) [77][ 800/5047] lr: 1.9066e-05 eta: 3 days, 18:39:03 time: 0.8676 data_time: 0.0024 memory: 41766 loss: 0.1095 loss_ce: 0.1095 2023/02/27 13:45:38 - mmengine - INFO - Epoch(train) [77][ 900/5047] lr: 1.9066e-05 eta: 3 days, 18:37:34 time: 0.8402 data_time: 0.0034 memory: 44956 loss: 0.1124 loss_ce: 0.1124 2023/02/27 13:47:03 - mmengine - INFO - Epoch(train) [77][1000/5047] lr: 1.9066e-05 eta: 3 days, 18:36:04 time: 0.8231 data_time: 0.0023 memory: 42239 loss: 0.1184 loss_ce: 0.1184 2023/02/27 13:48:28 - mmengine - INFO - Epoch(train) [77][1100/5047] lr: 1.9066e-05 eta: 3 days, 18:34:34 time: 0.8484 data_time: 0.0027 memory: 41026 loss: 0.1182 loss_ce: 0.1182 2023/02/27 13:49:56 - mmengine - INFO - Epoch(train) [77][1200/5047] lr: 1.9066e-05 eta: 3 days, 18:33:07 time: 0.8673 data_time: 0.0024 memory: 52882 loss: 0.1245 loss_ce: 0.1245 2023/02/27 13:51:23 - mmengine - INFO - Epoch(train) [77][1300/5047] lr: 1.9066e-05 eta: 3 days, 18:31:38 time: 0.8665 data_time: 0.0022 memory: 40590 loss: 0.1071 loss_ce: 0.1071 2023/02/27 13:52:47 - mmengine - INFO - Epoch(train) [77][1400/5047] lr: 1.9066e-05 eta: 3 days, 18:30:07 time: 0.8529 data_time: 0.0061 memory: 43511 loss: 0.1277 loss_ce: 0.1277 2023/02/27 13:53:12 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 13:54:14 - mmengine - INFO - Epoch(train) [77][1500/5047] lr: 1.9066e-05 eta: 3 days, 18:28:38 time: 0.8656 data_time: 0.0025 memory: 39960 loss: 0.1192 loss_ce: 0.1192 2023/02/27 13:55:38 - mmengine - INFO - Epoch(train) [77][1600/5047] lr: 1.9066e-05 eta: 3 days, 18:27:07 time: 0.8320 data_time: 0.0025 memory: 40241 loss: 0.1259 loss_ce: 0.1259 2023/02/27 13:57:04 - mmengine - INFO - Epoch(train) [77][1700/5047] lr: 1.9066e-05 eta: 3 days, 18:25:39 time: 0.8471 data_time: 0.0029 memory: 54205 loss: 0.1108 loss_ce: 0.1108 2023/02/27 13:58:31 - mmengine - INFO - Epoch(train) [77][1800/5047] lr: 1.9066e-05 eta: 3 days, 18:24:10 time: 0.8873 data_time: 0.0043 memory: 51308 loss: 0.1150 loss_ce: 0.1150 2023/02/27 13:59:57 - mmengine - INFO - Epoch(train) [77][1900/5047] lr: 1.9066e-05 eta: 3 days, 18:22:41 time: 0.9276 data_time: 0.0023 memory: 43947 loss: 0.1410 loss_ce: 0.1410 2023/02/27 14:01:24 - mmengine - INFO - Epoch(train) [77][2000/5047] lr: 1.9066e-05 eta: 3 days, 18:21:13 time: 0.8728 data_time: 0.0020 memory: 42282 loss: 0.1127 loss_ce: 0.1127 2023/02/27 14:02:52 - mmengine - INFO - Epoch(train) [77][2100/5047] lr: 1.9066e-05 eta: 3 days, 18:19:46 time: 0.8959 data_time: 0.0021 memory: 43217 loss: 0.1010 loss_ce: 0.1010 2023/02/27 14:04:18 - mmengine - INFO - Epoch(train) [77][2200/5047] lr: 1.9066e-05 eta: 3 days, 18:18:16 time: 0.8104 data_time: 0.0026 memory: 44565 loss: 0.1174 loss_ce: 0.1174 2023/02/27 14:05:44 - mmengine - INFO - Epoch(train) [77][2300/5047] lr: 1.9066e-05 eta: 3 days, 18:16:47 time: 0.8737 data_time: 0.0024 memory: 43947 loss: 0.1102 loss_ce: 0.1102 2023/02/27 14:07:12 - mmengine - INFO - Epoch(train) [77][2400/5047] lr: 1.9066e-05 eta: 3 days, 18:15:20 time: 0.8559 data_time: 0.0030 memory: 51639 loss: 0.1222 loss_ce: 0.1222 2023/02/27 14:07:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 14:08:40 - mmengine - INFO - Epoch(train) [77][2500/5047] lr: 1.9066e-05 eta: 3 days, 18:13:53 time: 0.8698 data_time: 0.0022 memory: 49334 loss: 0.1204 loss_ce: 0.1204 2023/02/27 14:10:05 - mmengine - INFO - Epoch(train) [77][2600/5047] lr: 1.9066e-05 eta: 3 days, 18:12:22 time: 0.8459 data_time: 0.0020 memory: 43289 loss: 0.1110 loss_ce: 0.1110 2023/02/27 14:11:30 - mmengine - INFO - Epoch(train) [77][2700/5047] lr: 1.9066e-05 eta: 3 days, 18:10:52 time: 0.8851 data_time: 0.0021 memory: 44976 loss: 0.1199 loss_ce: 0.1199 2023/02/27 14:12:56 - mmengine - INFO - Epoch(train) [77][2800/5047] lr: 1.9066e-05 eta: 3 days, 18:09:23 time: 0.8745 data_time: 0.0021 memory: 45168 loss: 0.1291 loss_ce: 0.1291 2023/02/27 14:14:22 - mmengine - INFO - Epoch(train) [77][2900/5047] lr: 1.9066e-05 eta: 3 days, 18:07:54 time: 0.8544 data_time: 0.0020 memory: 45643 loss: 0.1102 loss_ce: 0.1102 2023/02/27 14:15:49 - mmengine - INFO - Epoch(train) [77][3000/5047] lr: 1.9066e-05 eta: 3 days, 18:06:26 time: 0.8526 data_time: 0.0042 memory: 55562 loss: 0.1262 loss_ce: 0.1262 2023/02/27 14:17:15 - mmengine - INFO - Epoch(train) [77][3100/5047] lr: 1.9066e-05 eta: 3 days, 18:04:57 time: 0.8387 data_time: 0.0022 memory: 42336 loss: 0.1081 loss_ce: 0.1081 2023/02/27 14:18:40 - mmengine - INFO - Epoch(train) [77][3200/5047] lr: 1.9066e-05 eta: 3 days, 18:03:27 time: 0.9171 data_time: 0.0023 memory: 41943 loss: 0.1071 loss_ce: 0.1071 2023/02/27 14:20:05 - mmengine - INFO - Epoch(train) [77][3300/5047] lr: 1.9066e-05 eta: 3 days, 18:01:57 time: 0.8468 data_time: 0.0021 memory: 42965 loss: 0.1136 loss_ce: 0.1136 2023/02/27 14:21:31 - mmengine - INFO - Epoch(train) [77][3400/5047] lr: 1.9066e-05 eta: 3 days, 18:00:27 time: 0.8778 data_time: 0.0028 memory: 43289 loss: 0.1118 loss_ce: 0.1118 2023/02/27 14:21:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 14:22:57 - mmengine - INFO - Epoch(train) [77][3500/5047] lr: 1.9066e-05 eta: 3 days, 17:58:59 time: 0.8214 data_time: 0.0022 memory: 55364 loss: 0.1273 loss_ce: 0.1273 2023/02/27 14:24:26 - mmengine - INFO - Epoch(train) [77][3600/5047] lr: 1.9066e-05 eta: 3 days, 17:57:32 time: 0.8799 data_time: 0.0022 memory: 42965 loss: 0.1265 loss_ce: 0.1265 2023/02/27 14:25:52 - mmengine - INFO - Epoch(train) [77][3700/5047] lr: 1.9066e-05 eta: 3 days, 17:56:03 time: 0.8760 data_time: 0.0024 memory: 43947 loss: 0.1195 loss_ce: 0.1195 2023/02/27 14:27:17 - mmengine - INFO - Epoch(train) [77][3800/5047] lr: 1.9066e-05 eta: 3 days, 17:54:33 time: 0.8557 data_time: 0.0032 memory: 49296 loss: 0.1186 loss_ce: 0.1186 2023/02/27 14:28:43 - mmengine - INFO - Epoch(train) [77][3900/5047] lr: 1.9066e-05 eta: 3 days, 17:53:04 time: 0.8422 data_time: 0.0033 memory: 43252 loss: 0.1220 loss_ce: 0.1220 2023/02/27 14:30:09 - mmengine - INFO - Epoch(train) [77][4000/5047] lr: 1.9066e-05 eta: 3 days, 17:51:34 time: 0.8784 data_time: 0.0026 memory: 41521 loss: 0.1065 loss_ce: 0.1065 2023/02/27 14:31:34 - mmengine - INFO - Epoch(train) [77][4100/5047] lr: 1.9066e-05 eta: 3 days, 17:50:04 time: 0.8516 data_time: 0.0024 memory: 45302 loss: 0.1188 loss_ce: 0.1188 2023/02/27 14:33:01 - mmengine - INFO - Epoch(train) [77][4200/5047] lr: 1.9066e-05 eta: 3 days, 17:48:36 time: 0.8753 data_time: 0.0029 memory: 42268 loss: 0.1257 loss_ce: 0.1257 2023/02/27 14:34:27 - mmengine - INFO - Epoch(train) [77][4300/5047] lr: 1.9066e-05 eta: 3 days, 17:47:07 time: 0.8029 data_time: 0.0035 memory: 41452 loss: 0.1212 loss_ce: 0.1212 2023/02/27 14:35:54 - mmengine - INFO - Epoch(train) [77][4400/5047] lr: 1.9066e-05 eta: 3 days, 17:45:39 time: 0.8962 data_time: 0.0020 memory: 52543 loss: 0.1154 loss_ce: 0.1154 2023/02/27 14:36:19 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 14:37:20 - mmengine - INFO - Epoch(train) [77][4500/5047] lr: 1.9066e-05 eta: 3 days, 17:44:09 time: 0.8445 data_time: 0.0022 memory: 41419 loss: 0.1015 loss_ce: 0.1015 2023/02/27 14:38:46 - mmengine - INFO - Epoch(train) [77][4600/5047] lr: 1.9066e-05 eta: 3 days, 17:42:41 time: 0.8735 data_time: 0.0023 memory: 55562 loss: 0.1097 loss_ce: 0.1097 2023/02/27 14:40:12 - mmengine - INFO - Epoch(train) [77][4700/5047] lr: 1.9066e-05 eta: 3 days, 17:41:12 time: 0.8768 data_time: 0.0022 memory: 43613 loss: 0.1139 loss_ce: 0.1139 2023/02/27 14:41:38 - mmengine - INFO - Epoch(train) [77][4800/5047] lr: 1.9066e-05 eta: 3 days, 17:39:42 time: 0.8690 data_time: 0.0032 memory: 43554 loss: 0.1041 loss_ce: 0.1041 2023/02/27 14:43:04 - mmengine - INFO - Epoch(train) [77][4900/5047] lr: 1.9066e-05 eta: 3 days, 17:38:13 time: 0.8280 data_time: 0.0021 memory: 47983 loss: 0.1408 loss_ce: 0.1408 2023/02/27 14:44:30 - mmengine - INFO - Epoch(train) [77][5000/5047] lr: 1.9066e-05 eta: 3 days, 17:36:44 time: 0.8738 data_time: 0.0022 memory: 50608 loss: 0.1215 loss_ce: 0.1215 2023/02/27 14:45:10 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 14:45:10 - mmengine - INFO - Saving checkpoint at 77 epochs 2023/02/27 14:46:41 - mmengine - INFO - Epoch(train) [78][ 100/5047] lr: 1.8865e-05 eta: 3 days, 17:34:32 time: 0.8363 data_time: 0.0024 memory: 47178 loss: 0.1142 loss_ce: 0.1142 2023/02/27 14:48:07 - mmengine - INFO - Epoch(train) [78][ 200/5047] lr: 1.8865e-05 eta: 3 days, 17:33:03 time: 0.8643 data_time: 0.0040 memory: 47324 loss: 0.1147 loss_ce: 0.1147 2023/02/27 14:49:33 - mmengine - INFO - Epoch(train) [78][ 300/5047] lr: 1.8865e-05 eta: 3 days, 17:31:34 time: 0.8572 data_time: 0.0021 memory: 49407 loss: 0.1304 loss_ce: 0.1304 2023/02/27 14:50:42 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 14:50:59 - mmengine - INFO - Epoch(train) [78][ 400/5047] lr: 1.8865e-05 eta: 3 days, 17:30:05 time: 0.8832 data_time: 0.0022 memory: 44278 loss: 0.1156 loss_ce: 0.1156 2023/02/27 14:52:26 - mmengine - INFO - Epoch(train) [78][ 500/5047] lr: 1.8865e-05 eta: 3 days, 17:28:37 time: 0.8867 data_time: 0.0021 memory: 40722 loss: 0.1256 loss_ce: 0.1256 2023/02/27 14:53:52 - mmengine - INFO - Epoch(train) [78][ 600/5047] lr: 1.8865e-05 eta: 3 days, 17:27:08 time: 0.8159 data_time: 0.0024 memory: 44631 loss: 0.1049 loss_ce: 0.1049 2023/02/27 14:55:18 - mmengine - INFO - Epoch(train) [78][ 700/5047] lr: 1.8865e-05 eta: 3 days, 17:25:39 time: 0.8503 data_time: 0.0030 memory: 45302 loss: 0.1098 loss_ce: 0.1098 2023/02/27 14:56:44 - mmengine - INFO - Epoch(train) [78][ 800/5047] lr: 1.8865e-05 eta: 3 days, 17:24:10 time: 0.8775 data_time: 0.0022 memory: 55562 loss: 0.1036 loss_ce: 0.1036 2023/02/27 14:58:10 - mmengine - INFO - Epoch(train) [78][ 900/5047] lr: 1.8865e-05 eta: 3 days, 17:22:40 time: 0.8675 data_time: 0.0024 memory: 45572 loss: 0.0975 loss_ce: 0.0975 2023/02/27 14:59:36 - mmengine - INFO - Epoch(train) [78][1000/5047] lr: 1.8865e-05 eta: 3 days, 17:21:11 time: 0.8423 data_time: 0.0037 memory: 39960 loss: 0.1212 loss_ce: 0.1212 2023/02/27 15:01:01 - mmengine - INFO - Epoch(train) [78][1100/5047] lr: 1.8865e-05 eta: 3 days, 17:19:42 time: 0.8865 data_time: 0.0021 memory: 49242 loss: 0.1128 loss_ce: 0.1128 2023/02/27 15:02:27 - mmengine - INFO - Epoch(train) [78][1200/5047] lr: 1.8865e-05 eta: 3 days, 17:18:12 time: 0.8491 data_time: 0.0021 memory: 40154 loss: 0.1117 loss_ce: 0.1117 2023/02/27 15:03:51 - mmengine - INFO - Epoch(train) [78][1300/5047] lr: 1.8865e-05 eta: 3 days, 17:16:41 time: 0.8645 data_time: 0.0047 memory: 41724 loss: 0.1260 loss_ce: 0.1260 2023/02/27 15:05:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 15:05:17 - mmengine - INFO - Epoch(train) [78][1400/5047] lr: 1.8865e-05 eta: 3 days, 17:15:12 time: 0.8548 data_time: 0.0022 memory: 41724 loss: 0.1162 loss_ce: 0.1162 2023/02/27 15:06:43 - mmengine - INFO - Epoch(train) [78][1500/5047] lr: 1.8865e-05 eta: 3 days, 17:13:43 time: 0.8799 data_time: 0.0023 memory: 47037 loss: 0.1114 loss_ce: 0.1114 2023/02/27 15:08:10 - mmengine - INFO - Epoch(train) [78][1600/5047] lr: 1.8865e-05 eta: 3 days, 17:12:15 time: 0.8500 data_time: 0.0026 memory: 55562 loss: 0.1121 loss_ce: 0.1121 2023/02/27 15:09:37 - mmengine - INFO - Epoch(train) [78][1700/5047] lr: 1.8865e-05 eta: 3 days, 17:10:47 time: 0.9272 data_time: 0.0021 memory: 40825 loss: 0.1190 loss_ce: 0.1190 2023/02/27 15:11:04 - mmengine - INFO - Epoch(train) [78][1800/5047] lr: 1.8865e-05 eta: 3 days, 17:09:18 time: 0.8636 data_time: 0.0022 memory: 45644 loss: 0.1203 loss_ce: 0.1203 2023/02/27 15:12:31 - mmengine - INFO - Epoch(train) [78][1900/5047] lr: 1.8865e-05 eta: 3 days, 17:07:50 time: 0.8795 data_time: 0.0026 memory: 41419 loss: 0.1121 loss_ce: 0.1121 2023/02/27 15:13:57 - mmengine - INFO - Epoch(train) [78][2000/5047] lr: 1.8865e-05 eta: 3 days, 17:06:21 time: 0.8138 data_time: 0.0023 memory: 43289 loss: 0.1115 loss_ce: 0.1115 2023/02/27 15:15:22 - mmengine - INFO - Epoch(train) [78][2100/5047] lr: 1.8865e-05 eta: 3 days, 17:04:51 time: 0.8509 data_time: 0.0058 memory: 50106 loss: 0.1227 loss_ce: 0.1227 2023/02/27 15:16:48 - mmengine - INFO - Epoch(train) [78][2200/5047] lr: 1.8865e-05 eta: 3 days, 17:03:22 time: 0.8417 data_time: 0.0020 memory: 45069 loss: 0.1171 loss_ce: 0.1171 2023/02/27 15:18:14 - mmengine - INFO - Epoch(train) [78][2300/5047] lr: 1.8865e-05 eta: 3 days, 17:01:53 time: 0.8626 data_time: 0.0022 memory: 47227 loss: 0.1073 loss_ce: 0.1073 2023/02/27 15:19:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 15:19:40 - mmengine - INFO - Epoch(train) [78][2400/5047] lr: 1.8865e-05 eta: 3 days, 17:00:24 time: 0.8725 data_time: 0.0021 memory: 47813 loss: 0.1019 loss_ce: 0.1019 2023/02/27 15:21:06 - mmengine - INFO - Epoch(train) [78][2500/5047] lr: 1.8865e-05 eta: 3 days, 16:58:55 time: 0.8481 data_time: 0.0062 memory: 43508 loss: 0.1006 loss_ce: 0.1006 2023/02/27 15:22:33 - mmengine - INFO - Epoch(train) [78][2600/5047] lr: 1.8865e-05 eta: 3 days, 16:57:26 time: 0.8860 data_time: 0.0024 memory: 55562 loss: 0.1201 loss_ce: 0.1201 2023/02/27 15:23:58 - mmengine - INFO - Epoch(train) [78][2700/5047] lr: 1.8865e-05 eta: 3 days, 16:55:57 time: 0.8832 data_time: 0.0024 memory: 55562 loss: 0.1193 loss_ce: 0.1193 2023/02/27 15:25:25 - mmengine - INFO - Epoch(train) [78][2800/5047] lr: 1.8865e-05 eta: 3 days, 16:54:29 time: 0.8728 data_time: 0.0023 memory: 51719 loss: 0.1238 loss_ce: 0.1238 2023/02/27 15:26:51 - mmengine - INFO - Epoch(train) [78][2900/5047] lr: 1.8865e-05 eta: 3 days, 16:52:59 time: 0.8235 data_time: 0.0027 memory: 44956 loss: 0.1220 loss_ce: 0.1220 2023/02/27 15:28:17 - mmengine - INFO - Epoch(train) [78][3000/5047] lr: 1.8865e-05 eta: 3 days, 16:51:30 time: 0.8559 data_time: 0.0021 memory: 55562 loss: 0.1261 loss_ce: 0.1261 2023/02/27 15:29:45 - mmengine - INFO - Epoch(train) [78][3100/5047] lr: 1.8865e-05 eta: 3 days, 16:50:03 time: 0.9245 data_time: 0.0071 memory: 46713 loss: 0.1277 loss_ce: 0.1277 2023/02/27 15:31:11 - mmengine - INFO - Epoch(train) [78][3200/5047] lr: 1.8865e-05 eta: 3 days, 16:48:34 time: 0.8207 data_time: 0.0022 memory: 39960 loss: 0.1286 loss_ce: 0.1286 2023/02/27 15:32:37 - mmengine - INFO - Epoch(train) [78][3300/5047] lr: 1.8865e-05 eta: 3 days, 16:47:05 time: 0.8338 data_time: 0.0022 memory: 40662 loss: 0.1146 loss_ce: 0.1146 2023/02/27 15:33:47 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 15:34:04 - mmengine - INFO - Epoch(train) [78][3400/5047] lr: 1.8865e-05 eta: 3 days, 16:45:36 time: 0.8509 data_time: 0.0021 memory: 51658 loss: 0.1213 loss_ce: 0.1213 2023/02/27 15:35:30 - mmengine - INFO - Epoch(train) [78][3500/5047] lr: 1.8865e-05 eta: 3 days, 16:44:08 time: 0.8648 data_time: 0.0023 memory: 41533 loss: 0.1221 loss_ce: 0.1221 2023/02/27 15:36:56 - mmengine - INFO - Epoch(train) [78][3600/5047] lr: 1.8865e-05 eta: 3 days, 16:42:38 time: 0.8665 data_time: 0.0057 memory: 42791 loss: 0.1176 loss_ce: 0.1176 2023/02/27 15:38:23 - mmengine - INFO - Epoch(train) [78][3700/5047] lr: 1.8865e-05 eta: 3 days, 16:41:11 time: 0.8408 data_time: 0.0021 memory: 54116 loss: 0.1104 loss_ce: 0.1104 2023/02/27 15:39:50 - mmengine - INFO - Epoch(train) [78][3800/5047] lr: 1.8865e-05 eta: 3 days, 16:39:42 time: 0.8834 data_time: 0.0022 memory: 46267 loss: 0.1088 loss_ce: 0.1088 2023/02/27 15:41:15 - mmengine - INFO - Epoch(train) [78][3900/5047] lr: 1.8865e-05 eta: 3 days, 16:38:12 time: 0.8620 data_time: 0.0023 memory: 39656 loss: 0.1263 loss_ce: 0.1263 2023/02/27 15:42:42 - mmengine - INFO - Epoch(train) [78][4000/5047] lr: 1.8865e-05 eta: 3 days, 16:36:44 time: 0.8795 data_time: 0.0050 memory: 52817 loss: 0.1174 loss_ce: 0.1174 2023/02/27 15:44:08 - mmengine - INFO - Epoch(train) [78][4100/5047] lr: 1.8865e-05 eta: 3 days, 16:35:15 time: 0.8514 data_time: 0.0031 memory: 44524 loss: 0.1130 loss_ce: 0.1130 2023/02/27 15:45:34 - mmengine - INFO - Epoch(train) [78][4200/5047] lr: 1.8865e-05 eta: 3 days, 16:33:46 time: 0.8236 data_time: 0.0026 memory: 50540 loss: 0.1078 loss_ce: 0.1078 2023/02/27 15:46:59 - mmengine - INFO - Epoch(train) [78][4300/5047] lr: 1.8865e-05 eta: 3 days, 16:32:16 time: 0.8665 data_time: 0.0023 memory: 53387 loss: 0.1164 loss_ce: 0.1164 2023/02/27 15:48:11 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 15:48:26 - mmengine - INFO - Epoch(train) [78][4400/5047] lr: 1.8865e-05 eta: 3 days, 16:30:47 time: 0.7979 data_time: 0.0026 memory: 42556 loss: 0.1148 loss_ce: 0.1148 2023/02/27 15:49:52 - mmengine - INFO - Epoch(train) [78][4500/5047] lr: 1.8865e-05 eta: 3 days, 16:29:19 time: 0.8833 data_time: 0.0023 memory: 48565 loss: 0.1333 loss_ce: 0.1333 2023/02/27 15:51:18 - mmengine - INFO - Epoch(train) [78][4600/5047] lr: 1.8865e-05 eta: 3 days, 16:27:50 time: 0.8663 data_time: 0.0023 memory: 44544 loss: 0.1149 loss_ce: 0.1149 2023/02/27 15:52:44 - mmengine - INFO - Epoch(train) [78][4700/5047] lr: 1.8865e-05 eta: 3 days, 16:26:21 time: 0.8672 data_time: 0.0020 memory: 44537 loss: 0.1174 loss_ce: 0.1174 2023/02/27 15:54:09 - mmengine - INFO - Epoch(train) [78][4800/5047] lr: 1.8865e-05 eta: 3 days, 16:24:51 time: 0.8210 data_time: 0.0022 memory: 46355 loss: 0.1145 loss_ce: 0.1145 2023/02/27 15:55:36 - mmengine - INFO - Epoch(train) [78][4900/5047] lr: 1.8865e-05 eta: 3 days, 16:23:23 time: 0.8167 data_time: 0.0025 memory: 50271 loss: 0.1174 loss_ce: 0.1174 2023/02/27 16:02:39 - mmengine - INFO - Epoch(train) [78][5000/5047] lr: 1.8865e-05 eta: 3 days, 16:27:17 time: 0.8333 data_time: 0.0019 memory: 47447 loss: 0.1220 loss_ce: 0.1220 2023/02/27 16:03:20 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 16:03:20 - mmengine - INFO - Saving checkpoint at 78 epochs 2023/02/27 16:04:52 - mmengine - INFO - Epoch(train) [79][ 100/5047] lr: 1.8664e-05 eta: 3 days, 16:25:07 time: 0.8633 data_time: 0.0025 memory: 52955 loss: 0.1087 loss_ce: 0.1087 2023/02/27 16:06:18 - mmengine - INFO - Epoch(train) [79][ 200/5047] lr: 1.8664e-05 eta: 3 days, 16:23:38 time: 0.8484 data_time: 0.0038 memory: 41419 loss: 0.1220 loss_ce: 0.1220 2023/02/27 16:07:44 - mmengine - INFO - Epoch(train) [79][ 300/5047] lr: 1.8664e-05 eta: 3 days, 16:22:09 time: 0.8701 data_time: 0.0022 memory: 47241 loss: 0.1163 loss_ce: 0.1163 2023/02/27 16:08:14 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 16:09:09 - mmengine - INFO - Epoch(train) [79][ 400/5047] lr: 1.8664e-05 eta: 3 days, 16:20:38 time: 0.8347 data_time: 0.0026 memory: 42965 loss: 0.1112 loss_ce: 0.1112 2023/02/27 16:10:35 - mmengine - INFO - Epoch(train) [79][ 500/5047] lr: 1.8664e-05 eta: 3 days, 16:19:09 time: 0.8217 data_time: 0.0021 memory: 48169 loss: 0.1160 loss_ce: 0.1160 2023/02/27 16:12:02 - mmengine - INFO - Epoch(train) [79][ 600/5047] lr: 1.8664e-05 eta: 3 days, 16:17:41 time: 0.7930 data_time: 0.0022 memory: 41342 loss: 0.1161 loss_ce: 0.1161 2023/02/27 16:13:28 - mmengine - INFO - Epoch(train) [79][ 700/5047] lr: 1.8664e-05 eta: 3 days, 16:16:12 time: 0.8512 data_time: 0.0066 memory: 52964 loss: 0.1139 loss_ce: 0.1139 2023/02/27 16:14:53 - mmengine - INFO - Epoch(train) [79][ 800/5047] lr: 1.8664e-05 eta: 3 days, 16:14:42 time: 0.9182 data_time: 0.0020 memory: 41151 loss: 0.1347 loss_ce: 0.1347 2023/02/27 16:16:19 - mmengine - INFO - Epoch(train) [79][ 900/5047] lr: 1.8664e-05 eta: 3 days, 16:13:13 time: 0.8775 data_time: 0.0024 memory: 44956 loss: 0.1162 loss_ce: 0.1162 2023/02/27 16:17:46 - mmengine - INFO - Epoch(train) [79][1000/5047] lr: 1.8664e-05 eta: 3 days, 16:11:44 time: 0.8486 data_time: 0.0023 memory: 43947 loss: 0.1104 loss_ce: 0.1104 2023/02/27 16:19:12 - mmengine - INFO - Epoch(train) [79][1100/5047] lr: 1.8664e-05 eta: 3 days, 16:10:14 time: 0.8707 data_time: 0.0030 memory: 49378 loss: 0.1037 loss_ce: 0.1037 2023/02/27 16:20:36 - mmengine - INFO - Epoch(train) [79][1200/5047] lr: 1.8664e-05 eta: 3 days, 16:08:44 time: 0.8556 data_time: 0.0023 memory: 42336 loss: 0.1078 loss_ce: 0.1078 2023/02/27 16:22:03 - mmengine - INFO - Epoch(train) [79][1300/5047] lr: 1.8664e-05 eta: 3 days, 16:07:16 time: 0.8453 data_time: 0.0032 memory: 50471 loss: 0.1077 loss_ce: 0.1077 2023/02/27 16:22:32 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 16:23:29 - mmengine - INFO - Epoch(train) [79][1400/5047] lr: 1.8664e-05 eta: 3 days, 16:05:46 time: 0.9014 data_time: 0.0032 memory: 43613 loss: 0.1250 loss_ce: 0.1250 2023/02/27 16:24:56 - mmengine - INFO - Epoch(train) [79][1500/5047] lr: 1.8664e-05 eta: 3 days, 16:04:18 time: 0.8683 data_time: 0.0022 memory: 54242 loss: 0.1133 loss_ce: 0.1133 2023/02/27 16:26:22 - mmengine - INFO - Epoch(train) [79][1600/5047] lr: 1.8664e-05 eta: 3 days, 16:02:49 time: 0.8533 data_time: 0.0023 memory: 40535 loss: 0.1017 loss_ce: 0.1017 2023/02/27 16:27:48 - mmengine - INFO - Epoch(train) [79][1700/5047] lr: 1.8664e-05 eta: 3 days, 16:01:19 time: 0.8382 data_time: 0.0020 memory: 43947 loss: 0.1148 loss_ce: 0.1148 2023/02/27 16:29:24 - mmengine - INFO - Epoch(train) [79][1800/5047] lr: 1.8664e-05 eta: 3 days, 16:00:00 time: 0.8336 data_time: 0.0021 memory: 44956 loss: 0.1144 loss_ce: 0.1144 2023/02/27 16:30:52 - mmengine - INFO - Epoch(train) [79][1900/5047] lr: 1.8664e-05 eta: 3 days, 15:58:32 time: 0.8091 data_time: 0.0022 memory: 42749 loss: 0.1179 loss_ce: 0.1179 2023/02/27 16:32:17 - mmengine - INFO - Epoch(train) [79][2000/5047] lr: 1.8664e-05 eta: 3 days, 15:57:02 time: 0.8332 data_time: 0.0024 memory: 51719 loss: 0.1104 loss_ce: 0.1104 2023/02/27 16:33:43 - mmengine - INFO - Epoch(train) [79][2100/5047] lr: 1.8664e-05 eta: 3 days, 15:55:34 time: 0.8814 data_time: 0.0022 memory: 55364 loss: 0.1176 loss_ce: 0.1176 2023/02/27 16:35:08 - mmengine - INFO - Epoch(train) [79][2200/5047] lr: 1.8664e-05 eta: 3 days, 15:54:03 time: 0.8080 data_time: 0.0023 memory: 40825 loss: 0.1257 loss_ce: 0.1257 2023/02/27 16:36:35 - mmengine - INFO - Epoch(train) [79][2300/5047] lr: 1.8664e-05 eta: 3 days, 15:52:35 time: 0.8885 data_time: 0.0024 memory: 55562 loss: 0.1039 loss_ce: 0.1039 2023/02/27 16:37:06 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 16:38:02 - mmengine - INFO - Epoch(train) [79][2400/5047] lr: 1.8664e-05 eta: 3 days, 15:51:07 time: 0.8677 data_time: 0.0058 memory: 49334 loss: 0.1087 loss_ce: 0.1087 2023/02/27 16:39:29 - mmengine - INFO - Epoch(train) [79][2500/5047] lr: 1.8664e-05 eta: 3 days, 15:49:39 time: 0.8956 data_time: 0.0021 memory: 41122 loss: 0.1127 loss_ce: 0.1127 2023/02/27 16:40:56 - mmengine - INFO - Epoch(train) [79][2600/5047] lr: 1.8664e-05 eta: 3 days, 15:48:10 time: 0.8370 data_time: 0.0026 memory: 39873 loss: 0.1138 loss_ce: 0.1138 2023/02/27 16:42:25 - mmengine - INFO - Epoch(train) [79][2700/5047] lr: 1.8664e-05 eta: 3 days, 15:46:44 time: 0.8884 data_time: 0.0023 memory: 45990 loss: 0.1106 loss_ce: 0.1106 2023/02/27 16:43:55 - mmengine - INFO - Epoch(train) [79][2800/5047] lr: 1.8664e-05 eta: 3 days, 15:45:18 time: 0.8757 data_time: 0.0024 memory: 46876 loss: 0.1026 loss_ce: 0.1026 2023/02/27 16:45:22 - mmengine - INFO - Epoch(train) [79][2900/5047] lr: 1.8664e-05 eta: 3 days, 15:43:50 time: 0.9034 data_time: 0.0024 memory: 43001 loss: 0.1056 loss_ce: 0.1056 2023/02/27 16:46:48 - mmengine - INFO - Epoch(train) [79][3000/5047] lr: 1.8664e-05 eta: 3 days, 15:42:21 time: 0.8485 data_time: 0.0027 memory: 39960 loss: 0.1004 loss_ce: 0.1004 2023/02/27 16:48:14 - mmengine - INFO - Epoch(train) [79][3100/5047] lr: 1.8664e-05 eta: 3 days, 15:40:52 time: 0.8415 data_time: 0.0026 memory: 42441 loss: 0.1076 loss_ce: 0.1076 2023/02/27 16:49:41 - mmengine - INFO - Epoch(train) [79][3200/5047] lr: 1.8664e-05 eta: 3 days, 15:39:23 time: 0.8332 data_time: 0.0029 memory: 43613 loss: 0.1185 loss_ce: 0.1185 2023/02/27 16:51:08 - mmengine - INFO - Epoch(train) [79][3300/5047] lr: 1.8664e-05 eta: 3 days, 15:37:55 time: 0.8021 data_time: 0.0023 memory: 45545 loss: 0.1146 loss_ce: 0.1146 2023/02/27 16:51:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 16:52:34 - mmengine - INFO - Epoch(train) [79][3400/5047] lr: 1.8664e-05 eta: 3 days, 15:36:26 time: 0.8540 data_time: 0.0021 memory: 46925 loss: 0.1338 loss_ce: 0.1338 2023/02/27 16:54:00 - mmengine - INFO - Epoch(train) [79][3500/5047] lr: 1.8664e-05 eta: 3 days, 15:34:57 time: 0.8598 data_time: 0.0022 memory: 39398 loss: 0.1091 loss_ce: 0.1091 2023/02/27 16:55:27 - mmengine - INFO - Epoch(train) [79][3600/5047] lr: 1.8664e-05 eta: 3 days, 15:33:28 time: 0.9013 data_time: 0.0022 memory: 45302 loss: 0.1107 loss_ce: 0.1107 2023/02/27 16:56:53 - mmengine - INFO - Epoch(train) [79][3700/5047] lr: 1.8664e-05 eta: 3 days, 15:31:59 time: 0.8327 data_time: 0.0021 memory: 46744 loss: 0.1228 loss_ce: 0.1228 2023/02/27 16:58:18 - mmengine - INFO - Epoch(train) [79][3800/5047] lr: 1.8664e-05 eta: 3 days, 15:30:29 time: 0.8795 data_time: 0.0021 memory: 47673 loss: 0.1220 loss_ce: 0.1220 2023/02/27 16:59:43 - mmengine - INFO - Epoch(train) [79][3900/5047] lr: 1.8664e-05 eta: 3 days, 15:29:00 time: 0.8330 data_time: 0.0022 memory: 50589 loss: 0.0971 loss_ce: 0.0971 2023/02/27 17:01:10 - mmengine - INFO - Epoch(train) [79][4000/5047] lr: 1.8664e-05 eta: 3 days, 15:27:31 time: 0.8461 data_time: 0.0031 memory: 40263 loss: 0.1058 loss_ce: 0.1058 2023/02/27 17:02:38 - mmengine - INFO - Epoch(train) [79][4100/5047] lr: 1.8664e-05 eta: 3 days, 15:26:03 time: 0.8520 data_time: 0.0022 memory: 43613 loss: 0.1107 loss_ce: 0.1107 2023/02/27 17:04:04 - mmengine - INFO - Epoch(train) [79][4200/5047] lr: 1.8664e-05 eta: 3 days, 15:24:34 time: 0.9002 data_time: 0.0025 memory: 55562 loss: 0.1196 loss_ce: 0.1196 2023/02/27 17:05:29 - mmengine - INFO - Epoch(train) [79][4300/5047] lr: 1.8664e-05 eta: 3 days, 15:23:04 time: 0.8757 data_time: 0.0024 memory: 44956 loss: 0.1146 loss_ce: 0.1146 2023/02/27 17:05:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 17:06:55 - mmengine - INFO - Epoch(train) [79][4400/5047] lr: 1.8664e-05 eta: 3 days, 15:21:35 time: 0.8419 data_time: 0.0021 memory: 49334 loss: 0.1092 loss_ce: 0.1092 2023/02/27 17:08:21 - mmengine - INFO - Epoch(train) [79][4500/5047] lr: 1.8664e-05 eta: 3 days, 15:20:06 time: 0.8586 data_time: 0.0024 memory: 42809 loss: 0.1264 loss_ce: 0.1264 2023/02/27 17:09:46 - mmengine - INFO - Epoch(train) [79][4600/5047] lr: 1.8664e-05 eta: 3 days, 15:18:36 time: 0.8765 data_time: 0.0039 memory: 49216 loss: 0.1085 loss_ce: 0.1085 2023/02/27 17:11:12 - mmengine - INFO - Epoch(train) [79][4700/5047] lr: 1.8664e-05 eta: 3 days, 15:17:07 time: 0.8558 data_time: 0.0072 memory: 42965 loss: 0.1145 loss_ce: 0.1145 2023/02/27 17:12:37 - mmengine - INFO - Epoch(train) [79][4800/5047] lr: 1.8664e-05 eta: 3 days, 15:15:37 time: 0.8361 data_time: 0.0031 memory: 48472 loss: 0.1177 loss_ce: 0.1177 2023/02/27 17:14:03 - mmengine - INFO - Epoch(train) [79][4900/5047] lr: 1.8664e-05 eta: 3 days, 15:14:08 time: 0.8859 data_time: 0.0043 memory: 43613 loss: 0.1166 loss_ce: 0.1166 2023/02/27 17:15:31 - mmengine - INFO - Epoch(train) [79][5000/5047] lr: 1.8664e-05 eta: 3 days, 15:12:40 time: 0.8766 data_time: 0.0026 memory: 45549 loss: 0.1011 loss_ce: 0.1011 2023/02/27 17:16:11 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 17:16:11 - mmengine - INFO - Saving checkpoint at 79 epochs 2023/02/27 17:17:42 - mmengine - INFO - Epoch(train) [80][ 100/5047] lr: 1.8463e-05 eta: 3 days, 15:10:28 time: 0.8899 data_time: 0.0022 memory: 44956 loss: 0.1120 loss_ce: 0.1120 2023/02/27 17:19:08 - mmengine - INFO - Epoch(train) [80][ 200/5047] lr: 1.8463e-05 eta: 3 days, 15:08:59 time: 0.8447 data_time: 0.0021 memory: 40825 loss: 0.1255 loss_ce: 0.1255 2023/02/27 17:20:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 17:20:35 - mmengine - INFO - Epoch(train) [80][ 300/5047] lr: 1.8463e-05 eta: 3 days, 15:07:31 time: 0.8792 data_time: 0.0022 memory: 47882 loss: 0.1095 loss_ce: 0.1095 2023/02/27 17:22:02 - mmengine - INFO - Epoch(train) [80][ 400/5047] lr: 1.8463e-05 eta: 3 days, 15:06:03 time: 0.8565 data_time: 0.0051 memory: 44956 loss: 0.1046 loss_ce: 0.1046 2023/02/27 17:23:28 - mmengine - INFO - Epoch(train) [80][ 500/5047] lr: 1.8463e-05 eta: 3 days, 15:04:34 time: 0.8331 data_time: 0.0024 memory: 51373 loss: 0.1154 loss_ce: 0.1154 2023/02/27 17:24:56 - mmengine - INFO - Epoch(train) [80][ 600/5047] lr: 1.8463e-05 eta: 3 days, 15:03:07 time: 0.8890 data_time: 0.0027 memory: 48948 loss: 0.1193 loss_ce: 0.1193 2023/02/27 17:26:23 - mmengine - INFO - Epoch(train) [80][ 700/5047] lr: 1.8463e-05 eta: 3 days, 15:01:38 time: 0.8584 data_time: 0.0056 memory: 42452 loss: 0.1116 loss_ce: 0.1116 2023/02/27 17:27:51 - mmengine - INFO - Epoch(train) [80][ 800/5047] lr: 1.8463e-05 eta: 3 days, 15:00:11 time: 0.8985 data_time: 0.0026 memory: 42336 loss: 0.1190 loss_ce: 0.1190 2023/02/27 17:29:19 - mmengine - INFO - Epoch(train) [80][ 900/5047] lr: 1.8463e-05 eta: 3 days, 14:58:44 time: 0.8704 data_time: 0.0025 memory: 55535 loss: 0.1007 loss_ce: 0.1007 2023/02/27 17:30:47 - mmengine - INFO - Epoch(train) [80][1000/5047] lr: 1.8463e-05 eta: 3 days, 14:57:17 time: 0.8630 data_time: 0.0048 memory: 51435 loss: 0.1091 loss_ce: 0.1091 2023/02/27 17:32:15 - mmengine - INFO - Epoch(train) [80][1100/5047] lr: 1.8463e-05 eta: 3 days, 14:55:49 time: 0.8721 data_time: 0.0062 memory: 46951 loss: 0.1231 loss_ce: 0.1231 2023/02/27 17:33:42 - mmengine - INFO - Epoch(train) [80][1200/5047] lr: 1.8463e-05 eta: 3 days, 14:54:21 time: 0.8847 data_time: 0.0022 memory: 42488 loss: 0.1282 loss_ce: 0.1282 2023/02/27 17:34:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 17:35:08 - mmengine - INFO - Epoch(train) [80][1300/5047] lr: 1.8463e-05 eta: 3 days, 14:52:52 time: 0.8113 data_time: 0.0023 memory: 55562 loss: 0.1224 loss_ce: 0.1224 2023/02/27 17:36:35 - mmengine - INFO - Epoch(train) [80][1400/5047] lr: 1.8463e-05 eta: 3 days, 14:51:24 time: 0.8794 data_time: 0.0021 memory: 45116 loss: 0.1184 loss_ce: 0.1184 2023/02/27 17:38:02 - mmengine - INFO - Epoch(train) [80][1500/5047] lr: 1.8463e-05 eta: 3 days, 14:49:55 time: 0.8692 data_time: 0.0027 memory: 47813 loss: 0.1188 loss_ce: 0.1188 2023/02/27 17:39:29 - mmengine - INFO - Epoch(train) [80][1600/5047] lr: 1.8463e-05 eta: 3 days, 14:48:27 time: 0.8515 data_time: 0.0023 memory: 41372 loss: 0.1266 loss_ce: 0.1266 2023/02/27 17:40:55 - mmengine - INFO - Epoch(train) [80][1700/5047] lr: 1.8463e-05 eta: 3 days, 14:46:58 time: 0.8511 data_time: 0.0022 memory: 41724 loss: 0.1216 loss_ce: 0.1216 2023/02/27 17:42:20 - mmengine - INFO - Epoch(train) [80][1800/5047] lr: 1.8463e-05 eta: 3 days, 14:45:28 time: 0.8631 data_time: 0.0023 memory: 55562 loss: 0.1098 loss_ce: 0.1098 2023/02/27 17:43:45 - mmengine - INFO - Epoch(train) [80][1900/5047] lr: 1.8463e-05 eta: 3 days, 14:43:58 time: 0.8930 data_time: 0.0024 memory: 43947 loss: 0.1038 loss_ce: 0.1038 2023/02/27 17:45:10 - mmengine - INFO - Epoch(train) [80][2000/5047] lr: 1.8463e-05 eta: 3 days, 14:42:27 time: 0.8608 data_time: 0.0032 memory: 55562 loss: 0.1223 loss_ce: 0.1223 2023/02/27 17:46:36 - mmengine - INFO - Epoch(train) [80][2100/5047] lr: 1.8463e-05 eta: 3 days, 14:40:59 time: 0.8796 data_time: 0.0045 memory: 45689 loss: 0.1110 loss_ce: 0.1110 2023/02/27 17:48:02 - mmengine - INFO - Epoch(train) [80][2200/5047] lr: 1.8463e-05 eta: 3 days, 14:39:30 time: 0.8681 data_time: 0.0033 memory: 43406 loss: 0.1217 loss_ce: 0.1217 2023/02/27 17:49:16 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 17:49:27 - mmengine - INFO - Epoch(train) [80][2300/5047] lr: 1.8463e-05 eta: 3 days, 14:38:00 time: 0.8826 data_time: 0.0021 memory: 42024 loss: 0.1092 loss_ce: 0.1092 2023/02/27 17:50:53 - mmengine - INFO - Epoch(train) [80][2400/5047] lr: 1.8463e-05 eta: 3 days, 14:36:30 time: 0.8727 data_time: 0.0023 memory: 44278 loss: 0.1075 loss_ce: 0.1075 2023/02/27 17:52:20 - mmengine - INFO - Epoch(train) [80][2500/5047] lr: 1.8463e-05 eta: 3 days, 14:35:02 time: 0.8356 data_time: 0.0028 memory: 50894 loss: 0.1149 loss_ce: 0.1149 2023/02/27 17:53:48 - mmengine - INFO - Epoch(train) [80][2600/5047] lr: 1.8463e-05 eta: 3 days, 14:33:35 time: 0.9247 data_time: 0.0021 memory: 48133 loss: 0.1201 loss_ce: 0.1201 2023/02/27 17:55:12 - mmengine - INFO - Epoch(train) [80][2700/5047] lr: 1.8463e-05 eta: 3 days, 14:32:04 time: 0.8862 data_time: 0.0025 memory: 46524 loss: 0.1078 loss_ce: 0.1078 2023/02/27 17:56:38 - mmengine - INFO - Epoch(train) [80][2800/5047] lr: 1.8463e-05 eta: 3 days, 14:30:35 time: 0.8314 data_time: 0.0024 memory: 42965 loss: 0.1229 loss_ce: 0.1229 2023/02/27 17:58:06 - mmengine - INFO - Epoch(train) [80][2900/5047] lr: 1.8463e-05 eta: 3 days, 14:29:07 time: 0.8390 data_time: 0.0021 memory: 44954 loss: 0.1148 loss_ce: 0.1148 2023/02/27 17:59:31 - mmengine - INFO - Epoch(train) [80][3000/5047] lr: 1.8463e-05 eta: 3 days, 14:27:38 time: 0.8341 data_time: 0.0021 memory: 55562 loss: 0.1258 loss_ce: 0.1258 2023/02/27 18:00:58 - mmengine - INFO - Epoch(train) [80][3100/5047] lr: 1.8463e-05 eta: 3 days, 14:26:09 time: 0.8585 data_time: 0.0031 memory: 44956 loss: 0.1046 loss_ce: 0.1046 2023/02/27 18:02:22 - mmengine - INFO - Epoch(train) [80][3200/5047] lr: 1.8463e-05 eta: 3 days, 14:24:38 time: 0.8597 data_time: 0.0074 memory: 40535 loss: 0.1067 loss_ce: 0.1067 2023/02/27 18:03:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 18:03:48 - mmengine - INFO - Epoch(train) [80][3300/5047] lr: 1.8463e-05 eta: 3 days, 14:23:09 time: 0.8759 data_time: 0.0023 memory: 47523 loss: 0.1097 loss_ce: 0.1097 2023/02/27 18:05:14 - mmengine - INFO - Epoch(train) [80][3400/5047] lr: 1.8463e-05 eta: 3 days, 14:21:40 time: 0.8506 data_time: 0.0041 memory: 51657 loss: 0.1006 loss_ce: 0.1006 2023/02/27 18:06:40 - mmengine - INFO - Epoch(train) [80][3500/5047] lr: 1.8463e-05 eta: 3 days, 14:20:11 time: 0.8290 data_time: 0.0027 memory: 42336 loss: 0.1151 loss_ce: 0.1151 2023/02/27 18:08:06 - mmengine - INFO - Epoch(train) [80][3600/5047] lr: 1.8463e-05 eta: 3 days, 14:18:42 time: 0.8869 data_time: 0.0021 memory: 45909 loss: 0.1094 loss_ce: 0.1094 2023/02/27 18:09:34 - mmengine - INFO - Epoch(train) [80][3700/5047] lr: 1.8463e-05 eta: 3 days, 14:17:15 time: 0.8726 data_time: 0.0069 memory: 42024 loss: 0.1190 loss_ce: 0.1190 2023/02/27 18:11:00 - mmengine - INFO - Epoch(train) [80][3800/5047] lr: 1.8463e-05 eta: 3 days, 14:15:46 time: 0.8791 data_time: 0.0023 memory: 43947 loss: 0.1176 loss_ce: 0.1176 2023/02/27 18:12:26 - mmengine - INFO - Epoch(train) [80][3900/5047] lr: 1.8463e-05 eta: 3 days, 14:14:16 time: 0.8996 data_time: 0.0022 memory: 45643 loss: 0.1147 loss_ce: 0.1147 2023/02/27 18:13:52 - mmengine - INFO - Epoch(train) [80][4000/5047] lr: 1.8463e-05 eta: 3 days, 14:12:47 time: 0.8408 data_time: 0.0022 memory: 50106 loss: 0.1245 loss_ce: 0.1245 2023/02/27 18:15:18 - mmengine - INFO - Epoch(train) [80][4100/5047] lr: 1.8463e-05 eta: 3 days, 14:11:18 time: 0.8592 data_time: 0.0025 memory: 41419 loss: 0.1179 loss_ce: 0.1179 2023/02/27 18:16:43 - mmengine - INFO - Epoch(train) [80][4200/5047] lr: 1.8463e-05 eta: 3 days, 14:09:49 time: 0.8631 data_time: 0.0064 memory: 51795 loss: 0.1152 loss_ce: 0.1152 2023/02/27 18:17:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 18:18:10 - mmengine - INFO - Epoch(train) [80][4300/5047] lr: 1.8463e-05 eta: 3 days, 14:08:20 time: 0.8898 data_time: 0.0022 memory: 41472 loss: 0.1199 loss_ce: 0.1199 2023/02/27 18:19:36 - mmengine - INFO - Epoch(train) [80][4400/5047] lr: 1.8463e-05 eta: 3 days, 14:06:51 time: 0.8873 data_time: 0.0021 memory: 41766 loss: 0.1092 loss_ce: 0.1092 2023/02/27 18:21:03 - mmengine - INFO - Epoch(train) [80][4500/5047] lr: 1.8463e-05 eta: 3 days, 14:05:23 time: 0.8481 data_time: 0.0022 memory: 46005 loss: 0.1088 loss_ce: 0.1088 2023/02/27 18:22:29 - mmengine - INFO - Epoch(train) [80][4600/5047] lr: 1.8463e-05 eta: 3 days, 14:03:53 time: 0.8645 data_time: 0.0021 memory: 41419 loss: 0.1016 loss_ce: 0.1016 2023/02/27 18:23:54 - mmengine - INFO - Epoch(train) [80][4700/5047] lr: 1.8463e-05 eta: 3 days, 14:02:23 time: 0.8996 data_time: 0.0037 memory: 44956 loss: 0.0949 loss_ce: 0.0949 2023/02/27 18:25:21 - mmengine - INFO - Epoch(train) [80][4800/5047] lr: 1.8463e-05 eta: 3 days, 14:00:55 time: 0.8829 data_time: 0.0027 memory: 55562 loss: 0.1024 loss_ce: 0.1024 2023/02/27 18:26:48 - mmengine - INFO - Epoch(train) [80][4900/5047] lr: 1.8463e-05 eta: 3 days, 13:59:27 time: 0.8812 data_time: 0.0023 memory: 42336 loss: 0.1050 loss_ce: 0.1050 2023/02/27 18:28:14 - mmengine - INFO - Epoch(train) [80][5000/5047] lr: 1.8463e-05 eta: 3 days, 13:57:58 time: 0.8805 data_time: 0.0033 memory: 46005 loss: 0.1064 loss_ce: 0.1064 2023/02/27 18:28:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 18:28:55 - mmengine - INFO - Saving checkpoint at 80 epochs 2023/02/27 18:30:26 - mmengine - INFO - Epoch(train) [81][ 100/5047] lr: 1.8262e-05 eta: 3 days, 13:55:48 time: 0.9010 data_time: 0.0026 memory: 47074 loss: 0.1056 loss_ce: 0.1056 2023/02/27 18:31:51 - mmengine - INFO - Epoch(train) [81][ 200/5047] lr: 1.8262e-05 eta: 3 days, 13:54:18 time: 0.8596 data_time: 0.0023 memory: 43289 loss: 0.1123 loss_ce: 0.1123 2023/02/27 18:32:26 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 18:33:18 - mmengine - INFO - Epoch(train) [81][ 300/5047] lr: 1.8262e-05 eta: 3 days, 13:52:49 time: 0.8872 data_time: 0.0036 memory: 44956 loss: 0.1097 loss_ce: 0.1097 2023/02/27 18:34:44 - mmengine - INFO - Epoch(train) [81][ 400/5047] lr: 1.8262e-05 eta: 3 days, 13:51:20 time: 0.8975 data_time: 0.0023 memory: 55562 loss: 0.0997 loss_ce: 0.0997 2023/02/27 18:36:10 - mmengine - INFO - Epoch(train) [81][ 500/5047] lr: 1.8262e-05 eta: 3 days, 13:49:51 time: 0.8855 data_time: 0.0035 memory: 48188 loss: 0.1064 loss_ce: 0.1064 2023/02/27 18:37:35 - mmengine - INFO - Epoch(train) [81][ 600/5047] lr: 1.8262e-05 eta: 3 days, 13:48:21 time: 0.8512 data_time: 0.0089 memory: 52792 loss: 0.1191 loss_ce: 0.1191 2023/02/27 18:39:03 - mmengine - INFO - Epoch(train) [81][ 700/5047] lr: 1.8262e-05 eta: 3 days, 13:46:54 time: 0.8610 data_time: 0.0025 memory: 49178 loss: 0.1204 loss_ce: 0.1204 2023/02/27 18:40:29 - mmengine - INFO - Epoch(train) [81][ 800/5047] lr: 1.8262e-05 eta: 3 days, 13:45:25 time: 0.8794 data_time: 0.0022 memory: 51637 loss: 0.1174 loss_ce: 0.1174 2023/02/27 18:41:55 - mmengine - INFO - Epoch(train) [81][ 900/5047] lr: 1.8262e-05 eta: 3 days, 13:43:56 time: 0.8504 data_time: 0.0021 memory: 41724 loss: 0.1216 loss_ce: 0.1216 2023/02/27 18:43:21 - mmengine - INFO - Epoch(train) [81][1000/5047] lr: 1.8262e-05 eta: 3 days, 13:42:26 time: 0.8483 data_time: 0.0025 memory: 44956 loss: 0.1284 loss_ce: 0.1284 2023/02/27 18:44:47 - mmengine - INFO - Epoch(train) [81][1100/5047] lr: 1.8262e-05 eta: 3 days, 13:40:57 time: 0.9116 data_time: 0.0021 memory: 42396 loss: 0.1122 loss_ce: 0.1122 2023/02/27 18:46:11 - mmengine - INFO - Epoch(train) [81][1200/5047] lr: 1.8262e-05 eta: 3 days, 13:39:27 time: 0.8292 data_time: 0.0069 memory: 48215 loss: 0.1079 loss_ce: 0.1079 2023/02/27 18:46:46 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 18:47:36 - mmengine - INFO - Epoch(train) [81][1300/5047] lr: 1.8262e-05 eta: 3 days, 13:37:57 time: 0.8699 data_time: 0.0022 memory: 45879 loss: 0.1342 loss_ce: 0.1342 2023/02/27 18:49:04 - mmengine - INFO - Epoch(train) [81][1400/5047] lr: 1.8262e-05 eta: 3 days, 13:36:29 time: 0.8296 data_time: 0.0030 memory: 44925 loss: 0.0944 loss_ce: 0.0944 2023/02/27 18:50:30 - mmengine - INFO - Epoch(train) [81][1500/5047] lr: 1.8262e-05 eta: 3 days, 13:35:01 time: 0.8776 data_time: 0.0022 memory: 44278 loss: 0.1035 loss_ce: 0.1035 2023/02/27 18:51:57 - mmengine - INFO - Epoch(train) [81][1600/5047] lr: 1.8262e-05 eta: 3 days, 13:33:32 time: 0.7842 data_time: 0.0023 memory: 40055 loss: 0.1115 loss_ce: 0.1115 2023/02/27 18:53:20 - mmengine - INFO - Epoch(train) [81][1700/5047] lr: 1.8262e-05 eta: 3 days, 13:32:01 time: 0.8086 data_time: 0.0026 memory: 43316 loss: 0.1043 loss_ce: 0.1043 2023/02/27 18:54:48 - mmengine - INFO - Epoch(train) [81][1800/5047] lr: 1.8262e-05 eta: 3 days, 13:30:34 time: 0.8759 data_time: 0.0022 memory: 43289 loss: 0.1112 loss_ce: 0.1112 2023/02/27 18:56:13 - mmengine - INFO - Epoch(train) [81][1900/5047] lr: 1.8262e-05 eta: 3 days, 13:29:04 time: 0.8169 data_time: 0.0027 memory: 42965 loss: 0.1139 loss_ce: 0.1139 2023/02/27 18:57:39 - mmengine - INFO - Epoch(train) [81][2000/5047] lr: 1.8262e-05 eta: 3 days, 13:27:34 time: 0.8552 data_time: 0.0024 memory: 44617 loss: 0.1037 loss_ce: 0.1037 2023/02/27 18:59:06 - mmengine - INFO - Epoch(train) [81][2100/5047] lr: 1.8262e-05 eta: 3 days, 13:26:06 time: 0.8673 data_time: 0.0022 memory: 48565 loss: 0.1051 loss_ce: 0.1051 2023/02/27 19:00:32 - mmengine - INFO - Epoch(train) [81][2200/5047] lr: 1.8262e-05 eta: 3 days, 13:24:37 time: 0.8987 data_time: 0.0043 memory: 42965 loss: 0.1147 loss_ce: 0.1147 2023/02/27 19:01:07 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 19:01:58 - mmengine - INFO - Epoch(train) [81][2300/5047] lr: 1.8262e-05 eta: 3 days, 13:23:08 time: 0.8214 data_time: 0.0023 memory: 54673 loss: 0.1229 loss_ce: 0.1229 2023/02/27 19:03:23 - mmengine - INFO - Epoch(train) [81][2400/5047] lr: 1.8262e-05 eta: 3 days, 13:21:38 time: 0.8456 data_time: 0.0037 memory: 42965 loss: 0.1078 loss_ce: 0.1078 2023/02/27 19:04:49 - mmengine - INFO - Epoch(train) [81][2500/5047] lr: 1.8262e-05 eta: 3 days, 13:20:09 time: 0.8426 data_time: 0.0023 memory: 54303 loss: 0.1170 loss_ce: 0.1170 2023/02/27 19:06:17 - mmengine - INFO - Epoch(train) [81][2600/5047] lr: 1.8262e-05 eta: 3 days, 13:18:41 time: 0.8072 data_time: 0.0023 memory: 43613 loss: 0.1234 loss_ce: 0.1234 2023/02/27 19:07:42 - mmengine - INFO - Epoch(train) [81][2700/5047] lr: 1.8262e-05 eta: 3 days, 13:17:12 time: 0.8442 data_time: 0.0032 memory: 41792 loss: 0.1288 loss_ce: 0.1288 2023/02/27 19:09:07 - mmengine - INFO - Epoch(train) [81][2800/5047] lr: 1.8262e-05 eta: 3 days, 13:15:42 time: 0.8578 data_time: 0.0046 memory: 55323 loss: 0.1151 loss_ce: 0.1151 2023/02/27 19:10:29 - mmengine - INFO - Epoch(train) [81][2900/5047] lr: 1.8262e-05 eta: 3 days, 13:14:09 time: 0.7630 data_time: 0.0024 memory: 48503 loss: 0.1089 loss_ce: 0.1089 2023/02/27 19:11:55 - mmengine - INFO - Epoch(train) [81][3000/5047] lr: 1.8262e-05 eta: 3 days, 13:12:41 time: 0.8369 data_time: 0.0046 memory: 45733 loss: 0.1186 loss_ce: 0.1186 2023/02/27 19:13:21 - mmengine - INFO - Epoch(train) [81][3100/5047] lr: 1.8262e-05 eta: 3 days, 13:11:11 time: 0.8435 data_time: 0.0059 memory: 40825 loss: 0.1031 loss_ce: 0.1031 2023/02/27 19:14:47 - mmengine - INFO - Epoch(train) [81][3200/5047] lr: 1.8262e-05 eta: 3 days, 13:09:42 time: 0.8597 data_time: 0.0054 memory: 43613 loss: 0.1112 loss_ce: 0.1112 2023/02/27 19:15:21 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 19:16:13 - mmengine - INFO - Epoch(train) [81][3300/5047] lr: 1.8262e-05 eta: 3 days, 13:08:13 time: 0.8438 data_time: 0.0024 memory: 43188 loss: 0.1129 loss_ce: 0.1129 2023/02/27 19:17:39 - mmengine - INFO - Epoch(train) [81][3400/5047] lr: 1.8262e-05 eta: 3 days, 13:06:44 time: 0.8133 data_time: 0.0021 memory: 46005 loss: 0.1123 loss_ce: 0.1123 2023/02/27 19:19:05 - mmengine - INFO - Epoch(train) [81][3500/5047] lr: 1.8262e-05 eta: 3 days, 13:05:15 time: 0.8467 data_time: 0.0024 memory: 50540 loss: 0.1134 loss_ce: 0.1134 2023/02/27 19:20:32 - mmengine - INFO - Epoch(train) [81][3600/5047] lr: 1.8262e-05 eta: 3 days, 13:03:47 time: 0.8050 data_time: 0.0025 memory: 46355 loss: 0.1091 loss_ce: 0.1091 2023/02/27 19:21:59 - mmengine - INFO - Epoch(train) [81][3700/5047] lr: 1.8262e-05 eta: 3 days, 13:02:18 time: 0.8461 data_time: 0.0024 memory: 41538 loss: 0.1328 loss_ce: 0.1328 2023/02/27 19:23:24 - mmengine - INFO - Epoch(train) [81][3800/5047] lr: 1.8262e-05 eta: 3 days, 13:00:49 time: 0.8099 data_time: 0.0022 memory: 42707 loss: 0.1027 loss_ce: 0.1027 2023/02/27 19:24:50 - mmengine - INFO - Epoch(train) [81][3900/5047] lr: 1.8262e-05 eta: 3 days, 12:59:20 time: 0.8677 data_time: 0.0060 memory: 50477 loss: 0.1052 loss_ce: 0.1052 2023/02/27 19:26:16 - mmengine - INFO - Epoch(train) [81][4000/5047] lr: 1.8262e-05 eta: 3 days, 12:57:51 time: 0.8530 data_time: 0.0022 memory: 46005 loss: 0.1208 loss_ce: 0.1208 2023/02/27 19:27:42 - mmengine - INFO - Epoch(train) [81][4100/5047] lr: 1.8262e-05 eta: 3 days, 12:56:22 time: 0.9114 data_time: 0.0023 memory: 46355 loss: 0.1112 loss_ce: 0.1112 2023/02/27 19:29:10 - mmengine - INFO - Epoch(train) [81][4200/5047] lr: 1.8262e-05 eta: 3 days, 12:54:54 time: 0.8759 data_time: 0.0029 memory: 50435 loss: 0.1070 loss_ce: 0.1070 2023/02/27 19:29:45 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 19:30:37 - mmengine - INFO - Epoch(train) [81][4300/5047] lr: 1.8262e-05 eta: 3 days, 12:53:27 time: 0.8935 data_time: 0.0026 memory: 55562 loss: 0.1141 loss_ce: 0.1141 2023/02/27 19:32:03 - mmengine - INFO - Epoch(train) [81][4400/5047] lr: 1.8262e-05 eta: 3 days, 12:51:58 time: 0.8572 data_time: 0.0027 memory: 39960 loss: 0.1306 loss_ce: 0.1306 2023/02/27 19:33:30 - mmengine - INFO - Epoch(train) [81][4500/5047] lr: 1.8262e-05 eta: 3 days, 12:50:29 time: 0.8735 data_time: 0.0052 memory: 55537 loss: 0.1066 loss_ce: 0.1066 2023/02/27 19:34:56 - mmengine - INFO - Epoch(train) [81][4600/5047] lr: 1.8262e-05 eta: 3 days, 12:49:01 time: 0.8364 data_time: 0.0021 memory: 44278 loss: 0.0977 loss_ce: 0.0977 2023/02/27 19:36:23 - mmengine - INFO - Epoch(train) [81][4700/5047] lr: 1.8262e-05 eta: 3 days, 12:47:32 time: 0.8794 data_time: 0.0023 memory: 55562 loss: 0.1116 loss_ce: 0.1116 2023/02/27 19:37:48 - mmengine - INFO - Epoch(train) [81][4800/5047] lr: 1.8262e-05 eta: 3 days, 12:46:02 time: 0.8775 data_time: 0.0020 memory: 46355 loss: 0.1142 loss_ce: 0.1142 2023/02/27 19:39:14 - mmengine - INFO - Epoch(train) [81][4900/5047] lr: 1.8262e-05 eta: 3 days, 12:44:33 time: 0.8679 data_time: 0.0023 memory: 55562 loss: 0.1355 loss_ce: 0.1355 2023/02/27 19:40:41 - mmengine - INFO - Epoch(train) [81][5000/5047] lr: 1.8262e-05 eta: 3 days, 12:43:05 time: 0.8766 data_time: 0.0038 memory: 51719 loss: 0.1120 loss_ce: 0.1120 2023/02/27 19:41:22 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 19:41:22 - mmengine - INFO - Saving checkpoint at 81 epochs 2023/02/27 19:42:53 - mmengine - INFO - Epoch(train) [82][ 100/5047] lr: 1.8061e-05 eta: 3 days, 12:40:55 time: 0.8666 data_time: 0.0021 memory: 53809 loss: 0.1155 loss_ce: 0.1155 2023/02/27 19:44:13 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 19:44:19 - mmengine - INFO - Epoch(train) [82][ 200/5047] lr: 1.8061e-05 eta: 3 days, 12:39:25 time: 0.8516 data_time: 0.0024 memory: 44722 loss: 0.1080 loss_ce: 0.1080 2023/02/27 19:45:46 - mmengine - INFO - Epoch(train) [82][ 300/5047] lr: 1.8061e-05 eta: 3 days, 12:37:57 time: 0.8772 data_time: 0.0029 memory: 47813 loss: 0.1111 loss_ce: 0.1111 2023/02/27 19:47:10 - mmengine - INFO - Epoch(train) [82][ 400/5047] lr: 1.8061e-05 eta: 3 days, 12:36:26 time: 0.8163 data_time: 0.0022 memory: 52964 loss: 0.1030 loss_ce: 0.1030 2023/02/27 19:48:35 - mmengine - INFO - Epoch(train) [82][ 500/5047] lr: 1.8061e-05 eta: 3 days, 12:34:57 time: 0.8537 data_time: 0.0030 memory: 41115 loss: 0.1125 loss_ce: 0.1125 2023/02/27 19:50:02 - mmengine - INFO - Epoch(train) [82][ 600/5047] lr: 1.8061e-05 eta: 3 days, 12:33:29 time: 0.8765 data_time: 0.0037 memory: 47963 loss: 0.1210 loss_ce: 0.1210 2023/02/27 19:51:28 - mmengine - INFO - Epoch(train) [82][ 700/5047] lr: 1.8061e-05 eta: 3 days, 12:32:00 time: 0.8845 data_time: 0.0046 memory: 48948 loss: 0.1036 loss_ce: 0.1036 2023/02/27 19:52:54 - mmengine - INFO - Epoch(train) [82][ 800/5047] lr: 1.8061e-05 eta: 3 days, 12:30:31 time: 0.8991 data_time: 0.0022 memory: 41122 loss: 0.1145 loss_ce: 0.1145 2023/02/27 19:54:20 - mmengine - INFO - Epoch(train) [82][ 900/5047] lr: 1.8061e-05 eta: 3 days, 12:29:02 time: 0.9055 data_time: 0.0021 memory: 51719 loss: 0.1117 loss_ce: 0.1117 2023/02/27 19:55:46 - mmengine - INFO - Epoch(train) [82][1000/5047] lr: 1.8061e-05 eta: 3 days, 12:27:33 time: 0.8425 data_time: 0.0022 memory: 45643 loss: 0.1060 loss_ce: 0.1060 2023/02/27 19:57:12 - mmengine - INFO - Epoch(train) [82][1100/5047] lr: 1.8061e-05 eta: 3 days, 12:26:04 time: 0.8944 data_time: 0.0023 memory: 43403 loss: 0.1144 loss_ce: 0.1144 2023/02/27 19:58:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 19:58:39 - mmengine - INFO - Epoch(train) [82][1200/5047] lr: 1.8061e-05 eta: 3 days, 12:24:36 time: 0.8715 data_time: 0.0022 memory: 49168 loss: 0.1228 loss_ce: 0.1228 2023/02/27 20:00:05 - mmengine - INFO - Epoch(train) [82][1300/5047] lr: 1.8061e-05 eta: 3 days, 12:23:07 time: 0.8780 data_time: 0.0032 memory: 43001 loss: 0.1078 loss_ce: 0.1078 2023/02/27 20:01:30 - mmengine - INFO - Epoch(train) [82][1400/5047] lr: 1.8061e-05 eta: 3 days, 12:21:37 time: 0.8786 data_time: 0.0030 memory: 44787 loss: 0.1101 loss_ce: 0.1101 2023/02/27 20:02:58 - mmengine - INFO - Epoch(train) [82][1500/5047] lr: 1.8061e-05 eta: 3 days, 12:20:09 time: 0.8775 data_time: 0.0026 memory: 52127 loss: 0.1168 loss_ce: 0.1168 2023/02/27 20:04:25 - mmengine - INFO - Epoch(train) [82][1600/5047] lr: 1.8061e-05 eta: 3 days, 12:18:41 time: 0.8767 data_time: 0.0021 memory: 49242 loss: 0.0918 loss_ce: 0.0918 2023/02/27 20:05:52 - mmengine - INFO - Epoch(train) [82][1700/5047] lr: 1.8061e-05 eta: 3 days, 12:17:14 time: 0.8780 data_time: 0.0025 memory: 41724 loss: 0.1129 loss_ce: 0.1129 2023/02/27 20:07:19 - mmengine - INFO - Epoch(train) [82][1800/5047] lr: 1.8061e-05 eta: 3 days, 12:15:46 time: 0.8865 data_time: 0.0030 memory: 44632 loss: 0.1207 loss_ce: 0.1207 2023/02/27 20:08:45 - mmengine - INFO - Epoch(train) [82][1900/5047] lr: 1.8061e-05 eta: 3 days, 12:14:16 time: 0.8757 data_time: 0.0030 memory: 46772 loss: 0.1004 loss_ce: 0.1004 2023/02/27 20:10:11 - mmengine - INFO - Epoch(train) [82][2000/5047] lr: 1.8061e-05 eta: 3 days, 12:12:47 time: 0.8198 data_time: 0.0022 memory: 44278 loss: 0.1168 loss_ce: 0.1168 2023/02/27 20:11:39 - mmengine - INFO - Epoch(train) [82][2100/5047] lr: 1.8061e-05 eta: 3 days, 12:11:20 time: 0.8667 data_time: 0.0024 memory: 55562 loss: 0.1200 loss_ce: 0.1200 2023/02/27 20:12:59 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 20:13:05 - mmengine - INFO - Epoch(train) [82][2200/5047] lr: 1.8061e-05 eta: 3 days, 12:09:51 time: 0.8688 data_time: 0.0022 memory: 45808 loss: 0.1128 loss_ce: 0.1128 2023/02/27 20:14:29 - mmengine - INFO - Epoch(train) [82][2300/5047] lr: 1.8061e-05 eta: 3 days, 12:08:21 time: 0.8500 data_time: 0.0023 memory: 43524 loss: 0.1167 loss_ce: 0.1167 2023/02/27 20:15:56 - mmengine - INFO - Epoch(train) [82][2400/5047] lr: 1.8061e-05 eta: 3 days, 12:06:52 time: 0.8835 data_time: 0.0021 memory: 46964 loss: 0.1170 loss_ce: 0.1170 2023/02/27 20:17:21 - mmengine - INFO - Epoch(train) [82][2500/5047] lr: 1.8061e-05 eta: 3 days, 12:05:22 time: 0.8244 data_time: 0.0023 memory: 48948 loss: 0.1167 loss_ce: 0.1167 2023/02/27 20:18:46 - mmengine - INFO - Epoch(train) [82][2600/5047] lr: 1.8061e-05 eta: 3 days, 12:03:53 time: 0.9103 data_time: 0.0023 memory: 42024 loss: 0.1159 loss_ce: 0.1159 2023/02/27 20:20:12 - mmengine - INFO - Epoch(train) [82][2700/5047] lr: 1.8061e-05 eta: 3 days, 12:02:24 time: 0.8458 data_time: 0.0026 memory: 47963 loss: 0.1148 loss_ce: 0.1148 2023/02/27 20:21:40 - mmengine - INFO - Epoch(train) [82][2800/5047] lr: 1.8061e-05 eta: 3 days, 12:00:56 time: 0.8513 data_time: 0.0025 memory: 43429 loss: 0.1219 loss_ce: 0.1219 2023/02/27 20:23:07 - mmengine - INFO - Epoch(train) [82][2900/5047] lr: 1.8061e-05 eta: 3 days, 11:59:28 time: 0.8786 data_time: 0.0022 memory: 43409 loss: 0.1248 loss_ce: 0.1248 2023/02/27 20:24:33 - mmengine - INFO - Epoch(train) [82][3000/5047] lr: 1.8061e-05 eta: 3 days, 11:57:59 time: 0.8284 data_time: 0.0027 memory: 46872 loss: 0.1289 loss_ce: 0.1289 2023/02/27 20:25:59 - mmengine - INFO - Epoch(train) [82][3100/5047] lr: 1.8061e-05 eta: 3 days, 11:56:30 time: 0.8626 data_time: 0.0021 memory: 42965 loss: 0.1001 loss_ce: 0.1001 2023/02/27 20:27:18 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 20:27:25 - mmengine - INFO - Epoch(train) [82][3200/5047] lr: 1.8061e-05 eta: 3 days, 11:55:01 time: 0.8574 data_time: 0.0024 memory: 41419 loss: 0.0965 loss_ce: 0.0965 2023/02/27 20:28:50 - mmengine - INFO - Epoch(train) [82][3300/5047] lr: 1.8061e-05 eta: 3 days, 11:53:32 time: 0.8277 data_time: 0.0025 memory: 42336 loss: 0.1007 loss_ce: 0.1007 2023/02/27 20:30:17 - mmengine - INFO - Epoch(train) [82][3400/5047] lr: 1.8061e-05 eta: 3 days, 11:52:04 time: 0.8721 data_time: 0.0025 memory: 43947 loss: 0.1137 loss_ce: 0.1137 2023/02/27 20:31:41 - mmengine - INFO - Epoch(train) [82][3500/5047] lr: 1.8061e-05 eta: 3 days, 11:50:33 time: 0.8589 data_time: 0.0022 memory: 45785 loss: 0.1122 loss_ce: 0.1122 2023/02/27 20:33:07 - mmengine - INFO - Epoch(train) [82][3600/5047] lr: 1.8061e-05 eta: 3 days, 11:49:04 time: 0.8653 data_time: 0.0023 memory: 47982 loss: 0.1314 loss_ce: 0.1314 2023/02/27 20:34:33 - mmengine - INFO - Epoch(train) [82][3700/5047] lr: 1.8061e-05 eta: 3 days, 11:47:35 time: 0.8652 data_time: 0.0023 memory: 55562 loss: 0.1099 loss_ce: 0.1099 2023/02/27 20:35:59 - mmengine - INFO - Epoch(train) [82][3800/5047] lr: 1.8061e-05 eta: 3 days, 11:46:06 time: 0.8571 data_time: 0.0024 memory: 55562 loss: 0.1348 loss_ce: 0.1348 2023/02/27 20:37:26 - mmengine - INFO - Epoch(train) [82][3900/5047] lr: 1.8061e-05 eta: 3 days, 11:44:38 time: 0.8937 data_time: 0.0023 memory: 42024 loss: 0.1132 loss_ce: 0.1132 2023/02/27 20:38:52 - mmengine - INFO - Epoch(train) [82][4000/5047] lr: 1.8061e-05 eta: 3 days, 11:43:09 time: 0.8457 data_time: 0.0040 memory: 41724 loss: 0.1315 loss_ce: 0.1315 2023/02/27 20:40:19 - mmengine - INFO - Epoch(train) [82][4100/5047] lr: 1.8061e-05 eta: 3 days, 11:41:40 time: 0.8338 data_time: 0.0059 memory: 54876 loss: 0.1153 loss_ce: 0.1153 2023/02/27 20:41:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 20:41:45 - mmengine - INFO - Epoch(train) [82][4200/5047] lr: 1.8061e-05 eta: 3 days, 11:40:12 time: 0.8675 data_time: 0.0025 memory: 47813 loss: 0.0997 loss_ce: 0.0997 2023/02/27 20:43:12 - mmengine - INFO - Epoch(train) [82][4300/5047] lr: 1.8061e-05 eta: 3 days, 11:38:44 time: 0.8432 data_time: 0.0038 memory: 46355 loss: 0.1243 loss_ce: 0.1243 2023/02/27 20:44:39 - mmengine - INFO - Epoch(train) [82][4400/5047] lr: 1.8061e-05 eta: 3 days, 11:37:16 time: 0.8267 data_time: 0.0022 memory: 42233 loss: 0.1087 loss_ce: 0.1087 2023/02/27 20:46:05 - mmengine - INFO - Epoch(train) [82][4500/5047] lr: 1.8061e-05 eta: 3 days, 11:35:47 time: 0.8426 data_time: 0.0024 memory: 43613 loss: 0.1081 loss_ce: 0.1081 2023/02/27 20:47:30 - mmengine - INFO - Epoch(train) [82][4600/5047] lr: 1.8061e-05 eta: 3 days, 11:34:17 time: 0.8830 data_time: 0.0021 memory: 41701 loss: 0.1187 loss_ce: 0.1187 2023/02/27 20:48:56 - mmengine - INFO - Epoch(train) [82][4700/5047] lr: 1.8061e-05 eta: 3 days, 11:32:48 time: 0.8581 data_time: 0.0023 memory: 54205 loss: 0.1117 loss_ce: 0.1117 2023/02/27 20:50:22 - mmengine - INFO - Epoch(train) [82][4800/5047] lr: 1.8061e-05 eta: 3 days, 11:31:19 time: 0.8788 data_time: 0.0024 memory: 55562 loss: 0.1202 loss_ce: 0.1202 2023/02/27 20:51:47 - mmengine - INFO - Epoch(train) [82][4900/5047] lr: 1.8061e-05 eta: 3 days, 11:29:50 time: 0.8706 data_time: 0.0028 memory: 43219 loss: 0.1124 loss_ce: 0.1124 2023/02/27 20:53:12 - mmengine - INFO - Epoch(train) [82][5000/5047] lr: 1.8061e-05 eta: 3 days, 11:28:20 time: 0.8204 data_time: 0.0024 memory: 55562 loss: 0.1120 loss_ce: 0.1120 2023/02/27 20:53:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 20:53:52 - mmengine - INFO - Saving checkpoint at 82 epochs 2023/02/27 20:55:25 - mmengine - INFO - Epoch(train) [83][ 100/5047] lr: 1.7861e-05 eta: 3 days, 11:26:09 time: 0.8278 data_time: 0.0023 memory: 41285 loss: 0.1183 loss_ce: 0.1183 2023/02/27 20:56:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 20:56:50 - mmengine - INFO - Epoch(train) [83][ 200/5047] lr: 1.7861e-05 eta: 3 days, 11:24:40 time: 0.8895 data_time: 0.0024 memory: 55562 loss: 0.1175 loss_ce: 0.1175 2023/02/27 20:58:16 - mmengine - INFO - Epoch(train) [83][ 300/5047] lr: 1.7861e-05 eta: 3 days, 11:23:11 time: 0.8151 data_time: 0.0024 memory: 46146 loss: 0.1219 loss_ce: 0.1219 2023/02/27 20:59:43 - mmengine - INFO - Epoch(train) [83][ 400/5047] lr: 1.7861e-05 eta: 3 days, 11:21:43 time: 0.8979 data_time: 0.0025 memory: 45426 loss: 0.1077 loss_ce: 0.1077 2023/02/27 21:01:09 - mmengine - INFO - Epoch(train) [83][ 500/5047] lr: 1.7861e-05 eta: 3 days, 11:20:14 time: 0.8843 data_time: 0.0024 memory: 44617 loss: 0.1042 loss_ce: 0.1042 2023/02/27 21:02:33 - mmengine - INFO - Epoch(train) [83][ 600/5047] lr: 1.7861e-05 eta: 3 days, 11:18:43 time: 0.8297 data_time: 0.0024 memory: 40886 loss: 0.1116 loss_ce: 0.1116 2023/02/27 21:03:59 - mmengine - INFO - Epoch(train) [83][ 700/5047] lr: 1.7861e-05 eta: 3 days, 11:17:14 time: 0.8369 data_time: 0.0027 memory: 39778 loss: 0.1086 loss_ce: 0.1086 2023/02/27 21:05:25 - mmengine - INFO - Epoch(train) [83][ 800/5047] lr: 1.7861e-05 eta: 3 days, 11:15:45 time: 0.8875 data_time: 0.0023 memory: 43557 loss: 0.1146 loss_ce: 0.1146 2023/02/27 21:06:51 - mmengine - INFO - Epoch(train) [83][ 900/5047] lr: 1.7861e-05 eta: 3 days, 11:14:16 time: 0.8671 data_time: 0.0023 memory: 40825 loss: 0.1179 loss_ce: 0.1179 2023/02/27 21:08:18 - mmengine - INFO - Epoch(train) [83][1000/5047] lr: 1.7861e-05 eta: 3 days, 11:12:48 time: 0.8892 data_time: 0.0022 memory: 41915 loss: 0.1155 loss_ce: 0.1155 2023/02/27 21:09:45 - mmengine - INFO - Epoch(train) [83][1100/5047] lr: 1.7861e-05 eta: 3 days, 11:11:21 time: 0.8699 data_time: 0.0022 memory: 55562 loss: 0.1279 loss_ce: 0.1279 2023/02/27 21:10:25 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 21:11:11 - mmengine - INFO - Epoch(train) [83][1200/5047] lr: 1.7861e-05 eta: 3 days, 11:09:52 time: 0.8593 data_time: 0.0022 memory: 45320 loss: 0.1036 loss_ce: 0.1036 2023/02/27 21:12:38 - mmengine - INFO - Epoch(train) [83][1300/5047] lr: 1.7861e-05 eta: 3 days, 11:08:23 time: 0.8534 data_time: 0.0021 memory: 44727 loss: 0.1130 loss_ce: 0.1130 2023/02/27 21:14:03 - mmengine - INFO - Epoch(train) [83][1400/5047] lr: 1.7861e-05 eta: 3 days, 11:06:54 time: 0.8387 data_time: 0.0040 memory: 46854 loss: 0.1315 loss_ce: 0.1315 2023/02/27 21:15:29 - mmengine - INFO - Epoch(train) [83][1500/5047] lr: 1.7861e-05 eta: 3 days, 11:05:25 time: 0.8335 data_time: 0.0032 memory: 45787 loss: 0.1133 loss_ce: 0.1133 2023/02/27 21:16:54 - mmengine - INFO - Epoch(train) [83][1600/5047] lr: 1.7861e-05 eta: 3 days, 11:03:55 time: 0.8577 data_time: 0.0024 memory: 48857 loss: 0.1216 loss_ce: 0.1216 2023/02/27 21:18:20 - mmengine - INFO - Epoch(train) [83][1700/5047] lr: 1.7861e-05 eta: 3 days, 11:02:26 time: 0.8781 data_time: 0.0022 memory: 42024 loss: 0.1081 loss_ce: 0.1081 2023/02/27 21:19:47 - mmengine - INFO - Epoch(train) [83][1800/5047] lr: 1.7861e-05 eta: 3 days, 11:00:58 time: 0.8558 data_time: 0.0026 memory: 44539 loss: 0.1233 loss_ce: 0.1233 2023/02/27 21:21:15 - mmengine - INFO - Epoch(train) [83][1900/5047] lr: 1.7861e-05 eta: 3 days, 10:59:31 time: 0.8519 data_time: 0.0030 memory: 55562 loss: 0.1252 loss_ce: 0.1252 2023/02/27 21:22:40 - mmengine - INFO - Epoch(train) [83][2000/5047] lr: 1.7861e-05 eta: 3 days, 10:58:01 time: 0.9144 data_time: 0.0025 memory: 55562 loss: 0.1056 loss_ce: 0.1056 2023/02/27 21:24:06 - mmengine - INFO - Epoch(train) [83][2100/5047] lr: 1.7861e-05 eta: 3 days, 10:56:32 time: 0.7636 data_time: 0.0025 memory: 42398 loss: 0.1244 loss_ce: 0.1244 2023/02/27 21:24:46 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 21:25:32 - mmengine - INFO - Epoch(train) [83][2200/5047] lr: 1.7861e-05 eta: 3 days, 10:55:03 time: 0.9098 data_time: 0.0027 memory: 44563 loss: 0.1163 loss_ce: 0.1163 2023/02/27 21:26:58 - mmengine - INFO - Epoch(train) [83][2300/5047] lr: 1.7861e-05 eta: 3 days, 10:53:34 time: 0.8603 data_time: 0.0026 memory: 42336 loss: 0.1164 loss_ce: 0.1164 2023/02/27 21:28:26 - mmengine - INFO - Epoch(train) [83][2400/5047] lr: 1.7861e-05 eta: 3 days, 10:52:07 time: 0.8925 data_time: 0.0026 memory: 44631 loss: 0.1200 loss_ce: 0.1200 2023/02/27 21:29:51 - mmengine - INFO - Epoch(train) [83][2500/5047] lr: 1.7861e-05 eta: 3 days, 10:50:37 time: 0.8703 data_time: 0.0023 memory: 43947 loss: 0.1251 loss_ce: 0.1251 2023/02/27 21:31:16 - mmengine - INFO - Epoch(train) [83][2600/5047] lr: 1.7861e-05 eta: 3 days, 10:49:08 time: 0.8648 data_time: 0.0021 memory: 45642 loss: 0.1139 loss_ce: 0.1139 2023/02/27 21:32:42 - mmengine - INFO - Epoch(train) [83][2700/5047] lr: 1.7861e-05 eta: 3 days, 10:47:39 time: 0.8990 data_time: 0.0022 memory: 41724 loss: 0.1107 loss_ce: 0.1107 2023/02/27 21:34:09 - mmengine - INFO - Epoch(train) [83][2800/5047] lr: 1.7861e-05 eta: 3 days, 10:46:11 time: 0.8342 data_time: 0.0029 memory: 43289 loss: 0.1141 loss_ce: 0.1141 2023/02/27 21:35:35 - mmengine - INFO - Epoch(train) [83][2900/5047] lr: 1.7861e-05 eta: 3 days, 10:44:42 time: 0.8698 data_time: 0.0034 memory: 43613 loss: 0.1042 loss_ce: 0.1042 2023/02/27 21:37:02 - mmengine - INFO - Epoch(train) [83][3000/5047] lr: 1.7861e-05 eta: 3 days, 10:43:14 time: 0.8455 data_time: 0.0026 memory: 44956 loss: 0.1064 loss_ce: 0.1064 2023/02/27 21:38:28 - mmengine - INFO - Epoch(train) [83][3100/5047] lr: 1.7861e-05 eta: 3 days, 10:41:45 time: 0.8765 data_time: 0.0022 memory: 45217 loss: 0.1139 loss_ce: 0.1139 2023/02/27 21:39:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 21:39:54 - mmengine - INFO - Epoch(train) [83][3200/5047] lr: 1.7861e-05 eta: 3 days, 10:40:16 time: 0.8193 data_time: 0.0024 memory: 46561 loss: 0.1237 loss_ce: 0.1237 2023/02/27 21:41:22 - mmengine - INFO - Epoch(train) [83][3300/5047] lr: 1.7861e-05 eta: 3 days, 10:38:49 time: 0.8893 data_time: 0.0024 memory: 45983 loss: 0.1120 loss_ce: 0.1120 2023/02/27 21:42:50 - mmengine - INFO - Epoch(train) [83][3400/5047] lr: 1.7861e-05 eta: 3 days, 10:37:22 time: 0.9290 data_time: 0.0024 memory: 52127 loss: 0.1173 loss_ce: 0.1173 2023/02/27 21:44:16 - mmengine - INFO - Epoch(train) [83][3500/5047] lr: 1.7861e-05 eta: 3 days, 10:35:53 time: 0.8214 data_time: 0.0023 memory: 43613 loss: 0.1348 loss_ce: 0.1348 2023/02/27 21:45:42 - mmengine - INFO - Epoch(train) [83][3600/5047] lr: 1.7861e-05 eta: 3 days, 10:34:24 time: 0.8694 data_time: 0.0031 memory: 43735 loss: 0.1161 loss_ce: 0.1161 2023/02/27 21:47:09 - mmengine - INFO - Epoch(train) [83][3700/5047] lr: 1.7861e-05 eta: 3 days, 10:32:56 time: 0.8203 data_time: 0.0045 memory: 45786 loss: 0.1159 loss_ce: 0.1159 2023/02/27 21:48:36 - mmengine - INFO - Epoch(train) [83][3800/5047] lr: 1.7861e-05 eta: 3 days, 10:31:28 time: 0.8631 data_time: 0.0031 memory: 44956 loss: 0.1266 loss_ce: 0.1266 2023/02/27 21:50:02 - mmengine - INFO - Epoch(train) [83][3900/5047] lr: 1.7861e-05 eta: 3 days, 10:29:59 time: 0.8672 data_time: 0.0021 memory: 39652 loss: 0.1226 loss_ce: 0.1226 2023/02/27 21:51:31 - mmengine - INFO - Epoch(train) [83][4000/5047] lr: 1.7861e-05 eta: 3 days, 10:28:33 time: 0.8579 data_time: 0.0025 memory: 48096 loss: 0.1134 loss_ce: 0.1134 2023/02/27 21:52:57 - mmengine - INFO - Epoch(train) [83][4100/5047] lr: 1.7861e-05 eta: 3 days, 10:27:04 time: 0.8504 data_time: 0.0024 memory: 42239 loss: 0.1211 loss_ce: 0.1211 2023/02/27 21:53:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 21:54:24 - mmengine - INFO - Epoch(train) [83][4200/5047] lr: 1.7861e-05 eta: 3 days, 10:25:36 time: 0.9268 data_time: 0.0023 memory: 43919 loss: 0.1012 loss_ce: 0.1012 2023/02/27 21:55:51 - mmengine - INFO - Epoch(train) [83][4300/5047] lr: 1.7861e-05 eta: 3 days, 10:24:07 time: 0.8283 data_time: 0.0021 memory: 43050 loss: 0.1290 loss_ce: 0.1290 2023/02/27 21:57:16 - mmengine - INFO - Epoch(train) [83][4400/5047] lr: 1.7861e-05 eta: 3 days, 10:22:38 time: 0.7956 data_time: 0.0022 memory: 55535 loss: 0.1070 loss_ce: 0.1070 2023/02/27 21:58:41 - mmengine - INFO - Epoch(train) [83][4500/5047] lr: 1.7861e-05 eta: 3 days, 10:21:08 time: 0.8674 data_time: 0.0024 memory: 49334 loss: 0.1063 loss_ce: 0.1063 2023/02/27 22:00:07 - mmengine - INFO - Epoch(train) [83][4600/5047] lr: 1.7861e-05 eta: 3 days, 10:19:40 time: 0.8931 data_time: 0.0048 memory: 44301 loss: 0.1096 loss_ce: 0.1096 2023/02/27 22:01:34 - mmengine - INFO - Epoch(train) [83][4700/5047] lr: 1.7861e-05 eta: 3 days, 10:18:11 time: 0.8491 data_time: 0.0022 memory: 49378 loss: 0.1146 loss_ce: 0.1146 2023/02/27 22:03:00 - mmengine - INFO - Epoch(train) [83][4800/5047] lr: 1.7861e-05 eta: 3 days, 10:16:42 time: 0.8309 data_time: 0.0023 memory: 42243 loss: 0.1245 loss_ce: 0.1245 2023/02/27 22:04:26 - mmengine - INFO - Epoch(train) [83][4900/5047] lr: 1.7861e-05 eta: 3 days, 10:15:14 time: 0.8368 data_time: 0.0022 memory: 55562 loss: 0.1099 loss_ce: 0.1099 2023/02/27 22:05:53 - mmengine - INFO - Epoch(train) [83][5000/5047] lr: 1.7861e-05 eta: 3 days, 10:13:46 time: 0.8690 data_time: 0.0199 memory: 55562 loss: 0.1310 loss_ce: 0.1310 2023/02/27 22:06:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 22:06:33 - mmengine - INFO - Saving checkpoint at 83 epochs 2023/02/27 22:08:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 22:08:05 - mmengine - INFO - Epoch(train) [84][ 100/5047] lr: 1.7660e-05 eta: 3 days, 10:11:36 time: 0.8852 data_time: 0.0022 memory: 49954 loss: 0.1235 loss_ce: 0.1235 2023/02/27 22:09:31 - mmengine - INFO - Epoch(train) [84][ 200/5047] lr: 1.7660e-05 eta: 3 days, 10:10:06 time: 0.8325 data_time: 0.0023 memory: 40241 loss: 0.1129 loss_ce: 0.1129 2023/02/27 22:10:57 - mmengine - INFO - Epoch(train) [84][ 300/5047] lr: 1.7660e-05 eta: 3 days, 10:08:38 time: 0.8494 data_time: 0.0029 memory: 40535 loss: 0.1062 loss_ce: 0.1062 2023/02/27 22:12:24 - mmengine - INFO - Epoch(train) [84][ 400/5047] lr: 1.7660e-05 eta: 3 days, 10:07:10 time: 0.8560 data_time: 0.0053 memory: 55562 loss: 0.0989 loss_ce: 0.0989 2023/02/27 22:13:52 - mmengine - INFO - Epoch(train) [84][ 500/5047] lr: 1.7660e-05 eta: 3 days, 10:05:42 time: 0.9032 data_time: 0.0023 memory: 55562 loss: 0.1161 loss_ce: 0.1161 2023/02/27 22:15:19 - mmengine - INFO - Epoch(train) [84][ 600/5047] lr: 1.7660e-05 eta: 3 days, 10:04:14 time: 0.9448 data_time: 0.0030 memory: 41724 loss: 0.1047 loss_ce: 0.1047 2023/02/27 22:16:44 - mmengine - INFO - Epoch(train) [84][ 700/5047] lr: 1.7660e-05 eta: 3 days, 10:02:44 time: 0.8457 data_time: 0.0050 memory: 44687 loss: 0.1281 loss_ce: 0.1281 2023/02/27 22:18:09 - mmengine - INFO - Epoch(train) [84][ 800/5047] lr: 1.7660e-05 eta: 3 days, 10:01:15 time: 0.8532 data_time: 0.0031 memory: 45302 loss: 0.1100 loss_ce: 0.1100 2023/02/27 22:19:36 - mmengine - INFO - Epoch(train) [84][ 900/5047] lr: 1.7660e-05 eta: 3 days, 9:59:47 time: 0.8498 data_time: 0.0021 memory: 52543 loss: 0.1106 loss_ce: 0.1106 2023/02/27 22:21:00 - mmengine - INFO - Epoch(train) [84][1000/5047] lr: 1.7660e-05 eta: 3 days, 9:58:17 time: 0.8501 data_time: 0.0027 memory: 55562 loss: 0.1028 loss_ce: 0.1028 2023/02/27 22:22:26 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 22:22:27 - mmengine - INFO - Epoch(train) [84][1100/5047] lr: 1.7660e-05 eta: 3 days, 9:56:48 time: 0.8671 data_time: 0.0028 memory: 55562 loss: 0.1007 loss_ce: 0.1007 2023/02/27 22:23:53 - mmengine - INFO - Epoch(train) [84][1200/5047] lr: 1.7660e-05 eta: 3 days, 9:55:20 time: 0.8712 data_time: 0.0026 memory: 55562 loss: 0.1149 loss_ce: 0.1149 2023/02/27 22:25:19 - mmengine - INFO - Epoch(train) [84][1300/5047] lr: 1.7660e-05 eta: 3 days, 9:53:50 time: 0.8067 data_time: 0.0025 memory: 49484 loss: 0.1137 loss_ce: 0.1137 2023/02/27 22:26:45 - mmengine - INFO - Epoch(train) [84][1400/5047] lr: 1.7660e-05 eta: 3 days, 9:52:22 time: 0.8539 data_time: 0.0024 memory: 55562 loss: 0.1163 loss_ce: 0.1163 2023/02/27 22:28:10 - mmengine - INFO - Epoch(train) [84][1500/5047] lr: 1.7660e-05 eta: 3 days, 9:50:52 time: 0.8300 data_time: 0.0047 memory: 42336 loss: 0.1259 loss_ce: 0.1259 2023/02/27 22:29:37 - mmengine - INFO - Epoch(train) [84][1600/5047] lr: 1.7660e-05 eta: 3 days, 9:49:24 time: 0.8264 data_time: 0.0022 memory: 53809 loss: 0.1077 loss_ce: 0.1077 2023/02/27 22:31:03 - mmengine - INFO - Epoch(train) [84][1700/5047] lr: 1.7660e-05 eta: 3 days, 9:47:55 time: 0.8328 data_time: 0.0020 memory: 41391 loss: 0.1129 loss_ce: 0.1129 2023/02/27 22:32:31 - mmengine - INFO - Epoch(train) [84][1800/5047] lr: 1.7660e-05 eta: 3 days, 9:46:28 time: 0.8800 data_time: 0.0022 memory: 40544 loss: 0.1246 loss_ce: 0.1246 2023/02/27 22:33:57 - mmengine - INFO - Epoch(train) [84][1900/5047] lr: 1.7660e-05 eta: 3 days, 9:44:59 time: 0.8596 data_time: 0.0026 memory: 43947 loss: 0.1106 loss_ce: 0.1106 2023/02/27 22:35:24 - mmengine - INFO - Epoch(train) [84][2000/5047] lr: 1.7660e-05 eta: 3 days, 9:43:31 time: 0.8917 data_time: 0.0021 memory: 42839 loss: 0.1255 loss_ce: 0.1255 2023/02/27 22:36:48 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 22:36:49 - mmengine - INFO - Epoch(train) [84][2100/5047] lr: 1.7660e-05 eta: 3 days, 9:42:01 time: 0.8944 data_time: 0.0021 memory: 38618 loss: 0.1063 loss_ce: 0.1063 2023/02/27 22:38:14 - mmengine - INFO - Epoch(train) [84][2200/5047] lr: 1.7660e-05 eta: 3 days, 9:40:32 time: 0.8424 data_time: 0.0030 memory: 55562 loss: 0.1188 loss_ce: 0.1188 2023/02/27 22:39:42 - mmengine - INFO - Epoch(train) [84][2300/5047] lr: 1.7660e-05 eta: 3 days, 9:39:04 time: 0.9280 data_time: 0.0024 memory: 46005 loss: 0.1028 loss_ce: 0.1028 2023/02/27 22:41:09 - mmengine - INFO - Epoch(train) [84][2400/5047] lr: 1.7660e-05 eta: 3 days, 9:37:37 time: 0.9223 data_time: 0.0054 memory: 55562 loss: 0.1127 loss_ce: 0.1127 2023/02/27 22:42:36 - mmengine - INFO - Epoch(train) [84][2500/5047] lr: 1.7660e-05 eta: 3 days, 9:36:08 time: 0.9199 data_time: 0.0026 memory: 45517 loss: 0.1210 loss_ce: 0.1210 2023/02/27 22:44:02 - mmengine - INFO - Epoch(train) [84][2600/5047] lr: 1.7660e-05 eta: 3 days, 9:34:40 time: 0.8327 data_time: 0.0022 memory: 55562 loss: 0.1153 loss_ce: 0.1153 2023/02/27 22:45:28 - mmengine - INFO - Epoch(train) [84][2700/5047] lr: 1.7660e-05 eta: 3 days, 9:33:11 time: 0.8350 data_time: 0.0024 memory: 43773 loss: 0.1158 loss_ce: 0.1158 2023/02/27 22:46:54 - mmengine - INFO - Epoch(train) [84][2800/5047] lr: 1.7660e-05 eta: 3 days, 9:31:42 time: 0.8467 data_time: 0.0022 memory: 45302 loss: 0.1284 loss_ce: 0.1284 2023/02/27 22:48:21 - mmengine - INFO - Epoch(train) [84][2900/5047] lr: 1.7660e-05 eta: 3 days, 9:30:14 time: 0.8990 data_time: 0.0023 memory: 55562 loss: 0.1148 loss_ce: 0.1148 2023/02/27 22:49:46 - mmengine - INFO - Epoch(train) [84][3000/5047] lr: 1.7660e-05 eta: 3 days, 9:28:45 time: 0.8268 data_time: 0.0024 memory: 43289 loss: 0.1132 loss_ce: 0.1132 2023/02/27 22:51:11 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 22:51:12 - mmengine - INFO - Epoch(train) [84][3100/5047] lr: 1.7660e-05 eta: 3 days, 9:27:16 time: 0.9205 data_time: 0.0026 memory: 48565 loss: 0.1264 loss_ce: 0.1264 2023/02/27 22:52:40 - mmengine - INFO - Epoch(train) [84][3200/5047] lr: 1.7660e-05 eta: 3 days, 9:25:48 time: 0.8633 data_time: 0.0026 memory: 42878 loss: 0.1265 loss_ce: 0.1265 2023/02/27 22:54:03 - mmengine - INFO - Epoch(train) [84][3300/5047] lr: 1.7660e-05 eta: 3 days, 9:24:17 time: 0.8456 data_time: 0.0022 memory: 42332 loss: 0.1211 loss_ce: 0.1211 2023/02/27 22:55:26 - mmengine - INFO - Epoch(train) [84][3400/5047] lr: 1.7660e-05 eta: 3 days, 9:22:46 time: 0.8270 data_time: 0.0023 memory: 55562 loss: 0.1016 loss_ce: 0.1016 2023/02/27 22:56:53 - mmengine - INFO - Epoch(train) [84][3500/5047] lr: 1.7660e-05 eta: 3 days, 9:21:18 time: 0.8009 data_time: 0.0022 memory: 48116 loss: 0.1258 loss_ce: 0.1258 2023/02/27 22:58:18 - mmengine - INFO - Epoch(train) [84][3600/5047] lr: 1.7660e-05 eta: 3 days, 9:19:49 time: 0.8557 data_time: 0.0020 memory: 42024 loss: 0.1119 loss_ce: 0.1119 2023/02/27 22:59:46 - mmengine - INFO - Epoch(train) [84][3700/5047] lr: 1.7660e-05 eta: 3 days, 9:18:21 time: 0.8806 data_time: 0.0021 memory: 41724 loss: 0.1158 loss_ce: 0.1158 2023/02/27 23:01:13 - mmengine - INFO - Epoch(train) [84][3800/5047] lr: 1.7660e-05 eta: 3 days, 9:16:53 time: 0.8612 data_time: 0.0023 memory: 44896 loss: 0.1087 loss_ce: 0.1087 2023/02/27 23:02:38 - mmengine - INFO - Epoch(train) [84][3900/5047] lr: 1.7660e-05 eta: 3 days, 9:15:23 time: 0.8721 data_time: 0.0025 memory: 39960 loss: 0.1152 loss_ce: 0.1152 2023/02/27 23:04:04 - mmengine - INFO - Epoch(train) [84][4000/5047] lr: 1.7660e-05 eta: 3 days, 9:13:55 time: 0.8299 data_time: 0.0022 memory: 48053 loss: 0.1112 loss_ce: 0.1112 2023/02/27 23:05:28 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 23:05:29 - mmengine - INFO - Epoch(train) [84][4100/5047] lr: 1.7660e-05 eta: 3 days, 9:12:25 time: 0.8389 data_time: 0.0024 memory: 45708 loss: 0.1191 loss_ce: 0.1191 2023/02/27 23:06:55 - mmengine - INFO - Epoch(train) [84][4200/5047] lr: 1.7660e-05 eta: 3 days, 9:10:56 time: 0.8542 data_time: 0.0022 memory: 44278 loss: 0.1184 loss_ce: 0.1184 2023/02/27 23:08:21 - mmengine - INFO - Epoch(train) [84][4300/5047] lr: 1.7660e-05 eta: 3 days, 9:09:27 time: 0.8590 data_time: 0.0024 memory: 51896 loss: 0.1175 loss_ce: 0.1175 2023/02/27 23:09:47 - mmengine - INFO - Epoch(train) [84][4400/5047] lr: 1.7660e-05 eta: 3 days, 9:07:58 time: 0.8500 data_time: 0.0024 memory: 55562 loss: 0.1178 loss_ce: 0.1178 2023/02/27 23:11:13 - mmengine - INFO - Epoch(train) [84][4500/5047] lr: 1.7660e-05 eta: 3 days, 9:06:30 time: 0.8590 data_time: 0.0023 memory: 45401 loss: 0.1094 loss_ce: 0.1094 2023/02/27 23:12:38 - mmengine - INFO - Epoch(train) [84][4600/5047] lr: 1.7660e-05 eta: 3 days, 9:05:00 time: 0.8392 data_time: 0.0026 memory: 40858 loss: 0.1179 loss_ce: 0.1179 2023/02/27 23:14:03 - mmengine - INFO - Epoch(train) [84][4700/5047] lr: 1.7660e-05 eta: 3 days, 9:03:30 time: 0.8021 data_time: 0.0021 memory: 40001 loss: 0.1176 loss_ce: 0.1176 2023/02/27 23:15:29 - mmengine - INFO - Epoch(train) [84][4800/5047] lr: 1.7660e-05 eta: 3 days, 9:02:01 time: 0.8535 data_time: 0.0020 memory: 40574 loss: 0.1096 loss_ce: 0.1096 2023/02/27 23:16:55 - mmengine - INFO - Epoch(train) [84][4900/5047] lr: 1.7660e-05 eta: 3 days, 9:00:33 time: 0.8798 data_time: 0.0022 memory: 43289 loss: 0.1184 loss_ce: 0.1184 2023/02/27 23:18:22 - mmengine - INFO - Epoch(train) [84][5000/5047] lr: 1.7660e-05 eta: 3 days, 8:59:05 time: 0.9159 data_time: 0.0032 memory: 51731 loss: 0.1031 loss_ce: 0.1031 2023/02/27 23:19:02 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 23:19:02 - mmengine - INFO - Saving checkpoint at 84 epochs 2023/02/27 23:19:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 23:20:34 - mmengine - INFO - Epoch(train) [85][ 100/5047] lr: 1.7459e-05 eta: 3 days, 8:56:55 time: 0.9064 data_time: 0.0048 memory: 52863 loss: 0.1180 loss_ce: 0.1180 2023/02/27 23:22:00 - mmengine - INFO - Epoch(train) [85][ 200/5047] lr: 1.7459e-05 eta: 3 days, 8:55:26 time: 0.8890 data_time: 0.0023 memory: 42649 loss: 0.1125 loss_ce: 0.1125 2023/02/27 23:23:27 - mmengine - INFO - Epoch(train) [85][ 300/5047] lr: 1.7459e-05 eta: 3 days, 8:53:58 time: 0.8870 data_time: 0.0029 memory: 42024 loss: 0.1003 loss_ce: 0.1003 2023/02/27 23:24:55 - mmengine - INFO - Epoch(train) [85][ 400/5047] lr: 1.7459e-05 eta: 3 days, 8:52:31 time: 0.8711 data_time: 0.0028 memory: 48565 loss: 0.1066 loss_ce: 0.1066 2023/02/27 23:26:21 - mmengine - INFO - Epoch(train) [85][ 500/5047] lr: 1.7459e-05 eta: 3 days, 8:51:02 time: 0.8986 data_time: 0.0026 memory: 44956 loss: 0.1416 loss_ce: 0.1416 2023/02/27 23:27:48 - mmengine - INFO - Epoch(train) [85][ 600/5047] lr: 1.7459e-05 eta: 3 days, 8:49:34 time: 0.8801 data_time: 0.0026 memory: 42649 loss: 0.1095 loss_ce: 0.1095 2023/02/27 23:29:14 - mmengine - INFO - Epoch(train) [85][ 700/5047] lr: 1.7459e-05 eta: 3 days, 8:48:05 time: 0.8890 data_time: 0.0022 memory: 42336 loss: 0.1063 loss_ce: 0.1063 2023/02/27 23:30:39 - mmengine - INFO - Epoch(train) [85][ 800/5047] lr: 1.7459e-05 eta: 3 days, 8:46:35 time: 0.8160 data_time: 0.0027 memory: 41032 loss: 0.1102 loss_ce: 0.1102 2023/02/27 23:32:05 - mmengine - INFO - Epoch(train) [85][ 900/5047] lr: 1.7459e-05 eta: 3 days, 8:45:07 time: 0.9063 data_time: 0.0022 memory: 42649 loss: 0.1289 loss_ce: 0.1289 2023/02/27 23:33:30 - mmengine - INFO - Epoch(train) [85][1000/5047] lr: 1.7459e-05 eta: 3 days, 8:43:38 time: 0.8597 data_time: 0.0022 memory: 48948 loss: 0.1118 loss_ce: 0.1118 2023/02/27 23:34:15 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 23:34:56 - mmengine - INFO - Epoch(train) [85][1100/5047] lr: 1.7459e-05 eta: 3 days, 8:42:09 time: 0.9332 data_time: 0.0023 memory: 40627 loss: 0.1024 loss_ce: 0.1024 2023/02/27 23:36:21 - mmengine - INFO - Epoch(train) [85][1200/5047] lr: 1.7459e-05 eta: 3 days, 8:40:39 time: 0.8164 data_time: 0.0026 memory: 42649 loss: 0.1040 loss_ce: 0.1040 2023/02/27 23:37:48 - mmengine - INFO - Epoch(train) [85][1300/5047] lr: 1.7459e-05 eta: 3 days, 8:39:11 time: 0.8292 data_time: 0.0027 memory: 42559 loss: 0.1200 loss_ce: 0.1200 2023/02/27 23:39:15 - mmengine - INFO - Epoch(train) [85][1400/5047] lr: 1.7459e-05 eta: 3 days, 8:37:43 time: 0.8831 data_time: 0.0026 memory: 48948 loss: 0.1193 loss_ce: 0.1193 2023/02/27 23:40:40 - mmengine - INFO - Epoch(train) [85][1500/5047] lr: 1.7459e-05 eta: 3 days, 8:36:13 time: 0.8891 data_time: 0.0028 memory: 50500 loss: 0.1062 loss_ce: 0.1062 2023/02/27 23:42:07 - mmengine - INFO - Epoch(train) [85][1600/5047] lr: 1.7459e-05 eta: 3 days, 8:34:45 time: 0.8803 data_time: 0.0027 memory: 44498 loss: 0.1007 loss_ce: 0.1007 2023/02/27 23:43:32 - mmengine - INFO - Epoch(train) [85][1700/5047] lr: 1.7459e-05 eta: 3 days, 8:33:15 time: 0.8699 data_time: 0.0032 memory: 42024 loss: 0.1048 loss_ce: 0.1048 2023/02/27 23:44:57 - mmengine - INFO - Epoch(train) [85][1800/5047] lr: 1.7459e-05 eta: 3 days, 8:31:46 time: 0.7980 data_time: 0.0025 memory: 47074 loss: 0.1103 loss_ce: 0.1103 2023/02/27 23:46:22 - mmengine - INFO - Epoch(train) [85][1900/5047] lr: 1.7459e-05 eta: 3 days, 8:30:17 time: 0.8735 data_time: 0.0022 memory: 45217 loss: 0.1202 loss_ce: 0.1202 2023/02/27 23:47:48 - mmengine - INFO - Epoch(train) [85][2000/5047] lr: 1.7459e-05 eta: 3 days, 8:28:48 time: 0.9044 data_time: 0.0023 memory: 41122 loss: 0.1143 loss_ce: 0.1143 2023/02/27 23:48:32 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/27 23:49:13 - mmengine - INFO - Epoch(train) [85][2100/5047] lr: 1.7459e-05 eta: 3 days, 8:27:19 time: 0.8587 data_time: 0.0048 memory: 44278 loss: 0.1111 loss_ce: 0.1111 2023/02/27 23:50:40 - mmengine - INFO - Epoch(train) [85][2200/5047] lr: 1.7459e-05 eta: 3 days, 8:25:50 time: 0.8340 data_time: 0.0024 memory: 54232 loss: 0.1061 loss_ce: 0.1061 2023/02/27 23:52:09 - mmengine - INFO - Epoch(train) [85][2300/5047] lr: 1.7459e-05 eta: 3 days, 8:24:24 time: 0.8678 data_time: 0.0037 memory: 44617 loss: 0.1225 loss_ce: 0.1225 2023/02/27 23:53:36 - mmengine - INFO - Epoch(train) [85][2400/5047] lr: 1.7459e-05 eta: 3 days, 8:22:56 time: 0.8495 data_time: 0.0024 memory: 43289 loss: 0.1219 loss_ce: 0.1219 2023/02/27 23:55:02 - mmengine - INFO - Epoch(train) [85][2500/5047] lr: 1.7459e-05 eta: 3 days, 8:21:28 time: 0.8884 data_time: 0.0028 memory: 43440 loss: 0.1205 loss_ce: 0.1205 2023/02/27 23:56:28 - mmengine - INFO - Epoch(train) [85][2600/5047] lr: 1.7459e-05 eta: 3 days, 8:19:58 time: 0.8345 data_time: 0.0054 memory: 45302 loss: 0.1282 loss_ce: 0.1282 2023/02/27 23:57:54 - mmengine - INFO - Epoch(train) [85][2700/5047] lr: 1.7459e-05 eta: 3 days, 8:18:30 time: 0.8623 data_time: 0.0025 memory: 43852 loss: 0.1148 loss_ce: 0.1148 2023/02/27 23:59:20 - mmengine - INFO - Epoch(train) [85][2800/5047] lr: 1.7459e-05 eta: 3 days, 8:17:01 time: 0.8516 data_time: 0.0023 memory: 54137 loss: 0.1110 loss_ce: 0.1110 2023/02/28 00:00:46 - mmengine - INFO - Epoch(train) [85][2900/5047] lr: 1.7459e-05 eta: 3 days, 8:15:32 time: 0.8562 data_time: 0.0024 memory: 46355 loss: 0.1073 loss_ce: 0.1073 2023/02/28 00:02:11 - mmengine - INFO - Epoch(train) [85][3000/5047] lr: 1.7459e-05 eta: 3 days, 8:14:03 time: 0.8204 data_time: 0.0038 memory: 46621 loss: 0.0998 loss_ce: 0.0998 2023/02/28 00:02:57 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 00:03:39 - mmengine - INFO - Epoch(train) [85][3100/5047] lr: 1.7459e-05 eta: 3 days, 8:12:35 time: 0.8553 data_time: 0.0023 memory: 54242 loss: 0.1110 loss_ce: 0.1110 2023/02/28 00:05:05 - mmengine - INFO - Epoch(train) [85][3200/5047] lr: 1.7459e-05 eta: 3 days, 8:11:07 time: 0.8573 data_time: 0.0027 memory: 47074 loss: 0.1151 loss_ce: 0.1151 2023/02/28 00:06:30 - mmengine - INFO - Epoch(train) [85][3300/5047] lr: 1.7459e-05 eta: 3 days, 8:09:37 time: 0.8519 data_time: 0.0023 memory: 49302 loss: 0.1220 loss_ce: 0.1220 2023/02/28 00:07:57 - mmengine - INFO - Epoch(train) [85][3400/5047] lr: 1.7459e-05 eta: 3 days, 8:08:09 time: 0.9027 data_time: 0.0023 memory: 46167 loss: 0.1154 loss_ce: 0.1154 2023/02/28 00:09:22 - mmengine - INFO - Epoch(train) [85][3500/5047] lr: 1.7459e-05 eta: 3 days, 8:06:40 time: 0.8312 data_time: 0.0063 memory: 46005 loss: 0.1053 loss_ce: 0.1053 2023/02/28 00:10:49 - mmengine - INFO - Epoch(train) [85][3600/5047] lr: 1.7459e-05 eta: 3 days, 8:05:12 time: 0.8301 data_time: 0.0031 memory: 42336 loss: 0.1212 loss_ce: 0.1212 2023/02/28 00:12:15 - mmengine - INFO - Epoch(train) [85][3700/5047] lr: 1.7459e-05 eta: 3 days, 8:03:43 time: 0.8589 data_time: 0.0034 memory: 44496 loss: 0.1214 loss_ce: 0.1214 2023/02/28 00:13:39 - mmengine - INFO - Epoch(train) [85][3800/5047] lr: 1.7459e-05 eta: 3 days, 8:02:13 time: 0.8656 data_time: 0.0022 memory: 44537 loss: 0.1231 loss_ce: 0.1231 2023/02/28 00:15:07 - mmengine - INFO - Epoch(train) [85][3900/5047] lr: 1.7459e-05 eta: 3 days, 8:00:46 time: 0.8635 data_time: 0.0036 memory: 47074 loss: 0.1235 loss_ce: 0.1235 2023/02/28 00:16:33 - mmengine - INFO - Epoch(train) [85][4000/5047] lr: 1.7459e-05 eta: 3 days, 7:59:16 time: 0.8567 data_time: 0.0025 memory: 43684 loss: 0.1183 loss_ce: 0.1183 2023/02/28 00:17:16 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 00:17:59 - mmengine - INFO - Epoch(train) [85][4100/5047] lr: 1.7459e-05 eta: 3 days, 7:57:48 time: 0.8758 data_time: 0.0022 memory: 43201 loss: 0.1113 loss_ce: 0.1113 2023/02/28 00:19:25 - mmengine - INFO - Epoch(train) [85][4200/5047] lr: 1.7459e-05 eta: 3 days, 7:56:19 time: 0.9240 data_time: 0.0029 memory: 55485 loss: 0.1173 loss_ce: 0.1173 2023/02/28 00:20:53 - mmengine - INFO - Epoch(train) [85][4300/5047] lr: 1.7459e-05 eta: 3 days, 7:54:52 time: 0.9065 data_time: 0.0040 memory: 52866 loss: 0.1182 loss_ce: 0.1182 2023/02/28 00:22:18 - mmengine - INFO - Epoch(train) [85][4400/5047] lr: 1.7459e-05 eta: 3 days, 7:53:23 time: 0.8761 data_time: 0.0022 memory: 46546 loss: 0.1046 loss_ce: 0.1046 2023/02/28 00:23:45 - mmengine - INFO - Epoch(train) [85][4500/5047] lr: 1.7459e-05 eta: 3 days, 7:51:55 time: 0.8443 data_time: 0.0022 memory: 44956 loss: 0.1144 loss_ce: 0.1144 2023/02/28 00:25:12 - mmengine - INFO - Epoch(train) [85][4600/5047] lr: 1.7459e-05 eta: 3 days, 7:50:26 time: 0.8603 data_time: 0.0024 memory: 49537 loss: 0.0999 loss_ce: 0.0999 2023/02/28 00:26:39 - mmengine - INFO - Epoch(train) [85][4700/5047] lr: 1.7459e-05 eta: 3 days, 7:48:59 time: 0.8741 data_time: 0.0022 memory: 55562 loss: 0.1019 loss_ce: 0.1019 2023/02/28 00:28:06 - mmengine - INFO - Epoch(train) [85][4800/5047] lr: 1.7459e-05 eta: 3 days, 7:47:31 time: 0.8813 data_time: 0.0023 memory: 51043 loss: 0.1041 loss_ce: 0.1041 2023/02/28 00:29:33 - mmengine - INFO - Epoch(train) [85][4900/5047] lr: 1.7459e-05 eta: 3 days, 7:46:03 time: 0.8711 data_time: 0.0032 memory: 48055 loss: 0.1074 loss_ce: 0.1074 2023/02/28 00:30:59 - mmengine - INFO - Epoch(train) [85][5000/5047] lr: 1.7459e-05 eta: 3 days, 7:44:34 time: 0.8467 data_time: 0.0027 memory: 55562 loss: 0.1214 loss_ce: 0.1214 2023/02/28 00:31:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 00:31:39 - mmengine - INFO - Saving checkpoint at 85 epochs 2023/02/28 00:31:49 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 00:33:11 - mmengine - INFO - Epoch(train) [86][ 100/5047] lr: 1.7258e-05 eta: 3 days, 7:42:24 time: 0.8888 data_time: 0.0022 memory: 43270 loss: 0.1105 loss_ce: 0.1105 2023/02/28 00:34:38 - mmengine - INFO - Epoch(train) [86][ 200/5047] lr: 1.7258e-05 eta: 3 days, 7:40:56 time: 0.8605 data_time: 0.0045 memory: 50482 loss: 0.1073 loss_ce: 0.1073 2023/02/28 00:36:03 - mmengine - INFO - Epoch(train) [86][ 300/5047] lr: 1.7258e-05 eta: 3 days, 7:39:27 time: 0.8491 data_time: 0.0022 memory: 42643 loss: 0.1239 loss_ce: 0.1239 2023/02/28 00:37:29 - mmengine - INFO - Epoch(train) [86][ 400/5047] lr: 1.7258e-05 eta: 3 days, 7:37:58 time: 0.8249 data_time: 0.0052 memory: 41487 loss: 0.1146 loss_ce: 0.1146 2023/02/28 00:38:56 - mmengine - INFO - Epoch(train) [86][ 500/5047] lr: 1.7258e-05 eta: 3 days, 7:36:30 time: 0.8577 data_time: 0.0022 memory: 41345 loss: 0.1183 loss_ce: 0.1183 2023/02/28 00:40:22 - mmengine - INFO - Epoch(train) [86][ 600/5047] lr: 1.7258e-05 eta: 3 days, 7:35:01 time: 0.9023 data_time: 0.0043 memory: 47261 loss: 0.1190 loss_ce: 0.1190 2023/02/28 00:41:47 - mmengine - INFO - Epoch(train) [86][ 700/5047] lr: 1.7258e-05 eta: 3 days, 7:33:31 time: 0.8406 data_time: 0.0024 memory: 50906 loss: 0.1118 loss_ce: 0.1118 2023/02/28 00:43:13 - mmengine - INFO - Epoch(train) [86][ 800/5047] lr: 1.7258e-05 eta: 3 days, 7:32:03 time: 0.8989 data_time: 0.0022 memory: 47265 loss: 0.1040 loss_ce: 0.1040 2023/02/28 00:44:40 - mmengine - INFO - Epoch(train) [86][ 900/5047] lr: 1.7258e-05 eta: 3 days, 7:30:35 time: 0.9254 data_time: 0.0021 memory: 46047 loss: 0.1114 loss_ce: 0.1114 2023/02/28 00:46:05 - mmengine - INFO - Epoch(train) [86][1000/5047] lr: 1.7258e-05 eta: 3 days, 7:29:05 time: 0.8431 data_time: 0.0022 memory: 47447 loss: 0.1006 loss_ce: 0.1006 2023/02/28 00:46:09 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 00:47:31 - mmengine - INFO - Epoch(train) [86][1100/5047] lr: 1.7258e-05 eta: 3 days, 7:27:37 time: 0.8619 data_time: 0.0053 memory: 46346 loss: 0.1071 loss_ce: 0.1071 2023/02/28 00:49:00 - mmengine - INFO - Epoch(train) [86][1200/5047] lr: 1.7258e-05 eta: 3 days, 7:26:11 time: 0.9204 data_time: 0.0022 memory: 49224 loss: 0.1006 loss_ce: 0.1006 2023/02/28 00:50:26 - mmengine - INFO - Epoch(train) [86][1300/5047] lr: 1.7258e-05 eta: 3 days, 7:24:42 time: 0.9294 data_time: 0.0026 memory: 43280 loss: 0.1228 loss_ce: 0.1228 2023/02/28 00:51:52 - mmengine - INFO - Epoch(train) [86][1400/5047] lr: 1.7258e-05 eta: 3 days, 7:23:13 time: 0.8589 data_time: 0.0023 memory: 41724 loss: 0.1049 loss_ce: 0.1049 2023/02/28 00:53:17 - mmengine - INFO - Epoch(train) [86][1500/5047] lr: 1.7258e-05 eta: 3 days, 7:21:44 time: 0.9002 data_time: 0.0023 memory: 49242 loss: 0.1146 loss_ce: 0.1146 2023/02/28 00:54:44 - mmengine - INFO - Epoch(train) [86][1600/5047] lr: 1.7258e-05 eta: 3 days, 7:20:16 time: 0.8335 data_time: 0.0028 memory: 53387 loss: 0.1256 loss_ce: 0.1256 2023/02/28 00:56:09 - mmengine - INFO - Epoch(train) [86][1700/5047] lr: 1.7258e-05 eta: 3 days, 7:18:46 time: 0.8293 data_time: 0.0029 memory: 41724 loss: 0.1057 loss_ce: 0.1057 2023/02/28 00:57:37 - mmengine - INFO - Epoch(train) [86][1800/5047] lr: 1.7258e-05 eta: 3 days, 7:17:19 time: 0.9171 data_time: 0.0024 memory: 55562 loss: 0.0958 loss_ce: 0.0958 2023/02/28 00:59:03 - mmengine - INFO - Epoch(train) [86][1900/5047] lr: 1.7258e-05 eta: 3 days, 7:15:50 time: 0.8856 data_time: 0.0125 memory: 49334 loss: 0.1029 loss_ce: 0.1029 2023/02/28 01:00:28 - mmengine - INFO - Epoch(train) [86][2000/5047] lr: 1.7258e-05 eta: 3 days, 7:14:21 time: 0.8073 data_time: 0.0024 memory: 50906 loss: 0.0998 loss_ce: 0.0998 2023/02/28 01:00:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 01:01:54 - mmengine - INFO - Epoch(train) [86][2100/5047] lr: 1.7258e-05 eta: 3 days, 7:12:52 time: 0.8695 data_time: 0.0032 memory: 43947 loss: 0.1174 loss_ce: 0.1174 2023/02/28 01:03:20 - mmengine - INFO - Epoch(train) [86][2200/5047] lr: 1.7258e-05 eta: 3 days, 7:11:23 time: 0.8580 data_time: 0.0023 memory: 42649 loss: 0.1132 loss_ce: 0.1132 2023/02/28 01:04:46 - mmengine - INFO - Epoch(train) [86][2300/5047] lr: 1.7258e-05 eta: 3 days, 7:09:55 time: 0.8205 data_time: 0.0025 memory: 43947 loss: 0.1092 loss_ce: 0.1092 2023/02/28 01:06:12 - mmengine - INFO - Epoch(train) [86][2400/5047] lr: 1.7258e-05 eta: 3 days, 7:08:26 time: 0.8941 data_time: 0.0023 memory: 41676 loss: 0.1136 loss_ce: 0.1136 2023/02/28 01:07:40 - mmengine - INFO - Epoch(train) [86][2500/5047] lr: 1.7258e-05 eta: 3 days, 7:06:59 time: 0.8876 data_time: 0.0022 memory: 41104 loss: 0.1276 loss_ce: 0.1276 2023/02/28 01:09:07 - mmengine - INFO - Epoch(train) [86][2600/5047] lr: 1.7258e-05 eta: 3 days, 7:05:31 time: 0.8933 data_time: 0.0023 memory: 55298 loss: 0.1228 loss_ce: 0.1228 2023/02/28 01:10:33 - mmengine - INFO - Epoch(train) [86][2700/5047] lr: 1.7258e-05 eta: 3 days, 7:04:02 time: 0.8692 data_time: 0.0023 memory: 42024 loss: 0.1082 loss_ce: 0.1082 2023/02/28 01:11:58 - mmengine - INFO - Epoch(train) [86][2800/5047] lr: 1.7258e-05 eta: 3 days, 7:02:33 time: 0.8590 data_time: 0.0026 memory: 41252 loss: 0.1065 loss_ce: 0.1065 2023/02/28 01:13:24 - mmengine - INFO - Epoch(train) [86][2900/5047] lr: 1.7258e-05 eta: 3 days, 7:01:04 time: 0.8352 data_time: 0.0030 memory: 43613 loss: 0.1091 loss_ce: 0.1091 2023/02/28 01:14:50 - mmengine - INFO - Epoch(train) [86][3000/5047] lr: 1.7258e-05 eta: 3 days, 6:59:35 time: 0.8520 data_time: 0.0022 memory: 44038 loss: 0.1298 loss_ce: 0.1298 2023/02/28 01:14:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 01:16:17 - mmengine - INFO - Epoch(train) [86][3100/5047] lr: 1.7258e-05 eta: 3 days, 6:58:07 time: 0.8752 data_time: 0.0023 memory: 48035 loss: 0.1072 loss_ce: 0.1072 2023/02/28 01:17:42 - mmengine - INFO - Epoch(train) [86][3200/5047] lr: 1.7258e-05 eta: 3 days, 6:56:38 time: 0.8695 data_time: 0.0025 memory: 45302 loss: 0.1273 loss_ce: 0.1273 2023/02/28 01:19:07 - mmengine - INFO - Epoch(train) [86][3300/5047] lr: 1.7258e-05 eta: 3 days, 6:55:08 time: 0.8381 data_time: 0.0023 memory: 42024 loss: 0.1134 loss_ce: 0.1134 2023/02/28 01:20:33 - mmengine - INFO - Epoch(train) [86][3400/5047] lr: 1.7258e-05 eta: 3 days, 6:53:40 time: 0.8975 data_time: 0.0021 memory: 44250 loss: 0.0963 loss_ce: 0.0963 2023/02/28 01:21:58 - mmengine - INFO - Epoch(train) [86][3500/5047] lr: 1.7258e-05 eta: 3 days, 6:52:11 time: 0.8884 data_time: 0.0022 memory: 41757 loss: 0.1226 loss_ce: 0.1226 2023/02/28 01:23:23 - mmengine - INFO - Epoch(train) [86][3600/5047] lr: 1.7258e-05 eta: 3 days, 6:50:41 time: 0.8864 data_time: 0.0051 memory: 42649 loss: 0.1063 loss_ce: 0.1063 2023/02/28 01:24:49 - mmengine - INFO - Epoch(train) [86][3700/5047] lr: 1.7258e-05 eta: 3 days, 6:49:12 time: 0.8752 data_time: 0.0043 memory: 43804 loss: 0.1121 loss_ce: 0.1121 2023/02/28 01:26:15 - mmengine - INFO - Epoch(train) [86][3800/5047] lr: 1.7258e-05 eta: 3 days, 6:47:43 time: 0.8578 data_time: 0.0033 memory: 42772 loss: 0.1137 loss_ce: 0.1137 2023/02/28 01:27:42 - mmengine - INFO - Epoch(train) [86][3900/5047] lr: 1.7258e-05 eta: 3 days, 6:46:15 time: 0.8538 data_time: 0.0051 memory: 45302 loss: 0.1263 loss_ce: 0.1263 2023/02/28 01:29:08 - mmengine - INFO - Epoch(train) [86][4000/5047] lr: 1.7258e-05 eta: 3 days, 6:44:47 time: 0.8346 data_time: 0.0023 memory: 43613 loss: 0.1043 loss_ce: 0.1043 2023/02/28 01:29:12 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 01:30:34 - mmengine - INFO - Epoch(train) [86][4100/5047] lr: 1.7258e-05 eta: 3 days, 6:43:19 time: 0.8852 data_time: 0.0082 memory: 44390 loss: 0.1065 loss_ce: 0.1065 2023/02/28 01:32:01 - mmengine - INFO - Epoch(train) [86][4200/5047] lr: 1.7258e-05 eta: 3 days, 6:41:50 time: 0.8276 data_time: 0.0033 memory: 55562 loss: 0.1102 loss_ce: 0.1102 2023/02/28 01:33:27 - mmengine - INFO - Epoch(train) [86][4300/5047] lr: 1.7258e-05 eta: 3 days, 6:40:21 time: 0.8769 data_time: 0.0022 memory: 41668 loss: 0.1265 loss_ce: 0.1265 2023/02/28 01:34:53 - mmengine - INFO - Epoch(train) [86][4400/5047] lr: 1.7258e-05 eta: 3 days, 6:38:53 time: 0.8125 data_time: 0.0023 memory: 43346 loss: 0.1204 loss_ce: 0.1204 2023/02/28 01:36:18 - mmengine - INFO - Epoch(train) [86][4500/5047] lr: 1.7258e-05 eta: 3 days, 6:37:23 time: 0.7886 data_time: 0.0024 memory: 55114 loss: 0.1252 loss_ce: 0.1252 2023/02/28 01:37:44 - mmengine - INFO - Epoch(train) [86][4600/5047] lr: 1.7258e-05 eta: 3 days, 6:35:55 time: 0.9195 data_time: 0.0022 memory: 44583 loss: 0.1044 loss_ce: 0.1044 2023/02/28 01:39:11 - mmengine - INFO - Epoch(train) [86][4700/5047] lr: 1.7258e-05 eta: 3 days, 6:34:27 time: 0.8497 data_time: 0.0025 memory: 40535 loss: 0.1057 loss_ce: 0.1057 2023/02/28 01:40:39 - mmengine - INFO - Epoch(train) [86][4800/5047] lr: 1.7258e-05 eta: 3 days, 6:32:59 time: 0.8800 data_time: 0.0051 memory: 40535 loss: 0.1003 loss_ce: 0.1003 2023/02/28 01:42:04 - mmengine - INFO - Epoch(train) [86][4900/5047] lr: 1.7258e-05 eta: 3 days, 6:31:30 time: 0.8609 data_time: 0.0025 memory: 50569 loss: 0.1324 loss_ce: 0.1324 2023/02/28 01:43:31 - mmengine - INFO - Epoch(train) [86][5000/5047] lr: 1.7258e-05 eta: 3 days, 6:30:02 time: 0.8530 data_time: 0.0024 memory: 45643 loss: 0.1098 loss_ce: 0.1098 2023/02/28 01:43:35 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 01:44:11 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 01:44:11 - mmengine - INFO - Saving checkpoint at 86 epochs 2023/02/28 01:45:42 - mmengine - INFO - Epoch(train) [87][ 100/5047] lr: 1.7057e-05 eta: 3 days, 6:27:51 time: 0.8541 data_time: 0.0044 memory: 54277 loss: 0.1170 loss_ce: 0.1170 2023/02/28 01:47:08 - mmengine - INFO - Epoch(train) [87][ 200/5047] lr: 1.7057e-05 eta: 3 days, 6:26:23 time: 0.8508 data_time: 0.0025 memory: 55114 loss: 0.1012 loss_ce: 0.1012 2023/02/28 01:48:34 - mmengine - INFO - Epoch(train) [87][ 300/5047] lr: 1.7057e-05 eta: 3 days, 6:24:54 time: 0.8455 data_time: 0.0077 memory: 43947 loss: 0.1208 loss_ce: 0.1208 2023/02/28 01:49:58 - mmengine - INFO - Epoch(train) [87][ 400/5047] lr: 1.7057e-05 eta: 3 days, 6:23:24 time: 0.8390 data_time: 0.0066 memory: 42649 loss: 0.0948 loss_ce: 0.0948 2023/02/28 01:51:23 - mmengine - INFO - Epoch(train) [87][ 500/5047] lr: 1.7057e-05 eta: 3 days, 6:21:54 time: 0.8706 data_time: 0.0022 memory: 45711 loss: 0.1145 loss_ce: 0.1145 2023/02/28 01:52:49 - mmengine - INFO - Epoch(train) [87][ 600/5047] lr: 1.7057e-05 eta: 3 days, 6:20:26 time: 0.8838 data_time: 0.0027 memory: 40227 loss: 0.1008 loss_ce: 0.1008 2023/02/28 01:54:16 - mmengine - INFO - Epoch(train) [87][ 700/5047] lr: 1.7057e-05 eta: 3 days, 6:18:58 time: 0.8576 data_time: 0.0040 memory: 55562 loss: 0.1188 loss_ce: 0.1188 2023/02/28 01:55:41 - mmengine - INFO - Epoch(train) [87][ 800/5047] lr: 1.7057e-05 eta: 3 days, 6:17:28 time: 0.8422 data_time: 0.0025 memory: 42336 loss: 0.1181 loss_ce: 0.1181 2023/02/28 01:57:06 - mmengine - INFO - Epoch(train) [87][ 900/5047] lr: 1.7057e-05 eta: 3 days, 6:15:59 time: 0.8487 data_time: 0.0046 memory: 45037 loss: 0.1253 loss_ce: 0.1253 2023/02/28 01:57:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 01:58:31 - mmengine - INFO - Epoch(train) [87][1000/5047] lr: 1.7057e-05 eta: 3 days, 6:14:30 time: 0.8541 data_time: 0.0024 memory: 42965 loss: 0.1282 loss_ce: 0.1282 2023/02/28 01:59:57 - mmengine - INFO - Epoch(train) [87][1100/5047] lr: 1.7057e-05 eta: 3 days, 6:13:01 time: 0.8563 data_time: 0.0022 memory: 50347 loss: 0.1075 loss_ce: 0.1075 2023/02/28 02:01:23 - mmengine - INFO - Epoch(train) [87][1200/5047] lr: 1.7057e-05 eta: 3 days, 6:11:33 time: 0.8697 data_time: 0.0023 memory: 45850 loss: 0.1044 loss_ce: 0.1044 2023/02/28 02:02:49 - mmengine - INFO - Epoch(train) [87][1300/5047] lr: 1.7057e-05 eta: 3 days, 6:10:04 time: 0.8590 data_time: 0.0027 memory: 41427 loss: 0.1183 loss_ce: 0.1183 2023/02/28 02:04:14 - mmengine - INFO - Epoch(train) [87][1400/5047] lr: 1.7057e-05 eta: 3 days, 6:08:34 time: 0.8984 data_time: 0.0022 memory: 45302 loss: 0.0976 loss_ce: 0.0976 2023/02/28 02:05:40 - mmengine - INFO - Epoch(train) [87][1500/5047] lr: 1.7057e-05 eta: 3 days, 6:07:05 time: 0.8323 data_time: 0.0026 memory: 55562 loss: 0.1063 loss_ce: 0.1063 2023/02/28 02:07:06 - mmengine - INFO - Epoch(train) [87][1600/5047] lr: 1.7057e-05 eta: 3 days, 6:05:37 time: 0.8372 data_time: 0.0027 memory: 42649 loss: 0.1088 loss_ce: 0.1088 2023/02/28 02:08:31 - mmengine - INFO - Epoch(train) [87][1700/5047] lr: 1.7057e-05 eta: 3 days, 6:04:08 time: 0.8423 data_time: 0.0024 memory: 46005 loss: 0.1365 loss_ce: 0.1365 2023/02/28 02:09:57 - mmengine - INFO - Epoch(train) [87][1800/5047] lr: 1.7057e-05 eta: 3 days, 6:02:39 time: 0.8082 data_time: 0.0026 memory: 43289 loss: 0.1181 loss_ce: 0.1181 2023/02/28 02:11:22 - mmengine - INFO - Epoch(train) [87][1900/5047] lr: 1.7057e-05 eta: 3 days, 6:01:10 time: 0.8640 data_time: 0.0029 memory: 45623 loss: 0.1210 loss_ce: 0.1210 2023/02/28 02:12:13 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 02:12:50 - mmengine - INFO - Epoch(train) [87][2000/5047] lr: 1.7057e-05 eta: 3 days, 5:59:43 time: 0.8630 data_time: 0.0026 memory: 49334 loss: 0.1195 loss_ce: 0.1195 2023/02/28 02:14:16 - mmengine - INFO - Epoch(train) [87][2100/5047] lr: 1.7057e-05 eta: 3 days, 5:58:14 time: 0.8484 data_time: 0.0023 memory: 43139 loss: 0.1141 loss_ce: 0.1141 2023/02/28 02:15:42 - mmengine - INFO - Epoch(train) [87][2200/5047] lr: 1.7057e-05 eta: 3 days, 5:56:45 time: 0.8737 data_time: 0.0022 memory: 55562 loss: 0.1153 loss_ce: 0.1153 2023/02/28 02:17:09 - mmengine - INFO - Epoch(train) [87][2300/5047] lr: 1.7057e-05 eta: 3 days, 5:55:18 time: 0.8648 data_time: 0.0023 memory: 43404 loss: 0.1003 loss_ce: 0.1003 2023/02/28 02:18:36 - mmengine - INFO - Epoch(train) [87][2400/5047] lr: 1.7057e-05 eta: 3 days, 5:53:49 time: 0.8744 data_time: 0.0077 memory: 46005 loss: 0.1218 loss_ce: 0.1218 2023/02/28 02:20:02 - mmengine - INFO - Epoch(train) [87][2500/5047] lr: 1.7057e-05 eta: 3 days, 5:52:21 time: 0.8699 data_time: 0.0022 memory: 46713 loss: 0.1185 loss_ce: 0.1185 2023/02/28 02:21:29 - mmengine - INFO - Epoch(train) [87][2600/5047] lr: 1.7057e-05 eta: 3 days, 5:50:53 time: 0.8920 data_time: 0.0026 memory: 43289 loss: 0.1172 loss_ce: 0.1172 2023/02/28 02:22:54 - mmengine - INFO - Epoch(train) [87][2700/5047] lr: 1.7057e-05 eta: 3 days, 5:49:24 time: 0.8691 data_time: 0.0023 memory: 50370 loss: 0.1188 loss_ce: 0.1188 2023/02/28 02:24:20 - mmengine - INFO - Epoch(train) [87][2800/5047] lr: 1.7057e-05 eta: 3 days, 5:47:55 time: 0.8553 data_time: 0.0024 memory: 42649 loss: 0.1235 loss_ce: 0.1235 2023/02/28 02:25:46 - mmengine - INFO - Epoch(train) [87][2900/5047] lr: 1.7057e-05 eta: 3 days, 5:46:26 time: 0.8355 data_time: 0.0028 memory: 43680 loss: 0.1043 loss_ce: 0.1043 2023/02/28 02:26:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 02:27:12 - mmengine - INFO - Epoch(train) [87][3000/5047] lr: 1.7057e-05 eta: 3 days, 5:44:58 time: 0.8221 data_time: 0.0024 memory: 43613 loss: 0.1258 loss_ce: 0.1258 2023/02/28 02:28:38 - mmengine - INFO - Epoch(train) [87][3100/5047] lr: 1.7057e-05 eta: 3 days, 5:43:29 time: 0.8605 data_time: 0.0031 memory: 43947 loss: 0.1004 loss_ce: 0.1004 2023/02/28 02:30:06 - mmengine - INFO - Epoch(train) [87][3200/5047] lr: 1.7057e-05 eta: 3 days, 5:42:02 time: 0.8612 data_time: 0.0021 memory: 51372 loss: 0.1261 loss_ce: 0.1261 2023/02/28 02:31:32 - mmengine - INFO - Epoch(train) [87][3300/5047] lr: 1.7057e-05 eta: 3 days, 5:40:33 time: 0.8674 data_time: 0.0027 memory: 41419 loss: 0.0996 loss_ce: 0.0996 2023/02/28 02:32:58 - mmengine - INFO - Epoch(train) [87][3400/5047] lr: 1.7057e-05 eta: 3 days, 5:39:05 time: 0.8479 data_time: 0.0021 memory: 40461 loss: 0.0966 loss_ce: 0.0966 2023/02/28 02:34:23 - mmengine - INFO - Epoch(train) [87][3500/5047] lr: 1.7057e-05 eta: 3 days, 5:37:35 time: 0.8505 data_time: 0.0022 memory: 42924 loss: 0.1243 loss_ce: 0.1243 2023/02/28 02:35:50 - mmengine - INFO - Epoch(train) [87][3600/5047] lr: 1.7057e-05 eta: 3 days, 5:36:08 time: 0.8954 data_time: 0.0027 memory: 41117 loss: 0.1115 loss_ce: 0.1115 2023/02/28 02:37:17 - mmengine - INFO - Epoch(train) [87][3700/5047] lr: 1.7057e-05 eta: 3 days, 5:34:39 time: 0.8505 data_time: 0.0022 memory: 42561 loss: 0.1139 loss_ce: 0.1139 2023/02/28 02:38:44 - mmengine - INFO - Epoch(train) [87][3800/5047] lr: 1.7057e-05 eta: 3 days, 5:33:12 time: 0.8876 data_time: 0.0024 memory: 42628 loss: 0.0979 loss_ce: 0.0979 2023/02/28 02:40:10 - mmengine - INFO - Epoch(train) [87][3900/5047] lr: 1.7057e-05 eta: 3 days, 5:31:43 time: 0.8667 data_time: 0.0022 memory: 40825 loss: 0.1141 loss_ce: 0.1141 2023/02/28 02:41:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 02:41:35 - mmengine - INFO - Epoch(train) [87][4000/5047] lr: 1.7057e-05 eta: 3 days, 5:30:14 time: 0.8144 data_time: 0.0023 memory: 42649 loss: 0.1175 loss_ce: 0.1175 2023/02/28 02:43:01 - mmengine - INFO - Epoch(train) [87][4100/5047] lr: 1.7057e-05 eta: 3 days, 5:28:45 time: 0.8917 data_time: 0.0027 memory: 54072 loss: 0.1171 loss_ce: 0.1171 2023/02/28 02:44:26 - mmengine - INFO - Epoch(train) [87][4200/5047] lr: 1.7057e-05 eta: 3 days, 5:27:16 time: 0.8819 data_time: 0.0028 memory: 44278 loss: 0.1166 loss_ce: 0.1166 2023/02/28 02:45:53 - mmengine - INFO - Epoch(train) [87][4300/5047] lr: 1.7057e-05 eta: 3 days, 5:25:47 time: 0.8375 data_time: 0.0026 memory: 48188 loss: 0.1254 loss_ce: 0.1254 2023/02/28 02:47:17 - mmengine - INFO - Epoch(train) [87][4400/5047] lr: 1.7057e-05 eta: 3 days, 5:24:18 time: 0.8320 data_time: 0.0022 memory: 42336 loss: 0.1181 loss_ce: 0.1181 2023/02/28 02:48:44 - mmengine - INFO - Epoch(train) [87][4500/5047] lr: 1.7057e-05 eta: 3 days, 5:22:49 time: 0.8800 data_time: 0.0027 memory: 55114 loss: 0.1008 loss_ce: 0.1008 2023/02/28 02:50:11 - mmengine - INFO - Epoch(train) [87][4600/5047] lr: 1.7057e-05 eta: 3 days, 5:21:22 time: 0.9078 data_time: 0.0028 memory: 50106 loss: 0.1052 loss_ce: 0.1052 2023/02/28 02:51:35 - mmengine - INFO - Epoch(train) [87][4700/5047] lr: 1.7057e-05 eta: 3 days, 5:19:52 time: 0.8959 data_time: 0.0022 memory: 43289 loss: 0.1191 loss_ce: 0.1191 2023/02/28 02:53:02 - mmengine - INFO - Epoch(train) [87][4800/5047] lr: 1.7057e-05 eta: 3 days, 5:18:24 time: 0.8729 data_time: 0.0022 memory: 50541 loss: 0.1157 loss_ce: 0.1157 2023/02/28 02:54:26 - mmengine - INFO - Epoch(train) [87][4900/5047] lr: 1.7057e-05 eta: 3 days, 5:16:54 time: 0.8710 data_time: 0.0024 memory: 46713 loss: 0.1317 loss_ce: 0.1317 2023/02/28 02:55:16 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 02:55:52 - mmengine - INFO - Epoch(train) [87][5000/5047] lr: 1.7057e-05 eta: 3 days, 5:15:25 time: 0.8749 data_time: 0.0027 memory: 44725 loss: 0.1193 loss_ce: 0.1193 2023/02/28 02:56:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 02:56:33 - mmengine - INFO - Saving checkpoint at 87 epochs 2023/02/28 02:58:04 - mmengine - INFO - Epoch(train) [88][ 100/5047] lr: 1.6856e-05 eta: 3 days, 5:13:15 time: 0.8060 data_time: 0.0035 memory: 53043 loss: 0.1175 loss_ce: 0.1175 2023/02/28 02:59:29 - mmengine - INFO - Epoch(train) [88][ 200/5047] lr: 1.6856e-05 eta: 3 days, 5:11:46 time: 0.8792 data_time: 0.0044 memory: 51308 loss: 0.1079 loss_ce: 0.1079 2023/02/28 03:00:56 - mmengine - INFO - Epoch(train) [88][ 300/5047] lr: 1.6856e-05 eta: 3 days, 5:10:18 time: 0.8463 data_time: 0.0029 memory: 52862 loss: 0.1024 loss_ce: 0.1024 2023/02/28 03:02:22 - mmengine - INFO - Epoch(train) [88][ 400/5047] lr: 1.6856e-05 eta: 3 days, 5:08:50 time: 0.8609 data_time: 0.0028 memory: 43947 loss: 0.1078 loss_ce: 0.1078 2023/02/28 03:03:46 - mmengine - INFO - Epoch(train) [88][ 500/5047] lr: 1.6856e-05 eta: 3 days, 5:07:19 time: 0.8332 data_time: 0.0077 memory: 42965 loss: 0.1119 loss_ce: 0.1119 2023/02/28 03:05:13 - mmengine - INFO - Epoch(train) [88][ 600/5047] lr: 1.6856e-05 eta: 3 days, 5:05:51 time: 0.8419 data_time: 0.0030 memory: 42203 loss: 0.1069 loss_ce: 0.1069 2023/02/28 03:06:40 - mmengine - INFO - Epoch(train) [88][ 700/5047] lr: 1.6856e-05 eta: 3 days, 5:04:24 time: 0.8618 data_time: 0.0024 memory: 55562 loss: 0.1091 loss_ce: 0.1091 2023/02/28 03:08:06 - mmengine - INFO - Epoch(train) [88][ 800/5047] lr: 1.6856e-05 eta: 3 days, 5:02:55 time: 0.9042 data_time: 0.0021 memory: 51755 loss: 0.1142 loss_ce: 0.1142 2023/02/28 03:09:34 - mmengine - INFO - Epoch(train) [88][ 900/5047] lr: 1.6856e-05 eta: 3 days, 5:01:28 time: 0.8746 data_time: 0.0032 memory: 52862 loss: 0.1110 loss_ce: 0.1110 2023/02/28 03:09:43 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 03:10:59 - mmengine - INFO - Epoch(train) [88][1000/5047] lr: 1.6856e-05 eta: 3 days, 4:59:59 time: 0.8708 data_time: 0.0037 memory: 40241 loss: 0.1309 loss_ce: 0.1309 2023/02/28 03:12:25 - mmengine - INFO - Epoch(train) [88][1100/5047] lr: 1.6856e-05 eta: 3 days, 4:58:30 time: 0.8332 data_time: 0.0023 memory: 44617 loss: 0.1080 loss_ce: 0.1080 2023/02/28 03:13:50 - mmengine - INFO - Epoch(train) [88][1200/5047] lr: 1.6856e-05 eta: 3 days, 4:57:01 time: 0.8808 data_time: 0.0023 memory: 43947 loss: 0.1094 loss_ce: 0.1094 2023/02/28 03:15:16 - mmengine - INFO - Epoch(train) [88][1300/5047] lr: 1.6856e-05 eta: 3 days, 4:55:32 time: 0.8328 data_time: 0.0027 memory: 44496 loss: 0.0964 loss_ce: 0.0964 2023/02/28 03:16:41 - mmengine - INFO - Epoch(train) [88][1400/5047] lr: 1.6856e-05 eta: 3 days, 4:54:03 time: 0.8118 data_time: 0.0027 memory: 42718 loss: 0.1020 loss_ce: 0.1020 2023/02/28 03:18:08 - mmengine - INFO - Epoch(train) [88][1500/5047] lr: 1.6856e-05 eta: 3 days, 4:52:35 time: 0.8460 data_time: 0.0025 memory: 55562 loss: 0.1031 loss_ce: 0.1031 2023/02/28 03:19:34 - mmengine - INFO - Epoch(train) [88][1600/5047] lr: 1.6856e-05 eta: 3 days, 4:51:06 time: 0.8304 data_time: 0.0025 memory: 49235 loss: 0.1094 loss_ce: 0.1094 2023/02/28 03:21:00 - mmengine - INFO - Epoch(train) [88][1700/5047] lr: 1.6856e-05 eta: 3 days, 4:49:38 time: 0.8468 data_time: 0.0025 memory: 41419 loss: 0.0969 loss_ce: 0.0969 2023/02/28 03:22:25 - mmengine - INFO - Epoch(train) [88][1800/5047] lr: 1.6856e-05 eta: 3 days, 4:48:08 time: 0.8707 data_time: 0.0030 memory: 40825 loss: 0.1203 loss_ce: 0.1203 2023/02/28 03:23:50 - mmengine - INFO - Epoch(train) [88][1900/5047] lr: 1.6856e-05 eta: 3 days, 4:46:39 time: 0.8052 data_time: 0.0028 memory: 50431 loss: 0.1045 loss_ce: 0.1045 2023/02/28 03:23:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 03:25:15 - mmengine - INFO - Epoch(train) [88][2000/5047] lr: 1.6856e-05 eta: 3 days, 4:45:09 time: 0.8257 data_time: 0.0028 memory: 48948 loss: 0.1145 loss_ce: 0.1145 2023/02/28 03:26:41 - mmengine - INFO - Epoch(train) [88][2100/5047] lr: 1.6856e-05 eta: 3 days, 4:43:42 time: 0.8582 data_time: 0.0024 memory: 45302 loss: 0.1130 loss_ce: 0.1130 2023/02/28 03:28:07 - mmengine - INFO - Epoch(train) [88][2200/5047] lr: 1.6856e-05 eta: 3 days, 4:42:12 time: 0.8485 data_time: 0.0027 memory: 48149 loss: 0.1036 loss_ce: 0.1036 2023/02/28 03:29:32 - mmengine - INFO - Epoch(train) [88][2300/5047] lr: 1.6856e-05 eta: 3 days, 4:40:43 time: 0.8648 data_time: 0.0048 memory: 52387 loss: 0.1144 loss_ce: 0.1144 2023/02/28 03:30:59 - mmengine - INFO - Epoch(train) [88][2400/5047] lr: 1.6856e-05 eta: 3 days, 4:39:15 time: 0.8784 data_time: 0.0030 memory: 40241 loss: 0.1007 loss_ce: 0.1007 2023/02/28 03:32:27 - mmengine - INFO - Epoch(train) [88][2500/5047] lr: 1.6856e-05 eta: 3 days, 4:37:48 time: 0.8697 data_time: 0.0024 memory: 43289 loss: 0.1293 loss_ce: 0.1293 2023/02/28 03:33:53 - mmengine - INFO - Epoch(train) [88][2600/5047] lr: 1.6856e-05 eta: 3 days, 4:36:20 time: 0.8971 data_time: 0.0027 memory: 47300 loss: 0.1033 loss_ce: 0.1033 2023/02/28 03:35:18 - mmengine - INFO - Epoch(train) [88][2700/5047] lr: 1.6856e-05 eta: 3 days, 4:34:51 time: 0.8574 data_time: 0.0024 memory: 44121 loss: 0.1071 loss_ce: 0.1071 2023/02/28 03:36:45 - mmengine - INFO - Epoch(train) [88][2800/5047] lr: 1.6856e-05 eta: 3 days, 4:33:23 time: 0.8588 data_time: 0.0062 memory: 40460 loss: 0.1267 loss_ce: 0.1267 2023/02/28 03:38:12 - mmengine - INFO - Epoch(train) [88][2900/5047] lr: 1.6856e-05 eta: 3 days, 4:31:55 time: 0.8845 data_time: 0.0024 memory: 43613 loss: 0.1119 loss_ce: 0.1119 2023/02/28 03:38:21 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 03:39:38 - mmengine - INFO - Epoch(train) [88][3000/5047] lr: 1.6856e-05 eta: 3 days, 4:30:26 time: 0.8275 data_time: 0.0032 memory: 43327 loss: 0.1193 loss_ce: 0.1193 2023/02/28 03:41:04 - mmengine - INFO - Epoch(train) [88][3100/5047] lr: 1.6856e-05 eta: 3 days, 4:28:58 time: 0.8752 data_time: 0.0031 memory: 47447 loss: 0.1058 loss_ce: 0.1058 2023/02/28 03:42:29 - mmengine - INFO - Epoch(train) [88][3200/5047] lr: 1.6856e-05 eta: 3 days, 4:27:29 time: 0.8823 data_time: 0.0026 memory: 43613 loss: 0.1186 loss_ce: 0.1186 2023/02/28 03:43:56 - mmengine - INFO - Epoch(train) [88][3300/5047] lr: 1.6856e-05 eta: 3 days, 4:26:01 time: 0.8873 data_time: 0.0029 memory: 53213 loss: 0.1132 loss_ce: 0.1132 2023/02/28 03:45:22 - mmengine - INFO - Epoch(train) [88][3400/5047] lr: 1.6856e-05 eta: 3 days, 4:24:32 time: 0.8514 data_time: 0.0022 memory: 46532 loss: 0.1269 loss_ce: 0.1269 2023/02/28 03:46:48 - mmengine - INFO - Epoch(train) [88][3500/5047] lr: 1.6856e-05 eta: 3 days, 4:23:03 time: 0.8835 data_time: 0.0036 memory: 41074 loss: 0.1051 loss_ce: 0.1051 2023/02/28 03:48:14 - mmengine - INFO - Epoch(train) [88][3600/5047] lr: 1.6856e-05 eta: 3 days, 4:21:35 time: 0.8688 data_time: 0.0024 memory: 46088 loss: 0.1145 loss_ce: 0.1145 2023/02/28 03:49:42 - mmengine - INFO - Epoch(train) [88][3700/5047] lr: 1.6856e-05 eta: 3 days, 4:20:08 time: 0.8072 data_time: 0.0026 memory: 43491 loss: 0.1275 loss_ce: 0.1275 2023/02/28 03:51:08 - mmengine - INFO - Epoch(train) [88][3800/5047] lr: 1.6856e-05 eta: 3 days, 4:18:39 time: 0.8948 data_time: 0.0023 memory: 55089 loss: 0.1009 loss_ce: 0.1009 2023/02/28 03:52:32 - mmengine - INFO - Epoch(train) [88][3900/5047] lr: 1.6856e-05 eta: 3 days, 4:17:09 time: 0.8457 data_time: 0.0027 memory: 41724 loss: 0.1123 loss_ce: 0.1123 2023/02/28 03:52:41 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 03:53:58 - mmengine - INFO - Epoch(train) [88][4000/5047] lr: 1.6856e-05 eta: 3 days, 4:15:41 time: 0.8745 data_time: 0.0025 memory: 42024 loss: 0.1264 loss_ce: 0.1264 2023/02/28 03:55:24 - mmengine - INFO - Epoch(train) [88][4100/5047] lr: 1.6856e-05 eta: 3 days, 4:14:12 time: 0.8709 data_time: 0.0026 memory: 41724 loss: 0.1220 loss_ce: 0.1220 2023/02/28 03:56:49 - mmengine - INFO - Epoch(train) [88][4200/5047] lr: 1.6856e-05 eta: 3 days, 4:12:43 time: 0.8077 data_time: 0.0029 memory: 42336 loss: 0.0991 loss_ce: 0.0991 2023/02/28 03:58:15 - mmengine - INFO - Epoch(train) [88][4300/5047] lr: 1.6856e-05 eta: 3 days, 4:11:14 time: 0.8623 data_time: 0.0048 memory: 43947 loss: 0.1207 loss_ce: 0.1207 2023/02/28 03:59:41 - mmengine - INFO - Epoch(train) [88][4400/5047] lr: 1.6856e-05 eta: 3 days, 4:09:46 time: 0.7990 data_time: 0.0024 memory: 43557 loss: 0.1135 loss_ce: 0.1135 2023/02/28 04:01:08 - mmengine - INFO - Epoch(train) [88][4500/5047] lr: 1.6856e-05 eta: 3 days, 4:08:18 time: 0.8694 data_time: 0.0026 memory: 46875 loss: 0.1109 loss_ce: 0.1109 2023/02/28 04:02:33 - mmengine - INFO - Epoch(train) [88][4600/5047] lr: 1.6856e-05 eta: 3 days, 4:06:49 time: 0.8454 data_time: 0.0023 memory: 50460 loss: 0.1104 loss_ce: 0.1104 2023/02/28 04:04:00 - mmengine - INFO - Epoch(train) [88][4700/5047] lr: 1.6856e-05 eta: 3 days, 4:05:21 time: 0.8876 data_time: 0.0023 memory: 40535 loss: 0.1043 loss_ce: 0.1043 2023/02/28 04:05:28 - mmengine - INFO - Epoch(train) [88][4800/5047] lr: 1.6856e-05 eta: 3 days, 4:03:54 time: 0.8763 data_time: 0.0025 memory: 39398 loss: 0.1229 loss_ce: 0.1229 2023/02/28 04:06:55 - mmengine - INFO - Epoch(train) [88][4900/5047] lr: 1.6856e-05 eta: 3 days, 4:02:26 time: 0.8581 data_time: 0.0026 memory: 55562 loss: 0.1292 loss_ce: 0.1292 2023/02/28 04:07:05 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 04:08:23 - mmengine - INFO - Epoch(train) [88][5000/5047] lr: 1.6856e-05 eta: 3 days, 4:00:59 time: 0.8745 data_time: 0.0029 memory: 55562 loss: 0.1252 loss_ce: 0.1252 2023/02/28 04:09:02 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 04:09:02 - mmengine - INFO - Saving checkpoint at 88 epochs 2023/02/28 04:10:33 - mmengine - INFO - Epoch(train) [89][ 100/5047] lr: 1.6655e-05 eta: 3 days, 3:58:48 time: 0.8435 data_time: 0.0079 memory: 46713 loss: 0.1234 loss_ce: 0.1234 2023/02/28 04:12:00 - mmengine - INFO - Epoch(train) [89][ 200/5047] lr: 1.6655e-05 eta: 3 days, 3:57:20 time: 0.9096 data_time: 0.0026 memory: 50308 loss: 0.0987 loss_ce: 0.0987 2023/02/28 04:13:26 - mmengine - INFO - Epoch(train) [89][ 300/5047] lr: 1.6655e-05 eta: 3 days, 3:55:52 time: 0.8694 data_time: 0.0024 memory: 53771 loss: 0.0995 loss_ce: 0.0995 2023/02/28 04:14:53 - mmengine - INFO - Epoch(train) [89][ 400/5047] lr: 1.6655e-05 eta: 3 days, 3:54:24 time: 0.8908 data_time: 0.0021 memory: 40293 loss: 0.1144 loss_ce: 0.1144 2023/02/28 04:16:19 - mmengine - INFO - Epoch(train) [89][ 500/5047] lr: 1.6655e-05 eta: 3 days, 3:52:55 time: 0.8587 data_time: 0.0023 memory: 41724 loss: 0.1123 loss_ce: 0.1123 2023/02/28 04:17:44 - mmengine - INFO - Epoch(train) [89][ 600/5047] lr: 1.6655e-05 eta: 3 days, 3:51:26 time: 0.8309 data_time: 0.0023 memory: 41323 loss: 0.1129 loss_ce: 0.1129 2023/02/28 04:19:11 - mmengine - INFO - Epoch(train) [89][ 700/5047] lr: 1.6655e-05 eta: 3 days, 3:49:58 time: 0.8959 data_time: 0.0050 memory: 44138 loss: 0.0890 loss_ce: 0.0890 2023/02/28 04:20:35 - mmengine - INFO - Epoch(train) [89][ 800/5047] lr: 1.6655e-05 eta: 3 days, 3:48:28 time: 0.8650 data_time: 0.0028 memory: 43289 loss: 0.1081 loss_ce: 0.1081 2023/02/28 04:21:30 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 04:22:01 - mmengine - INFO - Epoch(train) [89][ 900/5047] lr: 1.6655e-05 eta: 3 days, 3:47:00 time: 0.8836 data_time: 0.0041 memory: 55562 loss: 0.1133 loss_ce: 0.1133 2023/02/28 04:23:27 - mmengine - INFO - Epoch(train) [89][1000/5047] lr: 1.6655e-05 eta: 3 days, 3:45:31 time: 0.8439 data_time: 0.0043 memory: 45302 loss: 0.1011 loss_ce: 0.1011 2023/02/28 04:24:53 - mmengine - INFO - Epoch(train) [89][1100/5047] lr: 1.6655e-05 eta: 3 days, 3:44:03 time: 0.8393 data_time: 0.0027 memory: 52792 loss: 0.0961 loss_ce: 0.0961 2023/02/28 04:26:20 - mmengine - INFO - Epoch(train) [89][1200/5047] lr: 1.6655e-05 eta: 3 days, 3:42:34 time: 0.8956 data_time: 0.0023 memory: 55535 loss: 0.1174 loss_ce: 0.1174 2023/02/28 04:27:45 - mmengine - INFO - Epoch(train) [89][1300/5047] lr: 1.6655e-05 eta: 3 days, 3:41:05 time: 0.8909 data_time: 0.0027 memory: 46005 loss: 0.1158 loss_ce: 0.1158 2023/02/28 04:29:12 - mmengine - INFO - Epoch(train) [89][1400/5047] lr: 1.6655e-05 eta: 3 days, 3:39:37 time: 0.8663 data_time: 0.0027 memory: 40535 loss: 0.0982 loss_ce: 0.0982 2023/02/28 04:30:38 - mmengine - INFO - Epoch(train) [89][1500/5047] lr: 1.6655e-05 eta: 3 days, 3:38:09 time: 0.8672 data_time: 0.0022 memory: 43613 loss: 0.1160 loss_ce: 0.1160 2023/02/28 04:32:03 - mmengine - INFO - Epoch(train) [89][1600/5047] lr: 1.6655e-05 eta: 3 days, 3:36:40 time: 0.8630 data_time: 0.0026 memory: 54276 loss: 0.1263 loss_ce: 0.1263 2023/02/28 04:33:30 - mmengine - INFO - Epoch(train) [89][1700/5047] lr: 1.6655e-05 eta: 3 days, 3:35:12 time: 0.9726 data_time: 0.0025 memory: 55487 loss: 0.1108 loss_ce: 0.1108 2023/02/28 04:34:55 - mmengine - INFO - Epoch(train) [89][1800/5047] lr: 1.6655e-05 eta: 3 days, 3:33:42 time: 0.8481 data_time: 0.0024 memory: 41122 loss: 0.1181 loss_ce: 0.1181 2023/02/28 04:35:51 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 04:36:22 - mmengine - INFO - Epoch(train) [89][1900/5047] lr: 1.6655e-05 eta: 3 days, 3:32:15 time: 0.9144 data_time: 0.0040 memory: 52953 loss: 0.1143 loss_ce: 0.1143 2023/02/28 04:37:49 - mmengine - INFO - Epoch(train) [89][2000/5047] lr: 1.6655e-05 eta: 3 days, 3:30:47 time: 0.8344 data_time: 0.0023 memory: 55562 loss: 0.1189 loss_ce: 0.1189 2023/02/28 04:39:14 - mmengine - INFO - Epoch(train) [89][2100/5047] lr: 1.6655e-05 eta: 3 days, 3:29:18 time: 0.8374 data_time: 0.0022 memory: 43709 loss: 0.1280 loss_ce: 0.1280 2023/02/28 04:40:40 - mmengine - INFO - Epoch(train) [89][2200/5047] lr: 1.6655e-05 eta: 3 days, 3:27:49 time: 0.8856 data_time: 0.0026 memory: 48188 loss: 0.1106 loss_ce: 0.1106 2023/02/28 04:42:05 - mmengine - INFO - Epoch(train) [89][2300/5047] lr: 1.6655e-05 eta: 3 days, 3:26:20 time: 0.8508 data_time: 0.0023 memory: 48188 loss: 0.1056 loss_ce: 0.1056 2023/02/28 04:43:31 - mmengine - INFO - Epoch(train) [89][2400/5047] lr: 1.6655e-05 eta: 3 days, 3:24:52 time: 0.8463 data_time: 0.0022 memory: 42469 loss: 0.1044 loss_ce: 0.1044 2023/02/28 04:44:57 - mmengine - INFO - Epoch(train) [89][2500/5047] lr: 1.6655e-05 eta: 3 days, 3:23:23 time: 0.8835 data_time: 0.0022 memory: 49240 loss: 0.1230 loss_ce: 0.1230 2023/02/28 04:46:24 - mmengine - INFO - Epoch(train) [89][2600/5047] lr: 1.6655e-05 eta: 3 days, 3:21:55 time: 0.8518 data_time: 0.0025 memory: 50906 loss: 0.1146 loss_ce: 0.1146 2023/02/28 04:47:48 - mmengine - INFO - Epoch(train) [89][2700/5047] lr: 1.6655e-05 eta: 3 days, 3:20:25 time: 0.8827 data_time: 0.0024 memory: 45302 loss: 0.1205 loss_ce: 0.1205 2023/02/28 04:49:13 - mmengine - INFO - Epoch(train) [89][2800/5047] lr: 1.6655e-05 eta: 3 days, 3:18:56 time: 0.8329 data_time: 0.0022 memory: 43289 loss: 0.1062 loss_ce: 0.1062 2023/02/28 04:50:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 04:50:39 - mmengine - INFO - Epoch(train) [89][2900/5047] lr: 1.6655e-05 eta: 3 days, 3:17:28 time: 0.9170 data_time: 0.0025 memory: 43289 loss: 0.1169 loss_ce: 0.1169 2023/02/28 04:52:05 - mmengine - INFO - Epoch(train) [89][3000/5047] lr: 1.6655e-05 eta: 3 days, 3:15:59 time: 0.8581 data_time: 0.0026 memory: 55562 loss: 0.1116 loss_ce: 0.1116 2023/02/28 04:53:31 - mmengine - INFO - Epoch(train) [89][3100/5047] lr: 1.6655e-05 eta: 3 days, 3:14:31 time: 0.8690 data_time: 0.0047 memory: 46794 loss: 0.0996 loss_ce: 0.0996 2023/02/28 04:54:57 - mmengine - INFO - Epoch(train) [89][3200/5047] lr: 1.6655e-05 eta: 3 days, 3:13:02 time: 0.8077 data_time: 0.0022 memory: 43947 loss: 0.0972 loss_ce: 0.0972 2023/02/28 04:56:23 - mmengine - INFO - Epoch(train) [89][3300/5047] lr: 1.6655e-05 eta: 3 days, 3:11:33 time: 0.8356 data_time: 0.0023 memory: 39681 loss: 0.1139 loss_ce: 0.1139 2023/02/28 04:57:51 - mmengine - INFO - Epoch(train) [89][3400/5047] lr: 1.6655e-05 eta: 3 days, 3:10:07 time: 0.8586 data_time: 0.0023 memory: 40535 loss: 0.0989 loss_ce: 0.0989 2023/02/28 04:59:19 - mmengine - INFO - Epoch(train) [89][3500/5047] lr: 1.6655e-05 eta: 3 days, 3:08:39 time: 0.8763 data_time: 0.0023 memory: 46713 loss: 0.1221 loss_ce: 0.1221 2023/02/28 05:00:45 - mmengine - INFO - Epoch(train) [89][3600/5047] lr: 1.6655e-05 eta: 3 days, 3:07:11 time: 0.8746 data_time: 0.0026 memory: 41827 loss: 0.0979 loss_ce: 0.0979 2023/02/28 05:02:11 - mmengine - INFO - Epoch(train) [89][3700/5047] lr: 1.6655e-05 eta: 3 days, 3:05:42 time: 0.8485 data_time: 0.0025 memory: 44956 loss: 0.1135 loss_ce: 0.1135 2023/02/28 05:03:37 - mmengine - INFO - Epoch(train) [89][3800/5047] lr: 1.6655e-05 eta: 3 days, 3:04:14 time: 0.8584 data_time: 0.0031 memory: 43027 loss: 0.1150 loss_ce: 0.1150 2023/02/28 05:04:32 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 05:05:02 - mmengine - INFO - Epoch(train) [89][3900/5047] lr: 1.6655e-05 eta: 3 days, 3:02:45 time: 0.8349 data_time: 0.0025 memory: 42740 loss: 0.1232 loss_ce: 0.1232 2023/02/28 05:06:29 - mmengine - INFO - Epoch(train) [89][4000/5047] lr: 1.6655e-05 eta: 3 days, 3:01:17 time: 0.8886 data_time: 0.0026 memory: 55114 loss: 0.1035 loss_ce: 0.1035 2023/02/28 05:07:56 - mmengine - INFO - Epoch(train) [89][4100/5047] lr: 1.6655e-05 eta: 3 days, 2:59:49 time: 0.7958 data_time: 0.0038 memory: 55562 loss: 0.1026 loss_ce: 0.1026 2023/02/28 05:09:23 - mmengine - INFO - Epoch(train) [89][4200/5047] lr: 1.6655e-05 eta: 3 days, 2:58:21 time: 0.7949 data_time: 0.0023 memory: 51251 loss: 0.1182 loss_ce: 0.1182 2023/02/28 05:10:48 - mmengine - INFO - Epoch(train) [89][4300/5047] lr: 1.6655e-05 eta: 3 days, 2:56:52 time: 0.8885 data_time: 0.0024 memory: 39562 loss: 0.1015 loss_ce: 0.1015 2023/02/28 05:12:14 - mmengine - INFO - Epoch(train) [89][4400/5047] lr: 1.6655e-05 eta: 3 days, 2:55:24 time: 0.8505 data_time: 0.0025 memory: 48796 loss: 0.1150 loss_ce: 0.1150 2023/02/28 05:13:41 - mmengine - INFO - Epoch(train) [89][4500/5047] lr: 1.6655e-05 eta: 3 days, 2:53:56 time: 0.8689 data_time: 0.0023 memory: 41488 loss: 0.1125 loss_ce: 0.1125 2023/02/28 05:15:06 - mmengine - INFO - Epoch(train) [89][4600/5047] lr: 1.6655e-05 eta: 3 days, 2:52:27 time: 0.8101 data_time: 0.0032 memory: 41724 loss: 0.1162 loss_ce: 0.1162 2023/02/28 05:16:32 - mmengine - INFO - Epoch(train) [89][4700/5047] lr: 1.6655e-05 eta: 3 days, 2:50:58 time: 0.8489 data_time: 0.0032 memory: 43613 loss: 0.1063 loss_ce: 0.1063 2023/02/28 05:17:56 - mmengine - INFO - Epoch(train) [89][4800/5047] lr: 1.6655e-05 eta: 3 days, 2:49:28 time: 0.8764 data_time: 0.0034 memory: 43613 loss: 0.1142 loss_ce: 0.1142 2023/02/28 05:18:51 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 05:19:22 - mmengine - INFO - Epoch(train) [89][4900/5047] lr: 1.6655e-05 eta: 3 days, 2:48:00 time: 0.8497 data_time: 0.0047 memory: 41724 loss: 0.1208 loss_ce: 0.1208 2023/02/28 05:20:48 - mmengine - INFO - Epoch(train) [89][5000/5047] lr: 1.6655e-05 eta: 3 days, 2:46:31 time: 0.8821 data_time: 0.0042 memory: 42991 loss: 0.0994 loss_ce: 0.0994 2023/02/28 05:21:28 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 05:21:28 - mmengine - INFO - Saving checkpoint at 89 epochs 2023/02/28 05:23:00 - mmengine - INFO - Epoch(train) [90][ 100/5047] lr: 1.6454e-05 eta: 3 days, 2:44:21 time: 0.8848 data_time: 0.0023 memory: 55562 loss: 0.1108 loss_ce: 0.1108 2023/02/28 05:24:26 - mmengine - INFO - Epoch(train) [90][ 200/5047] lr: 1.6454e-05 eta: 3 days, 2:42:53 time: 0.8673 data_time: 0.0030 memory: 42024 loss: 0.1036 loss_ce: 0.1036 2023/02/28 05:25:51 - mmengine - INFO - Epoch(train) [90][ 300/5047] lr: 1.6454e-05 eta: 3 days, 2:41:24 time: 0.8875 data_time: 0.0025 memory: 37942 loss: 0.1091 loss_ce: 0.1091 2023/02/28 05:27:19 - mmengine - INFO - Epoch(train) [90][ 400/5047] lr: 1.6454e-05 eta: 3 days, 2:39:56 time: 0.8747 data_time: 0.0025 memory: 45876 loss: 0.1173 loss_ce: 0.1173 2023/02/28 05:28:44 - mmengine - INFO - Epoch(train) [90][ 500/5047] lr: 1.6454e-05 eta: 3 days, 2:38:28 time: 0.8333 data_time: 0.0057 memory: 45302 loss: 0.1040 loss_ce: 0.1040 2023/02/28 05:30:10 - mmengine - INFO - Epoch(train) [90][ 600/5047] lr: 1.6454e-05 eta: 3 days, 2:36:59 time: 0.8731 data_time: 0.0024 memory: 44278 loss: 0.0992 loss_ce: 0.0992 2023/02/28 05:31:36 - mmengine - INFO - Epoch(train) [90][ 700/5047] lr: 1.6454e-05 eta: 3 days, 2:35:31 time: 0.9107 data_time: 0.0023 memory: 42602 loss: 0.1068 loss_ce: 0.1068 2023/02/28 05:33:05 - mmengine - INFO - Epoch(train) [90][ 800/5047] lr: 1.6454e-05 eta: 3 days, 2:34:04 time: 0.8989 data_time: 0.0027 memory: 47813 loss: 0.1218 loss_ce: 0.1218 2023/02/28 05:33:20 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 05:34:30 - mmengine - INFO - Epoch(train) [90][ 900/5047] lr: 1.6454e-05 eta: 3 days, 2:32:35 time: 0.8322 data_time: 0.0027 memory: 45642 loss: 0.1140 loss_ce: 0.1140 2023/02/28 05:35:57 - mmengine - INFO - Epoch(train) [90][1000/5047] lr: 1.6454e-05 eta: 3 days, 2:31:07 time: 0.8430 data_time: 0.0024 memory: 51507 loss: 0.1103 loss_ce: 0.1103 2023/02/28 05:37:23 - mmengine - INFO - Epoch(train) [90][1100/5047] lr: 1.6454e-05 eta: 3 days, 2:29:39 time: 0.8472 data_time: 0.0035 memory: 42336 loss: 0.1041 loss_ce: 0.1041 2023/02/28 05:38:48 - mmengine - INFO - Epoch(train) [90][1200/5047] lr: 1.6454e-05 eta: 3 days, 2:28:10 time: 0.8814 data_time: 0.0023 memory: 39439 loss: 0.1093 loss_ce: 0.1093 2023/02/28 05:40:12 - mmengine - INFO - Epoch(train) [90][1300/5047] lr: 1.6454e-05 eta: 3 days, 2:26:40 time: 0.8130 data_time: 0.0050 memory: 44956 loss: 0.1300 loss_ce: 0.1300 2023/02/28 05:41:38 - mmengine - INFO - Epoch(train) [90][1400/5047] lr: 1.6454e-05 eta: 3 days, 2:25:11 time: 0.8455 data_time: 0.0023 memory: 46942 loss: 0.1245 loss_ce: 0.1245 2023/02/28 05:43:04 - mmengine - INFO - Epoch(train) [90][1500/5047] lr: 1.6454e-05 eta: 3 days, 2:23:43 time: 0.8333 data_time: 0.0022 memory: 53387 loss: 0.1070 loss_ce: 0.1070 2023/02/28 05:44:30 - mmengine - INFO - Epoch(train) [90][1600/5047] lr: 1.6454e-05 eta: 3 days, 2:22:14 time: 0.8746 data_time: 0.0043 memory: 55562 loss: 0.1292 loss_ce: 0.1292 2023/02/28 05:45:57 - mmengine - INFO - Epoch(train) [90][1700/5047] lr: 1.6454e-05 eta: 3 days, 2:20:47 time: 0.9345 data_time: 0.0027 memory: 54072 loss: 0.1127 loss_ce: 0.1127 2023/02/28 05:47:23 - mmengine - INFO - Epoch(train) [90][1800/5047] lr: 1.6454e-05 eta: 3 days, 2:19:18 time: 0.8250 data_time: 0.0028 memory: 41255 loss: 0.0942 loss_ce: 0.0942 2023/02/28 05:47:38 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 05:48:49 - mmengine - INFO - Epoch(train) [90][1900/5047] lr: 1.6454e-05 eta: 3 days, 2:17:50 time: 0.8275 data_time: 0.0023 memory: 42965 loss: 0.1109 loss_ce: 0.1109 2023/02/28 05:50:13 - mmengine - INFO - Epoch(train) [90][2000/5047] lr: 1.6454e-05 eta: 3 days, 2:16:20 time: 0.8476 data_time: 0.0023 memory: 43289 loss: 0.1154 loss_ce: 0.1154 2023/02/28 05:51:39 - mmengine - INFO - Epoch(train) [90][2100/5047] lr: 1.6454e-05 eta: 3 days, 2:14:51 time: 0.8619 data_time: 0.0025 memory: 42311 loss: 0.1174 loss_ce: 0.1174 2023/02/28 05:53:04 - mmengine - INFO - Epoch(train) [90][2200/5047] lr: 1.6454e-05 eta: 3 days, 2:13:22 time: 0.8204 data_time: 0.0024 memory: 47037 loss: 0.1111 loss_ce: 0.1111 2023/02/28 05:54:31 - mmengine - INFO - Epoch(train) [90][2300/5047] lr: 1.6454e-05 eta: 3 days, 2:11:55 time: 0.8318 data_time: 0.0024 memory: 48948 loss: 0.1290 loss_ce: 0.1290 2023/02/28 05:55:57 - mmengine - INFO - Epoch(train) [90][2400/5047] lr: 1.6454e-05 eta: 3 days, 2:10:26 time: 0.8038 data_time: 0.0031 memory: 42965 loss: 0.1253 loss_ce: 0.1253 2023/02/28 05:57:22 - mmengine - INFO - Epoch(train) [90][2500/5047] lr: 1.6454e-05 eta: 3 days, 2:08:57 time: 0.8817 data_time: 0.0023 memory: 49373 loss: 0.1033 loss_ce: 0.1033 2023/02/28 05:58:48 - mmengine - INFO - Epoch(train) [90][2600/5047] lr: 1.6454e-05 eta: 3 days, 2:07:28 time: 0.8796 data_time: 0.0022 memory: 40535 loss: 0.1210 loss_ce: 0.1210 2023/02/28 06:00:14 - mmengine - INFO - Epoch(train) [90][2700/5047] lr: 1.6454e-05 eta: 3 days, 2:05:59 time: 0.8949 data_time: 0.0022 memory: 48053 loss: 0.0987 loss_ce: 0.0987 2023/02/28 06:01:39 - mmengine - INFO - Epoch(train) [90][2800/5047] lr: 1.6454e-05 eta: 3 days, 2:04:31 time: 0.8329 data_time: 0.0024 memory: 55562 loss: 0.0940 loss_ce: 0.0940 2023/02/28 06:01:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 06:03:07 - mmengine - INFO - Epoch(train) [90][2900/5047] lr: 1.6454e-05 eta: 3 days, 2:03:03 time: 0.8849 data_time: 0.0027 memory: 54205 loss: 0.1126 loss_ce: 0.1126 2023/02/28 06:04:32 - mmengine - INFO - Epoch(train) [90][3000/5047] lr: 1.6454e-05 eta: 3 days, 2:01:35 time: 0.8145 data_time: 0.0029 memory: 42024 loss: 0.1159 loss_ce: 0.1159 2023/02/28 06:05:59 - mmengine - INFO - Epoch(train) [90][3100/5047] lr: 1.6454e-05 eta: 3 days, 2:00:06 time: 0.9294 data_time: 0.0069 memory: 41719 loss: 0.1010 loss_ce: 0.1010 2023/02/28 06:07:25 - mmengine - INFO - Epoch(train) [90][3200/5047] lr: 1.6454e-05 eta: 3 days, 1:58:38 time: 0.8726 data_time: 0.0024 memory: 49845 loss: 0.1127 loss_ce: 0.1127 2023/02/28 06:08:51 - mmengine - INFO - Epoch(train) [90][3300/5047] lr: 1.6454e-05 eta: 3 days, 1:57:10 time: 0.8433 data_time: 0.0023 memory: 46009 loss: 0.1174 loss_ce: 0.1174 2023/02/28 06:10:17 - mmengine - INFO - Epoch(train) [90][3400/5047] lr: 1.6454e-05 eta: 3 days, 1:55:41 time: 0.8471 data_time: 0.0044 memory: 55562 loss: 0.1249 loss_ce: 0.1249 2023/02/28 06:11:43 - mmengine - INFO - Epoch(train) [90][3500/5047] lr: 1.6454e-05 eta: 3 days, 1:54:13 time: 0.8630 data_time: 0.0023 memory: 42991 loss: 0.0979 loss_ce: 0.0979 2023/02/28 06:13:09 - mmengine - INFO - Epoch(train) [90][3600/5047] lr: 1.6454e-05 eta: 3 days, 1:52:44 time: 0.8784 data_time: 0.0024 memory: 49048 loss: 0.0935 loss_ce: 0.0935 2023/02/28 06:14:35 - mmengine - INFO - Epoch(train) [90][3700/5047] lr: 1.6454e-05 eta: 3 days, 1:51:16 time: 0.8281 data_time: 0.0023 memory: 43263 loss: 0.1096 loss_ce: 0.1096 2023/02/28 06:16:00 - mmengine - INFO - Epoch(train) [90][3800/5047] lr: 1.6454e-05 eta: 3 days, 1:49:47 time: 0.8647 data_time: 0.0031 memory: 53364 loss: 0.1204 loss_ce: 0.1204 2023/02/28 06:16:14 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 06:17:27 - mmengine - INFO - Epoch(train) [90][3900/5047] lr: 1.6454e-05 eta: 3 days, 1:48:19 time: 0.8913 data_time: 0.0032 memory: 55562 loss: 0.1110 loss_ce: 0.1110 2023/02/28 06:18:54 - mmengine - INFO - Epoch(train) [90][4000/5047] lr: 1.6454e-05 eta: 3 days, 1:46:51 time: 0.8820 data_time: 0.0024 memory: 48384 loss: 0.0997 loss_ce: 0.0997 2023/02/28 06:20:20 - mmengine - INFO - Epoch(train) [90][4100/5047] lr: 1.6454e-05 eta: 3 days, 1:45:23 time: 0.8364 data_time: 0.0026 memory: 48035 loss: 0.1239 loss_ce: 0.1239 2023/02/28 06:21:46 - mmengine - INFO - Epoch(train) [90][4200/5047] lr: 1.6454e-05 eta: 3 days, 1:43:54 time: 0.8598 data_time: 0.0024 memory: 49312 loss: 0.1212 loss_ce: 0.1212 2023/02/28 06:23:12 - mmengine - INFO - Epoch(train) [90][4300/5047] lr: 1.6454e-05 eta: 3 days, 1:42:26 time: 0.8801 data_time: 0.0025 memory: 53021 loss: 0.1171 loss_ce: 0.1171 2023/02/28 06:24:37 - mmengine - INFO - Epoch(train) [90][4400/5047] lr: 1.6454e-05 eta: 3 days, 1:40:57 time: 0.8846 data_time: 0.0056 memory: 48948 loss: 0.1072 loss_ce: 0.1072 2023/02/28 06:26:04 - mmengine - INFO - Epoch(train) [90][4500/5047] lr: 1.6454e-05 eta: 3 days, 1:39:29 time: 0.8564 data_time: 0.0033 memory: 54116 loss: 0.1166 loss_ce: 0.1166 2023/02/28 06:27:31 - mmengine - INFO - Epoch(train) [90][4600/5047] lr: 1.6454e-05 eta: 3 days, 1:38:01 time: 0.8998 data_time: 0.0025 memory: 43613 loss: 0.1104 loss_ce: 0.1104 2023/02/28 06:28:58 - mmengine - INFO - Epoch(train) [90][4700/5047] lr: 1.6454e-05 eta: 3 days, 1:36:34 time: 0.8475 data_time: 0.0025 memory: 51637 loss: 0.1060 loss_ce: 0.1060 2023/02/28 06:30:23 - mmengine - INFO - Epoch(train) [90][4800/5047] lr: 1.6454e-05 eta: 3 days, 1:35:04 time: 0.8114 data_time: 0.0023 memory: 44565 loss: 0.1181 loss_ce: 0.1181 2023/02/28 06:30:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 06:31:47 - mmengine - INFO - Epoch(train) [90][4900/5047] lr: 1.6454e-05 eta: 3 days, 1:33:35 time: 0.8699 data_time: 0.0025 memory: 42965 loss: 0.1148 loss_ce: 0.1148 2023/02/28 06:33:13 - mmengine - INFO - Epoch(train) [90][5000/5047] lr: 1.6454e-05 eta: 3 days, 1:32:06 time: 0.8543 data_time: 0.0025 memory: 45549 loss: 0.0978 loss_ce: 0.0978 2023/02/28 06:33:53 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 06:33:53 - mmengine - INFO - Saving checkpoint at 90 epochs 2023/02/28 06:35:23 - mmengine - INFO - Epoch(train) [91][ 100/5047] lr: 1.6253e-05 eta: 3 days, 1:29:55 time: 0.8474 data_time: 0.0023 memory: 46713 loss: 0.1221 loss_ce: 0.1221 2023/02/28 06:36:48 - mmengine - INFO - Epoch(train) [91][ 200/5047] lr: 1.6253e-05 eta: 3 days, 1:28:26 time: 0.8877 data_time: 0.0022 memory: 40825 loss: 0.1151 loss_ce: 0.1151 2023/02/28 06:38:14 - mmengine - INFO - Epoch(train) [91][ 300/5047] lr: 1.6253e-05 eta: 3 days, 1:26:58 time: 0.8930 data_time: 0.0025 memory: 49715 loss: 0.1070 loss_ce: 0.1070 2023/02/28 06:39:40 - mmengine - INFO - Epoch(train) [91][ 400/5047] lr: 1.6253e-05 eta: 3 days, 1:25:30 time: 0.8694 data_time: 0.0058 memory: 49334 loss: 0.1118 loss_ce: 0.1118 2023/02/28 06:41:06 - mmengine - INFO - Epoch(train) [91][ 500/5047] lr: 1.6253e-05 eta: 3 days, 1:24:01 time: 0.8241 data_time: 0.0033 memory: 55562 loss: 0.1215 loss_ce: 0.1215 2023/02/28 06:42:32 - mmengine - INFO - Epoch(train) [91][ 600/5047] lr: 1.6253e-05 eta: 3 days, 1:22:33 time: 0.8762 data_time: 0.0024 memory: 46005 loss: 0.1248 loss_ce: 0.1248 2023/02/28 06:43:59 - mmengine - INFO - Epoch(train) [91][ 700/5047] lr: 1.6253e-05 eta: 3 days, 1:21:05 time: 0.8685 data_time: 0.0043 memory: 51574 loss: 0.1149 loss_ce: 0.1149 2023/02/28 06:44:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 06:45:25 - mmengine - INFO - Epoch(train) [91][ 800/5047] lr: 1.6253e-05 eta: 3 days, 1:19:36 time: 0.8601 data_time: 0.0024 memory: 49241 loss: 0.1065 loss_ce: 0.1065 2023/02/28 06:46:50 - mmengine - INFO - Epoch(train) [91][ 900/5047] lr: 1.6253e-05 eta: 3 days, 1:18:08 time: 0.8438 data_time: 0.0022 memory: 43420 loss: 0.1174 loss_ce: 0.1174 2023/02/28 06:48:17 - mmengine - INFO - Epoch(train) [91][1000/5047] lr: 1.6253e-05 eta: 3 days, 1:16:40 time: 0.8989 data_time: 0.0024 memory: 44278 loss: 0.1142 loss_ce: 0.1142 2023/02/28 06:49:44 - mmengine - INFO - Epoch(train) [91][1100/5047] lr: 1.6253e-05 eta: 3 days, 1:15:12 time: 0.8846 data_time: 0.0022 memory: 43613 loss: 0.1060 loss_ce: 0.1060 2023/02/28 06:51:11 - mmengine - INFO - Epoch(train) [91][1200/5047] lr: 1.6253e-05 eta: 3 days, 1:13:44 time: 0.8695 data_time: 0.0036 memory: 39398 loss: 0.1113 loss_ce: 0.1113 2023/02/28 06:52:35 - mmengine - INFO - Epoch(train) [91][1300/5047] lr: 1.6253e-05 eta: 3 days, 1:12:14 time: 0.8700 data_time: 0.0025 memory: 43613 loss: 0.1140 loss_ce: 0.1140 2023/02/28 06:54:00 - mmengine - INFO - Epoch(train) [91][1400/5047] lr: 1.6253e-05 eta: 3 days, 1:10:45 time: 0.8561 data_time: 0.0025 memory: 45302 loss: 0.1251 loss_ce: 0.1251 2023/02/28 06:55:27 - mmengine - INFO - Epoch(train) [91][1500/5047] lr: 1.6253e-05 eta: 3 days, 1:09:17 time: 0.8493 data_time: 0.0024 memory: 49219 loss: 0.1200 loss_ce: 0.1200 2023/02/28 06:56:51 - mmengine - INFO - Epoch(train) [91][1600/5047] lr: 1.6253e-05 eta: 3 days, 1:07:48 time: 0.8261 data_time: 0.0025 memory: 46838 loss: 0.1141 loss_ce: 0.1141 2023/02/28 06:58:17 - mmengine - INFO - Epoch(train) [91][1700/5047] lr: 1.6253e-05 eta: 3 days, 1:06:19 time: 0.8273 data_time: 0.0028 memory: 48565 loss: 0.1221 loss_ce: 0.1221 2023/02/28 06:59:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 06:59:43 - mmengine - INFO - Epoch(train) [91][1800/5047] lr: 1.6253e-05 eta: 3 days, 1:04:51 time: 0.8419 data_time: 0.0028 memory: 50295 loss: 0.1130 loss_ce: 0.1130 2023/02/28 07:01:07 - mmengine - INFO - Epoch(train) [91][1900/5047] lr: 1.6253e-05 eta: 3 days, 1:03:21 time: 0.8257 data_time: 0.0026 memory: 41724 loss: 0.1267 loss_ce: 0.1267 2023/02/28 07:02:34 - mmengine - INFO - Epoch(train) [91][2000/5047] lr: 1.6253e-05 eta: 3 days, 1:01:54 time: 0.9122 data_time: 0.0022 memory: 44587 loss: 0.1103 loss_ce: 0.1103 2023/02/28 07:03:58 - mmengine - INFO - Epoch(train) [91][2100/5047] lr: 1.6253e-05 eta: 3 days, 1:00:24 time: 0.8865 data_time: 0.0022 memory: 45944 loss: 0.0986 loss_ce: 0.0986 2023/02/28 07:05:23 - mmengine - INFO - Epoch(train) [91][2200/5047] lr: 1.6253e-05 eta: 3 days, 0:58:55 time: 0.8374 data_time: 0.0025 memory: 42024 loss: 0.1114 loss_ce: 0.1114 2023/02/28 07:06:49 - mmengine - INFO - Epoch(train) [91][2300/5047] lr: 1.6253e-05 eta: 3 days, 0:57:26 time: 0.7970 data_time: 0.0029 memory: 44656 loss: 0.1264 loss_ce: 0.1264 2023/02/28 07:08:14 - mmengine - INFO - Epoch(train) [91][2400/5047] lr: 1.6253e-05 eta: 3 days, 0:55:57 time: 0.8342 data_time: 0.0067 memory: 45709 loss: 0.0993 loss_ce: 0.0993 2023/02/28 07:09:41 - mmengine - INFO - Epoch(train) [91][2500/5047] lr: 1.6253e-05 eta: 3 days, 0:54:29 time: 0.8098 data_time: 0.0024 memory: 54072 loss: 0.1108 loss_ce: 0.1108 2023/02/28 07:11:08 - mmengine - INFO - Epoch(train) [91][2600/5047] lr: 1.6253e-05 eta: 3 days, 0:53:02 time: 0.8465 data_time: 0.0025 memory: 44593 loss: 0.1028 loss_ce: 0.1028 2023/02/28 07:12:34 - mmengine - INFO - Epoch(train) [91][2700/5047] lr: 1.6253e-05 eta: 3 days, 0:51:33 time: 0.9466 data_time: 0.0026 memory: 42965 loss: 0.1199 loss_ce: 0.1199 2023/02/28 07:13:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 07:14:00 - mmengine - INFO - Epoch(train) [91][2800/5047] lr: 1.6253e-05 eta: 3 days, 0:50:05 time: 0.8200 data_time: 0.0029 memory: 44617 loss: 0.1163 loss_ce: 0.1163 2023/02/28 07:15:26 - mmengine - INFO - Epoch(train) [91][2900/5047] lr: 1.6253e-05 eta: 3 days, 0:48:37 time: 0.8842 data_time: 0.0024 memory: 43445 loss: 0.1222 loss_ce: 0.1222 2023/02/28 07:16:52 - mmengine - INFO - Epoch(train) [91][3000/5047] lr: 1.6253e-05 eta: 3 days, 0:47:08 time: 0.8487 data_time: 0.0023 memory: 40813 loss: 0.1176 loss_ce: 0.1176 2023/02/28 07:18:20 - mmengine - INFO - Epoch(train) [91][3100/5047] lr: 1.6253e-05 eta: 3 days, 0:45:41 time: 0.8747 data_time: 0.0083 memory: 45302 loss: 0.1147 loss_ce: 0.1147 2023/02/28 07:19:46 - mmengine - INFO - Epoch(train) [91][3200/5047] lr: 1.6253e-05 eta: 3 days, 0:44:13 time: 0.8791 data_time: 0.0023 memory: 51465 loss: 0.1112 loss_ce: 0.1112 2023/02/28 07:21:11 - mmengine - INFO - Epoch(train) [91][3300/5047] lr: 1.6253e-05 eta: 3 days, 0:42:44 time: 0.8301 data_time: 0.0024 memory: 43289 loss: 0.1060 loss_ce: 0.1060 2023/02/28 07:22:37 - mmengine - INFO - Epoch(train) [91][3400/5047] lr: 1.6253e-05 eta: 3 days, 0:41:16 time: 0.8714 data_time: 0.0023 memory: 51658 loss: 0.1208 loss_ce: 0.1208 2023/02/28 07:24:03 - mmengine - INFO - Epoch(train) [91][3500/5047] lr: 1.6253e-05 eta: 3 days, 0:39:47 time: 0.8381 data_time: 0.0027 memory: 44278 loss: 0.1027 loss_ce: 0.1027 2023/02/28 07:25:28 - mmengine - INFO - Epoch(train) [91][3600/5047] lr: 1.6253e-05 eta: 3 days, 0:38:18 time: 0.8579 data_time: 0.0025 memory: 43492 loss: 0.1005 loss_ce: 0.1005 2023/02/28 07:26:56 - mmengine - INFO - Epoch(train) [91][3700/5047] lr: 1.6253e-05 eta: 3 days, 0:36:51 time: 0.9325 data_time: 0.0023 memory: 52882 loss: 0.1179 loss_ce: 0.1179 2023/02/28 07:27:57 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 07:28:21 - mmengine - INFO - Epoch(train) [91][3800/5047] lr: 1.6253e-05 eta: 3 days, 0:35:22 time: 0.7842 data_time: 0.0024 memory: 42336 loss: 0.1092 loss_ce: 0.1092 2023/02/28 07:29:48 - mmengine - INFO - Epoch(train) [91][3900/5047] lr: 1.6253e-05 eta: 3 days, 0:33:54 time: 0.8494 data_time: 0.0022 memory: 48948 loss: 0.1291 loss_ce: 0.1291 2023/02/28 07:31:12 - mmengine - INFO - Epoch(train) [91][4000/5047] lr: 1.6253e-05 eta: 3 days, 0:32:25 time: 0.8418 data_time: 0.0027 memory: 55562 loss: 0.1063 loss_ce: 0.1063 2023/02/28 07:32:38 - mmengine - INFO - Epoch(train) [91][4100/5047] lr: 1.6253e-05 eta: 3 days, 0:30:56 time: 0.8260 data_time: 0.0028 memory: 42336 loss: 0.1019 loss_ce: 0.1019 2023/02/28 07:34:03 - mmengine - INFO - Epoch(train) [91][4200/5047] lr: 1.6253e-05 eta: 3 days, 0:29:27 time: 0.8498 data_time: 0.0048 memory: 41382 loss: 0.1056 loss_ce: 0.1056 2023/02/28 07:35:28 - mmengine - INFO - Epoch(train) [91][4300/5047] lr: 1.6253e-05 eta: 3 days, 0:27:58 time: 0.8762 data_time: 0.0032 memory: 42649 loss: 0.1137 loss_ce: 0.1137 2023/02/28 07:36:54 - mmengine - INFO - Epoch(train) [91][4400/5047] lr: 1.6253e-05 eta: 3 days, 0:26:30 time: 0.8446 data_time: 0.0026 memory: 46080 loss: 0.0945 loss_ce: 0.0945 2023/02/28 07:38:21 - mmengine - INFO - Epoch(train) [91][4500/5047] lr: 1.6253e-05 eta: 3 days, 0:25:02 time: 0.8660 data_time: 0.0022 memory: 41894 loss: 0.1214 loss_ce: 0.1214 2023/02/28 07:39:47 - mmengine - INFO - Epoch(train) [91][4600/5047] lr: 1.6253e-05 eta: 3 days, 0:23:33 time: 0.8523 data_time: 0.0028 memory: 48796 loss: 0.1182 loss_ce: 0.1182 2023/02/28 07:41:13 - mmengine - INFO - Epoch(train) [91][4700/5047] lr: 1.6253e-05 eta: 3 days, 0:22:05 time: 0.9021 data_time: 0.0027 memory: 48187 loss: 0.1053 loss_ce: 0.1053 2023/02/28 07:42:14 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 07:42:39 - mmengine - INFO - Epoch(train) [91][4800/5047] lr: 1.6253e-05 eta: 3 days, 0:20:37 time: 0.8442 data_time: 0.0023 memory: 42302 loss: 0.1152 loss_ce: 0.1152 2023/02/28 07:44:04 - mmengine - INFO - Epoch(train) [91][4900/5047] lr: 1.6253e-05 eta: 3 days, 0:19:08 time: 0.8549 data_time: 0.0022 memory: 44278 loss: 0.1070 loss_ce: 0.1070 2023/02/28 07:45:30 - mmengine - INFO - Epoch(train) [91][5000/5047] lr: 1.6253e-05 eta: 3 days, 0:17:39 time: 0.8542 data_time: 0.0029 memory: 44956 loss: 0.1190 loss_ce: 0.1190 2023/02/28 07:46:10 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 07:46:10 - mmengine - INFO - Saving checkpoint at 91 epochs 2023/02/28 07:47:42 - mmengine - INFO - Epoch(train) [92][ 100/5047] lr: 1.6052e-05 eta: 3 days, 0:15:30 time: 0.8971 data_time: 0.0027 memory: 48188 loss: 0.1116 loss_ce: 0.1116 2023/02/28 07:49:09 - mmengine - INFO - Epoch(train) [92][ 200/5047] lr: 1.6052e-05 eta: 3 days, 0:14:02 time: 0.8737 data_time: 0.0028 memory: 41315 loss: 0.1084 loss_ce: 0.1084 2023/02/28 07:50:35 - mmengine - INFO - Epoch(train) [92][ 300/5047] lr: 1.6052e-05 eta: 3 days, 0:12:34 time: 0.8118 data_time: 0.0030 memory: 42336 loss: 0.1248 loss_ce: 0.1248 2023/02/28 07:52:01 - mmengine - INFO - Epoch(train) [92][ 400/5047] lr: 1.6052e-05 eta: 3 days, 0:11:05 time: 0.8359 data_time: 0.0027 memory: 44278 loss: 0.1060 loss_ce: 0.1060 2023/02/28 07:53:28 - mmengine - INFO - Epoch(train) [92][ 500/5047] lr: 1.6052e-05 eta: 3 days, 0:09:37 time: 0.8921 data_time: 0.0030 memory: 42965 loss: 0.1103 loss_ce: 0.1103 2023/02/28 07:54:54 - mmengine - INFO - Epoch(train) [92][ 600/5047] lr: 1.6052e-05 eta: 3 days, 0:08:09 time: 0.8453 data_time: 0.0032 memory: 52056 loss: 0.1159 loss_ce: 0.1159 2023/02/28 07:56:19 - mmengine - INFO - Epoch(train) [92][ 700/5047] lr: 1.6052e-05 eta: 3 days, 0:06:40 time: 0.8730 data_time: 0.0031 memory: 46382 loss: 0.1016 loss_ce: 0.1016 2023/02/28 07:56:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 07:57:44 - mmengine - INFO - Epoch(train) [92][ 800/5047] lr: 1.6052e-05 eta: 3 days, 0:05:11 time: 0.8910 data_time: 0.0045 memory: 54045 loss: 0.0967 loss_ce: 0.0967 2023/02/28 07:59:11 - mmengine - INFO - Epoch(train) [92][ 900/5047] lr: 1.6052e-05 eta: 3 days, 0:03:43 time: 0.8717 data_time: 0.0024 memory: 42024 loss: 0.1061 loss_ce: 0.1061 2023/02/28 08:00:36 - mmengine - INFO - Epoch(train) [92][1000/5047] lr: 1.6052e-05 eta: 3 days, 0:02:14 time: 0.8538 data_time: 0.0023 memory: 42649 loss: 0.1105 loss_ce: 0.1105 2023/02/28 08:02:01 - mmengine - INFO - Epoch(train) [92][1100/5047] lr: 1.6052e-05 eta: 3 days, 0:00:45 time: 0.8445 data_time: 0.0023 memory: 45689 loss: 0.1075 loss_ce: 0.1075 2023/02/28 08:03:29 - mmengine - INFO - Epoch(train) [92][1200/5047] lr: 1.6052e-05 eta: 2 days, 23:59:18 time: 0.8554 data_time: 0.0027 memory: 47813 loss: 0.1065 loss_ce: 0.1065 2023/02/28 08:04:56 - mmengine - INFO - Epoch(train) [92][1300/5047] lr: 1.6052e-05 eta: 2 days, 23:57:50 time: 0.8956 data_time: 0.0024 memory: 44925 loss: 0.1099 loss_ce: 0.1099 2023/02/28 08:06:21 - mmengine - INFO - Epoch(train) [92][1400/5047] lr: 1.6052e-05 eta: 2 days, 23:56:22 time: 0.8405 data_time: 0.0023 memory: 49216 loss: 0.1165 loss_ce: 0.1165 2023/02/28 08:07:48 - mmengine - INFO - Epoch(train) [92][1500/5047] lr: 1.6052e-05 eta: 2 days, 23:54:54 time: 0.8472 data_time: 0.0049 memory: 52976 loss: 0.1227 loss_ce: 0.1227 2023/02/28 08:09:15 - mmengine - INFO - Epoch(train) [92][1600/5047] lr: 1.6052e-05 eta: 2 days, 23:53:26 time: 0.9376 data_time: 0.0026 memory: 50443 loss: 0.1188 loss_ce: 0.1188 2023/02/28 08:10:41 - mmengine - INFO - Epoch(train) [92][1700/5047] lr: 1.6052e-05 eta: 2 days, 23:51:57 time: 0.8777 data_time: 0.0034 memory: 46005 loss: 0.1092 loss_ce: 0.1092 2023/02/28 08:11:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 08:12:06 - mmengine - INFO - Epoch(train) [92][1800/5047] lr: 1.6052e-05 eta: 2 days, 23:50:29 time: 0.8442 data_time: 0.0024 memory: 42649 loss: 0.0957 loss_ce: 0.0957 2023/02/28 08:13:32 - mmengine - INFO - Epoch(train) [92][1900/5047] lr: 1.6052e-05 eta: 2 days, 23:49:00 time: 0.8140 data_time: 0.0025 memory: 55562 loss: 0.1161 loss_ce: 0.1161 2023/02/28 08:14:57 - mmengine - INFO - Epoch(train) [92][2000/5047] lr: 1.6052e-05 eta: 2 days, 23:47:32 time: 0.8188 data_time: 0.0023 memory: 55562 loss: 0.1166 loss_ce: 0.1166 2023/02/28 08:16:23 - mmengine - INFO - Epoch(train) [92][2100/5047] lr: 1.6052e-05 eta: 2 days, 23:46:03 time: 0.8976 data_time: 0.0029 memory: 43613 loss: 0.1028 loss_ce: 0.1028 2023/02/28 08:17:48 - mmengine - INFO - Epoch(train) [92][2200/5047] lr: 1.6052e-05 eta: 2 days, 23:44:34 time: 0.8552 data_time: 0.0031 memory: 43613 loss: 0.1207 loss_ce: 0.1207 2023/02/28 08:19:15 - mmengine - INFO - Epoch(train) [92][2300/5047] lr: 1.6052e-05 eta: 2 days, 23:43:06 time: 0.8747 data_time: 0.0026 memory: 43289 loss: 0.1166 loss_ce: 0.1166 2023/02/28 08:20:41 - mmengine - INFO - Epoch(train) [92][2400/5047] lr: 1.6052e-05 eta: 2 days, 23:41:38 time: 0.9091 data_time: 0.0026 memory: 48134 loss: 0.1261 loss_ce: 0.1261 2023/02/28 08:22:06 - mmengine - INFO - Epoch(train) [92][2500/5047] lr: 1.6052e-05 eta: 2 days, 23:40:09 time: 0.8533 data_time: 0.0022 memory: 49075 loss: 0.1068 loss_ce: 0.1068 2023/02/28 08:23:31 - mmengine - INFO - Epoch(train) [92][2600/5047] lr: 1.6052e-05 eta: 2 days, 23:38:40 time: 0.8864 data_time: 0.0027 memory: 43289 loss: 0.1039 loss_ce: 0.1039 2023/02/28 08:24:58 - mmengine - INFO - Epoch(train) [92][2700/5047] lr: 1.6052e-05 eta: 2 days, 23:37:12 time: 0.8195 data_time: 0.0022 memory: 43289 loss: 0.1118 loss_ce: 0.1118 2023/02/28 08:25:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 08:26:22 - mmengine - INFO - Epoch(train) [92][2800/5047] lr: 1.6052e-05 eta: 2 days, 23:35:42 time: 0.8551 data_time: 0.0034 memory: 40535 loss: 0.1315 loss_ce: 0.1315 2023/02/28 08:27:48 - mmengine - INFO - Epoch(train) [92][2900/5047] lr: 1.6052e-05 eta: 2 days, 23:34:15 time: 0.8101 data_time: 0.0026 memory: 55562 loss: 0.1178 loss_ce: 0.1178 2023/02/28 08:29:14 - mmengine - INFO - Epoch(train) [92][3000/5047] lr: 1.6052e-05 eta: 2 days, 23:32:46 time: 0.8172 data_time: 0.0025 memory: 55562 loss: 0.1000 loss_ce: 0.1000 2023/02/28 08:30:39 - mmengine - INFO - Epoch(train) [92][3100/5047] lr: 1.6052e-05 eta: 2 days, 23:31:17 time: 0.8443 data_time: 0.0023 memory: 42678 loss: 0.1083 loss_ce: 0.1083 2023/02/28 08:32:05 - mmengine - INFO - Epoch(train) [92][3200/5047] lr: 1.6052e-05 eta: 2 days, 23:29:49 time: 0.8960 data_time: 0.0026 memory: 46258 loss: 0.1048 loss_ce: 0.1048 2023/02/28 08:33:31 - mmengine - INFO - Epoch(train) [92][3300/5047] lr: 1.6052e-05 eta: 2 days, 23:28:20 time: 0.8652 data_time: 0.0030 memory: 40241 loss: 0.1122 loss_ce: 0.1122 2023/02/28 08:34:56 - mmengine - INFO - Epoch(train) [92][3400/5047] lr: 1.6052e-05 eta: 2 days, 23:26:51 time: 0.8305 data_time: 0.0026 memory: 44715 loss: 0.1320 loss_ce: 0.1320 2023/02/28 08:36:22 - mmengine - INFO - Epoch(train) [92][3500/5047] lr: 1.6052e-05 eta: 2 days, 23:25:23 time: 0.8187 data_time: 0.0026 memory: 43218 loss: 0.1107 loss_ce: 0.1107 2023/02/28 08:37:48 - mmengine - INFO - Epoch(train) [92][3600/5047] lr: 1.6052e-05 eta: 2 days, 23:23:55 time: 0.8351 data_time: 0.0026 memory: 45875 loss: 0.1126 loss_ce: 0.1126 2023/02/28 08:39:14 - mmengine - INFO - Epoch(train) [92][3700/5047] lr: 1.6052e-05 eta: 2 days, 23:22:26 time: 0.8514 data_time: 0.0026 memory: 46772 loss: 0.1139 loss_ce: 0.1139 2023/02/28 08:39:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 08:40:39 - mmengine - INFO - Epoch(train) [92][3800/5047] lr: 1.6052e-05 eta: 2 days, 23:20:57 time: 0.8875 data_time: 0.0023 memory: 43195 loss: 0.1237 loss_ce: 0.1237 2023/02/28 08:42:04 - mmengine - INFO - Epoch(train) [92][3900/5047] lr: 1.6052e-05 eta: 2 days, 23:19:28 time: 0.8389 data_time: 0.0021 memory: 45887 loss: 0.0918 loss_ce: 0.0918 2023/02/28 08:43:31 - mmengine - INFO - Epoch(train) [92][4000/5047] lr: 1.6052e-05 eta: 2 days, 23:18:01 time: 0.8898 data_time: 0.0024 memory: 42965 loss: 0.1130 loss_ce: 0.1130 2023/02/28 08:44:57 - mmengine - INFO - Epoch(train) [92][4100/5047] lr: 1.6052e-05 eta: 2 days, 23:16:32 time: 0.8634 data_time: 0.0025 memory: 44617 loss: 0.1010 loss_ce: 0.1010 2023/02/28 08:46:22 - mmengine - INFO - Epoch(train) [92][4200/5047] lr: 1.6052e-05 eta: 2 days, 23:15:03 time: 0.8847 data_time: 0.0079 memory: 43613 loss: 0.1089 loss_ce: 0.1089 2023/02/28 08:47:47 - mmengine - INFO - Epoch(train) [92][4300/5047] lr: 1.6052e-05 eta: 2 days, 23:13:34 time: 0.8581 data_time: 0.0070 memory: 46444 loss: 0.1097 loss_ce: 0.1097 2023/02/28 08:49:14 - mmengine - INFO - Epoch(train) [92][4400/5047] lr: 1.6052e-05 eta: 2 days, 23:12:07 time: 0.8508 data_time: 0.0024 memory: 48948 loss: 0.1173 loss_ce: 0.1173 2023/02/28 08:50:39 - mmengine - INFO - Epoch(train) [92][4500/5047] lr: 1.6052e-05 eta: 2 days, 23:10:38 time: 0.8465 data_time: 0.0026 memory: 43613 loss: 0.1064 loss_ce: 0.1064 2023/02/28 08:52:05 - mmengine - INFO - Epoch(train) [92][4600/5047] lr: 1.6052e-05 eta: 2 days, 23:09:10 time: 0.8750 data_time: 0.0032 memory: 47074 loss: 0.1117 loss_ce: 0.1117 2023/02/28 08:53:30 - mmengine - INFO - Epoch(train) [92][4700/5047] lr: 1.6052e-05 eta: 2 days, 23:07:41 time: 0.8702 data_time: 0.0023 memory: 52127 loss: 0.1101 loss_ce: 0.1101 2023/02/28 08:53:50 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 08:54:56 - mmengine - INFO - Epoch(train) [92][4800/5047] lr: 1.6052e-05 eta: 2 days, 23:06:13 time: 0.9061 data_time: 0.0023 memory: 44632 loss: 0.1156 loss_ce: 0.1156 2023/02/28 08:56:22 - mmengine - INFO - Epoch(train) [92][4900/5047] lr: 1.6052e-05 eta: 2 days, 23:04:44 time: 0.8815 data_time: 0.0028 memory: 47452 loss: 0.1164 loss_ce: 0.1164 2023/02/28 08:57:47 - mmengine - INFO - Epoch(train) [92][5000/5047] lr: 1.6052e-05 eta: 2 days, 23:03:15 time: 0.8567 data_time: 0.0027 memory: 40825 loss: 0.1203 loss_ce: 0.1203 2023/02/28 08:58:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 08:58:27 - mmengine - INFO - Saving checkpoint at 92 epochs 2023/02/28 08:59:59 - mmengine - INFO - Epoch(train) [93][ 100/5047] lr: 1.5851e-05 eta: 2 days, 23:01:06 time: 0.8804 data_time: 0.0025 memory: 44278 loss: 0.1100 loss_ce: 0.1100 2023/02/28 09:01:26 - mmengine - INFO - Epoch(train) [93][ 200/5047] lr: 1.5851e-05 eta: 2 days, 22:59:38 time: 0.8682 data_time: 0.0023 memory: 41174 loss: 0.1081 loss_ce: 0.1081 2023/02/28 09:02:52 - mmengine - INFO - Epoch(train) [93][ 300/5047] lr: 1.5851e-05 eta: 2 days, 22:58:09 time: 0.8331 data_time: 0.0028 memory: 42024 loss: 0.1103 loss_ce: 0.1103 2023/02/28 09:04:16 - mmengine - INFO - Epoch(train) [93][ 400/5047] lr: 1.5851e-05 eta: 2 days, 22:56:40 time: 0.8149 data_time: 0.0052 memory: 47074 loss: 0.1109 loss_ce: 0.1109 2023/02/28 09:05:43 - mmengine - INFO - Epoch(train) [93][ 500/5047] lr: 1.5851e-05 eta: 2 days, 22:55:13 time: 0.8609 data_time: 0.0024 memory: 42965 loss: 0.1003 loss_ce: 0.1003 2023/02/28 09:07:09 - mmengine - INFO - Epoch(train) [93][ 600/5047] lr: 1.5851e-05 eta: 2 days, 22:53:44 time: 0.8424 data_time: 0.0052 memory: 44956 loss: 0.1016 loss_ce: 0.1016 2023/02/28 09:08:16 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 09:08:36 - mmengine - INFO - Epoch(train) [93][ 700/5047] lr: 1.5851e-05 eta: 2 days, 22:52:17 time: 0.8573 data_time: 0.0025 memory: 43613 loss: 0.1280 loss_ce: 0.1280 2023/02/28 09:10:03 - mmengine - INFO - Epoch(train) [93][ 800/5047] lr: 1.5851e-05 eta: 2 days, 22:50:49 time: 0.8545 data_time: 0.0024 memory: 42336 loss: 0.1141 loss_ce: 0.1141 2023/02/28 09:11:28 - mmengine - INFO - Epoch(train) [93][ 900/5047] lr: 1.5851e-05 eta: 2 days, 22:49:20 time: 0.8354 data_time: 0.0023 memory: 55562 loss: 0.1064 loss_ce: 0.1064 2023/02/28 09:12:55 - mmengine - INFO - Epoch(train) [93][1000/5047] lr: 1.5851e-05 eta: 2 days, 22:47:52 time: 0.8569 data_time: 0.0031 memory: 45643 loss: 0.1111 loss_ce: 0.1111 2023/02/28 09:14:20 - mmengine - INFO - Epoch(train) [93][1100/5047] lr: 1.5851e-05 eta: 2 days, 22:46:23 time: 0.8596 data_time: 0.0024 memory: 44617 loss: 0.0984 loss_ce: 0.0984 2023/02/28 09:15:45 - mmengine - INFO - Epoch(train) [93][1200/5047] lr: 1.5851e-05 eta: 2 days, 22:44:55 time: 0.8536 data_time: 0.0026 memory: 42336 loss: 0.1098 loss_ce: 0.1098 2023/02/28 09:17:10 - mmengine - INFO - Epoch(train) [93][1300/5047] lr: 1.5851e-05 eta: 2 days, 22:43:26 time: 0.8075 data_time: 0.0052 memory: 40825 loss: 0.1154 loss_ce: 0.1154 2023/02/28 09:18:37 - mmengine - INFO - Epoch(train) [93][1400/5047] lr: 1.5851e-05 eta: 2 days, 22:41:58 time: 0.8742 data_time: 0.0022 memory: 45030 loss: 0.0958 loss_ce: 0.0958 2023/02/28 09:20:06 - mmengine - INFO - Epoch(train) [93][1500/5047] lr: 1.5851e-05 eta: 2 days, 22:40:31 time: 0.8860 data_time: 0.0033 memory: 47138 loss: 0.1245 loss_ce: 0.1245 2023/02/28 09:21:33 - mmengine - INFO - Epoch(train) [93][1600/5047] lr: 1.5851e-05 eta: 2 days, 22:39:03 time: 0.8757 data_time: 0.0042 memory: 53021 loss: 0.1098 loss_ce: 0.1098 2023/02/28 09:22:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 09:22:57 - mmengine - INFO - Epoch(train) [93][1700/5047] lr: 1.5851e-05 eta: 2 days, 22:37:34 time: 0.8874 data_time: 0.0032 memory: 52926 loss: 0.1019 loss_ce: 0.1019 2023/02/28 09:24:23 - mmengine - INFO - Epoch(train) [93][1800/5047] lr: 1.5851e-05 eta: 2 days, 22:36:06 time: 0.8576 data_time: 0.0024 memory: 39681 loss: 0.1144 loss_ce: 0.1144 2023/02/28 09:25:49 - mmengine - INFO - Epoch(train) [93][1900/5047] lr: 1.5851e-05 eta: 2 days, 22:34:38 time: 0.8985 data_time: 0.0024 memory: 47813 loss: 0.1043 loss_ce: 0.1043 2023/02/28 09:27:14 - mmengine - INFO - Epoch(train) [93][2000/5047] lr: 1.5851e-05 eta: 2 days, 22:33:09 time: 0.8885 data_time: 0.0028 memory: 48994 loss: 0.1130 loss_ce: 0.1130 2023/02/28 09:28:42 - mmengine - INFO - Epoch(train) [93][2100/5047] lr: 1.5851e-05 eta: 2 days, 22:31:42 time: 0.9135 data_time: 0.0028 memory: 47982 loss: 0.1127 loss_ce: 0.1127 2023/02/28 09:30:09 - mmengine - INFO - Epoch(train) [93][2200/5047] lr: 1.5851e-05 eta: 2 days, 22:30:14 time: 0.8376 data_time: 0.0025 memory: 40001 loss: 0.1181 loss_ce: 0.1181 2023/02/28 09:31:37 - mmengine - INFO - Epoch(train) [93][2300/5047] lr: 1.5851e-05 eta: 2 days, 22:28:47 time: 0.8848 data_time: 0.0025 memory: 46713 loss: 0.1110 loss_ce: 0.1110 2023/02/28 09:33:02 - mmengine - INFO - Epoch(train) [93][2400/5047] lr: 1.5851e-05 eta: 2 days, 22:27:18 time: 0.8239 data_time: 0.0023 memory: 42566 loss: 0.1211 loss_ce: 0.1211 2023/02/28 09:34:27 - mmengine - INFO - Epoch(train) [93][2500/5047] lr: 1.5851e-05 eta: 2 days, 22:25:49 time: 0.8746 data_time: 0.0030 memory: 49144 loss: 0.1109 loss_ce: 0.1109 2023/02/28 09:35:53 - mmengine - INFO - Epoch(train) [93][2600/5047] lr: 1.5851e-05 eta: 2 days, 22:24:21 time: 0.8473 data_time: 0.0025 memory: 47447 loss: 0.1167 loss_ce: 0.1167 2023/02/28 09:36:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 09:37:18 - mmengine - INFO - Epoch(train) [93][2700/5047] lr: 1.5851e-05 eta: 2 days, 22:22:52 time: 0.8664 data_time: 0.0029 memory: 42649 loss: 0.1091 loss_ce: 0.1091 2023/02/28 09:38:44 - mmengine - INFO - Epoch(train) [93][2800/5047] lr: 1.5851e-05 eta: 2 days, 22:21:23 time: 0.8508 data_time: 0.0026 memory: 41093 loss: 0.1042 loss_ce: 0.1042 2023/02/28 09:40:11 - mmengine - INFO - Epoch(train) [93][2900/5047] lr: 1.5851e-05 eta: 2 days, 22:19:56 time: 0.8431 data_time: 0.0041 memory: 40547 loss: 0.1210 loss_ce: 0.1210 2023/02/28 09:41:36 - mmengine - INFO - Epoch(train) [93][3000/5047] lr: 1.5851e-05 eta: 2 days, 22:18:27 time: 0.8821 data_time: 0.0023 memory: 40799 loss: 0.1180 loss_ce: 0.1180 2023/02/28 09:43:01 - mmengine - INFO - Epoch(train) [93][3100/5047] lr: 1.5851e-05 eta: 2 days, 22:16:58 time: 0.8808 data_time: 0.0032 memory: 42965 loss: 0.1166 loss_ce: 0.1166 2023/02/28 09:44:26 - mmengine - INFO - Epoch(train) [93][3200/5047] lr: 1.5851e-05 eta: 2 days, 22:15:29 time: 0.8451 data_time: 0.0050 memory: 41724 loss: 0.1157 loss_ce: 0.1157 2023/02/28 09:45:54 - mmengine - INFO - Epoch(train) [93][3300/5047] lr: 1.5851e-05 eta: 2 days, 22:14:02 time: 0.8421 data_time: 0.0059 memory: 41095 loss: 0.1023 loss_ce: 0.1023 2023/02/28 09:47:21 - mmengine - INFO - Epoch(train) [93][3400/5047] lr: 1.5851e-05 eta: 2 days, 22:12:34 time: 0.8125 data_time: 0.0029 memory: 50505 loss: 0.1126 loss_ce: 0.1126 2023/02/28 09:48:47 - mmengine - INFO - Epoch(train) [93][3500/5047] lr: 1.5851e-05 eta: 2 days, 22:11:06 time: 0.9224 data_time: 0.0025 memory: 55562 loss: 0.1066 loss_ce: 0.1066 2023/02/28 09:50:13 - mmengine - INFO - Epoch(train) [93][3600/5047] lr: 1.5851e-05 eta: 2 days, 22:09:38 time: 0.8348 data_time: 0.0026 memory: 43068 loss: 0.1065 loss_ce: 0.1065 2023/02/28 09:51:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 09:51:37 - mmengine - INFO - Epoch(train) [93][3700/5047] lr: 1.5851e-05 eta: 2 days, 22:08:09 time: 0.7945 data_time: 0.0024 memory: 46005 loss: 0.1226 loss_ce: 0.1226 2023/02/28 09:53:05 - mmengine - INFO - Epoch(train) [93][3800/5047] lr: 1.5851e-05 eta: 2 days, 22:06:42 time: 0.8465 data_time: 0.0030 memory: 50540 loss: 0.1255 loss_ce: 0.1255 2023/02/28 09:54:31 - mmengine - INFO - Epoch(train) [93][3900/5047] lr: 1.5851e-05 eta: 2 days, 22:05:13 time: 0.8973 data_time: 0.0023 memory: 43289 loss: 0.1175 loss_ce: 0.1175 2023/02/28 09:55:56 - mmengine - INFO - Epoch(train) [93][4000/5047] lr: 1.5851e-05 eta: 2 days, 22:03:44 time: 0.8752 data_time: 0.0037 memory: 47447 loss: 0.1087 loss_ce: 0.1087 2023/02/28 09:57:21 - mmengine - INFO - Epoch(train) [93][4100/5047] lr: 1.5851e-05 eta: 2 days, 22:02:16 time: 0.8711 data_time: 0.0024 memory: 43524 loss: 0.1067 loss_ce: 0.1067 2023/02/28 09:58:48 - mmengine - INFO - Epoch(train) [93][4200/5047] lr: 1.5851e-05 eta: 2 days, 22:00:48 time: 0.8514 data_time: 0.0027 memory: 43348 loss: 0.1127 loss_ce: 0.1127 2023/02/28 10:00:14 - mmengine - INFO - Epoch(train) [93][4300/5047] lr: 1.5851e-05 eta: 2 days, 21:59:19 time: 0.8433 data_time: 0.0024 memory: 42579 loss: 0.1211 loss_ce: 0.1211 2023/02/28 10:01:41 - mmengine - INFO - Epoch(train) [93][4400/5047] lr: 1.5851e-05 eta: 2 days, 21:57:52 time: 0.9036 data_time: 0.0027 memory: 44617 loss: 0.1131 loss_ce: 0.1131 2023/02/28 10:03:07 - mmengine - INFO - Epoch(train) [93][4500/5047] lr: 1.5851e-05 eta: 2 days, 21:56:24 time: 0.8602 data_time: 0.0024 memory: 54072 loss: 0.1019 loss_ce: 0.1019 2023/02/28 10:04:33 - mmengine - INFO - Epoch(train) [93][4600/5047] lr: 1.5851e-05 eta: 2 days, 21:54:56 time: 0.8928 data_time: 0.0030 memory: 49453 loss: 0.1135 loss_ce: 0.1135 2023/02/28 10:05:38 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 10:05:59 - mmengine - INFO - Epoch(train) [93][4700/5047] lr: 1.5851e-05 eta: 2 days, 21:53:27 time: 0.8945 data_time: 0.0027 memory: 40825 loss: 0.1109 loss_ce: 0.1109 2023/02/28 10:07:26 - mmengine - INFO - Epoch(train) [93][4800/5047] lr: 1.5851e-05 eta: 2 days, 21:52:00 time: 0.8527 data_time: 0.0022 memory: 45751 loss: 0.1276 loss_ce: 0.1276 2023/02/28 10:08:53 - mmengine - INFO - Epoch(train) [93][4900/5047] lr: 1.5851e-05 eta: 2 days, 21:50:32 time: 0.8640 data_time: 0.0025 memory: 46005 loss: 0.1167 loss_ce: 0.1167 2023/02/28 10:10:20 - mmengine - INFO - Epoch(train) [93][5000/5047] lr: 1.5851e-05 eta: 2 days, 21:49:04 time: 0.8950 data_time: 0.0024 memory: 43289 loss: 0.1044 loss_ce: 0.1044 2023/02/28 10:11:01 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 10:11:01 - mmengine - INFO - Saving checkpoint at 93 epochs 2023/02/28 10:12:32 - mmengine - INFO - Epoch(train) [94][ 100/5047] lr: 1.5650e-05 eta: 2 days, 21:46:55 time: 0.8614 data_time: 0.0025 memory: 44476 loss: 0.1180 loss_ce: 0.1180 2023/02/28 10:13:57 - mmengine - INFO - Epoch(train) [94][ 200/5047] lr: 1.5650e-05 eta: 2 days, 21:45:26 time: 0.8693 data_time: 0.0024 memory: 41724 loss: 0.1046 loss_ce: 0.1046 2023/02/28 10:15:23 - mmengine - INFO - Epoch(train) [94][ 300/5047] lr: 1.5650e-05 eta: 2 days, 21:43:58 time: 0.8553 data_time: 0.0027 memory: 49077 loss: 0.1112 loss_ce: 0.1112 2023/02/28 10:16:49 - mmengine - INFO - Epoch(train) [94][ 400/5047] lr: 1.5650e-05 eta: 2 days, 21:42:29 time: 0.8193 data_time: 0.0034 memory: 42336 loss: 0.1154 loss_ce: 0.1154 2023/02/28 10:18:14 - mmengine - INFO - Epoch(train) [94][ 500/5047] lr: 1.5650e-05 eta: 2 days, 21:41:01 time: 0.8393 data_time: 0.0027 memory: 42663 loss: 0.1269 loss_ce: 0.1269 2023/02/28 10:19:42 - mmengine - INFO - Epoch(train) [94][ 600/5047] lr: 1.5650e-05 eta: 2 days, 21:39:33 time: 0.8563 data_time: 0.0028 memory: 42245 loss: 0.1143 loss_ce: 0.1143 2023/02/28 10:20:06 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 10:21:08 - mmengine - INFO - Epoch(train) [94][ 700/5047] lr: 1.5650e-05 eta: 2 days, 21:38:05 time: 0.8429 data_time: 0.0028 memory: 40825 loss: 0.1300 loss_ce: 0.1300 2023/02/28 10:22:36 - mmengine - INFO - Epoch(train) [94][ 800/5047] lr: 1.5650e-05 eta: 2 days, 21:36:38 time: 0.8026 data_time: 0.0023 memory: 46854 loss: 0.1047 loss_ce: 0.1047 2023/02/28 10:24:02 - mmengine - INFO - Epoch(train) [94][ 900/5047] lr: 1.5650e-05 eta: 2 days, 21:35:10 time: 0.8527 data_time: 0.0051 memory: 45643 loss: 0.0875 loss_ce: 0.0875 2023/02/28 10:25:27 - mmengine - INFO - Epoch(train) [94][1000/5047] lr: 1.5650e-05 eta: 2 days, 21:33:41 time: 0.8339 data_time: 0.0026 memory: 39126 loss: 0.1168 loss_ce: 0.1168 2023/02/28 10:26:53 - mmengine - INFO - Epoch(train) [94][1100/5047] lr: 1.5650e-05 eta: 2 days, 21:32:13 time: 0.8491 data_time: 0.0032 memory: 49552 loss: 0.1142 loss_ce: 0.1142 2023/02/28 10:28:20 - mmengine - INFO - Epoch(train) [94][1200/5047] lr: 1.5650e-05 eta: 2 days, 21:30:45 time: 0.8053 data_time: 0.0032 memory: 50607 loss: 0.1030 loss_ce: 0.1030 2023/02/28 10:29:45 - mmengine - INFO - Epoch(train) [94][1300/5047] lr: 1.5650e-05 eta: 2 days, 21:29:17 time: 0.8424 data_time: 0.0023 memory: 46681 loss: 0.1172 loss_ce: 0.1172 2023/02/28 10:31:13 - mmengine - INFO - Epoch(train) [94][1400/5047] lr: 1.5650e-05 eta: 2 days, 21:27:49 time: 0.8371 data_time: 0.0024 memory: 46685 loss: 0.1026 loss_ce: 0.1026 2023/02/28 10:32:39 - mmengine - INFO - Epoch(train) [94][1500/5047] lr: 1.5650e-05 eta: 2 days, 21:26:21 time: 0.8952 data_time: 0.0025 memory: 43289 loss: 0.1191 loss_ce: 0.1191 2023/02/28 10:34:05 - mmengine - INFO - Epoch(train) [94][1600/5047] lr: 1.5650e-05 eta: 2 days, 21:24:53 time: 0.8921 data_time: 0.0023 memory: 43511 loss: 0.1231 loss_ce: 0.1231 2023/02/28 10:34:30 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 10:35:31 - mmengine - INFO - Epoch(train) [94][1700/5047] lr: 1.5650e-05 eta: 2 days, 21:23:24 time: 0.8574 data_time: 0.0024 memory: 47813 loss: 0.1046 loss_ce: 0.1046 2023/02/28 10:36:56 - mmengine - INFO - Epoch(train) [94][1800/5047] lr: 1.5650e-05 eta: 2 days, 21:21:56 time: 0.8301 data_time: 0.0065 memory: 55562 loss: 0.1210 loss_ce: 0.1210 2023/02/28 10:38:21 - mmengine - INFO - Epoch(train) [94][1900/5047] lr: 1.5650e-05 eta: 2 days, 21:20:27 time: 0.8636 data_time: 0.0028 memory: 48948 loss: 0.0975 loss_ce: 0.0975 2023/02/28 10:39:49 - mmengine - INFO - Epoch(train) [94][2000/5047] lr: 1.5650e-05 eta: 2 days, 21:19:00 time: 0.8512 data_time: 0.0065 memory: 51308 loss: 0.1157 loss_ce: 0.1157 2023/02/28 10:41:16 - mmengine - INFO - Epoch(train) [94][2100/5047] lr: 1.5650e-05 eta: 2 days, 21:17:32 time: 0.8294 data_time: 0.0025 memory: 46713 loss: 0.1108 loss_ce: 0.1108 2023/02/28 10:42:41 - mmengine - INFO - Epoch(train) [94][2200/5047] lr: 1.5650e-05 eta: 2 days, 21:16:04 time: 0.8307 data_time: 0.0026 memory: 50372 loss: 0.1133 loss_ce: 0.1133 2023/02/28 10:44:09 - mmengine - INFO - Epoch(train) [94][2300/5047] lr: 1.5650e-05 eta: 2 days, 21:14:36 time: 0.8507 data_time: 0.0028 memory: 51562 loss: 0.1395 loss_ce: 0.1395 2023/02/28 10:45:34 - mmengine - INFO - Epoch(train) [94][2400/5047] lr: 1.5650e-05 eta: 2 days, 21:13:08 time: 0.8726 data_time: 0.0029 memory: 45643 loss: 0.1011 loss_ce: 0.1011 2023/02/28 10:47:00 - mmengine - INFO - Epoch(train) [94][2500/5047] lr: 1.5650e-05 eta: 2 days, 21:11:40 time: 0.8256 data_time: 0.0024 memory: 55562 loss: 0.1071 loss_ce: 0.1071 2023/02/28 10:48:26 - mmengine - INFO - Epoch(train) [94][2600/5047] lr: 1.5650e-05 eta: 2 days, 21:10:11 time: 0.8669 data_time: 0.0024 memory: 46339 loss: 0.1160 loss_ce: 0.1160 2023/02/28 10:48:51 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 10:49:52 - mmengine - INFO - Epoch(train) [94][2700/5047] lr: 1.5650e-05 eta: 2 days, 21:08:43 time: 0.8777 data_time: 0.0026 memory: 42626 loss: 0.1165 loss_ce: 0.1165 2023/02/28 10:51:18 - mmengine - INFO - Epoch(train) [94][2800/5047] lr: 1.5650e-05 eta: 2 days, 21:07:15 time: 0.8705 data_time: 0.0027 memory: 49373 loss: 0.1148 loss_ce: 0.1148 2023/02/28 10:52:44 - mmengine - INFO - Epoch(train) [94][2900/5047] lr: 1.5650e-05 eta: 2 days, 21:05:47 time: 0.8695 data_time: 0.0034 memory: 50514 loss: 0.1138 loss_ce: 0.1138 2023/02/28 10:54:09 - mmengine - INFO - Epoch(train) [94][3000/5047] lr: 1.5650e-05 eta: 2 days, 21:04:17 time: 0.8422 data_time: 0.0024 memory: 42336 loss: 0.1099 loss_ce: 0.1099 2023/02/28 10:55:35 - mmengine - INFO - Epoch(train) [94][3100/5047] lr: 1.5650e-05 eta: 2 days, 21:02:49 time: 0.8878 data_time: 0.0029 memory: 43947 loss: 0.1222 loss_ce: 0.1222 2023/02/28 10:57:01 - mmengine - INFO - Epoch(train) [94][3200/5047] lr: 1.5650e-05 eta: 2 days, 21:01:21 time: 0.8068 data_time: 0.0025 memory: 44563 loss: 0.1042 loss_ce: 0.1042 2023/02/28 10:58:26 - mmengine - INFO - Epoch(train) [94][3300/5047] lr: 1.5650e-05 eta: 2 days, 20:59:52 time: 0.8294 data_time: 0.0028 memory: 41792 loss: 0.1194 loss_ce: 0.1194 2023/02/28 10:59:53 - mmengine - INFO - Epoch(train) [94][3400/5047] lr: 1.5650e-05 eta: 2 days, 20:58:24 time: 0.8417 data_time: 0.0055 memory: 46452 loss: 0.1153 loss_ce: 0.1153 2023/02/28 11:01:19 - mmengine - INFO - Epoch(train) [94][3500/5047] lr: 1.5650e-05 eta: 2 days, 20:56:56 time: 0.8968 data_time: 0.0043 memory: 45302 loss: 0.1078 loss_ce: 0.1078 2023/02/28 11:02:44 - mmengine - INFO - Epoch(train) [94][3600/5047] lr: 1.5650e-05 eta: 2 days, 20:55:28 time: 0.9266 data_time: 0.0025 memory: 50887 loss: 0.1161 loss_ce: 0.1161 2023/02/28 11:03:09 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 11:04:11 - mmengine - INFO - Epoch(train) [94][3700/5047] lr: 1.5650e-05 eta: 2 days, 20:54:00 time: 0.8258 data_time: 0.0024 memory: 44617 loss: 0.1242 loss_ce: 0.1242 2023/02/28 11:05:38 - mmengine - INFO - Epoch(train) [94][3800/5047] lr: 1.5650e-05 eta: 2 days, 20:52:32 time: 0.8695 data_time: 0.0024 memory: 41530 loss: 0.1141 loss_ce: 0.1141 2023/02/28 11:07:03 - mmengine - INFO - Epoch(train) [94][3900/5047] lr: 1.5650e-05 eta: 2 days, 20:51:03 time: 0.8501 data_time: 0.0048 memory: 42934 loss: 0.1119 loss_ce: 0.1119 2023/02/28 11:08:28 - mmengine - INFO - Epoch(train) [94][4000/5047] lr: 1.5650e-05 eta: 2 days, 20:49:35 time: 0.8155 data_time: 0.0024 memory: 42649 loss: 0.1461 loss_ce: 0.1461 2023/02/28 11:09:55 - mmengine - INFO - Epoch(train) [94][4100/5047] lr: 1.5650e-05 eta: 2 days, 20:48:07 time: 0.8820 data_time: 0.0023 memory: 42649 loss: 0.1085 loss_ce: 0.1085 2023/02/28 11:11:20 - mmengine - INFO - Epoch(train) [94][4200/5047] lr: 1.5650e-05 eta: 2 days, 20:46:39 time: 0.8471 data_time: 0.0030 memory: 45689 loss: 0.1201 loss_ce: 0.1201 2023/02/28 11:12:47 - mmengine - INFO - Epoch(train) [94][4300/5047] lr: 1.5650e-05 eta: 2 days, 20:45:11 time: 0.8749 data_time: 0.0025 memory: 44956 loss: 0.1276 loss_ce: 0.1276 2023/02/28 11:14:12 - mmengine - INFO - Epoch(train) [94][4400/5047] lr: 1.5650e-05 eta: 2 days, 20:43:42 time: 0.7846 data_time: 0.0030 memory: 43289 loss: 0.1053 loss_ce: 0.1053 2023/02/28 11:15:40 - mmengine - INFO - Epoch(train) [94][4500/5047] lr: 1.5650e-05 eta: 2 days, 20:42:15 time: 0.8408 data_time: 0.0029 memory: 45643 loss: 0.1162 loss_ce: 0.1162 2023/02/28 11:17:05 - mmengine - INFO - Epoch(train) [94][4600/5047] lr: 1.5650e-05 eta: 2 days, 20:40:46 time: 0.8426 data_time: 0.0023 memory: 43289 loss: 0.0959 loss_ce: 0.0959 2023/02/28 11:17:30 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 11:18:29 - mmengine - INFO - Epoch(train) [94][4700/5047] lr: 1.5650e-05 eta: 2 days, 20:39:17 time: 0.8569 data_time: 0.0047 memory: 55562 loss: 0.1173 loss_ce: 0.1173 2023/02/28 11:19:56 - mmengine - INFO - Epoch(train) [94][4800/5047] lr: 1.5650e-05 eta: 2 days, 20:37:49 time: 0.9418 data_time: 0.0027 memory: 55562 loss: 0.1267 loss_ce: 0.1267 2023/02/28 11:21:21 - mmengine - INFO - Epoch(train) [94][4900/5047] lr: 1.5650e-05 eta: 2 days, 20:36:20 time: 0.9115 data_time: 0.0025 memory: 55562 loss: 0.1189 loss_ce: 0.1189 2023/02/28 11:22:46 - mmengine - INFO - Epoch(train) [94][5000/5047] lr: 1.5650e-05 eta: 2 days, 20:34:51 time: 0.8121 data_time: 0.0027 memory: 52543 loss: 0.1122 loss_ce: 0.1122 2023/02/28 11:23:26 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 11:23:26 - mmengine - INFO - Saving checkpoint at 94 epochs 2023/02/28 11:24:59 - mmengine - INFO - Epoch(train) [95][ 100/5047] lr: 1.5449e-05 eta: 2 days, 20:32:43 time: 0.8889 data_time: 0.0024 memory: 55562 loss: 0.1170 loss_ce: 0.1170 2023/02/28 11:26:27 - mmengine - INFO - Epoch(train) [95][ 200/5047] lr: 1.5449e-05 eta: 2 days, 20:31:15 time: 0.8594 data_time: 0.0027 memory: 43351 loss: 0.1157 loss_ce: 0.1157 2023/02/28 11:27:53 - mmengine - INFO - Epoch(train) [95][ 300/5047] lr: 1.5449e-05 eta: 2 days, 20:29:47 time: 0.8466 data_time: 0.0024 memory: 40697 loss: 0.1215 loss_ce: 0.1215 2023/02/28 11:29:17 - mmengine - INFO - Epoch(train) [95][ 400/5047] lr: 1.5449e-05 eta: 2 days, 20:28:18 time: 0.8479 data_time: 0.0049 memory: 38251 loss: 0.1237 loss_ce: 0.1237 2023/02/28 11:30:43 - mmengine - INFO - Epoch(train) [95][ 500/5047] lr: 1.5449e-05 eta: 2 days, 20:26:50 time: 0.8559 data_time: 0.0046 memory: 41998 loss: 0.1132 loss_ce: 0.1132 2023/02/28 11:31:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 11:32:10 - mmengine - INFO - Epoch(train) [95][ 600/5047] lr: 1.5449e-05 eta: 2 days, 20:25:22 time: 0.8920 data_time: 0.0024 memory: 46997 loss: 0.0888 loss_ce: 0.0888 2023/02/28 11:33:37 - mmengine - INFO - Epoch(train) [95][ 700/5047] lr: 1.5449e-05 eta: 2 days, 20:23:54 time: 0.8275 data_time: 0.0025 memory: 49334 loss: 0.1062 loss_ce: 0.1062 2023/02/28 11:35:02 - mmengine - INFO - Epoch(train) [95][ 800/5047] lr: 1.5449e-05 eta: 2 days, 20:22:26 time: 0.8312 data_time: 0.0035 memory: 44951 loss: 0.1156 loss_ce: 0.1156 2023/02/28 11:36:28 - mmengine - INFO - Epoch(train) [95][ 900/5047] lr: 1.5449e-05 eta: 2 days, 20:20:58 time: 0.8713 data_time: 0.0030 memory: 51755 loss: 0.1085 loss_ce: 0.1085 2023/02/28 11:37:53 - mmengine - INFO - Epoch(train) [95][1000/5047] lr: 1.5449e-05 eta: 2 days, 20:19:29 time: 0.8670 data_time: 0.0024 memory: 45643 loss: 0.1180 loss_ce: 0.1180 2023/02/28 11:39:21 - mmengine - INFO - Epoch(train) [95][1100/5047] lr: 1.5449e-05 eta: 2 days, 20:18:02 time: 0.8766 data_time: 0.0040 memory: 50420 loss: 0.1044 loss_ce: 0.1044 2023/02/28 11:40:46 - mmengine - INFO - Epoch(train) [95][1200/5047] lr: 1.5449e-05 eta: 2 days, 20:16:33 time: 0.8415 data_time: 0.0022 memory: 47116 loss: 0.1118 loss_ce: 0.1118 2023/02/28 11:42:11 - mmengine - INFO - Epoch(train) [95][1300/5047] lr: 1.5449e-05 eta: 2 days, 20:15:04 time: 0.8486 data_time: 0.0023 memory: 42649 loss: 0.1283 loss_ce: 0.1283 2023/02/28 11:43:36 - mmengine - INFO - Epoch(train) [95][1400/5047] lr: 1.5449e-05 eta: 2 days, 20:13:35 time: 0.8529 data_time: 0.0029 memory: 42731 loss: 0.1180 loss_ce: 0.1180 2023/02/28 11:45:02 - mmengine - INFO - Epoch(train) [95][1500/5047] lr: 1.5449e-05 eta: 2 days, 20:12:07 time: 0.8466 data_time: 0.0027 memory: 45547 loss: 0.1172 loss_ce: 0.1172 2023/02/28 11:46:11 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 11:46:26 - mmengine - INFO - Epoch(train) [95][1600/5047] lr: 1.5449e-05 eta: 2 days, 20:10:38 time: 0.8455 data_time: 0.0024 memory: 52862 loss: 0.1123 loss_ce: 0.1123 2023/02/28 11:47:52 - mmengine - INFO - Epoch(train) [95][1700/5047] lr: 1.5449e-05 eta: 2 days, 20:09:10 time: 0.8567 data_time: 0.0033 memory: 53021 loss: 0.1095 loss_ce: 0.1095 2023/02/28 11:49:18 - mmengine - INFO - Epoch(train) [95][1800/5047] lr: 1.5449e-05 eta: 2 days, 20:07:41 time: 0.8822 data_time: 0.0023 memory: 42965 loss: 0.1099 loss_ce: 0.1099 2023/02/28 11:50:44 - mmengine - INFO - Epoch(train) [95][1900/5047] lr: 1.5449e-05 eta: 2 days, 20:06:13 time: 0.8340 data_time: 0.0029 memory: 42606 loss: 0.1038 loss_ce: 0.1038 2023/02/28 11:52:09 - mmengine - INFO - Epoch(train) [95][2000/5047] lr: 1.5449e-05 eta: 2 days, 20:04:44 time: 0.8675 data_time: 0.0059 memory: 42273 loss: 0.1063 loss_ce: 0.1063 2023/02/28 11:53:35 - mmengine - INFO - Epoch(train) [95][2100/5047] lr: 1.5449e-05 eta: 2 days, 20:03:16 time: 0.8097 data_time: 0.0026 memory: 43613 loss: 0.1121 loss_ce: 0.1121 2023/02/28 11:54:59 - mmengine - INFO - Epoch(train) [95][2200/5047] lr: 1.5449e-05 eta: 2 days, 20:01:47 time: 0.8401 data_time: 0.0022 memory: 39398 loss: 0.1237 loss_ce: 0.1237 2023/02/28 11:56:26 - mmengine - INFO - Epoch(train) [95][2300/5047] lr: 1.5449e-05 eta: 2 days, 20:00:19 time: 0.8798 data_time: 0.0023 memory: 42649 loss: 0.1130 loss_ce: 0.1130 2023/02/28 11:57:54 - mmengine - INFO - Epoch(train) [95][2400/5047] lr: 1.5449e-05 eta: 2 days, 19:58:52 time: 0.9237 data_time: 0.0023 memory: 46997 loss: 0.1062 loss_ce: 0.1062 2023/02/28 11:59:21 - mmengine - INFO - Epoch(train) [95][2500/5047] lr: 1.5449e-05 eta: 2 days, 19:57:25 time: 0.8701 data_time: 0.0024 memory: 45813 loss: 0.1174 loss_ce: 0.1174 2023/02/28 12:00:32 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 12:00:47 - mmengine - INFO - Epoch(train) [95][2600/5047] lr: 1.5449e-05 eta: 2 days, 19:55:57 time: 0.8461 data_time: 0.0029 memory: 42024 loss: 0.1085 loss_ce: 0.1085 2023/02/28 12:02:13 - mmengine - INFO - Epoch(train) [95][2700/5047] lr: 1.5449e-05 eta: 2 days, 19:54:29 time: 0.8791 data_time: 0.0038 memory: 44037 loss: 0.1030 loss_ce: 0.1030 2023/02/28 12:03:40 - mmengine - INFO - Epoch(train) [95][2800/5047] lr: 1.5449e-05 eta: 2 days, 19:53:01 time: 0.8563 data_time: 0.0023 memory: 40016 loss: 0.1240 loss_ce: 0.1240 2023/02/28 12:05:06 - mmengine - INFO - Epoch(train) [95][2900/5047] lr: 1.5449e-05 eta: 2 days, 19:51:32 time: 0.8511 data_time: 0.0051 memory: 43613 loss: 0.1083 loss_ce: 0.1083 2023/02/28 12:06:32 - mmengine - INFO - Epoch(train) [95][3000/5047] lr: 1.5449e-05 eta: 2 days, 19:50:04 time: 0.8569 data_time: 0.0030 memory: 40825 loss: 0.1160 loss_ce: 0.1160 2023/02/28 12:07:58 - mmengine - INFO - Epoch(train) [95][3100/5047] lr: 1.5449e-05 eta: 2 days, 19:48:36 time: 0.8546 data_time: 0.0023 memory: 47247 loss: 0.1061 loss_ce: 0.1061 2023/02/28 12:09:25 - mmengine - INFO - Epoch(train) [95][3200/5047] lr: 1.5449e-05 eta: 2 days, 19:47:09 time: 0.9013 data_time: 0.0025 memory: 54072 loss: 0.1225 loss_ce: 0.1225 2023/02/28 12:10:52 - mmengine - INFO - Epoch(train) [95][3300/5047] lr: 1.5449e-05 eta: 2 days, 19:45:41 time: 0.9150 data_time: 0.0025 memory: 52543 loss: 0.1115 loss_ce: 0.1115 2023/02/28 12:12:18 - mmengine - INFO - Epoch(train) [95][3400/5047] lr: 1.5449e-05 eta: 2 days, 19:44:13 time: 0.8565 data_time: 0.0037 memory: 43613 loss: 0.1136 loss_ce: 0.1136 2023/02/28 12:13:45 - mmengine - INFO - Epoch(train) [95][3500/5047] lr: 1.5449e-05 eta: 2 days, 19:42:45 time: 0.8541 data_time: 0.0025 memory: 46005 loss: 0.1165 loss_ce: 0.1165 2023/02/28 12:14:56 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 12:15:11 - mmengine - INFO - Epoch(train) [95][3600/5047] lr: 1.5449e-05 eta: 2 days, 19:41:17 time: 0.8169 data_time: 0.0030 memory: 52796 loss: 0.1140 loss_ce: 0.1140 2023/02/28 12:16:38 - mmengine - INFO - Epoch(train) [95][3700/5047] lr: 1.5449e-05 eta: 2 days, 19:39:50 time: 0.8789 data_time: 0.0043 memory: 39681 loss: 0.1105 loss_ce: 0.1105 2023/02/28 12:18:03 - mmengine - INFO - Epoch(train) [95][3800/5047] lr: 1.5449e-05 eta: 2 days, 19:38:21 time: 0.8663 data_time: 0.0058 memory: 50513 loss: 0.1253 loss_ce: 0.1253 2023/02/28 12:19:31 - mmengine - INFO - Epoch(train) [95][3900/5047] lr: 1.5449e-05 eta: 2 days, 19:36:53 time: 0.8832 data_time: 0.0024 memory: 48565 loss: 0.1209 loss_ce: 0.1209 2023/02/28 12:20:56 - mmengine - INFO - Epoch(train) [95][4000/5047] lr: 1.5449e-05 eta: 2 days, 19:35:25 time: 0.8426 data_time: 0.0024 memory: 42649 loss: 0.1087 loss_ce: 0.1087 2023/02/28 12:22:22 - mmengine - INFO - Epoch(train) [95][4100/5047] lr: 1.5449e-05 eta: 2 days, 19:33:57 time: 0.8678 data_time: 0.0026 memory: 45079 loss: 0.1171 loss_ce: 0.1171 2023/02/28 12:23:50 - mmengine - INFO - Epoch(train) [95][4200/5047] lr: 1.5449e-05 eta: 2 days, 19:32:30 time: 0.8572 data_time: 0.0027 memory: 45643 loss: 0.1113 loss_ce: 0.1113 2023/02/28 12:25:15 - mmengine - INFO - Epoch(train) [95][4300/5047] lr: 1.5449e-05 eta: 2 days, 19:31:01 time: 0.8514 data_time: 0.0024 memory: 42965 loss: 0.1157 loss_ce: 0.1157 2023/02/28 12:26:41 - mmengine - INFO - Epoch(train) [95][4400/5047] lr: 1.5449e-05 eta: 2 days, 19:29:33 time: 0.8740 data_time: 0.0080 memory: 43289 loss: 0.1044 loss_ce: 0.1044 2023/02/28 12:28:07 - mmengine - INFO - Epoch(train) [95][4500/5047] lr: 1.5449e-05 eta: 2 days, 19:28:05 time: 0.8701 data_time: 0.0028 memory: 55562 loss: 0.1169 loss_ce: 0.1169 2023/02/28 12:29:18 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 12:29:34 - mmengine - INFO - Epoch(train) [95][4600/5047] lr: 1.5449e-05 eta: 2 days, 19:26:37 time: 0.8412 data_time: 0.0025 memory: 52543 loss: 0.1097 loss_ce: 0.1097 2023/02/28 12:31:00 - mmengine - INFO - Epoch(train) [95][4700/5047] lr: 1.5449e-05 eta: 2 days, 19:25:09 time: 0.8603 data_time: 0.0029 memory: 46794 loss: 0.1024 loss_ce: 0.1024 2023/02/28 12:32:25 - mmengine - INFO - Epoch(train) [95][4800/5047] lr: 1.5449e-05 eta: 2 days, 19:23:40 time: 0.8848 data_time: 0.0055 memory: 42649 loss: 0.1166 loss_ce: 0.1166 2023/02/28 12:33:51 - mmengine - INFO - Epoch(train) [95][4900/5047] lr: 1.5449e-05 eta: 2 days, 19:22:12 time: 0.8643 data_time: 0.0032 memory: 44823 loss: 0.1040 loss_ce: 0.1040 2023/02/28 12:35:16 - mmengine - INFO - Epoch(train) [95][5000/5047] lr: 1.5449e-05 eta: 2 days, 19:20:44 time: 0.8508 data_time: 0.0025 memory: 42649 loss: 0.1065 loss_ce: 0.1065 2023/02/28 12:35:57 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 12:35:57 - mmengine - INFO - Saving checkpoint at 95 epochs 2023/02/28 12:37:30 - mmengine - INFO - Epoch(train) [96][ 100/5047] lr: 1.5248e-05 eta: 2 days, 19:18:35 time: 0.8426 data_time: 0.0027 memory: 47958 loss: 0.1020 loss_ce: 0.1020 2023/02/28 12:38:55 - mmengine - INFO - Epoch(train) [96][ 200/5047] lr: 1.5248e-05 eta: 2 days, 19:17:06 time: 0.8557 data_time: 0.0026 memory: 41724 loss: 0.1158 loss_ce: 0.1158 2023/02/28 12:40:20 - mmengine - INFO - Epoch(train) [96][ 300/5047] lr: 1.5248e-05 eta: 2 days, 19:15:37 time: 0.8656 data_time: 0.0024 memory: 43947 loss: 0.1044 loss_ce: 0.1044 2023/02/28 12:41:47 - mmengine - INFO - Epoch(train) [96][ 400/5047] lr: 1.5248e-05 eta: 2 days, 19:14:10 time: 0.8924 data_time: 0.0028 memory: 45746 loss: 0.1029 loss_ce: 0.1029 2023/02/28 12:43:13 - mmengine - INFO - Epoch(train) [96][ 500/5047] lr: 1.5248e-05 eta: 2 days, 19:12:41 time: 0.8859 data_time: 0.0026 memory: 42699 loss: 0.1096 loss_ce: 0.1096 2023/02/28 12:43:43 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 12:44:39 - mmengine - INFO - Epoch(train) [96][ 600/5047] lr: 1.5248e-05 eta: 2 days, 19:11:14 time: 0.8687 data_time: 0.0024 memory: 42965 loss: 0.1297 loss_ce: 0.1297 2023/02/28 12:46:06 - mmengine - INFO - Epoch(train) [96][ 700/5047] lr: 1.5248e-05 eta: 2 days, 19:09:46 time: 0.8748 data_time: 0.0099 memory: 42406 loss: 0.1055 loss_ce: 0.1055 2023/02/28 12:47:32 - mmengine - INFO - Epoch(train) [96][ 800/5047] lr: 1.5248e-05 eta: 2 days, 19:08:18 time: 0.8514 data_time: 0.0026 memory: 53809 loss: 0.1029 loss_ce: 0.1029 2023/02/28 12:48:59 - mmengine - INFO - Epoch(train) [96][ 900/5047] lr: 1.5248e-05 eta: 2 days, 19:06:50 time: 0.8593 data_time: 0.0028 memory: 42418 loss: 0.1142 loss_ce: 0.1142 2023/02/28 12:50:25 - mmengine - INFO - Epoch(train) [96][1000/5047] lr: 1.5248e-05 eta: 2 days, 19:05:22 time: 0.8423 data_time: 0.0045 memory: 53025 loss: 0.1138 loss_ce: 0.1138 2023/02/28 12:51:51 - mmengine - INFO - Epoch(train) [96][1100/5047] lr: 1.5248e-05 eta: 2 days, 19:03:54 time: 0.8513 data_time: 0.0027 memory: 52955 loss: 0.1111 loss_ce: 0.1111 2023/02/28 12:53:16 - mmengine - INFO - Epoch(train) [96][1200/5047] lr: 1.5248e-05 eta: 2 days, 19:02:25 time: 0.8012 data_time: 0.0031 memory: 44617 loss: 0.1106 loss_ce: 0.1106 2023/02/28 12:54:43 - mmengine - INFO - Epoch(train) [96][1300/5047] lr: 1.5248e-05 eta: 2 days, 19:00:58 time: 0.7842 data_time: 0.0028 memory: 45927 loss: 0.1298 loss_ce: 0.1298 2023/02/28 12:56:09 - mmengine - INFO - Epoch(train) [96][1400/5047] lr: 1.5248e-05 eta: 2 days, 18:59:29 time: 0.9075 data_time: 0.0068 memory: 54276 loss: 0.1232 loss_ce: 0.1232 2023/02/28 12:57:35 - mmengine - INFO - Epoch(train) [96][1500/5047] lr: 1.5248e-05 eta: 2 days, 18:58:01 time: 0.8411 data_time: 0.0027 memory: 48948 loss: 0.1067 loss_ce: 0.1067 2023/02/28 12:58:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 12:59:00 - mmengine - INFO - Epoch(train) [96][1600/5047] lr: 1.5248e-05 eta: 2 days, 18:56:33 time: 0.8770 data_time: 0.0027 memory: 41097 loss: 0.1222 loss_ce: 0.1222 2023/02/28 13:00:26 - mmengine - INFO - Epoch(train) [96][1700/5047] lr: 1.5248e-05 eta: 2 days, 18:55:04 time: 0.8380 data_time: 0.0026 memory: 55562 loss: 0.1182 loss_ce: 0.1182 2023/02/28 13:01:53 - mmengine - INFO - Epoch(train) [96][1800/5047] lr: 1.5248e-05 eta: 2 days, 18:53:37 time: 0.8864 data_time: 0.0054 memory: 41724 loss: 0.1145 loss_ce: 0.1145 2023/02/28 13:03:19 - mmengine - INFO - Epoch(train) [96][1900/5047] lr: 1.5248e-05 eta: 2 days, 18:52:08 time: 0.8753 data_time: 0.0031 memory: 51350 loss: 0.1212 loss_ce: 0.1212 2023/02/28 13:04:44 - mmengine - INFO - Epoch(train) [96][2000/5047] lr: 1.5248e-05 eta: 2 days, 18:50:40 time: 0.8908 data_time: 0.0027 memory: 40241 loss: 0.1113 loss_ce: 0.1113 2023/02/28 13:06:10 - mmengine - INFO - Epoch(train) [96][2100/5047] lr: 1.5248e-05 eta: 2 days, 18:49:12 time: 0.8534 data_time: 0.0034 memory: 39681 loss: 0.1207 loss_ce: 0.1207 2023/02/28 13:07:36 - mmengine - INFO - Epoch(train) [96][2200/5047] lr: 1.5248e-05 eta: 2 days, 18:47:44 time: 0.8943 data_time: 0.0027 memory: 49715 loss: 0.1134 loss_ce: 0.1134 2023/02/28 13:09:03 - mmengine - INFO - Epoch(train) [96][2300/5047] lr: 1.5248e-05 eta: 2 days, 18:46:16 time: 0.8514 data_time: 0.0028 memory: 42336 loss: 0.1053 loss_ce: 0.1053 2023/02/28 13:10:29 - mmengine - INFO - Epoch(train) [96][2400/5047] lr: 1.5248e-05 eta: 2 days, 18:44:48 time: 0.8447 data_time: 0.0026 memory: 47447 loss: 0.1211 loss_ce: 0.1211 2023/02/28 13:11:54 - mmengine - INFO - Epoch(train) [96][2500/5047] lr: 1.5248e-05 eta: 2 days, 18:43:19 time: 0.8642 data_time: 0.0034 memory: 43630 loss: 0.1111 loss_ce: 0.1111 2023/02/28 13:12:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 13:13:21 - mmengine - INFO - Epoch(train) [96][2600/5047] lr: 1.5248e-05 eta: 2 days, 18:41:51 time: 0.9156 data_time: 0.0027 memory: 48948 loss: 0.1158 loss_ce: 0.1158 2023/02/28 13:14:46 - mmengine - INFO - Epoch(train) [96][2700/5047] lr: 1.5248e-05 eta: 2 days, 18:40:23 time: 0.8714 data_time: 0.0023 memory: 43141 loss: 0.0999 loss_ce: 0.0999 2023/02/28 13:16:11 - mmengine - INFO - Epoch(train) [96][2800/5047] lr: 1.5248e-05 eta: 2 days, 18:38:54 time: 0.8323 data_time: 0.0071 memory: 45457 loss: 0.1189 loss_ce: 0.1189 2023/02/28 13:17:38 - mmengine - INFO - Epoch(train) [96][2900/5047] lr: 1.5248e-05 eta: 2 days, 18:37:27 time: 0.8887 data_time: 0.0028 memory: 44810 loss: 0.1092 loss_ce: 0.1092 2023/02/28 13:19:04 - mmengine - INFO - Epoch(train) [96][3000/5047] lr: 1.5248e-05 eta: 2 days, 18:35:59 time: 0.8456 data_time: 0.0023 memory: 51658 loss: 0.1176 loss_ce: 0.1176 2023/02/28 13:20:30 - mmengine - INFO - Epoch(train) [96][3100/5047] lr: 1.5248e-05 eta: 2 days, 18:34:31 time: 0.8705 data_time: 0.0030 memory: 49712 loss: 0.1054 loss_ce: 0.1054 2023/02/28 13:21:58 - mmengine - INFO - Epoch(train) [96][3200/5047] lr: 1.5248e-05 eta: 2 days, 18:33:03 time: 0.8998 data_time: 0.0026 memory: 55562 loss: 0.1220 loss_ce: 0.1220 2023/02/28 13:23:24 - mmengine - INFO - Epoch(train) [96][3300/5047] lr: 1.5248e-05 eta: 2 days, 18:31:35 time: 0.8699 data_time: 0.0062 memory: 43585 loss: 0.1143 loss_ce: 0.1143 2023/02/28 13:24:50 - mmengine - INFO - Epoch(train) [96][3400/5047] lr: 1.5248e-05 eta: 2 days, 18:30:07 time: 0.8878 data_time: 0.0030 memory: 49648 loss: 0.1250 loss_ce: 0.1250 2023/02/28 13:26:16 - mmengine - INFO - Epoch(train) [96][3500/5047] lr: 1.5248e-05 eta: 2 days, 18:28:39 time: 0.8891 data_time: 0.0026 memory: 42649 loss: 0.1429 loss_ce: 0.1429 2023/02/28 13:26:47 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 13:27:44 - mmengine - INFO - Epoch(train) [96][3600/5047] lr: 1.5248e-05 eta: 2 days, 18:27:12 time: 0.8477 data_time: 0.0023 memory: 40825 loss: 0.1183 loss_ce: 0.1183 2023/02/28 13:29:08 - mmengine - INFO - Epoch(train) [96][3700/5047] lr: 1.5248e-05 eta: 2 days, 18:25:43 time: 0.8200 data_time: 0.0025 memory: 54303 loss: 0.1055 loss_ce: 0.1055 2023/02/28 13:30:33 - mmengine - INFO - Epoch(train) [96][3800/5047] lr: 1.5248e-05 eta: 2 days, 18:24:14 time: 0.8616 data_time: 0.0024 memory: 41998 loss: 0.1151 loss_ce: 0.1151 2023/02/28 13:31:59 - mmengine - INFO - Epoch(train) [96][3900/5047] lr: 1.5248e-05 eta: 2 days, 18:22:46 time: 0.8508 data_time: 0.0024 memory: 47920 loss: 0.1057 loss_ce: 0.1057 2023/02/28 13:33:30 - mmengine - INFO - Epoch(train) [96][4000/5047] lr: 1.5248e-05 eta: 2 days, 18:21:21 time: 0.8632 data_time: 0.0025 memory: 53021 loss: 0.1085 loss_ce: 0.1085 2023/02/28 13:34:56 - mmengine - INFO - Epoch(train) [96][4100/5047] lr: 1.5248e-05 eta: 2 days, 18:19:53 time: 0.8699 data_time: 0.0023 memory: 55562 loss: 0.1144 loss_ce: 0.1144 2023/02/28 13:36:23 - mmengine - INFO - Epoch(train) [96][4200/5047] lr: 1.5248e-05 eta: 2 days, 18:18:25 time: 0.8440 data_time: 0.0028 memory: 43613 loss: 0.0951 loss_ce: 0.0951 2023/02/28 13:37:48 - mmengine - INFO - Epoch(train) [96][4300/5047] lr: 1.5248e-05 eta: 2 days, 18:16:57 time: 0.8286 data_time: 0.0024 memory: 55327 loss: 0.1151 loss_ce: 0.1151 2023/02/28 13:39:14 - mmengine - INFO - Epoch(train) [96][4400/5047] lr: 1.5248e-05 eta: 2 days, 18:15:28 time: 0.8752 data_time: 0.0025 memory: 42024 loss: 0.1055 loss_ce: 0.1055 2023/02/28 13:40:38 - mmengine - INFO - Epoch(train) [96][4500/5047] lr: 1.5248e-05 eta: 2 days, 18:13:59 time: 0.8364 data_time: 0.0023 memory: 43289 loss: 0.1204 loss_ce: 0.1204 2023/02/28 13:41:09 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 13:42:05 - mmengine - INFO - Epoch(train) [96][4600/5047] lr: 1.5248e-05 eta: 2 days, 18:12:32 time: 0.8967 data_time: 0.0085 memory: 44956 loss: 0.1058 loss_ce: 0.1058 2023/02/28 13:43:31 - mmengine - INFO - Epoch(train) [96][4700/5047] lr: 1.5248e-05 eta: 2 days, 18:11:03 time: 0.8560 data_time: 0.0026 memory: 40535 loss: 0.1167 loss_ce: 0.1167 2023/02/28 13:44:56 - mmengine - INFO - Epoch(train) [96][4800/5047] lr: 1.5248e-05 eta: 2 days, 18:09:34 time: 0.8370 data_time: 0.0044 memory: 42649 loss: 0.1078 loss_ce: 0.1078 2023/02/28 13:46:21 - mmengine - INFO - Epoch(train) [96][4900/5047] lr: 1.5248e-05 eta: 2 days, 18:08:06 time: 0.8405 data_time: 0.0025 memory: 51719 loss: 0.1082 loss_ce: 0.1082 2023/02/28 13:47:48 - mmengine - INFO - Epoch(train) [96][5000/5047] lr: 1.5248e-05 eta: 2 days, 18:06:39 time: 0.8490 data_time: 0.0027 memory: 41996 loss: 0.1284 loss_ce: 0.1284 2023/02/28 13:48:28 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 13:48:28 - mmengine - INFO - Saving checkpoint at 96 epochs 2023/02/28 13:50:01 - mmengine - INFO - Epoch(train) [97][ 100/5047] lr: 1.5047e-05 eta: 2 days, 18:04:29 time: 0.8704 data_time: 0.0030 memory: 46720 loss: 0.1039 loss_ce: 0.1039 2023/02/28 13:51:27 - mmengine - INFO - Epoch(train) [97][ 200/5047] lr: 1.5047e-05 eta: 2 days, 18:03:01 time: 0.8511 data_time: 0.0022 memory: 46976 loss: 0.1154 loss_ce: 0.1154 2023/02/28 13:52:53 - mmengine - INFO - Epoch(train) [97][ 300/5047] lr: 1.5047e-05 eta: 2 days, 18:01:33 time: 0.8270 data_time: 0.0069 memory: 42965 loss: 0.1110 loss_ce: 0.1110 2023/02/28 13:54:19 - mmengine - INFO - Epoch(train) [97][ 400/5047] lr: 1.5047e-05 eta: 2 days, 18:00:05 time: 0.7946 data_time: 0.0036 memory: 41724 loss: 0.1154 loss_ce: 0.1154 2023/02/28 13:55:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 13:55:44 - mmengine - INFO - Epoch(train) [97][ 500/5047] lr: 1.5047e-05 eta: 2 days, 17:58:36 time: 0.8638 data_time: 0.0053 memory: 45448 loss: 0.1115 loss_ce: 0.1115 2023/02/28 13:57:09 - mmengine - INFO - Epoch(train) [97][ 600/5047] lr: 1.5047e-05 eta: 2 days, 17:57:07 time: 0.8914 data_time: 0.0053 memory: 44278 loss: 0.1022 loss_ce: 0.1022 2023/02/28 13:58:36 - mmengine - INFO - Epoch(train) [97][ 700/5047] lr: 1.5047e-05 eta: 2 days, 17:55:40 time: 0.8733 data_time: 0.0030 memory: 52964 loss: 0.1095 loss_ce: 0.1095 2023/02/28 14:00:01 - mmengine - INFO - Epoch(train) [97][ 800/5047] lr: 1.5047e-05 eta: 2 days, 17:54:11 time: 0.8244 data_time: 0.0040 memory: 44477 loss: 0.1083 loss_ce: 0.1083 2023/02/28 14:01:28 - mmengine - INFO - Epoch(train) [97][ 900/5047] lr: 1.5047e-05 eta: 2 days, 17:52:43 time: 0.8503 data_time: 0.0025 memory: 44927 loss: 0.1039 loss_ce: 0.1039 2023/02/28 14:02:52 - mmengine - INFO - Epoch(train) [97][1000/5047] lr: 1.5047e-05 eta: 2 days, 17:51:14 time: 0.8262 data_time: 0.0029 memory: 47813 loss: 0.1035 loss_ce: 0.1035 2023/02/28 14:04:17 - mmengine - INFO - Epoch(train) [97][1100/5047] lr: 1.5047e-05 eta: 2 days, 17:49:45 time: 0.8541 data_time: 0.0024 memory: 43289 loss: 0.0958 loss_ce: 0.0958 2023/02/28 14:05:43 - mmengine - INFO - Epoch(train) [97][1200/5047] lr: 1.5047e-05 eta: 2 days, 17:48:17 time: 0.8220 data_time: 0.0026 memory: 43289 loss: 0.1051 loss_ce: 0.1051 2023/02/28 14:07:09 - mmengine - INFO - Epoch(train) [97][1300/5047] lr: 1.5047e-05 eta: 2 days, 17:46:50 time: 0.8546 data_time: 0.0026 memory: 50589 loss: 0.1164 loss_ce: 0.1164 2023/02/28 14:08:33 - mmengine - INFO - Epoch(train) [97][1400/5047] lr: 1.5047e-05 eta: 2 days, 17:45:20 time: 0.8362 data_time: 0.0024 memory: 41122 loss: 0.1014 loss_ce: 0.1014 2023/02/28 14:09:49 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 14:09:58 - mmengine - INFO - Epoch(train) [97][1500/5047] lr: 1.5047e-05 eta: 2 days, 17:43:52 time: 0.8171 data_time: 0.0027 memory: 41161 loss: 0.1246 loss_ce: 0.1246 2023/02/28 14:11:25 - mmengine - INFO - Epoch(train) [97][1600/5047] lr: 1.5047e-05 eta: 2 days, 17:42:24 time: 0.8508 data_time: 0.0024 memory: 43683 loss: 0.1222 loss_ce: 0.1222 2023/02/28 14:12:51 - mmengine - INFO - Epoch(train) [97][1700/5047] lr: 1.5047e-05 eta: 2 days, 17:40:56 time: 0.8705 data_time: 0.0046 memory: 42965 loss: 0.1153 loss_ce: 0.1153 2023/02/28 14:18:15 - mmengine - INFO - Epoch(train) [97][1800/5047] lr: 1.5047e-05 eta: 2 days, 17:41:44 time: 0.8732 data_time: 0.0026 memory: 43090 loss: 0.1078 loss_ce: 0.1078 2023/02/28 14:19:41 - mmengine - INFO - Epoch(train) [97][1900/5047] lr: 1.5047e-05 eta: 2 days, 17:40:16 time: 0.8472 data_time: 0.0054 memory: 44565 loss: 0.0966 loss_ce: 0.0966 2023/02/28 14:21:08 - mmengine - INFO - Epoch(train) [97][2000/5047] lr: 1.5047e-05 eta: 2 days, 17:38:48 time: 0.8818 data_time: 0.0027 memory: 42494 loss: 0.1131 loss_ce: 0.1131 2023/02/28 14:22:35 - mmengine - INFO - Epoch(train) [97][2100/5047] lr: 1.5047e-05 eta: 2 days, 17:37:21 time: 0.8895 data_time: 0.0030 memory: 55562 loss: 0.1085 loss_ce: 0.1085 2023/02/28 14:24:01 - mmengine - INFO - Epoch(train) [97][2200/5047] lr: 1.5047e-05 eta: 2 days, 17:35:53 time: 0.8500 data_time: 0.0026 memory: 53025 loss: 0.1110 loss_ce: 0.1110 2023/02/28 14:25:26 - mmengine - INFO - Epoch(train) [97][2300/5047] lr: 1.5047e-05 eta: 2 days, 17:34:24 time: 0.9036 data_time: 0.0035 memory: 54044 loss: 0.1086 loss_ce: 0.1086 2023/02/28 14:26:51 - mmengine - INFO - Epoch(train) [97][2400/5047] lr: 1.5047e-05 eta: 2 days, 17:32:56 time: 0.8504 data_time: 0.0036 memory: 55562 loss: 0.1308 loss_ce: 0.1308 2023/02/28 14:28:07 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 14:28:17 - mmengine - INFO - Epoch(train) [97][2500/5047] lr: 1.5047e-05 eta: 2 days, 17:31:27 time: 0.8298 data_time: 0.0026 memory: 49117 loss: 0.1131 loss_ce: 0.1131 2023/02/28 14:29:42 - mmengine - INFO - Epoch(train) [97][2600/5047] lr: 1.5047e-05 eta: 2 days, 17:29:58 time: 0.8455 data_time: 0.0033 memory: 48539 loss: 0.1071 loss_ce: 0.1071 2023/02/28 14:31:07 - mmengine - INFO - Epoch(train) [97][2700/5047] lr: 1.5047e-05 eta: 2 days, 17:28:30 time: 0.8368 data_time: 0.0071 memory: 45652 loss: 0.0930 loss_ce: 0.0930 2023/02/28 14:32:34 - mmengine - INFO - Epoch(train) [97][2800/5047] lr: 1.5047e-05 eta: 2 days, 17:27:02 time: 0.8720 data_time: 0.0024 memory: 46005 loss: 0.1130 loss_ce: 0.1130 2023/02/28 14:34:00 - mmengine - INFO - Epoch(train) [97][2900/5047] lr: 1.5047e-05 eta: 2 days, 17:25:34 time: 0.8557 data_time: 0.0027 memory: 40241 loss: 0.1048 loss_ce: 0.1048 2023/02/28 14:35:25 - mmengine - INFO - Epoch(train) [97][3000/5047] lr: 1.5047e-05 eta: 2 days, 17:24:05 time: 0.8271 data_time: 0.0034 memory: 41724 loss: 0.1127 loss_ce: 0.1127 2023/02/28 14:36:50 - mmengine - INFO - Epoch(train) [97][3100/5047] lr: 1.5047e-05 eta: 2 days, 17:22:36 time: 0.8460 data_time: 0.0024 memory: 45643 loss: 0.1249 loss_ce: 0.1249 2023/02/28 14:38:16 - mmengine - INFO - Epoch(train) [97][3200/5047] lr: 1.5047e-05 eta: 2 days, 17:21:08 time: 0.8827 data_time: 0.0024 memory: 43294 loss: 0.1110 loss_ce: 0.1110 2023/02/28 14:39:41 - mmengine - INFO - Epoch(train) [97][3300/5047] lr: 1.5047e-05 eta: 2 days, 17:19:39 time: 0.8682 data_time: 0.0040 memory: 46524 loss: 0.1125 loss_ce: 0.1125 2023/02/28 14:41:07 - mmengine - INFO - Epoch(train) [97][3400/5047] lr: 1.5047e-05 eta: 2 days, 17:18:11 time: 0.8377 data_time: 0.0025 memory: 43947 loss: 0.1003 loss_ce: 0.1003 2023/02/28 14:42:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 14:42:34 - mmengine - INFO - Epoch(train) [97][3500/5047] lr: 1.5047e-05 eta: 2 days, 17:16:44 time: 0.8462 data_time: 0.0022 memory: 42024 loss: 0.1167 loss_ce: 0.1167 2023/02/28 14:43:58 - mmengine - INFO - Epoch(train) [97][3600/5047] lr: 1.5047e-05 eta: 2 days, 17:15:14 time: 0.8334 data_time: 0.0027 memory: 55562 loss: 0.1065 loss_ce: 0.1065 2023/02/28 14:45:25 - mmengine - INFO - Epoch(train) [97][3700/5047] lr: 1.5047e-05 eta: 2 days, 17:13:47 time: 0.8590 data_time: 0.0030 memory: 50220 loss: 0.0966 loss_ce: 0.0966 2023/02/28 14:46:50 - mmengine - INFO - Epoch(train) [97][3800/5047] lr: 1.5047e-05 eta: 2 days, 17:12:18 time: 0.8562 data_time: 0.0031 memory: 41027 loss: 0.1071 loss_ce: 0.1071 2023/02/28 14:48:17 - mmengine - INFO - Epoch(train) [97][3900/5047] lr: 1.5047e-05 eta: 2 days, 17:10:50 time: 0.8492 data_time: 0.0033 memory: 40535 loss: 0.1160 loss_ce: 0.1160 2023/02/28 14:49:42 - mmengine - INFO - Epoch(train) [97][4000/5047] lr: 1.5047e-05 eta: 2 days, 17:09:22 time: 0.8221 data_time: 0.0030 memory: 47918 loss: 0.1143 loss_ce: 0.1143 2023/02/28 14:51:10 - mmengine - INFO - Epoch(train) [97][4100/5047] lr: 1.5047e-05 eta: 2 days, 17:07:55 time: 0.8625 data_time: 0.0027 memory: 43919 loss: 0.1025 loss_ce: 0.1025 2023/02/28 14:52:36 - mmengine - INFO - Epoch(train) [97][4200/5047] lr: 1.5047e-05 eta: 2 days, 17:06:27 time: 0.8414 data_time: 0.0029 memory: 43774 loss: 0.1211 loss_ce: 0.1211 2023/02/28 14:54:03 - mmengine - INFO - Epoch(train) [97][4300/5047] lr: 1.5047e-05 eta: 2 days, 17:04:59 time: 0.8282 data_time: 0.0046 memory: 42649 loss: 0.1004 loss_ce: 0.1004 2023/02/28 14:55:29 - mmengine - INFO - Epoch(train) [97][4400/5047] lr: 1.5047e-05 eta: 2 days, 17:03:31 time: 0.8538 data_time: 0.0070 memory: 48657 loss: 0.1053 loss_ce: 0.1053 2023/02/28 14:56:45 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 14:56:55 - mmengine - INFO - Epoch(train) [97][4500/5047] lr: 1.5047e-05 eta: 2 days, 17:02:03 time: 0.8817 data_time: 0.0080 memory: 49312 loss: 0.1070 loss_ce: 0.1070 2023/02/28 14:58:22 - mmengine - INFO - Epoch(train) [97][4600/5047] lr: 1.5047e-05 eta: 2 days, 17:00:35 time: 0.8948 data_time: 0.0024 memory: 49217 loss: 0.1161 loss_ce: 0.1161 2023/02/28 14:59:46 - mmengine - INFO - Epoch(train) [97][4700/5047] lr: 1.5047e-05 eta: 2 days, 16:59:06 time: 0.8260 data_time: 0.0029 memory: 51308 loss: 0.1066 loss_ce: 0.1066 2023/02/28 15:01:12 - mmengine - INFO - Epoch(train) [97][4800/5047] lr: 1.5047e-05 eta: 2 days, 16:57:38 time: 0.8297 data_time: 0.0025 memory: 44956 loss: 0.1101 loss_ce: 0.1101 2023/02/28 15:02:38 - mmengine - INFO - Epoch(train) [97][4900/5047] lr: 1.5047e-05 eta: 2 days, 16:56:09 time: 0.8473 data_time: 0.0025 memory: 42336 loss: 0.1177 loss_ce: 0.1177 2023/02/28 15:04:04 - mmengine - INFO - Epoch(train) [97][5000/5047] lr: 1.5047e-05 eta: 2 days, 16:54:41 time: 0.8852 data_time: 0.0023 memory: 48565 loss: 0.1184 loss_ce: 0.1184 2023/02/28 15:04:44 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 15:04:44 - mmengine - INFO - Saving checkpoint at 97 epochs 2023/02/28 15:06:14 - mmengine - INFO - Epoch(train) [98][ 100/5047] lr: 1.4846e-05 eta: 2 days, 16:52:31 time: 0.8546 data_time: 0.0027 memory: 41461 loss: 0.1082 loss_ce: 0.1082 2023/02/28 15:07:39 - mmengine - INFO - Epoch(train) [98][ 200/5047] lr: 1.4846e-05 eta: 2 days, 16:51:02 time: 0.8059 data_time: 0.0026 memory: 41419 loss: 0.1132 loss_ce: 0.1132 2023/02/28 15:09:05 - mmengine - INFO - Epoch(train) [98][ 300/5047] lr: 1.4846e-05 eta: 2 days, 16:49:34 time: 0.8663 data_time: 0.0026 memory: 54137 loss: 0.1277 loss_ce: 0.1277 2023/02/28 15:10:30 - mmengine - INFO - Epoch(train) [98][ 400/5047] lr: 1.4846e-05 eta: 2 days, 16:48:05 time: 0.8622 data_time: 0.0024 memory: 40825 loss: 0.1000 loss_ce: 0.1000 2023/02/28 15:11:06 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 15:11:58 - mmengine - INFO - Epoch(train) [98][ 500/5047] lr: 1.4846e-05 eta: 2 days, 16:46:38 time: 0.8658 data_time: 0.0024 memory: 45785 loss: 0.0994 loss_ce: 0.0994 2023/02/28 15:13:23 - mmengine - INFO - Epoch(train) [98][ 600/5047] lr: 1.4846e-05 eta: 2 days, 16:45:09 time: 0.8955 data_time: 0.0026 memory: 41122 loss: 0.1121 loss_ce: 0.1121 2023/02/28 15:14:49 - mmengine - INFO - Epoch(train) [98][ 700/5047] lr: 1.4846e-05 eta: 2 days, 16:43:41 time: 0.8590 data_time: 0.0060 memory: 41122 loss: 0.1132 loss_ce: 0.1132 2023/02/28 15:16:15 - mmengine - INFO - Epoch(train) [98][ 800/5047] lr: 1.4846e-05 eta: 2 days, 16:42:14 time: 0.8794 data_time: 0.0026 memory: 50106 loss: 0.1093 loss_ce: 0.1093 2023/02/28 15:17:40 - mmengine - INFO - Epoch(train) [98][ 900/5047] lr: 1.4846e-05 eta: 2 days, 16:40:45 time: 0.7977 data_time: 0.0026 memory: 50106 loss: 0.1158 loss_ce: 0.1158 2023/02/28 15:19:07 - mmengine - INFO - Epoch(train) [98][1000/5047] lr: 1.4846e-05 eta: 2 days, 16:39:17 time: 0.8127 data_time: 0.0027 memory: 41724 loss: 0.1110 loss_ce: 0.1110 2023/02/28 15:20:34 - mmengine - INFO - Epoch(train) [98][1100/5047] lr: 1.4846e-05 eta: 2 days, 16:37:49 time: 0.8830 data_time: 0.0052 memory: 43604 loss: 0.1129 loss_ce: 0.1129 2023/02/28 15:22:01 - mmengine - INFO - Epoch(train) [98][1200/5047] lr: 1.4846e-05 eta: 2 days, 16:36:22 time: 0.8964 data_time: 0.0024 memory: 45908 loss: 0.1143 loss_ce: 0.1143 2023/02/28 15:23:27 - mmengine - INFO - Epoch(train) [98][1300/5047] lr: 1.4846e-05 eta: 2 days, 16:34:54 time: 0.8190 data_time: 0.0023 memory: 40522 loss: 0.1199 loss_ce: 0.1199 2023/02/28 15:24:55 - mmengine - INFO - Epoch(train) [98][1400/5047] lr: 1.4846e-05 eta: 2 days, 16:33:27 time: 0.8570 data_time: 0.0024 memory: 55562 loss: 0.1240 loss_ce: 0.1240 2023/02/28 15:25:30 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 15:26:21 - mmengine - INFO - Epoch(train) [98][1500/5047] lr: 1.4846e-05 eta: 2 days, 16:31:59 time: 0.8394 data_time: 0.0025 memory: 44617 loss: 0.1050 loss_ce: 0.1050 2023/02/28 15:27:45 - mmengine - INFO - Epoch(train) [98][1600/5047] lr: 1.4846e-05 eta: 2 days, 16:30:30 time: 0.8070 data_time: 0.0022 memory: 49590 loss: 0.1151 loss_ce: 0.1151 2023/02/28 15:29:12 - mmengine - INFO - Epoch(train) [98][1700/5047] lr: 1.4846e-05 eta: 2 days, 16:29:02 time: 0.9064 data_time: 0.0054 memory: 51719 loss: 0.1018 loss_ce: 0.1018 2023/02/28 15:30:38 - mmengine - INFO - Epoch(train) [98][1800/5047] lr: 1.4846e-05 eta: 2 days, 16:27:34 time: 0.9115 data_time: 0.0024 memory: 48074 loss: 0.1119 loss_ce: 0.1119 2023/02/28 15:32:02 - mmengine - INFO - Epoch(train) [98][1900/5047] lr: 1.4846e-05 eta: 2 days, 16:26:04 time: 0.7924 data_time: 0.0027 memory: 40825 loss: 0.1065 loss_ce: 0.1065 2023/02/28 15:33:27 - mmengine - INFO - Epoch(train) [98][2000/5047] lr: 1.4846e-05 eta: 2 days, 16:24:36 time: 0.8381 data_time: 0.0023 memory: 45268 loss: 0.1159 loss_ce: 0.1159 2023/02/28 15:34:52 - mmengine - INFO - Epoch(train) [98][2100/5047] lr: 1.4846e-05 eta: 2 days, 16:23:07 time: 0.8684 data_time: 0.0023 memory: 42328 loss: 0.1077 loss_ce: 0.1077 2023/02/28 15:36:18 - mmengine - INFO - Epoch(train) [98][2200/5047] lr: 1.4846e-05 eta: 2 days, 16:21:39 time: 0.8062 data_time: 0.0033 memory: 44867 loss: 0.1113 loss_ce: 0.1113 2023/02/28 15:37:42 - mmengine - INFO - Epoch(train) [98][2300/5047] lr: 1.4846e-05 eta: 2 days, 16:20:10 time: 0.8363 data_time: 0.0024 memory: 55562 loss: 0.1216 loss_ce: 0.1216 2023/02/28 15:39:11 - mmengine - INFO - Epoch(train) [98][2400/5047] lr: 1.4846e-05 eta: 2 days, 16:18:43 time: 0.8569 data_time: 0.0027 memory: 49121 loss: 0.1049 loss_ce: 0.1049 2023/02/28 15:39:47 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 15:40:38 - mmengine - INFO - Epoch(train) [98][2500/5047] lr: 1.4846e-05 eta: 2 days, 16:17:16 time: 0.8143 data_time: 0.0028 memory: 55114 loss: 0.1059 loss_ce: 0.1059 2023/02/28 15:42:02 - mmengine - INFO - Epoch(train) [98][2600/5047] lr: 1.4846e-05 eta: 2 days, 16:15:47 time: 0.8310 data_time: 0.0024 memory: 43044 loss: 0.1033 loss_ce: 0.1033 2023/02/28 15:43:30 - mmengine - INFO - Epoch(train) [98][2700/5047] lr: 1.4846e-05 eta: 2 days, 16:14:19 time: 0.8557 data_time: 0.0024 memory: 41552 loss: 0.1060 loss_ce: 0.1060 2023/02/28 15:44:59 - mmengine - INFO - Epoch(train) [98][2800/5047] lr: 1.4846e-05 eta: 2 days, 16:12:53 time: 0.8158 data_time: 0.0025 memory: 47011 loss: 0.1117 loss_ce: 0.1117 2023/02/28 15:46:24 - mmengine - INFO - Epoch(train) [98][2900/5047] lr: 1.4846e-05 eta: 2 days, 16:11:25 time: 0.8431 data_time: 0.0024 memory: 45927 loss: 0.1167 loss_ce: 0.1167 2023/02/28 15:47:50 - mmengine - INFO - Epoch(train) [98][3000/5047] lr: 1.4846e-05 eta: 2 days, 16:09:56 time: 0.8220 data_time: 0.0087 memory: 42965 loss: 0.1064 loss_ce: 0.1064 2023/02/28 15:49:15 - mmengine - INFO - Epoch(train) [98][3100/5047] lr: 1.4846e-05 eta: 2 days, 16:08:27 time: 0.8674 data_time: 0.0024 memory: 50505 loss: 0.1114 loss_ce: 0.1114 2023/02/28 15:50:41 - mmengine - INFO - Epoch(train) [98][3200/5047] lr: 1.4846e-05 eta: 2 days, 16:06:59 time: 0.8992 data_time: 0.0022 memory: 48948 loss: 0.1183 loss_ce: 0.1183 2023/02/28 15:52:06 - mmengine - INFO - Epoch(train) [98][3300/5047] lr: 1.4846e-05 eta: 2 days, 16:05:31 time: 0.8731 data_time: 0.0027 memory: 40825 loss: 0.1083 loss_ce: 0.1083 2023/02/28 15:53:32 - mmengine - INFO - Epoch(train) [98][3400/5047] lr: 1.4846e-05 eta: 2 days, 16:04:03 time: 0.8480 data_time: 0.0069 memory: 39960 loss: 0.1132 loss_ce: 0.1132 2023/02/28 15:54:07 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 15:54:59 - mmengine - INFO - Epoch(train) [98][3500/5047] lr: 1.4846e-05 eta: 2 days, 16:02:35 time: 0.8500 data_time: 0.0054 memory: 40241 loss: 0.1233 loss_ce: 0.1233 2023/02/28 15:56:26 - mmengine - INFO - Epoch(train) [98][3600/5047] lr: 1.4846e-05 eta: 2 days, 16:01:07 time: 0.8755 data_time: 0.0024 memory: 44478 loss: 0.1029 loss_ce: 0.1029 2023/02/28 15:57:52 - mmengine - INFO - Epoch(train) [98][3700/5047] lr: 1.4846e-05 eta: 2 days, 15:59:39 time: 0.8086 data_time: 0.0026 memory: 47813 loss: 0.1201 loss_ce: 0.1201 2023/02/28 15:59:19 - mmengine - INFO - Epoch(train) [98][3800/5047] lr: 1.4846e-05 eta: 2 days, 15:58:12 time: 0.8799 data_time: 0.0027 memory: 42915 loss: 0.1075 loss_ce: 0.1075 2023/02/28 16:00:45 - mmengine - INFO - Epoch(train) [98][3900/5047] lr: 1.4846e-05 eta: 2 days, 15:56:44 time: 0.8385 data_time: 0.0027 memory: 43942 loss: 0.0976 loss_ce: 0.0976 2023/02/28 16:02:12 - mmengine - INFO - Epoch(train) [98][4000/5047] lr: 1.4846e-05 eta: 2 days, 15:55:16 time: 0.8364 data_time: 0.0029 memory: 53809 loss: 0.1158 loss_ce: 0.1158 2023/02/28 16:03:38 - mmengine - INFO - Epoch(train) [98][4100/5047] lr: 1.4846e-05 eta: 2 days, 15:53:48 time: 0.9020 data_time: 0.0026 memory: 45302 loss: 0.1147 loss_ce: 0.1147 2023/02/28 16:05:04 - mmengine - INFO - Epoch(train) [98][4200/5047] lr: 1.4846e-05 eta: 2 days, 15:52:20 time: 0.8530 data_time: 0.0024 memory: 43967 loss: 0.1106 loss_ce: 0.1106 2023/02/28 16:06:31 - mmengine - INFO - Epoch(train) [98][4300/5047] lr: 1.4846e-05 eta: 2 days, 15:50:52 time: 0.8737 data_time: 0.0025 memory: 40261 loss: 0.1057 loss_ce: 0.1057 2023/02/28 16:07:57 - mmengine - INFO - Epoch(train) [98][4400/5047] lr: 1.4846e-05 eta: 2 days, 15:49:24 time: 0.8563 data_time: 0.0026 memory: 42965 loss: 0.1109 loss_ce: 0.1109 2023/02/28 16:08:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 16:09:24 - mmengine - INFO - Epoch(train) [98][4500/5047] lr: 1.4846e-05 eta: 2 days, 15:47:57 time: 0.9137 data_time: 0.0023 memory: 48035 loss: 0.0984 loss_ce: 0.0984 2023/02/28 16:10:49 - mmengine - INFO - Epoch(train) [98][4600/5047] lr: 1.4846e-05 eta: 2 days, 15:46:28 time: 0.8454 data_time: 0.0025 memory: 42593 loss: 0.1178 loss_ce: 0.1178 2023/02/28 16:12:16 - mmengine - INFO - Epoch(train) [98][4700/5047] lr: 1.4846e-05 eta: 2 days, 15:45:01 time: 0.9164 data_time: 0.0025 memory: 47447 loss: 0.1279 loss_ce: 0.1279 2023/02/28 16:13:43 - mmengine - INFO - Epoch(train) [98][4800/5047] lr: 1.4846e-05 eta: 2 days, 15:43:33 time: 0.8637 data_time: 0.0029 memory: 42336 loss: 0.1190 loss_ce: 0.1190 2023/02/28 16:15:08 - mmengine - INFO - Epoch(train) [98][4900/5047] lr: 1.4846e-05 eta: 2 days, 15:42:04 time: 0.8122 data_time: 0.0025 memory: 49280 loss: 0.1179 loss_ce: 0.1179 2023/02/28 16:16:37 - mmengine - INFO - Epoch(train) [98][5000/5047] lr: 1.4846e-05 eta: 2 days, 15:40:38 time: 0.8226 data_time: 0.0024 memory: 41419 loss: 0.1178 loss_ce: 0.1178 2023/02/28 16:17:18 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 16:17:18 - mmengine - INFO - Saving checkpoint at 98 epochs 2023/02/28 16:18:52 - mmengine - INFO - Epoch(train) [99][ 100/5047] lr: 1.4645e-05 eta: 2 days, 15:38:29 time: 0.9184 data_time: 0.0053 memory: 51817 loss: 0.1167 loss_ce: 0.1167 2023/02/28 16:20:18 - mmengine - INFO - Epoch(train) [99][ 200/5047] lr: 1.4645e-05 eta: 2 days, 15:37:01 time: 0.8164 data_time: 0.0023 memory: 42418 loss: 0.1145 loss_ce: 0.1145 2023/02/28 16:21:44 - mmengine - INFO - Epoch(train) [99][ 300/5047] lr: 1.4645e-05 eta: 2 days, 15:35:33 time: 0.8682 data_time: 0.0025 memory: 41425 loss: 0.1052 loss_ce: 0.1052 2023/02/28 16:23:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 16:23:09 - mmengine - INFO - Epoch(train) [99][ 400/5047] lr: 1.4645e-05 eta: 2 days, 15:34:04 time: 0.8045 data_time: 0.0045 memory: 53387 loss: 0.1128 loss_ce: 0.1128 2023/02/28 16:24:38 - mmengine - INFO - Epoch(train) [99][ 500/5047] lr: 1.4645e-05 eta: 2 days, 15:32:38 time: 0.8654 data_time: 0.0029 memory: 40241 loss: 0.1170 loss_ce: 0.1170 2023/02/28 16:26:04 - mmengine - INFO - Epoch(train) [99][ 600/5047] lr: 1.4645e-05 eta: 2 days, 15:31:10 time: 0.9138 data_time: 0.0063 memory: 42024 loss: 0.1032 loss_ce: 0.1032 2023/02/28 16:27:30 - mmengine - INFO - Epoch(train) [99][ 700/5047] lr: 1.4645e-05 eta: 2 days, 15:29:42 time: 0.8825 data_time: 0.0023 memory: 50106 loss: 0.1216 loss_ce: 0.1216 2023/02/28 16:28:56 - mmengine - INFO - Epoch(train) [99][ 800/5047] lr: 1.4645e-05 eta: 2 days, 15:28:13 time: 0.8417 data_time: 0.0024 memory: 49270 loss: 0.1058 loss_ce: 0.1058 2023/02/28 16:30:21 - mmengine - INFO - Epoch(train) [99][ 900/5047] lr: 1.4645e-05 eta: 2 days, 15:26:45 time: 0.8200 data_time: 0.0026 memory: 51719 loss: 0.1221 loss_ce: 0.1221 2023/02/28 16:31:47 - mmengine - INFO - Epoch(train) [99][1000/5047] lr: 1.4645e-05 eta: 2 days, 15:25:17 time: 0.8403 data_time: 0.0025 memory: 51755 loss: 0.1202 loss_ce: 0.1202 2023/02/28 16:33:14 - mmengine - INFO - Epoch(train) [99][1100/5047] lr: 1.4645e-05 eta: 2 days, 15:23:49 time: 0.8223 data_time: 0.0027 memory: 44023 loss: 0.1085 loss_ce: 0.1085 2023/02/28 16:34:41 - mmengine - INFO - Epoch(train) [99][1200/5047] lr: 1.4645e-05 eta: 2 days, 15:22:22 time: 0.8581 data_time: 0.0034 memory: 51732 loss: 0.0950 loss_ce: 0.0950 2023/02/28 16:36:07 - mmengine - INFO - Epoch(train) [99][1300/5047] lr: 1.4645e-05 eta: 2 days, 15:20:54 time: 0.8472 data_time: 0.0047 memory: 43960 loss: 0.1132 loss_ce: 0.1132 2023/02/28 16:37:26 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 16:37:31 - mmengine - INFO - Epoch(train) [99][1400/5047] lr: 1.4645e-05 eta: 2 days, 15:19:25 time: 0.8277 data_time: 0.0026 memory: 43745 loss: 0.1071 loss_ce: 0.1071 2023/02/28 16:38:57 - mmengine - INFO - Epoch(train) [99][1500/5047] lr: 1.4645e-05 eta: 2 days, 15:17:56 time: 0.8237 data_time: 0.0023 memory: 41601 loss: 0.0973 loss_ce: 0.0973 2023/02/28 16:40:23 - mmengine - INFO - Epoch(train) [99][1600/5047] lr: 1.4645e-05 eta: 2 days, 15:16:28 time: 0.7970 data_time: 0.0022 memory: 42336 loss: 0.1072 loss_ce: 0.1072 2023/02/28 16:41:49 - mmengine - INFO - Epoch(train) [99][1700/5047] lr: 1.4645e-05 eta: 2 days, 15:15:00 time: 0.8513 data_time: 0.0027 memory: 43463 loss: 0.1146 loss_ce: 0.1146 2023/02/28 16:43:16 - mmengine - INFO - Epoch(train) [99][1800/5047] lr: 1.4645e-05 eta: 2 days, 15:13:32 time: 0.8659 data_time: 0.0026 memory: 46713 loss: 0.1084 loss_ce: 0.1084 2023/02/28 16:44:43 - mmengine - INFO - Epoch(train) [99][1900/5047] lr: 1.4645e-05 eta: 2 days, 15:12:05 time: 0.8891 data_time: 0.0024 memory: 42233 loss: 0.1024 loss_ce: 0.1024 2023/02/28 16:46:10 - mmengine - INFO - Epoch(train) [99][2000/5047] lr: 1.4645e-05 eta: 2 days, 15:10:37 time: 0.8340 data_time: 0.0025 memory: 42649 loss: 0.1037 loss_ce: 0.1037 2023/02/28 16:47:38 - mmengine - INFO - Epoch(train) [99][2100/5047] lr: 1.4645e-05 eta: 2 days, 15:09:11 time: 1.0282 data_time: 0.0031 memory: 48519 loss: 0.1156 loss_ce: 0.1156 2023/02/28 16:49:11 - mmengine - INFO - Epoch(train) [99][2200/5047] lr: 1.4645e-05 eta: 2 days, 15:07:46 time: 0.8959 data_time: 0.0027 memory: 42574 loss: 0.1062 loss_ce: 0.1062 2023/02/28 16:50:36 - mmengine - INFO - Epoch(train) [99][2300/5047] lr: 1.4645e-05 eta: 2 days, 15:06:18 time: 0.8301 data_time: 0.0036 memory: 43613 loss: 0.1119 loss_ce: 0.1119 2023/02/28 16:51:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 16:52:02 - mmengine - INFO - Epoch(train) [99][2400/5047] lr: 1.4645e-05 eta: 2 days, 15:04:50 time: 0.8575 data_time: 0.0039 memory: 43872 loss: 0.1069 loss_ce: 0.1069 2023/02/28 16:53:30 - mmengine - INFO - Epoch(train) [99][2500/5047] lr: 1.4645e-05 eta: 2 days, 15:03:23 time: 0.8120 data_time: 0.0027 memory: 43289 loss: 0.1056 loss_ce: 0.1056 2023/02/28 16:54:56 - mmengine - INFO - Epoch(train) [99][2600/5047] lr: 1.4645e-05 eta: 2 days, 15:01:55 time: 0.8702 data_time: 0.0027 memory: 41724 loss: 0.1129 loss_ce: 0.1129 2023/02/28 16:56:23 - mmengine - INFO - Epoch(train) [99][2700/5047] lr: 1.4645e-05 eta: 2 days, 15:00:27 time: 0.8606 data_time: 0.0025 memory: 54135 loss: 0.1027 loss_ce: 0.1027 2023/02/28 16:57:49 - mmengine - INFO - Epoch(train) [99][2800/5047] lr: 1.4645e-05 eta: 2 days, 14:58:59 time: 0.8438 data_time: 0.0024 memory: 45851 loss: 0.1023 loss_ce: 0.1023 2023/02/28 16:59:16 - mmengine - INFO - Epoch(train) [99][2900/5047] lr: 1.4645e-05 eta: 2 days, 14:57:31 time: 0.8459 data_time: 0.0029 memory: 44956 loss: 0.1046 loss_ce: 0.1046 2023/02/28 17:00:43 - mmengine - INFO - Epoch(train) [99][3000/5047] lr: 1.4645e-05 eta: 2 days, 14:56:04 time: 0.8573 data_time: 0.0027 memory: 48188 loss: 0.1136 loss_ce: 0.1136 2023/02/28 17:02:08 - mmengine - INFO - Epoch(train) [99][3100/5047] lr: 1.4645e-05 eta: 2 days, 14:54:35 time: 0.8280 data_time: 0.0024 memory: 42649 loss: 0.1048 loss_ce: 0.1048 2023/02/28 17:03:35 - mmengine - INFO - Epoch(train) [99][3200/5047] lr: 1.4645e-05 eta: 2 days, 14:53:08 time: 0.9072 data_time: 0.0022 memory: 46764 loss: 0.1003 loss_ce: 0.1003 2023/02/28 17:05:02 - mmengine - INFO - Epoch(train) [99][3300/5047] lr: 1.4645e-05 eta: 2 days, 14:51:40 time: 0.8551 data_time: 0.0036 memory: 49334 loss: 0.1191 loss_ce: 0.1191 2023/02/28 17:06:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 17:06:28 - mmengine - INFO - Epoch(train) [99][3400/5047] lr: 1.4645e-05 eta: 2 days, 14:50:12 time: 0.8473 data_time: 0.0023 memory: 55562 loss: 0.1084 loss_ce: 0.1084 2023/02/28 17:07:54 - mmengine - INFO - Epoch(train) [99][3500/5047] lr: 1.4645e-05 eta: 2 days, 14:48:44 time: 0.8745 data_time: 0.0025 memory: 41824 loss: 0.1039 loss_ce: 0.1039 2023/02/28 17:09:19 - mmengine - INFO - Epoch(train) [99][3600/5047] lr: 1.4645e-05 eta: 2 days, 14:47:15 time: 0.8749 data_time: 0.0031 memory: 42168 loss: 0.1076 loss_ce: 0.1076 2023/02/28 17:10:46 - mmengine - INFO - Epoch(train) [99][3700/5047] lr: 1.4645e-05 eta: 2 days, 14:45:48 time: 0.8451 data_time: 0.0025 memory: 42336 loss: 0.1026 loss_ce: 0.1026 2023/02/28 17:12:10 - mmengine - INFO - Epoch(train) [99][3800/5047] lr: 1.4645e-05 eta: 2 days, 14:44:19 time: 0.8511 data_time: 0.0023 memory: 43613 loss: 0.1177 loss_ce: 0.1177 2023/02/28 17:13:36 - mmengine - INFO - Epoch(train) [99][3900/5047] lr: 1.4645e-05 eta: 2 days, 14:42:51 time: 0.8941 data_time: 0.0022 memory: 55562 loss: 0.0966 loss_ce: 0.0966 2023/02/28 17:15:04 - mmengine - INFO - Epoch(train) [99][4000/5047] lr: 1.4645e-05 eta: 2 days, 14:41:23 time: 0.8761 data_time: 0.0023 memory: 41724 loss: 0.1031 loss_ce: 0.1031 2023/02/28 17:16:29 - mmengine - INFO - Epoch(train) [99][4100/5047] lr: 1.4645e-05 eta: 2 days, 14:39:55 time: 0.8292 data_time: 0.0024 memory: 41964 loss: 0.1027 loss_ce: 0.1027 2023/02/28 17:17:56 - mmengine - INFO - Epoch(train) [99][4200/5047] lr: 1.4645e-05 eta: 2 days, 14:38:27 time: 0.8693 data_time: 0.0024 memory: 45297 loss: 0.1029 loss_ce: 0.1029 2023/02/28 17:19:22 - mmengine - INFO - Epoch(train) [99][4300/5047] lr: 1.4645e-05 eta: 2 days, 14:36:59 time: 0.8561 data_time: 0.0037 memory: 55562 loss: 0.1219 loss_ce: 0.1219 2023/02/28 17:20:41 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 17:20:47 - mmengine - INFO - Epoch(train) [99][4400/5047] lr: 1.4645e-05 eta: 2 days, 14:35:31 time: 0.8591 data_time: 0.0023 memory: 41419 loss: 0.1029 loss_ce: 0.1029 2023/02/28 17:22:13 - mmengine - INFO - Epoch(train) [99][4500/5047] lr: 1.4645e-05 eta: 2 days, 14:34:03 time: 0.8422 data_time: 0.0023 memory: 44754 loss: 0.1092 loss_ce: 0.1092 2023/02/28 17:23:40 - mmengine - INFO - Epoch(train) [99][4600/5047] lr: 1.4645e-05 eta: 2 days, 14:32:35 time: 0.8936 data_time: 0.0032 memory: 46191 loss: 0.1125 loss_ce: 0.1125 2023/02/28 17:25:04 - mmengine - INFO - Epoch(train) [99][4700/5047] lr: 1.4645e-05 eta: 2 days, 14:31:06 time: 0.8107 data_time: 0.0025 memory: 44956 loss: 0.1066 loss_ce: 0.1066 2023/02/28 17:26:31 - mmengine - INFO - Epoch(train) [99][4800/5047] lr: 1.4645e-05 eta: 2 days, 14:29:39 time: 0.8794 data_time: 0.0024 memory: 51585 loss: 0.1144 loss_ce: 0.1144 2023/02/28 17:27:58 - mmengine - INFO - Epoch(train) [99][4900/5047] lr: 1.4645e-05 eta: 2 days, 14:28:11 time: 0.9057 data_time: 0.0025 memory: 45302 loss: 0.1166 loss_ce: 0.1166 2023/02/28 17:29:23 - mmengine - INFO - Epoch(train) [99][5000/5047] lr: 1.4645e-05 eta: 2 days, 14:26:42 time: 0.8394 data_time: 0.0027 memory: 42234 loss: 0.1067 loss_ce: 0.1067 2023/02/28 17:30:03 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 17:30:03 - mmengine - INFO - Saving checkpoint at 99 epochs 2023/02/28 17:31:32 - mmengine - INFO - Epoch(train) [100][ 100/5047] lr: 1.4444e-05 eta: 2 days, 14:24:32 time: 0.7853 data_time: 0.0023 memory: 41985 loss: 0.1203 loss_ce: 0.1203 2023/02/28 17:32:58 - mmengine - INFO - Epoch(train) [100][ 200/5047] lr: 1.4444e-05 eta: 2 days, 14:23:04 time: 0.8755 data_time: 0.0025 memory: 55562 loss: 0.1023 loss_ce: 0.1023 2023/02/28 17:34:24 - mmengine - INFO - Epoch(train) [100][ 300/5047] lr: 1.4444e-05 eta: 2 days, 14:21:36 time: 0.8513 data_time: 0.0029 memory: 52349 loss: 0.0999 loss_ce: 0.0999 2023/02/28 17:35:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 17:35:51 - mmengine - INFO - Epoch(train) [100][ 400/5047] lr: 1.4444e-05 eta: 2 days, 14:20:08 time: 0.8443 data_time: 0.0025 memory: 44546 loss: 0.1158 loss_ce: 0.1158 2023/02/28 17:37:18 - mmengine - INFO - Epoch(train) [100][ 500/5047] lr: 1.4444e-05 eta: 2 days, 14:18:40 time: 0.8928 data_time: 0.0030 memory: 46713 loss: 0.1254 loss_ce: 0.1254 2023/02/28 17:38:44 - mmengine - INFO - Epoch(train) [100][ 600/5047] lr: 1.4444e-05 eta: 2 days, 14:17:12 time: 0.8323 data_time: 0.0023 memory: 41115 loss: 0.1256 loss_ce: 0.1256 2023/02/28 17:40:10 - mmengine - INFO - Epoch(train) [100][ 700/5047] lr: 1.4444e-05 eta: 2 days, 14:15:45 time: 0.8899 data_time: 0.0026 memory: 44956 loss: 0.1246 loss_ce: 0.1246 2023/02/28 17:41:36 - mmengine - INFO - Epoch(train) [100][ 800/5047] lr: 1.4444e-05 eta: 2 days, 14:14:16 time: 0.8182 data_time: 0.0023 memory: 54041 loss: 0.1069 loss_ce: 0.1069 2023/02/28 17:43:02 - mmengine - INFO - Epoch(train) [100][ 900/5047] lr: 1.4444e-05 eta: 2 days, 14:12:48 time: 0.9057 data_time: 0.0023 memory: 44788 loss: 0.1219 loss_ce: 0.1219 2023/02/28 17:44:29 - mmengine - INFO - Epoch(train) [100][1000/5047] lr: 1.4444e-05 eta: 2 days, 14:11:20 time: 0.8460 data_time: 0.0023 memory: 41419 loss: 0.1128 loss_ce: 0.1128 2023/02/28 17:45:54 - mmengine - INFO - Epoch(train) [100][1100/5047] lr: 1.4444e-05 eta: 2 days, 14:09:52 time: 0.8475 data_time: 0.0026 memory: 46629 loss: 0.1012 loss_ce: 0.1012 2023/02/28 17:47:21 - mmengine - INFO - Epoch(train) [100][1200/5047] lr: 1.4444e-05 eta: 2 days, 14:08:24 time: 0.8835 data_time: 0.0034 memory: 47037 loss: 0.0994 loss_ce: 0.0994 2023/02/28 17:48:47 - mmengine - INFO - Epoch(train) [100][1300/5047] lr: 1.4444e-05 eta: 2 days, 14:06:56 time: 0.9210 data_time: 0.0024 memory: 42649 loss: 0.0984 loss_ce: 0.0984 2023/02/28 17:49:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 17:50:12 - mmengine - INFO - Epoch(train) [100][1400/5047] lr: 1.4444e-05 eta: 2 days, 14:05:28 time: 0.8449 data_time: 0.0038 memory: 55562 loss: 0.1029 loss_ce: 0.1029 2023/02/28 17:51:37 - mmengine - INFO - Epoch(train) [100][1500/5047] lr: 1.4444e-05 eta: 2 days, 14:03:59 time: 0.8295 data_time: 0.0027 memory: 41724 loss: 0.1053 loss_ce: 0.1053 2023/02/28 17:53:04 - mmengine - INFO - Epoch(train) [100][1600/5047] lr: 1.4444e-05 eta: 2 days, 14:02:32 time: 0.8992 data_time: 0.0024 memory: 41984 loss: 0.0972 loss_ce: 0.0972 2023/02/28 17:54:30 - mmengine - INFO - Epoch(train) [100][1700/5047] lr: 1.4444e-05 eta: 2 days, 14:01:03 time: 0.8508 data_time: 0.0025 memory: 43947 loss: 0.1024 loss_ce: 0.1024 2023/02/28 17:55:55 - mmengine - INFO - Epoch(train) [100][1800/5047] lr: 1.4444e-05 eta: 2 days, 13:59:35 time: 0.8875 data_time: 0.0033 memory: 42649 loss: 0.1016 loss_ce: 0.1016 2023/02/28 17:57:21 - mmengine - INFO - Epoch(train) [100][1900/5047] lr: 1.4444e-05 eta: 2 days, 13:58:07 time: 0.8610 data_time: 0.0024 memory: 45643 loss: 0.1016 loss_ce: 0.1016 2023/02/28 17:58:46 - mmengine - INFO - Epoch(train) [100][2000/5047] lr: 1.4444e-05 eta: 2 days, 13:56:38 time: 0.8254 data_time: 0.0027 memory: 45294 loss: 0.1187 loss_ce: 0.1187 2023/02/28 18:00:11 - mmengine - INFO - Epoch(train) [100][2100/5047] lr: 1.4444e-05 eta: 2 days, 13:55:10 time: 0.8687 data_time: 0.0074 memory: 45302 loss: 0.1256 loss_ce: 0.1256 2023/02/28 18:01:37 - mmengine - INFO - Epoch(train) [100][2200/5047] lr: 1.4444e-05 eta: 2 days, 13:53:42 time: 0.8872 data_time: 0.0025 memory: 47138 loss: 0.1293 loss_ce: 0.1293 2023/02/28 18:03:02 - mmengine - INFO - Epoch(train) [100][2300/5047] lr: 1.4444e-05 eta: 2 days, 13:52:13 time: 0.8333 data_time: 0.0028 memory: 42216 loss: 0.1054 loss_ce: 0.1054 2023/02/28 18:03:43 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 18:04:29 - mmengine - INFO - Epoch(train) [100][2400/5047] lr: 1.4444e-05 eta: 2 days, 13:50:46 time: 0.8762 data_time: 0.0028 memory: 41391 loss: 0.1135 loss_ce: 0.1135 2023/02/28 18:05:54 - mmengine - INFO - Epoch(train) [100][2500/5047] lr: 1.4444e-05 eta: 2 days, 13:49:17 time: 0.8688 data_time: 0.0055 memory: 41724 loss: 0.1134 loss_ce: 0.1134 2023/02/28 18:07:20 - mmengine - INFO - Epoch(train) [100][2600/5047] lr: 1.4444e-05 eta: 2 days, 13:47:49 time: 0.8299 data_time: 0.0027 memory: 45642 loss: 0.1279 loss_ce: 0.1279 2023/02/28 18:08:44 - mmengine - INFO - Epoch(train) [100][2700/5047] lr: 1.4444e-05 eta: 2 days, 13:46:20 time: 0.8456 data_time: 0.0050 memory: 42336 loss: 0.1079 loss_ce: 0.1079 2023/02/28 18:10:10 - mmengine - INFO - Epoch(train) [100][2800/5047] lr: 1.4444e-05 eta: 2 days, 13:44:52 time: 0.8773 data_time: 0.0023 memory: 39960 loss: 0.1094 loss_ce: 0.1094 2023/02/28 18:11:36 - mmengine - INFO - Epoch(train) [100][2900/5047] lr: 1.4444e-05 eta: 2 days, 13:43:24 time: 0.8568 data_time: 0.0030 memory: 43289 loss: 0.1052 loss_ce: 0.1052 2023/02/28 18:13:02 - mmengine - INFO - Epoch(train) [100][3000/5047] lr: 1.4444e-05 eta: 2 days, 13:41:56 time: 0.8700 data_time: 0.0030 memory: 51817 loss: 0.0917 loss_ce: 0.0917 2023/02/28 18:14:28 - mmengine - INFO - Epoch(train) [100][3100/5047] lr: 1.4444e-05 eta: 2 days, 13:40:28 time: 0.8036 data_time: 0.0029 memory: 41724 loss: 0.1126 loss_ce: 0.1126 2023/02/28 18:15:55 - mmengine - INFO - Epoch(train) [100][3200/5047] lr: 1.4444e-05 eta: 2 days, 13:39:00 time: 0.8987 data_time: 0.0025 memory: 51732 loss: 0.1056 loss_ce: 0.1056 2023/02/28 18:17:21 - mmengine - INFO - Epoch(train) [100][3300/5047] lr: 1.4444e-05 eta: 2 days, 13:37:32 time: 0.8950 data_time: 0.0023 memory: 47037 loss: 0.1244 loss_ce: 0.1244 2023/02/28 18:18:01 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 18:18:46 - mmengine - INFO - Epoch(train) [100][3400/5047] lr: 1.4444e-05 eta: 2 days, 13:36:04 time: 0.8202 data_time: 0.0043 memory: 46355 loss: 0.1063 loss_ce: 0.1063 2023/02/28 18:20:12 - mmengine - INFO - Epoch(train) [100][3500/5047] lr: 1.4444e-05 eta: 2 days, 13:34:36 time: 0.8792 data_time: 0.0024 memory: 49389 loss: 0.1070 loss_ce: 0.1070 2023/02/28 18:21:36 - mmengine - INFO - Epoch(train) [100][3600/5047] lr: 1.4444e-05 eta: 2 days, 13:33:07 time: 0.8673 data_time: 0.0028 memory: 44869 loss: 0.1002 loss_ce: 0.1002 2023/02/28 18:23:03 - mmengine - INFO - Epoch(train) [100][3700/5047] lr: 1.4444e-05 eta: 2 days, 13:31:39 time: 0.8358 data_time: 0.0025 memory: 42649 loss: 0.1064 loss_ce: 0.1064 2023/02/28 18:24:29 - mmengine - INFO - Epoch(train) [100][3800/5047] lr: 1.4444e-05 eta: 2 days, 13:30:11 time: 0.8784 data_time: 0.0026 memory: 40323 loss: 0.0997 loss_ce: 0.0997 2023/02/28 18:25:53 - mmengine - INFO - Epoch(train) [100][3900/5047] lr: 1.4444e-05 eta: 2 days, 13:28:42 time: 0.8261 data_time: 0.0026 memory: 43348 loss: 0.0998 loss_ce: 0.0998 2023/02/28 18:27:19 - mmengine - INFO - Epoch(train) [100][4000/5047] lr: 1.4444e-05 eta: 2 days, 13:27:14 time: 0.9018 data_time: 0.0045 memory: 49378 loss: 0.0992 loss_ce: 0.0992 2023/02/28 18:28:44 - mmengine - INFO - Epoch(train) [100][4100/5047] lr: 1.4444e-05 eta: 2 days, 13:25:46 time: 0.8855 data_time: 0.0029 memory: 44648 loss: 0.1097 loss_ce: 0.1097 2023/02/28 18:30:11 - mmengine - INFO - Epoch(train) [100][4200/5047] lr: 1.4444e-05 eta: 2 days, 13:24:18 time: 0.9299 data_time: 0.0027 memory: 44278 loss: 0.1162 loss_ce: 0.1162 2023/02/28 18:31:35 - mmengine - INFO - Epoch(train) [100][4300/5047] lr: 1.4444e-05 eta: 2 days, 13:22:49 time: 0.8174 data_time: 0.0023 memory: 45809 loss: 0.1038 loss_ce: 0.1038 2023/02/28 18:32:15 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 18:33:00 - mmengine - INFO - Epoch(train) [100][4400/5047] lr: 1.4444e-05 eta: 2 days, 13:21:21 time: 0.8175 data_time: 0.0022 memory: 45302 loss: 0.0990 loss_ce: 0.0990 2023/02/28 18:34:26 - mmengine - INFO - Epoch(train) [100][4500/5047] lr: 1.4444e-05 eta: 2 days, 13:19:53 time: 0.8091 data_time: 0.0023 memory: 44278 loss: 0.1026 loss_ce: 0.1026 2023/02/28 18:35:53 - mmengine - INFO - Epoch(train) [100][4600/5047] lr: 1.4444e-05 eta: 2 days, 13:18:25 time: 0.8413 data_time: 0.0027 memory: 47718 loss: 0.1011 loss_ce: 0.1011 2023/02/28 18:37:18 - mmengine - INFO - Epoch(train) [100][4700/5047] lr: 1.4444e-05 eta: 2 days, 13:16:57 time: 0.8344 data_time: 0.0025 memory: 51755 loss: 0.1149 loss_ce: 0.1149 2023/02/28 18:38:45 - mmengine - INFO - Epoch(train) [100][4800/5047] lr: 1.4444e-05 eta: 2 days, 13:15:29 time: 0.8578 data_time: 0.0024 memory: 55327 loss: 0.1075 loss_ce: 0.1075 2023/02/28 18:40:11 - mmengine - INFO - Epoch(train) [100][4900/5047] lr: 1.4444e-05 eta: 2 days, 13:14:01 time: 0.8790 data_time: 0.0024 memory: 55392 loss: 0.1102 loss_ce: 0.1102 2023/02/28 18:41:37 - mmengine - INFO - Epoch(train) [100][5000/5047] lr: 1.4444e-05 eta: 2 days, 13:12:33 time: 0.8637 data_time: 0.0025 memory: 49151 loss: 0.1243 loss_ce: 0.1243 2023/02/28 18:42:18 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 18:42:18 - mmengine - INFO - Saving checkpoint at 100 epochs 2023/02/28 18:43:49 - mmengine - INFO - Epoch(train) [101][ 100/5047] lr: 1.4243e-05 eta: 2 days, 13:10:24 time: 0.8336 data_time: 0.0025 memory: 45104 loss: 0.1157 loss_ce: 0.1157 2023/02/28 18:45:13 - mmengine - INFO - Epoch(train) [101][ 200/5047] lr: 1.4243e-05 eta: 2 days, 13:08:55 time: 0.8465 data_time: 0.0066 memory: 48734 loss: 0.1015 loss_ce: 0.1015 2023/02/28 18:46:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 18:46:39 - mmengine - INFO - Epoch(train) [101][ 300/5047] lr: 1.4243e-05 eta: 2 days, 13:07:26 time: 0.8760 data_time: 0.0037 memory: 44632 loss: 0.1102 loss_ce: 0.1102 2023/02/28 18:48:05 - mmengine - INFO - Epoch(train) [101][ 400/5047] lr: 1.4243e-05 eta: 2 days, 13:05:58 time: 0.8620 data_time: 0.0025 memory: 52903 loss: 0.1041 loss_ce: 0.1041 2023/02/28 18:49:32 - mmengine - INFO - Epoch(train) [101][ 500/5047] lr: 1.4243e-05 eta: 2 days, 13:04:31 time: 0.9015 data_time: 0.0059 memory: 45302 loss: 0.1027 loss_ce: 0.1027 2023/02/28 18:50:58 - mmengine - INFO - Epoch(train) [101][ 600/5047] lr: 1.4243e-05 eta: 2 days, 13:03:03 time: 0.8887 data_time: 0.0023 memory: 45643 loss: 0.1104 loss_ce: 0.1104 2023/02/28 18:52:23 - mmengine - INFO - Epoch(train) [101][ 700/5047] lr: 1.4243e-05 eta: 2 days, 13:01:35 time: 0.8907 data_time: 0.0026 memory: 42649 loss: 0.1084 loss_ce: 0.1084 2023/02/28 18:53:50 - mmengine - INFO - Epoch(train) [101][ 800/5047] lr: 1.4243e-05 eta: 2 days, 13:00:07 time: 0.8211 data_time: 0.0027 memory: 44324 loss: 0.1187 loss_ce: 0.1187 2023/02/28 18:55:17 - mmengine - INFO - Epoch(train) [101][ 900/5047] lr: 1.4243e-05 eta: 2 days, 12:58:39 time: 0.8134 data_time: 0.0027 memory: 42336 loss: 0.1010 loss_ce: 0.1010 2023/02/28 18:56:42 - mmengine - INFO - Epoch(train) [101][1000/5047] lr: 1.4243e-05 eta: 2 days, 12:57:11 time: 0.8758 data_time: 0.0024 memory: 51308 loss: 0.1052 loss_ce: 0.1052 2023/02/28 18:58:08 - mmengine - INFO - Epoch(train) [101][1100/5047] lr: 1.4243e-05 eta: 2 days, 12:55:43 time: 0.8812 data_time: 0.0025 memory: 45875 loss: 0.1207 loss_ce: 0.1207 2023/02/28 18:59:33 - mmengine - INFO - Epoch(train) [101][1200/5047] lr: 1.4243e-05 eta: 2 days, 12:54:15 time: 0.8499 data_time: 0.0070 memory: 41425 loss: 0.1160 loss_ce: 0.1160 2023/02/28 19:01:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 19:01:00 - mmengine - INFO - Epoch(train) [101][1300/5047] lr: 1.4243e-05 eta: 2 days, 12:52:47 time: 0.8593 data_time: 0.0022 memory: 53044 loss: 0.1217 loss_ce: 0.1217 2023/02/28 19:02:28 - mmengine - INFO - Epoch(train) [101][1400/5047] lr: 1.4243e-05 eta: 2 days, 12:51:20 time: 0.8888 data_time: 0.0026 memory: 41724 loss: 0.0973 loss_ce: 0.0973 2023/02/28 19:03:55 - mmengine - INFO - Epoch(train) [101][1500/5047] lr: 1.4243e-05 eta: 2 days, 12:49:52 time: 0.8664 data_time: 0.0033 memory: 47784 loss: 0.1130 loss_ce: 0.1130 2023/02/28 19:05:22 - mmengine - INFO - Epoch(train) [101][1600/5047] lr: 1.4243e-05 eta: 2 days, 12:48:25 time: 0.9023 data_time: 0.0024 memory: 46964 loss: 0.1117 loss_ce: 0.1117 2023/02/28 19:06:48 - mmengine - INFO - Epoch(train) [101][1700/5047] lr: 1.4243e-05 eta: 2 days, 12:46:57 time: 0.8952 data_time: 0.0023 memory: 41910 loss: 0.1046 loss_ce: 0.1046 2023/02/28 19:08:14 - mmengine - INFO - Epoch(train) [101][1800/5047] lr: 1.4243e-05 eta: 2 days, 12:45:29 time: 0.8700 data_time: 0.0027 memory: 43613 loss: 0.1142 loss_ce: 0.1142 2023/02/28 19:09:39 - mmengine - INFO - Epoch(train) [101][1900/5047] lr: 1.4243e-05 eta: 2 days, 12:44:01 time: 0.8750 data_time: 0.0022 memory: 51815 loss: 0.1130 loss_ce: 0.1130 2023/02/28 19:11:02 - mmengine - INFO - Epoch(train) [101][2000/5047] lr: 1.4243e-05 eta: 2 days, 12:42:31 time: 0.8148 data_time: 0.0024 memory: 42024 loss: 0.1034 loss_ce: 0.1034 2023/02/28 19:12:30 - mmengine - INFO - Epoch(train) [101][2100/5047] lr: 1.4243e-05 eta: 2 days, 12:41:04 time: 0.9391 data_time: 0.0023 memory: 55562 loss: 0.1147 loss_ce: 0.1147 2023/02/28 19:13:57 - mmengine - INFO - Epoch(train) [101][2200/5047] lr: 1.4243e-05 eta: 2 days, 12:39:36 time: 0.9001 data_time: 0.0023 memory: 44878 loss: 0.1079 loss_ce: 0.1079 2023/02/28 19:15:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 19:15:24 - mmengine - INFO - Epoch(train) [101][2300/5047] lr: 1.4243e-05 eta: 2 days, 12:38:09 time: 0.8734 data_time: 0.0024 memory: 42911 loss: 0.1214 loss_ce: 0.1214 2023/02/28 19:16:51 - mmengine - INFO - Epoch(train) [101][2400/5047] lr: 1.4243e-05 eta: 2 days, 12:36:41 time: 0.8117 data_time: 0.0026 memory: 43457 loss: 0.0995 loss_ce: 0.0995 2023/02/28 19:18:16 - mmengine - INFO - Epoch(train) [101][2500/5047] lr: 1.4243e-05 eta: 2 days, 12:35:13 time: 0.8927 data_time: 0.0028 memory: 44031 loss: 0.1130 loss_ce: 0.1130 2023/02/28 19:19:44 - mmengine - INFO - Epoch(train) [101][2600/5047] lr: 1.4243e-05 eta: 2 days, 12:33:46 time: 0.8984 data_time: 0.0025 memory: 55562 loss: 0.1045 loss_ce: 0.1045 2023/02/28 19:21:08 - mmengine - INFO - Epoch(train) [101][2700/5047] lr: 1.4243e-05 eta: 2 days, 12:32:17 time: 0.8536 data_time: 0.0043 memory: 46874 loss: 0.1115 loss_ce: 0.1115 2023/02/28 19:22:35 - mmengine - INFO - Epoch(train) [101][2800/5047] lr: 1.4243e-05 eta: 2 days, 12:30:50 time: 0.8852 data_time: 0.0049 memory: 49167 loss: 0.1033 loss_ce: 0.1033 2023/02/28 19:24:01 - mmengine - INFO - Epoch(train) [101][2900/5047] lr: 1.4243e-05 eta: 2 days, 12:29:21 time: 0.8675 data_time: 0.0030 memory: 52964 loss: 0.1048 loss_ce: 0.1048 2023/02/28 19:25:27 - mmengine - INFO - Epoch(train) [101][3000/5047] lr: 1.4243e-05 eta: 2 days, 12:27:53 time: 0.9164 data_time: 0.0024 memory: 44278 loss: 0.1168 loss_ce: 0.1168 2023/02/28 19:26:52 - mmengine - INFO - Epoch(train) [101][3100/5047] lr: 1.4243e-05 eta: 2 days, 12:26:25 time: 0.8589 data_time: 0.0027 memory: 43198 loss: 0.1101 loss_ce: 0.1101 2023/02/28 19:28:17 - mmengine - INFO - Epoch(train) [101][3200/5047] lr: 1.4243e-05 eta: 2 days, 12:24:57 time: 0.8143 data_time: 0.0041 memory: 46005 loss: 0.1161 loss_ce: 0.1161 2023/02/28 19:29:45 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 19:29:45 - mmengine - INFO - Epoch(train) [101][3300/5047] lr: 1.4243e-05 eta: 2 days, 12:23:29 time: 0.8647 data_time: 0.0023 memory: 46355 loss: 0.0919 loss_ce: 0.0919 2023/02/28 19:31:13 - mmengine - INFO - Epoch(train) [101][3400/5047] lr: 1.4243e-05 eta: 2 days, 12:22:02 time: 0.8685 data_time: 0.0026 memory: 48517 loss: 0.1078 loss_ce: 0.1078 2023/02/28 19:32:38 - mmengine - INFO - Epoch(train) [101][3500/5047] lr: 1.4243e-05 eta: 2 days, 12:20:34 time: 0.8264 data_time: 0.0026 memory: 48406 loss: 0.1322 loss_ce: 0.1322 2023/02/28 19:34:03 - mmengine - INFO - Epoch(train) [101][3600/5047] lr: 1.4243e-05 eta: 2 days, 12:19:06 time: 0.8191 data_time: 0.0025 memory: 43736 loss: 0.1180 loss_ce: 0.1180 2023/02/28 19:35:27 - mmengine - INFO - Epoch(train) [101][3700/5047] lr: 1.4243e-05 eta: 2 days, 12:17:36 time: 0.8513 data_time: 0.0023 memory: 39416 loss: 0.1165 loss_ce: 0.1165 2023/02/28 19:36:52 - mmengine - INFO - Epoch(train) [101][3800/5047] lr: 1.4243e-05 eta: 2 days, 12:16:08 time: 0.8264 data_time: 0.0028 memory: 40535 loss: 0.1054 loss_ce: 0.1054 2023/02/28 19:38:17 - mmengine - INFO - Epoch(train) [101][3900/5047] lr: 1.4243e-05 eta: 2 days, 12:14:39 time: 0.8656 data_time: 0.0024 memory: 43348 loss: 0.1069 loss_ce: 0.1069 2023/02/28 19:39:42 - mmengine - INFO - Epoch(train) [101][4000/5047] lr: 1.4243e-05 eta: 2 days, 12:13:11 time: 0.8654 data_time: 0.0028 memory: 42559 loss: 0.1172 loss_ce: 0.1172 2023/02/28 19:41:08 - mmengine - INFO - Epoch(train) [101][4100/5047] lr: 1.4243e-05 eta: 2 days, 12:11:43 time: 0.8785 data_time: 0.0040 memory: 44617 loss: 0.1092 loss_ce: 0.1092 2023/02/28 19:42:35 - mmengine - INFO - Epoch(train) [101][4200/5047] lr: 1.4243e-05 eta: 2 days, 12:10:15 time: 0.8264 data_time: 0.0022 memory: 47504 loss: 0.1039 loss_ce: 0.1039 2023/02/28 19:44:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 19:44:00 - mmengine - INFO - Epoch(train) [101][4300/5047] lr: 1.4243e-05 eta: 2 days, 12:08:47 time: 0.8315 data_time: 0.0031 memory: 45640 loss: 0.1178 loss_ce: 0.1178 2023/02/28 19:45:26 - mmengine - INFO - Epoch(train) [101][4400/5047] lr: 1.4243e-05 eta: 2 days, 12:07:19 time: 0.8579 data_time: 0.0021 memory: 40825 loss: 0.1151 loss_ce: 0.1151 2023/02/28 19:46:51 - mmengine - INFO - Epoch(train) [101][4500/5047] lr: 1.4243e-05 eta: 2 days, 12:05:51 time: 0.8784 data_time: 0.0024 memory: 51795 loss: 0.0971 loss_ce: 0.0971 2023/02/28 19:48:16 - mmengine - INFO - Epoch(train) [101][4600/5047] lr: 1.4243e-05 eta: 2 days, 12:04:22 time: 0.8694 data_time: 0.0057 memory: 42965 loss: 0.0917 loss_ce: 0.0917 2023/02/28 19:49:45 - mmengine - INFO - Epoch(train) [101][4700/5047] lr: 1.4243e-05 eta: 2 days, 12:02:56 time: 0.8712 data_time: 0.0026 memory: 39398 loss: 0.1061 loss_ce: 0.1061 2023/02/28 19:51:11 - mmengine - INFO - Epoch(train) [101][4800/5047] lr: 1.4243e-05 eta: 2 days, 12:01:28 time: 0.8760 data_time: 0.0031 memory: 54041 loss: 0.1089 loss_ce: 0.1089 2023/02/28 19:52:37 - mmengine - INFO - Epoch(train) [101][4900/5047] lr: 1.4243e-05 eta: 2 days, 12:00:00 time: 0.8410 data_time: 0.0024 memory: 41122 loss: 0.1142 loss_ce: 0.1142 2023/02/28 19:54:02 - mmengine - INFO - Epoch(train) [101][5000/5047] lr: 1.4243e-05 eta: 2 days, 11:58:31 time: 0.8744 data_time: 0.0051 memory: 43947 loss: 0.1102 loss_ce: 0.1102 2023/02/28 19:54:43 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 19:54:43 - mmengine - INFO - Saving checkpoint at 101 epochs 2023/02/28 19:56:13 - mmengine - INFO - Epoch(train) [102][ 100/5047] lr: 1.4043e-05 eta: 2 days, 11:56:22 time: 0.8588 data_time: 0.0024 memory: 44956 loss: 0.1191 loss_ce: 0.1191 2023/02/28 19:57:38 - mmengine - INFO - Epoch(train) [102][ 200/5047] lr: 1.4043e-05 eta: 2 days, 11:54:53 time: 0.8533 data_time: 0.0027 memory: 40456 loss: 0.1136 loss_ce: 0.1136 2023/02/28 19:58:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 19:59:04 - mmengine - INFO - Epoch(train) [102][ 300/5047] lr: 1.4043e-05 eta: 2 days, 11:53:25 time: 0.8337 data_time: 0.0025 memory: 41122 loss: 0.1052 loss_ce: 0.1052 2023/02/28 20:00:31 - mmengine - INFO - Epoch(train) [102][ 400/5047] lr: 1.4043e-05 eta: 2 days, 11:51:58 time: 0.8907 data_time: 0.0074 memory: 40241 loss: 0.1018 loss_ce: 0.1018 2023/02/28 20:01:58 - mmengine - INFO - Epoch(train) [102][ 500/5047] lr: 1.4043e-05 eta: 2 days, 11:50:30 time: 0.8713 data_time: 0.0024 memory: 42336 loss: 0.1139 loss_ce: 0.1139 2023/02/28 20:03:23 - mmengine - INFO - Epoch(train) [102][ 600/5047] lr: 1.4043e-05 eta: 2 days, 11:49:02 time: 0.8477 data_time: 0.0023 memory: 49407 loss: 0.1157 loss_ce: 0.1157 2023/02/28 20:04:50 - mmengine - INFO - Epoch(train) [102][ 700/5047] lr: 1.4043e-05 eta: 2 days, 11:47:34 time: 0.8863 data_time: 0.0024 memory: 50935 loss: 0.1086 loss_ce: 0.1086 2023/02/28 20:06:14 - mmengine - INFO - Epoch(train) [102][ 800/5047] lr: 1.4043e-05 eta: 2 days, 11:46:05 time: 0.7888 data_time: 0.0038 memory: 40241 loss: 0.1056 loss_ce: 0.1056 2023/02/28 20:07:41 - mmengine - INFO - Epoch(train) [102][ 900/5047] lr: 1.4043e-05 eta: 2 days, 11:44:38 time: 0.8771 data_time: 0.0033 memory: 42024 loss: 0.1037 loss_ce: 0.1037 2023/02/28 20:09:07 - mmengine - INFO - Epoch(train) [102][1000/5047] lr: 1.4043e-05 eta: 2 days, 11:43:10 time: 0.8004 data_time: 0.0046 memory: 42075 loss: 0.1063 loss_ce: 0.1063 2023/02/28 20:10:33 - mmengine - INFO - Epoch(train) [102][1100/5047] lr: 1.4043e-05 eta: 2 days, 11:41:42 time: 0.8483 data_time: 0.0024 memory: 44956 loss: 0.1022 loss_ce: 0.1022 2023/02/28 20:11:59 - mmengine - INFO - Epoch(train) [102][1200/5047] lr: 1.4043e-05 eta: 2 days, 11:40:14 time: 0.9132 data_time: 0.0024 memory: 41122 loss: 0.1068 loss_ce: 0.1068 2023/02/28 20:12:46 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 20:13:26 - mmengine - INFO - Epoch(train) [102][1300/5047] lr: 1.4043e-05 eta: 2 days, 11:38:46 time: 0.8664 data_time: 0.0022 memory: 55562 loss: 0.1131 loss_ce: 0.1131 2023/02/28 20:14:51 - mmengine - INFO - Epoch(train) [102][1400/5047] lr: 1.4043e-05 eta: 2 days, 11:37:18 time: 0.8900 data_time: 0.0031 memory: 43916 loss: 0.1111 loss_ce: 0.1111 2023/02/28 20:16:15 - mmengine - INFO - Epoch(train) [102][1500/5047] lr: 1.4043e-05 eta: 2 days, 11:35:49 time: 0.8352 data_time: 0.0023 memory: 43613 loss: 0.1041 loss_ce: 0.1041 2023/02/28 20:17:41 - mmengine - INFO - Epoch(train) [102][1600/5047] lr: 1.4043e-05 eta: 2 days, 11:34:21 time: 0.8664 data_time: 0.0025 memory: 43613 loss: 0.1089 loss_ce: 0.1089 2023/02/28 20:19:08 - mmengine - INFO - Epoch(train) [102][1700/5047] lr: 1.4043e-05 eta: 2 days, 11:32:54 time: 0.8153 data_time: 0.0036 memory: 40241 loss: 0.1217 loss_ce: 0.1217 2023/02/28 20:20:35 - mmengine - INFO - Epoch(train) [102][1800/5047] lr: 1.4043e-05 eta: 2 days, 11:31:26 time: 0.8777 data_time: 0.0028 memory: 44617 loss: 0.1184 loss_ce: 0.1184 2023/02/28 20:21:59 - mmengine - INFO - Epoch(train) [102][1900/5047] lr: 1.4043e-05 eta: 2 days, 11:29:57 time: 0.8046 data_time: 0.0024 memory: 44340 loss: 0.1058 loss_ce: 0.1058 2023/02/28 20:23:25 - mmengine - INFO - Epoch(train) [102][2000/5047] lr: 1.4043e-05 eta: 2 days, 11:28:30 time: 0.8700 data_time: 0.0025 memory: 41174 loss: 0.1198 loss_ce: 0.1198 2023/02/28 20:24:52 - mmengine - INFO - Epoch(train) [102][2100/5047] lr: 1.4043e-05 eta: 2 days, 11:27:02 time: 0.8616 data_time: 0.0025 memory: 42455 loss: 0.1056 loss_ce: 0.1056 2023/02/28 20:26:18 - mmengine - INFO - Epoch(train) [102][2200/5047] lr: 1.4043e-05 eta: 2 days, 11:25:34 time: 0.8137 data_time: 0.0023 memory: 48325 loss: 0.1203 loss_ce: 0.1203 2023/02/28 20:27:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 20:27:44 - mmengine - INFO - Epoch(train) [102][2300/5047] lr: 1.4043e-05 eta: 2 days, 11:24:06 time: 0.8638 data_time: 0.0037 memory: 44498 loss: 0.1061 loss_ce: 0.1061 2023/02/28 20:29:09 - mmengine - INFO - Epoch(train) [102][2400/5047] lr: 1.4043e-05 eta: 2 days, 11:22:38 time: 0.8553 data_time: 0.0028 memory: 44632 loss: 0.1002 loss_ce: 0.1002 2023/02/28 20:30:36 - mmengine - INFO - Epoch(train) [102][2500/5047] lr: 1.4043e-05 eta: 2 days, 11:21:10 time: 0.8085 data_time: 0.0026 memory: 46739 loss: 0.1015 loss_ce: 0.1015 2023/02/28 20:32:03 - mmengine - INFO - Epoch(train) [102][2600/5047] lr: 1.4043e-05 eta: 2 days, 11:19:43 time: 0.8584 data_time: 0.0057 memory: 49457 loss: 0.1095 loss_ce: 0.1095 2023/02/28 20:33:29 - mmengine - INFO - Epoch(train) [102][2700/5047] lr: 1.4043e-05 eta: 2 days, 11:18:15 time: 0.8303 data_time: 0.0024 memory: 48139 loss: 0.1085 loss_ce: 0.1085 2023/02/28 20:34:55 - mmengine - INFO - Epoch(train) [102][2800/5047] lr: 1.4043e-05 eta: 2 days, 11:16:47 time: 0.8165 data_time: 0.0039 memory: 41419 loss: 0.1179 loss_ce: 0.1179 2023/02/28 20:36:22 - mmengine - INFO - Epoch(train) [102][2900/5047] lr: 1.4043e-05 eta: 2 days, 11:15:19 time: 0.9035 data_time: 0.0022 memory: 42150 loss: 0.1157 loss_ce: 0.1157 2023/02/28 20:37:48 - mmengine - INFO - Epoch(train) [102][3000/5047] lr: 1.4043e-05 eta: 2 days, 11:13:51 time: 0.8386 data_time: 0.0022 memory: 42965 loss: 0.1122 loss_ce: 0.1122 2023/02/28 20:39:13 - mmengine - INFO - Epoch(train) [102][3100/5047] lr: 1.4043e-05 eta: 2 days, 11:12:23 time: 0.8490 data_time: 0.0023 memory: 43947 loss: 0.1066 loss_ce: 0.1066 2023/02/28 20:40:39 - mmengine - INFO - Epoch(train) [102][3200/5047] lr: 1.4043e-05 eta: 2 days, 11:10:55 time: 0.8512 data_time: 0.0028 memory: 41122 loss: 0.1135 loss_ce: 0.1135 2023/02/28 20:41:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 20:42:04 - mmengine - INFO - Epoch(train) [102][3300/5047] lr: 1.4043e-05 eta: 2 days, 11:09:26 time: 0.8528 data_time: 0.0023 memory: 42024 loss: 0.1019 loss_ce: 0.1019 2023/02/28 20:43:30 - mmengine - INFO - Epoch(train) [102][3400/5047] lr: 1.4043e-05 eta: 2 days, 11:07:58 time: 0.9121 data_time: 0.0022 memory: 42592 loss: 0.1029 loss_ce: 0.1029 2023/02/28 20:44:56 - mmengine - INFO - Epoch(train) [102][3500/5047] lr: 1.4043e-05 eta: 2 days, 11:06:31 time: 0.8685 data_time: 0.0048 memory: 48188 loss: 0.1109 loss_ce: 0.1109 2023/02/28 20:46:23 - mmengine - INFO - Epoch(train) [102][3600/5047] lr: 1.4043e-05 eta: 2 days, 11:05:03 time: 0.8513 data_time: 0.0027 memory: 44617 loss: 0.1035 loss_ce: 0.1035 2023/02/28 20:47:49 - mmengine - INFO - Epoch(train) [102][3700/5047] lr: 1.4043e-05 eta: 2 days, 11:03:35 time: 0.8716 data_time: 0.0038 memory: 42493 loss: 0.1104 loss_ce: 0.1104 2023/02/28 20:49:16 - mmengine - INFO - Epoch(train) [102][3800/5047] lr: 1.4043e-05 eta: 2 days, 11:02:08 time: 0.8750 data_time: 0.0024 memory: 44978 loss: 0.1165 loss_ce: 0.1165 2023/02/28 20:50:42 - mmengine - INFO - Epoch(train) [102][3900/5047] lr: 1.4043e-05 eta: 2 days, 11:00:40 time: 0.8711 data_time: 0.0022 memory: 43613 loss: 0.1082 loss_ce: 0.1082 2023/02/28 20:52:10 - mmengine - INFO - Epoch(train) [102][4000/5047] lr: 1.4043e-05 eta: 2 days, 10:59:13 time: 0.8537 data_time: 0.0034 memory: 46568 loss: 0.1007 loss_ce: 0.1007 2023/02/28 20:53:33 - mmengine - INFO - Epoch(train) [102][4100/5047] lr: 1.4043e-05 eta: 2 days, 10:57:43 time: 0.8346 data_time: 0.0024 memory: 41122 loss: 0.1137 loss_ce: 0.1137 2023/02/28 20:55:01 - mmengine - INFO - Epoch(train) [102][4200/5047] lr: 1.4043e-05 eta: 2 days, 10:56:16 time: 0.8383 data_time: 0.0025 memory: 44012 loss: 0.1166 loss_ce: 0.1166 2023/02/28 20:55:46 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 20:56:26 - mmengine - INFO - Epoch(train) [102][4300/5047] lr: 1.4043e-05 eta: 2 days, 10:54:48 time: 0.8487 data_time: 0.0038 memory: 44142 loss: 0.0939 loss_ce: 0.0939 2023/02/28 20:57:54 - mmengine - INFO - Epoch(train) [102][4400/5047] lr: 1.4043e-05 eta: 2 days, 10:53:21 time: 0.8524 data_time: 0.0029 memory: 55562 loss: 0.1030 loss_ce: 0.1030 2023/02/28 20:59:19 - mmengine - INFO - Epoch(train) [102][4500/5047] lr: 1.4043e-05 eta: 2 days, 10:51:53 time: 0.8236 data_time: 0.0034 memory: 48822 loss: 0.1154 loss_ce: 0.1154 2023/02/28 21:00:45 - mmengine - INFO - Epoch(train) [102][4600/5047] lr: 1.4043e-05 eta: 2 days, 10:50:25 time: 0.7951 data_time: 0.0023 memory: 46005 loss: 0.1127 loss_ce: 0.1127 2023/02/28 21:02:10 - mmengine - INFO - Epoch(train) [102][4700/5047] lr: 1.4043e-05 eta: 2 days, 10:48:57 time: 0.8509 data_time: 0.0024 memory: 41927 loss: 0.1306 loss_ce: 0.1306 2023/02/28 21:03:38 - mmengine - INFO - Epoch(train) [102][4800/5047] lr: 1.4043e-05 eta: 2 days, 10:47:29 time: 0.8597 data_time: 0.0031 memory: 42159 loss: 0.0996 loss_ce: 0.0996 2023/02/28 21:05:04 - mmengine - INFO - Epoch(train) [102][4900/5047] lr: 1.4043e-05 eta: 2 days, 10:46:01 time: 0.9033 data_time: 0.0024 memory: 42965 loss: 0.1065 loss_ce: 0.1065 2023/02/28 21:06:29 - mmengine - INFO - Epoch(train) [102][5000/5047] lr: 1.4043e-05 eta: 2 days, 10:44:33 time: 0.8699 data_time: 0.0027 memory: 45302 loss: 0.1084 loss_ce: 0.1084 2023/02/28 21:07:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 21:07:08 - mmengine - INFO - Saving checkpoint at 102 epochs 2023/02/28 21:08:40 - mmengine - INFO - Epoch(train) [103][ 100/5047] lr: 1.3842e-05 eta: 2 days, 10:42:23 time: 0.8511 data_time: 0.0025 memory: 41998 loss: 0.1155 loss_ce: 0.1155 2023/02/28 21:10:05 - mmengine - INFO - Epoch(train) [103][ 200/5047] lr: 1.3842e-05 eta: 2 days, 10:40:55 time: 0.8809 data_time: 0.0026 memory: 51637 loss: 0.1059 loss_ce: 0.1059 2023/02/28 21:10:10 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 21:11:31 - mmengine - INFO - Epoch(train) [103][ 300/5047] lr: 1.3842e-05 eta: 2 days, 10:39:27 time: 0.9027 data_time: 0.0024 memory: 41975 loss: 0.0996 loss_ce: 0.0996 2023/02/28 21:12:57 - mmengine - INFO - Epoch(train) [103][ 400/5047] lr: 1.3842e-05 eta: 2 days, 10:37:59 time: 0.8507 data_time: 0.0038 memory: 43947 loss: 0.1088 loss_ce: 0.1088 2023/02/28 21:14:22 - mmengine - INFO - Epoch(train) [103][ 500/5047] lr: 1.3842e-05 eta: 2 days, 10:36:31 time: 0.8474 data_time: 0.0075 memory: 55562 loss: 0.0954 loss_ce: 0.0954 2023/02/28 21:15:48 - mmengine - INFO - Epoch(train) [103][ 600/5047] lr: 1.3842e-05 eta: 2 days, 10:35:03 time: 0.8578 data_time: 0.0023 memory: 53809 loss: 0.1156 loss_ce: 0.1156 2023/02/28 21:17:15 - mmengine - INFO - Epoch(train) [103][ 700/5047] lr: 1.3842e-05 eta: 2 days, 10:33:35 time: 0.8398 data_time: 0.0026 memory: 51308 loss: 0.1153 loss_ce: 0.1153 2023/02/28 21:18:40 - mmengine - INFO - Epoch(train) [103][ 800/5047] lr: 1.3842e-05 eta: 2 days, 10:32:07 time: 0.8638 data_time: 0.0025 memory: 41961 loss: 0.1123 loss_ce: 0.1123 2023/02/28 21:20:08 - mmengine - INFO - Epoch(train) [103][ 900/5047] lr: 1.3842e-05 eta: 2 days, 10:30:40 time: 0.8855 data_time: 0.0027 memory: 43947 loss: 0.1191 loss_ce: 0.1191 2023/02/28 21:21:35 - mmengine - INFO - Epoch(train) [103][1000/5047] lr: 1.3842e-05 eta: 2 days, 10:29:12 time: 0.9039 data_time: 0.0029 memory: 55366 loss: 0.1093 loss_ce: 0.1093 2023/02/28 21:23:00 - mmengine - INFO - Epoch(train) [103][1100/5047] lr: 1.3842e-05 eta: 2 days, 10:27:44 time: 0.8337 data_time: 0.0033 memory: 43058 loss: 0.1043 loss_ce: 0.1043 2023/02/28 21:24:26 - mmengine - INFO - Epoch(train) [103][1200/5047] lr: 1.3842e-05 eta: 2 days, 10:26:16 time: 0.8766 data_time: 0.0065 memory: 40535 loss: 0.1119 loss_ce: 0.1119 2023/02/28 21:24:32 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 21:25:53 - mmengine - INFO - Epoch(train) [103][1300/5047] lr: 1.3842e-05 eta: 2 days, 10:24:49 time: 0.8461 data_time: 0.0031 memory: 47430 loss: 0.1017 loss_ce: 0.1017 2023/02/28 21:27:18 - mmengine - INFO - Epoch(train) [103][1400/5047] lr: 1.3842e-05 eta: 2 days, 10:23:21 time: 0.8119 data_time: 0.0023 memory: 44496 loss: 0.1154 loss_ce: 0.1154 2023/02/28 21:28:44 - mmengine - INFO - Epoch(train) [103][1500/5047] lr: 1.3842e-05 eta: 2 days, 10:21:52 time: 0.8360 data_time: 0.0023 memory: 43289 loss: 0.1214 loss_ce: 0.1214 2023/02/28 21:30:08 - mmengine - INFO - Epoch(train) [103][1600/5047] lr: 1.3842e-05 eta: 2 days, 10:20:24 time: 0.7744 data_time: 0.0066 memory: 44617 loss: 0.1159 loss_ce: 0.1159 2023/02/28 21:31:36 - mmengine - INFO - Epoch(train) [103][1700/5047] lr: 1.3842e-05 eta: 2 days, 10:18:57 time: 0.9131 data_time: 0.0027 memory: 44278 loss: 0.1120 loss_ce: 0.1120 2023/02/28 21:33:03 - mmengine - INFO - Epoch(train) [103][1800/5047] lr: 1.3842e-05 eta: 2 days, 10:17:29 time: 0.8445 data_time: 0.0027 memory: 41122 loss: 0.1212 loss_ce: 0.1212 2023/02/28 21:34:28 - mmengine - INFO - Epoch(train) [103][1900/5047] lr: 1.3842e-05 eta: 2 days, 10:16:01 time: 0.8659 data_time: 0.0024 memory: 41724 loss: 0.1047 loss_ce: 0.1047 2023/02/28 21:35:55 - mmengine - INFO - Epoch(train) [103][2000/5047] lr: 1.3842e-05 eta: 2 days, 10:14:33 time: 0.8357 data_time: 0.0024 memory: 43947 loss: 0.1029 loss_ce: 0.1029 2023/02/28 21:37:28 - mmengine - INFO - Epoch(train) [103][2100/5047] lr: 1.3842e-05 eta: 2 days, 10:13:09 time: 0.8578 data_time: 0.0023 memory: 45643 loss: 0.1180 loss_ce: 0.1180 2023/02/28 21:38:55 - mmengine - INFO - Epoch(train) [103][2200/5047] lr: 1.3842e-05 eta: 2 days, 10:11:41 time: 0.8611 data_time: 0.0036 memory: 45643 loss: 0.1221 loss_ce: 0.1221 2023/02/28 21:38:59 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 21:40:21 - mmengine - INFO - Epoch(train) [103][2300/5047] lr: 1.3842e-05 eta: 2 days, 10:10:13 time: 0.8396 data_time: 0.0025 memory: 41122 loss: 0.1178 loss_ce: 0.1178 2023/02/28 21:49:45 - mmengine - INFO - Epoch(train) [103][2400/5047] lr: 1.3842e-05 eta: 2 days, 10:12:34 time: 0.8366 data_time: 0.0052 memory: 42649 loss: 0.1051 loss_ce: 0.1051 2023/02/28 21:51:12 - mmengine - INFO - Epoch(train) [103][2500/5047] lr: 1.3842e-05 eta: 2 days, 10:11:06 time: 0.9012 data_time: 0.0024 memory: 41419 loss: 0.1107 loss_ce: 0.1107 2023/02/28 21:52:37 - mmengine - INFO - Epoch(train) [103][2600/5047] lr: 1.3842e-05 eta: 2 days, 10:09:38 time: 0.8599 data_time: 0.0070 memory: 50338 loss: 0.1266 loss_ce: 0.1266 2023/02/28 21:54:02 - mmengine - INFO - Epoch(train) [103][2700/5047] lr: 1.3842e-05 eta: 2 days, 10:08:09 time: 0.8198 data_time: 0.0049 memory: 43430 loss: 0.1177 loss_ce: 0.1177 2023/02/28 21:55:27 - mmengine - INFO - Epoch(train) [103][2800/5047] lr: 1.3842e-05 eta: 2 days, 10:06:40 time: 0.8579 data_time: 0.0024 memory: 48210 loss: 0.1051 loss_ce: 0.1051 2023/02/28 21:56:53 - mmengine - INFO - Epoch(train) [103][2900/5047] lr: 1.3842e-05 eta: 2 days, 10:05:13 time: 0.8892 data_time: 0.0026 memory: 45879 loss: 0.1105 loss_ce: 0.1105 2023/02/28 21:58:19 - mmengine - INFO - Epoch(train) [103][3000/5047] lr: 1.3842e-05 eta: 2 days, 10:03:45 time: 0.8791 data_time: 0.0051 memory: 42965 loss: 0.1163 loss_ce: 0.1163 2023/02/28 21:59:46 - mmengine - INFO - Epoch(train) [103][3100/5047] lr: 1.3842e-05 eta: 2 days, 10:02:17 time: 0.8493 data_time: 0.0029 memory: 49715 loss: 0.0871 loss_ce: 0.0871 2023/02/28 22:01:12 - mmengine - INFO - Epoch(train) [103][3200/5047] lr: 1.3842e-05 eta: 2 days, 10:00:49 time: 0.8303 data_time: 0.0025 memory: 48188 loss: 0.1127 loss_ce: 0.1127 2023/02/28 22:01:18 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 22:02:40 - mmengine - INFO - Epoch(train) [103][3300/5047] lr: 1.3842e-05 eta: 2 days, 9:59:22 time: 0.8945 data_time: 0.0025 memory: 43947 loss: 0.1042 loss_ce: 0.1042 2023/02/28 22:04:05 - mmengine - INFO - Epoch(train) [103][3400/5047] lr: 1.3842e-05 eta: 2 days, 9:57:53 time: 0.8896 data_time: 0.0034 memory: 49809 loss: 0.1095 loss_ce: 0.1095 2023/02/28 22:05:30 - mmengine - INFO - Epoch(train) [103][3500/5047] lr: 1.3842e-05 eta: 2 days, 9:56:25 time: 0.8134 data_time: 0.0027 memory: 50106 loss: 0.1080 loss_ce: 0.1080 2023/02/28 22:06:56 - mmengine - INFO - Epoch(train) [103][3600/5047] lr: 1.3842e-05 eta: 2 days, 9:54:57 time: 0.9060 data_time: 0.0024 memory: 45681 loss: 0.1027 loss_ce: 0.1027 2023/02/28 22:08:22 - mmengine - INFO - Epoch(train) [103][3700/5047] lr: 1.3842e-05 eta: 2 days, 9:53:29 time: 0.8603 data_time: 0.0023 memory: 45488 loss: 0.1183 loss_ce: 0.1183 2023/02/28 22:09:46 - mmengine - INFO - Epoch(train) [103][3800/5047] lr: 1.3842e-05 eta: 2 days, 9:51:59 time: 0.8217 data_time: 0.0031 memory: 42024 loss: 0.1051 loss_ce: 0.1051 2023/02/28 22:11:12 - mmengine - INFO - Epoch(train) [103][3900/5047] lr: 1.3842e-05 eta: 2 days, 9:50:32 time: 0.8510 data_time: 0.0027 memory: 42965 loss: 0.1106 loss_ce: 0.1106 2023/02/28 22:12:38 - mmengine - INFO - Epoch(train) [103][4000/5047] lr: 1.3842e-05 eta: 2 days, 9:49:03 time: 0.8585 data_time: 0.0037 memory: 46853 loss: 0.1239 loss_ce: 0.1239 2023/02/28 22:14:04 - mmengine - INFO - Epoch(train) [103][4100/5047] lr: 1.3842e-05 eta: 2 days, 9:47:35 time: 0.8991 data_time: 0.0025 memory: 55562 loss: 0.1166 loss_ce: 0.1166 2023/02/28 22:15:28 - mmengine - INFO - Epoch(train) [103][4200/5047] lr: 1.3842e-05 eta: 2 days, 9:46:06 time: 0.8427 data_time: 0.0024 memory: 42965 loss: 0.1253 loss_ce: 0.1253 2023/02/28 22:15:32 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 22:16:52 - mmengine - INFO - Epoch(train) [103][4300/5047] lr: 1.3842e-05 eta: 2 days, 9:44:38 time: 0.8615 data_time: 0.0026 memory: 42649 loss: 0.0930 loss_ce: 0.0930 2023/02/28 22:18:19 - mmengine - INFO - Epoch(train) [103][4400/5047] lr: 1.3842e-05 eta: 2 days, 9:43:10 time: 0.8983 data_time: 0.0024 memory: 45377 loss: 0.1163 loss_ce: 0.1163 2023/02/28 22:19:44 - mmengine - INFO - Epoch(train) [103][4500/5047] lr: 1.3842e-05 eta: 2 days, 9:41:42 time: 0.8671 data_time: 0.0025 memory: 45643 loss: 0.1178 loss_ce: 0.1178 2023/02/28 22:21:08 - mmengine - INFO - Epoch(train) [103][4600/5047] lr: 1.3842e-05 eta: 2 days, 9:40:13 time: 0.8704 data_time: 0.0031 memory: 44109 loss: 0.1205 loss_ce: 0.1205 2023/02/28 22:22:35 - mmengine - INFO - Epoch(train) [103][4700/5047] lr: 1.3842e-05 eta: 2 days, 9:38:45 time: 0.8890 data_time: 0.0029 memory: 44956 loss: 0.1181 loss_ce: 0.1181 2023/02/28 22:24:00 - mmengine - INFO - Epoch(train) [103][4800/5047] lr: 1.3842e-05 eta: 2 days, 9:37:16 time: 0.8541 data_time: 0.0027 memory: 43648 loss: 0.1056 loss_ce: 0.1056 2023/02/28 22:25:26 - mmengine - INFO - Epoch(train) [103][4900/5047] lr: 1.3842e-05 eta: 2 days, 9:35:49 time: 0.8957 data_time: 0.0028 memory: 49373 loss: 0.1000 loss_ce: 0.1000 2023/02/28 22:26:52 - mmengine - INFO - Epoch(train) [103][5000/5047] lr: 1.3842e-05 eta: 2 days, 9:34:21 time: 0.8522 data_time: 0.0033 memory: 40119 loss: 0.0983 loss_ce: 0.0983 2023/02/28 22:27:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 22:27:33 - mmengine - INFO - Saving checkpoint at 103 epochs 2023/02/28 22:29:03 - mmengine - INFO - Epoch(train) [104][ 100/5047] lr: 1.3641e-05 eta: 2 days, 9:32:11 time: 0.8073 data_time: 0.0034 memory: 42024 loss: 0.1153 loss_ce: 0.1153 2023/02/28 22:29:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 22:30:30 - mmengine - INFO - Epoch(train) [104][ 200/5047] lr: 1.3641e-05 eta: 2 days, 9:30:43 time: 0.9068 data_time: 0.0024 memory: 44956 loss: 0.1229 loss_ce: 0.1229 2023/02/28 22:31:55 - mmengine - INFO - Epoch(train) [104][ 300/5047] lr: 1.3641e-05 eta: 2 days, 9:29:14 time: 0.8395 data_time: 0.0023 memory: 46713 loss: 0.1176 loss_ce: 0.1176 2023/02/28 22:33:20 - mmengine - INFO - Epoch(train) [104][ 400/5047] lr: 1.3641e-05 eta: 2 days, 9:27:46 time: 0.8982 data_time: 0.0030 memory: 39398 loss: 0.0990 loss_ce: 0.0990 2023/02/28 22:34:45 - mmengine - INFO - Epoch(train) [104][ 500/5047] lr: 1.3641e-05 eta: 2 days, 9:26:18 time: 0.8202 data_time: 0.0027 memory: 40535 loss: 0.0902 loss_ce: 0.0902 2023/02/28 22:36:12 - mmengine - INFO - Epoch(train) [104][ 600/5047] lr: 1.3641e-05 eta: 2 days, 9:24:50 time: 0.8870 data_time: 0.0027 memory: 51607 loss: 0.0931 loss_ce: 0.0931 2023/02/28 22:37:36 - mmengine - INFO - Epoch(train) [104][ 700/5047] lr: 1.3641e-05 eta: 2 days, 9:23:21 time: 0.8133 data_time: 0.0025 memory: 55562 loss: 0.1171 loss_ce: 0.1171 2023/02/28 22:39:03 - mmengine - INFO - Epoch(train) [104][ 800/5047] lr: 1.3641e-05 eta: 2 days, 9:21:53 time: 0.9103 data_time: 0.0023 memory: 40535 loss: 0.1236 loss_ce: 0.1236 2023/02/28 22:40:30 - mmengine - INFO - Epoch(train) [104][ 900/5047] lr: 1.3641e-05 eta: 2 days, 9:20:26 time: 0.8567 data_time: 0.0023 memory: 50372 loss: 0.1058 loss_ce: 0.1058 2023/02/28 22:41:57 - mmengine - INFO - Epoch(train) [104][1000/5047] lr: 1.3641e-05 eta: 2 days, 9:18:59 time: 0.9030 data_time: 0.0024 memory: 45302 loss: 0.1206 loss_ce: 0.1206 2023/02/28 22:43:24 - mmengine - INFO - Epoch(train) [104][1100/5047] lr: 1.3641e-05 eta: 2 days, 9:17:31 time: 0.8383 data_time: 0.0031 memory: 55562 loss: 0.1126 loss_ce: 0.1126 2023/02/28 22:44:15 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 22:44:51 - mmengine - INFO - Epoch(train) [104][1200/5047] lr: 1.3641e-05 eta: 2 days, 9:16:03 time: 0.8976 data_time: 0.0032 memory: 55562 loss: 0.1151 loss_ce: 0.1151 2023/02/28 22:46:17 - mmengine - INFO - Epoch(train) [104][1300/5047] lr: 1.3641e-05 eta: 2 days, 9:14:35 time: 0.8269 data_time: 0.0027 memory: 42939 loss: 0.1068 loss_ce: 0.1068 2023/02/28 22:47:43 - mmengine - INFO - Epoch(train) [104][1400/5047] lr: 1.3641e-05 eta: 2 days, 9:13:07 time: 0.8467 data_time: 0.0029 memory: 42336 loss: 0.0998 loss_ce: 0.0998 2023/02/28 22:49:09 - mmengine - INFO - Epoch(train) [104][1500/5047] lr: 1.3641e-05 eta: 2 days, 9:11:39 time: 0.7977 data_time: 0.0030 memory: 40939 loss: 0.1031 loss_ce: 0.1031 2023/02/28 22:50:35 - mmengine - INFO - Epoch(train) [104][1600/5047] lr: 1.3641e-05 eta: 2 days, 9:10:11 time: 0.8523 data_time: 0.0025 memory: 44956 loss: 0.0908 loss_ce: 0.0908 2023/02/28 22:52:01 - mmengine - INFO - Epoch(train) [104][1700/5047] lr: 1.3641e-05 eta: 2 days, 9:08:43 time: 0.8070 data_time: 0.0026 memory: 44956 loss: 0.1168 loss_ce: 0.1168 2023/02/28 22:53:26 - mmengine - INFO - Epoch(train) [104][1800/5047] lr: 1.3641e-05 eta: 2 days, 9:07:15 time: 0.8538 data_time: 0.0023 memory: 50417 loss: 0.1204 loss_ce: 0.1204 2023/02/28 22:54:50 - mmengine - INFO - Epoch(train) [104][1900/5047] lr: 1.3641e-05 eta: 2 days, 9:05:46 time: 0.8578 data_time: 0.0029 memory: 39398 loss: 0.1193 loss_ce: 0.1193 2023/02/28 22:56:17 - mmengine - INFO - Epoch(train) [104][2000/5047] lr: 1.3641e-05 eta: 2 days, 9:04:18 time: 0.8608 data_time: 0.0027 memory: 55562 loss: 0.1043 loss_ce: 0.1043 2023/02/28 22:57:44 - mmengine - INFO - Epoch(train) [104][2100/5047] lr: 1.3641e-05 eta: 2 days, 9:02:51 time: 0.8955 data_time: 0.0049 memory: 55392 loss: 0.1095 loss_ce: 0.1095 2023/02/28 22:58:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 22:59:12 - mmengine - INFO - Epoch(train) [104][2200/5047] lr: 1.3641e-05 eta: 2 days, 9:01:23 time: 0.8973 data_time: 0.0034 memory: 50608 loss: 0.1165 loss_ce: 0.1165 2023/02/28 23:00:38 - mmengine - INFO - Epoch(train) [104][2300/5047] lr: 1.3641e-05 eta: 2 days, 8:59:55 time: 0.8598 data_time: 0.0025 memory: 55562 loss: 0.0984 loss_ce: 0.0984 2023/02/28 23:02:04 - mmengine - INFO - Epoch(train) [104][2400/5047] lr: 1.3641e-05 eta: 2 days, 8:58:28 time: 0.8459 data_time: 0.0039 memory: 43289 loss: 0.1191 loss_ce: 0.1191 2023/02/28 23:03:31 - mmengine - INFO - Epoch(train) [104][2500/5047] lr: 1.3641e-05 eta: 2 days, 8:57:00 time: 0.8689 data_time: 0.0028 memory: 39222 loss: 0.1123 loss_ce: 0.1123 2023/02/28 23:04:57 - mmengine - INFO - Epoch(train) [104][2600/5047] lr: 1.3641e-05 eta: 2 days, 8:55:32 time: 0.8710 data_time: 0.0053 memory: 41122 loss: 0.1031 loss_ce: 0.1031 2023/02/28 23:06:23 - mmengine - INFO - Epoch(train) [104][2700/5047] lr: 1.3641e-05 eta: 2 days, 8:54:04 time: 0.9073 data_time: 0.0028 memory: 47824 loss: 0.1025 loss_ce: 0.1025 2023/02/28 23:07:50 - mmengine - INFO - Epoch(train) [104][2800/5047] lr: 1.3641e-05 eta: 2 days, 8:52:36 time: 0.8961 data_time: 0.0028 memory: 42024 loss: 0.1089 loss_ce: 0.1089 2023/02/28 23:09:14 - mmengine - INFO - Epoch(train) [104][2900/5047] lr: 1.3641e-05 eta: 2 days, 8:51:07 time: 0.8296 data_time: 0.0022 memory: 41531 loss: 0.1219 loss_ce: 0.1219 2023/02/28 23:10:40 - mmengine - INFO - Epoch(train) [104][3000/5047] lr: 1.3641e-05 eta: 2 days, 8:49:39 time: 0.8813 data_time: 0.0024 memory: 55562 loss: 0.0954 loss_ce: 0.0954 2023/02/28 23:12:04 - mmengine - INFO - Epoch(train) [104][3100/5047] lr: 1.3641e-05 eta: 2 days, 8:48:11 time: 0.8265 data_time: 0.0025 memory: 44375 loss: 0.1192 loss_ce: 0.1192 2023/02/28 23:12:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 23:13:31 - mmengine - INFO - Epoch(train) [104][3200/5047] lr: 1.3641e-05 eta: 2 days, 8:46:43 time: 0.8884 data_time: 0.0023 memory: 42965 loss: 0.1126 loss_ce: 0.1126 2023/02/28 23:14:56 - mmengine - INFO - Epoch(train) [104][3300/5047] lr: 1.3641e-05 eta: 2 days, 8:45:15 time: 0.8725 data_time: 0.0027 memory: 42965 loss: 0.1010 loss_ce: 0.1010 2023/02/28 23:16:21 - mmengine - INFO - Epoch(train) [104][3400/5047] lr: 1.3641e-05 eta: 2 days, 8:43:46 time: 0.8298 data_time: 0.0024 memory: 54242 loss: 0.1047 loss_ce: 0.1047 2023/02/28 23:17:46 - mmengine - INFO - Epoch(train) [104][3500/5047] lr: 1.3641e-05 eta: 2 days, 8:42:18 time: 0.8362 data_time: 0.0028 memory: 51734 loss: 0.1079 loss_ce: 0.1079 2023/02/28 23:19:14 - mmengine - INFO - Epoch(train) [104][3600/5047] lr: 1.3641e-05 eta: 2 days, 8:40:51 time: 0.8755 data_time: 0.0022 memory: 42962 loss: 0.1120 loss_ce: 0.1120 2023/02/28 23:20:40 - mmengine - INFO - Epoch(train) [104][3700/5047] lr: 1.3641e-05 eta: 2 days, 8:39:23 time: 0.8503 data_time: 0.0027 memory: 46744 loss: 0.1129 loss_ce: 0.1129 2023/02/28 23:22:07 - mmengine - INFO - Epoch(train) [104][3800/5047] lr: 1.3641e-05 eta: 2 days, 8:37:55 time: 0.8973 data_time: 0.0025 memory: 46005 loss: 0.1118 loss_ce: 0.1118 2023/02/28 23:23:33 - mmengine - INFO - Epoch(train) [104][3900/5047] lr: 1.3641e-05 eta: 2 days, 8:36:27 time: 0.8503 data_time: 0.0023 memory: 41564 loss: 0.1033 loss_ce: 0.1033 2023/02/28 23:25:00 - mmengine - INFO - Epoch(train) [104][4000/5047] lr: 1.3641e-05 eta: 2 days, 8:34:59 time: 0.8810 data_time: 0.0024 memory: 42649 loss: 0.1066 loss_ce: 0.1066 2023/02/28 23:26:24 - mmengine - INFO - Epoch(train) [104][4100/5047] lr: 1.3641e-05 eta: 2 days, 8:33:30 time: 0.8503 data_time: 0.0025 memory: 43508 loss: 0.1154 loss_ce: 0.1154 2023/02/28 23:27:15 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 23:27:48 - mmengine - INFO - Epoch(train) [104][4200/5047] lr: 1.3641e-05 eta: 2 days, 8:32:02 time: 0.8122 data_time: 0.0050 memory: 46005 loss: 0.0917 loss_ce: 0.0917 2023/02/28 23:29:13 - mmengine - INFO - Epoch(train) [104][4300/5047] lr: 1.3641e-05 eta: 2 days, 8:30:33 time: 0.8375 data_time: 0.0023 memory: 41122 loss: 0.1093 loss_ce: 0.1093 2023/02/28 23:30:38 - mmengine - INFO - Epoch(train) [104][4400/5047] lr: 1.3641e-05 eta: 2 days, 8:29:05 time: 0.8384 data_time: 0.0023 memory: 42336 loss: 0.1352 loss_ce: 0.1352 2023/02/28 23:32:04 - mmengine - INFO - Epoch(train) [104][4500/5047] lr: 1.3641e-05 eta: 2 days, 8:27:37 time: 0.8661 data_time: 0.0025 memory: 49177 loss: 0.1078 loss_ce: 0.1078 2023/02/28 23:33:30 - mmengine - INFO - Epoch(train) [104][4600/5047] lr: 1.3641e-05 eta: 2 days, 8:26:08 time: 0.8522 data_time: 0.0028 memory: 55562 loss: 0.1294 loss_ce: 0.1294 2023/02/28 23:34:55 - mmengine - INFO - Epoch(train) [104][4700/5047] lr: 1.3641e-05 eta: 2 days, 8:24:40 time: 0.8543 data_time: 0.0024 memory: 42024 loss: 0.1069 loss_ce: 0.1069 2023/02/28 23:36:22 - mmengine - INFO - Epoch(train) [104][4800/5047] lr: 1.3641e-05 eta: 2 days, 8:23:13 time: 0.8751 data_time: 0.0024 memory: 42649 loss: 0.1128 loss_ce: 0.1128 2023/02/28 23:37:49 - mmengine - INFO - Epoch(train) [104][4900/5047] lr: 1.3641e-05 eta: 2 days, 8:21:45 time: 0.8261 data_time: 0.0024 memory: 45642 loss: 0.1065 loss_ce: 0.1065 2023/02/28 23:39:15 - mmengine - INFO - Epoch(train) [104][5000/5047] lr: 1.3641e-05 eta: 2 days, 8:20:17 time: 0.8470 data_time: 0.0028 memory: 41724 loss: 0.1179 loss_ce: 0.1179 2023/02/28 23:39:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 23:39:55 - mmengine - INFO - Saving checkpoint at 104 epochs 2023/02/28 23:41:25 - mmengine - INFO - Epoch(train) [105][ 100/5047] lr: 1.3440e-05 eta: 2 days, 8:18:07 time: 0.8356 data_time: 0.0077 memory: 44204 loss: 0.1196 loss_ce: 0.1196 2023/02/28 23:41:35 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 23:42:52 - mmengine - INFO - Epoch(train) [105][ 200/5047] lr: 1.3440e-05 eta: 2 days, 8:16:40 time: 0.8712 data_time: 0.0029 memory: 54303 loss: 0.1152 loss_ce: 0.1152 2023/02/28 23:44:17 - mmengine - INFO - Epoch(train) [105][ 300/5047] lr: 1.3440e-05 eta: 2 days, 8:15:11 time: 0.8292 data_time: 0.0028 memory: 44956 loss: 0.1086 loss_ce: 0.1086 2023/02/28 23:45:43 - mmengine - INFO - Epoch(train) [105][ 400/5047] lr: 1.3440e-05 eta: 2 days, 8:13:43 time: 0.8640 data_time: 0.0023 memory: 42649 loss: 0.1041 loss_ce: 0.1041 2023/02/28 23:47:07 - mmengine - INFO - Epoch(train) [105][ 500/5047] lr: 1.3440e-05 eta: 2 days, 8:12:14 time: 0.8809 data_time: 0.0026 memory: 50505 loss: 0.1176 loss_ce: 0.1176 2023/02/28 23:48:33 - mmengine - INFO - Epoch(train) [105][ 600/5047] lr: 1.3440e-05 eta: 2 days, 8:10:46 time: 0.8823 data_time: 0.0026 memory: 41252 loss: 0.1202 loss_ce: 0.1202 2023/02/28 23:49:59 - mmengine - INFO - Epoch(train) [105][ 700/5047] lr: 1.3440e-05 eta: 2 days, 8:09:18 time: 0.8690 data_time: 0.0030 memory: 41419 loss: 0.1058 loss_ce: 0.1058 2023/02/28 23:51:23 - mmengine - INFO - Epoch(train) [105][ 800/5047] lr: 1.3440e-05 eta: 2 days, 8:07:49 time: 0.8354 data_time: 0.0030 memory: 49933 loss: 0.1133 loss_ce: 0.1133 2023/02/28 23:52:50 - mmengine - INFO - Epoch(train) [105][ 900/5047] lr: 1.3440e-05 eta: 2 days, 8:06:22 time: 0.8592 data_time: 0.0064 memory: 55562 loss: 0.1054 loss_ce: 0.1054 2023/02/28 23:54:16 - mmengine - INFO - Epoch(train) [105][1000/5047] lr: 1.3440e-05 eta: 2 days, 8:04:54 time: 0.8685 data_time: 0.0037 memory: 40917 loss: 0.0991 loss_ce: 0.0991 2023/02/28 23:55:42 - mmengine - INFO - Epoch(train) [105][1100/5047] lr: 1.3440e-05 eta: 2 days, 8:03:26 time: 0.9166 data_time: 0.0025 memory: 48948 loss: 0.1042 loss_ce: 0.1042 2023/02/28 23:55:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/02/28 23:57:09 - mmengine - INFO - Epoch(train) [105][1200/5047] lr: 1.3440e-05 eta: 2 days, 8:01:59 time: 0.8717 data_time: 0.0025 memory: 55562 loss: 0.1211 loss_ce: 0.1211 2023/02/28 23:58:37 - mmengine - INFO - Epoch(train) [105][1300/5047] lr: 1.3440e-05 eta: 2 days, 8:00:31 time: 0.9070 data_time: 0.0025 memory: 52964 loss: 0.1084 loss_ce: 0.1084 2023/03/01 00:00:02 - mmengine - INFO - Epoch(train) [105][1400/5047] lr: 1.3440e-05 eta: 2 days, 7:59:03 time: 0.8390 data_time: 0.0035 memory: 40535 loss: 0.0967 loss_ce: 0.0967 2023/03/01 00:01:29 - mmengine - INFO - Epoch(train) [105][1500/5047] lr: 1.3440e-05 eta: 2 days, 7:57:35 time: 0.8831 data_time: 0.0067 memory: 55485 loss: 0.1071 loss_ce: 0.1071 2023/03/01 00:02:56 - mmengine - INFO - Epoch(train) [105][1600/5047] lr: 1.3440e-05 eta: 2 days, 7:56:08 time: 0.9181 data_time: 0.0023 memory: 45643 loss: 0.1195 loss_ce: 0.1195 2023/03/01 00:04:23 - mmengine - INFO - Epoch(train) [105][1700/5047] lr: 1.3440e-05 eta: 2 days, 7:54:40 time: 0.8815 data_time: 0.0025 memory: 42649 loss: 0.1201 loss_ce: 0.1201 2023/03/01 00:05:49 - mmengine - INFO - Epoch(train) [105][1800/5047] lr: 1.3440e-05 eta: 2 days, 7:53:12 time: 0.8934 data_time: 0.0025 memory: 47484 loss: 0.1159 loss_ce: 0.1159 2023/03/01 00:07:14 - mmengine - INFO - Epoch(train) [105][1900/5047] lr: 1.3440e-05 eta: 2 days, 7:51:44 time: 0.8107 data_time: 0.0046 memory: 42336 loss: 0.1098 loss_ce: 0.1098 2023/03/01 00:08:40 - mmengine - INFO - Epoch(train) [105][2000/5047] lr: 1.3440e-05 eta: 2 days, 7:50:16 time: 0.8665 data_time: 0.0030 memory: 41780 loss: 0.1169 loss_ce: 0.1169 2023/03/01 00:10:06 - mmengine - INFO - Epoch(train) [105][2100/5047] lr: 1.3440e-05 eta: 2 days, 7:48:48 time: 0.8277 data_time: 0.0026 memory: 45362 loss: 0.1027 loss_ce: 0.1027 2023/03/01 00:10:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 00:11:32 - mmengine - INFO - Epoch(train) [105][2200/5047] lr: 1.3440e-05 eta: 2 days, 7:47:20 time: 0.8870 data_time: 0.0023 memory: 42649 loss: 0.1130 loss_ce: 0.1130 2023/03/01 00:12:58 - mmengine - INFO - Epoch(train) [105][2300/5047] lr: 1.3440e-05 eta: 2 days, 7:45:52 time: 0.8322 data_time: 0.0027 memory: 42336 loss: 0.1148 loss_ce: 0.1148 2023/03/01 00:14:24 - mmengine - INFO - Epoch(train) [105][2400/5047] lr: 1.3440e-05 eta: 2 days, 7:44:24 time: 0.8374 data_time: 0.0037 memory: 43613 loss: 0.1165 loss_ce: 0.1165 2023/03/01 00:15:50 - mmengine - INFO - Epoch(train) [105][2500/5047] lr: 1.3440e-05 eta: 2 days, 7:42:56 time: 0.8533 data_time: 0.0023 memory: 41552 loss: 0.1095 loss_ce: 0.1095 2023/03/01 00:17:15 - mmengine - INFO - Epoch(train) [105][2600/5047] lr: 1.3440e-05 eta: 2 days, 7:41:28 time: 0.8578 data_time: 0.0024 memory: 41419 loss: 0.1133 loss_ce: 0.1133 2023/03/01 00:18:41 - mmengine - INFO - Epoch(train) [105][2700/5047] lr: 1.3440e-05 eta: 2 days, 7:40:00 time: 0.8474 data_time: 0.0027 memory: 40147 loss: 0.1219 loss_ce: 0.1219 2023/03/01 00:20:06 - mmengine - INFO - Epoch(train) [105][2800/5047] lr: 1.3440e-05 eta: 2 days, 7:38:31 time: 0.8821 data_time: 0.0066 memory: 45302 loss: 0.1064 loss_ce: 0.1064 2023/03/01 00:21:34 - mmengine - INFO - Epoch(train) [105][2900/5047] lr: 1.3440e-05 eta: 2 days, 7:37:04 time: 0.8403 data_time: 0.0023 memory: 46355 loss: 0.1008 loss_ce: 0.1008 2023/03/01 00:23:00 - mmengine - INFO - Epoch(train) [105][3000/5047] lr: 1.3440e-05 eta: 2 days, 7:35:36 time: 0.8079 data_time: 0.0028 memory: 43220 loss: 0.1095 loss_ce: 0.1095 2023/03/01 00:24:26 - mmengine - INFO - Epoch(train) [105][3100/5047] lr: 1.3440e-05 eta: 2 days, 7:34:08 time: 0.8615 data_time: 0.0024 memory: 41724 loss: 0.0963 loss_ce: 0.0963 2023/03/01 00:24:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 00:25:55 - mmengine - INFO - Epoch(train) [105][3200/5047] lr: 1.3440e-05 eta: 2 days, 7:32:41 time: 0.9354 data_time: 0.0026 memory: 55296 loss: 0.1170 loss_ce: 0.1170 2023/03/01 00:27:19 - mmengine - INFO - Epoch(train) [105][3300/5047] lr: 1.3440e-05 eta: 2 days, 7:31:13 time: 0.8617 data_time: 0.0026 memory: 41142 loss: 0.1113 loss_ce: 0.1113 2023/03/01 00:28:44 - mmengine - INFO - Epoch(train) [105][3400/5047] lr: 1.3440e-05 eta: 2 days, 7:29:44 time: 0.8637 data_time: 0.0033 memory: 43947 loss: 0.1197 loss_ce: 0.1197 2023/03/01 00:30:09 - mmengine - INFO - Epoch(train) [105][3500/5047] lr: 1.3440e-05 eta: 2 days, 7:28:16 time: 0.8500 data_time: 0.0025 memory: 43613 loss: 0.0911 loss_ce: 0.0911 2023/03/01 00:31:35 - mmengine - INFO - Epoch(train) [105][3600/5047] lr: 1.3440e-05 eta: 2 days, 7:26:48 time: 0.8651 data_time: 0.0025 memory: 41011 loss: 0.1059 loss_ce: 0.1059 2023/03/01 00:33:01 - mmengine - INFO - Epoch(train) [105][3700/5047] lr: 1.3440e-05 eta: 2 days, 7:25:20 time: 0.8769 data_time: 0.0025 memory: 49312 loss: 0.1130 loss_ce: 0.1130 2023/03/01 00:34:27 - mmengine - INFO - Epoch(train) [105][3800/5047] lr: 1.3440e-05 eta: 2 days, 7:23:52 time: 0.8977 data_time: 0.0047 memory: 46017 loss: 0.1178 loss_ce: 0.1178 2023/03/01 00:35:54 - mmengine - INFO - Epoch(train) [105][3900/5047] lr: 1.3440e-05 eta: 2 days, 7:22:24 time: 0.8631 data_time: 0.0028 memory: 41419 loss: 0.0964 loss_ce: 0.0964 2023/03/01 00:37:22 - mmengine - INFO - Epoch(train) [105][4000/5047] lr: 1.3440e-05 eta: 2 days, 7:20:57 time: 0.8641 data_time: 0.0025 memory: 42649 loss: 0.1091 loss_ce: 0.1091 2023/03/01 00:38:48 - mmengine - INFO - Epoch(train) [105][4100/5047] lr: 1.3440e-05 eta: 2 days, 7:19:30 time: 0.8690 data_time: 0.0026 memory: 47223 loss: 0.1089 loss_ce: 0.1089 2023/03/01 00:39:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 00:40:15 - mmengine - INFO - Epoch(train) [105][4200/5047] lr: 1.3440e-05 eta: 2 days, 7:18:02 time: 0.8397 data_time: 0.0034 memory: 44956 loss: 0.1171 loss_ce: 0.1171 2023/03/01 00:41:40 - mmengine - INFO - Epoch(train) [105][4300/5047] lr: 1.3440e-05 eta: 2 days, 7:16:33 time: 0.8668 data_time: 0.0049 memory: 41419 loss: 0.1033 loss_ce: 0.1033 2023/03/01 00:43:04 - mmengine - INFO - Epoch(train) [105][4400/5047] lr: 1.3440e-05 eta: 2 days, 7:15:05 time: 0.8185 data_time: 0.0029 memory: 43546 loss: 0.1083 loss_ce: 0.1083 2023/03/01 00:44:30 - mmengine - INFO - Epoch(train) [105][4500/5047] lr: 1.3440e-05 eta: 2 days, 7:13:37 time: 0.8159 data_time: 0.0029 memory: 41165 loss: 0.1084 loss_ce: 0.1084 2023/03/01 00:45:58 - mmengine - INFO - Epoch(train) [105][4600/5047] lr: 1.3440e-05 eta: 2 days, 7:12:10 time: 0.8674 data_time: 0.0023 memory: 54232 loss: 0.1020 loss_ce: 0.1020 2023/03/01 00:47:26 - mmengine - INFO - Epoch(train) [105][4700/5047] lr: 1.3440e-05 eta: 2 days, 7:10:42 time: 0.8473 data_time: 0.0027 memory: 43532 loss: 0.1170 loss_ce: 0.1170 2023/03/01 00:48:52 - mmengine - INFO - Epoch(train) [105][4800/5047] lr: 1.3440e-05 eta: 2 days, 7:09:14 time: 0.8130 data_time: 0.0027 memory: 48129 loss: 0.1083 loss_ce: 0.1083 2023/03/01 00:50:18 - mmengine - INFO - Epoch(train) [105][4900/5047] lr: 1.3440e-05 eta: 2 days, 7:07:47 time: 0.8558 data_time: 0.0053 memory: 50514 loss: 0.0964 loss_ce: 0.0964 2023/03/01 00:51:45 - mmengine - INFO - Epoch(train) [105][5000/5047] lr: 1.3440e-05 eta: 2 days, 7:06:19 time: 0.8839 data_time: 0.0026 memory: 44865 loss: 0.1168 loss_ce: 0.1168 2023/03/01 00:52:25 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 00:52:25 - mmengine - INFO - Saving checkpoint at 105 epochs 2023/03/01 00:53:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 00:53:56 - mmengine - INFO - Epoch(train) [106][ 100/5047] lr: 1.3239e-05 eta: 2 days, 7:04:10 time: 0.8271 data_time: 0.0025 memory: 52864 loss: 0.1127 loss_ce: 0.1127 2023/03/01 00:55:22 - mmengine - INFO - Epoch(train) [106][ 200/5047] lr: 1.3239e-05 eta: 2 days, 7:02:42 time: 0.8567 data_time: 0.0025 memory: 44563 loss: 0.1260 loss_ce: 0.1260 2023/03/01 00:56:47 - mmengine - INFO - Epoch(train) [106][ 300/5047] lr: 1.3239e-05 eta: 2 days, 7:01:13 time: 0.8972 data_time: 0.0025 memory: 43030 loss: 0.1118 loss_ce: 0.1118 2023/03/01 00:58:13 - mmengine - INFO - Epoch(train) [106][ 400/5047] lr: 1.3239e-05 eta: 2 days, 6:59:45 time: 0.8796 data_time: 0.0031 memory: 43271 loss: 0.1032 loss_ce: 0.1032 2023/03/01 00:59:40 - mmengine - INFO - Epoch(train) [106][ 500/5047] lr: 1.3239e-05 eta: 2 days, 6:58:18 time: 0.9012 data_time: 0.0027 memory: 46964 loss: 0.1168 loss_ce: 0.1168 2023/03/01 01:01:07 - mmengine - INFO - Epoch(train) [106][ 600/5047] lr: 1.3239e-05 eta: 2 days, 6:56:50 time: 0.9174 data_time: 0.0028 memory: 48188 loss: 0.1144 loss_ce: 0.1144 2023/03/01 01:02:34 - mmengine - INFO - Epoch(train) [106][ 700/5047] lr: 1.3239e-05 eta: 2 days, 6:55:23 time: 0.8790 data_time: 0.0030 memory: 52517 loss: 0.1174 loss_ce: 0.1174 2023/03/01 01:04:01 - mmengine - INFO - Epoch(train) [106][ 800/5047] lr: 1.3239e-05 eta: 2 days, 6:53:55 time: 0.8677 data_time: 0.0027 memory: 54242 loss: 0.1133 loss_ce: 0.1133 2023/03/01 01:05:27 - mmengine - INFO - Epoch(train) [106][ 900/5047] lr: 1.3239e-05 eta: 2 days, 6:52:27 time: 0.8823 data_time: 0.0031 memory: 40241 loss: 0.1110 loss_ce: 0.1110 2023/03/01 01:06:53 - mmengine - INFO - Epoch(train) [106][1000/5047] lr: 1.3239e-05 eta: 2 days, 6:50:59 time: 0.8578 data_time: 0.0025 memory: 55562 loss: 0.1032 loss_ce: 0.1032 2023/03/01 01:07:49 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 01:08:19 - mmengine - INFO - Epoch(train) [106][1100/5047] lr: 1.3239e-05 eta: 2 days, 6:49:32 time: 0.8334 data_time: 0.0023 memory: 42048 loss: 0.1136 loss_ce: 0.1136 2023/03/01 01:09:45 - mmengine - INFO - Epoch(train) [106][1200/5047] lr: 1.3239e-05 eta: 2 days, 6:48:04 time: 0.8155 data_time: 0.0025 memory: 42649 loss: 0.1171 loss_ce: 0.1171 2023/03/01 01:11:13 - mmengine - INFO - Epoch(train) [106][1300/5047] lr: 1.3239e-05 eta: 2 days, 6:46:36 time: 0.8744 data_time: 0.0035 memory: 42901 loss: 0.1145 loss_ce: 0.1145 2023/03/01 01:12:38 - mmengine - INFO - Epoch(train) [106][1400/5047] lr: 1.3239e-05 eta: 2 days, 6:45:08 time: 0.8560 data_time: 0.0025 memory: 42336 loss: 0.1118 loss_ce: 0.1118 2023/03/01 01:14:03 - mmengine - INFO - Epoch(train) [106][1500/5047] lr: 1.3239e-05 eta: 2 days, 6:43:40 time: 0.8610 data_time: 0.0028 memory: 40825 loss: 0.1078 loss_ce: 0.1078 2023/03/01 01:15:28 - mmengine - INFO - Epoch(train) [106][1600/5047] lr: 1.3239e-05 eta: 2 days, 6:42:12 time: 0.8697 data_time: 0.0049 memory: 42965 loss: 0.1008 loss_ce: 0.1008 2023/03/01 01:16:56 - mmengine - INFO - Epoch(train) [106][1700/5047] lr: 1.3239e-05 eta: 2 days, 6:40:44 time: 0.9031 data_time: 0.0025 memory: 43613 loss: 0.1025 loss_ce: 0.1025 2023/03/01 01:18:20 - mmengine - INFO - Epoch(train) [106][1800/5047] lr: 1.3239e-05 eta: 2 days, 6:39:16 time: 0.8474 data_time: 0.0027 memory: 39283 loss: 0.1038 loss_ce: 0.1038 2023/03/01 01:19:46 - mmengine - INFO - Epoch(train) [106][1900/5047] lr: 1.3239e-05 eta: 2 days, 6:37:47 time: 0.8692 data_time: 0.0025 memory: 43378 loss: 0.1162 loss_ce: 0.1162 2023/03/01 01:21:13 - mmengine - INFO - Epoch(train) [106][2000/5047] lr: 1.3239e-05 eta: 2 days, 6:36:20 time: 0.9224 data_time: 0.0025 memory: 41145 loss: 0.0950 loss_ce: 0.0950 2023/03/01 01:22:10 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 01:22:40 - mmengine - INFO - Epoch(train) [106][2100/5047] lr: 1.3239e-05 eta: 2 days, 6:34:53 time: 0.8750 data_time: 0.0034 memory: 46854 loss: 0.1144 loss_ce: 0.1144 2023/03/01 01:24:08 - mmengine - INFO - Epoch(train) [106][2200/5047] lr: 1.3239e-05 eta: 2 days, 6:33:25 time: 0.9043 data_time: 0.0031 memory: 45319 loss: 0.0988 loss_ce: 0.0988 2023/03/01 01:25:33 - mmengine - INFO - Epoch(train) [106][2300/5047] lr: 1.3239e-05 eta: 2 days, 6:31:57 time: 0.8211 data_time: 0.0033 memory: 40270 loss: 0.1021 loss_ce: 0.1021 2023/03/01 01:26:59 - mmengine - INFO - Epoch(train) [106][2400/5047] lr: 1.3239e-05 eta: 2 days, 6:30:29 time: 0.8611 data_time: 0.0025 memory: 47012 loss: 0.1228 loss_ce: 0.1228 2023/03/01 01:28:26 - mmengine - INFO - Epoch(train) [106][2500/5047] lr: 1.3239e-05 eta: 2 days, 6:29:02 time: 0.9010 data_time: 0.0052 memory: 54242 loss: 0.0996 loss_ce: 0.0996 2023/03/01 01:29:53 - mmengine - INFO - Epoch(train) [106][2600/5047] lr: 1.3239e-05 eta: 2 days, 6:27:34 time: 0.9207 data_time: 0.0030 memory: 46553 loss: 0.0984 loss_ce: 0.0984 2023/03/01 01:31:19 - mmengine - INFO - Epoch(train) [106][2700/5047] lr: 1.3239e-05 eta: 2 days, 6:26:06 time: 0.8306 data_time: 0.0026 memory: 54072 loss: 0.1040 loss_ce: 0.1040 2023/03/01 01:32:45 - mmengine - INFO - Epoch(train) [106][2800/5047] lr: 1.3239e-05 eta: 2 days, 6:24:39 time: 0.8742 data_time: 0.0028 memory: 45343 loss: 0.0948 loss_ce: 0.0948 2023/03/01 01:34:11 - mmengine - INFO - Epoch(train) [106][2900/5047] lr: 1.3239e-05 eta: 2 days, 6:23:11 time: 0.8619 data_time: 0.0026 memory: 41724 loss: 0.1177 loss_ce: 0.1177 2023/03/01 01:35:38 - mmengine - INFO - Epoch(train) [106][3000/5047] lr: 1.3239e-05 eta: 2 days, 6:21:43 time: 0.8196 data_time: 0.0026 memory: 43613 loss: 0.1212 loss_ce: 0.1212 2023/03/01 01:36:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 01:37:07 - mmengine - INFO - Epoch(train) [106][3100/5047] lr: 1.3239e-05 eta: 2 days, 6:20:16 time: 0.8744 data_time: 0.0025 memory: 40535 loss: 0.1094 loss_ce: 0.1094 2023/03/01 01:38:33 - mmengine - INFO - Epoch(train) [106][3200/5047] lr: 1.3239e-05 eta: 2 days, 6:18:48 time: 0.8296 data_time: 0.0026 memory: 46995 loss: 0.1164 loss_ce: 0.1164 2023/03/01 01:39:59 - mmengine - INFO - Epoch(train) [106][3300/5047] lr: 1.3239e-05 eta: 2 days, 6:17:21 time: 0.8649 data_time: 0.0048 memory: 38870 loss: 0.1074 loss_ce: 0.1074 2023/03/01 01:41:24 - mmengine - INFO - Epoch(train) [106][3400/5047] lr: 1.3239e-05 eta: 2 days, 6:15:52 time: 0.8366 data_time: 0.0037 memory: 50542 loss: 0.1107 loss_ce: 0.1107 2023/03/01 01:42:53 - mmengine - INFO - Epoch(train) [106][3500/5047] lr: 1.3239e-05 eta: 2 days, 6:14:25 time: 0.8616 data_time: 0.0026 memory: 55562 loss: 0.1150 loss_ce: 0.1150 2023/03/01 01:44:20 - mmengine - INFO - Epoch(train) [106][3600/5047] lr: 1.3239e-05 eta: 2 days, 6:12:58 time: 0.8769 data_time: 0.0130 memory: 42024 loss: 0.1125 loss_ce: 0.1125 2023/03/01 01:45:48 - mmengine - INFO - Epoch(train) [106][3700/5047] lr: 1.3239e-05 eta: 2 days, 6:11:31 time: 0.8257 data_time: 0.0025 memory: 55562 loss: 0.1126 loss_ce: 0.1126 2023/03/01 01:47:14 - mmengine - INFO - Epoch(train) [106][3800/5047] lr: 1.3239e-05 eta: 2 days, 6:10:03 time: 0.8299 data_time: 0.0047 memory: 50348 loss: 0.1189 loss_ce: 0.1189 2023/03/01 01:48:40 - mmengine - INFO - Epoch(train) [106][3900/5047] lr: 1.3239e-05 eta: 2 days, 6:08:35 time: 0.8616 data_time: 0.0024 memory: 49312 loss: 0.1123 loss_ce: 0.1123 2023/03/01 01:50:06 - mmengine - INFO - Epoch(train) [106][4000/5047] lr: 1.3239e-05 eta: 2 days, 6:07:07 time: 0.8366 data_time: 0.0029 memory: 49343 loss: 0.1217 loss_ce: 0.1217 2023/03/01 01:51:02 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 01:51:33 - mmengine - INFO - Epoch(train) [106][4100/5047] lr: 1.3239e-05 eta: 2 days, 6:05:40 time: 0.8705 data_time: 0.0024 memory: 44953 loss: 0.0978 loss_ce: 0.0978 2023/03/01 01:52:59 - mmengine - INFO - Epoch(train) [106][4200/5047] lr: 1.3239e-05 eta: 2 days, 6:04:12 time: 0.8414 data_time: 0.0029 memory: 45302 loss: 0.1069 loss_ce: 0.1069 2023/03/01 01:54:27 - mmengine - INFO - Epoch(train) [106][4300/5047] lr: 1.3239e-05 eta: 2 days, 6:02:45 time: 0.9413 data_time: 0.0030 memory: 50419 loss: 0.1165 loss_ce: 0.1165 2023/03/01 01:55:54 - mmengine - INFO - Epoch(train) [106][4400/5047] lr: 1.3239e-05 eta: 2 days, 6:01:17 time: 0.8256 data_time: 0.0025 memory: 47968 loss: 0.1023 loss_ce: 0.1023 2023/03/01 01:57:19 - mmengine - INFO - Epoch(train) [106][4500/5047] lr: 1.3239e-05 eta: 2 days, 5:59:49 time: 0.8450 data_time: 0.0025 memory: 43624 loss: 0.1072 loss_ce: 0.1072 2023/03/01 01:58:46 - mmengine - INFO - Epoch(train) [106][4600/5047] lr: 1.3239e-05 eta: 2 days, 5:58:21 time: 0.8709 data_time: 0.0029 memory: 41724 loss: 0.1022 loss_ce: 0.1022 2023/03/01 02:00:11 - mmengine - INFO - Epoch(train) [106][4700/5047] lr: 1.3239e-05 eta: 2 days, 5:56:53 time: 0.8719 data_time: 0.0026 memory: 42024 loss: 0.1147 loss_ce: 0.1147 2023/03/01 02:01:36 - mmengine - INFO - Epoch(train) [106][4800/5047] lr: 1.3239e-05 eta: 2 days, 5:55:25 time: 0.8683 data_time: 0.0023 memory: 40535 loss: 0.0991 loss_ce: 0.0991 2023/03/01 02:03:05 - mmengine - INFO - Epoch(train) [106][4900/5047] lr: 1.3239e-05 eta: 2 days, 5:53:58 time: 0.8737 data_time: 0.0024 memory: 55562 loss: 0.1052 loss_ce: 0.1052 2023/03/01 02:04:32 - mmengine - INFO - Epoch(train) [106][5000/5047] lr: 1.3239e-05 eta: 2 days, 5:52:30 time: 0.8904 data_time: 0.0025 memory: 47984 loss: 0.1354 loss_ce: 0.1354 2023/03/01 02:05:13 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 02:05:13 - mmengine - INFO - Saving checkpoint at 106 epochs 2023/03/01 02:05:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 02:06:45 - mmengine - INFO - Epoch(train) [107][ 100/5047] lr: 1.3038e-05 eta: 2 days, 5:50:22 time: 0.8433 data_time: 0.0024 memory: 48948 loss: 0.1030 loss_ce: 0.1030 2023/03/01 02:08:12 - mmengine - INFO - Epoch(train) [107][ 200/5047] lr: 1.3038e-05 eta: 2 days, 5:48:54 time: 0.9028 data_time: 0.0024 memory: 49170 loss: 0.1044 loss_ce: 0.1044 2023/03/01 02:09:38 - mmengine - INFO - Epoch(train) [107][ 300/5047] lr: 1.3038e-05 eta: 2 days, 5:47:26 time: 0.8550 data_time: 0.0053 memory: 43396 loss: 0.1069 loss_ce: 0.1069 2023/03/01 02:11:04 - mmengine - INFO - Epoch(train) [107][ 400/5047] lr: 1.3038e-05 eta: 2 days, 5:45:58 time: 0.8317 data_time: 0.0023 memory: 43609 loss: 0.1162 loss_ce: 0.1162 2023/03/01 02:12:29 - mmengine - INFO - Epoch(train) [107][ 500/5047] lr: 1.3038e-05 eta: 2 days, 5:44:30 time: 0.8679 data_time: 0.0024 memory: 39960 loss: 0.1187 loss_ce: 0.1187 2023/03/01 02:13:55 - mmengine - INFO - Epoch(train) [107][ 600/5047] lr: 1.3038e-05 eta: 2 days, 5:43:02 time: 0.9169 data_time: 0.0024 memory: 43743 loss: 0.1046 loss_ce: 0.1046 2023/03/01 02:15:21 - mmengine - INFO - Epoch(train) [107][ 700/5047] lr: 1.3038e-05 eta: 2 days, 5:41:34 time: 0.8409 data_time: 0.0023 memory: 46883 loss: 0.1031 loss_ce: 0.1031 2023/03/01 02:16:48 - mmengine - INFO - Epoch(train) [107][ 800/5047] lr: 1.3038e-05 eta: 2 days, 5:40:07 time: 0.8594 data_time: 0.0024 memory: 47826 loss: 0.1236 loss_ce: 0.1236 2023/03/01 02:18:16 - mmengine - INFO - Epoch(train) [107][ 900/5047] lr: 1.3038e-05 eta: 2 days, 5:38:40 time: 0.8838 data_time: 0.0030 memory: 47813 loss: 0.1163 loss_ce: 0.1163 2023/03/01 02:19:42 - mmengine - INFO - Epoch(train) [107][1000/5047] lr: 1.3038e-05 eta: 2 days, 5:37:12 time: 0.8807 data_time: 0.0046 memory: 45015 loss: 0.1217 loss_ce: 0.1217 2023/03/01 02:19:57 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 02:21:08 - mmengine - INFO - Epoch(train) [107][1100/5047] lr: 1.3038e-05 eta: 2 days, 5:35:44 time: 0.8949 data_time: 0.0024 memory: 54242 loss: 0.1169 loss_ce: 0.1169 2023/03/01 02:22:35 - mmengine - INFO - Epoch(train) [107][1200/5047] lr: 1.3038e-05 eta: 2 days, 5:34:17 time: 0.8596 data_time: 0.0033 memory: 39126 loss: 0.1103 loss_ce: 0.1103 2023/03/01 02:24:02 - mmengine - INFO - Epoch(train) [107][1300/5047] lr: 1.3038e-05 eta: 2 days, 5:32:49 time: 0.8734 data_time: 0.0025 memory: 41281 loss: 0.1255 loss_ce: 0.1255 2023/03/01 02:25:29 - mmengine - INFO - Epoch(train) [107][1400/5047] lr: 1.3038e-05 eta: 2 days, 5:31:21 time: 0.8065 data_time: 0.0025 memory: 42024 loss: 0.1179 loss_ce: 0.1179 2023/03/01 02:26:54 - mmengine - INFO - Epoch(train) [107][1500/5047] lr: 1.3038e-05 eta: 2 days, 5:29:53 time: 0.8584 data_time: 0.0025 memory: 44477 loss: 0.1084 loss_ce: 0.1084 2023/03/01 02:28:21 - mmengine - INFO - Epoch(train) [107][1600/5047] lr: 1.3038e-05 eta: 2 days, 5:28:26 time: 0.8570 data_time: 0.0026 memory: 44756 loss: 0.1073 loss_ce: 0.1073 2023/03/01 02:29:51 - mmengine - INFO - Epoch(train) [107][1700/5047] lr: 1.3038e-05 eta: 2 days, 5:26:59 time: 0.9690 data_time: 0.0028 memory: 42465 loss: 0.1183 loss_ce: 0.1183 2023/03/01 02:31:30 - mmengine - INFO - Epoch(train) [107][1800/5047] lr: 1.3038e-05 eta: 2 days, 5:25:37 time: 1.3732 data_time: 0.0023 memory: 45130 loss: 0.1093 loss_ce: 0.1093 2023/03/01 02:33:05 - mmengine - INFO - Epoch(train) [107][1900/5047] lr: 1.3038e-05 eta: 2 days, 5:24:13 time: 0.8115 data_time: 0.0028 memory: 42336 loss: 0.1125 loss_ce: 0.1125 2023/03/01 02:34:31 - mmengine - INFO - Epoch(train) [107][2000/5047] lr: 1.3038e-05 eta: 2 days, 5:22:45 time: 0.8682 data_time: 0.0029 memory: 43289 loss: 0.1097 loss_ce: 0.1097 2023/03/01 02:34:46 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 02:35:59 - mmengine - INFO - Epoch(train) [107][2100/5047] lr: 1.3038e-05 eta: 2 days, 5:21:18 time: 0.8959 data_time: 0.0028 memory: 42772 loss: 0.1229 loss_ce: 0.1229 2023/03/01 02:37:25 - mmengine - INFO - Epoch(train) [107][2200/5047] lr: 1.3038e-05 eta: 2 days, 5:19:50 time: 0.8763 data_time: 0.0027 memory: 41285 loss: 0.1002 loss_ce: 0.1002 2023/03/01 02:38:51 - mmengine - INFO - Epoch(train) [107][2300/5047] lr: 1.3038e-05 eta: 2 days, 5:18:22 time: 0.9015 data_time: 0.0027 memory: 40694 loss: 0.1067 loss_ce: 0.1067 2023/03/01 02:40:18 - mmengine - INFO - Epoch(train) [107][2400/5047] lr: 1.3038e-05 eta: 2 days, 5:16:55 time: 0.9130 data_time: 0.0025 memory: 46355 loss: 0.1077 loss_ce: 0.1077 2023/03/01 02:41:46 - mmengine - INFO - Epoch(train) [107][2500/5047] lr: 1.3038e-05 eta: 2 days, 5:15:27 time: 0.8728 data_time: 0.0027 memory: 45302 loss: 0.0999 loss_ce: 0.0999 2023/03/01 02:43:14 - mmengine - INFO - Epoch(train) [107][2600/5047] lr: 1.3038e-05 eta: 2 days, 5:14:00 time: 0.8656 data_time: 0.0026 memory: 51586 loss: 0.1165 loss_ce: 0.1165 2023/03/01 02:44:41 - mmengine - INFO - Epoch(train) [107][2700/5047] lr: 1.3038e-05 eta: 2 days, 5:12:33 time: 0.8935 data_time: 0.0025 memory: 45815 loss: 0.1138 loss_ce: 0.1138 2023/03/01 02:46:09 - mmengine - INFO - Epoch(train) [107][2800/5047] lr: 1.3038e-05 eta: 2 days, 5:11:06 time: 0.8563 data_time: 0.0026 memory: 46875 loss: 0.1020 loss_ce: 0.1020 2023/03/01 02:47:34 - mmengine - INFO - Epoch(train) [107][2900/5047] lr: 1.3038e-05 eta: 2 days, 5:09:38 time: 0.8380 data_time: 0.0025 memory: 42336 loss: 0.1089 loss_ce: 0.1089 2023/03/01 02:49:01 - mmengine - INFO - Epoch(train) [107][3000/5047] lr: 1.3038e-05 eta: 2 days, 5:08:10 time: 0.8863 data_time: 0.0025 memory: 40535 loss: 0.1148 loss_ce: 0.1148 2023/03/01 02:49:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 02:50:28 - mmengine - INFO - Epoch(train) [107][3100/5047] lr: 1.3038e-05 eta: 2 days, 5:06:43 time: 0.8602 data_time: 0.0023 memory: 44278 loss: 0.1065 loss_ce: 0.1065 2023/03/01 02:51:56 - mmengine - INFO - Epoch(train) [107][3200/5047] lr: 1.3038e-05 eta: 2 days, 5:05:15 time: 0.9107 data_time: 0.0024 memory: 52543 loss: 0.1125 loss_ce: 0.1125 2023/03/01 02:53:22 - mmengine - INFO - Epoch(train) [107][3300/5047] lr: 1.3038e-05 eta: 2 days, 5:03:48 time: 0.8621 data_time: 0.0026 memory: 43947 loss: 0.0956 loss_ce: 0.0956 2023/03/01 02:54:50 - mmengine - INFO - Epoch(train) [107][3400/5047] lr: 1.3038e-05 eta: 2 days, 5:02:21 time: 0.8671 data_time: 0.0027 memory: 55562 loss: 0.1047 loss_ce: 0.1047 2023/03/01 02:56:16 - mmengine - INFO - Epoch(train) [107][3500/5047] lr: 1.3038e-05 eta: 2 days, 5:00:53 time: 0.8204 data_time: 0.0028 memory: 42024 loss: 0.1030 loss_ce: 0.1030 2023/03/01 02:57:41 - mmengine - INFO - Epoch(train) [107][3600/5047] lr: 1.3038e-05 eta: 2 days, 4:59:25 time: 0.8477 data_time: 0.0025 memory: 44278 loss: 0.1057 loss_ce: 0.1057 2023/03/01 02:59:07 - mmengine - INFO - Epoch(train) [107][3700/5047] lr: 1.3038e-05 eta: 2 days, 4:57:56 time: 0.8798 data_time: 0.0025 memory: 55562 loss: 0.1039 loss_ce: 0.1039 2023/03/01 03:00:34 - mmengine - INFO - Epoch(train) [107][3800/5047] lr: 1.3038e-05 eta: 2 days, 4:56:29 time: 0.8896 data_time: 0.0023 memory: 51732 loss: 0.1044 loss_ce: 0.1044 2023/03/01 03:01:59 - mmengine - INFO - Epoch(train) [107][3900/5047] lr: 1.3038e-05 eta: 2 days, 4:55:01 time: 0.8741 data_time: 0.0038 memory: 46941 loss: 0.0878 loss_ce: 0.0878 2023/03/01 03:03:28 - mmengine - INFO - Epoch(train) [107][4000/5047] lr: 1.3038e-05 eta: 2 days, 4:53:34 time: 0.8992 data_time: 0.0023 memory: 46772 loss: 0.1257 loss_ce: 0.1257 2023/03/01 03:03:44 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 03:04:55 - mmengine - INFO - Epoch(train) [107][4100/5047] lr: 1.3038e-05 eta: 2 days, 4:52:07 time: 0.8987 data_time: 0.0024 memory: 47813 loss: 0.1049 loss_ce: 0.1049 2023/03/01 03:06:20 - mmengine - INFO - Epoch(train) [107][4200/5047] lr: 1.3038e-05 eta: 2 days, 4:50:38 time: 0.8307 data_time: 0.0029 memory: 43964 loss: 0.0969 loss_ce: 0.0969 2023/03/01 03:07:47 - mmengine - INFO - Epoch(train) [107][4300/5047] lr: 1.3038e-05 eta: 2 days, 4:49:11 time: 0.8716 data_time: 0.0027 memory: 48727 loss: 0.1118 loss_ce: 0.1118 2023/03/01 03:09:14 - mmengine - INFO - Epoch(train) [107][4400/5047] lr: 1.3038e-05 eta: 2 days, 4:47:43 time: 0.8453 data_time: 0.0027 memory: 45689 loss: 0.1198 loss_ce: 0.1198 2023/03/01 03:10:41 - mmengine - INFO - Epoch(train) [107][4500/5047] lr: 1.3038e-05 eta: 2 days, 4:46:16 time: 0.8874 data_time: 0.0032 memory: 42649 loss: 0.1164 loss_ce: 0.1164 2023/03/01 03:12:09 - mmengine - INFO - Epoch(train) [107][4600/5047] lr: 1.3038e-05 eta: 2 days, 4:44:49 time: 0.8897 data_time: 0.0035 memory: 44852 loss: 0.0991 loss_ce: 0.0991 2023/03/01 03:13:36 - mmengine - INFO - Epoch(train) [107][4700/5047] lr: 1.3038e-05 eta: 2 days, 4:43:21 time: 0.8298 data_time: 0.0062 memory: 47799 loss: 0.1087 loss_ce: 0.1087 2023/03/01 03:15:03 - mmengine - INFO - Epoch(train) [107][4800/5047] lr: 1.3038e-05 eta: 2 days, 4:41:54 time: 0.8929 data_time: 0.0030 memory: 47813 loss: 0.1115 loss_ce: 0.1115 2023/03/01 03:16:28 - mmengine - INFO - Epoch(train) [107][4900/5047] lr: 1.3038e-05 eta: 2 days, 4:40:26 time: 0.8371 data_time: 0.0023 memory: 45642 loss: 0.0994 loss_ce: 0.0994 2023/03/01 03:17:55 - mmengine - INFO - Epoch(train) [107][5000/5047] lr: 1.3038e-05 eta: 2 days, 4:38:58 time: 0.8954 data_time: 0.0026 memory: 50540 loss: 0.1051 loss_ce: 0.1051 2023/03/01 03:18:10 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 03:18:35 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 03:18:35 - mmengine - INFO - Saving checkpoint at 107 epochs 2023/03/01 03:20:08 - mmengine - INFO - Epoch(train) [108][ 100/5047] lr: 1.2837e-05 eta: 2 days, 4:36:49 time: 0.8887 data_time: 0.0024 memory: 44722 loss: 0.1014 loss_ce: 0.1014 2023/03/01 03:21:35 - mmengine - INFO - Epoch(train) [108][ 200/5047] lr: 1.2837e-05 eta: 2 days, 4:35:22 time: 0.8841 data_time: 0.0024 memory: 46005 loss: 0.0979 loss_ce: 0.0979 2023/03/01 03:23:03 - mmengine - INFO - Epoch(train) [108][ 300/5047] lr: 1.2837e-05 eta: 2 days, 4:33:55 time: 0.8732 data_time: 0.0036 memory: 42965 loss: 0.1074 loss_ce: 0.1074 2023/03/01 03:24:28 - mmengine - INFO - Epoch(train) [108][ 400/5047] lr: 1.2837e-05 eta: 2 days, 4:32:26 time: 0.8527 data_time: 0.0025 memory: 43420 loss: 0.0957 loss_ce: 0.0957 2023/03/01 03:25:56 - mmengine - INFO - Epoch(train) [108][ 500/5047] lr: 1.2837e-05 eta: 2 days, 4:30:59 time: 0.8062 data_time: 0.0032 memory: 48789 loss: 0.1046 loss_ce: 0.1046 2023/03/01 03:27:22 - mmengine - INFO - Epoch(train) [108][ 600/5047] lr: 1.2837e-05 eta: 2 days, 4:29:31 time: 0.8317 data_time: 0.0055 memory: 44617 loss: 0.1091 loss_ce: 0.1091 2023/03/01 03:28:47 - mmengine - INFO - Epoch(train) [108][ 700/5047] lr: 1.2837e-05 eta: 2 days, 4:28:03 time: 0.7931 data_time: 0.0026 memory: 39960 loss: 0.0987 loss_ce: 0.0987 2023/03/01 03:30:12 - mmengine - INFO - Epoch(train) [108][ 800/5047] lr: 1.2837e-05 eta: 2 days, 4:26:35 time: 0.8274 data_time: 0.0026 memory: 44592 loss: 0.1061 loss_ce: 0.1061 2023/03/01 03:31:40 - mmengine - INFO - Epoch(train) [108][ 900/5047] lr: 1.2837e-05 eta: 2 days, 4:25:08 time: 0.8727 data_time: 0.0058 memory: 49240 loss: 0.1185 loss_ce: 0.1185 2023/03/01 03:32:41 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 03:33:07 - mmengine - INFO - Epoch(train) [108][1000/5047] lr: 1.2837e-05 eta: 2 days, 4:23:40 time: 0.9079 data_time: 0.0042 memory: 51755 loss: 0.1158 loss_ce: 0.1158 2023/03/01 03:34:33 - mmengine - INFO - Epoch(train) [108][1100/5047] lr: 1.2837e-05 eta: 2 days, 4:22:13 time: 0.8361 data_time: 0.0030 memory: 41624 loss: 0.1097 loss_ce: 0.1097 2023/03/01 03:35:59 - mmengine - INFO - Epoch(train) [108][1200/5047] lr: 1.2837e-05 eta: 2 days, 4:20:45 time: 0.8912 data_time: 0.0028 memory: 54242 loss: 0.1113 loss_ce: 0.1113 2023/03/01 03:37:25 - mmengine - INFO - Epoch(train) [108][1300/5047] lr: 1.2837e-05 eta: 2 days, 4:19:16 time: 0.8597 data_time: 0.0029 memory: 39681 loss: 0.1010 loss_ce: 0.1010 2023/03/01 03:38:51 - mmengine - INFO - Epoch(train) [108][1400/5047] lr: 1.2837e-05 eta: 2 days, 4:17:49 time: 0.8400 data_time: 0.0028 memory: 55562 loss: 0.1029 loss_ce: 0.1029 2023/03/01 03:40:18 - mmengine - INFO - Epoch(train) [108][1500/5047] lr: 1.2837e-05 eta: 2 days, 4:16:21 time: 0.8839 data_time: 0.0023 memory: 45959 loss: 0.1017 loss_ce: 0.1017 2023/03/01 03:41:46 - mmengine - INFO - Epoch(train) [108][1600/5047] lr: 1.2837e-05 eta: 2 days, 4:14:54 time: 0.8491 data_time: 0.0037 memory: 46854 loss: 0.0964 loss_ce: 0.0964 2023/03/01 03:43:12 - mmengine - INFO - Epoch(train) [108][1700/5047] lr: 1.2837e-05 eta: 2 days, 4:13:26 time: 0.9122 data_time: 0.0060 memory: 42336 loss: 0.1080 loss_ce: 0.1080 2023/03/01 03:44:38 - mmengine - INFO - Epoch(train) [108][1800/5047] lr: 1.2837e-05 eta: 2 days, 4:11:58 time: 0.8589 data_time: 0.0024 memory: 47261 loss: 0.0949 loss_ce: 0.0949 2023/03/01 03:46:03 - mmengine - INFO - Epoch(train) [108][1900/5047] lr: 1.2837e-05 eta: 2 days, 4:10:30 time: 0.8547 data_time: 0.0024 memory: 49334 loss: 0.1031 loss_ce: 0.1031 2023/03/01 03:47:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 03:47:30 - mmengine - INFO - Epoch(train) [108][2000/5047] lr: 1.2837e-05 eta: 2 days, 4:09:03 time: 0.9026 data_time: 0.0025 memory: 38341 loss: 0.1108 loss_ce: 0.1108 2023/03/01 03:48:57 - mmengine - INFO - Epoch(train) [108][2100/5047] lr: 1.2837e-05 eta: 2 days, 4:07:35 time: 0.8450 data_time: 0.0025 memory: 41782 loss: 0.1089 loss_ce: 0.1089 2023/03/01 03:50:24 - mmengine - INFO - Epoch(train) [108][2200/5047] lr: 1.2837e-05 eta: 2 days, 4:06:08 time: 0.8776 data_time: 0.0028 memory: 42096 loss: 0.1061 loss_ce: 0.1061 2023/03/01 03:51:49 - mmengine - INFO - Epoch(train) [108][2300/5047] lr: 1.2837e-05 eta: 2 days, 4:04:39 time: 0.8708 data_time: 0.0025 memory: 42965 loss: 0.1071 loss_ce: 0.1071 2023/03/01 03:53:16 - mmengine - INFO - Epoch(train) [108][2400/5047] lr: 1.2837e-05 eta: 2 days, 4:03:12 time: 0.8466 data_time: 0.0026 memory: 41998 loss: 0.1052 loss_ce: 0.1052 2023/03/01 03:54:41 - mmengine - INFO - Epoch(train) [108][2500/5047] lr: 1.2837e-05 eta: 2 days, 4:01:44 time: 0.8564 data_time: 0.0025 memory: 42475 loss: 0.1044 loss_ce: 0.1044 2023/03/01 03:56:06 - mmengine - INFO - Epoch(train) [108][2600/5047] lr: 1.2837e-05 eta: 2 days, 4:00:15 time: 0.7951 data_time: 0.0025 memory: 45643 loss: 0.1067 loss_ce: 0.1067 2023/03/01 03:57:34 - mmengine - INFO - Epoch(train) [108][2700/5047] lr: 1.2837e-05 eta: 2 days, 3:58:48 time: 0.8928 data_time: 0.0023 memory: 42336 loss: 0.0989 loss_ce: 0.0989 2023/03/01 03:59:01 - mmengine - INFO - Epoch(train) [108][2800/5047] lr: 1.2837e-05 eta: 2 days, 3:57:21 time: 0.8618 data_time: 0.0027 memory: 44278 loss: 0.1265 loss_ce: 0.1265 2023/03/01 04:00:28 - mmengine - INFO - Epoch(train) [108][2900/5047] lr: 1.2837e-05 eta: 2 days, 3:55:53 time: 0.8213 data_time: 0.0043 memory: 42649 loss: 0.1094 loss_ce: 0.1094 2023/03/01 04:01:29 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 04:01:54 - mmengine - INFO - Epoch(train) [108][3000/5047] lr: 1.2837e-05 eta: 2 days, 3:54:25 time: 0.8168 data_time: 0.0025 memory: 55562 loss: 0.1062 loss_ce: 0.1062 2023/03/01 04:03:21 - mmengine - INFO - Epoch(train) [108][3100/5047] lr: 1.2837e-05 eta: 2 days, 3:52:58 time: 0.8651 data_time: 0.0031 memory: 45688 loss: 0.1050 loss_ce: 0.1050 2023/03/01 04:04:47 - mmengine - INFO - Epoch(train) [108][3200/5047] lr: 1.2837e-05 eta: 2 days, 3:51:30 time: 0.8482 data_time: 0.0031 memory: 46005 loss: 0.1029 loss_ce: 0.1029 2023/03/01 04:06:12 - mmengine - INFO - Epoch(train) [108][3300/5047] lr: 1.2837e-05 eta: 2 days, 3:50:02 time: 0.8377 data_time: 0.0028 memory: 43613 loss: 0.1130 loss_ce: 0.1130 2023/03/01 04:07:39 - mmengine - INFO - Epoch(train) [108][3400/5047] lr: 1.2837e-05 eta: 2 days, 3:48:34 time: 0.8520 data_time: 0.0028 memory: 44956 loss: 0.1032 loss_ce: 0.1032 2023/03/01 04:09:05 - mmengine - INFO - Epoch(train) [108][3500/5047] lr: 1.2837e-05 eta: 2 days, 3:47:06 time: 0.8663 data_time: 0.0024 memory: 43289 loss: 0.1053 loss_ce: 0.1053 2023/03/01 04:10:31 - mmengine - INFO - Epoch(train) [108][3600/5047] lr: 1.2837e-05 eta: 2 days, 3:45:39 time: 0.8706 data_time: 0.0039 memory: 45850 loss: 0.1057 loss_ce: 0.1057 2023/03/01 04:11:57 - mmengine - INFO - Epoch(train) [108][3700/5047] lr: 1.2837e-05 eta: 2 days, 3:44:11 time: 0.8868 data_time: 0.0029 memory: 45191 loss: 0.1200 loss_ce: 0.1200 2023/03/01 04:13:23 - mmengine - INFO - Epoch(train) [108][3800/5047] lr: 1.2837e-05 eta: 2 days, 3:42:43 time: 0.9305 data_time: 0.0024 memory: 44659 loss: 0.1047 loss_ce: 0.1047 2023/03/01 04:14:51 - mmengine - INFO - Epoch(train) [108][3900/5047] lr: 1.2837e-05 eta: 2 days, 3:41:16 time: 0.8603 data_time: 0.0032 memory: 43947 loss: 0.1052 loss_ce: 0.1052 2023/03/01 04:15:53 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 04:16:17 - mmengine - INFO - Epoch(train) [108][4000/5047] lr: 1.2837e-05 eta: 2 days, 3:39:48 time: 0.8361 data_time: 0.0025 memory: 42719 loss: 0.1195 loss_ce: 0.1195 2023/03/01 04:17:45 - mmengine - INFO - Epoch(train) [108][4100/5047] lr: 1.2837e-05 eta: 2 days, 3:38:21 time: 0.9206 data_time: 0.0025 memory: 49373 loss: 0.1054 loss_ce: 0.1054 2023/03/01 04:19:09 - mmengine - INFO - Epoch(train) [108][4200/5047] lr: 1.2837e-05 eta: 2 days, 3:36:52 time: 0.8375 data_time: 0.0026 memory: 44041 loss: 0.1065 loss_ce: 0.1065 2023/03/01 04:20:37 - mmengine - INFO - Epoch(train) [108][4300/5047] lr: 1.2837e-05 eta: 2 days, 3:35:25 time: 0.8767 data_time: 0.0030 memory: 41624 loss: 0.1098 loss_ce: 0.1098 2023/03/01 04:22:03 - mmengine - INFO - Epoch(train) [108][4400/5047] lr: 1.2837e-05 eta: 2 days, 3:33:57 time: 0.8750 data_time: 0.0048 memory: 41392 loss: 0.1070 loss_ce: 0.1070 2023/03/01 04:23:30 - mmengine - INFO - Epoch(train) [108][4500/5047] lr: 1.2837e-05 eta: 2 days, 3:32:30 time: 0.8877 data_time: 0.0026 memory: 45302 loss: 0.1183 loss_ce: 0.1183 2023/03/01 04:24:57 - mmengine - INFO - Epoch(train) [108][4600/5047] lr: 1.2837e-05 eta: 2 days, 3:31:02 time: 0.8748 data_time: 0.0025 memory: 55562 loss: 0.1034 loss_ce: 0.1034 2023/03/01 04:26:23 - mmengine - INFO - Epoch(train) [108][4700/5047] lr: 1.2837e-05 eta: 2 days, 3:29:34 time: 0.8631 data_time: 0.0028 memory: 42795 loss: 0.1040 loss_ce: 0.1040 2023/03/01 04:27:48 - mmengine - INFO - Epoch(train) [108][4800/5047] lr: 1.2837e-05 eta: 2 days, 3:28:06 time: 0.8122 data_time: 0.0025 memory: 42336 loss: 0.0906 loss_ce: 0.0906 2023/03/01 04:29:14 - mmengine - INFO - Epoch(train) [108][4900/5047] lr: 1.2837e-05 eta: 2 days, 3:26:38 time: 0.8318 data_time: 0.0104 memory: 54242 loss: 0.1109 loss_ce: 0.1109 2023/03/01 04:30:16 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 04:30:41 - mmengine - INFO - Epoch(train) [108][5000/5047] lr: 1.2837e-05 eta: 2 days, 3:25:11 time: 0.8274 data_time: 0.0025 memory: 48086 loss: 0.1043 loss_ce: 0.1043 2023/03/01 04:31:21 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 04:31:21 - mmengine - INFO - Saving checkpoint at 108 epochs 2023/03/01 04:32:53 - mmengine - INFO - Epoch(train) [109][ 100/5047] lr: 1.2636e-05 eta: 2 days, 3:23:02 time: 0.8846 data_time: 0.0027 memory: 55562 loss: 0.1042 loss_ce: 0.1042 2023/03/01 04:34:19 - mmengine - INFO - Epoch(train) [109][ 200/5047] lr: 1.2636e-05 eta: 2 days, 3:21:34 time: 0.8292 data_time: 0.0024 memory: 46355 loss: 0.1055 loss_ce: 0.1055 2023/03/01 04:35:45 - mmengine - INFO - Epoch(train) [109][ 300/5047] lr: 1.2636e-05 eta: 2 days, 3:20:06 time: 0.8620 data_time: 0.0024 memory: 54232 loss: 0.1075 loss_ce: 0.1075 2023/03/01 04:37:10 - mmengine - INFO - Epoch(train) [109][ 400/5047] lr: 1.2636e-05 eta: 2 days, 3:18:38 time: 0.8476 data_time: 0.0026 memory: 41419 loss: 0.1027 loss_ce: 0.1027 2023/03/01 04:38:37 - mmengine - INFO - Epoch(train) [109][ 500/5047] lr: 1.2636e-05 eta: 2 days, 3:17:10 time: 0.8834 data_time: 0.0033 memory: 43289 loss: 0.1117 loss_ce: 0.1117 2023/03/01 04:40:03 - mmengine - INFO - Epoch(train) [109][ 600/5047] lr: 1.2636e-05 eta: 2 days, 3:15:43 time: 0.8505 data_time: 0.0027 memory: 46355 loss: 0.1365 loss_ce: 0.1365 2023/03/01 04:41:28 - mmengine - INFO - Epoch(train) [109][ 700/5047] lr: 1.2636e-05 eta: 2 days, 3:14:15 time: 0.8237 data_time: 0.0024 memory: 43613 loss: 0.1091 loss_ce: 0.1091 2023/03/01 04:42:53 - mmengine - INFO - Epoch(train) [109][ 800/5047] lr: 1.2636e-05 eta: 2 days, 3:12:46 time: 0.8569 data_time: 0.0040 memory: 43613 loss: 0.1205 loss_ce: 0.1205 2023/03/01 04:44:20 - mmengine - INFO - Epoch(train) [109][ 900/5047] lr: 1.2636e-05 eta: 2 days, 3:11:19 time: 0.8983 data_time: 0.0026 memory: 55562 loss: 0.1126 loss_ce: 0.1126 2023/03/01 04:44:41 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 04:45:47 - mmengine - INFO - Epoch(train) [109][1000/5047] lr: 1.2636e-05 eta: 2 days, 3:09:51 time: 0.8899 data_time: 0.0025 memory: 42965 loss: 0.1099 loss_ce: 0.1099 2023/03/01 04:47:13 - mmengine - INFO - Epoch(train) [109][1100/5047] lr: 1.2636e-05 eta: 2 days, 3:08:23 time: 0.8629 data_time: 0.0058 memory: 55562 loss: 0.1099 loss_ce: 0.1099 2023/03/01 04:48:41 - mmengine - INFO - Epoch(train) [109][1200/5047] lr: 1.2636e-05 eta: 2 days, 3:06:56 time: 0.8119 data_time: 0.0026 memory: 43613 loss: 0.1161 loss_ce: 0.1161 2023/03/01 04:50:07 - mmengine - INFO - Epoch(train) [109][1300/5047] lr: 1.2636e-05 eta: 2 days, 3:05:28 time: 0.8785 data_time: 0.0029 memory: 47108 loss: 0.1114 loss_ce: 0.1114 2023/03/01 04:51:34 - mmengine - INFO - Epoch(train) [109][1400/5047] lr: 1.2636e-05 eta: 2 days, 3:04:01 time: 0.8755 data_time: 0.0027 memory: 42965 loss: 0.1068 loss_ce: 0.1068 2023/03/01 04:53:01 - mmengine - INFO - Epoch(train) [109][1500/5047] lr: 1.2636e-05 eta: 2 days, 3:02:34 time: 0.8721 data_time: 0.0029 memory: 45851 loss: 0.1089 loss_ce: 0.1089 2023/03/01 04:54:26 - mmengine - INFO - Epoch(train) [109][1600/5047] lr: 1.2636e-05 eta: 2 days, 3:01:05 time: 0.8060 data_time: 0.0053 memory: 44721 loss: 0.1101 loss_ce: 0.1101 2023/03/01 04:55:53 - mmengine - INFO - Epoch(train) [109][1700/5047] lr: 1.2636e-05 eta: 2 days, 2:59:38 time: 0.8160 data_time: 0.0069 memory: 55562 loss: 0.1074 loss_ce: 0.1074 2023/03/01 04:57:18 - mmengine - INFO - Epoch(train) [109][1800/5047] lr: 1.2636e-05 eta: 2 days, 2:58:10 time: 0.8700 data_time: 0.0028 memory: 45681 loss: 0.0978 loss_ce: 0.0978 2023/03/01 04:58:42 - mmengine - INFO - Epoch(train) [109][1900/5047] lr: 1.2636e-05 eta: 2 days, 2:56:41 time: 0.8469 data_time: 0.0028 memory: 44956 loss: 0.0982 loss_ce: 0.0982 2023/03/01 04:59:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 05:00:09 - mmengine - INFO - Epoch(train) [109][2000/5047] lr: 1.2636e-05 eta: 2 days, 2:55:14 time: 0.8683 data_time: 0.0027 memory: 41419 loss: 0.1091 loss_ce: 0.1091 2023/03/01 05:01:37 - mmengine - INFO - Epoch(train) [109][2100/5047] lr: 1.2636e-05 eta: 2 days, 2:53:47 time: 0.8552 data_time: 0.0053 memory: 44956 loss: 0.1243 loss_ce: 0.1243 2023/03/01 05:03:03 - mmengine - INFO - Epoch(train) [109][2200/5047] lr: 1.2636e-05 eta: 2 days, 2:52:19 time: 0.8270 data_time: 0.0029 memory: 42336 loss: 0.1046 loss_ce: 0.1046 2023/03/01 05:04:29 - mmengine - INFO - Epoch(train) [109][2300/5047] lr: 1.2636e-05 eta: 2 days, 2:50:51 time: 0.8890 data_time: 0.0030 memory: 45939 loss: 0.1132 loss_ce: 0.1132 2023/03/01 05:05:55 - mmengine - INFO - Epoch(train) [109][2400/5047] lr: 1.2636e-05 eta: 2 days, 2:49:23 time: 0.8399 data_time: 0.0045 memory: 44617 loss: 0.1158 loss_ce: 0.1158 2023/03/01 05:07:23 - mmengine - INFO - Epoch(train) [109][2500/5047] lr: 1.2636e-05 eta: 2 days, 2:47:56 time: 0.8818 data_time: 0.0026 memory: 49240 loss: 0.1027 loss_ce: 0.1027 2023/03/01 05:08:47 - mmengine - INFO - Epoch(train) [109][2600/5047] lr: 1.2636e-05 eta: 2 days, 2:46:27 time: 0.8528 data_time: 0.0024 memory: 45643 loss: 0.1100 loss_ce: 0.1100 2023/03/01 05:10:13 - mmengine - INFO - Epoch(train) [109][2700/5047] lr: 1.2636e-05 eta: 2 days, 2:44:59 time: 0.8590 data_time: 0.0028 memory: 55562 loss: 0.1196 loss_ce: 0.1196 2023/03/01 05:11:40 - mmengine - INFO - Epoch(train) [109][2800/5047] lr: 1.2636e-05 eta: 2 days, 2:43:32 time: 0.9201 data_time: 0.0028 memory: 42336 loss: 0.1145 loss_ce: 0.1145 2023/03/01 05:13:07 - mmengine - INFO - Epoch(train) [109][2900/5047] lr: 1.2636e-05 eta: 2 days, 2:42:04 time: 0.8356 data_time: 0.0024 memory: 41122 loss: 0.0994 loss_ce: 0.0994 2023/03/01 05:13:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 05:14:32 - mmengine - INFO - Epoch(train) [109][3000/5047] lr: 1.2636e-05 eta: 2 days, 2:40:36 time: 0.8395 data_time: 0.0046 memory: 43947 loss: 0.1008 loss_ce: 0.1008 2023/03/01 05:15:58 - mmengine - INFO - Epoch(train) [109][3100/5047] lr: 1.2636e-05 eta: 2 days, 2:39:08 time: 0.8455 data_time: 0.0026 memory: 44956 loss: 0.1062 loss_ce: 0.1062 2023/03/01 05:17:24 - mmengine - INFO - Epoch(train) [109][3200/5047] lr: 1.2636e-05 eta: 2 days, 2:37:41 time: 0.8685 data_time: 0.0028 memory: 48188 loss: 0.1136 loss_ce: 0.1136 2023/03/01 05:18:50 - mmengine - INFO - Epoch(train) [109][3300/5047] lr: 1.2636e-05 eta: 2 days, 2:36:13 time: 0.8535 data_time: 0.0035 memory: 40825 loss: 0.1220 loss_ce: 0.1220 2023/03/01 05:20:15 - mmengine - INFO - Epoch(train) [109][3400/5047] lr: 1.2636e-05 eta: 2 days, 2:34:45 time: 0.8667 data_time: 0.0035 memory: 55562 loss: 0.1093 loss_ce: 0.1093 2023/03/01 05:21:43 - mmengine - INFO - Epoch(train) [109][3500/5047] lr: 1.2636e-05 eta: 2 days, 2:33:17 time: 0.8996 data_time: 0.0028 memory: 41122 loss: 0.1012 loss_ce: 0.1012 2023/03/01 05:23:07 - mmengine - INFO - Epoch(train) [109][3600/5047] lr: 1.2636e-05 eta: 2 days, 2:31:49 time: 0.8285 data_time: 0.0024 memory: 42336 loss: 0.1084 loss_ce: 0.1084 2023/03/01 05:24:35 - mmengine - INFO - Epoch(train) [109][3700/5047] lr: 1.2636e-05 eta: 2 days, 2:30:22 time: 0.8218 data_time: 0.0025 memory: 43816 loss: 0.1232 loss_ce: 0.1232 2023/03/01 05:26:00 - mmengine - INFO - Epoch(train) [109][3800/5047] lr: 1.2636e-05 eta: 2 days, 2:28:54 time: 0.8486 data_time: 0.0026 memory: 41724 loss: 0.1127 loss_ce: 0.1127 2023/03/01 05:27:27 - mmengine - INFO - Epoch(train) [109][3900/5047] lr: 1.2636e-05 eta: 2 days, 2:27:26 time: 0.8373 data_time: 0.0035 memory: 42024 loss: 0.1216 loss_ce: 0.1216 2023/03/01 05:27:47 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 05:28:55 - mmengine - INFO - Epoch(train) [109][4000/5047] lr: 1.2636e-05 eta: 2 days, 2:25:59 time: 0.9538 data_time: 0.0029 memory: 52976 loss: 0.1089 loss_ce: 0.1089 2023/03/01 05:30:21 - mmengine - INFO - Epoch(train) [109][4100/5047] lr: 1.2636e-05 eta: 2 days, 2:24:31 time: 0.8365 data_time: 0.0027 memory: 41793 loss: 0.0909 loss_ce: 0.0909 2023/03/01 05:31:47 - mmengine - INFO - Epoch(train) [109][4200/5047] lr: 1.2636e-05 eta: 2 days, 2:23:03 time: 0.8261 data_time: 0.0024 memory: 44582 loss: 0.1188 loss_ce: 0.1188 2023/03/01 05:33:13 - mmengine - INFO - Epoch(train) [109][4300/5047] lr: 1.2636e-05 eta: 2 days, 2:21:35 time: 0.8331 data_time: 0.0026 memory: 46713 loss: 0.1141 loss_ce: 0.1141 2023/03/01 05:34:39 - mmengine - INFO - Epoch(train) [109][4400/5047] lr: 1.2636e-05 eta: 2 days, 2:20:08 time: 0.9259 data_time: 0.0024 memory: 42024 loss: 0.1191 loss_ce: 0.1191 2023/03/01 05:36:06 - mmengine - INFO - Epoch(train) [109][4500/5047] lr: 1.2636e-05 eta: 2 days, 2:18:40 time: 0.8672 data_time: 0.0025 memory: 42649 loss: 0.1249 loss_ce: 0.1249 2023/03/01 05:37:30 - mmengine - INFO - Epoch(train) [109][4600/5047] lr: 1.2636e-05 eta: 2 days, 2:17:12 time: 0.8133 data_time: 0.0031 memory: 42965 loss: 0.1131 loss_ce: 0.1131 2023/03/01 05:38:55 - mmengine - INFO - Epoch(train) [109][4700/5047] lr: 1.2636e-05 eta: 2 days, 2:15:43 time: 0.8574 data_time: 0.0026 memory: 48309 loss: 0.1081 loss_ce: 0.1081 2023/03/01 05:40:23 - mmengine - INFO - Epoch(train) [109][4800/5047] lr: 1.2636e-05 eta: 2 days, 2:14:16 time: 0.8820 data_time: 0.0026 memory: 46198 loss: 0.1334 loss_ce: 0.1334 2023/03/01 05:41:51 - mmengine - INFO - Epoch(train) [109][4900/5047] lr: 1.2636e-05 eta: 2 days, 2:12:49 time: 0.9534 data_time: 0.0023 memory: 42336 loss: 0.1247 loss_ce: 0.1247 2023/03/01 05:42:12 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 05:43:17 - mmengine - INFO - Epoch(train) [109][5000/5047] lr: 1.2636e-05 eta: 2 days, 2:11:21 time: 0.8616 data_time: 0.0028 memory: 44278 loss: 0.1048 loss_ce: 0.1048 2023/03/01 05:43:59 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 05:43:59 - mmengine - INFO - Saving checkpoint at 109 epochs 2023/03/01 05:45:30 - mmengine - INFO - Epoch(train) [110][ 100/5047] lr: 1.2435e-05 eta: 2 days, 2:09:13 time: 0.8758 data_time: 0.0026 memory: 54673 loss: 0.1087 loss_ce: 0.1087 2023/03/01 05:46:58 - mmengine - INFO - Epoch(train) [110][ 200/5047] lr: 1.2435e-05 eta: 2 days, 2:07:46 time: 0.9153 data_time: 0.0027 memory: 42272 loss: 0.1063 loss_ce: 0.1063 2023/03/01 05:48:23 - mmengine - INFO - Epoch(train) [110][ 300/5047] lr: 1.2435e-05 eta: 2 days, 2:06:18 time: 0.8794 data_time: 0.0028 memory: 49474 loss: 0.1052 loss_ce: 0.1052 2023/03/01 05:49:49 - mmengine - INFO - Epoch(train) [110][ 400/5047] lr: 1.2435e-05 eta: 2 days, 2:04:49 time: 0.8349 data_time: 0.0027 memory: 43289 loss: 0.1242 loss_ce: 0.1242 2023/03/01 05:51:14 - mmengine - INFO - Epoch(train) [110][ 500/5047] lr: 1.2435e-05 eta: 2 days, 2:03:21 time: 0.8641 data_time: 0.0029 memory: 41122 loss: 0.1195 loss_ce: 0.1195 2023/03/01 05:52:40 - mmengine - INFO - Epoch(train) [110][ 600/5047] lr: 1.2435e-05 eta: 2 days, 2:01:53 time: 0.8268 data_time: 0.0037 memory: 48129 loss: 0.1143 loss_ce: 0.1143 2023/03/01 05:54:05 - mmengine - INFO - Epoch(train) [110][ 700/5047] lr: 1.2435e-05 eta: 2 days, 2:00:26 time: 0.7722 data_time: 0.0025 memory: 46711 loss: 0.1020 loss_ce: 0.1020 2023/03/01 05:55:32 - mmengine - INFO - Epoch(train) [110][ 800/5047] lr: 1.2435e-05 eta: 2 days, 1:58:58 time: 0.9118 data_time: 0.0028 memory: 44265 loss: 0.1141 loss_ce: 0.1141 2023/03/01 05:56:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 05:56:59 - mmengine - INFO - Epoch(train) [110][ 900/5047] lr: 1.2435e-05 eta: 2 days, 1:57:30 time: 0.8521 data_time: 0.0026 memory: 45643 loss: 0.1053 loss_ce: 0.1053 2023/03/01 05:58:26 - mmengine - INFO - Epoch(train) [110][1000/5047] lr: 1.2435e-05 eta: 2 days, 1:56:03 time: 0.8179 data_time: 0.0063 memory: 47011 loss: 0.1078 loss_ce: 0.1078 2023/03/01 05:59:53 - mmengine - INFO - Epoch(train) [110][1100/5047] lr: 1.2435e-05 eta: 2 days, 1:54:36 time: 0.8671 data_time: 0.0023 memory: 41724 loss: 0.1111 loss_ce: 0.1111 2023/03/01 06:01:18 - mmengine - INFO - Epoch(train) [110][1200/5047] lr: 1.2435e-05 eta: 2 days, 1:53:08 time: 0.8646 data_time: 0.0043 memory: 42965 loss: 0.1119 loss_ce: 0.1119 2023/03/01 06:02:44 - mmengine - INFO - Epoch(train) [110][1300/5047] lr: 1.2435e-05 eta: 2 days, 1:51:40 time: 0.8186 data_time: 0.0045 memory: 54879 loss: 0.1300 loss_ce: 0.1300 2023/03/01 06:04:08 - mmengine - INFO - Epoch(train) [110][1400/5047] lr: 1.2435e-05 eta: 2 days, 1:50:11 time: 0.8447 data_time: 0.0041 memory: 44539 loss: 0.1036 loss_ce: 0.1036 2023/03/01 06:05:34 - mmengine - INFO - Epoch(train) [110][1500/5047] lr: 1.2435e-05 eta: 2 days, 1:48:43 time: 0.8951 data_time: 0.0048 memory: 45643 loss: 0.1100 loss_ce: 0.1100 2023/03/01 06:06:59 - mmengine - INFO - Epoch(train) [110][1600/5047] lr: 1.2435e-05 eta: 2 days, 1:47:15 time: 0.8397 data_time: 0.0029 memory: 51308 loss: 0.1089 loss_ce: 0.1089 2023/03/01 06:08:24 - mmengine - INFO - Epoch(train) [110][1700/5047] lr: 1.2435e-05 eta: 2 days, 1:45:47 time: 0.8343 data_time: 0.0027 memory: 48035 loss: 0.1014 loss_ce: 0.1014 2023/03/01 06:09:50 - mmengine - INFO - Epoch(train) [110][1800/5047] lr: 1.2435e-05 eta: 2 days, 1:44:19 time: 0.8133 data_time: 0.0028 memory: 55562 loss: 0.1012 loss_ce: 0.1012 2023/03/01 06:10:56 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 06:11:16 - mmengine - INFO - Epoch(train) [110][1900/5047] lr: 1.2435e-05 eta: 2 days, 1:42:51 time: 0.8908 data_time: 0.0027 memory: 45463 loss: 0.1071 loss_ce: 0.1071 2023/03/01 06:12:41 - mmengine - INFO - Epoch(train) [110][2000/5047] lr: 1.2435e-05 eta: 2 days, 1:41:23 time: 0.8567 data_time: 0.0026 memory: 41455 loss: 0.0983 loss_ce: 0.0983 2023/03/01 06:14:09 - mmengine - INFO - Epoch(train) [110][2100/5047] lr: 1.2435e-05 eta: 2 days, 1:39:56 time: 0.8827 data_time: 0.0025 memory: 44278 loss: 0.1060 loss_ce: 0.1060 2023/03/01 06:15:35 - mmengine - INFO - Epoch(train) [110][2200/5047] lr: 1.2435e-05 eta: 2 days, 1:38:28 time: 0.8512 data_time: 0.0025 memory: 55562 loss: 0.1159 loss_ce: 0.1159 2023/03/01 06:17:01 - mmengine - INFO - Epoch(train) [110][2300/5047] lr: 1.2435e-05 eta: 2 days, 1:37:00 time: 0.9133 data_time: 0.0026 memory: 54242 loss: 0.0992 loss_ce: 0.0992 2023/03/01 06:18:26 - mmengine - INFO - Epoch(train) [110][2400/5047] lr: 1.2435e-05 eta: 2 days, 1:35:32 time: 0.8546 data_time: 0.0026 memory: 42239 loss: 0.1005 loss_ce: 0.1005 2023/03/01 06:19:52 - mmengine - INFO - Epoch(train) [110][2500/5047] lr: 1.2435e-05 eta: 2 days, 1:34:04 time: 0.8742 data_time: 0.0028 memory: 42024 loss: 0.1241 loss_ce: 0.1241 2023/03/01 06:21:18 - mmengine - INFO - Epoch(train) [110][2600/5047] lr: 1.2435e-05 eta: 2 days, 1:32:36 time: 0.8665 data_time: 0.0032 memory: 46005 loss: 0.0943 loss_ce: 0.0943 2023/03/01 06:22:44 - mmengine - INFO - Epoch(train) [110][2700/5047] lr: 1.2435e-05 eta: 2 days, 1:31:09 time: 0.9154 data_time: 0.0037 memory: 42513 loss: 0.1069 loss_ce: 0.1069 2023/03/01 06:24:10 - mmengine - INFO - Epoch(train) [110][2800/5047] lr: 1.2435e-05 eta: 2 days, 1:29:41 time: 0.8438 data_time: 0.0026 memory: 43613 loss: 0.1195 loss_ce: 0.1195 2023/03/01 06:25:15 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 06:25:35 - mmengine - INFO - Epoch(train) [110][2900/5047] lr: 1.2435e-05 eta: 2 days, 1:28:13 time: 0.8748 data_time: 0.0026 memory: 45643 loss: 0.1079 loss_ce: 0.1079 2023/03/01 06:27:01 - mmengine - INFO - Epoch(train) [110][3000/5047] lr: 1.2435e-05 eta: 2 days, 1:26:45 time: 0.8315 data_time: 0.0027 memory: 43613 loss: 0.1188 loss_ce: 0.1188 2023/03/01 06:28:26 - mmengine - INFO - Epoch(train) [110][3100/5047] lr: 1.2435e-05 eta: 2 days, 1:25:17 time: 0.8757 data_time: 0.0025 memory: 52543 loss: 0.1207 loss_ce: 0.1207 2023/03/01 06:29:52 - mmengine - INFO - Epoch(train) [110][3200/5047] lr: 1.2435e-05 eta: 2 days, 1:23:49 time: 0.8628 data_time: 0.0028 memory: 53044 loss: 0.1182 loss_ce: 0.1182 2023/03/01 06:31:19 - mmengine - INFO - Epoch(train) [110][3300/5047] lr: 1.2435e-05 eta: 2 days, 1:22:21 time: 0.9425 data_time: 0.0034 memory: 47447 loss: 0.1047 loss_ce: 0.1047 2023/03/01 06:32:47 - mmengine - INFO - Epoch(train) [110][3400/5047] lr: 1.2435e-05 eta: 2 days, 1:20:54 time: 0.8368 data_time: 0.0025 memory: 46996 loss: 0.1007 loss_ce: 0.1007 2023/03/01 06:34:13 - mmengine - INFO - Epoch(train) [110][3500/5047] lr: 1.2435e-05 eta: 2 days, 1:19:26 time: 0.8613 data_time: 0.0026 memory: 42024 loss: 0.0970 loss_ce: 0.0970 2023/03/01 06:35:40 - mmengine - INFO - Epoch(train) [110][3600/5047] lr: 1.2435e-05 eta: 2 days, 1:17:59 time: 0.8849 data_time: 0.0027 memory: 42965 loss: 0.1031 loss_ce: 0.1031 2023/03/01 06:37:05 - mmengine - INFO - Epoch(train) [110][3700/5047] lr: 1.2435e-05 eta: 2 days, 1:16:31 time: 0.8400 data_time: 0.0028 memory: 44617 loss: 0.1121 loss_ce: 0.1121 2023/03/01 06:38:31 - mmengine - INFO - Epoch(train) [110][3800/5047] lr: 1.2435e-05 eta: 2 days, 1:15:03 time: 0.8709 data_time: 0.0026 memory: 54113 loss: 0.1083 loss_ce: 0.1083 2023/03/01 06:39:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 06:39:57 - mmengine - INFO - Epoch(train) [110][3900/5047] lr: 1.2435e-05 eta: 2 days, 1:13:35 time: 0.8290 data_time: 0.0024 memory: 48407 loss: 0.1049 loss_ce: 0.1049 2023/03/01 06:41:23 - mmengine - INFO - Epoch(train) [110][4000/5047] lr: 1.2435e-05 eta: 2 days, 1:12:07 time: 0.8902 data_time: 0.0024 memory: 47729 loss: 0.1111 loss_ce: 0.1111 2023/03/01 06:42:51 - mmengine - INFO - Epoch(train) [110][4100/5047] lr: 1.2435e-05 eta: 2 days, 1:10:40 time: 0.8474 data_time: 0.0024 memory: 44956 loss: 0.1270 loss_ce: 0.1270 2023/03/01 06:44:18 - mmengine - INFO - Epoch(train) [110][4200/5047] lr: 1.2435e-05 eta: 2 days, 1:09:13 time: 0.8873 data_time: 0.0045 memory: 51308 loss: 0.0932 loss_ce: 0.0932 2023/03/01 06:45:42 - mmengine - INFO - Epoch(train) [110][4300/5047] lr: 1.2435e-05 eta: 2 days, 1:07:44 time: 0.8186 data_time: 0.0026 memory: 45302 loss: 0.1025 loss_ce: 0.1025 2023/03/01 06:47:07 - mmengine - INFO - Epoch(train) [110][4400/5047] lr: 1.2435e-05 eta: 2 days, 1:06:16 time: 0.8957 data_time: 0.0050 memory: 42239 loss: 0.1197 loss_ce: 0.1197 2023/03/01 06:48:33 - mmengine - INFO - Epoch(train) [110][4500/5047] lr: 1.2435e-05 eta: 2 days, 1:04:48 time: 0.8422 data_time: 0.0057 memory: 40825 loss: 0.1085 loss_ce: 0.1085 2023/03/01 06:49:57 - mmengine - INFO - Epoch(train) [110][4600/5047] lr: 1.2435e-05 eta: 2 days, 1:03:20 time: 0.8345 data_time: 0.0030 memory: 40991 loss: 0.1003 loss_ce: 0.1003 2023/03/01 06:51:24 - mmengine - INFO - Epoch(train) [110][4700/5047] lr: 1.2435e-05 eta: 2 days, 1:01:52 time: 0.8803 data_time: 0.0035 memory: 46355 loss: 0.1196 loss_ce: 0.1196 2023/03/01 06:52:49 - mmengine - INFO - Epoch(train) [110][4800/5047] lr: 1.2435e-05 eta: 2 days, 1:00:25 time: 0.8998 data_time: 0.0029 memory: 49456 loss: 0.1041 loss_ce: 0.1041 2023/03/01 06:53:56 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 06:54:16 - mmengine - INFO - Epoch(train) [110][4900/5047] lr: 1.2435e-05 eta: 2 days, 0:58:57 time: 0.8959 data_time: 0.0026 memory: 46966 loss: 0.1166 loss_ce: 0.1166 2023/03/01 06:55:44 - mmengine - INFO - Epoch(train) [110][5000/5047] lr: 1.2435e-05 eta: 2 days, 0:57:30 time: 0.8778 data_time: 0.0034 memory: 43511 loss: 0.1056 loss_ce: 0.1056 2023/03/01 06:56:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 06:56:24 - mmengine - INFO - Saving checkpoint at 110 epochs 2023/03/01 06:57:54 - mmengine - INFO - Epoch(train) [111][ 100/5047] lr: 1.2234e-05 eta: 2 days, 0:55:20 time: 0.8964 data_time: 0.0050 memory: 47348 loss: 0.1030 loss_ce: 0.1030 2023/03/01 06:59:20 - mmengine - INFO - Epoch(train) [111][ 200/5047] lr: 1.2234e-05 eta: 2 days, 0:53:52 time: 0.8661 data_time: 0.0027 memory: 41724 loss: 0.1166 loss_ce: 0.1166 2023/03/01 07:00:46 - mmengine - INFO - Epoch(train) [111][ 300/5047] lr: 1.2234e-05 eta: 2 days, 0:52:25 time: 0.8411 data_time: 0.0024 memory: 40825 loss: 0.0916 loss_ce: 0.0916 2023/03/01 07:02:12 - mmengine - INFO - Epoch(train) [111][ 400/5047] lr: 1.2234e-05 eta: 2 days, 0:50:57 time: 0.8792 data_time: 0.0027 memory: 43719 loss: 0.1105 loss_ce: 0.1105 2023/03/01 07:03:38 - mmengine - INFO - Epoch(train) [111][ 500/5047] lr: 1.2234e-05 eta: 2 days, 0:49:29 time: 0.8253 data_time: 0.0026 memory: 55393 loss: 0.1056 loss_ce: 0.1056 2023/03/01 07:05:04 - mmengine - INFO - Epoch(train) [111][ 600/5047] lr: 1.2234e-05 eta: 2 days, 0:48:01 time: 0.8257 data_time: 0.0045 memory: 52127 loss: 0.1197 loss_ce: 0.1197 2023/03/01 07:06:30 - mmengine - INFO - Epoch(train) [111][ 700/5047] lr: 1.2234e-05 eta: 2 days, 0:46:34 time: 0.9005 data_time: 0.0050 memory: 44989 loss: 0.0909 loss_ce: 0.0909 2023/03/01 07:07:55 - mmengine - INFO - Epoch(train) [111][ 800/5047] lr: 1.2234e-05 eta: 2 days, 0:45:05 time: 0.8287 data_time: 0.0055 memory: 39681 loss: 0.1216 loss_ce: 0.1216 2023/03/01 07:08:21 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 07:09:21 - mmengine - INFO - Epoch(train) [111][ 900/5047] lr: 1.2234e-05 eta: 2 days, 0:43:38 time: 0.8304 data_time: 0.0025 memory: 44617 loss: 0.1085 loss_ce: 0.1085 2023/03/01 07:10:49 - mmengine - INFO - Epoch(train) [111][1000/5047] lr: 1.2234e-05 eta: 2 days, 0:42:10 time: 0.8477 data_time: 0.0025 memory: 44278 loss: 0.1081 loss_ce: 0.1081 2023/03/01 07:12:14 - mmengine - INFO - Epoch(train) [111][1100/5047] lr: 1.2234e-05 eta: 2 days, 0:40:43 time: 0.8067 data_time: 0.0027 memory: 54044 loss: 0.0918 loss_ce: 0.0918 2023/03/01 07:13:42 - mmengine - INFO - Epoch(train) [111][1200/5047] lr: 1.2234e-05 eta: 2 days, 0:39:15 time: 0.8354 data_time: 0.0024 memory: 42649 loss: 0.0900 loss_ce: 0.0900 2023/03/01 07:15:07 - mmengine - INFO - Epoch(train) [111][1300/5047] lr: 1.2234e-05 eta: 2 days, 0:37:47 time: 0.8517 data_time: 0.0025 memory: 55562 loss: 0.1008 loss_ce: 0.1008 2023/03/01 07:16:32 - mmengine - INFO - Epoch(train) [111][1400/5047] lr: 1.2234e-05 eta: 2 days, 0:36:19 time: 0.8053 data_time: 0.0024 memory: 55562 loss: 0.1178 loss_ce: 0.1178 2023/03/01 07:17:58 - mmengine - INFO - Epoch(train) [111][1500/5047] lr: 1.2234e-05 eta: 2 days, 0:34:51 time: 0.8616 data_time: 0.0026 memory: 41492 loss: 0.1091 loss_ce: 0.1091 2023/03/01 07:19:23 - mmengine - INFO - Epoch(train) [111][1600/5047] lr: 1.2234e-05 eta: 2 days, 0:33:23 time: 0.8837 data_time: 0.0031 memory: 45907 loss: 0.1097 loss_ce: 0.1097 2023/03/01 07:20:49 - mmengine - INFO - Epoch(train) [111][1700/5047] lr: 1.2234e-05 eta: 2 days, 0:31:55 time: 0.8609 data_time: 0.0025 memory: 43289 loss: 0.1113 loss_ce: 0.1113 2023/03/01 07:22:16 - mmengine - INFO - Epoch(train) [111][1800/5047] lr: 1.2234e-05 eta: 2 days, 0:30:28 time: 0.8600 data_time: 0.0025 memory: 46875 loss: 0.1171 loss_ce: 0.1171 2023/03/01 07:22:42 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 07:23:42 - mmengine - INFO - Epoch(train) [111][1900/5047] lr: 1.2234e-05 eta: 2 days, 0:29:00 time: 0.8516 data_time: 0.0026 memory: 44278 loss: 0.1111 loss_ce: 0.1111 2023/03/01 07:25:07 - mmengine - INFO - Epoch(train) [111][2000/5047] lr: 1.2234e-05 eta: 2 days, 0:27:32 time: 0.8383 data_time: 0.0025 memory: 44617 loss: 0.1093 loss_ce: 0.1093 2023/03/01 07:26:33 - mmengine - INFO - Epoch(train) [111][2100/5047] lr: 1.2234e-05 eta: 2 days, 0:26:04 time: 0.8354 data_time: 0.0028 memory: 47582 loss: 0.1071 loss_ce: 0.1071 2023/03/01 07:27:58 - mmengine - INFO - Epoch(train) [111][2200/5047] lr: 1.2234e-05 eta: 2 days, 0:24:36 time: 0.8882 data_time: 0.0023 memory: 55562 loss: 0.1159 loss_ce: 0.1159 2023/03/01 07:29:24 - mmengine - INFO - Epoch(train) [111][2300/5047] lr: 1.2234e-05 eta: 2 days, 0:23:08 time: 0.8677 data_time: 0.0026 memory: 40825 loss: 0.1120 loss_ce: 0.1120 2023/03/01 07:30:51 - mmengine - INFO - Epoch(train) [111][2400/5047] lr: 1.2234e-05 eta: 2 days, 0:21:41 time: 0.9036 data_time: 0.0026 memory: 41656 loss: 0.0978 loss_ce: 0.0978 2023/03/01 07:32:17 - mmengine - INFO - Epoch(train) [111][2500/5047] lr: 1.2234e-05 eta: 2 days, 0:20:13 time: 0.9289 data_time: 0.0027 memory: 40535 loss: 0.1228 loss_ce: 0.1228 2023/03/01 07:33:44 - mmengine - INFO - Epoch(train) [111][2600/5047] lr: 1.2234e-05 eta: 2 days, 0:18:45 time: 0.8375 data_time: 0.0025 memory: 49334 loss: 0.1110 loss_ce: 0.1110 2023/03/01 07:35:09 - mmengine - INFO - Epoch(train) [111][2700/5047] lr: 1.2234e-05 eta: 2 days, 0:17:17 time: 0.8566 data_time: 0.0026 memory: 44956 loss: 0.1012 loss_ce: 0.1012 2023/03/01 07:36:34 - mmengine - INFO - Epoch(train) [111][2800/5047] lr: 1.2234e-05 eta: 2 days, 0:15:49 time: 0.8638 data_time: 0.0026 memory: 55393 loss: 0.0972 loss_ce: 0.0972 2023/03/01 07:37:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 07:38:00 - mmengine - INFO - Epoch(train) [111][2900/5047] lr: 1.2234e-05 eta: 2 days, 0:14:22 time: 0.8083 data_time: 0.0029 memory: 40825 loss: 0.1116 loss_ce: 0.1116 2023/03/01 07:39:25 - mmengine - INFO - Epoch(train) [111][3000/5047] lr: 1.2234e-05 eta: 2 days, 0:12:54 time: 0.8510 data_time: 0.0026 memory: 43613 loss: 0.1068 loss_ce: 0.1068 2023/03/01 07:40:52 - mmengine - INFO - Epoch(train) [111][3100/5047] lr: 1.2234e-05 eta: 2 days, 0:11:26 time: 0.8692 data_time: 0.0026 memory: 46355 loss: 0.1001 loss_ce: 0.1001 2023/03/01 07:42:19 - mmengine - INFO - Epoch(train) [111][3200/5047] lr: 1.2234e-05 eta: 2 days, 0:09:59 time: 0.8853 data_time: 0.0029 memory: 53387 loss: 0.1073 loss_ce: 0.1073 2023/03/01 07:43:46 - mmengine - INFO - Epoch(train) [111][3300/5047] lr: 1.2234e-05 eta: 2 days, 0:08:31 time: 0.8219 data_time: 0.0061 memory: 44580 loss: 0.1188 loss_ce: 0.1188 2023/03/01 07:45:12 - mmengine - INFO - Epoch(train) [111][3400/5047] lr: 1.2234e-05 eta: 2 days, 0:07:03 time: 0.8923 data_time: 0.0031 memory: 49378 loss: 0.1046 loss_ce: 0.1046 2023/03/01 07:46:37 - mmengine - INFO - Epoch(train) [111][3500/5047] lr: 1.2234e-05 eta: 2 days, 0:05:35 time: 0.8772 data_time: 0.0050 memory: 48811 loss: 0.0970 loss_ce: 0.0970 2023/03/01 07:48:02 - mmengine - INFO - Epoch(train) [111][3600/5047] lr: 1.2234e-05 eta: 2 days, 0:04:07 time: 0.9198 data_time: 0.0026 memory: 42336 loss: 0.1045 loss_ce: 0.1045 2023/03/01 07:49:28 - mmengine - INFO - Epoch(train) [111][3700/5047] lr: 1.2234e-05 eta: 2 days, 0:02:39 time: 0.7971 data_time: 0.0037 memory: 47500 loss: 0.1167 loss_ce: 0.1167 2023/03/01 07:50:55 - mmengine - INFO - Epoch(train) [111][3800/5047] lr: 1.2234e-05 eta: 2 days, 0:01:12 time: 0.8798 data_time: 0.0025 memory: 45643 loss: 0.1109 loss_ce: 0.1109 2023/03/01 07:51:21 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 07:52:21 - mmengine - INFO - Epoch(train) [111][3900/5047] lr: 1.2234e-05 eta: 1 day, 23:59:44 time: 0.8443 data_time: 0.0025 memory: 45447 loss: 0.0953 loss_ce: 0.0953 2023/03/01 07:53:45 - mmengine - INFO - Epoch(train) [111][4000/5047] lr: 1.2234e-05 eta: 1 day, 23:58:16 time: 0.7999 data_time: 0.0024 memory: 42655 loss: 0.1033 loss_ce: 0.1033 2023/03/01 07:55:13 - mmengine - INFO - Epoch(train) [111][4100/5047] lr: 1.2234e-05 eta: 1 day, 23:56:49 time: 0.9467 data_time: 0.0030 memory: 42649 loss: 0.1057 loss_ce: 0.1057 2023/03/01 07:56:39 - mmengine - INFO - Epoch(train) [111][4200/5047] lr: 1.2234e-05 eta: 1 day, 23:55:21 time: 0.8319 data_time: 0.0042 memory: 41982 loss: 0.1125 loss_ce: 0.1125 2023/03/01 07:58:06 - mmengine - INFO - Epoch(train) [111][4300/5047] lr: 1.2234e-05 eta: 1 day, 23:53:54 time: 0.8571 data_time: 0.0033 memory: 55535 loss: 0.1072 loss_ce: 0.1072 2023/03/01 07:59:31 - mmengine - INFO - Epoch(train) [111][4400/5047] lr: 1.2234e-05 eta: 1 day, 23:52:25 time: 0.8324 data_time: 0.0026 memory: 46713 loss: 0.1156 loss_ce: 0.1156 2023/03/01 08:00:57 - mmengine - INFO - Epoch(train) [111][4500/5047] lr: 1.2234e-05 eta: 1 day, 23:50:58 time: 0.8724 data_time: 0.0025 memory: 43327 loss: 0.1234 loss_ce: 0.1234 2023/03/01 08:02:23 - mmengine - INFO - Epoch(train) [111][4600/5047] lr: 1.2234e-05 eta: 1 day, 23:49:30 time: 0.8609 data_time: 0.0027 memory: 44781 loss: 0.0982 loss_ce: 0.0982 2023/03/01 08:03:49 - mmengine - INFO - Epoch(train) [111][4700/5047] lr: 1.2234e-05 eta: 1 day, 23:48:02 time: 0.8719 data_time: 0.0035 memory: 41122 loss: 0.1072 loss_ce: 0.1072 2023/03/01 08:05:14 - mmengine - INFO - Epoch(train) [111][4800/5047] lr: 1.2234e-05 eta: 1 day, 23:46:34 time: 0.8679 data_time: 0.0027 memory: 44617 loss: 0.1029 loss_ce: 0.1029 2023/03/01 08:05:40 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 08:06:40 - mmengine - INFO - Epoch(train) [111][4900/5047] lr: 1.2234e-05 eta: 1 day, 23:45:06 time: 0.8687 data_time: 0.0025 memory: 41419 loss: 0.1032 loss_ce: 0.1032 2023/03/01 08:08:06 - mmengine - INFO - Epoch(train) [111][5000/5047] lr: 1.2234e-05 eta: 1 day, 23:43:38 time: 0.8558 data_time: 0.0029 memory: 44816 loss: 0.1011 loss_ce: 0.1011 2023/03/01 08:08:46 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 08:08:46 - mmengine - INFO - Saving checkpoint at 111 epochs 2023/03/01 08:10:18 - mmengine - INFO - Epoch(train) [112][ 100/5047] lr: 1.2033e-05 eta: 1 day, 23:41:30 time: 0.8891 data_time: 0.0025 memory: 44617 loss: 0.1079 loss_ce: 0.1079 2023/03/01 08:11:43 - mmengine - INFO - Epoch(train) [112][ 200/5047] lr: 1.2033e-05 eta: 1 day, 23:40:01 time: 0.8614 data_time: 0.0025 memory: 43289 loss: 0.1070 loss_ce: 0.1070 2023/03/01 08:13:08 - mmengine - INFO - Epoch(train) [112][ 300/5047] lr: 1.2033e-05 eta: 1 day, 23:38:33 time: 0.8603 data_time: 0.0029 memory: 48266 loss: 0.1314 loss_ce: 0.1314 2023/03/01 08:14:33 - mmengine - INFO - Epoch(train) [112][ 400/5047] lr: 1.2033e-05 eta: 1 day, 23:37:05 time: 0.8540 data_time: 0.0025 memory: 50419 loss: 0.1108 loss_ce: 0.1108 2023/03/01 08:16:01 - mmengine - INFO - Epoch(train) [112][ 500/5047] lr: 1.2033e-05 eta: 1 day, 23:35:38 time: 0.8679 data_time: 0.0026 memory: 54041 loss: 0.1122 loss_ce: 0.1122 2023/03/01 08:17:28 - mmengine - INFO - Epoch(train) [112][ 600/5047] lr: 1.2033e-05 eta: 1 day, 23:34:11 time: 0.8356 data_time: 0.0031 memory: 55562 loss: 0.0971 loss_ce: 0.0971 2023/03/01 08:18:53 - mmengine - INFO - Epoch(train) [112][ 700/5047] lr: 1.2033e-05 eta: 1 day, 23:32:42 time: 0.8954 data_time: 0.0025 memory: 40500 loss: 0.1131 loss_ce: 0.1131 2023/03/01 08:20:03 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 08:20:18 - mmengine - INFO - Epoch(train) [112][ 800/5047] lr: 1.2033e-05 eta: 1 day, 23:31:14 time: 0.8442 data_time: 0.0025 memory: 47740 loss: 0.1010 loss_ce: 0.1010 2023/03/01 08:21:44 - mmengine - INFO - Epoch(train) [112][ 900/5047] lr: 1.2033e-05 eta: 1 day, 23:29:47 time: 0.8486 data_time: 0.0025 memory: 39398 loss: 0.1106 loss_ce: 0.1106 2023/03/01 08:23:10 - mmengine - INFO - Epoch(train) [112][1000/5047] lr: 1.2033e-05 eta: 1 day, 23:28:19 time: 0.8651 data_time: 0.0072 memory: 44788 loss: 0.1051 loss_ce: 0.1051 2023/03/01 08:24:37 - mmengine - INFO - Epoch(train) [112][1100/5047] lr: 1.2033e-05 eta: 1 day, 23:26:52 time: 0.8879 data_time: 0.0024 memory: 53979 loss: 0.1148 loss_ce: 0.1148 2023/03/01 08:26:02 - mmengine - INFO - Epoch(train) [112][1200/5047] lr: 1.2033e-05 eta: 1 day, 23:25:24 time: 0.8383 data_time: 0.0024 memory: 43289 loss: 0.1239 loss_ce: 0.1239 2023/03/01 08:27:30 - mmengine - INFO - Epoch(train) [112][1300/5047] lr: 1.2033e-05 eta: 1 day, 23:23:56 time: 0.9121 data_time: 0.0031 memory: 52127 loss: 0.1063 loss_ce: 0.1063 2023/03/01 08:28:55 - mmengine - INFO - Epoch(train) [112][1400/5047] lr: 1.2033e-05 eta: 1 day, 23:22:28 time: 0.8203 data_time: 0.0054 memory: 54116 loss: 0.1015 loss_ce: 0.1015 2023/03/01 08:30:23 - mmengine - INFO - Epoch(train) [112][1500/5047] lr: 1.2033e-05 eta: 1 day, 23:21:01 time: 0.9170 data_time: 0.0027 memory: 52964 loss: 0.1034 loss_ce: 0.1034 2023/03/01 08:31:49 - mmengine - INFO - Epoch(train) [112][1600/5047] lr: 1.2033e-05 eta: 1 day, 23:19:33 time: 0.8966 data_time: 0.0025 memory: 55562 loss: 0.1085 loss_ce: 0.1085 2023/03/01 08:33:15 - mmengine - INFO - Epoch(train) [112][1700/5047] lr: 1.2033e-05 eta: 1 day, 23:18:06 time: 0.8940 data_time: 0.0039 memory: 55562 loss: 0.0965 loss_ce: 0.0965 2023/03/01 08:34:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 08:34:41 - mmengine - INFO - Epoch(train) [112][1800/5047] lr: 1.2033e-05 eta: 1 day, 23:16:38 time: 0.8197 data_time: 0.0033 memory: 42336 loss: 0.1062 loss_ce: 0.1062 2023/03/01 08:36:08 - mmengine - INFO - Epoch(train) [112][1900/5047] lr: 1.2033e-05 eta: 1 day, 23:15:11 time: 0.8526 data_time: 0.0026 memory: 51731 loss: 0.1283 loss_ce: 0.1283 2023/03/01 08:37:33 - mmengine - INFO - Epoch(train) [112][2000/5047] lr: 1.2033e-05 eta: 1 day, 23:13:43 time: 0.8291 data_time: 0.0027 memory: 42934 loss: 0.0965 loss_ce: 0.0965 2023/03/01 08:39:01 - mmengine - INFO - Epoch(train) [112][2100/5047] lr: 1.2033e-05 eta: 1 day, 23:12:15 time: 0.8832 data_time: 0.0025 memory: 43947 loss: 0.1238 loss_ce: 0.1238 2023/03/01 08:40:27 - mmengine - INFO - Epoch(train) [112][2200/5047] lr: 1.2033e-05 eta: 1 day, 23:10:48 time: 0.8548 data_time: 0.0027 memory: 43613 loss: 0.1200 loss_ce: 0.1200 2023/03/01 08:41:54 - mmengine - INFO - Epoch(train) [112][2300/5047] lr: 1.2033e-05 eta: 1 day, 23:09:20 time: 0.8622 data_time: 0.0027 memory: 51719 loss: 0.1154 loss_ce: 0.1154 2023/03/01 08:43:19 - mmengine - INFO - Epoch(train) [112][2400/5047] lr: 1.2033e-05 eta: 1 day, 23:07:52 time: 0.8245 data_time: 0.0031 memory: 47158 loss: 0.1075 loss_ce: 0.1075 2023/03/01 08:44:45 - mmengine - INFO - Epoch(train) [112][2500/5047] lr: 1.2033e-05 eta: 1 day, 23:06:24 time: 0.8387 data_time: 0.0025 memory: 50505 loss: 0.1024 loss_ce: 0.1024 2023/03/01 08:46:11 - mmengine - INFO - Epoch(train) [112][2600/5047] lr: 1.2033e-05 eta: 1 day, 23:04:57 time: 0.8246 data_time: 0.0026 memory: 43947 loss: 0.1060 loss_ce: 0.1060 2023/03/01 08:47:37 - mmengine - INFO - Epoch(train) [112][2700/5047] lr: 1.2033e-05 eta: 1 day, 23:03:29 time: 0.8210 data_time: 0.0026 memory: 49334 loss: 0.1099 loss_ce: 0.1099 2023/03/01 08:48:50 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 08:49:05 - mmengine - INFO - Epoch(train) [112][2800/5047] lr: 1.2033e-05 eta: 1 day, 23:02:02 time: 0.8794 data_time: 0.0027 memory: 55562 loss: 0.1190 loss_ce: 0.1190 2023/03/01 08:50:32 - mmengine - INFO - Epoch(train) [112][2900/5047] lr: 1.2033e-05 eta: 1 day, 23:00:35 time: 0.8603 data_time: 0.0029 memory: 43613 loss: 0.1091 loss_ce: 0.1091 2023/03/01 08:51:58 - mmengine - INFO - Epoch(train) [112][3000/5047] lr: 1.2033e-05 eta: 1 day, 22:59:07 time: 0.8673 data_time: 0.0026 memory: 49620 loss: 0.1051 loss_ce: 0.1051 2023/03/01 08:53:24 - mmengine - INFO - Epoch(train) [112][3100/5047] lr: 1.2033e-05 eta: 1 day, 22:57:39 time: 0.8601 data_time: 0.0025 memory: 42371 loss: 0.1101 loss_ce: 0.1101 2023/03/01 08:54:53 - mmengine - INFO - Epoch(train) [112][3200/5047] lr: 1.2033e-05 eta: 1 day, 22:56:12 time: 0.8358 data_time: 0.0027 memory: 55114 loss: 0.1088 loss_ce: 0.1088 2023/03/01 08:56:18 - mmengine - INFO - Epoch(train) [112][3300/5047] lr: 1.2033e-05 eta: 1 day, 22:54:44 time: 0.8012 data_time: 0.0029 memory: 40825 loss: 0.1017 loss_ce: 0.1017 2023/03/01 08:57:43 - mmengine - INFO - Epoch(train) [112][3400/5047] lr: 1.2033e-05 eta: 1 day, 22:53:16 time: 0.8720 data_time: 0.0084 memory: 42965 loss: 0.1182 loss_ce: 0.1182 2023/03/01 08:59:07 - mmengine - INFO - Epoch(train) [112][3500/5047] lr: 1.2033e-05 eta: 1 day, 22:51:48 time: 0.8458 data_time: 0.0028 memory: 39681 loss: 0.1056 loss_ce: 0.1056 2023/03/01 09:00:31 - mmengine - INFO - Epoch(train) [112][3600/5047] lr: 1.2033e-05 eta: 1 day, 22:50:19 time: 0.8349 data_time: 0.0027 memory: 41419 loss: 0.0929 loss_ce: 0.0929 2023/03/01 09:01:58 - mmengine - INFO - Epoch(train) [112][3700/5047] lr: 1.2033e-05 eta: 1 day, 22:48:52 time: 0.8746 data_time: 0.0027 memory: 43613 loss: 0.0874 loss_ce: 0.0874 2023/03/01 09:03:10 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 09:03:24 - mmengine - INFO - Epoch(train) [112][3800/5047] lr: 1.2033e-05 eta: 1 day, 22:47:24 time: 0.8328 data_time: 0.0057 memory: 50243 loss: 0.1006 loss_ce: 0.1006 2023/03/01 09:04:50 - mmengine - INFO - Epoch(train) [112][3900/5047] lr: 1.2033e-05 eta: 1 day, 22:45:56 time: 0.7889 data_time: 0.0037 memory: 50106 loss: 0.1027 loss_ce: 0.1027 2023/03/01 09:06:15 - mmengine - INFO - Epoch(train) [112][4000/5047] lr: 1.2033e-05 eta: 1 day, 22:44:28 time: 0.8511 data_time: 0.0051 memory: 47447 loss: 0.0969 loss_ce: 0.0969 2023/03/01 09:07:40 - mmengine - INFO - Epoch(train) [112][4100/5047] lr: 1.2033e-05 eta: 1 day, 22:43:00 time: 0.8381 data_time: 0.0028 memory: 55366 loss: 0.1126 loss_ce: 0.1126 2023/03/01 09:09:05 - mmengine - INFO - Epoch(train) [112][4200/5047] lr: 1.2033e-05 eta: 1 day, 22:41:32 time: 0.8831 data_time: 0.0025 memory: 45033 loss: 0.1110 loss_ce: 0.1110 2023/03/01 09:10:30 - mmengine - INFO - Epoch(train) [112][4300/5047] lr: 1.2033e-05 eta: 1 day, 22:40:04 time: 0.8702 data_time: 0.0060 memory: 41724 loss: 0.1025 loss_ce: 0.1025 2023/03/01 09:11:58 - mmengine - INFO - Epoch(train) [112][4400/5047] lr: 1.2033e-05 eta: 1 day, 22:38:37 time: 0.8411 data_time: 0.0027 memory: 48948 loss: 0.1095 loss_ce: 0.1095 2023/03/01 09:13:23 - mmengine - INFO - Epoch(train) [112][4500/5047] lr: 1.2033e-05 eta: 1 day, 22:37:09 time: 0.8425 data_time: 0.0024 memory: 40939 loss: 0.1041 loss_ce: 0.1041 2023/03/01 09:14:50 - mmengine - INFO - Epoch(train) [112][4600/5047] lr: 1.2033e-05 eta: 1 day, 22:35:42 time: 0.8931 data_time: 0.0062 memory: 46651 loss: 0.1099 loss_ce: 0.1099 2023/03/01 09:16:17 - mmengine - INFO - Epoch(train) [112][4700/5047] lr: 1.2033e-05 eta: 1 day, 22:34:14 time: 0.8806 data_time: 0.0025 memory: 41122 loss: 0.1010 loss_ce: 0.1010 2023/03/01 09:17:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 09:17:41 - mmengine - INFO - Epoch(train) [112][4800/5047] lr: 1.2033e-05 eta: 1 day, 22:32:46 time: 0.8386 data_time: 0.0030 memory: 44278 loss: 0.1058 loss_ce: 0.1058 2023/03/01 09:19:08 - mmengine - INFO - Epoch(train) [112][4900/5047] lr: 1.2033e-05 eta: 1 day, 22:31:18 time: 0.8641 data_time: 0.0029 memory: 42292 loss: 0.1102 loss_ce: 0.1102 2023/03/01 09:20:33 - mmengine - INFO - Epoch(train) [112][5000/5047] lr: 1.2033e-05 eta: 1 day, 22:29:51 time: 0.8755 data_time: 0.0044 memory: 45302 loss: 0.1013 loss_ce: 0.1013 2023/03/01 09:21:13 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 09:21:13 - mmengine - INFO - Saving checkpoint at 112 epochs 2023/03/01 09:22:46 - mmengine - INFO - Epoch(train) [113][ 100/5047] lr: 1.1832e-05 eta: 1 day, 22:27:42 time: 0.8378 data_time: 0.0029 memory: 43624 loss: 0.1129 loss_ce: 0.1129 2023/03/01 09:24:12 - mmengine - INFO - Epoch(train) [113][ 200/5047] lr: 1.1832e-05 eta: 1 day, 22:26:14 time: 0.8266 data_time: 0.0026 memory: 46005 loss: 0.1035 loss_ce: 0.1035 2023/03/01 09:25:39 - mmengine - INFO - Epoch(train) [113][ 300/5047] lr: 1.1832e-05 eta: 1 day, 22:24:47 time: 0.8613 data_time: 0.0033 memory: 43947 loss: 0.0945 loss_ce: 0.0945 2023/03/01 09:27:05 - mmengine - INFO - Epoch(train) [113][ 400/5047] lr: 1.1832e-05 eta: 1 day, 22:23:19 time: 0.8684 data_time: 0.0050 memory: 49242 loss: 0.1003 loss_ce: 0.1003 2023/03/01 09:28:29 - mmengine - INFO - Epoch(train) [113][ 500/5047] lr: 1.1832e-05 eta: 1 day, 22:21:51 time: 0.8652 data_time: 0.0032 memory: 42965 loss: 0.1090 loss_ce: 0.1090 2023/03/01 09:29:57 - mmengine - INFO - Epoch(train) [113][ 600/5047] lr: 1.1832e-05 eta: 1 day, 22:20:24 time: 0.8995 data_time: 0.0030 memory: 55562 loss: 0.1066 loss_ce: 0.1066 2023/03/01 09:31:24 - mmengine - INFO - Epoch(train) [113][ 700/5047] lr: 1.1832e-05 eta: 1 day, 22:18:56 time: 0.8677 data_time: 0.0029 memory: 41515 loss: 0.1165 loss_ce: 0.1165 2023/03/01 09:31:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 09:32:50 - mmengine - INFO - Epoch(train) [113][ 800/5047] lr: 1.1832e-05 eta: 1 day, 22:17:28 time: 0.8437 data_time: 0.0024 memory: 41122 loss: 0.1154 loss_ce: 0.1154 2023/03/01 09:34:16 - mmengine - INFO - Epoch(train) [113][ 900/5047] lr: 1.1832e-05 eta: 1 day, 22:16:01 time: 0.9018 data_time: 0.0040 memory: 40851 loss: 0.1009 loss_ce: 0.1009 2023/03/01 09:35:42 - mmengine - INFO - Epoch(train) [113][1000/5047] lr: 1.1832e-05 eta: 1 day, 22:14:33 time: 0.8405 data_time: 0.0025 memory: 41419 loss: 0.1268 loss_ce: 0.1268 2023/03/01 09:37:09 - mmengine - INFO - Epoch(train) [113][1100/5047] lr: 1.1832e-05 eta: 1 day, 22:13:06 time: 0.8757 data_time: 0.0030 memory: 38897 loss: 0.1043 loss_ce: 0.1043 2023/03/01 09:38:35 - mmengine - INFO - Epoch(train) [113][1200/5047] lr: 1.1832e-05 eta: 1 day, 22:11:38 time: 0.8224 data_time: 0.0026 memory: 41175 loss: 0.1097 loss_ce: 0.1097 2023/03/01 09:40:01 - mmengine - INFO - Epoch(train) [113][1300/5047] lr: 1.1832e-05 eta: 1 day, 22:10:10 time: 0.8405 data_time: 0.0066 memory: 44617 loss: 0.0926 loss_ce: 0.0926 2023/03/01 09:41:28 - mmengine - INFO - Epoch(train) [113][1400/5047] lr: 1.1832e-05 eta: 1 day, 22:08:43 time: 0.8681 data_time: 0.0026 memory: 41419 loss: 0.1134 loss_ce: 0.1134 2023/03/01 09:42:55 - mmengine - INFO - Epoch(train) [113][1500/5047] lr: 1.1832e-05 eta: 1 day, 22:07:15 time: 0.8712 data_time: 0.0060 memory: 42662 loss: 0.1045 loss_ce: 0.1045 2023/03/01 09:44:20 - mmengine - INFO - Epoch(train) [113][1600/5047] lr: 1.1832e-05 eta: 1 day, 22:05:47 time: 0.9090 data_time: 0.0025 memory: 41122 loss: 0.0976 loss_ce: 0.0976 2023/03/01 09:45:45 - mmengine - INFO - Epoch(train) [113][1700/5047] lr: 1.1832e-05 eta: 1 day, 22:04:19 time: 0.8506 data_time: 0.0028 memory: 47305 loss: 0.0884 loss_ce: 0.0884 2023/03/01 09:46:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 09:47:12 - mmengine - INFO - Epoch(train) [113][1800/5047] lr: 1.1832e-05 eta: 1 day, 22:02:52 time: 0.9045 data_time: 0.0028 memory: 40715 loss: 0.1170 loss_ce: 0.1170 2023/03/01 09:48:38 - mmengine - INFO - Epoch(train) [113][1900/5047] lr: 1.1832e-05 eta: 1 day, 22:01:24 time: 0.8605 data_time: 0.0053 memory: 40825 loss: 0.1133 loss_ce: 0.1133 2023/03/01 09:50:03 - mmengine - INFO - Epoch(train) [113][2000/5047] lr: 1.1832e-05 eta: 1 day, 21:59:56 time: 0.8069 data_time: 0.0040 memory: 55562 loss: 0.1091 loss_ce: 0.1091 2023/03/01 09:51:29 - mmengine - INFO - Epoch(train) [113][2100/5047] lr: 1.1832e-05 eta: 1 day, 21:58:29 time: 0.8904 data_time: 0.0026 memory: 45643 loss: 0.1228 loss_ce: 0.1228 2023/03/01 09:52:56 - mmengine - INFO - Epoch(train) [113][2200/5047] lr: 1.1832e-05 eta: 1 day, 21:57:01 time: 0.8877 data_time: 0.0031 memory: 55562 loss: 0.1063 loss_ce: 0.1063 2023/03/01 09:54:22 - mmengine - INFO - Epoch(train) [113][2300/5047] lr: 1.1832e-05 eta: 1 day, 21:55:33 time: 0.9019 data_time: 0.0033 memory: 49334 loss: 0.0936 loss_ce: 0.0936 2023/03/01 09:55:47 - mmengine - INFO - Epoch(train) [113][2400/5047] lr: 1.1832e-05 eta: 1 day, 21:54:05 time: 0.8705 data_time: 0.0028 memory: 44742 loss: 0.1078 loss_ce: 0.1078 2023/03/01 09:57:13 - mmengine - INFO - Epoch(train) [113][2500/5047] lr: 1.1832e-05 eta: 1 day, 21:52:38 time: 0.8358 data_time: 0.0027 memory: 45590 loss: 0.1124 loss_ce: 0.1124 2023/03/01 09:58:39 - mmengine - INFO - Epoch(train) [113][2600/5047] lr: 1.1832e-05 eta: 1 day, 21:51:10 time: 0.8551 data_time: 0.0030 memory: 40126 loss: 0.0937 loss_ce: 0.0937 2023/03/01 10:00:05 - mmengine - INFO - Epoch(train) [113][2700/5047] lr: 1.1832e-05 eta: 1 day, 21:49:42 time: 0.9286 data_time: 0.0027 memory: 45302 loss: 0.1127 loss_ce: 0.1127 2023/03/01 10:00:35 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 10:01:32 - mmengine - INFO - Epoch(train) [113][2800/5047] lr: 1.1832e-05 eta: 1 day, 21:48:15 time: 0.8690 data_time: 0.0027 memory: 45624 loss: 0.1007 loss_ce: 0.1007 2023/03/01 10:02:59 - mmengine - INFO - Epoch(train) [113][2900/5047] lr: 1.1832e-05 eta: 1 day, 21:46:47 time: 0.8626 data_time: 0.0028 memory: 52891 loss: 0.1020 loss_ce: 0.1020 2023/03/01 10:04:24 - mmengine - INFO - Epoch(train) [113][3000/5047] lr: 1.1832e-05 eta: 1 day, 21:45:19 time: 0.8922 data_time: 0.0036 memory: 45787 loss: 0.1134 loss_ce: 0.1134 2023/03/01 10:05:49 - mmengine - INFO - Epoch(train) [113][3100/5047] lr: 1.1832e-05 eta: 1 day, 21:43:51 time: 0.8295 data_time: 0.0032 memory: 51731 loss: 0.1093 loss_ce: 0.1093 2023/03/01 10:07:13 - mmengine - INFO - Epoch(train) [113][3200/5047] lr: 1.1832e-05 eta: 1 day, 21:42:23 time: 0.8661 data_time: 0.0028 memory: 45643 loss: 0.1087 loss_ce: 0.1087 2023/03/01 10:08:39 - mmengine - INFO - Epoch(train) [113][3300/5047] lr: 1.1832e-05 eta: 1 day, 21:40:55 time: 0.8921 data_time: 0.0025 memory: 40991 loss: 0.1075 loss_ce: 0.1075 2023/03/01 10:10:06 - mmengine - INFO - Epoch(train) [113][3400/5047] lr: 1.1832e-05 eta: 1 day, 21:39:28 time: 0.9003 data_time: 0.0026 memory: 44738 loss: 0.1091 loss_ce: 0.1091 2023/03/01 10:11:31 - mmengine - INFO - Epoch(train) [113][3500/5047] lr: 1.1832e-05 eta: 1 day, 21:38:00 time: 0.8383 data_time: 0.0027 memory: 42965 loss: 0.1088 loss_ce: 0.1088 2023/03/01 10:12:57 - mmengine - INFO - Epoch(train) [113][3600/5047] lr: 1.1832e-05 eta: 1 day, 21:36:32 time: 0.8759 data_time: 0.0026 memory: 50482 loss: 0.1082 loss_ce: 0.1082 2023/03/01 10:14:24 - mmengine - INFO - Epoch(train) [113][3700/5047] lr: 1.1832e-05 eta: 1 day, 21:35:05 time: 0.8728 data_time: 0.0026 memory: 55323 loss: 0.1066 loss_ce: 0.1066 2023/03/01 10:14:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 10:15:49 - mmengine - INFO - Epoch(train) [113][3800/5047] lr: 1.1832e-05 eta: 1 day, 21:33:36 time: 0.8646 data_time: 0.0028 memory: 54276 loss: 0.0965 loss_ce: 0.0965 2023/03/01 10:17:16 - mmengine - INFO - Epoch(train) [113][3900/5047] lr: 1.1832e-05 eta: 1 day, 21:32:09 time: 0.9197 data_time: 0.0027 memory: 55562 loss: 0.1103 loss_ce: 0.1103 2023/03/01 10:18:41 - mmengine - INFO - Epoch(train) [113][4000/5047] lr: 1.1832e-05 eta: 1 day, 21:30:41 time: 0.8586 data_time: 0.0025 memory: 39938 loss: 0.1170 loss_ce: 0.1170 2023/03/01 10:20:09 - mmengine - INFO - Epoch(train) [113][4100/5047] lr: 1.1832e-05 eta: 1 day, 21:29:14 time: 0.8560 data_time: 0.0053 memory: 43289 loss: 0.1159 loss_ce: 0.1159 2023/03/01 10:21:36 - mmengine - INFO - Epoch(train) [113][4200/5047] lr: 1.1832e-05 eta: 1 day, 21:27:47 time: 0.8711 data_time: 0.0026 memory: 41419 loss: 0.0941 loss_ce: 0.0941 2023/03/01 10:23:01 - mmengine - INFO - Epoch(train) [113][4300/5047] lr: 1.1832e-05 eta: 1 day, 21:26:19 time: 0.9172 data_time: 0.0049 memory: 43613 loss: 0.1089 loss_ce: 0.1089 2023/03/01 10:24:30 - mmengine - INFO - Epoch(train) [113][4400/5047] lr: 1.1832e-05 eta: 1 day, 21:24:52 time: 0.8741 data_time: 0.0025 memory: 52517 loss: 0.1018 loss_ce: 0.1018 2023/03/01 10:25:56 - mmengine - INFO - Epoch(train) [113][4500/5047] lr: 1.1832e-05 eta: 1 day, 21:23:24 time: 0.9296 data_time: 0.0025 memory: 42024 loss: 0.1171 loss_ce: 0.1171 2023/03/01 10:27:22 - mmengine - INFO - Epoch(train) [113][4600/5047] lr: 1.1832e-05 eta: 1 day, 21:21:57 time: 0.8546 data_time: 0.0041 memory: 45405 loss: 0.1108 loss_ce: 0.1108 2023/03/01 10:28:47 - mmengine - INFO - Epoch(train) [113][4700/5047] lr: 1.1832e-05 eta: 1 day, 21:20:29 time: 0.8747 data_time: 0.0025 memory: 44240 loss: 0.1016 loss_ce: 0.1016 2023/03/01 10:29:19 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 10:30:14 - mmengine - INFO - Epoch(train) [113][4800/5047] lr: 1.1832e-05 eta: 1 day, 21:19:01 time: 0.8381 data_time: 0.0031 memory: 48035 loss: 0.0982 loss_ce: 0.0982 2023/03/01 10:31:40 - mmengine - INFO - Epoch(train) [113][4900/5047] lr: 1.1832e-05 eta: 1 day, 21:17:33 time: 0.9532 data_time: 0.0025 memory: 43289 loss: 0.1009 loss_ce: 0.1009 2023/03/01 10:33:05 - mmengine - INFO - Epoch(train) [113][5000/5047] lr: 1.1832e-05 eta: 1 day, 21:16:06 time: 0.8731 data_time: 0.0025 memory: 49219 loss: 0.1062 loss_ce: 0.1062 2023/03/01 10:33:46 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 10:33:46 - mmengine - INFO - Saving checkpoint at 113 epochs 2023/03/01 10:35:17 - mmengine - INFO - Epoch(train) [114][ 100/5047] lr: 1.1631e-05 eta: 1 day, 21:13:57 time: 0.8468 data_time: 0.0025 memory: 43947 loss: 0.1249 loss_ce: 0.1249 2023/03/01 10:36:43 - mmengine - INFO - Epoch(train) [114][ 200/5047] lr: 1.1631e-05 eta: 1 day, 21:12:29 time: 0.8627 data_time: 0.0048 memory: 41724 loss: 0.1179 loss_ce: 0.1179 2023/03/01 10:38:08 - mmengine - INFO - Epoch(train) [114][ 300/5047] lr: 1.1631e-05 eta: 1 day, 21:11:01 time: 0.8846 data_time: 0.0024 memory: 39553 loss: 0.1175 loss_ce: 0.1175 2023/03/01 10:39:35 - mmengine - INFO - Epoch(train) [114][ 400/5047] lr: 1.1631e-05 eta: 1 day, 21:09:34 time: 0.8481 data_time: 0.0025 memory: 41724 loss: 0.1038 loss_ce: 0.1038 2023/03/01 10:41:02 - mmengine - INFO - Epoch(train) [114][ 500/5047] lr: 1.1631e-05 eta: 1 day, 21:08:06 time: 0.8657 data_time: 0.0024 memory: 42336 loss: 0.1076 loss_ce: 0.1076 2023/03/01 10:42:29 - mmengine - INFO - Epoch(train) [114][ 600/5047] lr: 1.1631e-05 eta: 1 day, 21:06:39 time: 0.8335 data_time: 0.0053 memory: 41122 loss: 0.1178 loss_ce: 0.1178 2023/03/01 10:43:46 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 10:43:55 - mmengine - INFO - Epoch(train) [114][ 700/5047] lr: 1.1631e-05 eta: 1 day, 21:05:11 time: 0.8325 data_time: 0.0039 memory: 40650 loss: 0.1067 loss_ce: 0.1067 2023/03/01 10:45:21 - mmengine - INFO - Epoch(train) [114][ 800/5047] lr: 1.1631e-05 eta: 1 day, 21:03:44 time: 0.8608 data_time: 0.0033 memory: 48658 loss: 0.1206 loss_ce: 0.1206 2023/03/01 10:46:47 - mmengine - INFO - Epoch(train) [114][ 900/5047] lr: 1.1631e-05 eta: 1 day, 21:02:16 time: 0.8195 data_time: 0.0030 memory: 41724 loss: 0.1139 loss_ce: 0.1139 2023/03/01 10:48:13 - mmengine - INFO - Epoch(train) [114][1000/5047] lr: 1.1631e-05 eta: 1 day, 21:00:48 time: 0.8303 data_time: 0.0028 memory: 44278 loss: 0.1255 loss_ce: 0.1255 2023/03/01 10:49:39 - mmengine - INFO - Epoch(train) [114][1100/5047] lr: 1.1631e-05 eta: 1 day, 20:59:20 time: 0.8474 data_time: 0.0024 memory: 38855 loss: 0.1072 loss_ce: 0.1072 2023/03/01 10:51:05 - mmengine - INFO - Epoch(train) [114][1200/5047] lr: 1.1631e-05 eta: 1 day, 20:57:53 time: 0.8709 data_time: 0.0024 memory: 52127 loss: 0.1088 loss_ce: 0.1088 2023/03/01 10:52:31 - mmengine - INFO - Epoch(train) [114][1300/5047] lr: 1.1631e-05 eta: 1 day, 20:56:25 time: 0.8674 data_time: 0.0030 memory: 55562 loss: 0.1001 loss_ce: 0.1001 2023/03/01 10:53:57 - mmengine - INFO - Epoch(train) [114][1400/5047] lr: 1.1631e-05 eta: 1 day, 20:54:58 time: 0.8532 data_time: 0.0028 memory: 55562 loss: 0.1009 loss_ce: 0.1009 2023/03/01 10:55:23 - mmengine - INFO - Epoch(train) [114][1500/5047] lr: 1.1631e-05 eta: 1 day, 20:53:30 time: 0.8240 data_time: 0.0033 memory: 50344 loss: 0.1093 loss_ce: 0.1093 2023/03/01 10:56:48 - mmengine - INFO - Epoch(train) [114][1600/5047] lr: 1.1631e-05 eta: 1 day, 20:52:02 time: 0.8696 data_time: 0.0029 memory: 42718 loss: 0.1198 loss_ce: 0.1198 2023/03/01 10:58:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 10:58:14 - mmengine - INFO - Epoch(train) [114][1700/5047] lr: 1.1631e-05 eta: 1 day, 20:50:34 time: 0.9087 data_time: 0.0026 memory: 52964 loss: 0.1229 loss_ce: 0.1229 2023/03/01 10:59:39 - mmengine - INFO - Epoch(train) [114][1800/5047] lr: 1.1631e-05 eta: 1 day, 20:49:06 time: 0.8392 data_time: 0.0029 memory: 45813 loss: 0.1154 loss_ce: 0.1154 2023/03/01 11:01:04 - mmengine - INFO - Epoch(train) [114][1900/5047] lr: 1.1631e-05 eta: 1 day, 20:47:38 time: 0.9167 data_time: 0.0026 memory: 46050 loss: 0.0978 loss_ce: 0.0978 2023/03/01 11:02:30 - mmengine - INFO - Epoch(train) [114][2000/5047] lr: 1.1631e-05 eta: 1 day, 20:46:10 time: 0.8493 data_time: 0.0027 memory: 49342 loss: 0.1099 loss_ce: 0.1099 2023/03/01 11:03:57 - mmengine - INFO - Epoch(train) [114][2100/5047] lr: 1.1631e-05 eta: 1 day, 20:44:43 time: 0.8815 data_time: 0.0029 memory: 48454 loss: 0.1143 loss_ce: 0.1143 2023/03/01 11:05:25 - mmengine - INFO - Epoch(train) [114][2200/5047] lr: 1.1631e-05 eta: 1 day, 20:43:16 time: 0.9726 data_time: 0.0028 memory: 55393 loss: 0.1065 loss_ce: 0.1065 2023/03/01 11:06:52 - mmengine - INFO - Epoch(train) [114][2300/5047] lr: 1.1631e-05 eta: 1 day, 20:41:49 time: 0.8665 data_time: 0.0034 memory: 44524 loss: 0.1072 loss_ce: 0.1072 2023/03/01 11:08:19 - mmengine - INFO - Epoch(train) [114][2400/5047] lr: 1.1631e-05 eta: 1 day, 20:40:21 time: 0.8458 data_time: 0.0040 memory: 45643 loss: 0.1033 loss_ce: 0.1033 2023/03/01 11:09:46 - mmengine - INFO - Epoch(train) [114][2500/5047] lr: 1.1631e-05 eta: 1 day, 20:38:54 time: 0.8456 data_time: 0.0030 memory: 52975 loss: 0.0971 loss_ce: 0.0971 2023/03/01 11:11:12 - mmengine - INFO - Epoch(train) [114][2600/5047] lr: 1.1631e-05 eta: 1 day, 20:37:26 time: 0.8917 data_time: 0.0029 memory: 43289 loss: 0.1063 loss_ce: 0.1063 2023/03/01 11:12:29 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 11:12:38 - mmengine - INFO - Epoch(train) [114][2700/5047] lr: 1.1631e-05 eta: 1 day, 20:35:59 time: 0.8448 data_time: 0.0029 memory: 55562 loss: 0.1215 loss_ce: 0.1215 2023/03/01 11:14:05 - mmengine - INFO - Epoch(train) [114][2800/5047] lr: 1.1631e-05 eta: 1 day, 20:34:31 time: 0.8196 data_time: 0.0025 memory: 41419 loss: 0.1045 loss_ce: 0.1045 2023/03/01 11:15:33 - mmengine - INFO - Epoch(train) [114][2900/5047] lr: 1.1631e-05 eta: 1 day, 20:33:04 time: 0.8992 data_time: 0.0069 memory: 55562 loss: 0.1094 loss_ce: 0.1094 2023/03/01 11:16:59 - mmengine - INFO - Epoch(train) [114][3000/5047] lr: 1.1631e-05 eta: 1 day, 20:31:36 time: 0.8320 data_time: 0.0078 memory: 43289 loss: 0.1166 loss_ce: 0.1166 2023/03/01 11:18:25 - mmengine - INFO - Epoch(train) [114][3100/5047] lr: 1.1631e-05 eta: 1 day, 20:30:09 time: 0.8313 data_time: 0.0029 memory: 42336 loss: 0.1069 loss_ce: 0.1069 2023/03/01 11:19:51 - mmengine - INFO - Epoch(train) [114][3200/5047] lr: 1.1631e-05 eta: 1 day, 20:28:41 time: 0.8451 data_time: 0.0029 memory: 43198 loss: 0.1163 loss_ce: 0.1163 2023/03/01 11:21:18 - mmengine - INFO - Epoch(train) [114][3300/5047] lr: 1.1631e-05 eta: 1 day, 20:27:14 time: 0.8828 data_time: 0.0028 memory: 43420 loss: 0.1056 loss_ce: 0.1056 2023/03/01 11:22:43 - mmengine - INFO - Epoch(train) [114][3400/5047] lr: 1.1631e-05 eta: 1 day, 20:25:46 time: 0.8483 data_time: 0.0030 memory: 49147 loss: 0.1077 loss_ce: 0.1077 2023/03/01 11:24:08 - mmengine - INFO - Epoch(train) [114][3500/5047] lr: 1.1631e-05 eta: 1 day, 20:24:18 time: 0.8968 data_time: 0.0026 memory: 41371 loss: 0.1104 loss_ce: 0.1104 2023/03/01 11:25:33 - mmengine - INFO - Epoch(train) [114][3600/5047] lr: 1.1631e-05 eta: 1 day, 20:22:50 time: 0.8298 data_time: 0.0024 memory: 46949 loss: 0.0995 loss_ce: 0.0995 2023/03/01 11:26:49 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 11:26:58 - mmengine - INFO - Epoch(train) [114][3700/5047] lr: 1.1631e-05 eta: 1 day, 20:21:22 time: 0.8495 data_time: 0.0036 memory: 39681 loss: 0.1092 loss_ce: 0.1092 2023/03/01 11:28:25 - mmengine - INFO - Epoch(train) [114][3800/5047] lr: 1.1631e-05 eta: 1 day, 20:19:54 time: 0.8586 data_time: 0.0026 memory: 46713 loss: 0.1039 loss_ce: 0.1039 2023/03/01 11:29:50 - mmengine - INFO - Epoch(train) [114][3900/5047] lr: 1.1631e-05 eta: 1 day, 20:18:26 time: 0.8871 data_time: 0.0029 memory: 53387 loss: 0.1001 loss_ce: 0.1001 2023/03/01 11:31:15 - mmengine - INFO - Epoch(train) [114][4000/5047] lr: 1.1631e-05 eta: 1 day, 20:16:59 time: 0.8186 data_time: 0.0035 memory: 48055 loss: 0.1113 loss_ce: 0.1113 2023/03/01 11:32:41 - mmengine - INFO - Epoch(train) [114][4100/5047] lr: 1.1631e-05 eta: 1 day, 20:15:31 time: 0.8586 data_time: 0.0068 memory: 52792 loss: 0.1110 loss_ce: 0.1110 2023/03/01 11:34:08 - mmengine - INFO - Epoch(train) [114][4200/5047] lr: 1.1631e-05 eta: 1 day, 20:14:04 time: 0.8404 data_time: 0.0088 memory: 47447 loss: 0.1096 loss_ce: 0.1096 2023/03/01 11:35:35 - mmengine - INFO - Epoch(train) [114][4300/5047] lr: 1.1631e-05 eta: 1 day, 20:12:36 time: 0.8049 data_time: 0.0026 memory: 43289 loss: 0.1174 loss_ce: 0.1174 2023/03/01 11:37:00 - mmengine - INFO - Epoch(train) [114][4400/5047] lr: 1.1631e-05 eta: 1 day, 20:11:08 time: 0.7983 data_time: 0.0025 memory: 40241 loss: 0.1143 loss_ce: 0.1143 2023/03/01 11:38:26 - mmengine - INFO - Epoch(train) [114][4500/5047] lr: 1.1631e-05 eta: 1 day, 20:09:40 time: 0.8824 data_time: 0.0030 memory: 42649 loss: 0.1265 loss_ce: 0.1265 2023/03/01 11:39:53 - mmengine - INFO - Epoch(train) [114][4600/5047] lr: 1.1631e-05 eta: 1 day, 20:08:13 time: 0.8657 data_time: 0.0029 memory: 41516 loss: 0.1158 loss_ce: 0.1158 2023/03/01 11:41:09 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 11:41:19 - mmengine - INFO - Epoch(train) [114][4700/5047] lr: 1.1631e-05 eta: 1 day, 20:06:45 time: 0.8900 data_time: 0.0035 memory: 41122 loss: 0.1141 loss_ce: 0.1141 2023/03/01 11:42:47 - mmengine - INFO - Epoch(train) [114][4800/5047] lr: 1.1631e-05 eta: 1 day, 20:05:18 time: 0.9148 data_time: 0.0031 memory: 44934 loss: 0.1145 loss_ce: 0.1145 2023/03/01 11:44:13 - mmengine - INFO - Epoch(train) [114][4900/5047] lr: 1.1631e-05 eta: 1 day, 20:03:51 time: 0.8157 data_time: 0.0044 memory: 48187 loss: 0.1003 loss_ce: 0.1003 2023/03/01 11:45:39 - mmengine - INFO - Epoch(train) [114][5000/5047] lr: 1.1631e-05 eta: 1 day, 20:02:23 time: 0.8018 data_time: 0.0025 memory: 42024 loss: 0.1021 loss_ce: 0.1021 2023/03/01 11:46:20 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 11:46:20 - mmengine - INFO - Saving checkpoint at 114 epochs 2023/03/01 11:47:50 - mmengine - INFO - Epoch(train) [115][ 100/5047] lr: 1.1430e-05 eta: 1 day, 20:00:14 time: 0.8500 data_time: 0.0075 memory: 45302 loss: 0.1074 loss_ce: 0.1074 2023/03/01 11:49:16 - mmengine - INFO - Epoch(train) [115][ 200/5047] lr: 1.1430e-05 eta: 1 day, 19:58:46 time: 0.8050 data_time: 0.0040 memory: 42024 loss: 0.0927 loss_ce: 0.0927 2023/03/01 11:50:41 - mmengine - INFO - Epoch(train) [115][ 300/5047] lr: 1.1430e-05 eta: 1 day, 19:57:18 time: 0.8129 data_time: 0.0028 memory: 49130 loss: 0.1023 loss_ce: 0.1023 2023/03/01 11:52:06 - mmengine - INFO - Epoch(train) [115][ 400/5047] lr: 1.1430e-05 eta: 1 day, 19:55:50 time: 0.8987 data_time: 0.0027 memory: 43289 loss: 0.0951 loss_ce: 0.0951 2023/03/01 11:53:33 - mmengine - INFO - Epoch(train) [115][ 500/5047] lr: 1.1430e-05 eta: 1 day, 19:54:23 time: 0.8714 data_time: 0.0032 memory: 55562 loss: 0.1110 loss_ce: 0.1110 2023/03/01 11:54:58 - mmengine - INFO - Epoch(train) [115][ 600/5047] lr: 1.1430e-05 eta: 1 day, 19:52:55 time: 0.8295 data_time: 0.0025 memory: 48096 loss: 0.0939 loss_ce: 0.0939 2023/03/01 11:55:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 11:56:24 - mmengine - INFO - Epoch(train) [115][ 700/5047] lr: 1.1430e-05 eta: 1 day, 19:51:27 time: 0.8170 data_time: 0.0030 memory: 50505 loss: 0.1150 loss_ce: 0.1150 2023/03/01 11:57:49 - mmengine - INFO - Epoch(train) [115][ 800/5047] lr: 1.1430e-05 eta: 1 day, 19:49:59 time: 0.8601 data_time: 0.0031 memory: 47074 loss: 0.1204 loss_ce: 0.1204 2023/03/01 11:59:15 - mmengine - INFO - Epoch(train) [115][ 900/5047] lr: 1.1430e-05 eta: 1 day, 19:48:32 time: 0.8612 data_time: 0.0027 memory: 50505 loss: 0.1063 loss_ce: 0.1063 2023/03/01 12:00:40 - mmengine - INFO - Epoch(train) [115][1000/5047] lr: 1.1430e-05 eta: 1 day, 19:47:04 time: 0.8735 data_time: 0.0025 memory: 44956 loss: 0.1197 loss_ce: 0.1197 2023/03/01 12:02:07 - mmengine - INFO - Epoch(train) [115][1100/5047] lr: 1.1430e-05 eta: 1 day, 19:45:36 time: 0.8268 data_time: 0.0025 memory: 44956 loss: 0.1102 loss_ce: 0.1102 2023/03/01 12:03:32 - mmengine - INFO - Epoch(train) [115][1200/5047] lr: 1.1430e-05 eta: 1 day, 19:44:08 time: 0.8127 data_time: 0.0026 memory: 41724 loss: 0.1111 loss_ce: 0.1111 2023/03/01 12:04:56 - mmengine - INFO - Epoch(train) [115][1300/5047] lr: 1.1430e-05 eta: 1 day, 19:42:40 time: 0.8550 data_time: 0.0049 memory: 42336 loss: 0.1053 loss_ce: 0.1053 2023/03/01 12:06:22 - mmengine - INFO - Epoch(train) [115][1400/5047] lr: 1.1430e-05 eta: 1 day, 19:41:12 time: 0.8233 data_time: 0.0027 memory: 48035 loss: 0.1147 loss_ce: 0.1147 2023/03/01 12:07:48 - mmengine - INFO - Epoch(train) [115][1500/5047] lr: 1.1430e-05 eta: 1 day, 19:39:45 time: 0.7883 data_time: 0.0027 memory: 45711 loss: 0.1080 loss_ce: 0.1080 2023/03/01 12:09:14 - mmengine - INFO - Epoch(train) [115][1600/5047] lr: 1.1430e-05 eta: 1 day, 19:38:17 time: 0.8464 data_time: 0.0036 memory: 45302 loss: 0.1039 loss_ce: 0.1039 2023/03/01 12:09:50 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 12:10:40 - mmengine - INFO - Epoch(train) [115][1700/5047] lr: 1.1430e-05 eta: 1 day, 19:36:49 time: 0.8667 data_time: 0.0027 memory: 43244 loss: 0.1122 loss_ce: 0.1122 2023/03/01 12:12:07 - mmengine - INFO - Epoch(train) [115][1800/5047] lr: 1.1430e-05 eta: 1 day, 19:35:22 time: 0.9002 data_time: 0.0038 memory: 41703 loss: 0.1030 loss_ce: 0.1030 2023/03/01 12:13:32 - mmengine - INFO - Epoch(train) [115][1900/5047] lr: 1.1430e-05 eta: 1 day, 19:33:54 time: 0.8917 data_time: 0.0025 memory: 41852 loss: 0.1140 loss_ce: 0.1140 2023/03/01 12:14:58 - mmengine - INFO - Epoch(train) [115][2000/5047] lr: 1.1430e-05 eta: 1 day, 19:32:27 time: 0.8809 data_time: 0.0027 memory: 37003 loss: 0.1147 loss_ce: 0.1147 2023/03/01 12:16:24 - mmengine - INFO - Epoch(train) [115][2100/5047] lr: 1.1430e-05 eta: 1 day, 19:30:59 time: 0.8920 data_time: 0.0028 memory: 43346 loss: 0.1158 loss_ce: 0.1158 2023/03/01 12:17:50 - mmengine - INFO - Epoch(train) [115][2200/5047] lr: 1.1430e-05 eta: 1 day, 19:29:31 time: 0.8819 data_time: 0.0036 memory: 46005 loss: 0.1046 loss_ce: 0.1046 2023/03/01 12:19:18 - mmengine - INFO - Epoch(train) [115][2300/5047] lr: 1.1430e-05 eta: 1 day, 19:28:04 time: 0.8496 data_time: 0.0033 memory: 41419 loss: 0.1160 loss_ce: 0.1160 2023/03/01 12:20:43 - mmengine - INFO - Epoch(train) [115][2400/5047] lr: 1.1430e-05 eta: 1 day, 19:26:36 time: 0.8662 data_time: 0.0030 memory: 41122 loss: 0.1061 loss_ce: 0.1061 2023/03/01 12:22:10 - mmengine - INFO - Epoch(train) [115][2500/5047] lr: 1.1430e-05 eta: 1 day, 19:25:09 time: 0.8750 data_time: 0.0027 memory: 48188 loss: 0.1031 loss_ce: 0.1031 2023/03/01 12:23:36 - mmengine - INFO - Epoch(train) [115][2600/5047] lr: 1.1430e-05 eta: 1 day, 19:23:41 time: 0.8847 data_time: 0.0025 memory: 49334 loss: 0.1068 loss_ce: 0.1068 2023/03/01 12:24:11 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 12:25:01 - mmengine - INFO - Epoch(train) [115][2700/5047] lr: 1.1430e-05 eta: 1 day, 19:22:13 time: 0.8834 data_time: 0.0102 memory: 44429 loss: 0.1141 loss_ce: 0.1141 2023/03/01 12:26:29 - mmengine - INFO - Epoch(train) [115][2800/5047] lr: 1.1430e-05 eta: 1 day, 19:20:46 time: 0.8924 data_time: 0.0029 memory: 43289 loss: 0.1264 loss_ce: 0.1264 2023/03/01 12:27:54 - mmengine - INFO - Epoch(train) [115][2900/5047] lr: 1.1430e-05 eta: 1 day, 19:19:18 time: 0.8725 data_time: 0.0067 memory: 42965 loss: 0.0998 loss_ce: 0.0998 2023/03/01 12:29:19 - mmengine - INFO - Epoch(train) [115][3000/5047] lr: 1.1430e-05 eta: 1 day, 19:17:50 time: 0.8587 data_time: 0.0025 memory: 54205 loss: 0.1022 loss_ce: 0.1022 2023/03/01 12:30:45 - mmengine - INFO - Epoch(train) [115][3100/5047] lr: 1.1430e-05 eta: 1 day, 19:16:23 time: 0.8530 data_time: 0.0027 memory: 55562 loss: 0.1137 loss_ce: 0.1137 2023/03/01 12:32:10 - mmengine - INFO - Epoch(train) [115][3200/5047] lr: 1.1430e-05 eta: 1 day, 19:14:55 time: 0.8123 data_time: 0.0024 memory: 41419 loss: 0.1164 loss_ce: 0.1164 2023/03/01 12:33:37 - mmengine - INFO - Epoch(train) [115][3300/5047] lr: 1.1430e-05 eta: 1 day, 19:13:27 time: 0.8580 data_time: 0.0028 memory: 39738 loss: 0.1061 loss_ce: 0.1061 2023/03/01 12:35:02 - mmengine - INFO - Epoch(train) [115][3400/5047] lr: 1.1430e-05 eta: 1 day, 19:11:59 time: 0.8146 data_time: 0.0024 memory: 52953 loss: 0.1055 loss_ce: 0.1055 2023/03/01 12:40:23 - mmengine - INFO - Epoch(train) [115][3500/5047] lr: 1.1430e-05 eta: 1 day, 19:11:46 time: 0.8640 data_time: 0.0031 memory: 48948 loss: 0.1096 loss_ce: 0.1096 2023/03/01 12:41:51 - mmengine - INFO - Epoch(train) [115][3600/5047] lr: 1.1430e-05 eta: 1 day, 19:10:19 time: 0.8530 data_time: 0.0028 memory: 41324 loss: 0.1056 loss_ce: 0.1056 2023/03/01 12:42:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 12:43:16 - mmengine - INFO - Epoch(train) [115][3700/5047] lr: 1.1430e-05 eta: 1 day, 19:08:51 time: 0.8600 data_time: 0.0070 memory: 47074 loss: 0.0955 loss_ce: 0.0955 2023/03/01 12:44:43 - mmengine - INFO - Epoch(train) [115][3800/5047] lr: 1.1430e-05 eta: 1 day, 19:07:24 time: 0.8812 data_time: 0.0045 memory: 47447 loss: 0.1095 loss_ce: 0.1095 2023/03/01 12:46:08 - mmengine - INFO - Epoch(train) [115][3900/5047] lr: 1.1430e-05 eta: 1 day, 19:05:55 time: 0.8604 data_time: 0.0027 memory: 44617 loss: 0.1170 loss_ce: 0.1170 2023/03/01 12:47:34 - mmengine - INFO - Epoch(train) [115][4000/5047] lr: 1.1430e-05 eta: 1 day, 19:04:28 time: 0.8107 data_time: 0.0027 memory: 54673 loss: 0.1041 loss_ce: 0.1041 2023/03/01 12:48:59 - mmengine - INFO - Epoch(train) [115][4100/5047] lr: 1.1430e-05 eta: 1 day, 19:03:00 time: 0.8465 data_time: 0.0060 memory: 46713 loss: 0.0989 loss_ce: 0.0989 2023/03/01 12:50:25 - mmengine - INFO - Epoch(train) [115][4200/5047] lr: 1.1430e-05 eta: 1 day, 19:01:32 time: 0.8526 data_time: 0.0056 memory: 55562 loss: 0.1052 loss_ce: 0.1052 2023/03/01 12:51:50 - mmengine - INFO - Epoch(train) [115][4300/5047] lr: 1.1430e-05 eta: 1 day, 19:00:04 time: 0.8204 data_time: 0.0028 memory: 51081 loss: 0.1080 loss_ce: 0.1080 2023/03/01 12:53:15 - mmengine - INFO - Epoch(train) [115][4400/5047] lr: 1.1430e-05 eta: 1 day, 18:58:36 time: 0.8127 data_time: 0.0027 memory: 41419 loss: 0.1103 loss_ce: 0.1103 2023/03/01 12:54:40 - mmengine - INFO - Epoch(train) [115][4500/5047] lr: 1.1430e-05 eta: 1 day, 18:57:08 time: 0.8094 data_time: 0.0031 memory: 42649 loss: 0.1122 loss_ce: 0.1122 2023/03/01 12:56:06 - mmengine - INFO - Epoch(train) [115][4600/5047] lr: 1.1430e-05 eta: 1 day, 18:55:41 time: 0.8881 data_time: 0.0027 memory: 43289 loss: 0.1122 loss_ce: 0.1122 2023/03/01 12:56:43 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 12:57:32 - mmengine - INFO - Epoch(train) [115][4700/5047] lr: 1.1430e-05 eta: 1 day, 18:54:13 time: 0.8392 data_time: 0.0036 memory: 53809 loss: 0.1007 loss_ce: 0.1007 2023/03/01 12:58:58 - mmengine - INFO - Epoch(train) [115][4800/5047] lr: 1.1430e-05 eta: 1 day, 18:52:45 time: 0.8528 data_time: 0.0048 memory: 51734 loss: 0.1111 loss_ce: 0.1111 2023/03/01 13:00:22 - mmengine - INFO - Epoch(train) [115][4900/5047] lr: 1.1430e-05 eta: 1 day, 18:51:17 time: 0.8400 data_time: 0.0045 memory: 40825 loss: 0.1220 loss_ce: 0.1220 2023/03/01 13:01:48 - mmengine - INFO - Epoch(train) [115][5000/5047] lr: 1.1430e-05 eta: 1 day, 18:49:49 time: 0.8617 data_time: 0.0031 memory: 42965 loss: 0.0993 loss_ce: 0.0993 2023/03/01 13:02:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 13:02:27 - mmengine - INFO - Saving checkpoint at 115 epochs 2023/03/01 13:03:57 - mmengine - INFO - Epoch(train) [116][ 100/5047] lr: 1.1229e-05 eta: 1 day, 18:47:39 time: 0.8282 data_time: 0.0025 memory: 43289 loss: 0.1286 loss_ce: 0.1286 2023/03/01 13:05:22 - mmengine - INFO - Epoch(train) [116][ 200/5047] lr: 1.1229e-05 eta: 1 day, 18:46:11 time: 0.8334 data_time: 0.0024 memory: 46355 loss: 0.0898 loss_ce: 0.0898 2023/03/01 13:06:49 - mmengine - INFO - Epoch(train) [116][ 300/5047] lr: 1.1229e-05 eta: 1 day, 18:44:44 time: 0.8354 data_time: 0.0025 memory: 41826 loss: 0.1042 loss_ce: 0.1042 2023/03/01 13:08:13 - mmengine - INFO - Epoch(train) [116][ 400/5047] lr: 1.1229e-05 eta: 1 day, 18:43:16 time: 0.8407 data_time: 0.0027 memory: 41270 loss: 0.1041 loss_ce: 0.1041 2023/03/01 13:09:40 - mmengine - INFO - Epoch(train) [116][ 500/5047] lr: 1.1229e-05 eta: 1 day, 18:41:48 time: 0.8870 data_time: 0.0024 memory: 40513 loss: 0.1120 loss_ce: 0.1120 2023/03/01 13:11:03 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 13:11:07 - mmengine - INFO - Epoch(train) [116][ 600/5047] lr: 1.1229e-05 eta: 1 day, 18:40:21 time: 0.8174 data_time: 0.0027 memory: 44617 loss: 0.0974 loss_ce: 0.0974 2023/03/01 13:12:35 - mmengine - INFO - Epoch(train) [116][ 700/5047] lr: 1.1229e-05 eta: 1 day, 18:38:54 time: 0.9865 data_time: 0.0027 memory: 55562 loss: 0.1018 loss_ce: 0.1018 2023/03/01 13:14:00 - mmengine - INFO - Epoch(train) [116][ 800/5047] lr: 1.1229e-05 eta: 1 day, 18:37:26 time: 0.8292 data_time: 0.0029 memory: 41419 loss: 0.1091 loss_ce: 0.1091 2023/03/01 13:15:28 - mmengine - INFO - Epoch(train) [116][ 900/5047] lr: 1.1229e-05 eta: 1 day, 18:35:59 time: 0.8498 data_time: 0.0029 memory: 41122 loss: 0.0987 loss_ce: 0.0987 2023/03/01 13:16:52 - mmengine - INFO - Epoch(train) [116][1000/5047] lr: 1.1229e-05 eta: 1 day, 18:34:31 time: 0.8640 data_time: 0.0062 memory: 44278 loss: 0.1035 loss_ce: 0.1035 2023/03/01 13:18:18 - mmengine - INFO - Epoch(train) [116][1100/5047] lr: 1.1229e-05 eta: 1 day, 18:33:03 time: 0.8488 data_time: 0.0029 memory: 46005 loss: 0.1030 loss_ce: 0.1030 2023/03/01 13:19:43 - mmengine - INFO - Epoch(train) [116][1200/5047] lr: 1.1229e-05 eta: 1 day, 18:31:35 time: 0.8252 data_time: 0.0025 memory: 43947 loss: 0.1025 loss_ce: 0.1025 2023/03/01 13:21:09 - mmengine - INFO - Epoch(train) [116][1300/5047] lr: 1.1229e-05 eta: 1 day, 18:30:07 time: 0.8516 data_time: 0.0027 memory: 44108 loss: 0.1178 loss_ce: 0.1178 2023/03/01 13:22:36 - mmengine - INFO - Epoch(train) [116][1400/5047] lr: 1.1229e-05 eta: 1 day, 18:28:40 time: 0.8837 data_time: 0.0038 memory: 44278 loss: 0.1051 loss_ce: 0.1051 2023/03/01 13:24:02 - mmengine - INFO - Epoch(train) [116][1500/5047] lr: 1.1229e-05 eta: 1 day, 18:27:12 time: 0.8380 data_time: 0.0028 memory: 55562 loss: 0.1014 loss_ce: 0.1014 2023/03/01 13:25:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 13:25:27 - mmengine - INFO - Epoch(train) [116][1600/5047] lr: 1.1229e-05 eta: 1 day, 18:25:44 time: 0.8270 data_time: 0.0027 memory: 45642 loss: 0.1017 loss_ce: 0.1017 2023/03/01 13:26:54 - mmengine - INFO - Epoch(train) [116][1700/5047] lr: 1.1229e-05 eta: 1 day, 18:24:17 time: 0.8257 data_time: 0.0025 memory: 40002 loss: 0.0972 loss_ce: 0.0972 2023/03/01 13:28:19 - mmengine - INFO - Epoch(train) [116][1800/5047] lr: 1.1229e-05 eta: 1 day, 18:22:49 time: 0.8456 data_time: 0.0029 memory: 46568 loss: 0.0921 loss_ce: 0.0921 2023/03/01 13:29:44 - mmengine - INFO - Epoch(train) [116][1900/5047] lr: 1.1229e-05 eta: 1 day, 18:21:21 time: 0.8145 data_time: 0.0032 memory: 42628 loss: 0.0980 loss_ce: 0.0980 2023/03/01 13:31:11 - mmengine - INFO - Epoch(train) [116][2000/5047] lr: 1.1229e-05 eta: 1 day, 18:19:53 time: 0.8835 data_time: 0.0031 memory: 45478 loss: 0.0923 loss_ce: 0.0923 2023/03/01 13:32:37 - mmengine - INFO - Epoch(train) [116][2100/5047] lr: 1.1229e-05 eta: 1 day, 18:18:26 time: 0.8984 data_time: 0.0028 memory: 45813 loss: 0.1239 loss_ce: 0.1239 2023/03/01 13:34:04 - mmengine - INFO - Epoch(train) [116][2200/5047] lr: 1.1229e-05 eta: 1 day, 18:16:58 time: 0.8575 data_time: 0.0028 memory: 41721 loss: 0.1195 loss_ce: 0.1195 2023/03/01 13:35:31 - mmengine - INFO - Epoch(train) [116][2300/5047] lr: 1.1229e-05 eta: 1 day, 18:15:31 time: 0.8518 data_time: 0.0028 memory: 50277 loss: 0.1077 loss_ce: 0.1077 2023/03/01 13:36:57 - mmengine - INFO - Epoch(train) [116][2400/5047] lr: 1.1229e-05 eta: 1 day, 18:14:03 time: 0.9357 data_time: 0.0025 memory: 47813 loss: 0.1148 loss_ce: 0.1148 2023/03/01 13:38:23 - mmengine - INFO - Epoch(train) [116][2500/5047] lr: 1.1229e-05 eta: 1 day, 18:12:36 time: 0.8705 data_time: 0.0030 memory: 45408 loss: 0.1108 loss_ce: 0.1108 2023/03/01 13:39:46 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 13:39:50 - mmengine - INFO - Epoch(train) [116][2600/5047] lr: 1.1229e-05 eta: 1 day, 18:11:08 time: 0.9070 data_time: 0.0036 memory: 50505 loss: 0.1177 loss_ce: 0.1177 2023/03/01 13:41:15 - mmengine - INFO - Epoch(train) [116][2700/5047] lr: 1.1229e-05 eta: 1 day, 18:09:40 time: 0.8610 data_time: 0.0061 memory: 51719 loss: 0.1128 loss_ce: 0.1128 2023/03/01 13:42:41 - mmengine - INFO - Epoch(train) [116][2800/5047] lr: 1.1229e-05 eta: 1 day, 18:08:12 time: 0.8307 data_time: 0.0028 memory: 45302 loss: 0.1040 loss_ce: 0.1040 2023/03/01 13:44:05 - mmengine - INFO - Epoch(train) [116][2900/5047] lr: 1.1229e-05 eta: 1 day, 18:06:44 time: 0.8658 data_time: 0.0026 memory: 46457 loss: 0.1130 loss_ce: 0.1130 2023/03/01 13:45:30 - mmengine - INFO - Epoch(train) [116][3000/5047] lr: 1.1229e-05 eta: 1 day, 18:05:16 time: 0.8430 data_time: 0.0029 memory: 50347 loss: 0.0974 loss_ce: 0.0974 2023/03/01 13:46:56 - mmengine - INFO - Epoch(train) [116][3100/5047] lr: 1.1229e-05 eta: 1 day, 18:03:49 time: 0.8855 data_time: 0.0031 memory: 55562 loss: 0.1033 loss_ce: 0.1033 2023/03/01 13:48:21 - mmengine - INFO - Epoch(train) [116][3200/5047] lr: 1.1229e-05 eta: 1 day, 18:02:21 time: 0.8084 data_time: 0.0029 memory: 42348 loss: 0.1181 loss_ce: 0.1181 2023/03/01 13:49:46 - mmengine - INFO - Epoch(train) [116][3300/5047] lr: 1.1229e-05 eta: 1 day, 18:00:53 time: 0.8382 data_time: 0.0027 memory: 44617 loss: 0.0993 loss_ce: 0.0993 2023/03/01 13:51:10 - mmengine - INFO - Epoch(train) [116][3400/5047] lr: 1.1229e-05 eta: 1 day, 17:59:25 time: 0.8198 data_time: 0.0028 memory: 40397 loss: 0.1108 loss_ce: 0.1108 2023/03/01 13:52:36 - mmengine - INFO - Epoch(train) [116][3500/5047] lr: 1.1229e-05 eta: 1 day, 17:57:57 time: 0.8704 data_time: 0.0024 memory: 54277 loss: 0.1145 loss_ce: 0.1145 2023/03/01 13:53:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 13:54:02 - mmengine - INFO - Epoch(train) [116][3600/5047] lr: 1.1229e-05 eta: 1 day, 17:56:29 time: 0.8832 data_time: 0.0028 memory: 42649 loss: 0.1177 loss_ce: 0.1177 2023/03/01 13:55:26 - mmengine - INFO - Epoch(train) [116][3700/5047] lr: 1.1229e-05 eta: 1 day, 17:55:01 time: 0.8505 data_time: 0.0026 memory: 41122 loss: 0.1094 loss_ce: 0.1094 2023/03/01 13:56:54 - mmengine - INFO - Epoch(train) [116][3800/5047] lr: 1.1229e-05 eta: 1 day, 17:53:34 time: 0.8802 data_time: 0.0026 memory: 43010 loss: 0.1255 loss_ce: 0.1255 2023/03/01 13:58:19 - mmengine - INFO - Epoch(train) [116][3900/5047] lr: 1.1229e-05 eta: 1 day, 17:52:06 time: 0.8250 data_time: 0.0039 memory: 44760 loss: 0.1108 loss_ce: 0.1108 2023/03/01 13:59:44 - mmengine - INFO - Epoch(train) [116][4000/5047] lr: 1.1229e-05 eta: 1 day, 17:50:38 time: 0.8742 data_time: 0.0025 memory: 51510 loss: 0.1015 loss_ce: 0.1015 2023/03/01 14:01:11 - mmengine - INFO - Epoch(train) [116][4100/5047] lr: 1.1229e-05 eta: 1 day, 17:49:10 time: 0.8435 data_time: 0.0056 memory: 43749 loss: 0.1029 loss_ce: 0.1029 2023/03/01 14:02:38 - mmengine - INFO - Epoch(train) [116][4200/5047] lr: 1.1229e-05 eta: 1 day, 17:47:43 time: 0.8824 data_time: 0.0026 memory: 43683 loss: 0.0979 loss_ce: 0.0979 2023/03/01 14:04:04 - mmengine - INFO - Epoch(train) [116][4300/5047] lr: 1.1229e-05 eta: 1 day, 17:46:15 time: 0.8737 data_time: 0.0026 memory: 43947 loss: 0.0860 loss_ce: 0.0860 2023/03/01 14:05:32 - mmengine - INFO - Epoch(train) [116][4400/5047] lr: 1.1229e-05 eta: 1 day, 17:44:48 time: 0.9088 data_time: 0.0064 memory: 45689 loss: 0.1151 loss_ce: 0.1151 2023/03/01 14:06:58 - mmengine - INFO - Epoch(train) [116][4500/5047] lr: 1.1229e-05 eta: 1 day, 17:43:21 time: 0.8730 data_time: 0.0025 memory: 47087 loss: 0.0991 loss_ce: 0.0991 2023/03/01 14:08:20 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 14:08:24 - mmengine - INFO - Epoch(train) [116][4600/5047] lr: 1.1229e-05 eta: 1 day, 17:41:53 time: 0.8463 data_time: 0.0081 memory: 43177 loss: 0.1149 loss_ce: 0.1149 2023/03/01 14:09:51 - mmengine - INFO - Epoch(train) [116][4700/5047] lr: 1.1229e-05 eta: 1 day, 17:40:26 time: 0.8780 data_time: 0.0027 memory: 45643 loss: 0.1088 loss_ce: 0.1088 2023/03/01 14:11:18 - mmengine - INFO - Epoch(train) [116][4800/5047] lr: 1.1229e-05 eta: 1 day, 17:38:58 time: 0.8483 data_time: 0.0091 memory: 44617 loss: 0.1038 loss_ce: 0.1038 2023/03/01 14:12:44 - mmengine - INFO - Epoch(train) [116][4900/5047] lr: 1.1229e-05 eta: 1 day, 17:37:31 time: 0.8205 data_time: 0.0079 memory: 42205 loss: 0.1001 loss_ce: 0.1001 2023/03/01 14:14:11 - mmengine - INFO - Epoch(train) [116][5000/5047] lr: 1.1229e-05 eta: 1 day, 17:36:03 time: 0.8852 data_time: 0.0028 memory: 46179 loss: 0.1071 loss_ce: 0.1071 2023/03/01 14:14:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 14:14:52 - mmengine - INFO - Saving checkpoint at 116 epochs 2023/03/01 14:16:26 - mmengine - INFO - Epoch(train) [117][ 100/5047] lr: 1.1028e-05 eta: 1 day, 17:33:55 time: 0.8893 data_time: 0.0027 memory: 42024 loss: 0.0991 loss_ce: 0.0991 2023/03/01 14:17:50 - mmengine - INFO - Epoch(train) [117][ 200/5047] lr: 1.1028e-05 eta: 1 day, 17:32:27 time: 0.8497 data_time: 0.0026 memory: 45643 loss: 0.1100 loss_ce: 0.1100 2023/03/01 14:19:16 - mmengine - INFO - Epoch(train) [117][ 300/5047] lr: 1.1028e-05 eta: 1 day, 17:30:59 time: 0.8417 data_time: 0.0029 memory: 44748 loss: 0.1104 loss_ce: 0.1104 2023/03/01 14:20:42 - mmengine - INFO - Epoch(train) [117][ 400/5047] lr: 1.1028e-05 eta: 1 day, 17:29:32 time: 0.8322 data_time: 0.0025 memory: 55562 loss: 0.1009 loss_ce: 0.1009 2023/03/01 14:22:08 - mmengine - INFO - Epoch(train) [117][ 500/5047] lr: 1.1028e-05 eta: 1 day, 17:28:04 time: 0.8872 data_time: 0.0027 memory: 45643 loss: 0.0996 loss_ce: 0.0996 2023/03/01 14:22:49 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 14:23:34 - mmengine - INFO - Epoch(train) [117][ 600/5047] lr: 1.1028e-05 eta: 1 day, 17:26:36 time: 0.8257 data_time: 0.0025 memory: 45643 loss: 0.0951 loss_ce: 0.0951 2023/03/01 14:25:00 - mmengine - INFO - Epoch(train) [117][ 700/5047] lr: 1.1028e-05 eta: 1 day, 17:25:09 time: 0.8905 data_time: 0.0027 memory: 42592 loss: 0.1170 loss_ce: 0.1170 2023/03/01 14:26:27 - mmengine - INFO - Epoch(train) [117][ 800/5047] lr: 1.1028e-05 eta: 1 day, 17:23:41 time: 0.8677 data_time: 0.0025 memory: 55114 loss: 0.1054 loss_ce: 0.1054 2023/03/01 14:27:53 - mmengine - INFO - Epoch(train) [117][ 900/5047] lr: 1.1028e-05 eta: 1 day, 17:22:14 time: 0.8625 data_time: 0.0025 memory: 42336 loss: 0.1080 loss_ce: 0.1080 2023/03/01 14:29:19 - mmengine - INFO - Epoch(train) [117][1000/5047] lr: 1.1028e-05 eta: 1 day, 17:20:46 time: 0.8580 data_time: 0.0027 memory: 55562 loss: 0.1035 loss_ce: 0.1035 2023/03/01 14:30:45 - mmengine - INFO - Epoch(train) [117][1100/5047] lr: 1.1028e-05 eta: 1 day, 17:19:18 time: 0.8286 data_time: 0.0030 memory: 51574 loss: 0.1087 loss_ce: 0.1087 2023/03/01 14:32:10 - mmengine - INFO - Epoch(train) [117][1200/5047] lr: 1.1028e-05 eta: 1 day, 17:17:50 time: 0.8207 data_time: 0.0025 memory: 43289 loss: 0.1211 loss_ce: 0.1211 2023/03/01 14:33:36 - mmengine - INFO - Epoch(train) [117][1300/5047] lr: 1.1028e-05 eta: 1 day, 17:16:23 time: 0.8849 data_time: 0.0026 memory: 55562 loss: 0.1087 loss_ce: 0.1087 2023/03/01 14:35:03 - mmengine - INFO - Epoch(train) [117][1400/5047] lr: 1.1028e-05 eta: 1 day, 17:14:56 time: 0.8911 data_time: 0.0028 memory: 55297 loss: 0.1070 loss_ce: 0.1070 2023/03/01 14:36:31 - mmengine - INFO - Epoch(train) [117][1500/5047] lr: 1.1028e-05 eta: 1 day, 17:13:29 time: 0.8895 data_time: 0.0030 memory: 42649 loss: 0.1115 loss_ce: 0.1115 2023/03/01 14:37:13 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 14:37:56 - mmengine - INFO - Epoch(train) [117][1600/5047] lr: 1.1028e-05 eta: 1 day, 17:12:01 time: 0.8774 data_time: 0.0028 memory: 55562 loss: 0.1080 loss_ce: 0.1080 2023/03/01 14:39:24 - mmengine - INFO - Epoch(train) [117][1700/5047] lr: 1.1028e-05 eta: 1 day, 17:10:34 time: 0.8672 data_time: 0.0025 memory: 55562 loss: 0.1103 loss_ce: 0.1103 2023/03/01 14:40:49 - mmengine - INFO - Epoch(train) [117][1800/5047] lr: 1.1028e-05 eta: 1 day, 17:09:06 time: 0.8604 data_time: 0.0027 memory: 51719 loss: 0.1100 loss_ce: 0.1100 2023/03/01 14:42:16 - mmengine - INFO - Epoch(train) [117][1900/5047] lr: 1.1028e-05 eta: 1 day, 17:07:38 time: 0.8857 data_time: 0.0027 memory: 55562 loss: 0.1043 loss_ce: 0.1043 2023/03/01 14:43:43 - mmengine - INFO - Epoch(train) [117][2000/5047] lr: 1.1028e-05 eta: 1 day, 17:06:11 time: 0.8886 data_time: 0.0036 memory: 41171 loss: 0.0939 loss_ce: 0.0939 2023/03/01 14:45:09 - mmengine - INFO - Epoch(train) [117][2100/5047] lr: 1.1028e-05 eta: 1 day, 17:04:43 time: 0.8704 data_time: 0.0026 memory: 43613 loss: 0.1144 loss_ce: 0.1144 2023/03/01 14:46:35 - mmengine - INFO - Epoch(train) [117][2200/5047] lr: 1.1028e-05 eta: 1 day, 17:03:16 time: 0.8723 data_time: 0.0028 memory: 52127 loss: 0.1174 loss_ce: 0.1174 2023/03/01 14:48:00 - mmengine - INFO - Epoch(train) [117][2300/5047] lr: 1.1028e-05 eta: 1 day, 17:01:48 time: 0.8237 data_time: 0.0039 memory: 45569 loss: 0.1041 loss_ce: 0.1041 2023/03/01 14:49:27 - mmengine - INFO - Epoch(train) [117][2400/5047] lr: 1.1028e-05 eta: 1 day, 17:00:20 time: 0.8038 data_time: 0.0025 memory: 45191 loss: 0.0957 loss_ce: 0.0957 2023/03/01 14:50:54 - mmengine - INFO - Epoch(train) [117][2500/5047] lr: 1.1028e-05 eta: 1 day, 16:58:53 time: 0.8656 data_time: 0.0026 memory: 44632 loss: 0.0950 loss_ce: 0.0950 2023/03/01 14:51:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 14:52:21 - mmengine - INFO - Epoch(train) [117][2600/5047] lr: 1.1028e-05 eta: 1 day, 16:57:26 time: 0.8812 data_time: 0.0061 memory: 55562 loss: 0.1107 loss_ce: 0.1107 2023/03/01 14:53:48 - mmengine - INFO - Epoch(train) [117][2700/5047] lr: 1.1028e-05 eta: 1 day, 16:55:58 time: 0.8795 data_time: 0.0027 memory: 41115 loss: 0.1103 loss_ce: 0.1103 2023/03/01 14:55:14 - mmengine - INFO - Epoch(train) [117][2800/5047] lr: 1.1028e-05 eta: 1 day, 16:54:31 time: 0.8346 data_time: 0.0026 memory: 43289 loss: 0.1169 loss_ce: 0.1169 2023/03/01 14:56:40 - mmengine - INFO - Epoch(train) [117][2900/5047] lr: 1.1028e-05 eta: 1 day, 16:53:03 time: 0.8629 data_time: 0.0026 memory: 45616 loss: 0.1159 loss_ce: 0.1159 2023/03/01 14:58:05 - mmengine - INFO - Epoch(train) [117][3000/5047] lr: 1.1028e-05 eta: 1 day, 16:51:35 time: 0.8464 data_time: 0.0032 memory: 55485 loss: 0.0964 loss_ce: 0.0964 2023/03/01 14:59:30 - mmengine - INFO - Epoch(train) [117][3100/5047] lr: 1.1028e-05 eta: 1 day, 16:50:07 time: 0.8369 data_time: 0.0031 memory: 47505 loss: 0.1080 loss_ce: 0.1080 2023/03/01 15:00:56 - mmengine - INFO - Epoch(train) [117][3200/5047] lr: 1.1028e-05 eta: 1 day, 16:48:39 time: 0.8358 data_time: 0.0026 memory: 41419 loss: 0.1029 loss_ce: 0.1029 2023/03/01 15:02:22 - mmengine - INFO - Epoch(train) [117][3300/5047] lr: 1.1028e-05 eta: 1 day, 16:47:12 time: 0.8893 data_time: 0.0027 memory: 42024 loss: 0.1118 loss_ce: 0.1118 2023/03/01 15:03:49 - mmengine - INFO - Epoch(train) [117][3400/5047] lr: 1.1028e-05 eta: 1 day, 16:45:45 time: 0.8951 data_time: 0.0026 memory: 42375 loss: 0.1119 loss_ce: 0.1119 2023/03/01 15:05:14 - mmengine - INFO - Epoch(train) [117][3500/5047] lr: 1.1028e-05 eta: 1 day, 16:44:17 time: 0.8221 data_time: 0.0027 memory: 42024 loss: 0.1064 loss_ce: 0.1064 2023/03/01 15:05:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 15:06:41 - mmengine - INFO - Epoch(train) [117][3600/5047] lr: 1.1028e-05 eta: 1 day, 16:42:49 time: 0.8633 data_time: 0.0029 memory: 41939 loss: 0.1075 loss_ce: 0.1075 2023/03/01 15:08:06 - mmengine - INFO - Epoch(train) [117][3700/5047] lr: 1.1028e-05 eta: 1 day, 16:41:21 time: 0.8302 data_time: 0.0030 memory: 45329 loss: 0.1022 loss_ce: 0.1022 2023/03/01 15:09:34 - mmengine - INFO - Epoch(train) [117][3800/5047] lr: 1.1028e-05 eta: 1 day, 16:39:54 time: 0.8907 data_time: 0.0117 memory: 55562 loss: 0.1038 loss_ce: 0.1038 2023/03/01 15:11:01 - mmengine - INFO - Epoch(train) [117][3900/5047] lr: 1.1028e-05 eta: 1 day, 16:38:27 time: 0.8726 data_time: 0.0043 memory: 46257 loss: 0.1201 loss_ce: 0.1201 2023/03/01 15:12:28 - mmengine - INFO - Epoch(train) [117][4000/5047] lr: 1.1028e-05 eta: 1 day, 16:37:00 time: 0.8761 data_time: 0.0028 memory: 42649 loss: 0.1026 loss_ce: 0.1026 2023/03/01 15:13:54 - mmengine - INFO - Epoch(train) [117][4100/5047] lr: 1.1028e-05 eta: 1 day, 16:35:32 time: 0.8471 data_time: 0.0027 memory: 42991 loss: 0.0881 loss_ce: 0.0881 2023/03/01 15:15:21 - mmengine - INFO - Epoch(train) [117][4200/5047] lr: 1.1028e-05 eta: 1 day, 16:34:05 time: 0.8662 data_time: 0.0025 memory: 44587 loss: 0.1037 loss_ce: 0.1037 2023/03/01 15:16:47 - mmengine - INFO - Epoch(train) [117][4300/5047] lr: 1.1028e-05 eta: 1 day, 16:32:37 time: 0.8047 data_time: 0.0056 memory: 44716 loss: 0.1187 loss_ce: 0.1187 2023/03/01 15:18:14 - mmengine - INFO - Epoch(train) [117][4400/5047] lr: 1.1028e-05 eta: 1 day, 16:31:10 time: 0.8694 data_time: 0.0025 memory: 43613 loss: 0.0943 loss_ce: 0.0943 2023/03/01 15:19:39 - mmengine - INFO - Epoch(train) [117][4500/5047] lr: 1.1028e-05 eta: 1 day, 16:29:42 time: 0.8594 data_time: 0.0033 memory: 55562 loss: 0.1160 loss_ce: 0.1160 2023/03/01 15:20:21 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 15:21:05 - mmengine - INFO - Epoch(train) [117][4600/5047] lr: 1.1028e-05 eta: 1 day, 16:28:14 time: 0.8649 data_time: 0.0025 memory: 50542 loss: 0.1019 loss_ce: 0.1019 2023/03/01 15:22:30 - mmengine - INFO - Epoch(train) [117][4700/5047] lr: 1.1028e-05 eta: 1 day, 16:26:46 time: 0.8298 data_time: 0.0033 memory: 42336 loss: 0.1154 loss_ce: 0.1154 2023/03/01 15:23:56 - mmengine - INFO - Epoch(train) [117][4800/5047] lr: 1.1028e-05 eta: 1 day, 16:25:18 time: 0.8790 data_time: 0.0025 memory: 39491 loss: 0.1098 loss_ce: 0.1098 2023/03/01 15:25:20 - mmengine - INFO - Epoch(train) [117][4900/5047] lr: 1.1028e-05 eta: 1 day, 16:23:50 time: 0.8293 data_time: 0.0026 memory: 44705 loss: 0.1161 loss_ce: 0.1161 2023/03/01 15:26:48 - mmengine - INFO - Epoch(train) [117][5000/5047] lr: 1.1028e-05 eta: 1 day, 16:22:23 time: 0.9154 data_time: 0.0053 memory: 48948 loss: 0.1057 loss_ce: 0.1057 2023/03/01 15:27:28 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 15:27:28 - mmengine - INFO - Saving checkpoint at 117 epochs 2023/03/01 15:28:58 - mmengine - INFO - Epoch(train) [118][ 100/5047] lr: 1.0827e-05 eta: 1 day, 16:20:14 time: 0.8376 data_time: 0.0028 memory: 46181 loss: 0.1024 loss_ce: 0.1024 2023/03/01 15:30:24 - mmengine - INFO - Epoch(train) [118][ 200/5047] lr: 1.0827e-05 eta: 1 day, 16:18:46 time: 0.8700 data_time: 0.0057 memory: 44565 loss: 0.1053 loss_ce: 0.1053 2023/03/01 15:31:48 - mmengine - INFO - Epoch(train) [118][ 300/5047] lr: 1.0827e-05 eta: 1 day, 16:17:18 time: 0.8417 data_time: 0.0028 memory: 40535 loss: 0.1068 loss_ce: 0.1068 2023/03/01 15:33:14 - mmengine - INFO - Epoch(train) [118][ 400/5047] lr: 1.0827e-05 eta: 1 day, 16:15:50 time: 0.8740 data_time: 0.0029 memory: 47104 loss: 0.1020 loss_ce: 0.1020 2023/03/01 15:34:41 - mmengine - INFO - Epoch(train) [118][ 500/5047] lr: 1.0827e-05 eta: 1 day, 16:14:23 time: 0.8792 data_time: 0.0026 memory: 46005 loss: 0.1180 loss_ce: 0.1180 2023/03/01 15:34:42 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 15:36:06 - mmengine - INFO - Epoch(train) [118][ 600/5047] lr: 1.0827e-05 eta: 1 day, 16:12:55 time: 0.8670 data_time: 0.0027 memory: 42718 loss: 0.1112 loss_ce: 0.1112 2023/03/01 15:37:31 - mmengine - INFO - Epoch(train) [118][ 700/5047] lr: 1.0827e-05 eta: 1 day, 16:11:27 time: 0.8355 data_time: 0.0027 memory: 43717 loss: 0.1242 loss_ce: 0.1242 2023/03/01 15:38:57 - mmengine - INFO - Epoch(train) [118][ 800/5047] lr: 1.0827e-05 eta: 1 day, 16:10:00 time: 0.8191 data_time: 0.0056 memory: 52953 loss: 0.1190 loss_ce: 0.1190 2023/03/01 15:40:24 - mmengine - INFO - Epoch(train) [118][ 900/5047] lr: 1.0827e-05 eta: 1 day, 16:08:32 time: 0.8352 data_time: 0.0030 memory: 44705 loss: 0.0918 loss_ce: 0.0918 2023/03/01 15:41:50 - mmengine - INFO - Epoch(train) [118][1000/5047] lr: 1.0827e-05 eta: 1 day, 16:07:05 time: 0.8477 data_time: 0.0028 memory: 42649 loss: 0.1129 loss_ce: 0.1129 2023/03/01 15:43:16 - mmengine - INFO - Epoch(train) [118][1100/5047] lr: 1.0827e-05 eta: 1 day, 16:05:37 time: 0.9177 data_time: 0.0036 memory: 43872 loss: 0.0906 loss_ce: 0.0906 2023/03/01 15:44:43 - mmengine - INFO - Epoch(train) [118][1200/5047] lr: 1.0827e-05 eta: 1 day, 16:04:10 time: 0.8585 data_time: 0.0026 memory: 45642 loss: 0.1142 loss_ce: 0.1142 2023/03/01 15:46:10 - mmengine - INFO - Epoch(train) [118][1300/5047] lr: 1.0827e-05 eta: 1 day, 16:02:42 time: 0.9048 data_time: 0.0028 memory: 42077 loss: 0.1132 loss_ce: 0.1132 2023/03/01 15:47:36 - mmengine - INFO - Epoch(train) [118][1400/5047] lr: 1.0827e-05 eta: 1 day, 16:01:15 time: 0.8158 data_time: 0.0029 memory: 46948 loss: 0.1039 loss_ce: 0.1039 2023/03/01 15:49:03 - mmengine - INFO - Epoch(train) [118][1500/5047] lr: 1.0827e-05 eta: 1 day, 15:59:47 time: 0.8820 data_time: 0.0025 memory: 50343 loss: 0.1067 loss_ce: 0.1067 2023/03/01 15:49:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 15:50:28 - mmengine - INFO - Epoch(train) [118][1600/5047] lr: 1.0827e-05 eta: 1 day, 15:58:20 time: 0.8756 data_time: 0.0028 memory: 52976 loss: 0.1148 loss_ce: 0.1148 2023/03/01 15:51:54 - mmengine - INFO - Epoch(train) [118][1700/5047] lr: 1.0827e-05 eta: 1 day, 15:56:52 time: 0.8136 data_time: 0.0029 memory: 54042 loss: 0.0973 loss_ce: 0.0973 2023/03/01 15:53:20 - mmengine - INFO - Epoch(train) [118][1800/5047] lr: 1.0827e-05 eta: 1 day, 15:55:24 time: 0.8206 data_time: 0.0025 memory: 43079 loss: 0.1073 loss_ce: 0.1073 2023/03/01 15:54:44 - mmengine - INFO - Epoch(train) [118][1900/5047] lr: 1.0827e-05 eta: 1 day, 15:53:56 time: 0.8541 data_time: 0.0052 memory: 42965 loss: 0.1031 loss_ce: 0.1031 2023/03/01 15:56:11 - mmengine - INFO - Epoch(train) [118][2000/5047] lr: 1.0827e-05 eta: 1 day, 15:52:29 time: 0.8789 data_time: 0.0026 memory: 44058 loss: 0.1085 loss_ce: 0.1085 2023/03/01 15:57:38 - mmengine - INFO - Epoch(train) [118][2100/5047] lr: 1.0827e-05 eta: 1 day, 15:51:02 time: 0.8240 data_time: 0.0027 memory: 46598 loss: 0.1206 loss_ce: 0.1206 2023/03/01 15:59:05 - mmengine - INFO - Epoch(train) [118][2200/5047] lr: 1.0827e-05 eta: 1 day, 15:49:34 time: 0.9087 data_time: 0.0029 memory: 43613 loss: 0.1063 loss_ce: 0.1063 2023/03/01 16:00:32 - mmengine - INFO - Epoch(train) [118][2300/5047] lr: 1.0827e-05 eta: 1 day, 15:48:07 time: 0.9009 data_time: 0.0056 memory: 46733 loss: 0.1097 loss_ce: 0.1097 2023/03/01 16:01:58 - mmengine - INFO - Epoch(train) [118][2400/5047] lr: 1.0827e-05 eta: 1 day, 15:46:39 time: 0.8747 data_time: 0.0060 memory: 43613 loss: 0.1159 loss_ce: 0.1159 2023/03/01 16:03:26 - mmengine - INFO - Epoch(train) [118][2500/5047] lr: 1.0827e-05 eta: 1 day, 15:45:12 time: 0.8888 data_time: 0.0027 memory: 52964 loss: 0.0907 loss_ce: 0.0907 2023/03/01 16:03:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 16:04:52 - mmengine - INFO - Epoch(train) [118][2600/5047] lr: 1.0827e-05 eta: 1 day, 15:43:45 time: 0.8619 data_time: 0.0027 memory: 45879 loss: 0.1018 loss_ce: 0.1018 2023/03/01 16:06:21 - mmengine - INFO - Epoch(train) [118][2700/5047] lr: 1.0827e-05 eta: 1 day, 15:42:18 time: 0.8522 data_time: 0.0052 memory: 48215 loss: 0.1019 loss_ce: 0.1019 2023/03/01 16:07:48 - mmengine - INFO - Epoch(train) [118][2800/5047] lr: 1.0827e-05 eta: 1 day, 15:40:51 time: 0.8929 data_time: 0.0029 memory: 52295 loss: 0.0942 loss_ce: 0.0942 2023/03/01 16:09:13 - mmengine - INFO - Epoch(train) [118][2900/5047] lr: 1.0827e-05 eta: 1 day, 15:39:23 time: 0.8794 data_time: 0.0051 memory: 44956 loss: 0.1166 loss_ce: 0.1166 2023/03/01 16:10:40 - mmengine - INFO - Epoch(train) [118][3000/5047] lr: 1.0827e-05 eta: 1 day, 15:37:55 time: 0.8546 data_time: 0.0035 memory: 41724 loss: 0.1117 loss_ce: 0.1117 2023/03/01 16:12:05 - mmengine - INFO - Epoch(train) [118][3100/5047] lr: 1.0827e-05 eta: 1 day, 15:36:28 time: 0.8254 data_time: 0.0034 memory: 44617 loss: 0.1086 loss_ce: 0.1086 2023/03/01 16:13:31 - mmengine - INFO - Epoch(train) [118][3200/5047] lr: 1.0827e-05 eta: 1 day, 15:35:00 time: 0.8818 data_time: 0.0030 memory: 39960 loss: 0.1166 loss_ce: 0.1166 2023/03/01 16:14:57 - mmengine - INFO - Epoch(train) [118][3300/5047] lr: 1.0827e-05 eta: 1 day, 15:33:32 time: 0.8784 data_time: 0.0029 memory: 47982 loss: 0.1286 loss_ce: 0.1286 2023/03/01 16:16:22 - mmengine - INFO - Epoch(train) [118][3400/5047] lr: 1.0827e-05 eta: 1 day, 15:32:04 time: 0.8436 data_time: 0.0032 memory: 42465 loss: 0.1189 loss_ce: 0.1189 2023/03/01 16:17:46 - mmengine - INFO - Epoch(train) [118][3500/5047] lr: 1.0827e-05 eta: 1 day, 15:30:36 time: 0.8230 data_time: 0.0036 memory: 55562 loss: 0.1198 loss_ce: 0.1198 2023/03/01 16:17:47 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 16:19:13 - mmengine - INFO - Epoch(train) [118][3600/5047] lr: 1.0827e-05 eta: 1 day, 15:29:09 time: 0.8996 data_time: 0.0032 memory: 55487 loss: 0.1161 loss_ce: 0.1161 2023/03/01 16:20:38 - mmengine - INFO - Epoch(train) [118][3700/5047] lr: 1.0827e-05 eta: 1 day, 15:27:41 time: 0.8230 data_time: 0.0052 memory: 42336 loss: 0.1112 loss_ce: 0.1112 2023/03/01 16:22:04 - mmengine - INFO - Epoch(train) [118][3800/5047] lr: 1.0827e-05 eta: 1 day, 15:26:13 time: 0.9274 data_time: 0.0028 memory: 45302 loss: 0.1098 loss_ce: 0.1098 2023/03/01 16:23:30 - mmengine - INFO - Epoch(train) [118][3900/5047] lr: 1.0827e-05 eta: 1 day, 15:24:46 time: 0.8396 data_time: 0.0048 memory: 43199 loss: 0.1101 loss_ce: 0.1101 2023/03/01 16:24:55 - mmengine - INFO - Epoch(train) [118][4000/5047] lr: 1.0827e-05 eta: 1 day, 15:23:18 time: 0.8524 data_time: 0.0028 memory: 42336 loss: 0.0997 loss_ce: 0.0997 2023/03/01 16:26:22 - mmengine - INFO - Epoch(train) [118][4100/5047] lr: 1.0827e-05 eta: 1 day, 15:21:51 time: 0.8804 data_time: 0.0026 memory: 41371 loss: 0.1038 loss_ce: 0.1038 2023/03/01 16:27:47 - mmengine - INFO - Epoch(train) [118][4200/5047] lr: 1.0827e-05 eta: 1 day, 15:20:23 time: 0.8247 data_time: 0.0031 memory: 44956 loss: 0.1057 loss_ce: 0.1057 2023/03/01 16:29:13 - mmengine - INFO - Epoch(train) [118][4300/5047] lr: 1.0827e-05 eta: 1 day, 15:18:55 time: 0.8299 data_time: 0.0034 memory: 55562 loss: 0.1079 loss_ce: 0.1079 2023/03/01 16:30:40 - mmengine - INFO - Epoch(train) [118][4400/5047] lr: 1.0827e-05 eta: 1 day, 15:17:28 time: 0.8278 data_time: 0.0029 memory: 51658 loss: 0.0976 loss_ce: 0.0976 2023/03/01 16:32:07 - mmengine - INFO - Epoch(train) [118][4500/5047] lr: 1.0827e-05 eta: 1 day, 15:16:00 time: 0.8721 data_time: 0.0026 memory: 44705 loss: 0.0997 loss_ce: 0.0997 2023/03/01 16:32:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 16:33:33 - mmengine - INFO - Epoch(train) [118][4600/5047] lr: 1.0827e-05 eta: 1 day, 15:14:33 time: 0.8758 data_time: 0.0030 memory: 53211 loss: 0.1000 loss_ce: 0.1000 2023/03/01 16:35:00 - mmengine - INFO - Epoch(train) [118][4700/5047] lr: 1.0827e-05 eta: 1 day, 15:13:05 time: 0.8484 data_time: 0.0028 memory: 45549 loss: 0.1246 loss_ce: 0.1246 2023/03/01 16:36:26 - mmengine - INFO - Epoch(train) [118][4800/5047] lr: 1.0827e-05 eta: 1 day, 15:11:38 time: 0.8628 data_time: 0.0026 memory: 53043 loss: 0.1172 loss_ce: 0.1172 2023/03/01 16:37:52 - mmengine - INFO - Epoch(train) [118][4900/5047] lr: 1.0827e-05 eta: 1 day, 15:10:10 time: 0.8962 data_time: 0.0025 memory: 44631 loss: 0.1193 loss_ce: 0.1193 2023/03/01 16:39:19 - mmengine - INFO - Epoch(train) [118][5000/5047] lr: 1.0827e-05 eta: 1 day, 15:08:43 time: 0.8474 data_time: 0.0025 memory: 53021 loss: 0.1113 loss_ce: 0.1113 2023/03/01 16:39:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 16:39:58 - mmengine - INFO - Saving checkpoint at 118 epochs 2023/03/01 16:41:31 - mmengine - INFO - Epoch(train) [119][ 100/5047] lr: 1.0626e-05 eta: 1 day, 15:06:34 time: 0.8112 data_time: 0.0027 memory: 42149 loss: 0.1117 loss_ce: 0.1117 2023/03/01 16:42:56 - mmengine - INFO - Epoch(train) [119][ 200/5047] lr: 1.0626e-05 eta: 1 day, 15:05:06 time: 0.8498 data_time: 0.0029 memory: 43222 loss: 0.1207 loss_ce: 0.1207 2023/03/01 16:44:23 - mmengine - INFO - Epoch(train) [119][ 300/5047] lr: 1.0626e-05 eta: 1 day, 15:03:39 time: 0.8366 data_time: 0.0028 memory: 42469 loss: 0.1046 loss_ce: 0.1046 2023/03/01 16:45:49 - mmengine - INFO - Epoch(train) [119][ 400/5047] lr: 1.0626e-05 eta: 1 day, 15:02:11 time: 0.8381 data_time: 0.0031 memory: 42965 loss: 0.1124 loss_ce: 0.1124 2023/03/01 16:46:35 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 16:47:14 - mmengine - INFO - Epoch(train) [119][ 500/5047] lr: 1.0626e-05 eta: 1 day, 15:00:43 time: 0.8819 data_time: 0.0031 memory: 45369 loss: 0.1089 loss_ce: 0.1089 2023/03/01 16:48:42 - mmengine - INFO - Epoch(train) [119][ 600/5047] lr: 1.0626e-05 eta: 1 day, 14:59:16 time: 0.8961 data_time: 0.0025 memory: 52520 loss: 0.1033 loss_ce: 0.1033 2023/03/01 16:50:07 - mmengine - INFO - Epoch(train) [119][ 700/5047] lr: 1.0626e-05 eta: 1 day, 14:57:48 time: 0.8657 data_time: 0.0052 memory: 41566 loss: 0.1057 loss_ce: 0.1057 2023/03/01 16:51:32 - mmengine - INFO - Epoch(train) [119][ 800/5047] lr: 1.0626e-05 eta: 1 day, 14:56:21 time: 0.9192 data_time: 0.0028 memory: 45271 loss: 0.1002 loss_ce: 0.1002 2023/03/01 16:52:57 - mmengine - INFO - Epoch(train) [119][ 900/5047] lr: 1.0626e-05 eta: 1 day, 14:54:53 time: 0.8812 data_time: 0.0025 memory: 41829 loss: 0.1171 loss_ce: 0.1171 2023/03/01 16:54:23 - mmengine - INFO - Epoch(train) [119][1000/5047] lr: 1.0626e-05 eta: 1 day, 14:53:25 time: 0.8153 data_time: 0.0084 memory: 45805 loss: 0.1075 loss_ce: 0.1075 2023/03/01 16:55:49 - mmengine - INFO - Epoch(train) [119][1100/5047] lr: 1.0626e-05 eta: 1 day, 14:51:58 time: 0.8659 data_time: 0.0047 memory: 42877 loss: 0.1079 loss_ce: 0.1079 2023/03/01 16:57:14 - mmengine - INFO - Epoch(train) [119][1200/5047] lr: 1.0626e-05 eta: 1 day, 14:50:30 time: 0.8109 data_time: 0.0026 memory: 52862 loss: 0.0986 loss_ce: 0.0986 2023/03/01 16:58:39 - mmengine - INFO - Epoch(train) [119][1300/5047] lr: 1.0626e-05 eta: 1 day, 14:49:02 time: 0.8761 data_time: 0.0036 memory: 43289 loss: 0.1055 loss_ce: 0.1055 2023/03/01 17:00:06 - mmengine - INFO - Epoch(train) [119][1400/5047] lr: 1.0626e-05 eta: 1 day, 14:47:35 time: 0.9084 data_time: 0.0030 memory: 46324 loss: 0.1053 loss_ce: 0.1053 2023/03/01 17:00:53 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 17:01:33 - mmengine - INFO - Epoch(train) [119][1500/5047] lr: 1.0626e-05 eta: 1 day, 14:46:07 time: 0.8690 data_time: 0.0034 memory: 49306 loss: 0.0984 loss_ce: 0.0984 2023/03/01 17:02:59 - mmengine - INFO - Epoch(train) [119][1600/5047] lr: 1.0626e-05 eta: 1 day, 14:44:39 time: 0.8508 data_time: 0.0036 memory: 44477 loss: 0.0979 loss_ce: 0.0979 2023/03/01 17:04:25 - mmengine - INFO - Epoch(train) [119][1700/5047] lr: 1.0626e-05 eta: 1 day, 14:43:12 time: 0.8156 data_time: 0.0033 memory: 46554 loss: 0.1034 loss_ce: 0.1034 2023/03/01 17:05:51 - mmengine - INFO - Epoch(train) [119][1800/5047] lr: 1.0626e-05 eta: 1 day, 14:41:44 time: 0.8046 data_time: 0.0028 memory: 43613 loss: 0.1108 loss_ce: 0.1108 2023/03/01 17:07:16 - mmengine - INFO - Epoch(train) [119][1900/5047] lr: 1.0626e-05 eta: 1 day, 14:40:16 time: 0.8754 data_time: 0.0030 memory: 43351 loss: 0.1203 loss_ce: 0.1203 2023/03/01 17:08:42 - mmengine - INFO - Epoch(train) [119][2000/5047] lr: 1.0626e-05 eta: 1 day, 14:38:49 time: 0.8521 data_time: 0.0027 memory: 51792 loss: 0.1154 loss_ce: 0.1154 2023/03/01 17:10:07 - mmengine - INFO - Epoch(train) [119][2100/5047] lr: 1.0626e-05 eta: 1 day, 14:37:21 time: 0.8563 data_time: 0.0027 memory: 39681 loss: 0.1126 loss_ce: 0.1126 2023/03/01 17:11:32 - mmengine - INFO - Epoch(train) [119][2200/5047] lr: 1.0626e-05 eta: 1 day, 14:35:53 time: 0.8201 data_time: 0.0054 memory: 42965 loss: 0.1265 loss_ce: 0.1265 2023/03/01 17:12:59 - mmengine - INFO - Epoch(train) [119][2300/5047] lr: 1.0626e-05 eta: 1 day, 14:34:26 time: 0.8631 data_time: 0.0027 memory: 43947 loss: 0.1050 loss_ce: 0.1050 2023/03/01 17:14:26 - mmengine - INFO - Epoch(train) [119][2400/5047] lr: 1.0626e-05 eta: 1 day, 14:32:59 time: 0.8459 data_time: 0.0027 memory: 55562 loss: 0.1062 loss_ce: 0.1062 2023/03/01 17:15:12 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 17:15:52 - mmengine - INFO - Epoch(train) [119][2500/5047] lr: 1.0626e-05 eta: 1 day, 14:31:31 time: 0.9055 data_time: 0.0028 memory: 49334 loss: 0.0935 loss_ce: 0.0935 2023/03/01 17:17:19 - mmengine - INFO - Epoch(train) [119][2600/5047] lr: 1.0626e-05 eta: 1 day, 14:30:04 time: 0.8891 data_time: 0.0029 memory: 55114 loss: 0.0876 loss_ce: 0.0876 2023/03/01 17:18:49 - mmengine - INFO - Epoch(train) [119][2700/5047] lr: 1.0626e-05 eta: 1 day, 14:28:37 time: 0.8820 data_time: 0.0025 memory: 45658 loss: 0.1092 loss_ce: 0.1092 2023/03/01 17:20:14 - mmengine - INFO - Epoch(train) [119][2800/5047] lr: 1.0626e-05 eta: 1 day, 14:27:09 time: 0.8273 data_time: 0.0035 memory: 42965 loss: 0.1130 loss_ce: 0.1130 2023/03/01 17:21:42 - mmengine - INFO - Epoch(train) [119][2900/5047] lr: 1.0626e-05 eta: 1 day, 14:25:42 time: 0.8908 data_time: 0.0028 memory: 55562 loss: 0.1048 loss_ce: 0.1048 2023/03/01 17:23:07 - mmengine - INFO - Epoch(train) [119][3000/5047] lr: 1.0626e-05 eta: 1 day, 14:24:14 time: 0.8932 data_time: 0.0032 memory: 42336 loss: 0.1069 loss_ce: 0.1069 2023/03/01 17:24:34 - mmengine - INFO - Epoch(train) [119][3100/5047] lr: 1.0626e-05 eta: 1 day, 14:22:47 time: 0.8250 data_time: 0.0027 memory: 44012 loss: 0.1093 loss_ce: 0.1093 2023/03/01 17:25:59 - mmengine - INFO - Epoch(train) [119][3200/5047] lr: 1.0626e-05 eta: 1 day, 14:21:19 time: 0.8384 data_time: 0.0026 memory: 43614 loss: 0.1025 loss_ce: 0.1025 2023/03/01 17:27:25 - mmengine - INFO - Epoch(train) [119][3300/5047] lr: 1.0626e-05 eta: 1 day, 14:19:52 time: 0.8106 data_time: 0.0064 memory: 43791 loss: 0.0947 loss_ce: 0.0947 2023/03/01 17:28:52 - mmengine - INFO - Epoch(train) [119][3400/5047] lr: 1.0626e-05 eta: 1 day, 14:18:24 time: 0.8339 data_time: 0.0030 memory: 55562 loss: 0.1180 loss_ce: 0.1180 2023/03/01 17:29:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 17:30:16 - mmengine - INFO - Epoch(train) [119][3500/5047] lr: 1.0626e-05 eta: 1 day, 14:16:56 time: 0.8247 data_time: 0.0025 memory: 39398 loss: 0.1001 loss_ce: 0.1001 2023/03/01 17:31:42 - mmengine - INFO - Epoch(train) [119][3600/5047] lr: 1.0626e-05 eta: 1 day, 14:15:29 time: 0.8053 data_time: 0.0057 memory: 44409 loss: 0.1113 loss_ce: 0.1113 2023/03/01 17:33:08 - mmengine - INFO - Epoch(train) [119][3700/5047] lr: 1.0626e-05 eta: 1 day, 14:14:01 time: 0.8909 data_time: 0.0034 memory: 55562 loss: 0.0991 loss_ce: 0.0991 2023/03/01 17:34:33 - mmengine - INFO - Epoch(train) [119][3800/5047] lr: 1.0626e-05 eta: 1 day, 14:12:33 time: 0.8122 data_time: 0.0027 memory: 42965 loss: 0.1008 loss_ce: 0.1008 2023/03/01 17:35:59 - mmengine - INFO - Epoch(train) [119][3900/5047] lr: 1.0626e-05 eta: 1 day, 14:11:06 time: 0.8763 data_time: 0.0025 memory: 42239 loss: 0.1151 loss_ce: 0.1151 2023/03/01 17:37:25 - mmengine - INFO - Epoch(train) [119][4000/5047] lr: 1.0626e-05 eta: 1 day, 14:09:38 time: 0.8169 data_time: 0.0027 memory: 55114 loss: 0.1081 loss_ce: 0.1081 2023/03/01 17:38:50 - mmengine - INFO - Epoch(train) [119][4100/5047] lr: 1.0626e-05 eta: 1 day, 14:08:10 time: 0.8775 data_time: 0.0032 memory: 48053 loss: 0.1051 loss_ce: 0.1051 2023/03/01 17:40:15 - mmengine - INFO - Epoch(train) [119][4200/5047] lr: 1.0626e-05 eta: 1 day, 14:06:42 time: 0.8818 data_time: 0.0027 memory: 42965 loss: 0.1027 loss_ce: 0.1027 2023/03/01 17:41:42 - mmengine - INFO - Epoch(train) [119][4300/5047] lr: 1.0626e-05 eta: 1 day, 14:05:15 time: 0.8388 data_time: 0.0050 memory: 51637 loss: 0.0963 loss_ce: 0.0963 2023/03/01 17:43:09 - mmengine - INFO - Epoch(train) [119][4400/5047] lr: 1.0626e-05 eta: 1 day, 14:03:48 time: 0.8235 data_time: 0.0025 memory: 44821 loss: 0.1168 loss_ce: 0.1168 2023/03/01 17:43:56 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 17:44:36 - mmengine - INFO - Epoch(train) [119][4500/5047] lr: 1.0626e-05 eta: 1 day, 14:02:21 time: 0.8681 data_time: 0.0047 memory: 42649 loss: 0.0974 loss_ce: 0.0974 2023/03/01 17:46:02 - mmengine - INFO - Epoch(train) [119][4600/5047] lr: 1.0626e-05 eta: 1 day, 14:00:53 time: 0.8247 data_time: 0.0041 memory: 47021 loss: 0.0936 loss_ce: 0.0936 2023/03/01 17:47:27 - mmengine - INFO - Epoch(train) [119][4700/5047] lr: 1.0626e-05 eta: 1 day, 13:59:25 time: 0.8773 data_time: 0.0026 memory: 43324 loss: 0.0954 loss_ce: 0.0954 2023/03/01 17:48:53 - mmengine - INFO - Epoch(train) [119][4800/5047] lr: 1.0626e-05 eta: 1 day, 13:57:57 time: 0.8328 data_time: 0.0027 memory: 44539 loss: 0.0971 loss_ce: 0.0971 2023/03/01 17:50:19 - mmengine - INFO - Epoch(train) [119][4900/5047] lr: 1.0626e-05 eta: 1 day, 13:56:30 time: 0.9462 data_time: 0.0030 memory: 43613 loss: 0.1009 loss_ce: 0.1009 2023/03/01 17:51:44 - mmengine - INFO - Epoch(train) [119][5000/5047] lr: 1.0626e-05 eta: 1 day, 13:55:02 time: 0.8514 data_time: 0.0025 memory: 45902 loss: 0.0975 loss_ce: 0.0975 2023/03/01 17:52:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 17:52:24 - mmengine - INFO - Saving checkpoint at 119 epochs 2023/03/01 17:53:56 - mmengine - INFO - Epoch(train) [120][ 100/5047] lr: 1.0426e-05 eta: 1 day, 13:52:53 time: 0.8382 data_time: 0.0025 memory: 46713 loss: 0.1046 loss_ce: 0.1046 2023/03/01 17:55:20 - mmengine - INFO - Epoch(train) [120][ 200/5047] lr: 1.0426e-05 eta: 1 day, 13:51:25 time: 0.8106 data_time: 0.0026 memory: 46782 loss: 0.1050 loss_ce: 0.1050 2023/03/01 17:56:46 - mmengine - INFO - Epoch(train) [120][ 300/5047] lr: 1.0426e-05 eta: 1 day, 13:49:58 time: 0.8601 data_time: 0.0029 memory: 45305 loss: 0.1037 loss_ce: 0.1037 2023/03/01 17:58:11 - mmengine - INFO - Epoch(train) [120][ 400/5047] lr: 1.0426e-05 eta: 1 day, 13:48:30 time: 0.8749 data_time: 0.0040 memory: 54303 loss: 0.1022 loss_ce: 0.1022 2023/03/01 17:58:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 17:59:37 - mmengine - INFO - Epoch(train) [120][ 500/5047] lr: 1.0426e-05 eta: 1 day, 13:47:02 time: 0.8466 data_time: 0.0026 memory: 42024 loss: 0.1048 loss_ce: 0.1048 2023/03/01 18:01:03 - mmengine - INFO - Epoch(train) [120][ 600/5047] lr: 1.0426e-05 eta: 1 day, 13:45:35 time: 0.8339 data_time: 0.0025 memory: 42965 loss: 0.1005 loss_ce: 0.1005 2023/03/01 18:02:29 - mmengine - INFO - Epoch(train) [120][ 700/5047] lr: 1.0426e-05 eta: 1 day, 13:44:07 time: 0.8869 data_time: 0.0026 memory: 42649 loss: 0.1125 loss_ce: 0.1125 2023/03/01 18:03:54 - mmengine - INFO - Epoch(train) [120][ 800/5047] lr: 1.0426e-05 eta: 1 day, 13:42:39 time: 0.8633 data_time: 0.0104 memory: 42024 loss: 0.1014 loss_ce: 0.1014 2023/03/01 18:05:19 - mmengine - INFO - Epoch(train) [120][ 900/5047] lr: 1.0426e-05 eta: 1 day, 13:41:11 time: 0.8557 data_time: 0.0026 memory: 46027 loss: 0.1163 loss_ce: 0.1163 2023/03/01 18:06:46 - mmengine - INFO - Epoch(train) [120][1000/5047] lr: 1.0426e-05 eta: 1 day, 13:39:44 time: 0.8620 data_time: 0.0066 memory: 49312 loss: 0.1126 loss_ce: 0.1126 2023/03/01 18:08:10 - mmengine - INFO - Epoch(train) [120][1100/5047] lr: 1.0426e-05 eta: 1 day, 13:38:16 time: 0.8758 data_time: 0.0038 memory: 41150 loss: 0.1104 loss_ce: 0.1104 2023/03/01 18:09:36 - mmengine - INFO - Epoch(train) [120][1200/5047] lr: 1.0426e-05 eta: 1 day, 13:36:48 time: 0.8429 data_time: 0.0025 memory: 42387 loss: 0.0973 loss_ce: 0.0973 2023/03/01 18:11:01 - mmengine - INFO - Epoch(train) [120][1300/5047] lr: 1.0426e-05 eta: 1 day, 13:35:21 time: 0.8464 data_time: 0.0024 memory: 41419 loss: 0.1096 loss_ce: 0.1096 2023/03/01 18:12:27 - mmengine - INFO - Epoch(train) [120][1400/5047] lr: 1.0426e-05 eta: 1 day, 13:33:53 time: 0.8718 data_time: 0.0031 memory: 42649 loss: 0.1084 loss_ce: 0.1084 2023/03/01 18:12:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 18:13:54 - mmengine - INFO - Epoch(train) [120][1500/5047] lr: 1.0426e-05 eta: 1 day, 13:32:26 time: 0.8150 data_time: 0.0026 memory: 45957 loss: 0.0967 loss_ce: 0.0967 2023/03/01 18:15:20 - mmengine - INFO - Epoch(train) [120][1600/5047] lr: 1.0426e-05 eta: 1 day, 13:30:58 time: 0.8303 data_time: 0.0024 memory: 42024 loss: 0.1064 loss_ce: 0.1064 2023/03/01 18:16:46 - mmengine - INFO - Epoch(train) [120][1700/5047] lr: 1.0426e-05 eta: 1 day, 13:29:31 time: 0.8858 data_time: 0.0026 memory: 50106 loss: 0.1097 loss_ce: 0.1097 2023/03/01 18:18:10 - mmengine - INFO - Epoch(train) [120][1800/5047] lr: 1.0426e-05 eta: 1 day, 13:28:03 time: 0.8104 data_time: 0.0027 memory: 41724 loss: 0.1087 loss_ce: 0.1087 2023/03/01 18:19:37 - mmengine - INFO - Epoch(train) [120][1900/5047] lr: 1.0426e-05 eta: 1 day, 13:26:35 time: 0.8654 data_time: 0.0027 memory: 51586 loss: 0.1034 loss_ce: 0.1034 2023/03/01 18:21:03 - mmengine - INFO - Epoch(train) [120][2000/5047] lr: 1.0426e-05 eta: 1 day, 13:25:08 time: 0.8063 data_time: 0.0030 memory: 47813 loss: 0.1053 loss_ce: 0.1053 2023/03/01 18:22:29 - mmengine - INFO - Epoch(train) [120][2100/5047] lr: 1.0426e-05 eta: 1 day, 13:23:40 time: 0.8505 data_time: 0.0028 memory: 55562 loss: 0.1075 loss_ce: 0.1075 2023/03/01 18:23:54 - mmengine - INFO - Epoch(train) [120][2200/5047] lr: 1.0426e-05 eta: 1 day, 13:22:12 time: 0.8437 data_time: 0.0050 memory: 42997 loss: 0.1127 loss_ce: 0.1127 2023/03/01 18:25:20 - mmengine - INFO - Epoch(train) [120][2300/5047] lr: 1.0426e-05 eta: 1 day, 13:20:45 time: 0.8797 data_time: 0.0029 memory: 46794 loss: 0.1200 loss_ce: 0.1200 2023/03/01 18:26:46 - mmengine - INFO - Epoch(train) [120][2400/5047] lr: 1.0426e-05 eta: 1 day, 13:19:17 time: 0.8833 data_time: 0.0040 memory: 55562 loss: 0.1118 loss_ce: 0.1118 2023/03/01 18:26:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 18:28:12 - mmengine - INFO - Epoch(train) [120][2500/5047] lr: 1.0426e-05 eta: 1 day, 13:17:50 time: 0.8682 data_time: 0.0053 memory: 40564 loss: 0.1008 loss_ce: 0.1008 2023/03/01 18:29:38 - mmengine - INFO - Epoch(train) [120][2600/5047] lr: 1.0426e-05 eta: 1 day, 13:16:22 time: 0.8938 data_time: 0.0026 memory: 41626 loss: 0.1041 loss_ce: 0.1041 2023/03/01 18:31:03 - mmengine - INFO - Epoch(train) [120][2700/5047] lr: 1.0426e-05 eta: 1 day, 13:14:54 time: 0.9016 data_time: 0.0037 memory: 45850 loss: 0.1004 loss_ce: 0.1004 2023/03/01 18:32:29 - mmengine - INFO - Epoch(train) [120][2800/5047] lr: 1.0426e-05 eta: 1 day, 13:13:27 time: 0.8690 data_time: 0.0040 memory: 43613 loss: 0.1071 loss_ce: 0.1071 2023/03/01 18:33:54 - mmengine - INFO - Epoch(train) [120][2900/5047] lr: 1.0426e-05 eta: 1 day, 13:11:59 time: 0.8413 data_time: 0.0028 memory: 42965 loss: 0.1030 loss_ce: 0.1030 2023/03/01 18:35:20 - mmengine - INFO - Epoch(train) [120][3000/5047] lr: 1.0426e-05 eta: 1 day, 13:10:31 time: 0.8403 data_time: 0.0057 memory: 42369 loss: 0.1163 loss_ce: 0.1163 2023/03/01 18:36:46 - mmengine - INFO - Epoch(train) [120][3100/5047] lr: 1.0426e-05 eta: 1 day, 13:09:04 time: 0.8202 data_time: 0.0045 memory: 49277 loss: 0.1050 loss_ce: 0.1050 2023/03/01 18:38:14 - mmengine - INFO - Epoch(train) [120][3200/5047] lr: 1.0426e-05 eta: 1 day, 13:07:37 time: 0.9045 data_time: 0.0051 memory: 55562 loss: 0.0988 loss_ce: 0.0988 2023/03/01 18:39:41 - mmengine - INFO - Epoch(train) [120][3300/5047] lr: 1.0426e-05 eta: 1 day, 13:06:10 time: 0.8690 data_time: 0.0029 memory: 55562 loss: 0.1073 loss_ce: 0.1073 2023/03/01 18:41:07 - mmengine - INFO - Epoch(train) [120][3400/5047] lr: 1.0426e-05 eta: 1 day, 13:04:42 time: 0.8533 data_time: 0.0104 memory: 43420 loss: 0.1139 loss_ce: 0.1139 2023/03/01 18:41:13 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 18:42:32 - mmengine - INFO - Epoch(train) [120][3500/5047] lr: 1.0426e-05 eta: 1 day, 13:03:14 time: 0.8271 data_time: 0.0027 memory: 46713 loss: 0.1120 loss_ce: 0.1120 2023/03/01 18:43:57 - mmengine - INFO - Epoch(train) [120][3600/5047] lr: 1.0426e-05 eta: 1 day, 13:01:46 time: 0.8397 data_time: 0.0037 memory: 55562 loss: 0.1024 loss_ce: 0.1024 2023/03/01 18:45:22 - mmengine - INFO - Epoch(train) [120][3700/5047] lr: 1.0426e-05 eta: 1 day, 13:00:18 time: 0.8678 data_time: 0.0026 memory: 44707 loss: 0.0982 loss_ce: 0.0982 2023/03/01 18:46:48 - mmengine - INFO - Epoch(train) [120][3800/5047] lr: 1.0426e-05 eta: 1 day, 12:58:51 time: 0.9141 data_time: 0.0028 memory: 42965 loss: 0.1110 loss_ce: 0.1110 2023/03/01 18:48:13 - mmengine - INFO - Epoch(train) [120][3900/5047] lr: 1.0426e-05 eta: 1 day, 12:57:23 time: 0.8600 data_time: 0.0028 memory: 42336 loss: 0.0948 loss_ce: 0.0948 2023/03/01 18:49:39 - mmengine - INFO - Epoch(train) [120][4000/5047] lr: 1.0426e-05 eta: 1 day, 12:55:56 time: 0.8621 data_time: 0.0039 memory: 42024 loss: 0.1023 loss_ce: 0.1023 2023/03/01 18:51:05 - mmengine - INFO - Epoch(train) [120][4100/5047] lr: 1.0426e-05 eta: 1 day, 12:54:28 time: 0.9151 data_time: 0.0028 memory: 49715 loss: 0.1017 loss_ce: 0.1017 2023/03/01 18:52:31 - mmengine - INFO - Epoch(train) [120][4200/5047] lr: 1.0426e-05 eta: 1 day, 12:53:01 time: 0.8753 data_time: 0.0031 memory: 49715 loss: 0.1102 loss_ce: 0.1102 2023/03/01 18:53:57 - mmengine - INFO - Epoch(train) [120][4300/5047] lr: 1.0426e-05 eta: 1 day, 12:51:33 time: 0.8583 data_time: 0.0036 memory: 44617 loss: 0.1061 loss_ce: 0.1061 2023/03/01 18:55:23 - mmengine - INFO - Epoch(train) [120][4400/5047] lr: 1.0426e-05 eta: 1 day, 12:50:05 time: 0.8472 data_time: 0.0027 memory: 55562 loss: 0.0984 loss_ce: 0.0984 2023/03/01 18:55:29 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 18:56:48 - mmengine - INFO - Epoch(train) [120][4500/5047] lr: 1.0426e-05 eta: 1 day, 12:48:37 time: 0.8291 data_time: 0.0029 memory: 43947 loss: 0.1129 loss_ce: 0.1129 2023/03/01 18:58:13 - mmengine - INFO - Epoch(train) [120][4600/5047] lr: 1.0426e-05 eta: 1 day, 12:47:10 time: 0.8599 data_time: 0.0052 memory: 41122 loss: 0.0965 loss_ce: 0.0965 2023/03/01 18:59:39 - mmengine - INFO - Epoch(train) [120][4700/5047] lr: 1.0426e-05 eta: 1 day, 12:45:42 time: 0.8604 data_time: 0.0042 memory: 44202 loss: 0.1002 loss_ce: 0.1002 2023/03/01 19:01:04 - mmengine - INFO - Epoch(train) [120][4800/5047] lr: 1.0426e-05 eta: 1 day, 12:44:14 time: 0.7928 data_time: 0.0056 memory: 47813 loss: 0.1278 loss_ce: 0.1278 2023/03/01 19:02:30 - mmengine - INFO - Epoch(train) [120][4900/5047] lr: 1.0426e-05 eta: 1 day, 12:42:47 time: 0.8440 data_time: 0.0030 memory: 43947 loss: 0.1234 loss_ce: 0.1234 2023/03/01 19:03:56 - mmengine - INFO - Epoch(train) [120][5000/5047] lr: 1.0426e-05 eta: 1 day, 12:41:19 time: 0.8168 data_time: 0.0029 memory: 47267 loss: 0.1104 loss_ce: 0.1104 2023/03/01 19:04:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 19:04:37 - mmengine - INFO - Saving checkpoint at 120 epochs 2023/03/01 19:06:09 - mmengine - INFO - Epoch(train) [121][ 100/5047] lr: 1.0225e-05 eta: 1 day, 12:39:11 time: 0.8480 data_time: 0.0029 memory: 51732 loss: 0.1186 loss_ce: 0.1186 2023/03/01 19:07:36 - mmengine - INFO - Epoch(train) [121][ 200/5047] lr: 1.0225e-05 eta: 1 day, 12:37:44 time: 0.8214 data_time: 0.0025 memory: 40739 loss: 0.0945 loss_ce: 0.0945 2023/03/01 19:09:02 - mmengine - INFO - Epoch(train) [121][ 300/5047] lr: 1.0225e-05 eta: 1 day, 12:36:16 time: 0.8589 data_time: 0.0050 memory: 50106 loss: 0.0976 loss_ce: 0.0976 2023/03/01 19:09:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 19:10:29 - mmengine - INFO - Epoch(train) [121][ 400/5047] lr: 1.0225e-05 eta: 1 day, 12:34:49 time: 0.8498 data_time: 0.0026 memory: 41419 loss: 0.0996 loss_ce: 0.0996 2023/03/01 19:11:54 - mmengine - INFO - Epoch(train) [121][ 500/5047] lr: 1.0225e-05 eta: 1 day, 12:33:21 time: 0.8275 data_time: 0.0028 memory: 45945 loss: 0.1021 loss_ce: 0.1021 2023/03/01 19:13:20 - mmengine - INFO - Epoch(train) [121][ 600/5047] lr: 1.0225e-05 eta: 1 day, 12:31:54 time: 0.8406 data_time: 0.0080 memory: 45102 loss: 0.1053 loss_ce: 0.1053 2023/03/01 19:14:47 - mmengine - INFO - Epoch(train) [121][ 700/5047] lr: 1.0225e-05 eta: 1 day, 12:30:26 time: 0.8469 data_time: 0.0035 memory: 46878 loss: 0.1027 loss_ce: 0.1027 2023/03/01 19:16:12 - mmengine - INFO - Epoch(train) [121][ 800/5047] lr: 1.0225e-05 eta: 1 day, 12:28:59 time: 0.8502 data_time: 0.0057 memory: 44498 loss: 0.1004 loss_ce: 0.1004 2023/03/01 19:17:39 - mmengine - INFO - Epoch(train) [121][ 900/5047] lr: 1.0225e-05 eta: 1 day, 12:27:31 time: 0.8483 data_time: 0.0028 memory: 43926 loss: 0.1013 loss_ce: 0.1013 2023/03/01 19:19:05 - mmengine - INFO - Epoch(train) [121][1000/5047] lr: 1.0225e-05 eta: 1 day, 12:26:04 time: 0.8227 data_time: 0.0028 memory: 45302 loss: 0.1168 loss_ce: 0.1168 2023/03/01 19:20:33 - mmengine - INFO - Epoch(train) [121][1100/5047] lr: 1.0225e-05 eta: 1 day, 12:24:37 time: 0.8820 data_time: 0.0027 memory: 48116 loss: 0.1086 loss_ce: 0.1086 2023/03/01 19:22:00 - mmengine - INFO - Epoch(train) [121][1200/5047] lr: 1.0225e-05 eta: 1 day, 12:23:09 time: 0.8962 data_time: 0.0024 memory: 41444 loss: 0.1082 loss_ce: 0.1082 2023/03/01 19:23:25 - mmengine - INFO - Epoch(train) [121][1300/5047] lr: 1.0225e-05 eta: 1 day, 12:21:42 time: 0.8490 data_time: 0.0028 memory: 42368 loss: 0.1042 loss_ce: 0.1042 2023/03/01 19:24:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 19:24:50 - mmengine - INFO - Epoch(train) [121][1400/5047] lr: 1.0225e-05 eta: 1 day, 12:20:14 time: 0.8632 data_time: 0.0034 memory: 43947 loss: 0.1001 loss_ce: 0.1001 2023/03/01 19:26:15 - mmengine - INFO - Epoch(train) [121][1500/5047] lr: 1.0225e-05 eta: 1 day, 12:18:46 time: 0.8317 data_time: 0.0027 memory: 47447 loss: 0.1201 loss_ce: 0.1201 2023/03/01 19:27:39 - mmengine - INFO - Epoch(train) [121][1600/5047] lr: 1.0225e-05 eta: 1 day, 12:17:18 time: 0.8388 data_time: 0.0030 memory: 42143 loss: 0.1029 loss_ce: 0.1029 2023/03/01 19:29:05 - mmengine - INFO - Epoch(train) [121][1700/5047] lr: 1.0225e-05 eta: 1 day, 12:15:51 time: 0.8314 data_time: 0.0029 memory: 41241 loss: 0.1035 loss_ce: 0.1035 2023/03/01 19:30:31 - mmengine - INFO - Epoch(train) [121][1800/5047] lr: 1.0225e-05 eta: 1 day, 12:14:23 time: 0.8285 data_time: 0.0028 memory: 48030 loss: 0.1078 loss_ce: 0.1078 2023/03/01 19:31:57 - mmengine - INFO - Epoch(train) [121][1900/5047] lr: 1.0225e-05 eta: 1 day, 12:12:56 time: 0.8594 data_time: 0.0029 memory: 43289 loss: 0.1099 loss_ce: 0.1099 2023/03/01 19:33:22 - mmengine - INFO - Epoch(train) [121][2000/5047] lr: 1.0225e-05 eta: 1 day, 12:11:28 time: 0.8574 data_time: 0.0024 memory: 42024 loss: 0.1043 loss_ce: 0.1043 2023/03/01 19:34:49 - mmengine - INFO - Epoch(train) [121][2100/5047] lr: 1.0225e-05 eta: 1 day, 12:10:00 time: 0.8196 data_time: 0.0063 memory: 49500 loss: 0.0999 loss_ce: 0.0999 2023/03/01 19:36:16 - mmengine - INFO - Epoch(train) [121][2200/5047] lr: 1.0225e-05 eta: 1 day, 12:08:33 time: 0.8705 data_time: 0.0029 memory: 44278 loss: 0.1040 loss_ce: 0.1040 2023/03/01 19:37:42 - mmengine - INFO - Epoch(train) [121][2300/5047] lr: 1.0225e-05 eta: 1 day, 12:07:06 time: 0.8717 data_time: 0.0058 memory: 43289 loss: 0.0958 loss_ce: 0.0958 2023/03/01 19:38:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 19:39:08 - mmengine - INFO - Epoch(train) [121][2400/5047] lr: 1.0225e-05 eta: 1 day, 12:05:38 time: 0.8182 data_time: 0.0037 memory: 51248 loss: 0.1179 loss_ce: 0.1179 2023/03/01 19:40:34 - mmengine - INFO - Epoch(train) [121][2500/5047] lr: 1.0225e-05 eta: 1 day, 12:04:11 time: 0.8697 data_time: 0.0075 memory: 51719 loss: 0.1129 loss_ce: 0.1129 2023/03/01 19:41:59 - mmengine - INFO - Epoch(train) [121][2600/5047] lr: 1.0225e-05 eta: 1 day, 12:02:43 time: 0.8245 data_time: 0.0030 memory: 41419 loss: 0.0987 loss_ce: 0.0987 2023/03/01 19:43:26 - mmengine - INFO - Epoch(train) [121][2700/5047] lr: 1.0225e-05 eta: 1 day, 12:01:15 time: 0.8394 data_time: 0.0028 memory: 50419 loss: 0.1217 loss_ce: 0.1217 2023/03/01 19:44:52 - mmengine - INFO - Epoch(train) [121][2800/5047] lr: 1.0225e-05 eta: 1 day, 11:59:48 time: 0.8686 data_time: 0.0094 memory: 41419 loss: 0.1250 loss_ce: 0.1250 2023/03/01 19:46:19 - mmengine - INFO - Epoch(train) [121][2900/5047] lr: 1.0225e-05 eta: 1 day, 11:58:21 time: 0.8509 data_time: 0.0027 memory: 40535 loss: 0.1048 loss_ce: 0.1048 2023/03/01 19:47:45 - mmengine - INFO - Epoch(train) [121][3000/5047] lr: 1.0225e-05 eta: 1 day, 11:56:53 time: 0.8434 data_time: 0.0026 memory: 49334 loss: 0.1150 loss_ce: 0.1150 2023/03/01 19:49:10 - mmengine - INFO - Epoch(train) [121][3100/5047] lr: 1.0225e-05 eta: 1 day, 11:55:26 time: 0.8386 data_time: 0.0029 memory: 43895 loss: 0.1159 loss_ce: 0.1159 2023/03/01 19:50:35 - mmengine - INFO - Epoch(train) [121][3200/5047] lr: 1.0225e-05 eta: 1 day, 11:53:58 time: 0.8378 data_time: 0.0029 memory: 44440 loss: 0.1230 loss_ce: 0.1230 2023/03/01 19:52:01 - mmengine - INFO - Epoch(train) [121][3300/5047] lr: 1.0225e-05 eta: 1 day, 11:52:30 time: 0.8483 data_time: 0.0028 memory: 40825 loss: 0.1047 loss_ce: 0.1047 2023/03/01 19:52:53 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 19:53:27 - mmengine - INFO - Epoch(train) [121][3400/5047] lr: 1.0225e-05 eta: 1 day, 11:51:03 time: 0.8659 data_time: 0.0029 memory: 42965 loss: 0.0967 loss_ce: 0.0967 2023/03/01 19:54:52 - mmengine - INFO - Epoch(train) [121][3500/5047] lr: 1.0225e-05 eta: 1 day, 11:49:35 time: 0.8284 data_time: 0.0035 memory: 38894 loss: 0.1198 loss_ce: 0.1198 2023/03/01 19:56:18 - mmengine - INFO - Epoch(train) [121][3600/5047] lr: 1.0225e-05 eta: 1 day, 11:48:07 time: 0.9140 data_time: 0.0027 memory: 43289 loss: 0.1070 loss_ce: 0.1070 2023/03/01 19:57:43 - mmengine - INFO - Epoch(train) [121][3700/5047] lr: 1.0225e-05 eta: 1 day, 11:46:39 time: 0.8663 data_time: 0.0033 memory: 55562 loss: 0.1202 loss_ce: 0.1202 2023/03/01 19:59:09 - mmengine - INFO - Epoch(train) [121][3800/5047] lr: 1.0225e-05 eta: 1 day, 11:45:12 time: 0.8403 data_time: 0.0054 memory: 40535 loss: 0.1012 loss_ce: 0.1012 2023/03/01 20:00:34 - mmengine - INFO - Epoch(train) [121][3900/5047] lr: 1.0225e-05 eta: 1 day, 11:43:44 time: 0.8961 data_time: 0.0029 memory: 55562 loss: 0.0925 loss_ce: 0.0925 2023/03/01 20:02:01 - mmengine - INFO - Epoch(train) [121][4000/5047] lr: 1.0225e-05 eta: 1 day, 11:42:17 time: 0.9447 data_time: 0.0027 memory: 52964 loss: 0.0946 loss_ce: 0.0946 2023/03/01 20:03:27 - mmengine - INFO - Epoch(train) [121][4100/5047] lr: 1.0225e-05 eta: 1 day, 11:40:49 time: 0.8480 data_time: 0.0026 memory: 55562 loss: 0.1110 loss_ce: 0.1110 2023/03/01 20:04:54 - mmengine - INFO - Epoch(train) [121][4200/5047] lr: 1.0225e-05 eta: 1 day, 11:39:22 time: 0.8480 data_time: 0.0064 memory: 54673 loss: 0.1058 loss_ce: 0.1058 2023/03/01 20:06:20 - mmengine - INFO - Epoch(train) [121][4300/5047] lr: 1.0225e-05 eta: 1 day, 11:37:55 time: 0.7939 data_time: 0.0034 memory: 43363 loss: 0.1092 loss_ce: 0.1092 2023/03/01 20:07:12 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 20:07:47 - mmengine - INFO - Epoch(train) [121][4400/5047] lr: 1.0225e-05 eta: 1 day, 11:36:28 time: 0.8346 data_time: 0.0030 memory: 42463 loss: 0.1057 loss_ce: 0.1057 2023/03/01 20:09:13 - mmengine - INFO - Epoch(train) [121][4500/5047] lr: 1.0225e-05 eta: 1 day, 11:35:00 time: 0.8052 data_time: 0.0027 memory: 46904 loss: 0.1181 loss_ce: 0.1181 2023/03/01 20:10:38 - mmengine - INFO - Epoch(train) [121][4600/5047] lr: 1.0225e-05 eta: 1 day, 11:33:32 time: 0.8499 data_time: 0.0050 memory: 43289 loss: 0.1147 loss_ce: 0.1147 2023/03/01 20:12:04 - mmengine - INFO - Epoch(train) [121][4700/5047] lr: 1.0225e-05 eta: 1 day, 11:32:05 time: 0.9005 data_time: 0.0025 memory: 41514 loss: 0.0995 loss_ce: 0.0995 2023/03/01 20:13:31 - mmengine - INFO - Epoch(train) [121][4800/5047] lr: 1.0225e-05 eta: 1 day, 11:30:37 time: 0.8509 data_time: 0.0035 memory: 40241 loss: 0.1058 loss_ce: 0.1058 2023/03/01 20:14:57 - mmengine - INFO - Epoch(train) [121][4900/5047] lr: 1.0225e-05 eta: 1 day, 11:29:10 time: 0.8810 data_time: 0.0029 memory: 50106 loss: 0.1096 loss_ce: 0.1096 2023/03/01 20:16:24 - mmengine - INFO - Epoch(train) [121][5000/5047] lr: 1.0225e-05 eta: 1 day, 11:27:42 time: 0.8891 data_time: 0.0028 memory: 44617 loss: 0.1028 loss_ce: 0.1028 2023/03/01 20:17:05 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 20:17:05 - mmengine - INFO - Saving checkpoint at 121 epochs 2023/03/01 20:18:39 - mmengine - INFO - Epoch(train) [122][ 100/5047] lr: 1.0024e-05 eta: 1 day, 11:25:35 time: 0.8785 data_time: 0.0039 memory: 41419 loss: 0.0978 loss_ce: 0.0978 2023/03/01 20:20:08 - mmengine - INFO - Epoch(train) [122][ 200/5047] lr: 1.0024e-05 eta: 1 day, 11:24:08 time: 0.8953 data_time: 0.0032 memory: 48210 loss: 0.1136 loss_ce: 0.1136 2023/03/01 20:21:36 - mmengine - INFO - Epoch(train) [122][ 300/5047] lr: 1.0024e-05 eta: 1 day, 11:22:41 time: 0.8461 data_time: 0.0028 memory: 44956 loss: 0.1060 loss_ce: 0.1060 2023/03/01 20:21:47 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 20:23:02 - mmengine - INFO - Epoch(train) [122][ 400/5047] lr: 1.0024e-05 eta: 1 day, 11:21:13 time: 0.8632 data_time: 0.0032 memory: 46713 loss: 0.1060 loss_ce: 0.1060 2023/03/01 20:24:28 - mmengine - INFO - Epoch(train) [122][ 500/5047] lr: 1.0024e-05 eta: 1 day, 11:19:46 time: 0.8533 data_time: 0.0034 memory: 42675 loss: 0.1085 loss_ce: 0.1085 2023/03/01 20:25:54 - mmengine - INFO - Epoch(train) [122][ 600/5047] lr: 1.0024e-05 eta: 1 day, 11:18:18 time: 0.8873 data_time: 0.0027 memory: 53970 loss: 0.1073 loss_ce: 0.1073 2023/03/01 20:27:21 - mmengine - INFO - Epoch(train) [122][ 700/5047] lr: 1.0024e-05 eta: 1 day, 11:16:51 time: 0.8705 data_time: 0.0030 memory: 49715 loss: 0.1035 loss_ce: 0.1035 2023/03/01 20:28:46 - mmengine - INFO - Epoch(train) [122][ 800/5047] lr: 1.0024e-05 eta: 1 day, 11:15:23 time: 0.8824 data_time: 0.0029 memory: 41122 loss: 0.1106 loss_ce: 0.1106 2023/03/01 20:30:13 - mmengine - INFO - Epoch(train) [122][ 900/5047] lr: 1.0024e-05 eta: 1 day, 11:13:56 time: 0.9341 data_time: 0.0027 memory: 46893 loss: 0.1045 loss_ce: 0.1045 2023/03/01 20:31:38 - mmengine - INFO - Epoch(train) [122][1000/5047] lr: 1.0024e-05 eta: 1 day, 11:12:28 time: 0.8389 data_time: 0.0025 memory: 41419 loss: 0.0969 loss_ce: 0.0969 2023/03/01 20:33:07 - mmengine - INFO - Epoch(train) [122][1100/5047] lr: 1.0024e-05 eta: 1 day, 11:11:02 time: 0.9561 data_time: 0.0030 memory: 41257 loss: 0.1329 loss_ce: 0.1329 2023/03/01 20:34:32 - mmengine - INFO - Epoch(train) [122][1200/5047] lr: 1.0024e-05 eta: 1 day, 11:09:34 time: 0.8169 data_time: 0.0028 memory: 46355 loss: 0.1041 loss_ce: 0.1041 2023/03/01 20:35:58 - mmengine - INFO - Epoch(train) [122][1300/5047] lr: 1.0024e-05 eta: 1 day, 11:08:06 time: 0.8513 data_time: 0.0027 memory: 55562 loss: 0.1008 loss_ce: 0.1008 2023/03/01 20:36:09 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 20:37:26 - mmengine - INFO - Epoch(train) [122][1400/5047] lr: 1.0024e-05 eta: 1 day, 11:06:39 time: 0.8805 data_time: 0.0025 memory: 44278 loss: 0.1227 loss_ce: 0.1227 2023/03/01 20:38:53 - mmengine - INFO - Epoch(train) [122][1500/5047] lr: 1.0024e-05 eta: 1 day, 11:05:12 time: 0.8401 data_time: 0.0026 memory: 47447 loss: 0.1060 loss_ce: 0.1060 2023/03/01 20:40:19 - mmengine - INFO - Epoch(train) [122][1600/5047] lr: 1.0024e-05 eta: 1 day, 11:03:44 time: 0.9122 data_time: 0.0027 memory: 39884 loss: 0.0977 loss_ce: 0.0977 2023/03/01 20:41:45 - mmengine - INFO - Epoch(train) [122][1700/5047] lr: 1.0024e-05 eta: 1 day, 11:02:17 time: 0.8445 data_time: 0.0026 memory: 46910 loss: 0.1364 loss_ce: 0.1364 2023/03/01 20:43:09 - mmengine - INFO - Epoch(train) [122][1800/5047] lr: 1.0024e-05 eta: 1 day, 11:00:49 time: 0.8187 data_time: 0.0033 memory: 39681 loss: 0.1113 loss_ce: 0.1113 2023/03/01 20:44:34 - mmengine - INFO - Epoch(train) [122][1900/5047] lr: 1.0024e-05 eta: 1 day, 10:59:21 time: 0.8388 data_time: 0.0029 memory: 42336 loss: 0.1206 loss_ce: 0.1206 2023/03/01 20:46:00 - mmengine - INFO - Epoch(train) [122][2000/5047] lr: 1.0024e-05 eta: 1 day, 10:57:54 time: 0.8070 data_time: 0.0032 memory: 45742 loss: 0.1232 loss_ce: 0.1232 2023/03/01 20:47:26 - mmengine - INFO - Epoch(train) [122][2100/5047] lr: 1.0024e-05 eta: 1 day, 10:56:26 time: 0.7937 data_time: 0.0028 memory: 45302 loss: 0.1029 loss_ce: 0.1029 2023/03/01 20:48:51 - mmengine - INFO - Epoch(train) [122][2200/5047] lr: 1.0024e-05 eta: 1 day, 10:54:58 time: 0.8460 data_time: 0.0045 memory: 44617 loss: 0.1065 loss_ce: 0.1065 2023/03/01 20:50:15 - mmengine - INFO - Epoch(train) [122][2300/5047] lr: 1.0024e-05 eta: 1 day, 10:53:31 time: 0.8170 data_time: 0.0035 memory: 44238 loss: 0.1067 loss_ce: 0.1067 2023/03/01 20:50:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 20:51:41 - mmengine - INFO - Epoch(train) [122][2400/5047] lr: 1.0024e-05 eta: 1 day, 10:52:03 time: 0.8605 data_time: 0.0029 memory: 42698 loss: 0.1044 loss_ce: 0.1044 2023/03/01 20:53:08 - mmengine - INFO - Epoch(train) [122][2500/5047] lr: 1.0024e-05 eta: 1 day, 10:50:36 time: 0.8761 data_time: 0.0026 memory: 45851 loss: 0.1006 loss_ce: 0.1006 2023/03/01 20:54:37 - mmengine - INFO - Epoch(train) [122][2600/5047] lr: 1.0024e-05 eta: 1 day, 10:49:09 time: 0.8549 data_time: 0.0054 memory: 47066 loss: 0.1173 loss_ce: 0.1173 2023/03/01 20:56:02 - mmengine - INFO - Epoch(train) [122][2700/5047] lr: 1.0024e-05 eta: 1 day, 10:47:41 time: 0.8705 data_time: 0.0080 memory: 49312 loss: 0.1064 loss_ce: 0.1064 2023/03/01 20:57:29 - mmengine - INFO - Epoch(train) [122][2800/5047] lr: 1.0024e-05 eta: 1 day, 10:46:14 time: 0.8176 data_time: 0.0026 memory: 41724 loss: 0.1261 loss_ce: 0.1261 2023/03/01 20:58:55 - mmengine - INFO - Epoch(train) [122][2900/5047] lr: 1.0024e-05 eta: 1 day, 10:44:46 time: 0.8262 data_time: 0.0026 memory: 41419 loss: 0.1007 loss_ce: 0.1007 2023/03/01 21:00:20 - mmengine - INFO - Epoch(train) [122][3000/5047] lr: 1.0024e-05 eta: 1 day, 10:43:19 time: 0.8341 data_time: 0.0027 memory: 42965 loss: 0.1122 loss_ce: 0.1122 2023/03/01 21:01:47 - mmengine - INFO - Epoch(train) [122][3100/5047] lr: 1.0024e-05 eta: 1 day, 10:41:51 time: 0.8526 data_time: 0.0065 memory: 42965 loss: 0.1183 loss_ce: 0.1183 2023/03/01 21:03:16 - mmengine - INFO - Epoch(train) [122][3200/5047] lr: 1.0024e-05 eta: 1 day, 10:40:25 time: 0.9381 data_time: 0.0031 memory: 55323 loss: 0.1070 loss_ce: 0.1070 2023/03/01 21:04:41 - mmengine - INFO - Epoch(train) [122][3300/5047] lr: 1.0024e-05 eta: 1 day, 10:38:57 time: 0.9022 data_time: 0.0026 memory: 46964 loss: 0.1123 loss_ce: 0.1123 2023/03/01 21:04:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 21:06:05 - mmengine - INFO - Epoch(train) [122][3400/5047] lr: 1.0024e-05 eta: 1 day, 10:37:29 time: 0.8333 data_time: 0.0024 memory: 44956 loss: 0.1123 loss_ce: 0.1123 2023/03/01 21:07:30 - mmengine - INFO - Epoch(train) [122][3500/5047] lr: 1.0024e-05 eta: 1 day, 10:36:01 time: 0.8910 data_time: 0.0032 memory: 42336 loss: 0.1111 loss_ce: 0.1111 2023/03/01 21:08:56 - mmengine - INFO - Epoch(train) [122][3600/5047] lr: 1.0024e-05 eta: 1 day, 10:34:34 time: 0.8817 data_time: 0.0029 memory: 42574 loss: 0.1117 loss_ce: 0.1117 2023/03/01 21:10:20 - mmengine - INFO - Epoch(train) [122][3700/5047] lr: 1.0024e-05 eta: 1 day, 10:33:06 time: 0.8083 data_time: 0.0035 memory: 45643 loss: 0.1169 loss_ce: 0.1169 2023/03/01 21:11:47 - mmengine - INFO - Epoch(train) [122][3800/5047] lr: 1.0024e-05 eta: 1 day, 10:31:39 time: 0.8370 data_time: 0.0028 memory: 43044 loss: 0.0910 loss_ce: 0.0910 2023/03/01 21:13:12 - mmengine - INFO - Epoch(train) [122][3900/5047] lr: 1.0024e-05 eta: 1 day, 10:30:11 time: 0.8593 data_time: 0.0028 memory: 45770 loss: 0.1114 loss_ce: 0.1114 2023/03/01 21:14:36 - mmengine - INFO - Epoch(train) [122][4000/5047] lr: 1.0024e-05 eta: 1 day, 10:28:43 time: 0.8409 data_time: 0.0028 memory: 42336 loss: 0.1169 loss_ce: 0.1169 2023/03/01 21:16:02 - mmengine - INFO - Epoch(train) [122][4100/5047] lr: 1.0024e-05 eta: 1 day, 10:27:15 time: 0.8416 data_time: 0.0026 memory: 54113 loss: 0.1040 loss_ce: 0.1040 2023/03/01 21:17:28 - mmengine - INFO - Epoch(train) [122][4200/5047] lr: 1.0024e-05 eta: 1 day, 10:25:48 time: 0.8722 data_time: 0.0050 memory: 41724 loss: 0.1027 loss_ce: 0.1027 2023/03/01 21:18:55 - mmengine - INFO - Epoch(train) [122][4300/5047] lr: 1.0024e-05 eta: 1 day, 10:24:21 time: 0.9092 data_time: 0.0028 memory: 44202 loss: 0.1208 loss_ce: 0.1208 2023/03/01 21:19:06 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 21:20:21 - mmengine - INFO - Epoch(train) [122][4400/5047] lr: 1.0024e-05 eta: 1 day, 10:22:53 time: 0.8408 data_time: 0.0025 memory: 50505 loss: 0.0918 loss_ce: 0.0918 2023/03/01 21:21:49 - mmengine - INFO - Epoch(train) [122][4500/5047] lr: 1.0024e-05 eta: 1 day, 10:21:26 time: 0.8821 data_time: 0.0031 memory: 40241 loss: 0.1054 loss_ce: 0.1054 2023/03/01 21:23:16 - mmengine - INFO - Epoch(train) [122][4600/5047] lr: 1.0024e-05 eta: 1 day, 10:19:59 time: 0.8416 data_time: 0.0068 memory: 43289 loss: 0.1095 loss_ce: 0.1095 2023/03/01 21:24:42 - mmengine - INFO - Epoch(train) [122][4700/5047] lr: 1.0024e-05 eta: 1 day, 10:18:31 time: 0.8237 data_time: 0.0044 memory: 46285 loss: 0.1091 loss_ce: 0.1091 2023/03/01 21:26:09 - mmengine - INFO - Epoch(train) [122][4800/5047] lr: 1.0024e-05 eta: 1 day, 10:17:04 time: 0.8117 data_time: 0.0029 memory: 52127 loss: 0.1169 loss_ce: 0.1169 2023/03/01 21:27:36 - mmengine - INFO - Epoch(train) [122][4900/5047] lr: 1.0024e-05 eta: 1 day, 10:15:37 time: 0.8511 data_time: 0.0070 memory: 42099 loss: 0.1149 loss_ce: 0.1149 2023/03/01 21:29:03 - mmengine - INFO - Epoch(train) [122][5000/5047] lr: 1.0024e-05 eta: 1 day, 10:14:09 time: 0.8664 data_time: 0.0027 memory: 47813 loss: 0.1078 loss_ce: 0.1078 2023/03/01 21:29:42 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 21:29:43 - mmengine - INFO - Saving checkpoint at 122 epochs 2023/03/01 21:31:15 - mmengine - INFO - Epoch(train) [123][ 100/5047] lr: 9.8227e-06 eta: 1 day, 10:12:01 time: 0.8217 data_time: 0.0032 memory: 46005 loss: 0.0933 loss_ce: 0.0933 2023/03/01 21:32:40 - mmengine - INFO - Epoch(train) [123][ 200/5047] lr: 9.8227e-06 eta: 1 day, 10:10:33 time: 0.8526 data_time: 0.0028 memory: 44617 loss: 0.0947 loss_ce: 0.0947 2023/03/01 21:33:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 21:34:07 - mmengine - INFO - Epoch(train) [123][ 300/5047] lr: 9.8227e-06 eta: 1 day, 10:09:06 time: 0.8837 data_time: 0.0027 memory: 41419 loss: 0.0934 loss_ce: 0.0934 2023/03/01 21:35:33 - mmengine - INFO - Epoch(train) [123][ 400/5047] lr: 9.8227e-06 eta: 1 day, 10:07:38 time: 0.8546 data_time: 0.0027 memory: 47921 loss: 0.0966 loss_ce: 0.0966 2023/03/01 21:37:02 - mmengine - INFO - Epoch(train) [123][ 500/5047] lr: 9.8227e-06 eta: 1 day, 10:06:11 time: 0.8383 data_time: 0.0061 memory: 48761 loss: 0.1169 loss_ce: 0.1169 2023/03/01 21:38:28 - mmengine - INFO - Epoch(train) [123][ 600/5047] lr: 9.8227e-06 eta: 1 day, 10:04:44 time: 0.8243 data_time: 0.0033 memory: 42965 loss: 0.1036 loss_ce: 0.1036 2023/03/01 21:39:53 - mmengine - INFO - Epoch(train) [123][ 700/5047] lr: 9.8227e-06 eta: 1 day, 10:03:16 time: 0.8675 data_time: 0.0026 memory: 51773 loss: 0.1026 loss_ce: 0.1026 2023/03/01 21:41:20 - mmengine - INFO - Epoch(train) [123][ 800/5047] lr: 9.8227e-06 eta: 1 day, 10:01:49 time: 0.8582 data_time: 0.0031 memory: 48188 loss: 0.1020 loss_ce: 0.1020 2023/03/01 21:42:46 - mmengine - INFO - Epoch(train) [123][ 900/5047] lr: 9.8227e-06 eta: 1 day, 10:00:21 time: 0.8384 data_time: 0.0030 memory: 41419 loss: 0.1086 loss_ce: 0.1086 2023/03/01 21:44:11 - mmengine - INFO - Epoch(train) [123][1000/5047] lr: 9.8227e-06 eta: 1 day, 9:58:54 time: 0.8750 data_time: 0.0054 memory: 43404 loss: 0.1085 loss_ce: 0.1085 2023/03/01 21:45:38 - mmengine - INFO - Epoch(train) [123][1100/5047] lr: 9.8227e-06 eta: 1 day, 9:57:26 time: 0.8561 data_time: 0.0029 memory: 44667 loss: 0.1073 loss_ce: 0.1073 2023/03/01 21:47:04 - mmengine - INFO - Epoch(train) [123][1200/5047] lr: 9.8227e-06 eta: 1 day, 9:55:59 time: 0.9094 data_time: 0.0027 memory: 55562 loss: 0.1119 loss_ce: 0.1119 2023/03/01 21:48:01 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 21:48:30 - mmengine - INFO - Epoch(train) [123][1300/5047] lr: 9.8227e-06 eta: 1 day, 9:54:31 time: 0.8536 data_time: 0.0024 memory: 41151 loss: 0.1117 loss_ce: 0.1117 2023/03/01 21:49:56 - mmengine - INFO - Epoch(train) [123][1400/5047] lr: 9.8227e-06 eta: 1 day, 9:53:04 time: 0.8631 data_time: 0.0054 memory: 43819 loss: 0.1094 loss_ce: 0.1094 2023/03/01 21:51:22 - mmengine - INFO - Epoch(train) [123][1500/5047] lr: 9.8227e-06 eta: 1 day, 9:51:37 time: 0.9091 data_time: 0.0026 memory: 43947 loss: 0.1080 loss_ce: 0.1080 2023/03/01 21:52:47 - mmengine - INFO - Epoch(train) [123][1600/5047] lr: 9.8227e-06 eta: 1 day, 9:50:09 time: 0.8539 data_time: 0.0036 memory: 55562 loss: 0.1001 loss_ce: 0.1001 2023/03/01 21:54:13 - mmengine - INFO - Epoch(train) [123][1700/5047] lr: 9.8227e-06 eta: 1 day, 9:48:41 time: 0.8385 data_time: 0.0031 memory: 52978 loss: 0.1091 loss_ce: 0.1091 2023/03/01 21:55:39 - mmengine - INFO - Epoch(train) [123][1800/5047] lr: 9.8227e-06 eta: 1 day, 9:47:14 time: 0.8318 data_time: 0.0026 memory: 50269 loss: 0.1025 loss_ce: 0.1025 2023/03/01 21:57:03 - mmengine - INFO - Epoch(train) [123][1900/5047] lr: 9.8227e-06 eta: 1 day, 9:45:46 time: 0.8418 data_time: 0.0028 memory: 40740 loss: 0.1048 loss_ce: 0.1048 2023/03/01 21:58:29 - mmengine - INFO - Epoch(train) [123][2000/5047] lr: 9.8227e-06 eta: 1 day, 9:44:18 time: 0.8522 data_time: 0.0025 memory: 43947 loss: 0.1014 loss_ce: 0.1014 2023/03/01 21:59:54 - mmengine - INFO - Epoch(train) [123][2100/5047] lr: 9.8227e-06 eta: 1 day, 9:42:51 time: 0.8689 data_time: 0.0025 memory: 46713 loss: 0.1030 loss_ce: 0.1030 2023/03/01 22:01:19 - mmengine - INFO - Epoch(train) [123][2200/5047] lr: 9.8227e-06 eta: 1 day, 9:41:23 time: 0.8490 data_time: 0.0028 memory: 44563 loss: 0.1066 loss_ce: 0.1066 2023/03/01 22:02:15 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 22:02:45 - mmengine - INFO - Epoch(train) [123][2300/5047] lr: 9.8227e-06 eta: 1 day, 9:39:55 time: 0.8373 data_time: 0.0050 memory: 55562 loss: 0.1096 loss_ce: 0.1096 2023/03/01 22:04:10 - mmengine - INFO - Epoch(train) [123][2400/5047] lr: 9.8227e-06 eta: 1 day, 9:38:28 time: 0.8306 data_time: 0.0032 memory: 43613 loss: 0.1165 loss_ce: 0.1165 2023/03/01 22:05:36 - mmengine - INFO - Epoch(train) [123][2500/5047] lr: 9.8227e-06 eta: 1 day, 9:37:00 time: 0.8841 data_time: 0.0035 memory: 55562 loss: 0.1131 loss_ce: 0.1131 2023/03/01 22:07:01 - mmengine - INFO - Epoch(train) [123][2600/5047] lr: 9.8227e-06 eta: 1 day, 9:35:33 time: 0.8480 data_time: 0.0027 memory: 54042 loss: 0.1066 loss_ce: 0.1066 2023/03/01 22:08:28 - mmengine - INFO - Epoch(train) [123][2700/5047] lr: 9.8227e-06 eta: 1 day, 9:34:05 time: 0.8679 data_time: 0.0026 memory: 41724 loss: 0.0951 loss_ce: 0.0951 2023/03/01 22:09:56 - mmengine - INFO - Epoch(train) [123][2800/5047] lr: 9.8227e-06 eta: 1 day, 9:32:38 time: 1.0030 data_time: 0.0027 memory: 42965 loss: 0.1114 loss_ce: 0.1114 2023/03/01 22:11:22 - mmengine - INFO - Epoch(train) [123][2900/5047] lr: 9.8227e-06 eta: 1 day, 9:31:11 time: 0.8365 data_time: 0.0049 memory: 44185 loss: 0.1048 loss_ce: 0.1048 2023/03/01 22:12:48 - mmengine - INFO - Epoch(train) [123][3000/5047] lr: 9.8227e-06 eta: 1 day, 9:29:43 time: 0.8437 data_time: 0.0028 memory: 42940 loss: 0.1163 loss_ce: 0.1163 2023/03/01 22:14:15 - mmengine - INFO - Epoch(train) [123][3100/5047] lr: 9.8227e-06 eta: 1 day, 9:28:16 time: 0.8642 data_time: 0.0029 memory: 44272 loss: 0.1003 loss_ce: 0.1003 2023/03/01 22:15:41 - mmengine - INFO - Epoch(train) [123][3200/5047] lr: 9.8227e-06 eta: 1 day, 9:26:49 time: 0.8437 data_time: 0.0028 memory: 41122 loss: 0.1044 loss_ce: 0.1044 2023/03/01 22:16:38 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 22:17:08 - mmengine - INFO - Epoch(train) [123][3300/5047] lr: 9.8227e-06 eta: 1 day, 9:25:21 time: 0.8523 data_time: 0.0028 memory: 49378 loss: 0.1056 loss_ce: 0.1056 2023/03/01 22:18:33 - mmengine - INFO - Epoch(train) [123][3400/5047] lr: 9.8227e-06 eta: 1 day, 9:23:54 time: 0.8589 data_time: 0.0030 memory: 41044 loss: 0.1060 loss_ce: 0.1060 2023/03/01 22:19:59 - mmengine - INFO - Epoch(train) [123][3500/5047] lr: 9.8227e-06 eta: 1 day, 9:22:26 time: 0.8885 data_time: 0.0030 memory: 50349 loss: 0.1045 loss_ce: 0.1045 2023/03/01 22:21:24 - mmengine - INFO - Epoch(train) [123][3600/5047] lr: 9.8227e-06 eta: 1 day, 9:20:59 time: 0.8360 data_time: 0.0034 memory: 51562 loss: 0.1017 loss_ce: 0.1017 2023/03/01 22:22:50 - mmengine - INFO - Epoch(train) [123][3700/5047] lr: 9.8227e-06 eta: 1 day, 9:19:31 time: 0.8792 data_time: 0.0031 memory: 44956 loss: 0.0965 loss_ce: 0.0965 2023/03/01 22:24:16 - mmengine - INFO - Epoch(train) [123][3800/5047] lr: 9.8227e-06 eta: 1 day, 9:18:04 time: 0.9004 data_time: 0.0077 memory: 50372 loss: 0.1126 loss_ce: 0.1126 2023/03/01 22:25:44 - mmengine - INFO - Epoch(train) [123][3900/5047] lr: 9.8227e-06 eta: 1 day, 9:16:37 time: 0.8409 data_time: 0.0035 memory: 55562 loss: 0.1104 loss_ce: 0.1104 2023/03/01 22:27:11 - mmengine - INFO - Epoch(train) [123][4000/5047] lr: 9.8227e-06 eta: 1 day, 9:15:09 time: 0.9434 data_time: 0.0056 memory: 42649 loss: 0.1143 loss_ce: 0.1143 2023/03/01 22:28:37 - mmengine - INFO - Epoch(train) [123][4100/5047] lr: 9.8227e-06 eta: 1 day, 9:13:42 time: 0.8401 data_time: 0.0030 memory: 54205 loss: 0.1110 loss_ce: 0.1110 2023/03/01 22:30:02 - mmengine - INFO - Epoch(train) [123][4200/5047] lr: 9.8227e-06 eta: 1 day, 9:12:14 time: 0.8417 data_time: 0.0053 memory: 55468 loss: 0.1106 loss_ce: 0.1106 2023/03/01 22:30:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 22:31:29 - mmengine - INFO - Epoch(train) [123][4300/5047] lr: 9.8227e-06 eta: 1 day, 9:10:47 time: 0.8943 data_time: 0.0028 memory: 43445 loss: 0.1205 loss_ce: 0.1205 2023/03/01 22:32:54 - mmengine - INFO - Epoch(train) [123][4400/5047] lr: 9.8227e-06 eta: 1 day, 9:09:19 time: 0.8527 data_time: 0.0066 memory: 44617 loss: 0.0970 loss_ce: 0.0970 2023/03/01 22:34:18 - mmengine - INFO - Epoch(train) [123][4500/5047] lr: 9.8227e-06 eta: 1 day, 9:07:51 time: 0.8856 data_time: 0.0026 memory: 55298 loss: 0.1047 loss_ce: 0.1047 2023/03/01 22:35:46 - mmengine - INFO - Epoch(train) [123][4600/5047] lr: 9.8227e-06 eta: 1 day, 9:06:24 time: 0.9382 data_time: 0.0026 memory: 55562 loss: 0.1144 loss_ce: 0.1144 2023/03/01 22:37:12 - mmengine - INFO - Epoch(train) [123][4700/5047] lr: 9.8227e-06 eta: 1 day, 9:04:57 time: 0.8049 data_time: 0.0028 memory: 44901 loss: 0.1196 loss_ce: 0.1196 2023/03/01 22:38:39 - mmengine - INFO - Epoch(train) [123][4800/5047] lr: 9.8227e-06 eta: 1 day, 9:03:30 time: 0.8842 data_time: 0.0025 memory: 52127 loss: 0.1116 loss_ce: 0.1116 2023/03/01 22:40:04 - mmengine - INFO - Epoch(train) [123][4900/5047] lr: 9.8227e-06 eta: 1 day, 9:02:02 time: 0.8689 data_time: 0.0024 memory: 51719 loss: 0.1066 loss_ce: 0.1066 2023/03/01 22:41:31 - mmengine - INFO - Epoch(train) [123][5000/5047] lr: 9.8227e-06 eta: 1 day, 9:00:35 time: 0.8674 data_time: 0.0024 memory: 47963 loss: 0.1008 loss_ce: 0.1008 2023/03/01 22:42:11 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 22:42:11 - mmengine - INFO - Saving checkpoint at 123 epochs 2023/03/01 22:43:45 - mmengine - INFO - Epoch(train) [124][ 100/5047] lr: 9.6217e-06 eta: 1 day, 8:58:26 time: 0.8229 data_time: 0.0049 memory: 43289 loss: 0.1075 loss_ce: 0.1075 2023/03/01 22:45:12 - mmengine - INFO - Epoch(train) [124][ 200/5047] lr: 9.6217e-06 eta: 1 day, 8:56:59 time: 0.8548 data_time: 0.0029 memory: 43872 loss: 0.0993 loss_ce: 0.0993 2023/03/01 22:45:28 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 22:46:36 - mmengine - INFO - Epoch(train) [124][ 300/5047] lr: 9.6217e-06 eta: 1 day, 8:55:31 time: 0.8682 data_time: 0.0028 memory: 51732 loss: 0.1111 loss_ce: 0.1111 2023/03/01 22:48:02 - mmengine - INFO - Epoch(train) [124][ 400/5047] lr: 9.6217e-06 eta: 1 day, 8:54:03 time: 0.8258 data_time: 0.0034 memory: 47304 loss: 0.1226 loss_ce: 0.1226 2023/03/01 22:49:28 - mmengine - INFO - Epoch(train) [124][ 500/5047] lr: 9.6217e-06 eta: 1 day, 8:52:36 time: 0.8570 data_time: 0.0034 memory: 45967 loss: 0.1061 loss_ce: 0.1061 2023/03/01 22:50:55 - mmengine - INFO - Epoch(train) [124][ 600/5047] lr: 9.6217e-06 eta: 1 day, 8:51:09 time: 0.8864 data_time: 0.0031 memory: 44565 loss: 0.1047 loss_ce: 0.1047 2023/03/01 22:52:19 - mmengine - INFO - Epoch(train) [124][ 700/5047] lr: 9.6217e-06 eta: 1 day, 8:49:41 time: 0.8528 data_time: 0.0031 memory: 44539 loss: 0.0998 loss_ce: 0.0998 2023/03/01 22:53:44 - mmengine - INFO - Epoch(train) [124][ 800/5047] lr: 9.6217e-06 eta: 1 day, 8:48:13 time: 0.8787 data_time: 0.0027 memory: 46355 loss: 0.1047 loss_ce: 0.1047 2023/03/01 22:55:11 - mmengine - INFO - Epoch(train) [124][ 900/5047] lr: 9.6217e-06 eta: 1 day, 8:46:46 time: 0.8962 data_time: 0.0038 memory: 47281 loss: 0.1075 loss_ce: 0.1075 2023/03/01 22:56:37 - mmengine - INFO - Epoch(train) [124][1000/5047] lr: 9.6217e-06 eta: 1 day, 8:45:18 time: 0.8496 data_time: 0.0030 memory: 46005 loss: 0.1046 loss_ce: 0.1046 2023/03/01 22:58:04 - mmengine - INFO - Epoch(train) [124][1100/5047] lr: 9.6217e-06 eta: 1 day, 8:43:51 time: 0.8855 data_time: 0.0030 memory: 48789 loss: 0.1056 loss_ce: 0.1056 2023/03/01 22:59:30 - mmengine - INFO - Epoch(train) [124][1200/5047] lr: 9.6217e-06 eta: 1 day, 8:42:24 time: 0.8934 data_time: 0.0030 memory: 43091 loss: 0.0885 loss_ce: 0.0885 2023/03/01 22:59:46 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 23:00:55 - mmengine - INFO - Epoch(train) [124][1300/5047] lr: 9.6217e-06 eta: 1 day, 8:40:56 time: 0.8309 data_time: 0.0035 memory: 41724 loss: 0.1173 loss_ce: 0.1173 2023/03/01 23:02:21 - mmengine - INFO - Epoch(train) [124][1400/5047] lr: 9.6217e-06 eta: 1 day, 8:39:28 time: 0.8634 data_time: 0.0028 memory: 41419 loss: 0.1088 loss_ce: 0.1088 2023/03/01 23:03:48 - mmengine - INFO - Epoch(train) [124][1500/5047] lr: 9.6217e-06 eta: 1 day, 8:38:01 time: 0.8775 data_time: 0.0052 memory: 42239 loss: 0.1148 loss_ce: 0.1148 2023/03/01 23:05:13 - mmengine - INFO - Epoch(train) [124][1600/5047] lr: 9.6217e-06 eta: 1 day, 8:36:34 time: 0.8318 data_time: 0.0033 memory: 42649 loss: 0.1136 loss_ce: 0.1136 2023/03/01 23:06:39 - mmengine - INFO - Epoch(train) [124][1700/5047] lr: 9.6217e-06 eta: 1 day, 8:35:06 time: 0.8063 data_time: 0.0059 memory: 55562 loss: 0.0993 loss_ce: 0.0993 2023/03/01 23:08:04 - mmengine - INFO - Epoch(train) [124][1800/5047] lr: 9.6217e-06 eta: 1 day, 8:33:38 time: 0.8994 data_time: 0.0028 memory: 47959 loss: 0.1043 loss_ce: 0.1043 2023/03/01 23:09:29 - mmengine - INFO - Epoch(train) [124][1900/5047] lr: 9.6217e-06 eta: 1 day, 8:32:11 time: 0.8266 data_time: 0.0046 memory: 46005 loss: 0.1016 loss_ce: 0.1016 2023/03/01 23:10:55 - mmengine - INFO - Epoch(train) [124][2000/5047] lr: 9.6217e-06 eta: 1 day, 8:30:43 time: 0.8788 data_time: 0.0031 memory: 43947 loss: 0.1168 loss_ce: 0.1168 2023/03/01 23:12:20 - mmengine - INFO - Epoch(train) [124][2100/5047] lr: 9.6217e-06 eta: 1 day, 8:29:16 time: 0.8732 data_time: 0.0037 memory: 46118 loss: 0.1167 loss_ce: 0.1167 2023/03/01 23:13:44 - mmengine - INFO - Epoch(train) [124][2200/5047] lr: 9.6217e-06 eta: 1 day, 8:27:48 time: 0.8464 data_time: 0.0053 memory: 41419 loss: 0.1155 loss_ce: 0.1155 2023/03/01 23:14:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 23:15:11 - mmengine - INFO - Epoch(train) [124][2300/5047] lr: 9.6217e-06 eta: 1 day, 8:26:20 time: 0.8700 data_time: 0.0028 memory: 41720 loss: 0.1047 loss_ce: 0.1047 2023/03/01 23:16:36 - mmengine - INFO - Epoch(train) [124][2400/5047] lr: 9.6217e-06 eta: 1 day, 8:24:53 time: 0.8771 data_time: 0.0031 memory: 55114 loss: 0.0966 loss_ce: 0.0966 2023/03/01 23:18:03 - mmengine - INFO - Epoch(train) [124][2500/5047] lr: 9.6217e-06 eta: 1 day, 8:23:26 time: 0.8400 data_time: 0.0025 memory: 43613 loss: 0.1135 loss_ce: 0.1135 2023/03/01 23:19:28 - mmengine - INFO - Epoch(train) [124][2600/5047] lr: 9.6217e-06 eta: 1 day, 8:21:58 time: 0.8698 data_time: 0.0030 memory: 55562 loss: 0.1070 loss_ce: 0.1070 2023/03/01 23:20:54 - mmengine - INFO - Epoch(train) [124][2700/5047] lr: 9.6217e-06 eta: 1 day, 8:20:31 time: 0.8717 data_time: 0.0026 memory: 44617 loss: 0.1031 loss_ce: 0.1031 2023/03/01 23:22:20 - mmengine - INFO - Epoch(train) [124][2800/5047] lr: 9.6217e-06 eta: 1 day, 8:19:03 time: 0.8772 data_time: 0.0029 memory: 55562 loss: 0.1196 loss_ce: 0.1196 2023/03/01 23:23:48 - mmengine - INFO - Epoch(train) [124][2900/5047] lr: 9.6217e-06 eta: 1 day, 8:17:36 time: 0.8481 data_time: 0.0027 memory: 43020 loss: 0.0883 loss_ce: 0.0883 2023/03/01 23:25:13 - mmengine - INFO - Epoch(train) [124][3000/5047] lr: 9.6217e-06 eta: 1 day, 8:16:08 time: 0.8760 data_time: 0.0037 memory: 49173 loss: 0.0943 loss_ce: 0.0943 2023/03/01 23:26:40 - mmengine - INFO - Epoch(train) [124][3100/5047] lr: 9.6217e-06 eta: 1 day, 8:14:41 time: 0.9098 data_time: 0.0028 memory: 46219 loss: 0.1148 loss_ce: 0.1148 2023/03/01 23:28:07 - mmengine - INFO - Epoch(train) [124][3200/5047] lr: 9.6217e-06 eta: 1 day, 8:13:14 time: 0.8458 data_time: 0.0029 memory: 44617 loss: 0.1001 loss_ce: 0.1001 2023/03/01 23:28:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 23:29:32 - mmengine - INFO - Epoch(train) [124][3300/5047] lr: 9.6217e-06 eta: 1 day, 8:11:46 time: 0.8473 data_time: 0.0030 memory: 40825 loss: 0.1185 loss_ce: 0.1185 2023/03/01 23:30:57 - mmengine - INFO - Epoch(train) [124][3400/5047] lr: 9.6217e-06 eta: 1 day, 8:10:18 time: 0.7778 data_time: 0.0026 memory: 42336 loss: 0.1135 loss_ce: 0.1135 2023/03/01 23:32:21 - mmengine - INFO - Epoch(train) [124][3500/5047] lr: 9.6217e-06 eta: 1 day, 8:08:51 time: 0.8595 data_time: 0.0032 memory: 55296 loss: 0.0917 loss_ce: 0.0917 2023/03/01 23:33:46 - mmengine - INFO - Epoch(train) [124][3600/5047] lr: 9.6217e-06 eta: 1 day, 8:07:23 time: 0.8583 data_time: 0.0025 memory: 44278 loss: 0.1105 loss_ce: 0.1105 2023/03/01 23:35:14 - mmengine - INFO - Epoch(train) [124][3700/5047] lr: 9.6217e-06 eta: 1 day, 8:05:56 time: 0.8575 data_time: 0.0027 memory: 52344 loss: 0.1184 loss_ce: 0.1184 2023/03/01 23:36:41 - mmengine - INFO - Epoch(train) [124][3800/5047] lr: 9.6217e-06 eta: 1 day, 8:04:29 time: 0.9154 data_time: 0.0025 memory: 54673 loss: 0.1044 loss_ce: 0.1044 2023/03/01 23:38:07 - mmengine - INFO - Epoch(train) [124][3900/5047] lr: 9.6217e-06 eta: 1 day, 8:03:01 time: 0.8896 data_time: 0.0037 memory: 44632 loss: 0.1112 loss_ce: 0.1112 2023/03/01 23:39:31 - mmengine - INFO - Epoch(train) [124][4000/5047] lr: 9.6217e-06 eta: 1 day, 8:01:34 time: 0.8519 data_time: 0.0026 memory: 44278 loss: 0.0972 loss_ce: 0.0972 2023/03/01 23:40:57 - mmengine - INFO - Epoch(train) [124][4100/5047] lr: 9.6217e-06 eta: 1 day, 8:00:06 time: 0.8828 data_time: 0.0025 memory: 38357 loss: 0.0989 loss_ce: 0.0989 2023/03/01 23:42:22 - mmengine - INFO - Epoch(train) [124][4200/5047] lr: 9.6217e-06 eta: 1 day, 7:58:38 time: 0.8508 data_time: 0.0027 memory: 45850 loss: 0.1026 loss_ce: 0.1026 2023/03/01 23:42:38 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 23:43:47 - mmengine - INFO - Epoch(train) [124][4300/5047] lr: 9.6217e-06 eta: 1 day, 7:57:11 time: 0.8668 data_time: 0.0027 memory: 42681 loss: 0.1127 loss_ce: 0.1127 2023/03/01 23:45:13 - mmengine - INFO - Epoch(train) [124][4400/5047] lr: 9.6217e-06 eta: 1 day, 7:55:43 time: 0.8530 data_time: 0.0027 memory: 55562 loss: 0.1035 loss_ce: 0.1035 2023/03/01 23:46:38 - mmengine - INFO - Epoch(train) [124][4500/5047] lr: 9.6217e-06 eta: 1 day, 7:54:16 time: 0.8427 data_time: 0.0026 memory: 42306 loss: 0.1135 loss_ce: 0.1135 2023/03/01 23:48:04 - mmengine - INFO - Epoch(train) [124][4600/5047] lr: 9.6217e-06 eta: 1 day, 7:52:48 time: 0.8478 data_time: 0.0028 memory: 41122 loss: 0.1012 loss_ce: 0.1012 2023/03/01 23:49:32 - mmengine - INFO - Epoch(train) [124][4700/5047] lr: 9.6217e-06 eta: 1 day, 7:51:21 time: 0.9128 data_time: 0.0029 memory: 43289 loss: 0.0925 loss_ce: 0.0925 2023/03/01 23:50:58 - mmengine - INFO - Epoch(train) [124][4800/5047] lr: 9.6217e-06 eta: 1 day, 7:49:54 time: 0.8729 data_time: 0.0026 memory: 44617 loss: 0.1166 loss_ce: 0.1166 2023/03/01 23:52:25 - mmengine - INFO - Epoch(train) [124][4900/5047] lr: 9.6217e-06 eta: 1 day, 7:48:26 time: 0.8785 data_time: 0.0027 memory: 47013 loss: 0.1137 loss_ce: 0.1137 2023/03/01 23:53:52 - mmengine - INFO - Epoch(train) [124][5000/5047] lr: 9.6217e-06 eta: 1 day, 7:46:59 time: 0.8499 data_time: 0.0025 memory: 42649 loss: 0.1035 loss_ce: 0.1035 2023/03/01 23:54:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 23:54:33 - mmengine - INFO - Saving checkpoint at 124 epochs 2023/03/01 23:56:05 - mmengine - INFO - Epoch(train) [125][ 100/5047] lr: 9.4208e-06 eta: 1 day, 7:44:51 time: 0.8869 data_time: 0.0026 memory: 48055 loss: 0.1003 loss_ce: 0.1003 2023/03/01 23:57:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/01 23:57:32 - mmengine - INFO - Epoch(train) [125][ 200/5047] lr: 9.4208e-06 eta: 1 day, 7:43:24 time: 0.8394 data_time: 0.0026 memory: 55562 loss: 0.0981 loss_ce: 0.0981 2023/03/01 23:58:58 - mmengine - INFO - Epoch(train) [125][ 300/5047] lr: 9.4208e-06 eta: 1 day, 7:41:56 time: 0.8246 data_time: 0.0029 memory: 50906 loss: 0.0957 loss_ce: 0.0957 2023/03/02 00:00:23 - mmengine - INFO - Epoch(train) [125][ 400/5047] lr: 9.4208e-06 eta: 1 day, 7:40:29 time: 0.8507 data_time: 0.0030 memory: 46874 loss: 0.0967 loss_ce: 0.0967 2023/03/02 00:01:50 - mmengine - INFO - Epoch(train) [125][ 500/5047] lr: 9.4208e-06 eta: 1 day, 7:39:02 time: 0.8893 data_time: 0.0073 memory: 43289 loss: 0.1042 loss_ce: 0.1042 2023/03/02 00:03:17 - mmengine - INFO - Epoch(train) [125][ 600/5047] lr: 9.4208e-06 eta: 1 day, 7:37:34 time: 0.8918 data_time: 0.0030 memory: 40259 loss: 0.1055 loss_ce: 0.1055 2023/03/02 00:04:41 - mmengine - INFO - Epoch(train) [125][ 700/5047] lr: 9.4208e-06 eta: 1 day, 7:36:06 time: 0.8787 data_time: 0.0026 memory: 47882 loss: 0.1136 loss_ce: 0.1136 2023/03/02 00:06:06 - mmengine - INFO - Epoch(train) [125][ 800/5047] lr: 9.4208e-06 eta: 1 day, 7:34:39 time: 0.8096 data_time: 0.0027 memory: 43613 loss: 0.1117 loss_ce: 0.1117 2023/03/02 00:07:31 - mmengine - INFO - Epoch(train) [125][ 900/5047] lr: 9.4208e-06 eta: 1 day, 7:33:11 time: 0.8739 data_time: 0.0030 memory: 47011 loss: 0.0991 loss_ce: 0.0991 2023/03/02 00:08:56 - mmengine - INFO - Epoch(train) [125][1000/5047] lr: 9.4208e-06 eta: 1 day, 7:31:44 time: 0.8580 data_time: 0.0028 memory: 43947 loss: 0.1201 loss_ce: 0.1201 2023/03/02 00:10:22 - mmengine - INFO - Epoch(train) [125][1100/5047] lr: 9.4208e-06 eta: 1 day, 7:30:16 time: 0.8500 data_time: 0.0026 memory: 44956 loss: 0.1145 loss_ce: 0.1145 2023/03/02 00:11:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 00:11:47 - mmengine - INFO - Epoch(train) [125][1200/5047] lr: 9.4208e-06 eta: 1 day, 7:28:48 time: 0.8491 data_time: 0.0027 memory: 43027 loss: 0.1049 loss_ce: 0.1049 2023/03/02 00:13:12 - mmengine - INFO - Epoch(train) [125][1300/5047] lr: 9.4208e-06 eta: 1 day, 7:27:21 time: 0.8798 data_time: 0.0026 memory: 44278 loss: 0.1007 loss_ce: 0.1007 2023/03/02 00:14:39 - mmengine - INFO - Epoch(train) [125][1400/5047] lr: 9.4208e-06 eta: 1 day, 7:25:53 time: 0.8872 data_time: 0.0027 memory: 54168 loss: 0.1172 loss_ce: 0.1172 2023/03/02 00:16:05 - mmengine - INFO - Epoch(train) [125][1500/5047] lr: 9.4208e-06 eta: 1 day, 7:24:26 time: 0.8477 data_time: 0.0027 memory: 43378 loss: 0.1181 loss_ce: 0.1181 2023/03/02 00:17:31 - mmengine - INFO - Epoch(train) [125][1600/5047] lr: 9.4208e-06 eta: 1 day, 7:22:59 time: 0.8689 data_time: 0.0027 memory: 41724 loss: 0.1089 loss_ce: 0.1089 2023/03/02 00:18:58 - mmengine - INFO - Epoch(train) [125][1700/5047] lr: 9.4208e-06 eta: 1 day, 7:21:31 time: 0.8865 data_time: 0.0027 memory: 52956 loss: 0.1186 loss_ce: 0.1186 2023/03/02 00:20:21 - mmengine - INFO - Epoch(train) [125][1800/5047] lr: 9.4208e-06 eta: 1 day, 7:20:04 time: 0.8426 data_time: 0.0026 memory: 44749 loss: 0.1171 loss_ce: 0.1171 2023/03/02 00:21:47 - mmengine - INFO - Epoch(train) [125][1900/5047] lr: 9.4208e-06 eta: 1 day, 7:18:36 time: 0.9075 data_time: 0.0026 memory: 44915 loss: 0.1038 loss_ce: 0.1038 2023/03/02 00:23:12 - mmengine - INFO - Epoch(train) [125][2000/5047] lr: 9.4208e-06 eta: 1 day, 7:17:08 time: 0.8402 data_time: 0.0036 memory: 42649 loss: 0.0991 loss_ce: 0.0991 2023/03/02 00:24:37 - mmengine - INFO - Epoch(train) [125][2100/5047] lr: 9.4208e-06 eta: 1 day, 7:15:41 time: 0.8572 data_time: 0.0026 memory: 49312 loss: 0.1086 loss_ce: 0.1086 2023/03/02 00:25:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 00:26:04 - mmengine - INFO - Epoch(train) [125][2200/5047] lr: 9.4208e-06 eta: 1 day, 7:14:13 time: 0.8459 data_time: 0.0026 memory: 42965 loss: 0.1015 loss_ce: 0.1015 2023/03/02 00:27:31 - mmengine - INFO - Epoch(train) [125][2300/5047] lr: 9.4208e-06 eta: 1 day, 7:12:46 time: 0.9556 data_time: 0.0031 memory: 41770 loss: 0.1029 loss_ce: 0.1029 2023/03/02 00:28:55 - mmengine - INFO - Epoch(train) [125][2400/5047] lr: 9.4208e-06 eta: 1 day, 7:11:18 time: 0.7985 data_time: 0.0063 memory: 41363 loss: 0.0994 loss_ce: 0.0994 2023/03/02 00:30:21 - mmengine - INFO - Epoch(train) [125][2500/5047] lr: 9.4208e-06 eta: 1 day, 7:09:51 time: 0.8008 data_time: 0.0028 memory: 43748 loss: 0.1206 loss_ce: 0.1206 2023/03/02 00:31:48 - mmengine - INFO - Epoch(train) [125][2600/5047] lr: 9.4208e-06 eta: 1 day, 7:08:24 time: 0.8836 data_time: 0.0039 memory: 43409 loss: 0.1117 loss_ce: 0.1117 2023/03/02 00:33:14 - mmengine - INFO - Epoch(train) [125][2700/5047] lr: 9.4208e-06 eta: 1 day, 7:06:56 time: 0.8503 data_time: 0.0047 memory: 46192 loss: 0.1167 loss_ce: 0.1167 2023/03/02 00:34:39 - mmengine - INFO - Epoch(train) [125][2800/5047] lr: 9.4208e-06 eta: 1 day, 7:05:29 time: 0.8680 data_time: 0.0027 memory: 44934 loss: 0.0897 loss_ce: 0.0897 2023/03/02 00:36:03 - mmengine - INFO - Epoch(train) [125][2900/5047] lr: 9.4208e-06 eta: 1 day, 7:04:01 time: 0.8384 data_time: 0.0050 memory: 43403 loss: 0.1028 loss_ce: 0.1028 2023/03/02 00:37:28 - mmengine - INFO - Epoch(train) [125][3000/5047] lr: 9.4208e-06 eta: 1 day, 7:02:33 time: 0.8675 data_time: 0.0032 memory: 41122 loss: 0.1105 loss_ce: 0.1105 2023/03/02 00:38:55 - mmengine - INFO - Epoch(train) [125][3100/5047] lr: 9.4208e-06 eta: 1 day, 7:01:06 time: 0.7869 data_time: 0.0031 memory: 42965 loss: 0.1048 loss_ce: 0.1048 2023/03/02 00:39:56 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 00:40:20 - mmengine - INFO - Epoch(train) [125][3200/5047] lr: 9.4208e-06 eta: 1 day, 6:59:39 time: 0.8705 data_time: 0.0027 memory: 42556 loss: 0.1204 loss_ce: 0.1204 2023/03/02 00:41:46 - mmengine - INFO - Epoch(train) [125][3300/5047] lr: 9.4208e-06 eta: 1 day, 6:58:11 time: 0.8680 data_time: 0.0053 memory: 51563 loss: 0.1017 loss_ce: 0.1017 2023/03/02 00:43:11 - mmengine - INFO - Epoch(train) [125][3400/5047] lr: 9.4208e-06 eta: 1 day, 6:56:43 time: 0.8592 data_time: 0.0042 memory: 42649 loss: 0.1049 loss_ce: 0.1049 2023/03/02 00:44:37 - mmengine - INFO - Epoch(train) [125][3500/5047] lr: 9.4208e-06 eta: 1 day, 6:55:16 time: 0.8678 data_time: 0.0030 memory: 44956 loss: 0.1099 loss_ce: 0.1099 2023/03/02 00:46:02 - mmengine - INFO - Epoch(train) [125][3600/5047] lr: 9.4208e-06 eta: 1 day, 6:53:48 time: 0.8746 data_time: 0.0039 memory: 43947 loss: 0.1190 loss_ce: 0.1190 2023/03/02 00:47:28 - mmengine - INFO - Epoch(train) [125][3700/5047] lr: 9.4208e-06 eta: 1 day, 6:52:21 time: 0.8815 data_time: 0.0050 memory: 44707 loss: 0.1054 loss_ce: 0.1054 2023/03/02 00:48:52 - mmengine - INFO - Epoch(train) [125][3800/5047] lr: 9.4208e-06 eta: 1 day, 6:50:53 time: 0.8737 data_time: 0.0031 memory: 43613 loss: 0.1079 loss_ce: 0.1079 2023/03/02 00:50:18 - mmengine - INFO - Epoch(train) [125][3900/5047] lr: 9.4208e-06 eta: 1 day, 6:49:26 time: 0.9224 data_time: 0.0026 memory: 55562 loss: 0.1019 loss_ce: 0.1019 2023/03/02 00:51:44 - mmengine - INFO - Epoch(train) [125][4000/5047] lr: 9.4208e-06 eta: 1 day, 6:47:58 time: 0.8615 data_time: 0.0035 memory: 45302 loss: 0.1020 loss_ce: 0.1020 2023/03/02 00:53:11 - mmengine - INFO - Epoch(train) [125][4100/5047] lr: 9.4208e-06 eta: 1 day, 6:46:31 time: 0.8883 data_time: 0.0032 memory: 43403 loss: 0.0937 loss_ce: 0.0937 2023/03/02 00:54:13 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 00:54:37 - mmengine - INFO - Epoch(train) [125][4200/5047] lr: 9.4208e-06 eta: 1 day, 6:45:04 time: 0.8243 data_time: 0.0029 memory: 42024 loss: 0.1054 loss_ce: 0.1054 2023/03/02 00:56:03 - mmengine - INFO - Epoch(train) [125][4300/5047] lr: 9.4208e-06 eta: 1 day, 6:43:36 time: 0.8762 data_time: 0.0052 memory: 42668 loss: 0.1079 loss_ce: 0.1079 2023/03/02 00:57:31 - mmengine - INFO - Epoch(train) [125][4400/5047] lr: 9.4208e-06 eta: 1 day, 6:42:09 time: 0.8933 data_time: 0.0029 memory: 43951 loss: 0.1075 loss_ce: 0.1075 2023/03/02 00:58:56 - mmengine - INFO - Epoch(train) [125][4500/5047] lr: 9.4208e-06 eta: 1 day, 6:40:42 time: 0.8407 data_time: 0.0027 memory: 41724 loss: 0.1036 loss_ce: 0.1036 2023/03/02 01:00:22 - mmengine - INFO - Epoch(train) [125][4600/5047] lr: 9.4208e-06 eta: 1 day, 6:39:14 time: 0.8033 data_time: 0.0025 memory: 44614 loss: 0.1210 loss_ce: 0.1210 2023/03/02 01:01:46 - mmengine - INFO - Epoch(train) [125][4700/5047] lr: 9.4208e-06 eta: 1 day, 6:37:46 time: 0.9031 data_time: 0.0025 memory: 39810 loss: 0.1035 loss_ce: 0.1035 2023/03/02 01:03:13 - mmengine - INFO - Epoch(train) [125][4800/5047] lr: 9.4208e-06 eta: 1 day, 6:36:19 time: 0.8871 data_time: 0.0029 memory: 46875 loss: 0.1195 loss_ce: 0.1195 2023/03/02 01:04:39 - mmengine - INFO - Epoch(train) [125][4900/5047] lr: 9.4208e-06 eta: 1 day, 6:34:52 time: 0.8786 data_time: 0.0028 memory: 47813 loss: 0.1136 loss_ce: 0.1136 2023/03/02 01:06:04 - mmengine - INFO - Epoch(train) [125][5000/5047] lr: 9.4208e-06 eta: 1 day, 6:33:24 time: 0.8350 data_time: 0.0051 memory: 49312 loss: 0.1112 loss_ce: 0.1112 2023/03/02 01:06:45 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 01:06:45 - mmengine - INFO - Saving checkpoint at 125 epochs 2023/03/02 01:08:17 - mmengine - INFO - Epoch(train) [126][ 100/5047] lr: 9.2199e-06 eta: 1 day, 6:31:16 time: 0.8266 data_time: 0.0028 memory: 51991 loss: 0.1072 loss_ce: 0.1072 2023/03/02 01:08:38 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 01:09:43 - mmengine - INFO - Epoch(train) [126][ 200/5047] lr: 9.2199e-06 eta: 1 day, 6:29:48 time: 0.8393 data_time: 0.0025 memory: 52956 loss: 0.0998 loss_ce: 0.0998 2023/03/02 01:11:08 - mmengine - INFO - Epoch(train) [126][ 300/5047] lr: 9.2199e-06 eta: 1 day, 6:28:21 time: 0.8471 data_time: 0.0028 memory: 43947 loss: 0.1085 loss_ce: 0.1085 2023/03/02 01:12:35 - mmengine - INFO - Epoch(train) [126][ 400/5047] lr: 9.2199e-06 eta: 1 day, 6:26:54 time: 0.8348 data_time: 0.0026 memory: 40241 loss: 0.1051 loss_ce: 0.1051 2023/03/02 01:14:02 - mmengine - INFO - Epoch(train) [126][ 500/5047] lr: 9.2199e-06 eta: 1 day, 6:25:26 time: 0.8181 data_time: 0.0031 memory: 42336 loss: 0.1197 loss_ce: 0.1197 2023/03/02 01:15:28 - mmengine - INFO - Epoch(train) [126][ 600/5047] lr: 9.2199e-06 eta: 1 day, 6:23:59 time: 0.8553 data_time: 0.0027 memory: 50589 loss: 0.0990 loss_ce: 0.0990 2023/03/02 01:16:56 - mmengine - INFO - Epoch(train) [126][ 700/5047] lr: 9.2199e-06 eta: 1 day, 6:22:32 time: 0.8415 data_time: 0.0028 memory: 42649 loss: 0.1023 loss_ce: 0.1023 2023/03/02 01:18:21 - mmengine - INFO - Epoch(train) [126][ 800/5047] lr: 9.2199e-06 eta: 1 day, 6:21:04 time: 0.8896 data_time: 0.0026 memory: 49373 loss: 0.1000 loss_ce: 0.1000 2023/03/02 01:19:46 - mmengine - INFO - Epoch(train) [126][ 900/5047] lr: 9.2199e-06 eta: 1 day, 6:19:37 time: 0.8556 data_time: 0.0044 memory: 41724 loss: 0.1134 loss_ce: 0.1134 2023/03/02 01:21:12 - mmengine - INFO - Epoch(train) [126][1000/5047] lr: 9.2199e-06 eta: 1 day, 6:18:09 time: 0.8722 data_time: 0.0082 memory: 42965 loss: 0.1056 loss_ce: 0.1056 2023/03/02 01:22:39 - mmengine - INFO - Epoch(train) [126][1100/5047] lr: 9.2199e-06 eta: 1 day, 6:16:42 time: 0.8219 data_time: 0.0026 memory: 43947 loss: 0.1073 loss_ce: 0.1073 2023/03/02 01:23:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 01:24:04 - mmengine - INFO - Epoch(train) [126][1200/5047] lr: 9.2199e-06 eta: 1 day, 6:15:15 time: 0.8160 data_time: 0.0029 memory: 49715 loss: 0.0988 loss_ce: 0.0988 2023/03/02 01:25:31 - mmengine - INFO - Epoch(train) [126][1300/5047] lr: 9.2199e-06 eta: 1 day, 6:13:47 time: 0.8759 data_time: 0.0030 memory: 41419 loss: 0.1135 loss_ce: 0.1135 2023/03/02 01:26:57 - mmengine - INFO - Epoch(train) [126][1400/5047] lr: 9.2199e-06 eta: 1 day, 6:12:20 time: 0.8387 data_time: 0.0026 memory: 43613 loss: 0.1062 loss_ce: 0.1062 2023/03/02 01:28:22 - mmengine - INFO - Epoch(train) [126][1500/5047] lr: 9.2199e-06 eta: 1 day, 6:10:53 time: 0.8980 data_time: 0.0031 memory: 43591 loss: 0.1208 loss_ce: 0.1208 2023/03/02 01:29:47 - mmengine - INFO - Epoch(train) [126][1600/5047] lr: 9.2199e-06 eta: 1 day, 6:09:25 time: 0.8430 data_time: 0.0046 memory: 55562 loss: 0.0990 loss_ce: 0.0990 2023/03/02 01:31:14 - mmengine - INFO - Epoch(train) [126][1700/5047] lr: 9.2199e-06 eta: 1 day, 6:07:58 time: 0.8467 data_time: 0.0040 memory: 55562 loss: 0.0997 loss_ce: 0.0997 2023/03/02 01:32:40 - mmengine - INFO - Epoch(train) [126][1800/5047] lr: 9.2199e-06 eta: 1 day, 6:06:30 time: 0.8935 data_time: 0.0029 memory: 42649 loss: 0.1012 loss_ce: 0.1012 2023/03/02 01:34:06 - mmengine - INFO - Epoch(train) [126][1900/5047] lr: 9.2199e-06 eta: 1 day, 6:05:03 time: 0.8668 data_time: 0.0024 memory: 46355 loss: 0.1148 loss_ce: 0.1148 2023/03/02 01:35:32 - mmengine - INFO - Epoch(train) [126][2000/5047] lr: 9.2199e-06 eta: 1 day, 6:03:35 time: 0.8359 data_time: 0.0029 memory: 48323 loss: 0.0950 loss_ce: 0.0950 2023/03/02 01:36:57 - mmengine - INFO - Epoch(train) [126][2100/5047] lr: 9.2199e-06 eta: 1 day, 6:02:08 time: 0.9251 data_time: 0.0027 memory: 41146 loss: 0.0978 loss_ce: 0.0978 2023/03/02 01:37:18 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 01:38:22 - mmengine - INFO - Epoch(train) [126][2200/5047] lr: 9.2199e-06 eta: 1 day, 6:00:40 time: 0.8014 data_time: 0.0027 memory: 43112 loss: 0.1133 loss_ce: 0.1133 2023/03/02 01:39:48 - mmengine - INFO - Epoch(train) [126][2300/5047] lr: 9.2199e-06 eta: 1 day, 5:59:13 time: 0.8580 data_time: 0.0029 memory: 42707 loss: 0.1046 loss_ce: 0.1046 2023/03/02 01:41:17 - mmengine - INFO - Epoch(train) [126][2400/5047] lr: 9.2199e-06 eta: 1 day, 5:57:46 time: 0.8765 data_time: 0.0050 memory: 45302 loss: 0.1010 loss_ce: 0.1010 2023/03/02 01:42:42 - mmengine - INFO - Epoch(train) [126][2500/5047] lr: 9.2199e-06 eta: 1 day, 5:56:19 time: 0.8642 data_time: 0.0026 memory: 41268 loss: 0.1092 loss_ce: 0.1092 2023/03/02 01:44:08 - mmengine - INFO - Epoch(train) [126][2600/5047] lr: 9.2199e-06 eta: 1 day, 5:54:51 time: 0.8797 data_time: 0.0027 memory: 42649 loss: 0.1072 loss_ce: 0.1072 2023/03/02 01:45:33 - mmengine - INFO - Epoch(train) [126][2700/5047] lr: 9.2199e-06 eta: 1 day, 5:53:24 time: 0.8319 data_time: 0.0027 memory: 50542 loss: 0.1000 loss_ce: 0.1000 2023/03/02 01:47:00 - mmengine - INFO - Epoch(train) [126][2800/5047] lr: 9.2199e-06 eta: 1 day, 5:51:56 time: 0.8762 data_time: 0.0054 memory: 45711 loss: 0.1118 loss_ce: 0.1118 2023/03/02 01:48:26 - mmengine - INFO - Epoch(train) [126][2900/5047] lr: 9.2199e-06 eta: 1 day, 5:50:29 time: 0.8531 data_time: 0.0030 memory: 47447 loss: 0.1142 loss_ce: 0.1142 2023/03/02 01:49:51 - mmengine - INFO - Epoch(train) [126][3000/5047] lr: 9.2199e-06 eta: 1 day, 5:49:01 time: 0.8517 data_time: 0.0027 memory: 48565 loss: 0.1044 loss_ce: 0.1044 2023/03/02 01:51:16 - mmengine - INFO - Epoch(train) [126][3100/5047] lr: 9.2199e-06 eta: 1 day, 5:47:34 time: 0.9008 data_time: 0.0026 memory: 48035 loss: 0.1186 loss_ce: 0.1186 2023/03/02 01:51:38 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 01:52:42 - mmengine - INFO - Epoch(train) [126][3200/5047] lr: 9.2199e-06 eta: 1 day, 5:46:06 time: 0.8094 data_time: 0.0031 memory: 55562 loss: 0.1002 loss_ce: 0.1002 2023/03/02 01:54:07 - mmengine - INFO - Epoch(train) [126][3300/5047] lr: 9.2199e-06 eta: 1 day, 5:44:39 time: 0.8440 data_time: 0.0028 memory: 47132 loss: 0.1159 loss_ce: 0.1159 2023/03/02 01:55:32 - mmengine - INFO - Epoch(train) [126][3400/5047] lr: 9.2199e-06 eta: 1 day, 5:43:11 time: 0.8435 data_time: 0.0026 memory: 44773 loss: 0.0993 loss_ce: 0.0993 2023/03/02 01:56:57 - mmengine - INFO - Epoch(train) [126][3500/5047] lr: 9.2199e-06 eta: 1 day, 5:41:43 time: 0.8116 data_time: 0.0036 memory: 40586 loss: 0.1015 loss_ce: 0.1015 2023/03/02 01:58:23 - mmengine - INFO - Epoch(train) [126][3600/5047] lr: 9.2199e-06 eta: 1 day, 5:40:16 time: 0.8803 data_time: 0.0027 memory: 42923 loss: 0.1108 loss_ce: 0.1108 2023/03/02 01:59:50 - mmengine - INFO - Epoch(train) [126][3700/5047] lr: 9.2199e-06 eta: 1 day, 5:38:49 time: 0.8754 data_time: 0.0034 memory: 40825 loss: 0.1066 loss_ce: 0.1066 2023/03/02 02:01:14 - mmengine - INFO - Epoch(train) [126][3800/5047] lr: 9.2199e-06 eta: 1 day, 5:37:21 time: 0.8415 data_time: 0.0027 memory: 41419 loss: 0.1031 loss_ce: 0.1031 2023/03/02 02:02:41 - mmengine - INFO - Epoch(train) [126][3900/5047] lr: 9.2199e-06 eta: 1 day, 5:35:54 time: 0.9126 data_time: 0.0053 memory: 42336 loss: 0.1016 loss_ce: 0.1016 2023/03/02 02:04:06 - mmengine - INFO - Epoch(train) [126][4000/5047] lr: 9.2199e-06 eta: 1 day, 5:34:26 time: 0.8286 data_time: 0.0043 memory: 41724 loss: 0.1085 loss_ce: 0.1085 2023/03/02 02:05:33 - mmengine - INFO - Epoch(train) [126][4100/5047] lr: 9.2199e-06 eta: 1 day, 5:32:59 time: 0.8395 data_time: 0.0031 memory: 41122 loss: 0.1131 loss_ce: 0.1131 2023/03/02 02:05:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 02:06:59 - mmengine - INFO - Epoch(train) [126][4200/5047] lr: 9.2199e-06 eta: 1 day, 5:31:32 time: 0.9087 data_time: 0.0030 memory: 51561 loss: 0.1080 loss_ce: 0.1080 2023/03/02 02:08:23 - mmengine - INFO - Epoch(train) [126][4300/5047] lr: 9.2199e-06 eta: 1 day, 5:30:04 time: 0.8592 data_time: 0.0034 memory: 40825 loss: 0.1069 loss_ce: 0.1069 2023/03/02 02:09:49 - mmengine - INFO - Epoch(train) [126][4400/5047] lr: 9.2199e-06 eta: 1 day, 5:28:37 time: 0.8038 data_time: 0.0081 memory: 41649 loss: 0.0982 loss_ce: 0.0982 2023/03/02 02:11:16 - mmengine - INFO - Epoch(train) [126][4500/5047] lr: 9.2199e-06 eta: 1 day, 5:27:10 time: 0.9160 data_time: 0.0027 memory: 47319 loss: 0.0865 loss_ce: 0.0865 2023/03/02 02:12:43 - mmengine - INFO - Epoch(train) [126][4600/5047] lr: 9.2199e-06 eta: 1 day, 5:25:42 time: 0.8887 data_time: 0.0025 memory: 47885 loss: 0.0921 loss_ce: 0.0921 2023/03/02 02:14:09 - mmengine - INFO - Epoch(train) [126][4700/5047] lr: 9.2199e-06 eta: 1 day, 5:24:15 time: 0.9157 data_time: 0.0027 memory: 49500 loss: 0.1072 loss_ce: 0.1072 2023/03/02 02:15:36 - mmengine - INFO - Epoch(train) [126][4800/5047] lr: 9.2199e-06 eta: 1 day, 5:22:48 time: 0.8666 data_time: 0.0027 memory: 41658 loss: 0.1095 loss_ce: 0.1095 2023/03/02 02:17:02 - mmengine - INFO - Epoch(train) [126][4900/5047] lr: 9.2199e-06 eta: 1 day, 5:21:20 time: 0.8126 data_time: 0.0026 memory: 44278 loss: 0.1017 loss_ce: 0.1017 2023/03/02 02:18:29 - mmengine - INFO - Epoch(train) [126][5000/5047] lr: 9.2199e-06 eta: 1 day, 5:19:53 time: 0.8773 data_time: 0.0027 memory: 47813 loss: 0.1131 loss_ce: 0.1131 2023/03/02 02:19:09 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 02:19:09 - mmengine - INFO - Saving checkpoint at 126 epochs 2023/03/02 02:20:21 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 02:20:39 - mmengine - INFO - Epoch(train) [127][ 100/5047] lr: 9.0189e-06 eta: 1 day, 5:17:44 time: 0.8494 data_time: 0.0025 memory: 46900 loss: 0.1073 loss_ce: 0.1073 2023/03/02 02:22:06 - mmengine - INFO - Epoch(train) [127][ 200/5047] lr: 9.0189e-06 eta: 1 day, 5:16:17 time: 0.8461 data_time: 0.0065 memory: 43999 loss: 0.0985 loss_ce: 0.0985 2023/03/02 02:23:33 - mmengine - INFO - Epoch(train) [127][ 300/5047] lr: 9.0189e-06 eta: 1 day, 5:14:50 time: 0.8821 data_time: 0.0027 memory: 55562 loss: 0.0973 loss_ce: 0.0973 2023/03/02 02:24:57 - mmengine - INFO - Epoch(train) [127][ 400/5047] lr: 9.0189e-06 eta: 1 day, 5:13:22 time: 0.8204 data_time: 0.0028 memory: 44617 loss: 0.1111 loss_ce: 0.1111 2023/03/02 02:26:26 - mmengine - INFO - Epoch(train) [127][ 500/5047] lr: 9.0189e-06 eta: 1 day, 5:11:55 time: 0.9076 data_time: 0.0025 memory: 49075 loss: 0.1100 loss_ce: 0.1100 2023/03/02 02:27:51 - mmengine - INFO - Epoch(train) [127][ 600/5047] lr: 9.0189e-06 eta: 1 day, 5:10:28 time: 0.8837 data_time: 0.0035 memory: 44956 loss: 0.1089 loss_ce: 0.1089 2023/03/02 02:29:16 - mmengine - INFO - Epoch(train) [127][ 700/5047] lr: 9.0189e-06 eta: 1 day, 5:09:00 time: 0.8466 data_time: 0.0045 memory: 43289 loss: 0.1068 loss_ce: 0.1068 2023/03/02 02:30:41 - mmengine - INFO - Epoch(train) [127][ 800/5047] lr: 9.0189e-06 eta: 1 day, 5:07:33 time: 0.8519 data_time: 0.0030 memory: 46005 loss: 0.1048 loss_ce: 0.1048 2023/03/02 02:32:07 - mmengine - INFO - Epoch(train) [127][ 900/5047] lr: 9.0189e-06 eta: 1 day, 5:06:05 time: 0.8746 data_time: 0.0027 memory: 46949 loss: 0.0922 loss_ce: 0.0922 2023/03/02 02:33:33 - mmengine - INFO - Epoch(train) [127][1000/5047] lr: 9.0189e-06 eta: 1 day, 5:04:38 time: 0.8656 data_time: 0.0028 memory: 50372 loss: 0.0985 loss_ce: 0.0985 2023/03/02 02:34:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 02:34:58 - mmengine - INFO - Epoch(train) [127][1100/5047] lr: 9.0189e-06 eta: 1 day, 5:03:10 time: 0.8578 data_time: 0.0029 memory: 41724 loss: 0.1054 loss_ce: 0.1054 2023/03/02 02:36:25 - mmengine - INFO - Epoch(train) [127][1200/5047] lr: 9.0189e-06 eta: 1 day, 5:01:43 time: 0.8673 data_time: 0.0025 memory: 52127 loss: 0.1085 loss_ce: 0.1085 2023/03/02 02:37:53 - mmengine - INFO - Epoch(train) [127][1300/5047] lr: 9.0189e-06 eta: 1 day, 5:00:16 time: 0.9058 data_time: 0.0026 memory: 43289 loss: 0.1077 loss_ce: 0.1077 2023/03/02 02:39:18 - mmengine - INFO - Epoch(train) [127][1400/5047] lr: 9.0189e-06 eta: 1 day, 4:58:48 time: 0.8065 data_time: 0.0027 memory: 42024 loss: 0.1038 loss_ce: 0.1038 2023/03/02 02:40:43 - mmengine - INFO - Epoch(train) [127][1500/5047] lr: 9.0189e-06 eta: 1 day, 4:57:21 time: 0.8551 data_time: 0.0029 memory: 43947 loss: 0.1050 loss_ce: 0.1050 2023/03/02 02:42:08 - mmengine - INFO - Epoch(train) [127][1600/5047] lr: 9.0189e-06 eta: 1 day, 4:55:53 time: 0.8636 data_time: 0.0028 memory: 42336 loss: 0.1157 loss_ce: 0.1157 2023/03/02 02:43:35 - mmengine - INFO - Epoch(train) [127][1700/5047] lr: 9.0189e-06 eta: 1 day, 4:54:26 time: 0.8826 data_time: 0.0029 memory: 51731 loss: 0.1137 loss_ce: 0.1137 2023/03/02 02:45:01 - mmengine - INFO - Epoch(train) [127][1800/5047] lr: 9.0189e-06 eta: 1 day, 4:52:59 time: 0.8420 data_time: 0.0030 memory: 44956 loss: 0.1066 loss_ce: 0.1066 2023/03/02 02:46:27 - mmengine - INFO - Epoch(train) [127][1900/5047] lr: 9.0189e-06 eta: 1 day, 4:51:32 time: 0.8463 data_time: 0.0028 memory: 55562 loss: 0.1040 loss_ce: 0.1040 2023/03/02 02:47:53 - mmengine - INFO - Epoch(train) [127][2000/5047] lr: 9.0189e-06 eta: 1 day, 4:50:04 time: 0.8791 data_time: 0.0030 memory: 55562 loss: 0.0900 loss_ce: 0.0900 2023/03/02 02:49:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 02:49:19 - mmengine - INFO - Epoch(train) [127][2100/5047] lr: 9.0189e-06 eta: 1 day, 4:48:37 time: 0.8410 data_time: 0.0030 memory: 43947 loss: 0.1110 loss_ce: 0.1110 2023/03/02 02:50:44 - mmengine - INFO - Epoch(train) [127][2200/5047] lr: 9.0189e-06 eta: 1 day, 4:47:09 time: 0.8152 data_time: 0.0028 memory: 41765 loss: 0.1168 loss_ce: 0.1168 2023/03/02 02:52:10 - mmengine - INFO - Epoch(train) [127][2300/5047] lr: 9.0189e-06 eta: 1 day, 4:45:42 time: 0.8257 data_time: 0.0035 memory: 43585 loss: 0.1113 loss_ce: 0.1113 2023/03/02 02:53:37 - mmengine - INFO - Epoch(train) [127][2400/5047] lr: 9.0189e-06 eta: 1 day, 4:44:15 time: 0.9084 data_time: 0.0045 memory: 55562 loss: 0.0907 loss_ce: 0.0907 2023/03/02 02:55:04 - mmengine - INFO - Epoch(train) [127][2500/5047] lr: 9.0189e-06 eta: 1 day, 4:42:47 time: 0.8425 data_time: 0.0031 memory: 47813 loss: 0.0962 loss_ce: 0.0962 2023/03/02 02:56:30 - mmengine - INFO - Epoch(train) [127][2600/5047] lr: 9.0189e-06 eta: 1 day, 4:41:20 time: 0.8734 data_time: 0.0027 memory: 46005 loss: 0.0995 loss_ce: 0.0995 2023/03/02 02:57:56 - mmengine - INFO - Epoch(train) [127][2700/5047] lr: 9.0189e-06 eta: 1 day, 4:39:53 time: 0.8842 data_time: 0.0028 memory: 51755 loss: 0.1121 loss_ce: 0.1121 2023/03/02 02:59:23 - mmengine - INFO - Epoch(train) [127][2800/5047] lr: 9.0189e-06 eta: 1 day, 4:38:26 time: 0.8552 data_time: 0.0034 memory: 42649 loss: 0.1035 loss_ce: 0.1035 2023/03/02 03:00:51 - mmengine - INFO - Epoch(train) [127][2900/5047] lr: 9.0189e-06 eta: 1 day, 4:36:59 time: 0.8875 data_time: 0.0028 memory: 45302 loss: 0.1247 loss_ce: 0.1247 2023/03/02 03:02:18 - mmengine - INFO - Epoch(train) [127][3000/5047] lr: 9.0189e-06 eta: 1 day, 4:35:31 time: 0.8891 data_time: 0.0030 memory: 52816 loss: 0.0941 loss_ce: 0.0941 2023/03/02 03:03:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 03:03:44 - mmengine - INFO - Epoch(train) [127][3100/5047] lr: 9.0189e-06 eta: 1 day, 4:34:04 time: 0.8907 data_time: 0.0028 memory: 46355 loss: 0.1153 loss_ce: 0.1153 2023/03/02 03:05:11 - mmengine - INFO - Epoch(train) [127][3200/5047] lr: 9.0189e-06 eta: 1 day, 4:32:37 time: 0.8882 data_time: 0.0036 memory: 43289 loss: 0.1079 loss_ce: 0.1079 2023/03/02 03:06:36 - mmengine - INFO - Epoch(train) [127][3300/5047] lr: 9.0189e-06 eta: 1 day, 4:31:09 time: 0.9006 data_time: 0.0025 memory: 44563 loss: 0.0990 loss_ce: 0.0990 2023/03/02 03:08:01 - mmengine - INFO - Epoch(train) [127][3400/5047] lr: 9.0189e-06 eta: 1 day, 4:29:42 time: 0.8404 data_time: 0.0027 memory: 45230 loss: 0.1112 loss_ce: 0.1112 2023/03/02 03:09:28 - mmengine - INFO - Epoch(train) [127][3500/5047] lr: 9.0189e-06 eta: 1 day, 4:28:14 time: 0.8685 data_time: 0.0095 memory: 44184 loss: 0.1207 loss_ce: 0.1207 2023/03/02 03:10:55 - mmengine - INFO - Epoch(train) [127][3600/5047] lr: 9.0189e-06 eta: 1 day, 4:26:47 time: 0.8435 data_time: 0.0055 memory: 44661 loss: 0.1170 loss_ce: 0.1170 2023/03/02 03:12:19 - mmengine - INFO - Epoch(train) [127][3700/5047] lr: 9.0189e-06 eta: 1 day, 4:25:20 time: 0.8582 data_time: 0.0057 memory: 51637 loss: 0.0929 loss_ce: 0.0929 2023/03/02 03:13:46 - mmengine - INFO - Epoch(train) [127][3800/5047] lr: 9.0189e-06 eta: 1 day, 4:23:52 time: 0.9109 data_time: 0.0026 memory: 45280 loss: 0.1002 loss_ce: 0.1002 2023/03/02 03:15:12 - mmengine - INFO - Epoch(train) [127][3900/5047] lr: 9.0189e-06 eta: 1 day, 4:22:25 time: 0.8700 data_time: 0.0032 memory: 41419 loss: 0.1057 loss_ce: 0.1057 2023/03/02 03:16:37 - mmengine - INFO - Epoch(train) [127][4000/5047] lr: 9.0189e-06 eta: 1 day, 4:20:58 time: 0.8926 data_time: 0.0025 memory: 55562 loss: 0.1162 loss_ce: 0.1162 2023/03/02 03:17:44 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 03:18:03 - mmengine - INFO - Epoch(train) [127][4100/5047] lr: 9.0189e-06 eta: 1 day, 4:19:30 time: 0.8383 data_time: 0.0027 memory: 43289 loss: 0.1219 loss_ce: 0.1219 2023/03/02 03:19:30 - mmengine - INFO - Epoch(train) [127][4200/5047] lr: 9.0189e-06 eta: 1 day, 4:18:03 time: 0.8722 data_time: 0.0028 memory: 44787 loss: 0.1201 loss_ce: 0.1201 2023/03/02 03:20:54 - mmengine - INFO - Epoch(train) [127][4300/5047] lr: 9.0189e-06 eta: 1 day, 4:16:35 time: 0.8297 data_time: 0.0082 memory: 47447 loss: 0.1113 loss_ce: 0.1113 2023/03/02 03:22:20 - mmengine - INFO - Epoch(train) [127][4400/5047] lr: 9.0189e-06 eta: 1 day, 4:15:08 time: 0.8219 data_time: 0.0029 memory: 43947 loss: 0.0960 loss_ce: 0.0960 2023/03/02 03:23:45 - mmengine - INFO - Epoch(train) [127][4500/5047] lr: 9.0189e-06 eta: 1 day, 4:13:40 time: 0.8690 data_time: 0.0028 memory: 41531 loss: 0.1091 loss_ce: 0.1091 2023/03/02 03:25:10 - mmengine - INFO - Epoch(train) [127][4600/5047] lr: 9.0189e-06 eta: 1 day, 4:12:13 time: 0.8389 data_time: 0.0028 memory: 47447 loss: 0.1012 loss_ce: 0.1012 2023/03/02 03:26:36 - mmengine - INFO - Epoch(train) [127][4700/5047] lr: 9.0189e-06 eta: 1 day, 4:10:45 time: 0.8446 data_time: 0.0036 memory: 46123 loss: 0.1117 loss_ce: 0.1117 2023/03/02 03:28:02 - mmengine - INFO - Epoch(train) [127][4800/5047] lr: 9.0189e-06 eta: 1 day, 4:09:18 time: 0.8755 data_time: 0.0030 memory: 40241 loss: 0.1081 loss_ce: 0.1081 2023/03/02 03:29:28 - mmengine - INFO - Epoch(train) [127][4900/5047] lr: 9.0189e-06 eta: 1 day, 4:07:51 time: 0.8508 data_time: 0.0029 memory: 55562 loss: 0.0993 loss_ce: 0.0993 2023/03/02 03:30:54 - mmengine - INFO - Epoch(train) [127][5000/5047] lr: 9.0189e-06 eta: 1 day, 4:06:23 time: 0.9141 data_time: 0.0028 memory: 43919 loss: 0.1077 loss_ce: 0.1077 2023/03/02 03:31:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 03:31:34 - mmengine - INFO - Saving checkpoint at 127 epochs 2023/03/02 03:32:07 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 03:33:06 - mmengine - INFO - Epoch(train) [128][ 100/5047] lr: 8.8180e-06 eta: 1 day, 4:04:15 time: 0.8655 data_time: 0.0027 memory: 52543 loss: 0.0980 loss_ce: 0.0980 2023/03/02 03:34:31 - mmengine - INFO - Epoch(train) [128][ 200/5047] lr: 8.8180e-06 eta: 1 day, 4:02:47 time: 0.8318 data_time: 0.0026 memory: 43811 loss: 0.0968 loss_ce: 0.0968 2023/03/02 03:35:56 - mmengine - INFO - Epoch(train) [128][ 300/5047] lr: 8.8180e-06 eta: 1 day, 4:01:20 time: 0.8506 data_time: 0.0026 memory: 48565 loss: 0.1089 loss_ce: 0.1089 2023/03/02 03:37:21 - mmengine - INFO - Epoch(train) [128][ 400/5047] lr: 8.8180e-06 eta: 1 day, 3:59:52 time: 0.8546 data_time: 0.0027 memory: 39398 loss: 0.1118 loss_ce: 0.1118 2023/03/02 03:38:46 - mmengine - INFO - Epoch(train) [128][ 500/5047] lr: 8.8180e-06 eta: 1 day, 3:58:25 time: 0.8935 data_time: 0.0034 memory: 49217 loss: 0.1110 loss_ce: 0.1110 2023/03/02 03:40:12 - mmengine - INFO - Epoch(train) [128][ 600/5047] lr: 8.8180e-06 eta: 1 day, 3:56:58 time: 0.8725 data_time: 0.0029 memory: 42649 loss: 0.1004 loss_ce: 0.1004 2023/03/02 03:41:39 - mmengine - INFO - Epoch(train) [128][ 700/5047] lr: 8.8180e-06 eta: 1 day, 3:55:30 time: 0.8660 data_time: 0.0028 memory: 55366 loss: 0.1077 loss_ce: 0.1077 2023/03/02 03:43:06 - mmengine - INFO - Epoch(train) [128][ 800/5047] lr: 8.8180e-06 eta: 1 day, 3:54:03 time: 0.8484 data_time: 0.0027 memory: 54242 loss: 0.0932 loss_ce: 0.0932 2023/03/02 03:44:32 - mmengine - INFO - Epoch(train) [128][ 900/5047] lr: 8.8180e-06 eta: 1 day, 3:52:36 time: 0.8572 data_time: 0.0040 memory: 55562 loss: 0.1132 loss_ce: 0.1132 2023/03/02 03:45:59 - mmengine - INFO - Epoch(train) [128][1000/5047] lr: 8.8180e-06 eta: 1 day, 3:51:08 time: 0.8271 data_time: 0.0030 memory: 42396 loss: 0.1074 loss_ce: 0.1074 2023/03/02 03:46:26 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 03:47:24 - mmengine - INFO - Epoch(train) [128][1100/5047] lr: 8.8180e-06 eta: 1 day, 3:49:41 time: 0.8655 data_time: 0.0026 memory: 42965 loss: 0.1116 loss_ce: 0.1116 2023/03/02 03:48:49 - mmengine - INFO - Epoch(train) [128][1200/5047] lr: 8.8180e-06 eta: 1 day, 3:48:14 time: 0.8412 data_time: 0.0029 memory: 41419 loss: 0.1068 loss_ce: 0.1068 2023/03/02 03:50:15 - mmengine - INFO - Epoch(train) [128][1300/5047] lr: 8.8180e-06 eta: 1 day, 3:46:46 time: 0.8467 data_time: 0.0027 memory: 41419 loss: 0.1105 loss_ce: 0.1105 2023/03/02 03:51:41 - mmengine - INFO - Epoch(train) [128][1400/5047] lr: 8.8180e-06 eta: 1 day, 3:45:19 time: 0.8645 data_time: 0.0027 memory: 42649 loss: 0.1060 loss_ce: 0.1060 2023/03/02 03:53:08 - mmengine - INFO - Epoch(train) [128][1500/5047] lr: 8.8180e-06 eta: 1 day, 3:43:52 time: 0.8803 data_time: 0.0029 memory: 45302 loss: 0.1125 loss_ce: 0.1125 2023/03/02 03:54:32 - mmengine - INFO - Epoch(train) [128][1600/5047] lr: 8.8180e-06 eta: 1 day, 3:42:24 time: 0.8785 data_time: 0.0029 memory: 45462 loss: 0.0939 loss_ce: 0.0939 2023/03/02 03:55:58 - mmengine - INFO - Epoch(train) [128][1700/5047] lr: 8.8180e-06 eta: 1 day, 3:40:57 time: 0.7969 data_time: 0.0089 memory: 40901 loss: 0.1063 loss_ce: 0.1063 2023/03/02 03:57:23 - mmengine - INFO - Epoch(train) [128][1800/5047] lr: 8.8180e-06 eta: 1 day, 3:39:29 time: 0.8284 data_time: 0.0026 memory: 52543 loss: 0.0950 loss_ce: 0.0950 2023/03/02 03:58:48 - mmengine - INFO - Epoch(train) [128][1900/5047] lr: 8.8180e-06 eta: 1 day, 3:38:01 time: 0.8744 data_time: 0.0027 memory: 41514 loss: 0.1159 loss_ce: 0.1159 2023/03/02 04:00:13 - mmengine - INFO - Epoch(train) [128][2000/5047] lr: 8.8180e-06 eta: 1 day, 3:36:34 time: 0.8205 data_time: 0.0028 memory: 46355 loss: 0.1035 loss_ce: 0.1035 2023/03/02 04:00:41 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 04:01:40 - mmengine - INFO - Epoch(train) [128][2100/5047] lr: 8.8180e-06 eta: 1 day, 3:35:07 time: 0.8209 data_time: 0.0063 memory: 44278 loss: 0.0858 loss_ce: 0.0858 2023/03/02 04:03:04 - mmengine - INFO - Epoch(train) [128][2200/5047] lr: 8.8180e-06 eta: 1 day, 3:33:39 time: 0.8542 data_time: 0.0028 memory: 47074 loss: 0.1198 loss_ce: 0.1198 2023/03/02 04:04:30 - mmengine - INFO - Epoch(train) [128][2300/5047] lr: 8.8180e-06 eta: 1 day, 3:32:12 time: 0.8539 data_time: 0.0032 memory: 43289 loss: 0.1067 loss_ce: 0.1067 2023/03/02 04:05:57 - mmengine - INFO - Epoch(train) [128][2400/5047] lr: 8.8180e-06 eta: 1 day, 3:30:45 time: 0.8298 data_time: 0.0028 memory: 43289 loss: 0.0935 loss_ce: 0.0935 2023/03/02 04:07:22 - mmengine - INFO - Epoch(train) [128][2500/5047] lr: 8.8180e-06 eta: 1 day, 3:29:17 time: 0.8647 data_time: 0.0031 memory: 39960 loss: 0.1163 loss_ce: 0.1163 2023/03/02 04:08:48 - mmengine - INFO - Epoch(train) [128][2600/5047] lr: 8.8180e-06 eta: 1 day, 3:27:50 time: 0.8351 data_time: 0.0053 memory: 52863 loss: 0.1146 loss_ce: 0.1146 2023/03/02 04:10:13 - mmengine - INFO - Epoch(train) [128][2700/5047] lr: 8.8180e-06 eta: 1 day, 3:26:22 time: 0.8257 data_time: 0.0039 memory: 44477 loss: 0.1112 loss_ce: 0.1112 2023/03/02 04:11:39 - mmengine - INFO - Epoch(train) [128][2800/5047] lr: 8.8180e-06 eta: 1 day, 3:24:55 time: 0.8256 data_time: 0.0028 memory: 49270 loss: 0.1002 loss_ce: 0.1002 2023/03/02 04:13:04 - mmengine - INFO - Epoch(train) [128][2900/5047] lr: 8.8180e-06 eta: 1 day, 3:23:27 time: 0.8203 data_time: 0.0026 memory: 42649 loss: 0.1161 loss_ce: 0.1161 2023/03/02 04:14:29 - mmengine - INFO - Epoch(train) [128][3000/5047] lr: 8.8180e-06 eta: 1 day, 3:22:00 time: 0.8747 data_time: 0.0026 memory: 45302 loss: 0.1095 loss_ce: 0.1095 2023/03/02 04:14:56 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 04:15:56 - mmengine - INFO - Epoch(train) [128][3100/5047] lr: 8.8180e-06 eta: 1 day, 3:20:33 time: 0.8888 data_time: 0.0031 memory: 49035 loss: 0.1118 loss_ce: 0.1118 2023/03/02 04:17:21 - mmengine - INFO - Epoch(train) [128][3200/5047] lr: 8.8180e-06 eta: 1 day, 3:19:05 time: 0.8877 data_time: 0.0032 memory: 44278 loss: 0.1046 loss_ce: 0.1046 2023/03/02 04:18:47 - mmengine - INFO - Epoch(train) [128][3300/5047] lr: 8.8180e-06 eta: 1 day, 3:17:38 time: 0.8553 data_time: 0.0031 memory: 42273 loss: 0.1117 loss_ce: 0.1117 2023/03/02 04:20:12 - mmengine - INFO - Epoch(train) [128][3400/5047] lr: 8.8180e-06 eta: 1 day, 3:16:10 time: 0.8890 data_time: 0.0026 memory: 43491 loss: 0.1013 loss_ce: 0.1013 2023/03/02 04:21:36 - mmengine - INFO - Epoch(train) [128][3500/5047] lr: 8.8180e-06 eta: 1 day, 3:14:42 time: 0.8515 data_time: 0.0029 memory: 42024 loss: 0.1057 loss_ce: 0.1057 2023/03/02 04:23:02 - mmengine - INFO - Epoch(train) [128][3600/5047] lr: 8.8180e-06 eta: 1 day, 3:13:15 time: 0.8793 data_time: 0.0030 memory: 44278 loss: 0.1045 loss_ce: 0.1045 2023/03/02 04:24:28 - mmengine - INFO - Epoch(train) [128][3700/5047] lr: 8.8180e-06 eta: 1 day, 3:11:48 time: 0.8340 data_time: 0.0050 memory: 40358 loss: 0.1088 loss_ce: 0.1088 2023/03/02 04:25:55 - mmengine - INFO - Epoch(train) [128][3800/5047] lr: 8.8180e-06 eta: 1 day, 3:10:21 time: 0.8826 data_time: 0.0032 memory: 49333 loss: 0.1046 loss_ce: 0.1046 2023/03/02 04:27:20 - mmengine - INFO - Epoch(train) [128][3900/5047] lr: 8.8180e-06 eta: 1 day, 3:08:53 time: 0.8302 data_time: 0.0064 memory: 53044 loss: 0.1054 loss_ce: 0.1054 2023/03/02 04:28:46 - mmengine - INFO - Epoch(train) [128][4000/5047] lr: 8.8180e-06 eta: 1 day, 3:07:26 time: 0.8584 data_time: 0.0035 memory: 46899 loss: 0.1083 loss_ce: 0.1083 2023/03/02 04:29:13 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 04:30:13 - mmengine - INFO - Epoch(train) [128][4100/5047] lr: 8.8180e-06 eta: 1 day, 3:05:59 time: 0.8260 data_time: 0.0028 memory: 44477 loss: 0.1150 loss_ce: 0.1150 2023/03/02 04:31:39 - mmengine - INFO - Epoch(train) [128][4200/5047] lr: 8.8180e-06 eta: 1 day, 3:04:31 time: 0.8919 data_time: 0.0031 memory: 55562 loss: 0.1028 loss_ce: 0.1028 2023/03/02 04:33:04 - mmengine - INFO - Epoch(train) [128][4300/5047] lr: 8.8180e-06 eta: 1 day, 3:03:04 time: 0.8075 data_time: 0.0036 memory: 43508 loss: 0.1061 loss_ce: 0.1061 2023/03/02 04:34:29 - mmengine - INFO - Epoch(train) [128][4400/5047] lr: 8.8180e-06 eta: 1 day, 3:01:36 time: 0.8344 data_time: 0.0046 memory: 43947 loss: 0.0946 loss_ce: 0.0946 2023/03/02 04:35:55 - mmengine - INFO - Epoch(train) [128][4500/5047] lr: 8.8180e-06 eta: 1 day, 3:00:09 time: 0.8470 data_time: 0.0027 memory: 46005 loss: 0.1061 loss_ce: 0.1061 2023/03/02 04:37:20 - mmengine - INFO - Epoch(train) [128][4600/5047] lr: 8.8180e-06 eta: 1 day, 2:58:41 time: 0.8216 data_time: 0.0026 memory: 43245 loss: 0.1230 loss_ce: 0.1230 2023/03/02 04:38:47 - mmengine - INFO - Epoch(train) [128][4700/5047] lr: 8.8180e-06 eta: 1 day, 2:57:14 time: 0.8370 data_time: 0.0032 memory: 49312 loss: 0.1048 loss_ce: 0.1048 2023/03/02 04:40:12 - mmengine - INFO - Epoch(train) [128][4800/5047] lr: 8.8180e-06 eta: 1 day, 2:55:47 time: 0.8046 data_time: 0.0029 memory: 44956 loss: 0.1090 loss_ce: 0.1090 2023/03/02 04:41:40 - mmengine - INFO - Epoch(train) [128][4900/5047] lr: 8.8180e-06 eta: 1 day, 2:54:20 time: 0.8492 data_time: 0.0030 memory: 55562 loss: 0.1073 loss_ce: 0.1073 2023/03/02 04:43:03 - mmengine - INFO - Epoch(train) [128][5000/5047] lr: 8.8180e-06 eta: 1 day, 2:52:52 time: 0.8282 data_time: 0.0027 memory: 43947 loss: 0.1096 loss_ce: 0.1096 2023/03/02 04:43:30 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 04:43:43 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 04:43:43 - mmengine - INFO - Saving checkpoint at 128 epochs 2023/03/02 04:45:15 - mmengine - INFO - Epoch(train) [129][ 100/5047] lr: 8.6170e-06 eta: 1 day, 2:50:44 time: 0.8790 data_time: 0.0026 memory: 42965 loss: 0.1012 loss_ce: 0.1012 2023/03/02 04:46:39 - mmengine - INFO - Epoch(train) [129][ 200/5047] lr: 8.6170e-06 eta: 1 day, 2:49:16 time: 0.8659 data_time: 0.0028 memory: 39681 loss: 0.1135 loss_ce: 0.1135 2023/03/02 04:48:05 - mmengine - INFO - Epoch(train) [129][ 300/5047] lr: 8.6170e-06 eta: 1 day, 2:47:49 time: 0.8166 data_time: 0.0027 memory: 43613 loss: 0.1205 loss_ce: 0.1205 2023/03/02 04:49:32 - mmengine - INFO - Epoch(train) [129][ 400/5047] lr: 8.6170e-06 eta: 1 day, 2:46:22 time: 0.8893 data_time: 0.0026 memory: 49378 loss: 0.1218 loss_ce: 0.1218 2023/03/02 04:50:58 - mmengine - INFO - Epoch(train) [129][ 500/5047] lr: 8.6170e-06 eta: 1 day, 2:44:54 time: 0.8977 data_time: 0.0028 memory: 42965 loss: 0.0912 loss_ce: 0.0912 2023/03/02 04:52:23 - mmengine - INFO - Epoch(train) [129][ 600/5047] lr: 8.6170e-06 eta: 1 day, 2:43:27 time: 0.8367 data_time: 0.0027 memory: 46005 loss: 0.1052 loss_ce: 0.1052 2023/03/02 04:53:50 - mmengine - INFO - Epoch(train) [129][ 700/5047] lr: 8.6170e-06 eta: 1 day, 2:41:59 time: 0.8529 data_time: 0.0027 memory: 50420 loss: 0.1048 loss_ce: 0.1048 2023/03/02 04:55:13 - mmengine - INFO - Epoch(train) [129][ 800/5047] lr: 8.6170e-06 eta: 1 day, 2:40:32 time: 0.8514 data_time: 0.0054 memory: 39960 loss: 0.1000 loss_ce: 0.1000 2023/03/02 04:56:39 - mmengine - INFO - Epoch(train) [129][ 900/5047] lr: 8.6170e-06 eta: 1 day, 2:39:04 time: 0.8724 data_time: 0.0032 memory: 40825 loss: 0.0952 loss_ce: 0.0952 2023/03/02 04:57:50 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 04:58:04 - mmengine - INFO - Epoch(train) [129][1000/5047] lr: 8.6170e-06 eta: 1 day, 2:37:37 time: 0.8571 data_time: 0.0030 memory: 40877 loss: 0.1073 loss_ce: 0.1073 2023/03/02 04:59:29 - mmengine - INFO - Epoch(train) [129][1100/5047] lr: 8.6170e-06 eta: 1 day, 2:36:09 time: 0.8256 data_time: 0.0037 memory: 48638 loss: 0.1021 loss_ce: 0.1021 2023/03/02 05:00:54 - mmengine - INFO - Epoch(train) [129][1200/5047] lr: 8.6170e-06 eta: 1 day, 2:34:42 time: 0.8419 data_time: 0.0026 memory: 41724 loss: 0.1138 loss_ce: 0.1138 2023/03/02 05:02:19 - mmengine - INFO - Epoch(train) [129][1300/5047] lr: 8.6170e-06 eta: 1 day, 2:33:14 time: 0.8772 data_time: 0.0027 memory: 42024 loss: 0.1048 loss_ce: 0.1048 2023/03/02 05:03:46 - mmengine - INFO - Epoch(train) [129][1400/5047] lr: 8.6170e-06 eta: 1 day, 2:31:47 time: 0.8682 data_time: 0.0040 memory: 47973 loss: 0.1061 loss_ce: 0.1061 2023/03/02 05:05:11 - mmengine - INFO - Epoch(train) [129][1500/5047] lr: 8.6170e-06 eta: 1 day, 2:30:20 time: 0.8457 data_time: 0.0042 memory: 41724 loss: 0.1074 loss_ce: 0.1074 2023/03/02 05:06:37 - mmengine - INFO - Epoch(train) [129][1600/5047] lr: 8.6170e-06 eta: 1 day, 2:28:52 time: 0.8929 data_time: 0.0091 memory: 41724 loss: 0.1101 loss_ce: 0.1101 2023/03/02 05:08:02 - mmengine - INFO - Epoch(train) [129][1700/5047] lr: 8.6170e-06 eta: 1 day, 2:27:25 time: 0.8770 data_time: 0.0065 memory: 43613 loss: 0.1011 loss_ce: 0.1011 2023/03/02 05:09:27 - mmengine - INFO - Epoch(train) [129][1800/5047] lr: 8.6170e-06 eta: 1 day, 2:25:57 time: 0.8236 data_time: 0.0030 memory: 44866 loss: 0.1156 loss_ce: 0.1156 2023/03/02 05:10:53 - mmengine - INFO - Epoch(train) [129][1900/5047] lr: 8.6170e-06 eta: 1 day, 2:24:30 time: 0.8271 data_time: 0.0097 memory: 43613 loss: 0.1040 loss_ce: 0.1040 2023/03/02 05:12:06 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 05:12:21 - mmengine - INFO - Epoch(train) [129][2000/5047] lr: 8.6170e-06 eta: 1 day, 2:23:03 time: 0.9034 data_time: 0.0029 memory: 51586 loss: 0.1207 loss_ce: 0.1207 2023/03/02 05:13:44 - mmengine - INFO - Epoch(train) [129][2100/5047] lr: 8.6170e-06 eta: 1 day, 2:21:35 time: 0.8207 data_time: 0.0033 memory: 44667 loss: 0.1121 loss_ce: 0.1121 2023/03/02 05:15:10 - mmengine - INFO - Epoch(train) [129][2200/5047] lr: 8.6170e-06 eta: 1 day, 2:20:08 time: 0.8452 data_time: 0.0028 memory: 49069 loss: 0.1143 loss_ce: 0.1143 2023/03/02 05:16:35 - mmengine - INFO - Epoch(train) [129][2300/5047] lr: 8.6170e-06 eta: 1 day, 2:18:40 time: 0.9379 data_time: 0.0026 memory: 44617 loss: 0.1014 loss_ce: 0.1014 2023/03/02 05:18:02 - mmengine - INFO - Epoch(train) [129][2400/5047] lr: 8.6170e-06 eta: 1 day, 2:17:13 time: 0.8657 data_time: 0.0033 memory: 52127 loss: 0.1032 loss_ce: 0.1032 2023/03/02 05:19:26 - mmengine - INFO - Epoch(train) [129][2500/5047] lr: 8.6170e-06 eta: 1 day, 2:15:45 time: 0.8138 data_time: 0.0036 memory: 40241 loss: 0.1316 loss_ce: 0.1316 2023/03/02 05:20:50 - mmengine - INFO - Epoch(train) [129][2600/5047] lr: 8.6170e-06 eta: 1 day, 2:14:18 time: 0.8342 data_time: 0.0066 memory: 50906 loss: 0.1087 loss_ce: 0.1087 2023/03/02 05:22:15 - mmengine - INFO - Epoch(train) [129][2700/5047] lr: 8.6170e-06 eta: 1 day, 2:12:50 time: 0.8949 data_time: 0.0032 memory: 45302 loss: 0.1161 loss_ce: 0.1161 2023/03/02 05:23:40 - mmengine - INFO - Epoch(train) [129][2800/5047] lr: 8.6170e-06 eta: 1 day, 2:11:23 time: 0.8370 data_time: 0.0031 memory: 46713 loss: 0.0983 loss_ce: 0.0983 2023/03/02 05:25:05 - mmengine - INFO - Epoch(train) [129][2900/5047] lr: 8.6170e-06 eta: 1 day, 2:09:55 time: 0.8503 data_time: 0.0028 memory: 51795 loss: 0.1162 loss_ce: 0.1162 2023/03/02 05:26:19 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 05:26:33 - mmengine - INFO - Epoch(train) [129][3000/5047] lr: 8.6170e-06 eta: 1 day, 2:08:28 time: 0.8489 data_time: 0.0026 memory: 50417 loss: 0.1263 loss_ce: 0.1263 2023/03/02 05:27:57 - mmengine - INFO - Epoch(train) [129][3100/5047] lr: 8.6170e-06 eta: 1 day, 2:07:01 time: 0.8568 data_time: 0.0066 memory: 43289 loss: 0.1070 loss_ce: 0.1070 2023/03/02 05:29:24 - mmengine - INFO - Epoch(train) [129][3200/5047] lr: 8.6170e-06 eta: 1 day, 2:05:34 time: 0.8765 data_time: 0.0053 memory: 48565 loss: 0.1093 loss_ce: 0.1093 2023/03/02 05:30:50 - mmengine - INFO - Epoch(train) [129][3300/5047] lr: 8.6170e-06 eta: 1 day, 2:04:06 time: 0.8408 data_time: 0.0029 memory: 44278 loss: 0.1044 loss_ce: 0.1044 2023/03/02 05:32:14 - mmengine - INFO - Epoch(train) [129][3400/5047] lr: 8.6170e-06 eta: 1 day, 2:02:39 time: 0.8154 data_time: 0.0026 memory: 42054 loss: 0.1060 loss_ce: 0.1060 2023/03/02 05:33:41 - mmengine - INFO - Epoch(train) [129][3500/5047] lr: 8.6170e-06 eta: 1 day, 2:01:11 time: 0.9269 data_time: 0.0044 memory: 40256 loss: 0.0984 loss_ce: 0.0984 2023/03/02 05:35:06 - mmengine - INFO - Epoch(train) [129][3600/5047] lr: 8.6170e-06 eta: 1 day, 1:59:44 time: 0.8517 data_time: 0.0027 memory: 48565 loss: 0.0949 loss_ce: 0.0949 2023/03/02 05:36:31 - mmengine - INFO - Epoch(train) [129][3700/5047] lr: 8.6170e-06 eta: 1 day, 1:58:16 time: 0.8498 data_time: 0.0031 memory: 41966 loss: 0.1001 loss_ce: 0.1001 2023/03/02 05:37:56 - mmengine - INFO - Epoch(train) [129][3800/5047] lr: 8.6170e-06 eta: 1 day, 1:56:49 time: 0.8578 data_time: 0.0029 memory: 40402 loss: 0.0956 loss_ce: 0.0956 2023/03/02 05:39:23 - mmengine - INFO - Epoch(train) [129][3900/5047] lr: 8.6170e-06 eta: 1 day, 1:55:22 time: 0.9079 data_time: 0.0030 memory: 40535 loss: 0.1086 loss_ce: 0.1086 2023/03/02 05:40:35 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 05:40:48 - mmengine - INFO - Epoch(train) [129][4000/5047] lr: 8.6170e-06 eta: 1 day, 1:53:54 time: 0.8712 data_time: 0.0028 memory: 47813 loss: 0.1108 loss_ce: 0.1108 2023/03/02 05:42:14 - mmengine - INFO - Epoch(train) [129][4100/5047] lr: 8.6170e-06 eta: 1 day, 1:52:27 time: 0.8509 data_time: 0.0036 memory: 40535 loss: 0.1141 loss_ce: 0.1141 2023/03/02 05:43:41 - mmengine - INFO - Epoch(train) [129][4200/5047] lr: 8.6170e-06 eta: 1 day, 1:51:00 time: 0.8567 data_time: 0.0031 memory: 47074 loss: 0.1008 loss_ce: 0.1008 2023/03/02 05:45:05 - mmengine - INFO - Epoch(train) [129][4300/5047] lr: 8.6170e-06 eta: 1 day, 1:49:32 time: 0.8577 data_time: 0.0033 memory: 45734 loss: 0.1079 loss_ce: 0.1079 2023/03/02 05:46:31 - mmengine - INFO - Epoch(train) [129][4400/5047] lr: 8.6170e-06 eta: 1 day, 1:48:05 time: 0.8587 data_time: 0.0032 memory: 42965 loss: 0.0955 loss_ce: 0.0955 2023/03/02 05:47:57 - mmengine - INFO - Epoch(train) [129][4500/5047] lr: 8.6170e-06 eta: 1 day, 1:46:38 time: 0.8072 data_time: 0.0031 memory: 55562 loss: 0.1159 loss_ce: 0.1159 2023/03/02 05:49:24 - mmengine - INFO - Epoch(train) [129][4600/5047] lr: 8.6170e-06 eta: 1 day, 1:45:11 time: 0.8669 data_time: 0.0028 memory: 44956 loss: 0.1118 loss_ce: 0.1118 2023/03/02 05:50:50 - mmengine - INFO - Epoch(train) [129][4700/5047] lr: 8.6170e-06 eta: 1 day, 1:43:43 time: 0.8825 data_time: 0.0027 memory: 43846 loss: 0.1104 loss_ce: 0.1104 2023/03/02 05:52:16 - mmengine - INFO - Epoch(train) [129][4800/5047] lr: 8.6170e-06 eta: 1 day, 1:42:16 time: 0.8508 data_time: 0.0029 memory: 39960 loss: 0.1049 loss_ce: 0.1049 2023/03/02 05:53:42 - mmengine - INFO - Epoch(train) [129][4900/5047] lr: 8.6170e-06 eta: 1 day, 1:40:49 time: 0.8388 data_time: 0.0027 memory: 42649 loss: 0.1104 loss_ce: 0.1104 2023/03/02 05:54:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 05:55:08 - mmengine - INFO - Epoch(train) [129][5000/5047] lr: 8.6170e-06 eta: 1 day, 1:39:21 time: 0.8665 data_time: 0.0029 memory: 41427 loss: 0.1072 loss_ce: 0.1072 2023/03/02 05:55:48 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 05:55:48 - mmengine - INFO - Saving checkpoint at 129 epochs 2023/03/02 05:57:19 - mmengine - INFO - Epoch(train) [130][ 100/5047] lr: 8.4161e-06 eta: 1 day, 1:37:13 time: 0.8712 data_time: 0.0033 memory: 42649 loss: 0.1129 loss_ce: 0.1129 2023/03/02 05:58:44 - mmengine - INFO - Epoch(train) [130][ 200/5047] lr: 8.4161e-06 eta: 1 day, 1:35:45 time: 0.8874 data_time: 0.0042 memory: 55562 loss: 0.1194 loss_ce: 0.1194 2023/03/02 06:00:09 - mmengine - INFO - Epoch(train) [130][ 300/5047] lr: 8.4161e-06 eta: 1 day, 1:34:18 time: 0.8644 data_time: 0.0052 memory: 44617 loss: 0.1039 loss_ce: 0.1039 2023/03/02 06:01:35 - mmengine - INFO - Epoch(train) [130][ 400/5047] lr: 8.4161e-06 eta: 1 day, 1:32:51 time: 0.8322 data_time: 0.0049 memory: 44536 loss: 0.1040 loss_ce: 0.1040 2023/03/02 06:02:59 - mmengine - INFO - Epoch(train) [130][ 500/5047] lr: 8.4161e-06 eta: 1 day, 1:31:23 time: 0.8446 data_time: 0.0028 memory: 40535 loss: 0.0990 loss_ce: 0.0990 2023/03/02 06:04:25 - mmengine - INFO - Epoch(train) [130][ 600/5047] lr: 8.4161e-06 eta: 1 day, 1:29:56 time: 0.8760 data_time: 0.0027 memory: 47074 loss: 0.1180 loss_ce: 0.1180 2023/03/02 06:05:49 - mmengine - INFO - Epoch(train) [130][ 700/5047] lr: 8.4161e-06 eta: 1 day, 1:28:28 time: 0.8684 data_time: 0.0045 memory: 42046 loss: 0.1138 loss_ce: 0.1138 2023/03/02 06:07:15 - mmengine - INFO - Epoch(train) [130][ 800/5047] lr: 8.4161e-06 eta: 1 day, 1:27:01 time: 0.8311 data_time: 0.0033 memory: 46355 loss: 0.1181 loss_ce: 0.1181 2023/03/02 06:08:42 - mmengine - INFO - Epoch(train) [130][ 900/5047] lr: 8.4161e-06 eta: 1 day, 1:25:34 time: 0.8567 data_time: 0.0032 memory: 46472 loss: 0.1046 loss_ce: 0.1046 2023/03/02 06:09:13 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 06:10:06 - mmengine - INFO - Epoch(train) [130][1000/5047] lr: 8.4161e-06 eta: 1 day, 1:24:06 time: 0.8509 data_time: 0.0031 memory: 55562 loss: 0.1170 loss_ce: 0.1170 2023/03/02 06:11:32 - mmengine - INFO - Epoch(train) [130][1100/5047] lr: 8.4161e-06 eta: 1 day, 1:22:39 time: 0.8992 data_time: 0.0061 memory: 40825 loss: 0.1042 loss_ce: 0.1042 2023/03/02 06:12:57 - mmengine - INFO - Epoch(train) [130][1200/5047] lr: 8.4161e-06 eta: 1 day, 1:21:11 time: 0.8660 data_time: 0.0027 memory: 40535 loss: 0.1066 loss_ce: 0.1066 2023/03/02 06:14:22 - mmengine - INFO - Epoch(train) [130][1300/5047] lr: 8.4161e-06 eta: 1 day, 1:19:44 time: 0.8092 data_time: 0.0027 memory: 43289 loss: 0.1009 loss_ce: 0.1009 2023/03/02 06:15:48 - mmengine - INFO - Epoch(train) [130][1400/5047] lr: 8.4161e-06 eta: 1 day, 1:18:17 time: 0.8841 data_time: 0.0027 memory: 39059 loss: 0.1102 loss_ce: 0.1102 2023/03/02 06:17:14 - mmengine - INFO - Epoch(train) [130][1500/5047] lr: 8.4161e-06 eta: 1 day, 1:16:49 time: 0.9008 data_time: 0.0025 memory: 43289 loss: 0.1101 loss_ce: 0.1101 2023/03/02 06:18:38 - mmengine - INFO - Epoch(train) [130][1600/5047] lr: 8.4161e-06 eta: 1 day, 1:15:22 time: 0.8459 data_time: 0.0026 memory: 42024 loss: 0.1000 loss_ce: 0.1000 2023/03/02 06:20:03 - mmengine - INFO - Epoch(train) [130][1700/5047] lr: 8.4161e-06 eta: 1 day, 1:13:54 time: 0.8569 data_time: 0.0028 memory: 40825 loss: 0.1199 loss_ce: 0.1199 2023/03/02 06:21:29 - mmengine - INFO - Epoch(train) [130][1800/5047] lr: 8.4161e-06 eta: 1 day, 1:12:27 time: 0.8872 data_time: 0.0028 memory: 55389 loss: 0.1068 loss_ce: 0.1068 2023/03/02 06:22:54 - mmengine - INFO - Epoch(train) [130][1900/5047] lr: 8.4161e-06 eta: 1 day, 1:10:59 time: 0.8711 data_time: 0.0036 memory: 49242 loss: 0.1026 loss_ce: 0.1026 2023/03/02 06:23:26 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 06:24:20 - mmengine - INFO - Epoch(train) [130][2000/5047] lr: 8.4161e-06 eta: 1 day, 1:09:32 time: 0.8405 data_time: 0.0050 memory: 41122 loss: 0.1177 loss_ce: 0.1177 2023/03/02 06:25:47 - mmengine - INFO - Epoch(train) [130][2100/5047] lr: 8.4161e-06 eta: 1 day, 1:08:05 time: 0.8661 data_time: 0.0030 memory: 44278 loss: 0.0932 loss_ce: 0.0932 2023/03/02 06:27:13 - mmengine - INFO - Epoch(train) [130][2200/5047] lr: 8.4161e-06 eta: 1 day, 1:06:38 time: 0.8428 data_time: 0.0026 memory: 43289 loss: 0.0983 loss_ce: 0.0983 2023/03/02 06:28:38 - mmengine - INFO - Epoch(train) [130][2300/5047] lr: 8.4161e-06 eta: 1 day, 1:05:10 time: 0.8559 data_time: 0.0030 memory: 49429 loss: 0.1201 loss_ce: 0.1201 2023/03/02 06:30:04 - mmengine - INFO - Epoch(train) [130][2400/5047] lr: 8.4161e-06 eta: 1 day, 1:03:43 time: 0.8886 data_time: 0.0030 memory: 44865 loss: 0.1081 loss_ce: 0.1081 2023/03/02 06:31:29 - mmengine - INFO - Epoch(train) [130][2500/5047] lr: 8.4161e-06 eta: 1 day, 1:02:16 time: 0.8312 data_time: 0.0038 memory: 55562 loss: 0.1037 loss_ce: 0.1037 2023/03/02 06:32:54 - mmengine - INFO - Epoch(train) [130][2600/5047] lr: 8.4161e-06 eta: 1 day, 1:00:48 time: 0.8752 data_time: 0.0027 memory: 44614 loss: 0.1033 loss_ce: 0.1033 2023/03/02 06:34:20 - mmengine - INFO - Epoch(train) [130][2700/5047] lr: 8.4161e-06 eta: 1 day, 0:59:21 time: 0.8448 data_time: 0.0026 memory: 42336 loss: 0.0901 loss_ce: 0.0901 2023/03/02 06:35:46 - mmengine - INFO - Epoch(train) [130][2800/5047] lr: 8.4161e-06 eta: 1 day, 0:57:53 time: 0.9239 data_time: 0.0029 memory: 41724 loss: 0.0906 loss_ce: 0.0906 2023/03/02 06:37:12 - mmengine - INFO - Epoch(train) [130][2900/5047] lr: 8.4161e-06 eta: 1 day, 0:56:26 time: 0.8821 data_time: 0.0029 memory: 44278 loss: 0.1003 loss_ce: 0.1003 2023/03/02 06:37:44 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 06:38:38 - mmengine - INFO - Epoch(train) [130][3000/5047] lr: 8.4161e-06 eta: 1 day, 0:54:59 time: 0.8724 data_time: 0.0030 memory: 48641 loss: 0.1176 loss_ce: 0.1176 2023/03/02 06:40:04 - mmengine - INFO - Epoch(train) [130][3100/5047] lr: 8.4161e-06 eta: 1 day, 0:53:32 time: 0.8146 data_time: 0.0029 memory: 51132 loss: 0.1009 loss_ce: 0.1009 2023/03/02 06:41:29 - mmengine - INFO - Epoch(train) [130][3200/5047] lr: 8.4161e-06 eta: 1 day, 0:52:04 time: 0.8485 data_time: 0.0026 memory: 46355 loss: 0.1172 loss_ce: 0.1172 2023/03/02 06:42:57 - mmengine - INFO - Epoch(train) [130][3300/5047] lr: 8.4161e-06 eta: 1 day, 0:50:37 time: 0.8784 data_time: 0.0033 memory: 42965 loss: 0.1082 loss_ce: 0.1082 2023/03/02 06:44:24 - mmengine - INFO - Epoch(train) [130][3400/5047] lr: 8.4161e-06 eta: 1 day, 0:49:10 time: 0.9198 data_time: 0.0027 memory: 55562 loss: 0.0956 loss_ce: 0.0956 2023/03/02 06:45:48 - mmengine - INFO - Epoch(train) [130][3500/5047] lr: 8.4161e-06 eta: 1 day, 0:47:42 time: 0.8408 data_time: 0.0028 memory: 41122 loss: 0.1011 loss_ce: 0.1011 2023/03/02 06:47:15 - mmengine - INFO - Epoch(train) [130][3600/5047] lr: 8.4161e-06 eta: 1 day, 0:46:15 time: 0.8419 data_time: 0.0026 memory: 41122 loss: 0.0986 loss_ce: 0.0986 2023/03/02 06:48:40 - mmengine - INFO - Epoch(train) [130][3700/5047] lr: 8.4161e-06 eta: 1 day, 0:44:48 time: 0.8819 data_time: 0.0057 memory: 45302 loss: 0.1171 loss_ce: 0.1171 2023/03/02 06:50:07 - mmengine - INFO - Epoch(train) [130][3800/5047] lr: 8.4161e-06 eta: 1 day, 0:43:21 time: 0.8513 data_time: 0.0028 memory: 43714 loss: 0.1069 loss_ce: 0.1069 2023/03/02 06:51:32 - mmengine - INFO - Epoch(train) [130][3900/5047] lr: 8.4161e-06 eta: 1 day, 0:41:53 time: 0.8672 data_time: 0.0026 memory: 45689 loss: 0.1086 loss_ce: 0.1086 2023/03/02 06:52:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 06:52:57 - mmengine - INFO - Epoch(train) [130][4000/5047] lr: 8.4161e-06 eta: 1 day, 0:40:26 time: 0.8840 data_time: 0.0029 memory: 54072 loss: 0.0980 loss_ce: 0.0980 2023/03/02 06:54:23 - mmengine - INFO - Epoch(train) [130][4100/5047] lr: 8.4161e-06 eta: 1 day, 0:38:59 time: 0.8845 data_time: 0.0028 memory: 40535 loss: 0.1189 loss_ce: 0.1189 2023/03/02 06:55:48 - mmengine - INFO - Epoch(train) [130][4200/5047] lr: 8.4161e-06 eta: 1 day, 0:37:31 time: 0.8756 data_time: 0.0027 memory: 41724 loss: 0.0950 loss_ce: 0.0950 2023/03/02 06:57:18 - mmengine - INFO - Epoch(train) [130][4300/5047] lr: 8.4161e-06 eta: 1 day, 0:36:04 time: 0.8934 data_time: 0.0028 memory: 54232 loss: 0.1070 loss_ce: 0.1070 2023/03/02 06:58:42 - mmengine - INFO - Epoch(train) [130][4400/5047] lr: 8.4161e-06 eta: 1 day, 0:34:37 time: 0.8641 data_time: 0.0031 memory: 48215 loss: 0.1107 loss_ce: 0.1107 2023/03/02 07:00:09 - mmengine - INFO - Epoch(train) [130][4500/5047] lr: 8.4161e-06 eta: 1 day, 0:33:10 time: 0.8907 data_time: 0.0029 memory: 48948 loss: 0.1037 loss_ce: 0.1037 2023/03/02 07:01:36 - mmengine - INFO - Epoch(train) [130][4600/5047] lr: 8.4161e-06 eta: 1 day, 0:31:43 time: 0.8544 data_time: 0.0037 memory: 45302 loss: 0.1087 loss_ce: 0.1087 2023/03/02 07:03:04 - mmengine - INFO - Epoch(train) [130][4700/5047] lr: 8.4161e-06 eta: 1 day, 0:30:16 time: 0.8449 data_time: 0.0038 memory: 42465 loss: 0.1001 loss_ce: 0.1001 2023/03/02 07:04:30 - mmengine - INFO - Epoch(train) [130][4800/5047] lr: 8.4161e-06 eta: 1 day, 0:28:48 time: 0.8929 data_time: 0.0029 memory: 40535 loss: 0.1074 loss_ce: 0.1074 2023/03/02 07:05:55 - mmengine - INFO - Epoch(train) [130][4900/5047] lr: 8.4161e-06 eta: 1 day, 0:27:21 time: 0.8444 data_time: 0.0027 memory: 42623 loss: 0.1056 loss_ce: 0.1056 2023/03/02 07:06:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 07:07:21 - mmengine - INFO - Epoch(train) [130][5000/5047] lr: 8.4161e-06 eta: 1 day, 0:25:54 time: 0.8190 data_time: 0.0026 memory: 42965 loss: 0.1008 loss_ce: 0.1008 2023/03/02 07:08:01 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 07:08:01 - mmengine - INFO - Saving checkpoint at 130 epochs 2023/03/02 07:09:31 - mmengine - INFO - Epoch(train) [131][ 100/5047] lr: 8.2151e-06 eta: 1 day, 0:23:45 time: 0.8964 data_time: 0.0028 memory: 44896 loss: 0.1014 loss_ce: 0.1014 2023/03/02 07:10:58 - mmengine - INFO - Epoch(train) [131][ 200/5047] lr: 8.2151e-06 eta: 1 day, 0:22:18 time: 0.9037 data_time: 0.0091 memory: 43947 loss: 0.1088 loss_ce: 0.1088 2023/03/02 07:12:24 - mmengine - INFO - Epoch(train) [131][ 300/5047] lr: 8.2151e-06 eta: 1 day, 0:20:51 time: 0.8591 data_time: 0.0026 memory: 54113 loss: 0.0941 loss_ce: 0.0941 2023/03/02 07:13:49 - mmengine - INFO - Epoch(train) [131][ 400/5047] lr: 8.2151e-06 eta: 1 day, 0:19:23 time: 0.8680 data_time: 0.0028 memory: 50589 loss: 0.1061 loss_ce: 0.1061 2023/03/02 07:15:15 - mmengine - INFO - Epoch(train) [131][ 500/5047] lr: 8.2151e-06 eta: 1 day, 0:17:56 time: 0.8417 data_time: 0.0027 memory: 41122 loss: 0.0999 loss_ce: 0.0999 2023/03/02 07:16:43 - mmengine - INFO - Epoch(train) [131][ 600/5047] lr: 8.2151e-06 eta: 1 day, 0:16:29 time: 0.8893 data_time: 0.0027 memory: 41984 loss: 0.1040 loss_ce: 0.1040 2023/03/02 07:18:10 - mmengine - INFO - Epoch(train) [131][ 700/5047] lr: 8.2151e-06 eta: 1 day, 0:15:02 time: 0.8702 data_time: 0.0040 memory: 54242 loss: 0.0958 loss_ce: 0.0958 2023/03/02 07:19:35 - mmengine - INFO - Epoch(train) [131][ 800/5047] lr: 8.2151e-06 eta: 1 day, 0:13:34 time: 0.8734 data_time: 0.0027 memory: 52882 loss: 0.0963 loss_ce: 0.0963 2023/03/02 07:20:50 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 07:21:00 - mmengine - INFO - Epoch(train) [131][ 900/5047] lr: 8.2151e-06 eta: 1 day, 0:12:07 time: 0.9172 data_time: 0.0028 memory: 42265 loss: 0.1095 loss_ce: 0.1095 2023/03/02 07:22:25 - mmengine - INFO - Epoch(train) [131][1000/5047] lr: 8.2151e-06 eta: 1 day, 0:10:39 time: 0.7974 data_time: 0.0028 memory: 48541 loss: 0.1011 loss_ce: 0.1011 2023/03/02 07:23:50 - mmengine - INFO - Epoch(train) [131][1100/5047] lr: 8.2151e-06 eta: 1 day, 0:09:12 time: 0.8674 data_time: 0.0028 memory: 55562 loss: 0.0991 loss_ce: 0.0991 2023/03/02 07:25:17 - mmengine - INFO - Epoch(train) [131][1200/5047] lr: 8.2151e-06 eta: 1 day, 0:07:45 time: 0.8477 data_time: 0.0045 memory: 47447 loss: 0.1025 loss_ce: 0.1025 2023/03/02 07:26:43 - mmengine - INFO - Epoch(train) [131][1300/5047] lr: 8.2151e-06 eta: 1 day, 0:06:18 time: 0.8527 data_time: 0.0030 memory: 46964 loss: 0.1000 loss_ce: 0.1000 2023/03/02 07:28:10 - mmengine - INFO - Epoch(train) [131][1400/5047] lr: 8.2151e-06 eta: 1 day, 0:04:51 time: 0.8903 data_time: 0.0030 memory: 53025 loss: 0.0953 loss_ce: 0.0953 2023/03/02 07:29:37 - mmengine - INFO - Epoch(train) [131][1500/5047] lr: 8.2151e-06 eta: 1 day, 0:03:23 time: 0.8881 data_time: 0.0056 memory: 48189 loss: 0.1091 loss_ce: 0.1091 2023/03/02 07:31:04 - mmengine - INFO - Epoch(train) [131][1600/5047] lr: 8.2151e-06 eta: 1 day, 0:01:56 time: 0.9244 data_time: 0.0031 memory: 51436 loss: 0.1023 loss_ce: 0.1023 2023/03/02 07:32:29 - mmengine - INFO - Epoch(train) [131][1700/5047] lr: 8.2151e-06 eta: 1 day, 0:00:29 time: 0.8674 data_time: 0.0025 memory: 43947 loss: 0.1032 loss_ce: 0.1032 2023/03/02 07:33:54 - mmengine - INFO - Epoch(train) [131][1800/5047] lr: 8.2151e-06 eta: 23:59:01 time: 0.8690 data_time: 0.0032 memory: 46964 loss: 0.0979 loss_ce: 0.0979 2023/03/02 07:35:10 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 07:35:18 - mmengine - INFO - Epoch(train) [131][1900/5047] lr: 8.2151e-06 eta: 23:57:34 time: 0.8136 data_time: 0.0055 memory: 53021 loss: 0.1111 loss_ce: 0.1111 2023/03/02 07:36:44 - mmengine - INFO - Epoch(train) [131][2000/5047] lr: 8.2151e-06 eta: 23:56:07 time: 0.8426 data_time: 0.0051 memory: 42965 loss: 0.1046 loss_ce: 0.1046 2023/03/02 07:38:09 - mmengine - INFO - Epoch(train) [131][2100/5047] lr: 8.2151e-06 eta: 23:54:39 time: 0.8692 data_time: 0.0036 memory: 42391 loss: 0.1103 loss_ce: 0.1103 2023/03/02 07:39:34 - mmengine - INFO - Epoch(train) [131][2200/5047] lr: 8.2151e-06 eta: 23:53:12 time: 0.8258 data_time: 0.0029 memory: 44477 loss: 0.0969 loss_ce: 0.0969 2023/03/02 07:40:58 - mmengine - INFO - Epoch(train) [131][2300/5047] lr: 8.2151e-06 eta: 23:51:44 time: 0.8502 data_time: 0.0028 memory: 49217 loss: 0.1072 loss_ce: 0.1072 2023/03/02 07:42:23 - mmengine - INFO - Epoch(train) [131][2400/5047] lr: 8.2151e-06 eta: 23:50:17 time: 0.8640 data_time: 0.0063 memory: 53809 loss: 0.1035 loss_ce: 0.1035 2023/03/02 07:43:50 - mmengine - INFO - Epoch(train) [131][2500/5047] lr: 8.2151e-06 eta: 23:48:50 time: 0.8964 data_time: 0.0032 memory: 43289 loss: 0.1017 loss_ce: 0.1017 2023/03/02 07:45:16 - mmengine - INFO - Epoch(train) [131][2600/5047] lr: 8.2151e-06 eta: 23:47:22 time: 0.8533 data_time: 0.0034 memory: 42205 loss: 0.0985 loss_ce: 0.0985 2023/03/02 07:46:42 - mmengine - INFO - Epoch(train) [131][2700/5047] lr: 8.2151e-06 eta: 23:45:55 time: 0.8908 data_time: 0.0027 memory: 52862 loss: 0.0915 loss_ce: 0.0915 2023/03/02 07:48:07 - mmengine - INFO - Epoch(train) [131][2800/5047] lr: 8.2151e-06 eta: 23:44:28 time: 0.8017 data_time: 0.0028 memory: 44823 loss: 0.0977 loss_ce: 0.0977 2023/03/02 07:49:25 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 07:49:33 - mmengine - INFO - Epoch(train) [131][2900/5047] lr: 8.2151e-06 eta: 23:43:00 time: 0.8575 data_time: 0.0032 memory: 41419 loss: 0.0986 loss_ce: 0.0986 2023/03/02 07:50:57 - mmengine - INFO - Epoch(train) [131][3000/5047] lr: 8.2151e-06 eta: 23:41:33 time: 0.8078 data_time: 0.0028 memory: 43947 loss: 0.1035 loss_ce: 0.1035 2023/03/02 07:52:23 - mmengine - INFO - Epoch(train) [131][3100/5047] lr: 8.2151e-06 eta: 23:40:06 time: 0.8655 data_time: 0.0026 memory: 40590 loss: 0.0993 loss_ce: 0.0993 2023/03/02 07:53:49 - mmengine - INFO - Epoch(train) [131][3200/5047] lr: 8.2151e-06 eta: 23:38:38 time: 0.8015 data_time: 0.0028 memory: 43749 loss: 0.1066 loss_ce: 0.1066 2023/03/02 07:55:14 - mmengine - INFO - Epoch(train) [131][3300/5047] lr: 8.2151e-06 eta: 23:37:11 time: 0.7979 data_time: 0.0030 memory: 47813 loss: 0.1041 loss_ce: 0.1041 2023/03/02 07:56:40 - mmengine - INFO - Epoch(train) [131][3400/5047] lr: 8.2151e-06 eta: 23:35:44 time: 0.8441 data_time: 0.0055 memory: 46443 loss: 0.1126 loss_ce: 0.1126 2023/03/02 07:58:05 - mmengine - INFO - Epoch(train) [131][3500/5047] lr: 8.2151e-06 eta: 23:34:16 time: 0.8410 data_time: 0.0028 memory: 42336 loss: 0.1063 loss_ce: 0.1063 2023/03/02 07:59:31 - mmengine - INFO - Epoch(train) [131][3600/5047] lr: 8.2151e-06 eta: 23:32:49 time: 0.8848 data_time: 0.0034 memory: 41315 loss: 0.1168 loss_ce: 0.1168 2023/03/02 08:00:56 - mmengine - INFO - Epoch(train) [131][3700/5047] lr: 8.2151e-06 eta: 23:31:21 time: 0.9160 data_time: 0.0042 memory: 41419 loss: 0.0940 loss_ce: 0.0940 2023/03/02 08:02:23 - mmengine - INFO - Epoch(train) [131][3800/5047] lr: 8.2151e-06 eta: 23:29:54 time: 0.9024 data_time: 0.0033 memory: 42649 loss: 0.0948 loss_ce: 0.0948 2023/03/02 08:03:40 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 08:03:48 - mmengine - INFO - Epoch(train) [131][3900/5047] lr: 8.2151e-06 eta: 23:28:27 time: 0.8418 data_time: 0.0037 memory: 44275 loss: 0.1073 loss_ce: 0.1073 2023/03/02 08:05:14 - mmengine - INFO - Epoch(train) [131][4000/5047] lr: 8.2151e-06 eta: 23:27:00 time: 0.8763 data_time: 0.0027 memory: 40241 loss: 0.1111 loss_ce: 0.1111 2023/03/02 08:06:40 - mmengine - INFO - Epoch(train) [131][4100/5047] lr: 8.2151e-06 eta: 23:25:32 time: 0.8555 data_time: 0.0029 memory: 42530 loss: 0.1031 loss_ce: 0.1031 2023/03/02 08:08:05 - mmengine - INFO - Epoch(train) [131][4200/5047] lr: 8.2151e-06 eta: 23:24:05 time: 0.7846 data_time: 0.0027 memory: 50505 loss: 0.1023 loss_ce: 0.1023 2023/03/02 08:09:29 - mmengine - INFO - Epoch(train) [131][4300/5047] lr: 8.2151e-06 eta: 23:22:37 time: 0.8286 data_time: 0.0026 memory: 47037 loss: 0.1110 loss_ce: 0.1110 2023/03/02 08:10:54 - mmengine - INFO - Epoch(train) [131][4400/5047] lr: 8.2151e-06 eta: 23:21:10 time: 0.8185 data_time: 0.0036 memory: 45011 loss: 0.1049 loss_ce: 0.1049 2023/03/02 08:12:22 - mmengine - INFO - Epoch(train) [131][4500/5047] lr: 8.2151e-06 eta: 23:19:43 time: 0.8264 data_time: 0.0029 memory: 55562 loss: 0.1195 loss_ce: 0.1195 2023/03/02 08:13:47 - mmengine - INFO - Epoch(train) [131][4600/5047] lr: 8.2151e-06 eta: 23:18:16 time: 0.8579 data_time: 0.0028 memory: 46374 loss: 0.1143 loss_ce: 0.1143 2023/03/02 08:15:13 - mmengine - INFO - Epoch(train) [131][4700/5047] lr: 8.2151e-06 eta: 23:16:48 time: 0.8362 data_time: 0.0028 memory: 42024 loss: 0.1061 loss_ce: 0.1061 2023/03/02 08:16:39 - mmengine - INFO - Epoch(train) [131][4800/5047] lr: 8.2151e-06 eta: 23:15:21 time: 0.8429 data_time: 0.0096 memory: 46892 loss: 0.0951 loss_ce: 0.0951 2023/03/02 08:17:57 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 08:18:05 - mmengine - INFO - Epoch(train) [131][4900/5047] lr: 8.2151e-06 eta: 23:13:54 time: 0.8696 data_time: 0.0054 memory: 44467 loss: 0.1109 loss_ce: 0.1109 2023/03/02 08:19:31 - mmengine - INFO - Epoch(train) [131][5000/5047] lr: 8.2151e-06 eta: 23:12:27 time: 0.8994 data_time: 0.0028 memory: 52083 loss: 0.0927 loss_ce: 0.0927 2023/03/02 08:20:11 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 08:20:11 - mmengine - INFO - Saving checkpoint at 131 epochs 2023/03/02 08:21:41 - mmengine - INFO - Epoch(train) [132][ 100/5047] lr: 8.0142e-06 eta: 23:10:18 time: 0.8100 data_time: 0.0056 memory: 40535 loss: 0.1085 loss_ce: 0.1085 2023/03/02 08:23:06 - mmengine - INFO - Epoch(train) [132][ 200/5047] lr: 8.0142e-06 eta: 23:08:51 time: 0.8414 data_time: 0.0027 memory: 41146 loss: 0.1107 loss_ce: 0.1107 2023/03/02 08:24:34 - mmengine - INFO - Epoch(train) [132][ 300/5047] lr: 8.0142e-06 eta: 23:07:24 time: 0.8622 data_time: 0.0041 memory: 49334 loss: 0.0983 loss_ce: 0.0983 2023/03/02 08:26:01 - mmengine - INFO - Epoch(train) [132][ 400/5047] lr: 8.0142e-06 eta: 23:05:57 time: 0.8895 data_time: 0.0030 memory: 45711 loss: 0.1129 loss_ce: 0.1129 2023/03/02 08:27:28 - mmengine - INFO - Epoch(train) [132][ 500/5047] lr: 8.0142e-06 eta: 23:04:30 time: 0.8385 data_time: 0.0026 memory: 55562 loss: 0.1073 loss_ce: 0.1073 2023/03/02 08:28:52 - mmengine - INFO - Epoch(train) [132][ 600/5047] lr: 8.0142e-06 eta: 23:03:02 time: 0.8179 data_time: 0.0049 memory: 42024 loss: 0.1140 loss_ce: 0.1140 2023/03/02 08:30:17 - mmengine - INFO - Epoch(train) [132][ 700/5047] lr: 8.0142e-06 eta: 23:01:35 time: 0.8838 data_time: 0.0032 memory: 50414 loss: 0.0953 loss_ce: 0.0953 2023/03/02 08:31:44 - mmengine - INFO - Epoch(train) [132][ 800/5047] lr: 8.0142e-06 eta: 23:00:08 time: 0.8301 data_time: 0.0029 memory: 53577 loss: 0.1016 loss_ce: 0.1016 2023/03/02 08:32:20 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 08:33:09 - mmengine - INFO - Epoch(train) [132][ 900/5047] lr: 8.0142e-06 eta: 22:58:40 time: 0.8496 data_time: 0.0028 memory: 43947 loss: 0.0890 loss_ce: 0.0890 2023/03/02 08:34:35 - mmengine - INFO - Epoch(train) [132][1000/5047] lr: 8.0142e-06 eta: 22:57:13 time: 0.8655 data_time: 0.0033 memory: 46772 loss: 0.1050 loss_ce: 0.1050 2023/03/02 08:36:02 - mmengine - INFO - Epoch(train) [132][1100/5047] lr: 8.0142e-06 eta: 22:55:46 time: 0.8795 data_time: 0.0036 memory: 55562 loss: 0.1023 loss_ce: 0.1023 2023/03/02 08:37:27 - mmengine - INFO - Epoch(train) [132][1200/5047] lr: 8.0142e-06 eta: 22:54:18 time: 0.8998 data_time: 0.0027 memory: 42649 loss: 0.1007 loss_ce: 0.1007 2023/03/02 08:38:52 - mmengine - INFO - Epoch(train) [132][1300/5047] lr: 8.0142e-06 eta: 22:52:51 time: 0.8286 data_time: 0.0052 memory: 40241 loss: 0.0933 loss_ce: 0.0933 2023/03/02 08:40:19 - mmengine - INFO - Epoch(train) [132][1400/5047] lr: 8.0142e-06 eta: 22:51:24 time: 0.8874 data_time: 0.0030 memory: 49144 loss: 0.0968 loss_ce: 0.0968 2023/03/02 08:41:44 - mmengine - INFO - Epoch(train) [132][1500/5047] lr: 8.0142e-06 eta: 22:49:56 time: 0.7919 data_time: 0.0038 memory: 49241 loss: 0.1169 loss_ce: 0.1169 2023/03/02 08:43:09 - mmengine - INFO - Epoch(train) [132][1600/5047] lr: 8.0142e-06 eta: 22:48:29 time: 0.8477 data_time: 0.0035 memory: 45967 loss: 0.1040 loss_ce: 0.1040 2023/03/02 08:44:34 - mmengine - INFO - Epoch(train) [132][1700/5047] lr: 8.0142e-06 eta: 22:47:02 time: 0.7862 data_time: 0.0054 memory: 42965 loss: 0.1138 loss_ce: 0.1138 2023/03/02 08:46:00 - mmengine - INFO - Epoch(train) [132][1800/5047] lr: 8.0142e-06 eta: 22:45:34 time: 0.8659 data_time: 0.0029 memory: 44956 loss: 0.1078 loss_ce: 0.1078 2023/03/02 08:46:37 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 08:47:26 - mmengine - INFO - Epoch(train) [132][1900/5047] lr: 8.0142e-06 eta: 22:44:07 time: 0.8795 data_time: 0.0035 memory: 42024 loss: 0.1196 loss_ce: 0.1196 2023/03/02 08:48:53 - mmengine - INFO - Epoch(train) [132][2000/5047] lr: 8.0142e-06 eta: 22:42:40 time: 0.8684 data_time: 0.0029 memory: 44550 loss: 0.1094 loss_ce: 0.1094 2023/03/02 08:50:18 - mmengine - INFO - Epoch(train) [132][2100/5047] lr: 8.0142e-06 eta: 22:41:13 time: 0.9107 data_time: 0.0027 memory: 52543 loss: 0.1175 loss_ce: 0.1175 2023/03/02 08:51:46 - mmengine - INFO - Epoch(train) [132][2200/5047] lr: 8.0142e-06 eta: 22:39:46 time: 0.8793 data_time: 0.0026 memory: 44433 loss: 0.1038 loss_ce: 0.1038 2023/03/02 08:53:12 - mmengine - INFO - Epoch(train) [132][2300/5047] lr: 8.0142e-06 eta: 22:38:19 time: 0.8453 data_time: 0.0027 memory: 46005 loss: 0.1091 loss_ce: 0.1091 2023/03/02 08:54:38 - mmengine - INFO - Epoch(train) [132][2400/5047] lr: 8.0142e-06 eta: 22:36:51 time: 0.8691 data_time: 0.0036 memory: 52543 loss: 0.1065 loss_ce: 0.1065 2023/03/02 08:56:02 - mmengine - INFO - Epoch(train) [132][2500/5047] lr: 8.0142e-06 eta: 22:35:24 time: 0.8617 data_time: 0.0027 memory: 52540 loss: 0.1045 loss_ce: 0.1045 2023/03/02 08:57:28 - mmengine - INFO - Epoch(train) [132][2600/5047] lr: 8.0142e-06 eta: 22:33:56 time: 0.8864 data_time: 0.0030 memory: 51663 loss: 0.0811 loss_ce: 0.0811 2023/03/02 08:58:54 - mmengine - INFO - Epoch(train) [132][2700/5047] lr: 8.0142e-06 eta: 22:32:29 time: 0.8468 data_time: 0.0031 memory: 45302 loss: 0.0961 loss_ce: 0.0961 2023/03/02 09:00:21 - mmengine - INFO - Epoch(train) [132][2800/5047] lr: 8.0142e-06 eta: 22:31:02 time: 0.8781 data_time: 0.0026 memory: 44496 loss: 0.1054 loss_ce: 0.1054 2023/03/02 09:00:58 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 09:01:47 - mmengine - INFO - Epoch(train) [132][2900/5047] lr: 8.0142e-06 eta: 22:29:35 time: 0.8882 data_time: 0.0063 memory: 43611 loss: 0.1066 loss_ce: 0.1066 2023/03/02 09:03:13 - mmengine - INFO - Epoch(train) [132][3000/5047] lr: 8.0142e-06 eta: 22:28:08 time: 0.8612 data_time: 0.0029 memory: 44617 loss: 0.1063 loss_ce: 0.1063 2023/03/02 09:04:40 - mmengine - INFO - Epoch(train) [132][3100/5047] lr: 8.0142e-06 eta: 22:26:40 time: 0.8696 data_time: 0.0078 memory: 43289 loss: 0.1090 loss_ce: 0.1090 2023/03/02 09:06:05 - mmengine - INFO - Epoch(train) [132][3200/5047] lr: 8.0142e-06 eta: 22:25:13 time: 0.9078 data_time: 0.0028 memory: 55562 loss: 0.1161 loss_ce: 0.1161 2023/03/02 09:07:32 - mmengine - INFO - Epoch(train) [132][3300/5047] lr: 8.0142e-06 eta: 22:23:46 time: 0.8395 data_time: 0.0028 memory: 55562 loss: 0.1192 loss_ce: 0.1192 2023/03/02 09:08:57 - mmengine - INFO - Epoch(train) [132][3400/5047] lr: 8.0142e-06 eta: 22:22:19 time: 0.8380 data_time: 0.0027 memory: 40913 loss: 0.1066 loss_ce: 0.1066 2023/03/02 09:10:23 - mmengine - INFO - Epoch(train) [132][3500/5047] lr: 8.0142e-06 eta: 22:20:51 time: 0.8805 data_time: 0.0027 memory: 49373 loss: 0.0893 loss_ce: 0.0893 2023/03/02 09:11:49 - mmengine - INFO - Epoch(train) [132][3600/5047] lr: 8.0142e-06 eta: 22:19:24 time: 0.8568 data_time: 0.0032 memory: 39948 loss: 0.1066 loss_ce: 0.1066 2023/03/02 09:13:15 - mmengine - INFO - Epoch(train) [132][3700/5047] lr: 8.0142e-06 eta: 22:17:57 time: 0.8690 data_time: 0.0029 memory: 42649 loss: 0.1099 loss_ce: 0.1099 2023/03/02 09:14:40 - mmengine - INFO - Epoch(train) [132][3800/5047] lr: 8.0142e-06 eta: 22:16:30 time: 0.8705 data_time: 0.0028 memory: 41910 loss: 0.1099 loss_ce: 0.1099 2023/03/02 09:15:16 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 09:16:03 - mmengine - INFO - Epoch(train) [132][3900/5047] lr: 8.0142e-06 eta: 22:15:02 time: 0.8333 data_time: 0.0026 memory: 42561 loss: 0.1271 loss_ce: 0.1271 2023/03/02 09:17:27 - mmengine - INFO - Epoch(train) [132][4000/5047] lr: 8.0142e-06 eta: 22:13:34 time: 0.8434 data_time: 0.0030 memory: 44034 loss: 0.0913 loss_ce: 0.0913 2023/03/02 09:18:54 - mmengine - INFO - Epoch(train) [132][4100/5047] lr: 8.0142e-06 eta: 22:12:07 time: 0.8683 data_time: 0.0028 memory: 51755 loss: 0.1066 loss_ce: 0.1066 2023/03/02 09:20:20 - mmengine - INFO - Epoch(train) [132][4200/5047] lr: 8.0142e-06 eta: 22:10:40 time: 0.8615 data_time: 0.0028 memory: 54242 loss: 0.1049 loss_ce: 0.1049 2023/03/02 09:21:45 - mmengine - INFO - Epoch(train) [132][4300/5047] lr: 8.0142e-06 eta: 22:09:13 time: 0.8600 data_time: 0.0029 memory: 41419 loss: 0.1020 loss_ce: 0.1020 2023/03/02 09:23:08 - mmengine - INFO - Epoch(train) [132][4400/5047] lr: 8.0142e-06 eta: 22:07:45 time: 0.8419 data_time: 0.0029 memory: 40740 loss: 0.1067 loss_ce: 0.1067 2023/03/02 09:24:33 - mmengine - INFO - Epoch(train) [132][4500/5047] lr: 8.0142e-06 eta: 22:06:18 time: 0.8265 data_time: 0.0027 memory: 42336 loss: 0.1128 loss_ce: 0.1128 2023/03/02 09:25:58 - mmengine - INFO - Epoch(train) [132][4600/5047] lr: 8.0142e-06 eta: 22:04:50 time: 0.8514 data_time: 0.0032 memory: 46182 loss: 0.1118 loss_ce: 0.1118 2023/03/02 09:27:24 - mmengine - INFO - Epoch(train) [132][4700/5047] lr: 8.0142e-06 eta: 22:03:23 time: 0.8923 data_time: 0.0032 memory: 43613 loss: 0.1091 loss_ce: 0.1091 2023/03/02 09:28:50 - mmengine - INFO - Epoch(train) [132][4800/5047] lr: 8.0142e-06 eta: 22:01:56 time: 0.8696 data_time: 0.0031 memory: 49168 loss: 0.1087 loss_ce: 0.1087 2023/03/02 09:29:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 09:30:17 - mmengine - INFO - Epoch(train) [132][4900/5047] lr: 8.0142e-06 eta: 22:00:29 time: 0.8822 data_time: 0.0026 memory: 43454 loss: 0.0952 loss_ce: 0.0952 2023/03/02 09:31:45 - mmengine - INFO - Epoch(train) [132][5000/5047] lr: 8.0142e-06 eta: 21:59:02 time: 0.9095 data_time: 0.0039 memory: 55562 loss: 0.1042 loss_ce: 0.1042 2023/03/02 09:32:25 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 09:32:25 - mmengine - INFO - Saving checkpoint at 132 epochs 2023/03/02 09:33:56 - mmengine - INFO - Epoch(train) [133][ 100/5047] lr: 7.8132e-06 eta: 21:56:53 time: 0.8164 data_time: 0.0062 memory: 45302 loss: 0.1086 loss_ce: 0.1086 2023/03/02 09:35:23 - mmengine - INFO - Epoch(train) [133][ 200/5047] lr: 7.8132e-06 eta: 21:55:26 time: 0.9108 data_time: 0.0027 memory: 50540 loss: 0.0892 loss_ce: 0.0892 2023/03/02 09:36:48 - mmengine - INFO - Epoch(train) [133][ 300/5047] lr: 7.8132e-06 eta: 21:53:59 time: 0.8935 data_time: 0.0028 memory: 46964 loss: 0.0928 loss_ce: 0.0928 2023/03/02 09:38:13 - mmengine - INFO - Epoch(train) [133][ 400/5047] lr: 7.8132e-06 eta: 21:52:31 time: 0.8980 data_time: 0.0062 memory: 55562 loss: 0.1066 loss_ce: 0.1066 2023/03/02 09:39:39 - mmengine - INFO - Epoch(train) [133][ 500/5047] lr: 7.8132e-06 eta: 21:51:04 time: 0.8384 data_time: 0.0027 memory: 40535 loss: 0.1042 loss_ce: 0.1042 2023/03/02 09:41:04 - mmengine - INFO - Epoch(train) [133][ 600/5047] lr: 7.8132e-06 eta: 21:49:37 time: 0.8693 data_time: 0.0040 memory: 55562 loss: 0.1073 loss_ce: 0.1073 2023/03/02 09:42:31 - mmengine - INFO - Epoch(train) [133][ 700/5047] lr: 7.8132e-06 eta: 21:48:10 time: 0.8794 data_time: 0.0033 memory: 45137 loss: 0.1031 loss_ce: 0.1031 2023/03/02 09:43:55 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 09:43:58 - mmengine - INFO - Epoch(train) [133][ 800/5047] lr: 7.8132e-06 eta: 21:46:43 time: 0.8730 data_time: 0.0037 memory: 39681 loss: 0.1107 loss_ce: 0.1107 2023/03/02 09:45:23 - mmengine - INFO - Epoch(train) [133][ 900/5047] lr: 7.8132e-06 eta: 21:45:15 time: 0.8703 data_time: 0.0031 memory: 42649 loss: 0.1119 loss_ce: 0.1119 2023/03/02 09:46:48 - mmengine - INFO - Epoch(train) [133][1000/5047] lr: 7.8132e-06 eta: 21:43:48 time: 0.8833 data_time: 0.0028 memory: 41987 loss: 0.1015 loss_ce: 0.1015 2023/03/02 09:48:13 - mmengine - INFO - Epoch(train) [133][1100/5047] lr: 7.8132e-06 eta: 21:42:21 time: 0.8958 data_time: 0.0028 memory: 43947 loss: 0.1090 loss_ce: 0.1090 2023/03/02 09:49:39 - mmengine - INFO - Epoch(train) [133][1200/5047] lr: 7.8132e-06 eta: 21:40:53 time: 0.8813 data_time: 0.0033 memory: 41531 loss: 0.0933 loss_ce: 0.0933 2023/03/02 09:51:06 - mmengine - INFO - Epoch(train) [133][1300/5047] lr: 7.8132e-06 eta: 21:39:26 time: 0.8683 data_time: 0.0049 memory: 40825 loss: 0.1070 loss_ce: 0.1070 2023/03/02 09:52:32 - mmengine - INFO - Epoch(train) [133][1400/5047] lr: 7.8132e-06 eta: 21:37:59 time: 0.8757 data_time: 0.0052 memory: 43624 loss: 0.0955 loss_ce: 0.0955 2023/03/02 09:53:57 - mmengine - INFO - Epoch(train) [133][1500/5047] lr: 7.8132e-06 eta: 21:36:32 time: 0.8476 data_time: 0.0058 memory: 41122 loss: 0.1118 loss_ce: 0.1118 2023/03/02 09:55:23 - mmengine - INFO - Epoch(train) [133][1600/5047] lr: 7.8132e-06 eta: 21:35:04 time: 0.8290 data_time: 0.0026 memory: 47959 loss: 0.1105 loss_ce: 0.1105 2023/03/02 09:56:49 - mmengine - INFO - Epoch(train) [133][1700/5047] lr: 7.8132e-06 eta: 21:33:37 time: 0.8051 data_time: 0.0031 memory: 42649 loss: 0.1153 loss_ce: 0.1153 2023/03/02 09:58:12 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 09:58:15 - mmengine - INFO - Epoch(train) [133][1800/5047] lr: 7.8132e-06 eta: 21:32:10 time: 0.8759 data_time: 0.0027 memory: 55562 loss: 0.1135 loss_ce: 0.1135 2023/03/02 09:59:41 - mmengine - INFO - Epoch(train) [133][1900/5047] lr: 7.8132e-06 eta: 21:30:43 time: 0.8625 data_time: 0.0034 memory: 41419 loss: 0.1005 loss_ce: 0.1005 2023/03/02 10:01:08 - mmengine - INFO - Epoch(train) [133][2000/5047] lr: 7.8132e-06 eta: 21:29:16 time: 0.8493 data_time: 0.0028 memory: 42336 loss: 0.1063 loss_ce: 0.1063 2023/03/02 10:02:32 - mmengine - INFO - Epoch(train) [133][2100/5047] lr: 7.8132e-06 eta: 21:27:48 time: 0.8534 data_time: 0.0029 memory: 43289 loss: 0.0991 loss_ce: 0.0991 2023/03/02 10:03:59 - mmengine - INFO - Epoch(train) [133][2200/5047] lr: 7.8132e-06 eta: 21:26:21 time: 0.8295 data_time: 0.0029 memory: 42024 loss: 0.1280 loss_ce: 0.1280 2023/03/02 10:05:23 - mmengine - INFO - Epoch(train) [133][2300/5047] lr: 7.8132e-06 eta: 21:24:54 time: 0.8406 data_time: 0.0026 memory: 43289 loss: 0.0989 loss_ce: 0.0989 2023/03/02 10:06:48 - mmengine - INFO - Epoch(train) [133][2400/5047] lr: 7.8132e-06 eta: 21:23:26 time: 0.8702 data_time: 0.0027 memory: 40825 loss: 0.1080 loss_ce: 0.1080 2023/03/02 10:08:12 - mmengine - INFO - Epoch(train) [133][2500/5047] lr: 7.8132e-06 eta: 21:21:59 time: 0.8814 data_time: 0.0038 memory: 51731 loss: 0.1082 loss_ce: 0.1082 2023/03/02 10:09:40 - mmengine - INFO - Epoch(train) [133][2600/5047] lr: 7.8132e-06 eta: 21:20:32 time: 0.8569 data_time: 0.0030 memory: 39681 loss: 0.1095 loss_ce: 0.1095 2023/03/02 10:11:06 - mmengine - INFO - Epoch(train) [133][2700/5047] lr: 7.8132e-06 eta: 21:19:04 time: 0.8460 data_time: 0.0047 memory: 47074 loss: 0.1008 loss_ce: 0.1008 2023/03/02 10:12:25 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 10:12:29 - mmengine - INFO - Epoch(train) [133][2800/5047] lr: 7.8132e-06 eta: 21:17:37 time: 0.8834 data_time: 0.0033 memory: 43947 loss: 0.1048 loss_ce: 0.1048 2023/03/02 10:13:53 - mmengine - INFO - Epoch(train) [133][2900/5047] lr: 7.8132e-06 eta: 21:16:09 time: 0.8315 data_time: 0.0032 memory: 42907 loss: 0.1020 loss_ce: 0.1020 2023/03/02 10:15:19 - mmengine - INFO - Epoch(train) [133][3000/5047] lr: 7.8132e-06 eta: 21:14:42 time: 0.8685 data_time: 0.0025 memory: 45434 loss: 0.0830 loss_ce: 0.0830 2023/03/02 10:16:43 - mmengine - INFO - Epoch(train) [133][3100/5047] lr: 7.8132e-06 eta: 21:13:15 time: 0.8222 data_time: 0.0060 memory: 42336 loss: 0.1034 loss_ce: 0.1034 2023/03/02 10:18:10 - mmengine - INFO - Epoch(train) [133][3200/5047] lr: 7.8132e-06 eta: 21:11:48 time: 0.9226 data_time: 0.0029 memory: 50607 loss: 0.1094 loss_ce: 0.1094 2023/03/02 10:19:35 - mmengine - INFO - Epoch(train) [133][3300/5047] lr: 7.8132e-06 eta: 21:10:20 time: 0.8487 data_time: 0.0030 memory: 45456 loss: 0.1030 loss_ce: 0.1030 2023/03/02 10:21:01 - mmengine - INFO - Epoch(train) [133][3400/5047] lr: 7.8132e-06 eta: 21:08:53 time: 0.8123 data_time: 0.0029 memory: 51562 loss: 0.1059 loss_ce: 0.1059 2023/03/02 10:22:27 - mmengine - INFO - Epoch(train) [133][3500/5047] lr: 7.8132e-06 eta: 21:07:26 time: 0.8385 data_time: 0.0027 memory: 51732 loss: 0.1054 loss_ce: 0.1054 2023/03/02 10:23:52 - mmengine - INFO - Epoch(train) [133][3600/5047] lr: 7.8132e-06 eta: 21:05:58 time: 0.8564 data_time: 0.0030 memory: 40241 loss: 0.1057 loss_ce: 0.1057 2023/03/02 10:25:19 - mmengine - INFO - Epoch(train) [133][3700/5047] lr: 7.8132e-06 eta: 21:04:31 time: 0.8538 data_time: 0.0028 memory: 47170 loss: 0.1079 loss_ce: 0.1079 2023/03/02 10:26:42 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 10:26:45 - mmengine - INFO - Epoch(train) [133][3800/5047] lr: 7.8132e-06 eta: 21:03:04 time: 0.8791 data_time: 0.0030 memory: 42649 loss: 0.1043 loss_ce: 0.1043 2023/03/02 10:28:11 - mmengine - INFO - Epoch(train) [133][3900/5047] lr: 7.8132e-06 eta: 21:01:37 time: 0.8504 data_time: 0.0027 memory: 42649 loss: 0.1079 loss_ce: 0.1079 2023/03/02 10:29:36 - mmengine - INFO - Epoch(train) [133][4000/5047] lr: 7.8132e-06 eta: 21:00:10 time: 0.8366 data_time: 0.0051 memory: 42336 loss: 0.1067 loss_ce: 0.1067 2023/03/02 10:31:02 - mmengine - INFO - Epoch(train) [133][4100/5047] lr: 7.8132e-06 eta: 20:58:42 time: 0.8963 data_time: 0.0029 memory: 43557 loss: 0.1053 loss_ce: 0.1053 2023/03/02 10:32:28 - mmengine - INFO - Epoch(train) [133][4200/5047] lr: 7.8132e-06 eta: 20:57:15 time: 0.8451 data_time: 0.0028 memory: 49147 loss: 0.0967 loss_ce: 0.0967 2023/03/02 10:33:53 - mmengine - INFO - Epoch(train) [133][4300/5047] lr: 7.8132e-06 eta: 20:55:48 time: 0.8449 data_time: 0.0030 memory: 44956 loss: 0.1084 loss_ce: 0.1084 2023/03/02 10:35:19 - mmengine - INFO - Epoch(train) [133][4400/5047] lr: 7.8132e-06 eta: 20:54:21 time: 0.8295 data_time: 0.0032 memory: 39681 loss: 0.1178 loss_ce: 0.1178 2023/03/02 10:36:45 - mmengine - INFO - Epoch(train) [133][4500/5047] lr: 7.8132e-06 eta: 20:52:53 time: 0.8496 data_time: 0.0039 memory: 53809 loss: 0.1131 loss_ce: 0.1131 2023/03/02 10:38:11 - mmengine - INFO - Epoch(train) [133][4600/5047] lr: 7.8132e-06 eta: 20:51:26 time: 0.8469 data_time: 0.0033 memory: 53809 loss: 0.1140 loss_ce: 0.1140 2023/03/02 10:39:38 - mmengine - INFO - Epoch(train) [133][4700/5047] lr: 7.8132e-06 eta: 20:49:59 time: 0.8450 data_time: 0.0028 memory: 41419 loss: 0.1098 loss_ce: 0.1098 2023/03/02 10:41:01 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 10:41:05 - mmengine - INFO - Epoch(train) [133][4800/5047] lr: 7.8132e-06 eta: 20:48:32 time: 0.8168 data_time: 0.0027 memory: 51306 loss: 0.1128 loss_ce: 0.1128 2023/03/02 10:42:30 - mmengine - INFO - Epoch(train) [133][4900/5047] lr: 7.8132e-06 eta: 20:47:05 time: 0.8378 data_time: 0.0061 memory: 44539 loss: 0.0960 loss_ce: 0.0960 2023/03/02 10:43:54 - mmengine - INFO - Epoch(train) [133][5000/5047] lr: 7.8132e-06 eta: 20:45:37 time: 0.8549 data_time: 0.0028 memory: 45643 loss: 0.1013 loss_ce: 0.1013 2023/03/02 10:44:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 10:44:34 - mmengine - INFO - Saving checkpoint at 133 epochs 2023/03/02 10:46:07 - mmengine - INFO - Epoch(train) [134][ 100/5047] lr: 7.6123e-06 eta: 20:43:29 time: 0.8885 data_time: 0.0030 memory: 44278 loss: 0.1046 loss_ce: 0.1046 2023/03/02 10:47:33 - mmengine - INFO - Epoch(train) [134][ 200/5047] lr: 7.6123e-06 eta: 20:42:02 time: 0.8989 data_time: 0.0032 memory: 43454 loss: 0.1027 loss_ce: 0.1027 2023/03/02 10:48:59 - mmengine - INFO - Epoch(train) [134][ 300/5047] lr: 7.6123e-06 eta: 20:40:35 time: 0.8585 data_time: 0.0028 memory: 43627 loss: 0.1007 loss_ce: 0.1007 2023/03/02 10:50:24 - mmengine - INFO - Epoch(train) [134][ 400/5047] lr: 7.6123e-06 eta: 20:39:07 time: 0.8278 data_time: 0.0053 memory: 43611 loss: 0.1048 loss_ce: 0.1048 2023/03/02 10:51:50 - mmengine - INFO - Epoch(train) [134][ 500/5047] lr: 7.6123e-06 eta: 20:37:40 time: 0.8788 data_time: 0.0031 memory: 53273 loss: 0.0953 loss_ce: 0.0953 2023/03/02 10:53:15 - mmengine - INFO - Epoch(train) [134][ 600/5047] lr: 7.6123e-06 eta: 20:36:13 time: 0.9082 data_time: 0.0031 memory: 44617 loss: 0.1005 loss_ce: 0.1005 2023/03/02 10:54:40 - mmengine - INFO - Epoch(train) [134][ 700/5047] lr: 7.6123e-06 eta: 20:34:46 time: 0.8225 data_time: 0.0027 memory: 40241 loss: 0.1084 loss_ce: 0.1084 2023/03/02 10:55:22 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 10:56:07 - mmengine - INFO - Epoch(train) [134][ 800/5047] lr: 7.6123e-06 eta: 20:33:18 time: 0.8703 data_time: 0.0028 memory: 52964 loss: 0.1082 loss_ce: 0.1082 2023/03/02 10:57:31 - mmengine - INFO - Epoch(train) [134][ 900/5047] lr: 7.6123e-06 eta: 20:31:51 time: 0.8517 data_time: 0.0039 memory: 43289 loss: 0.1027 loss_ce: 0.1027 2023/03/02 10:58:57 - mmengine - INFO - Epoch(train) [134][1000/5047] lr: 7.6123e-06 eta: 20:30:24 time: 0.8537 data_time: 0.0047 memory: 55114 loss: 0.1067 loss_ce: 0.1067 2023/03/02 11:00:21 - mmengine - INFO - Epoch(train) [134][1100/5047] lr: 7.6123e-06 eta: 20:28:56 time: 0.8288 data_time: 0.0027 memory: 42024 loss: 0.1048 loss_ce: 0.1048 2023/03/02 11:01:47 - mmengine - INFO - Epoch(train) [134][1200/5047] lr: 7.6123e-06 eta: 20:27:29 time: 0.8397 data_time: 0.0036 memory: 45643 loss: 0.1002 loss_ce: 0.1002 2023/03/02 11:03:12 - mmengine - INFO - Epoch(train) [134][1300/5047] lr: 7.6123e-06 eta: 20:26:02 time: 0.8388 data_time: 0.0027 memory: 46713 loss: 0.1131 loss_ce: 0.1131 2023/03/02 11:04:38 - mmengine - INFO - Epoch(train) [134][1400/5047] lr: 7.6123e-06 eta: 20:24:35 time: 0.9067 data_time: 0.0030 memory: 45643 loss: 0.0940 loss_ce: 0.0940 2023/03/02 11:06:04 - mmengine - INFO - Epoch(train) [134][1500/5047] lr: 7.6123e-06 eta: 20:23:07 time: 0.8511 data_time: 0.0032 memory: 40139 loss: 0.1261 loss_ce: 0.1261 2023/03/02 11:07:30 - mmengine - INFO - Epoch(train) [134][1600/5047] lr: 7.6123e-06 eta: 20:21:40 time: 0.8189 data_time: 0.0029 memory: 55562 loss: 0.1045 loss_ce: 0.1045 2023/03/02 11:08:56 - mmengine - INFO - Epoch(train) [134][1700/5047] lr: 7.6123e-06 eta: 20:20:13 time: 0.8666 data_time: 0.0032 memory: 44956 loss: 0.1064 loss_ce: 0.1064 2023/03/02 11:09:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 11:10:19 - mmengine - INFO - Epoch(train) [134][1800/5047] lr: 7.6123e-06 eta: 20:18:45 time: 0.7941 data_time: 0.0026 memory: 42336 loss: 0.1040 loss_ce: 0.1040 2023/03/02 11:11:45 - mmengine - INFO - Epoch(train) [134][1900/5047] lr: 7.6123e-06 eta: 20:17:18 time: 0.8387 data_time: 0.0028 memory: 42843 loss: 0.1090 loss_ce: 0.1090 2023/03/02 11:13:11 - mmengine - INFO - Epoch(train) [134][2000/5047] lr: 7.6123e-06 eta: 20:15:51 time: 0.8814 data_time: 0.0065 memory: 45785 loss: 0.1072 loss_ce: 0.1072 2023/03/02 11:14:37 - mmengine - INFO - Epoch(train) [134][2100/5047] lr: 7.6123e-06 eta: 20:14:24 time: 0.8283 data_time: 0.0031 memory: 50232 loss: 0.0943 loss_ce: 0.0943 2023/03/02 11:16:03 - mmengine - INFO - Epoch(train) [134][2200/5047] lr: 7.6123e-06 eta: 20:12:57 time: 0.8732 data_time: 0.0028 memory: 46039 loss: 0.0941 loss_ce: 0.0941 2023/03/02 11:17:28 - mmengine - INFO - Epoch(train) [134][2300/5047] lr: 7.6123e-06 eta: 20:11:29 time: 0.8432 data_time: 0.0027 memory: 47716 loss: 0.1001 loss_ce: 0.1001 2023/03/02 11:18:53 - mmengine - INFO - Epoch(train) [134][2400/5047] lr: 7.6123e-06 eta: 20:10:02 time: 0.8432 data_time: 0.0041 memory: 50347 loss: 0.0996 loss_ce: 0.0996 2023/03/02 11:20:18 - mmengine - INFO - Epoch(train) [134][2500/5047] lr: 7.6123e-06 eta: 20:08:34 time: 0.8453 data_time: 0.0027 memory: 44956 loss: 0.1123 loss_ce: 0.1123 2023/03/02 11:21:44 - mmengine - INFO - Epoch(train) [134][2600/5047] lr: 7.6123e-06 eta: 20:07:07 time: 0.7886 data_time: 0.0034 memory: 42941 loss: 0.1109 loss_ce: 0.1109 2023/03/02 11:23:09 - mmengine - INFO - Epoch(train) [134][2700/5047] lr: 7.6123e-06 eta: 20:05:40 time: 0.8453 data_time: 0.0036 memory: 43011 loss: 0.1166 loss_ce: 0.1166 2023/03/02 11:23:51 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 11:24:35 - mmengine - INFO - Epoch(train) [134][2800/5047] lr: 7.6123e-06 eta: 20:04:13 time: 0.8819 data_time: 0.0028 memory: 44617 loss: 0.1013 loss_ce: 0.1013 2023/03/02 11:26:02 - mmengine - INFO - Epoch(train) [134][2900/5047] lr: 7.6123e-06 eta: 20:02:46 time: 0.8795 data_time: 0.0029 memory: 41122 loss: 0.1020 loss_ce: 0.1020 2023/03/02 11:27:28 - mmengine - INFO - Epoch(train) [134][3000/5047] lr: 7.6123e-06 eta: 20:01:18 time: 0.8403 data_time: 0.0048 memory: 41419 loss: 0.1019 loss_ce: 0.1019 2023/03/02 11:28:52 - mmengine - INFO - Epoch(train) [134][3100/5047] lr: 7.6123e-06 eta: 19:59:51 time: 0.8277 data_time: 0.0066 memory: 48071 loss: 0.1079 loss_ce: 0.1079 2023/03/02 11:30:17 - mmengine - INFO - Epoch(train) [134][3200/5047] lr: 7.6123e-06 eta: 19:58:24 time: 0.8507 data_time: 0.0032 memory: 43613 loss: 0.1084 loss_ce: 0.1084 2023/03/02 11:31:44 - mmengine - INFO - Epoch(train) [134][3300/5047] lr: 7.6123e-06 eta: 19:56:57 time: 0.8535 data_time: 0.0026 memory: 47447 loss: 0.1103 loss_ce: 0.1103 2023/03/02 11:33:09 - mmengine - INFO - Epoch(train) [134][3400/5047] lr: 7.6123e-06 eta: 19:55:29 time: 0.8202 data_time: 0.0065 memory: 44617 loss: 0.0974 loss_ce: 0.0974 2023/03/02 11:34:35 - mmengine - INFO - Epoch(train) [134][3500/5047] lr: 7.6123e-06 eta: 19:54:02 time: 0.8316 data_time: 0.0035 memory: 45849 loss: 0.0943 loss_ce: 0.0943 2023/03/02 11:36:01 - mmengine - INFO - Epoch(train) [134][3600/5047] lr: 7.6123e-06 eta: 19:52:35 time: 0.8268 data_time: 0.0028 memory: 42649 loss: 0.1040 loss_ce: 0.1040 2023/03/02 11:37:27 - mmengine - INFO - Epoch(train) [134][3700/5047] lr: 7.6123e-06 eta: 19:51:08 time: 0.8579 data_time: 0.0028 memory: 51970 loss: 0.1147 loss_ce: 0.1147 2023/03/02 11:38:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 11:38:52 - mmengine - INFO - Epoch(train) [134][3800/5047] lr: 7.6123e-06 eta: 19:49:40 time: 0.9103 data_time: 0.0026 memory: 40825 loss: 0.1096 loss_ce: 0.1096 2023/03/02 11:40:17 - mmengine - INFO - Epoch(train) [134][3900/5047] lr: 7.6123e-06 eta: 19:48:13 time: 0.8475 data_time: 0.0028 memory: 55562 loss: 0.1128 loss_ce: 0.1128 2023/03/02 11:41:44 - mmengine - INFO - Epoch(train) [134][4000/5047] lr: 7.6123e-06 eta: 19:46:46 time: 0.8737 data_time: 0.0028 memory: 44966 loss: 0.1023 loss_ce: 0.1023 2023/03/02 11:43:10 - mmengine - INFO - Epoch(train) [134][4100/5047] lr: 7.6123e-06 eta: 19:45:19 time: 0.8901 data_time: 0.0036 memory: 44498 loss: 0.1036 loss_ce: 0.1036 2023/03/02 11:44:34 - mmengine - INFO - Epoch(train) [134][4200/5047] lr: 7.6123e-06 eta: 19:43:51 time: 0.8464 data_time: 0.0027 memory: 43613 loss: 0.1005 loss_ce: 0.1005 2023/03/02 11:46:01 - mmengine - INFO - Epoch(train) [134][4300/5047] lr: 7.6123e-06 eta: 19:42:24 time: 0.8832 data_time: 0.0037 memory: 46951 loss: 0.1121 loss_ce: 0.1121 2023/03/02 11:47:27 - mmengine - INFO - Epoch(train) [134][4400/5047] lr: 7.6123e-06 eta: 19:40:57 time: 0.9012 data_time: 0.0031 memory: 55562 loss: 0.0954 loss_ce: 0.0954 2023/03/02 11:48:54 - mmengine - INFO - Epoch(train) [134][4500/5047] lr: 7.6123e-06 eta: 19:39:30 time: 0.8382 data_time: 0.0027 memory: 45878 loss: 0.1137 loss_ce: 0.1137 2023/03/02 11:50:18 - mmengine - INFO - Epoch(train) [134][4600/5047] lr: 7.6123e-06 eta: 19:38:03 time: 0.8610 data_time: 0.0036 memory: 43947 loss: 0.0999 loss_ce: 0.0999 2023/03/02 11:51:43 - mmengine - INFO - Epoch(train) [134][4700/5047] lr: 7.6123e-06 eta: 19:36:35 time: 0.8424 data_time: 0.0037 memory: 42965 loss: 0.0923 loss_ce: 0.0923 2023/03/02 11:52:26 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 11:53:09 - mmengine - INFO - Epoch(train) [134][4800/5047] lr: 7.6123e-06 eta: 19:35:08 time: 0.8433 data_time: 0.0075 memory: 47447 loss: 0.1002 loss_ce: 0.1002 2023/03/02 11:54:35 - mmengine - INFO - Epoch(train) [134][4900/5047] lr: 7.6123e-06 eta: 19:33:41 time: 0.8292 data_time: 0.0029 memory: 55562 loss: 0.1140 loss_ce: 0.1140 2023/03/02 11:56:01 - mmengine - INFO - Epoch(train) [134][5000/5047] lr: 7.6123e-06 eta: 19:32:14 time: 0.8681 data_time: 0.0037 memory: 48035 loss: 0.1018 loss_ce: 0.1018 2023/03/02 11:56:41 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 11:56:41 - mmengine - INFO - Saving checkpoint at 134 epochs 2023/03/02 11:58:12 - mmengine - INFO - Epoch(train) [135][ 100/5047] lr: 7.4113e-06 eta: 19:30:05 time: 0.8587 data_time: 0.0027 memory: 49378 loss: 0.1112 loss_ce: 0.1112 2023/03/02 11:59:36 - mmengine - INFO - Epoch(train) [135][ 200/5047] lr: 7.4113e-06 eta: 19:28:38 time: 0.8754 data_time: 0.0029 memory: 40241 loss: 0.1018 loss_ce: 0.1018 2023/03/02 12:01:02 - mmengine - INFO - Epoch(train) [135][ 300/5047] lr: 7.4113e-06 eta: 19:27:11 time: 0.8637 data_time: 0.0029 memory: 43475 loss: 0.0911 loss_ce: 0.0911 2023/03/02 12:02:29 - mmengine - INFO - Epoch(train) [135][ 400/5047] lr: 7.4113e-06 eta: 19:25:44 time: 0.8643 data_time: 0.0070 memory: 44565 loss: 0.1070 loss_ce: 0.1070 2023/03/02 12:03:54 - mmengine - INFO - Epoch(train) [135][ 500/5047] lr: 7.4113e-06 eta: 19:24:17 time: 0.8572 data_time: 0.0028 memory: 40057 loss: 0.0919 loss_ce: 0.0919 2023/03/02 12:05:21 - mmengine - INFO - Epoch(train) [135][ 600/5047] lr: 7.4113e-06 eta: 19:22:50 time: 0.8371 data_time: 0.0027 memory: 55366 loss: 0.1024 loss_ce: 0.1024 2023/03/02 12:06:47 - mmengine - INFO - Epoch(train) [135][ 700/5047] lr: 7.4113e-06 eta: 19:21:22 time: 0.8423 data_time: 0.0059 memory: 46101 loss: 0.1182 loss_ce: 0.1182 2023/03/02 12:06:48 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 12:08:12 - mmengine - INFO - Epoch(train) [135][ 800/5047] lr: 7.4113e-06 eta: 19:19:55 time: 0.8783 data_time: 0.0028 memory: 49360 loss: 0.0992 loss_ce: 0.0992 2023/03/02 12:09:38 - mmengine - INFO - Epoch(train) [135][ 900/5047] lr: 7.4113e-06 eta: 19:18:28 time: 0.8714 data_time: 0.0043 memory: 55559 loss: 0.0991 loss_ce: 0.0991 2023/03/02 12:11:04 - mmengine - INFO - Epoch(train) [135][1000/5047] lr: 7.4113e-06 eta: 19:17:01 time: 0.8760 data_time: 0.0056 memory: 55562 loss: 0.1088 loss_ce: 0.1088 2023/03/02 12:12:30 - mmengine - INFO - Epoch(train) [135][1100/5047] lr: 7.4113e-06 eta: 19:15:33 time: 0.8697 data_time: 0.0029 memory: 48565 loss: 0.1152 loss_ce: 0.1152 2023/03/02 12:13:55 - mmengine - INFO - Epoch(train) [135][1200/5047] lr: 7.4113e-06 eta: 19:14:06 time: 0.8035 data_time: 0.0025 memory: 52873 loss: 0.0999 loss_ce: 0.0999 2023/03/02 12:15:21 - mmengine - INFO - Epoch(train) [135][1300/5047] lr: 7.4113e-06 eta: 19:12:39 time: 0.8593 data_time: 0.0026 memory: 55562 loss: 0.1024 loss_ce: 0.1024 2023/03/02 12:16:47 - mmengine - INFO - Epoch(train) [135][1400/5047] lr: 7.4113e-06 eta: 19:11:12 time: 0.8835 data_time: 0.0067 memory: 41987 loss: 0.1058 loss_ce: 0.1058 2023/03/02 12:18:12 - mmengine - INFO - Epoch(train) [135][1500/5047] lr: 7.4113e-06 eta: 19:09:44 time: 0.8226 data_time: 0.0067 memory: 44607 loss: 0.1060 loss_ce: 0.1060 2023/03/02 12:19:38 - mmengine - INFO - Epoch(train) [135][1600/5047] lr: 7.4113e-06 eta: 19:08:17 time: 0.8559 data_time: 0.0029 memory: 39398 loss: 0.1074 loss_ce: 0.1074 2023/03/02 12:21:02 - mmengine - INFO - Epoch(train) [135][1700/5047] lr: 7.4113e-06 eta: 19:06:50 time: 0.8357 data_time: 0.0026 memory: 44617 loss: 0.1146 loss_ce: 0.1146 2023/03/02 12:21:03 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 12:22:26 - mmengine - INFO - Epoch(train) [135][1800/5047] lr: 7.4113e-06 eta: 19:05:22 time: 0.8766 data_time: 0.0029 memory: 44617 loss: 0.1040 loss_ce: 0.1040 2023/03/02 12:23:52 - mmengine - INFO - Epoch(train) [135][1900/5047] lr: 7.4113e-06 eta: 19:03:55 time: 0.8424 data_time: 0.0028 memory: 40825 loss: 0.1076 loss_ce: 0.1076 2023/03/02 12:25:17 - mmengine - INFO - Epoch(train) [135][2000/5047] lr: 7.4113e-06 eta: 19:02:28 time: 0.8448 data_time: 0.0033 memory: 50505 loss: 0.1020 loss_ce: 0.1020 2023/03/02 12:26:43 - mmengine - INFO - Epoch(train) [135][2100/5047] lr: 7.4113e-06 eta: 19:01:01 time: 0.8503 data_time: 0.0028 memory: 48948 loss: 0.0981 loss_ce: 0.0981 2023/03/02 12:28:09 - mmengine - INFO - Epoch(train) [135][2200/5047] lr: 7.4113e-06 eta: 18:59:34 time: 0.8858 data_time: 0.0028 memory: 47447 loss: 0.1071 loss_ce: 0.1071 2023/03/02 12:29:35 - mmengine - INFO - Epoch(train) [135][2300/5047] lr: 7.4113e-06 eta: 18:58:06 time: 0.8635 data_time: 0.0031 memory: 48148 loss: 0.1063 loss_ce: 0.1063 2023/03/02 12:31:01 - mmengine - INFO - Epoch(train) [135][2400/5047] lr: 7.4113e-06 eta: 18:56:39 time: 0.8341 data_time: 0.0047 memory: 40837 loss: 0.1093 loss_ce: 0.1093 2023/03/02 12:32:25 - mmengine - INFO - Epoch(train) [135][2500/5047] lr: 7.4113e-06 eta: 18:55:12 time: 0.8490 data_time: 0.0035 memory: 49171 loss: 0.0964 loss_ce: 0.0964 2023/03/02 12:33:51 - mmengine - INFO - Epoch(train) [135][2600/5047] lr: 7.4113e-06 eta: 18:53:45 time: 0.8559 data_time: 0.0035 memory: 41161 loss: 0.1165 loss_ce: 0.1165 2023/03/02 12:35:17 - mmengine - INFO - Epoch(train) [135][2700/5047] lr: 7.4113e-06 eta: 18:52:17 time: 0.8654 data_time: 0.0027 memory: 43115 loss: 0.1099 loss_ce: 0.1099 2023/03/02 12:35:18 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 12:36:41 - mmengine - INFO - Epoch(train) [135][2800/5047] lr: 7.4113e-06 eta: 18:50:50 time: 0.8896 data_time: 0.0027 memory: 45302 loss: 0.1029 loss_ce: 0.1029 2023/03/02 12:38:06 - mmengine - INFO - Epoch(train) [135][2900/5047] lr: 7.4113e-06 eta: 18:49:23 time: 0.8409 data_time: 0.0030 memory: 44432 loss: 0.1280 loss_ce: 0.1280 2023/03/02 12:39:31 - mmengine - INFO - Epoch(train) [135][3000/5047] lr: 7.4113e-06 eta: 18:47:56 time: 0.8334 data_time: 0.0032 memory: 41110 loss: 0.1064 loss_ce: 0.1064 2023/03/02 12:40:58 - mmengine - INFO - Epoch(train) [135][3100/5047] lr: 7.4113e-06 eta: 18:46:28 time: 0.8099 data_time: 0.0029 memory: 46005 loss: 0.1138 loss_ce: 0.1138 2023/03/02 12:42:25 - mmengine - INFO - Epoch(train) [135][3200/5047] lr: 7.4113e-06 eta: 18:45:01 time: 0.8881 data_time: 0.0052 memory: 42336 loss: 0.1060 loss_ce: 0.1060 2023/03/02 12:43:50 - mmengine - INFO - Epoch(train) [135][3300/5047] lr: 7.4113e-06 eta: 18:43:34 time: 0.8359 data_time: 0.0027 memory: 43613 loss: 0.1164 loss_ce: 0.1164 2023/03/02 12:45:16 - mmengine - INFO - Epoch(train) [135][3400/5047] lr: 7.4113e-06 eta: 18:42:07 time: 0.8390 data_time: 0.0024 memory: 43289 loss: 0.0976 loss_ce: 0.0976 2023/03/02 12:46:41 - mmengine - INFO - Epoch(train) [135][3500/5047] lr: 7.4113e-06 eta: 18:40:40 time: 0.8438 data_time: 0.0029 memory: 41724 loss: 0.0938 loss_ce: 0.0938 2023/03/02 12:48:06 - mmengine - INFO - Epoch(train) [135][3600/5047] lr: 7.4113e-06 eta: 18:39:12 time: 0.8481 data_time: 0.0031 memory: 47813 loss: 0.0879 loss_ce: 0.0879 2023/03/02 12:49:31 - mmengine - INFO - Epoch(train) [135][3700/5047] lr: 7.4113e-06 eta: 18:37:45 time: 0.8394 data_time: 0.0026 memory: 39960 loss: 0.1029 loss_ce: 0.1029 2023/03/02 12:49:32 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 12:50:56 - mmengine - INFO - Epoch(train) [135][3800/5047] lr: 7.4113e-06 eta: 18:36:18 time: 0.8184 data_time: 0.0027 memory: 40241 loss: 0.1086 loss_ce: 0.1086 2023/03/02 12:52:22 - mmengine - INFO - Epoch(train) [135][3900/5047] lr: 7.4113e-06 eta: 18:34:51 time: 0.8665 data_time: 0.0026 memory: 55562 loss: 0.0900 loss_ce: 0.0900 2023/03/02 12:53:47 - mmengine - INFO - Epoch(train) [135][4000/5047] lr: 7.4113e-06 eta: 18:33:23 time: 0.8444 data_time: 0.0027 memory: 42024 loss: 0.0960 loss_ce: 0.0960 2023/03/02 12:55:14 - mmengine - INFO - Epoch(train) [135][4100/5047] lr: 7.4113e-06 eta: 18:31:56 time: 0.8634 data_time: 0.0037 memory: 50906 loss: 0.1155 loss_ce: 0.1155 2023/03/02 12:56:39 - mmengine - INFO - Epoch(train) [135][4200/5047] lr: 7.4113e-06 eta: 18:30:29 time: 0.8222 data_time: 0.0026 memory: 44460 loss: 0.0919 loss_ce: 0.0919 2023/03/02 12:58:04 - mmengine - INFO - Epoch(train) [135][4300/5047] lr: 7.4113e-06 eta: 18:29:02 time: 0.8795 data_time: 0.0038 memory: 51658 loss: 0.1086 loss_ce: 0.1086 2023/03/02 12:59:30 - mmengine - INFO - Epoch(train) [135][4400/5047] lr: 7.4113e-06 eta: 18:27:34 time: 0.8173 data_time: 0.0030 memory: 42024 loss: 0.1073 loss_ce: 0.1073 2023/03/02 13:00:55 - mmengine - INFO - Epoch(train) [135][4500/5047] lr: 7.4113e-06 eta: 18:26:07 time: 0.8685 data_time: 0.0027 memory: 54072 loss: 0.1032 loss_ce: 0.1032 2023/03/02 13:02:21 - mmengine - INFO - Epoch(train) [135][4600/5047] lr: 7.4113e-06 eta: 18:24:40 time: 0.7895 data_time: 0.0028 memory: 43440 loss: 0.1053 loss_ce: 0.1053 2023/03/02 13:03:47 - mmengine - INFO - Epoch(train) [135][4700/5047] lr: 7.4113e-06 eta: 18:23:13 time: 0.8436 data_time: 0.0030 memory: 49044 loss: 0.1153 loss_ce: 0.1153 2023/03/02 13:03:49 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 13:05:12 - mmengine - INFO - Epoch(train) [135][4800/5047] lr: 7.4113e-06 eta: 18:21:46 time: 0.8055 data_time: 0.0028 memory: 55562 loss: 0.1038 loss_ce: 0.1038 2023/03/02 13:06:39 - mmengine - INFO - Epoch(train) [135][4900/5047] lr: 7.4113e-06 eta: 18:20:19 time: 0.9043 data_time: 0.0052 memory: 55562 loss: 0.1142 loss_ce: 0.1142 2023/03/02 13:08:05 - mmengine - INFO - Epoch(train) [135][5000/5047] lr: 7.4113e-06 eta: 18:18:51 time: 0.8833 data_time: 0.0035 memory: 50505 loss: 0.1172 loss_ce: 0.1172 2023/03/02 13:08:45 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 13:08:45 - mmengine - INFO - Saving checkpoint at 135 epochs 2023/03/02 13:10:16 - mmengine - INFO - Epoch(train) [136][ 100/5047] lr: 7.2104e-06 eta: 18:16:43 time: 0.8383 data_time: 0.0029 memory: 55562 loss: 0.1026 loss_ce: 0.1026 2023/03/02 13:11:42 - mmengine - INFO - Epoch(train) [136][ 200/5047] lr: 7.2104e-06 eta: 18:15:16 time: 0.8487 data_time: 0.0028 memory: 55562 loss: 0.1064 loss_ce: 0.1064 2023/03/02 13:13:10 - mmengine - INFO - Epoch(train) [136][ 300/5047] lr: 7.2104e-06 eta: 18:13:49 time: 0.9204 data_time: 0.0033 memory: 47311 loss: 0.1013 loss_ce: 0.1013 2023/03/02 13:14:36 - mmengine - INFO - Epoch(train) [136][ 400/5047] lr: 7.2104e-06 eta: 18:12:22 time: 0.9074 data_time: 0.0031 memory: 41694 loss: 0.1084 loss_ce: 0.1084 2023/03/02 13:16:01 - mmengine - INFO - Epoch(train) [136][ 500/5047] lr: 7.2104e-06 eta: 18:10:55 time: 0.8711 data_time: 0.0029 memory: 44617 loss: 0.0972 loss_ce: 0.0972 2023/03/02 13:17:28 - mmengine - INFO - Epoch(train) [136][ 600/5047] lr: 7.2104e-06 eta: 18:09:28 time: 0.8507 data_time: 0.0027 memory: 42965 loss: 0.0962 loss_ce: 0.0962 2023/03/02 13:18:15 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 13:18:54 - mmengine - INFO - Epoch(train) [136][ 700/5047] lr: 7.2104e-06 eta: 18:08:00 time: 0.8962 data_time: 0.0038 memory: 42293 loss: 0.1134 loss_ce: 0.1134 2023/03/02 13:20:20 - mmengine - INFO - Epoch(train) [136][ 800/5047] lr: 7.2104e-06 eta: 18:06:33 time: 0.8321 data_time: 0.0030 memory: 49312 loss: 0.1047 loss_ce: 0.1047 2023/03/02 13:21:46 - mmengine - INFO - Epoch(train) [136][ 900/5047] lr: 7.2104e-06 eta: 18:05:06 time: 0.8528 data_time: 0.0030 memory: 49301 loss: 0.1100 loss_ce: 0.1100 2023/03/02 13:23:13 - mmengine - INFO - Epoch(train) [136][1000/5047] lr: 7.2104e-06 eta: 18:03:39 time: 0.9547 data_time: 0.0065 memory: 50313 loss: 0.0995 loss_ce: 0.0995 2023/03/02 13:24:38 - mmengine - INFO - Epoch(train) [136][1100/5047] lr: 7.2104e-06 eta: 18:02:12 time: 0.8660 data_time: 0.0034 memory: 43613 loss: 0.1141 loss_ce: 0.1141 2023/03/02 13:26:04 - mmengine - INFO - Epoch(train) [136][1200/5047] lr: 7.2104e-06 eta: 18:00:45 time: 0.8239 data_time: 0.0029 memory: 44061 loss: 0.1088 loss_ce: 0.1088 2023/03/02 13:27:28 - mmengine - INFO - Epoch(train) [136][1300/5047] lr: 7.2104e-06 eta: 17:59:17 time: 0.9069 data_time: 0.0030 memory: 42649 loss: 0.1044 loss_ce: 0.1044 2023/03/02 13:28:53 - mmengine - INFO - Epoch(train) [136][1400/5047] lr: 7.2104e-06 eta: 17:57:50 time: 0.8618 data_time: 0.0116 memory: 43198 loss: 0.0968 loss_ce: 0.0968 2023/03/02 13:30:17 - mmengine - INFO - Epoch(train) [136][1500/5047] lr: 7.2104e-06 eta: 17:56:23 time: 0.8033 data_time: 0.0032 memory: 42655 loss: 0.0894 loss_ce: 0.0894 2023/03/02 13:31:42 - mmengine - INFO - Epoch(train) [136][1600/5047] lr: 7.2104e-06 eta: 17:54:55 time: 0.8414 data_time: 0.0027 memory: 42336 loss: 0.1009 loss_ce: 0.1009 2023/03/02 13:32:28 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 13:33:06 - mmengine - INFO - Epoch(train) [136][1700/5047] lr: 7.2104e-06 eta: 17:53:28 time: 0.8592 data_time: 0.0038 memory: 50592 loss: 0.1099 loss_ce: 0.1099 2023/03/02 13:34:33 - mmengine - INFO - Epoch(train) [136][1800/5047] lr: 7.2104e-06 eta: 17:52:01 time: 0.9131 data_time: 0.0028 memory: 48087 loss: 0.1110 loss_ce: 0.1110 2023/03/02 13:35:59 - mmengine - INFO - Epoch(train) [136][1900/5047] lr: 7.2104e-06 eta: 17:50:34 time: 0.8910 data_time: 0.0032 memory: 46772 loss: 0.0996 loss_ce: 0.0996 2023/03/02 13:37:26 - mmengine - INFO - Epoch(train) [136][2000/5047] lr: 7.2104e-06 eta: 17:49:07 time: 0.8777 data_time: 0.0028 memory: 49068 loss: 0.0923 loss_ce: 0.0923 2023/03/02 13:38:52 - mmengine - INFO - Epoch(train) [136][2100/5047] lr: 7.2104e-06 eta: 17:47:39 time: 0.8577 data_time: 0.0027 memory: 46794 loss: 0.1142 loss_ce: 0.1142 2023/03/02 13:40:19 - mmengine - INFO - Epoch(train) [136][2200/5047] lr: 7.2104e-06 eta: 17:46:12 time: 0.8348 data_time: 0.0085 memory: 51637 loss: 0.1036 loss_ce: 0.1036 2023/03/02 13:41:46 - mmengine - INFO - Epoch(train) [136][2300/5047] lr: 7.2104e-06 eta: 17:44:45 time: 0.8750 data_time: 0.0028 memory: 49715 loss: 0.0995 loss_ce: 0.0995 2023/03/02 13:43:11 - mmengine - INFO - Epoch(train) [136][2400/5047] lr: 7.2104e-06 eta: 17:43:18 time: 0.8511 data_time: 0.0050 memory: 48948 loss: 0.1037 loss_ce: 0.1037 2023/03/02 13:44:37 - mmengine - INFO - Epoch(train) [136][2500/5047] lr: 7.2104e-06 eta: 17:41:51 time: 0.8624 data_time: 0.0027 memory: 44563 loss: 0.0837 loss_ce: 0.0837 2023/03/02 13:46:02 - mmengine - INFO - Epoch(train) [136][2600/5047] lr: 7.2104e-06 eta: 17:40:24 time: 0.8304 data_time: 0.0027 memory: 43289 loss: 0.0952 loss_ce: 0.0952 2023/03/02 13:46:50 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 13:47:28 - mmengine - INFO - Epoch(train) [136][2700/5047] lr: 7.2104e-06 eta: 17:38:57 time: 0.8763 data_time: 0.0055 memory: 44956 loss: 0.0925 loss_ce: 0.0925 2023/03/02 13:48:54 - mmengine - INFO - Epoch(train) [136][2800/5047] lr: 7.2104e-06 eta: 17:37:29 time: 0.8566 data_time: 0.0053 memory: 46546 loss: 0.1054 loss_ce: 0.1054 2023/03/02 13:50:18 - mmengine - INFO - Epoch(train) [136][2900/5047] lr: 7.2104e-06 eta: 17:36:02 time: 0.7898 data_time: 0.0033 memory: 40876 loss: 0.1017 loss_ce: 0.1017 2023/03/02 13:51:44 - mmengine - INFO - Epoch(train) [136][3000/5047] lr: 7.2104e-06 eta: 17:34:35 time: 0.8270 data_time: 0.0030 memory: 41287 loss: 0.1006 loss_ce: 0.1006 2023/03/02 13:53:11 - mmengine - INFO - Epoch(train) [136][3100/5047] lr: 7.2104e-06 eta: 17:33:08 time: 0.8468 data_time: 0.0030 memory: 47162 loss: 0.1091 loss_ce: 0.1091 2023/03/02 13:54:37 - mmengine - INFO - Epoch(train) [136][3200/5047] lr: 7.2104e-06 eta: 17:31:41 time: 0.9008 data_time: 0.0042 memory: 51628 loss: 0.1001 loss_ce: 0.1001 2023/03/02 13:56:04 - mmengine - INFO - Epoch(train) [136][3300/5047] lr: 7.2104e-06 eta: 17:30:14 time: 0.8283 data_time: 0.0032 memory: 47074 loss: 0.1022 loss_ce: 0.1022 2023/03/02 13:57:30 - mmengine - INFO - Epoch(train) [136][3400/5047] lr: 7.2104e-06 eta: 17:28:46 time: 0.8503 data_time: 0.0030 memory: 46005 loss: 0.1028 loss_ce: 0.1028 2023/03/02 13:58:55 - mmengine - INFO - Epoch(train) [136][3500/5047] lr: 7.2104e-06 eta: 17:27:19 time: 0.8555 data_time: 0.0031 memory: 42076 loss: 0.1079 loss_ce: 0.1079 2023/03/02 14:00:19 - mmengine - INFO - Epoch(train) [136][3600/5047] lr: 7.2104e-06 eta: 17:25:52 time: 0.8590 data_time: 0.0095 memory: 45749 loss: 0.1229 loss_ce: 0.1229 2023/03/02 14:01:06 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 14:01:45 - mmengine - INFO - Epoch(train) [136][3700/5047] lr: 7.2104e-06 eta: 17:24:25 time: 0.8542 data_time: 0.0027 memory: 42664 loss: 0.1077 loss_ce: 0.1077 2023/03/02 14:03:11 - mmengine - INFO - Epoch(train) [136][3800/5047] lr: 7.2104e-06 eta: 17:22:58 time: 0.9353 data_time: 0.0028 memory: 51393 loss: 0.1132 loss_ce: 0.1132 2023/03/02 14:04:36 - mmengine - INFO - Epoch(train) [136][3900/5047] lr: 7.2104e-06 eta: 17:21:30 time: 0.8341 data_time: 0.0042 memory: 44956 loss: 0.1085 loss_ce: 0.1085 2023/03/02 14:06:01 - mmengine - INFO - Epoch(train) [136][4000/5047] lr: 7.2104e-06 eta: 17:20:03 time: 0.9030 data_time: 0.0027 memory: 52038 loss: 0.1070 loss_ce: 0.1070 2023/03/02 14:07:27 - mmengine - INFO - Epoch(train) [136][4100/5047] lr: 7.2104e-06 eta: 17:18:36 time: 0.8264 data_time: 0.0053 memory: 42024 loss: 0.1033 loss_ce: 0.1033 2023/03/02 14:08:55 - mmengine - INFO - Epoch(train) [136][4200/5047] lr: 7.2104e-06 eta: 17:17:09 time: 0.8768 data_time: 0.0031 memory: 45302 loss: 0.1212 loss_ce: 0.1212 2023/03/02 14:10:21 - mmengine - INFO - Epoch(train) [136][4300/5047] lr: 7.2104e-06 eta: 17:15:42 time: 0.8334 data_time: 0.0030 memory: 42649 loss: 0.1087 loss_ce: 0.1087 2023/03/02 14:11:49 - mmengine - INFO - Epoch(train) [136][4400/5047] lr: 7.2104e-06 eta: 17:14:15 time: 0.8545 data_time: 0.0029 memory: 45850 loss: 0.0929 loss_ce: 0.0929 2023/03/02 14:13:16 - mmengine - INFO - Epoch(train) [136][4500/5047] lr: 7.2104e-06 eta: 17:12:48 time: 0.8603 data_time: 0.0029 memory: 55562 loss: 0.0927 loss_ce: 0.0927 2023/03/02 14:14:42 - mmengine - INFO - Epoch(train) [136][4600/5047] lr: 7.2104e-06 eta: 17:11:21 time: 0.8255 data_time: 0.0027 memory: 43613 loss: 0.1037 loss_ce: 0.1037 2023/03/02 14:15:31 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 14:16:10 - mmengine - INFO - Epoch(train) [136][4700/5047] lr: 7.2104e-06 eta: 17:09:54 time: 0.8650 data_time: 0.0029 memory: 42941 loss: 0.1065 loss_ce: 0.1065 2023/03/02 14:17:37 - mmengine - INFO - Epoch(train) [136][4800/5047] lr: 7.2104e-06 eta: 17:08:27 time: 0.8493 data_time: 0.0026 memory: 48188 loss: 0.1018 loss_ce: 0.1018 2023/03/02 14:19:03 - mmengine - INFO - Epoch(train) [136][4900/5047] lr: 7.2104e-06 eta: 17:07:00 time: 0.8419 data_time: 0.0029 memory: 55562 loss: 0.1198 loss_ce: 0.1198 2023/03/02 14:20:30 - mmengine - INFO - Epoch(train) [136][5000/5047] lr: 7.2104e-06 eta: 17:05:32 time: 0.8851 data_time: 0.0027 memory: 46355 loss: 0.1028 loss_ce: 0.1028 2023/03/02 14:21:11 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 14:21:11 - mmengine - INFO - Saving checkpoint at 136 epochs 2023/03/02 14:22:43 - mmengine - INFO - Epoch(train) [137][ 100/5047] lr: 7.0095e-06 eta: 17:03:25 time: 0.8511 data_time: 0.0037 memory: 43289 loss: 0.1135 loss_ce: 0.1135 2023/03/02 14:24:11 - mmengine - INFO - Epoch(train) [137][ 200/5047] lr: 7.0095e-06 eta: 17:01:58 time: 0.8685 data_time: 0.0027 memory: 53044 loss: 0.1017 loss_ce: 0.1017 2023/03/02 14:25:37 - mmengine - INFO - Epoch(train) [137][ 300/5047] lr: 7.0095e-06 eta: 17:00:30 time: 0.8760 data_time: 0.0032 memory: 45361 loss: 0.1046 loss_ce: 0.1046 2023/03/02 14:27:03 - mmengine - INFO - Epoch(train) [137][ 400/5047] lr: 7.0095e-06 eta: 16:59:03 time: 0.8262 data_time: 0.0027 memory: 49116 loss: 0.0997 loss_ce: 0.0997 2023/03/02 14:28:28 - mmengine - INFO - Epoch(train) [137][ 500/5047] lr: 7.0095e-06 eta: 16:57:36 time: 0.8085 data_time: 0.0029 memory: 47813 loss: 0.1067 loss_ce: 0.1067 2023/03/02 14:29:55 - mmengine - INFO - Epoch(train) [137][ 600/5047] lr: 7.0095e-06 eta: 16:56:09 time: 0.8354 data_time: 0.0045 memory: 43457 loss: 0.1063 loss_ce: 0.1063 2023/03/02 14:30:02 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 14:31:22 - mmengine - INFO - Epoch(train) [137][ 700/5047] lr: 7.0095e-06 eta: 16:54:42 time: 0.8775 data_time: 0.0039 memory: 41724 loss: 0.1096 loss_ce: 0.1096 2023/03/02 14:32:46 - mmengine - INFO - Epoch(train) [137][ 800/5047] lr: 7.0095e-06 eta: 16:53:15 time: 0.8441 data_time: 0.0066 memory: 47799 loss: 0.1007 loss_ce: 0.1007 2023/03/02 14:34:13 - mmengine - INFO - Epoch(train) [137][ 900/5047] lr: 7.0095e-06 eta: 16:51:47 time: 0.8896 data_time: 0.0028 memory: 42024 loss: 0.1136 loss_ce: 0.1136 2023/03/02 14:35:39 - mmengine - INFO - Epoch(train) [137][1000/5047] lr: 7.0095e-06 eta: 16:50:20 time: 0.8318 data_time: 0.0031 memory: 43947 loss: 0.1164 loss_ce: 0.1164 2023/03/02 14:37:06 - mmengine - INFO - Epoch(train) [137][1100/5047] lr: 7.0095e-06 eta: 16:48:53 time: 0.8773 data_time: 0.0029 memory: 42430 loss: 0.1106 loss_ce: 0.1106 2023/03/02 14:38:31 - mmengine - INFO - Epoch(train) [137][1200/5047] lr: 7.0095e-06 eta: 16:47:26 time: 0.8653 data_time: 0.0028 memory: 46005 loss: 0.1141 loss_ce: 0.1141 2023/03/02 14:39:56 - mmengine - INFO - Epoch(train) [137][1300/5047] lr: 7.0095e-06 eta: 16:45:59 time: 0.8308 data_time: 0.0042 memory: 51705 loss: 0.1215 loss_ce: 0.1215 2023/03/02 14:41:22 - mmengine - INFO - Epoch(train) [137][1400/5047] lr: 7.0095e-06 eta: 16:44:32 time: 0.8788 data_time: 0.0027 memory: 52127 loss: 0.0965 loss_ce: 0.0965 2023/03/02 14:42:48 - mmengine - INFO - Epoch(train) [137][1500/5047] lr: 7.0095e-06 eta: 16:43:05 time: 0.8398 data_time: 0.0030 memory: 51561 loss: 0.0992 loss_ce: 0.0992 2023/03/02 14:44:12 - mmengine - INFO - Epoch(train) [137][1600/5047] lr: 7.0095e-06 eta: 16:41:37 time: 0.8180 data_time: 0.0034 memory: 42024 loss: 0.0946 loss_ce: 0.0946 2023/03/02 14:44:19 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 14:45:39 - mmengine - INFO - Epoch(train) [137][1700/5047] lr: 7.0095e-06 eta: 16:40:10 time: 0.8494 data_time: 0.0030 memory: 50607 loss: 0.1111 loss_ce: 0.1111 2023/03/02 14:47:05 - mmengine - INFO - Epoch(train) [137][1800/5047] lr: 7.0095e-06 eta: 16:38:43 time: 0.8690 data_time: 0.0069 memory: 47718 loss: 0.1118 loss_ce: 0.1118 2023/03/02 14:48:31 - mmengine - INFO - Epoch(train) [137][1900/5047] lr: 7.0095e-06 eta: 16:37:16 time: 0.8177 data_time: 0.0028 memory: 48188 loss: 0.1198 loss_ce: 0.1198 2023/03/02 14:49:55 - mmengine - INFO - Epoch(train) [137][2000/5047] lr: 7.0095e-06 eta: 16:35:49 time: 0.8301 data_time: 0.0028 memory: 39470 loss: 0.1001 loss_ce: 0.1001 2023/03/02 14:51:22 - mmengine - INFO - Epoch(train) [137][2100/5047] lr: 7.0095e-06 eta: 16:34:22 time: 0.8577 data_time: 0.0028 memory: 43269 loss: 0.1134 loss_ce: 0.1134 2023/03/02 14:52:47 - mmengine - INFO - Epoch(train) [137][2200/5047] lr: 7.0095e-06 eta: 16:32:54 time: 0.8754 data_time: 0.0034 memory: 42336 loss: 0.0959 loss_ce: 0.0959 2023/03/02 14:54:14 - mmengine - INFO - Epoch(train) [137][2300/5047] lr: 7.0095e-06 eta: 16:31:27 time: 0.8913 data_time: 0.0029 memory: 41724 loss: 0.1208 loss_ce: 0.1208 2023/03/02 14:55:41 - mmengine - INFO - Epoch(train) [137][2400/5047] lr: 7.0095e-06 eta: 16:30:00 time: 0.8755 data_time: 0.0028 memory: 42965 loss: 0.1016 loss_ce: 0.1016 2023/03/02 14:57:07 - mmengine - INFO - Epoch(train) [137][2500/5047] lr: 7.0095e-06 eta: 16:28:33 time: 0.8488 data_time: 0.0031 memory: 42649 loss: 0.1110 loss_ce: 0.1110 2023/03/02 14:58:33 - mmengine - INFO - Epoch(train) [137][2600/5047] lr: 7.0095e-06 eta: 16:27:06 time: 0.8141 data_time: 0.0033 memory: 49373 loss: 0.1166 loss_ce: 0.1166 2023/03/02 14:58:40 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 14:59:59 - mmengine - INFO - Epoch(train) [137][2700/5047] lr: 7.0095e-06 eta: 16:25:39 time: 0.8542 data_time: 0.0031 memory: 41724 loss: 0.1002 loss_ce: 0.1002 2023/03/02 15:01:24 - mmengine - INFO - Epoch(train) [137][2800/5047] lr: 7.0095e-06 eta: 16:24:12 time: 0.8728 data_time: 0.0032 memory: 55562 loss: 0.0959 loss_ce: 0.0959 2023/03/02 15:02:50 - mmengine - INFO - Epoch(train) [137][2900/5047] lr: 7.0095e-06 eta: 16:22:44 time: 0.8434 data_time: 0.0029 memory: 40069 loss: 0.0974 loss_ce: 0.0974 2023/03/02 15:04:15 - mmengine - INFO - Epoch(train) [137][3000/5047] lr: 7.0095e-06 eta: 16:21:17 time: 0.8293 data_time: 0.0027 memory: 52862 loss: 0.0982 loss_ce: 0.0982 2023/03/02 15:05:40 - mmengine - INFO - Epoch(train) [137][3100/5047] lr: 7.0095e-06 eta: 16:19:50 time: 0.8326 data_time: 0.0033 memory: 39938 loss: 0.1118 loss_ce: 0.1118 2023/03/02 15:07:06 - mmengine - INFO - Epoch(train) [137][3200/5047] lr: 7.0095e-06 eta: 16:18:23 time: 0.8902 data_time: 0.0032 memory: 51719 loss: 0.1085 loss_ce: 0.1085 2023/03/02 15:08:31 - mmengine - INFO - Epoch(train) [137][3300/5047] lr: 7.0095e-06 eta: 16:16:56 time: 0.8425 data_time: 0.0031 memory: 42649 loss: 0.0992 loss_ce: 0.0992 2023/03/02 15:09:57 - mmengine - INFO - Epoch(train) [137][3400/5047] lr: 7.0095e-06 eta: 16:15:28 time: 0.8535 data_time: 0.0026 memory: 41519 loss: 0.0867 loss_ce: 0.0867 2023/03/02 15:11:23 - mmengine - INFO - Epoch(train) [137][3500/5047] lr: 7.0095e-06 eta: 16:14:01 time: 0.8576 data_time: 0.0029 memory: 43222 loss: 0.1131 loss_ce: 0.1131 2023/03/02 15:12:50 - mmengine - INFO - Epoch(train) [137][3600/5047] lr: 7.0095e-06 eta: 16:12:34 time: 0.8192 data_time: 0.0030 memory: 41419 loss: 0.1006 loss_ce: 0.1006 2023/03/02 15:12:57 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 15:14:15 - mmengine - INFO - Epoch(train) [137][3700/5047] lr: 7.0095e-06 eta: 16:11:07 time: 0.8471 data_time: 0.0028 memory: 42022 loss: 0.1093 loss_ce: 0.1093 2023/03/02 15:15:40 - mmengine - INFO - Epoch(train) [137][3800/5047] lr: 7.0095e-06 eta: 16:09:40 time: 0.8734 data_time: 0.0029 memory: 55562 loss: 0.1061 loss_ce: 0.1061 2023/03/02 15:17:05 - mmengine - INFO - Epoch(train) [137][3900/5047] lr: 7.0095e-06 eta: 16:08:13 time: 0.8634 data_time: 0.0028 memory: 42802 loss: 0.1085 loss_ce: 0.1085 2023/03/02 15:18:30 - mmengine - INFO - Epoch(train) [137][4000/5047] lr: 7.0095e-06 eta: 16:06:45 time: 0.7834 data_time: 0.0074 memory: 42394 loss: 0.1070 loss_ce: 0.1070 2023/03/02 15:19:55 - mmengine - INFO - Epoch(train) [137][4100/5047] lr: 7.0095e-06 eta: 16:05:18 time: 0.8630 data_time: 0.0029 memory: 47983 loss: 0.1017 loss_ce: 0.1017 2023/03/02 15:21:22 - mmengine - INFO - Epoch(train) [137][4200/5047] lr: 7.0095e-06 eta: 16:03:51 time: 0.8893 data_time: 0.0030 memory: 42336 loss: 0.1041 loss_ce: 0.1041 2023/03/02 15:22:48 - mmengine - INFO - Epoch(train) [137][4300/5047] lr: 7.0095e-06 eta: 16:02:24 time: 0.8510 data_time: 0.0028 memory: 41480 loss: 0.1115 loss_ce: 0.1115 2023/03/02 15:24:13 - mmengine - INFO - Epoch(train) [137][4400/5047] lr: 7.0095e-06 eta: 16:00:57 time: 0.8408 data_time: 0.0036 memory: 42024 loss: 0.1130 loss_ce: 0.1130 2023/03/02 15:25:37 - mmengine - INFO - Epoch(train) [137][4500/5047] lr: 7.0095e-06 eta: 15:59:29 time: 0.8847 data_time: 0.0063 memory: 41724 loss: 0.1050 loss_ce: 0.1050 2023/03/02 15:27:05 - mmengine - INFO - Epoch(train) [137][4600/5047] lr: 7.0095e-06 eta: 15:58:02 time: 0.8756 data_time: 0.0028 memory: 45621 loss: 0.1081 loss_ce: 0.1081 2023/03/02 15:27:11 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 15:28:29 - mmengine - INFO - Epoch(train) [137][4700/5047] lr: 7.0095e-06 eta: 15:56:35 time: 0.8387 data_time: 0.0030 memory: 47959 loss: 0.0994 loss_ce: 0.0994 2023/03/02 15:29:54 - mmengine - INFO - Epoch(train) [137][4800/5047] lr: 7.0095e-06 eta: 15:55:08 time: 0.8803 data_time: 0.0027 memory: 48188 loss: 0.1062 loss_ce: 0.1062 2023/03/02 15:31:20 - mmengine - INFO - Epoch(train) [137][4900/5047] lr: 7.0095e-06 eta: 15:53:41 time: 0.8468 data_time: 0.0031 memory: 42556 loss: 0.1038 loss_ce: 0.1038 2023/03/02 15:32:46 - mmengine - INFO - Epoch(train) [137][5000/5047] lr: 7.0095e-06 eta: 15:52:14 time: 0.8126 data_time: 0.0032 memory: 41082 loss: 0.0999 loss_ce: 0.0999 2023/03/02 15:33:26 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 15:33:26 - mmengine - INFO - Saving checkpoint at 137 epochs 2023/03/02 15:34:56 - mmengine - INFO - Epoch(train) [138][ 100/5047] lr: 6.8085e-06 eta: 15:50:05 time: 0.8852 data_time: 0.0031 memory: 44956 loss: 0.1086 loss_ce: 0.1086 2023/03/02 15:36:21 - mmengine - INFO - Epoch(train) [138][ 200/5047] lr: 6.8085e-06 eta: 15:48:38 time: 0.8563 data_time: 0.0035 memory: 43613 loss: 0.1058 loss_ce: 0.1058 2023/03/02 15:37:49 - mmengine - INFO - Epoch(train) [138][ 300/5047] lr: 6.8085e-06 eta: 15:47:11 time: 0.8658 data_time: 0.0031 memory: 43289 loss: 0.0941 loss_ce: 0.0941 2023/03/02 15:39:15 - mmengine - INFO - Epoch(train) [138][ 400/5047] lr: 6.8085e-06 eta: 15:45:44 time: 0.8662 data_time: 0.0032 memory: 49715 loss: 0.1212 loss_ce: 0.1212 2023/03/02 15:40:41 - mmengine - INFO - Epoch(train) [138][ 500/5047] lr: 6.8085e-06 eta: 15:44:17 time: 0.8259 data_time: 0.0038 memory: 42273 loss: 0.0920 loss_ce: 0.0920 2023/03/02 15:41:33 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 15:42:06 - mmengine - INFO - Epoch(train) [138][ 600/5047] lr: 6.8085e-06 eta: 15:42:50 time: 0.8218 data_time: 0.0028 memory: 51308 loss: 0.1065 loss_ce: 0.1065 2023/03/02 15:43:32 - mmengine - INFO - Epoch(train) [138][ 700/5047] lr: 6.8085e-06 eta: 15:41:23 time: 0.8626 data_time: 0.0032 memory: 42205 loss: 0.1086 loss_ce: 0.1086 2023/03/02 15:44:56 - mmengine - INFO - Epoch(train) [138][ 800/5047] lr: 6.8085e-06 eta: 15:39:55 time: 0.8371 data_time: 0.0030 memory: 44843 loss: 0.0936 loss_ce: 0.0936 2023/03/02 15:46:21 - mmengine - INFO - Epoch(train) [138][ 900/5047] lr: 6.8085e-06 eta: 15:38:28 time: 0.8099 data_time: 0.0028 memory: 43115 loss: 0.1037 loss_ce: 0.1037 2023/03/02 15:47:46 - mmengine - INFO - Epoch(train) [138][1000/5047] lr: 6.8085e-06 eta: 15:37:01 time: 0.9105 data_time: 0.0029 memory: 55562 loss: 0.1061 loss_ce: 0.1061 2023/03/02 15:49:11 - mmengine - INFO - Epoch(train) [138][1100/5047] lr: 6.8085e-06 eta: 15:35:34 time: 0.8139 data_time: 0.0033 memory: 41724 loss: 0.1130 loss_ce: 0.1130 2023/03/02 15:50:37 - mmengine - INFO - Epoch(train) [138][1200/5047] lr: 6.8085e-06 eta: 15:34:07 time: 0.8737 data_time: 0.0032 memory: 42584 loss: 0.1060 loss_ce: 0.1060 2023/03/02 15:52:03 - mmengine - INFO - Epoch(train) [138][1300/5047] lr: 6.8085e-06 eta: 15:32:39 time: 0.8771 data_time: 0.0027 memory: 42336 loss: 0.1078 loss_ce: 0.1078 2023/03/02 15:53:31 - mmengine - INFO - Epoch(train) [138][1400/5047] lr: 6.8085e-06 eta: 15:31:13 time: 0.8660 data_time: 0.0034 memory: 42965 loss: 0.0934 loss_ce: 0.0934 2023/03/02 15:54:57 - mmengine - INFO - Epoch(train) [138][1500/5047] lr: 6.8085e-06 eta: 15:29:45 time: 0.8277 data_time: 0.0030 memory: 45643 loss: 0.1095 loss_ce: 0.1095 2023/03/02 15:55:48 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 15:56:22 - mmengine - INFO - Epoch(train) [138][1600/5047] lr: 6.8085e-06 eta: 15:28:18 time: 0.8229 data_time: 0.0097 memory: 42649 loss: 0.0968 loss_ce: 0.0968 2023/03/02 15:57:47 - mmengine - INFO - Epoch(train) [138][1700/5047] lr: 6.8085e-06 eta: 15:26:51 time: 0.8563 data_time: 0.0026 memory: 48129 loss: 0.0930 loss_ce: 0.0930 2023/03/02 15:59:12 - mmengine - INFO - Epoch(train) [138][1800/5047] lr: 6.8085e-06 eta: 15:25:24 time: 0.8475 data_time: 0.0032 memory: 45643 loss: 0.0980 loss_ce: 0.0980 2023/03/02 16:00:38 - mmengine - INFO - Epoch(train) [138][1900/5047] lr: 6.8085e-06 eta: 15:23:57 time: 0.8546 data_time: 0.0033 memory: 55562 loss: 0.0920 loss_ce: 0.0920 2023/03/02 16:02:04 - mmengine - INFO - Epoch(train) [138][2000/5047] lr: 6.8085e-06 eta: 15:22:30 time: 0.8661 data_time: 0.0048 memory: 41122 loss: 0.1047 loss_ce: 0.1047 2023/03/02 16:03:30 - mmengine - INFO - Epoch(train) [138][2100/5047] lr: 6.8085e-06 eta: 15:21:02 time: 0.9017 data_time: 0.0029 memory: 52127 loss: 0.0936 loss_ce: 0.0936 2023/03/02 16:04:57 - mmengine - INFO - Epoch(train) [138][2200/5047] lr: 6.8085e-06 eta: 15:19:35 time: 0.8129 data_time: 0.0031 memory: 46360 loss: 0.1020 loss_ce: 0.1020 2023/03/02 16:06:24 - mmengine - INFO - Epoch(train) [138][2300/5047] lr: 6.8085e-06 eta: 15:18:08 time: 0.8521 data_time: 0.0028 memory: 41609 loss: 0.1084 loss_ce: 0.1084 2023/03/02 16:07:50 - mmengine - INFO - Epoch(train) [138][2400/5047] lr: 6.8085e-06 eta: 15:16:41 time: 0.8164 data_time: 0.0031 memory: 45643 loss: 0.1015 loss_ce: 0.1015 2023/03/02 16:09:16 - mmengine - INFO - Epoch(train) [138][2500/5047] lr: 6.8085e-06 eta: 15:15:14 time: 0.8315 data_time: 0.0028 memory: 43141 loss: 0.0930 loss_ce: 0.0930 2023/03/02 16:10:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 16:10:43 - mmengine - INFO - Epoch(train) [138][2600/5047] lr: 6.8085e-06 eta: 15:13:47 time: 0.8861 data_time: 0.0032 memory: 45639 loss: 0.1001 loss_ce: 0.1001 2023/03/02 16:12:08 - mmengine - INFO - Epoch(train) [138][2700/5047] lr: 6.8085e-06 eta: 15:12:20 time: 0.8782 data_time: 0.0029 memory: 52816 loss: 0.1078 loss_ce: 0.1078 2023/03/02 16:13:34 - mmengine - INFO - Epoch(train) [138][2800/5047] lr: 6.8085e-06 eta: 15:10:53 time: 0.8464 data_time: 0.0027 memory: 44956 loss: 0.1094 loss_ce: 0.1094 2023/03/02 16:15:01 - mmengine - INFO - Epoch(train) [138][2900/5047] lr: 6.8085e-06 eta: 15:09:26 time: 0.8954 data_time: 0.0030 memory: 46005 loss: 0.1012 loss_ce: 0.1012 2023/03/02 16:16:27 - mmengine - INFO - Epoch(train) [138][3000/5047] lr: 6.8085e-06 eta: 15:07:59 time: 0.8382 data_time: 0.0026 memory: 42336 loss: 0.1093 loss_ce: 0.1093 2023/03/02 16:17:53 - mmengine - INFO - Epoch(train) [138][3100/5047] lr: 6.8085e-06 eta: 15:06:31 time: 0.8583 data_time: 0.0063 memory: 45588 loss: 0.1075 loss_ce: 0.1075 2023/03/02 16:19:18 - mmengine - INFO - Epoch(train) [138][3200/5047] lr: 6.8085e-06 eta: 15:05:04 time: 0.8345 data_time: 0.0039 memory: 46847 loss: 0.0955 loss_ce: 0.0955 2023/03/02 16:20:44 - mmengine - INFO - Epoch(train) [138][3300/5047] lr: 6.8085e-06 eta: 15:03:37 time: 0.8962 data_time: 0.0032 memory: 44479 loss: 0.1233 loss_ce: 0.1233 2023/03/02 16:22:08 - mmengine - INFO - Epoch(train) [138][3400/5047] lr: 6.8085e-06 eta: 15:02:10 time: 0.8349 data_time: 0.0028 memory: 41254 loss: 0.1031 loss_ce: 0.1031 2023/03/02 16:23:34 - mmengine - INFO - Epoch(train) [138][3500/5047] lr: 6.8085e-06 eta: 15:00:43 time: 0.8391 data_time: 0.0058 memory: 44539 loss: 0.1124 loss_ce: 0.1124 2023/03/02 16:24:26 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 16:24:59 - mmengine - INFO - Epoch(train) [138][3600/5047] lr: 6.8085e-06 eta: 14:59:16 time: 0.8352 data_time: 0.0026 memory: 44524 loss: 0.1030 loss_ce: 0.1030 2023/03/02 16:26:27 - mmengine - INFO - Epoch(train) [138][3700/5047] lr: 6.8085e-06 eta: 14:57:49 time: 0.9000 data_time: 0.0036 memory: 43289 loss: 0.1095 loss_ce: 0.1095 2023/03/02 16:27:53 - mmengine - INFO - Epoch(train) [138][3800/5047] lr: 6.8085e-06 eta: 14:56:22 time: 0.8685 data_time: 0.0031 memory: 51312 loss: 0.0989 loss_ce: 0.0989 2023/03/02 16:29:18 - mmengine - INFO - Epoch(train) [138][3900/5047] lr: 6.8085e-06 eta: 14:54:54 time: 0.8192 data_time: 0.0031 memory: 41419 loss: 0.1147 loss_ce: 0.1147 2023/03/02 16:30:42 - mmengine - INFO - Epoch(train) [138][4000/5047] lr: 6.8085e-06 eta: 14:53:27 time: 0.8567 data_time: 0.0031 memory: 45643 loss: 0.1134 loss_ce: 0.1134 2023/03/02 16:32:07 - mmengine - INFO - Epoch(train) [138][4100/5047] lr: 6.8085e-06 eta: 14:52:00 time: 0.8484 data_time: 0.0042 memory: 39652 loss: 0.0923 loss_ce: 0.0923 2023/03/02 16:33:33 - mmengine - INFO - Epoch(train) [138][4200/5047] lr: 6.8085e-06 eta: 14:50:33 time: 0.8516 data_time: 0.0052 memory: 44956 loss: 0.1168 loss_ce: 0.1168 2023/03/02 16:35:00 - mmengine - INFO - Epoch(train) [138][4300/5047] lr: 6.8085e-06 eta: 14:49:06 time: 0.8359 data_time: 0.0041 memory: 43289 loss: 0.0811 loss_ce: 0.0811 2023/03/02 16:36:25 - mmengine - INFO - Epoch(train) [138][4400/5047] lr: 6.8085e-06 eta: 14:47:39 time: 0.9162 data_time: 0.0033 memory: 43613 loss: 0.1022 loss_ce: 0.1022 2023/03/02 16:37:53 - mmengine - INFO - Epoch(train) [138][4500/5047] lr: 6.8085e-06 eta: 14:46:12 time: 0.9255 data_time: 0.0031 memory: 54242 loss: 0.1027 loss_ce: 0.1027 2023/03/02 16:38:45 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 16:39:19 - mmengine - INFO - Epoch(train) [138][4600/5047] lr: 6.8085e-06 eta: 14:44:44 time: 0.8773 data_time: 0.0094 memory: 49378 loss: 0.0985 loss_ce: 0.0985 2023/03/02 16:40:45 - mmengine - INFO - Epoch(train) [138][4700/5047] lr: 6.8085e-06 eta: 14:43:17 time: 0.9057 data_time: 0.0025 memory: 55366 loss: 0.1026 loss_ce: 0.1026 2023/03/02 16:42:11 - mmengine - INFO - Epoch(train) [138][4800/5047] lr: 6.8085e-06 eta: 14:41:50 time: 0.8697 data_time: 0.0043 memory: 48188 loss: 0.1001 loss_ce: 0.1001 2023/03/02 16:43:37 - mmengine - INFO - Epoch(train) [138][4900/5047] lr: 6.8085e-06 eta: 14:40:23 time: 0.8166 data_time: 0.0031 memory: 50589 loss: 0.1071 loss_ce: 0.1071 2023/03/02 16:45:03 - mmengine - INFO - Epoch(train) [138][5000/5047] lr: 6.8085e-06 eta: 14:38:56 time: 0.8465 data_time: 0.0028 memory: 42649 loss: 0.0969 loss_ce: 0.0969 2023/03/02 16:45:44 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 16:45:44 - mmengine - INFO - Saving checkpoint at 138 epochs 2023/03/02 16:47:14 - mmengine - INFO - Epoch(train) [139][ 100/5047] lr: 6.6076e-06 eta: 14:36:48 time: 0.8649 data_time: 0.0026 memory: 54673 loss: 0.0931 loss_ce: 0.0931 2023/03/02 16:48:39 - mmengine - INFO - Epoch(train) [139][ 200/5047] lr: 6.6076e-06 eta: 14:35:21 time: 0.8192 data_time: 0.0043 memory: 46005 loss: 0.1045 loss_ce: 0.1045 2023/03/02 16:50:06 - mmengine - INFO - Epoch(train) [139][ 300/5047] lr: 6.6076e-06 eta: 14:33:54 time: 0.8883 data_time: 0.0027 memory: 46608 loss: 0.1064 loss_ce: 0.1064 2023/03/02 16:51:33 - mmengine - INFO - Epoch(train) [139][ 400/5047] lr: 6.6076e-06 eta: 14:32:27 time: 0.9203 data_time: 0.0029 memory: 46566 loss: 0.1144 loss_ce: 0.1144 2023/03/02 16:52:59 - mmengine - INFO - Epoch(train) [139][ 500/5047] lr: 6.6076e-06 eta: 14:31:00 time: 0.8913 data_time: 0.0033 memory: 42336 loss: 0.1047 loss_ce: 0.1047 2023/03/02 16:53:11 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 16:54:27 - mmengine - INFO - Epoch(train) [139][ 600/5047] lr: 6.6076e-06 eta: 14:29:33 time: 0.8633 data_time: 0.0033 memory: 50446 loss: 0.1062 loss_ce: 0.1062 2023/03/02 16:55:51 - mmengine - INFO - Epoch(train) [139][ 700/5047] lr: 6.6076e-06 eta: 14:28:05 time: 0.8456 data_time: 0.0063 memory: 42649 loss: 0.0984 loss_ce: 0.0984 2023/03/02 16:57:17 - mmengine - INFO - Epoch(train) [139][ 800/5047] lr: 6.6076e-06 eta: 14:26:38 time: 0.8402 data_time: 0.0030 memory: 43872 loss: 0.0983 loss_ce: 0.0983 2023/03/02 16:58:41 - mmengine - INFO - Epoch(train) [139][ 900/5047] lr: 6.6076e-06 eta: 14:25:11 time: 0.8257 data_time: 0.0029 memory: 42069 loss: 0.1044 loss_ce: 0.1044 2023/03/02 17:00:08 - mmengine - INFO - Epoch(train) [139][1000/5047] lr: 6.6076e-06 eta: 14:23:44 time: 0.8841 data_time: 0.0031 memory: 46853 loss: 0.1053 loss_ce: 0.1053 2023/03/02 17:01:35 - mmengine - INFO - Epoch(train) [139][1100/5047] lr: 6.6076e-06 eta: 14:22:17 time: 0.8859 data_time: 0.0031 memory: 47596 loss: 0.1101 loss_ce: 0.1101 2023/03/02 17:03:00 - mmengine - INFO - Epoch(train) [139][1200/5047] lr: 6.6076e-06 eta: 14:20:50 time: 0.8221 data_time: 0.0036 memory: 41419 loss: 0.0975 loss_ce: 0.0975 2023/03/02 17:04:28 - mmengine - INFO - Epoch(train) [139][1300/5047] lr: 6.6076e-06 eta: 14:19:23 time: 0.8860 data_time: 0.0029 memory: 46355 loss: 0.1026 loss_ce: 0.1026 2023/03/02 17:05:54 - mmengine - INFO - Epoch(train) [139][1400/5047] lr: 6.6076e-06 eta: 14:17:56 time: 0.8223 data_time: 0.0029 memory: 42649 loss: 0.1088 loss_ce: 0.1088 2023/03/02 17:07:19 - mmengine - INFO - Epoch(train) [139][1500/5047] lr: 6.6076e-06 eta: 14:16:29 time: 0.8929 data_time: 0.0030 memory: 41419 loss: 0.1042 loss_ce: 0.1042 2023/03/02 17:07:31 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 17:08:45 - mmengine - INFO - Epoch(train) [139][1600/5047] lr: 6.6076e-06 eta: 14:15:01 time: 0.8890 data_time: 0.0039 memory: 42965 loss: 0.0992 loss_ce: 0.0992 2023/03/02 17:10:10 - mmengine - INFO - Epoch(train) [139][1700/5047] lr: 6.6076e-06 eta: 14:13:34 time: 0.9073 data_time: 0.0033 memory: 40030 loss: 0.0965 loss_ce: 0.0965 2023/03/02 17:11:36 - mmengine - INFO - Epoch(train) [139][1800/5047] lr: 6.6076e-06 eta: 14:12:07 time: 0.9189 data_time: 0.0037 memory: 47813 loss: 0.1023 loss_ce: 0.1023 2023/03/02 17:13:01 - mmengine - INFO - Epoch(train) [139][1900/5047] lr: 6.6076e-06 eta: 14:10:40 time: 0.7940 data_time: 0.0028 memory: 40535 loss: 0.1102 loss_ce: 0.1102 2023/03/02 17:14:26 - mmengine - INFO - Epoch(train) [139][2000/5047] lr: 6.6076e-06 eta: 14:09:13 time: 0.7646 data_time: 0.0032 memory: 55562 loss: 0.1066 loss_ce: 0.1066 2023/03/02 17:15:52 - mmengine - INFO - Epoch(train) [139][2100/5047] lr: 6.6076e-06 eta: 14:07:46 time: 0.8486 data_time: 0.0036 memory: 43947 loss: 0.1046 loss_ce: 0.1046 2023/03/02 17:17:17 - mmengine - INFO - Epoch(train) [139][2200/5047] lr: 6.6076e-06 eta: 14:06:19 time: 0.8363 data_time: 0.0029 memory: 43289 loss: 0.1124 loss_ce: 0.1124 2023/03/02 17:18:43 - mmengine - INFO - Epoch(train) [139][2300/5047] lr: 6.6076e-06 eta: 14:04:51 time: 0.8393 data_time: 0.0031 memory: 43289 loss: 0.0931 loss_ce: 0.0931 2023/03/02 17:20:10 - mmengine - INFO - Epoch(train) [139][2400/5047] lr: 6.6076e-06 eta: 14:03:24 time: 0.8473 data_time: 0.0029 memory: 55562 loss: 0.1032 loss_ce: 0.1032 2023/03/02 17:21:36 - mmengine - INFO - Epoch(train) [139][2500/5047] lr: 6.6076e-06 eta: 14:01:57 time: 0.8948 data_time: 0.0029 memory: 55562 loss: 0.1047 loss_ce: 0.1047 2023/03/02 17:21:48 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 17:23:01 - mmengine - INFO - Epoch(train) [139][2600/5047] lr: 6.6076e-06 eta: 14:00:30 time: 0.8572 data_time: 0.0029 memory: 55562 loss: 0.1128 loss_ce: 0.1128 2023/03/02 17:24:28 - mmengine - INFO - Epoch(train) [139][2700/5047] lr: 6.6076e-06 eta: 13:59:03 time: 0.8273 data_time: 0.0030 memory: 41625 loss: 0.0951 loss_ce: 0.0951 2023/03/02 17:25:53 - mmengine - INFO - Epoch(train) [139][2800/5047] lr: 6.6076e-06 eta: 13:57:36 time: 0.8462 data_time: 0.0028 memory: 45643 loss: 0.1029 loss_ce: 0.1029 2023/03/02 17:27:19 - mmengine - INFO - Epoch(train) [139][2900/5047] lr: 6.6076e-06 eta: 13:56:09 time: 0.8225 data_time: 0.0028 memory: 42649 loss: 0.0985 loss_ce: 0.0985 2023/03/02 17:28:46 - mmengine - INFO - Epoch(train) [139][3000/5047] lr: 6.6076e-06 eta: 13:54:42 time: 0.9042 data_time: 0.0027 memory: 40535 loss: 0.0913 loss_ce: 0.0913 2023/03/02 17:30:12 - mmengine - INFO - Epoch(train) [139][3100/5047] lr: 6.6076e-06 eta: 13:53:15 time: 0.8483 data_time: 0.0035 memory: 46874 loss: 0.0979 loss_ce: 0.0979 2023/03/02 17:31:38 - mmengine - INFO - Epoch(train) [139][3200/5047] lr: 6.6076e-06 eta: 13:51:48 time: 0.8206 data_time: 0.0029 memory: 45302 loss: 0.1084 loss_ce: 0.1084 2023/03/02 17:33:04 - mmengine - INFO - Epoch(train) [139][3300/5047] lr: 6.6076e-06 eta: 13:50:21 time: 0.8659 data_time: 0.0028 memory: 49334 loss: 0.0999 loss_ce: 0.0999 2023/03/02 17:34:30 - mmengine - INFO - Epoch(train) [139][3400/5047] lr: 6.6076e-06 eta: 13:48:53 time: 0.8231 data_time: 0.0030 memory: 42336 loss: 0.1121 loss_ce: 0.1121 2023/03/02 17:35:56 - mmengine - INFO - Epoch(train) [139][3500/5047] lr: 6.6076e-06 eta: 13:47:26 time: 0.9370 data_time: 0.0026 memory: 41419 loss: 0.1000 loss_ce: 0.1000 2023/03/02 17:36:07 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 17:37:21 - mmengine - INFO - Epoch(train) [139][3600/5047] lr: 6.6076e-06 eta: 13:45:59 time: 0.8174 data_time: 0.0029 memory: 42024 loss: 0.1051 loss_ce: 0.1051 2023/03/02 17:38:47 - mmengine - INFO - Epoch(train) [139][3700/5047] lr: 6.6076e-06 eta: 13:44:32 time: 0.9492 data_time: 0.0029 memory: 43613 loss: 0.0948 loss_ce: 0.0948 2023/03/02 17:40:14 - mmengine - INFO - Epoch(train) [139][3800/5047] lr: 6.6076e-06 eta: 13:43:05 time: 0.8462 data_time: 0.0027 memory: 43372 loss: 0.1135 loss_ce: 0.1135 2023/03/02 17:41:39 - mmengine - INFO - Epoch(train) [139][3900/5047] lr: 6.6076e-06 eta: 13:41:38 time: 0.8279 data_time: 0.0030 memory: 47813 loss: 0.0958 loss_ce: 0.0958 2023/03/02 17:43:04 - mmengine - INFO - Epoch(train) [139][4000/5047] lr: 6.6076e-06 eta: 13:40:11 time: 0.8118 data_time: 0.0029 memory: 40825 loss: 0.1012 loss_ce: 0.1012 2023/03/02 17:44:31 - mmengine - INFO - Epoch(train) [139][4100/5047] lr: 6.6076e-06 eta: 13:38:44 time: 0.8746 data_time: 0.0027 memory: 42336 loss: 0.1191 loss_ce: 0.1191 2023/03/02 17:45:56 - mmengine - INFO - Epoch(train) [139][4200/5047] lr: 6.6076e-06 eta: 13:37:17 time: 0.8364 data_time: 0.0027 memory: 39960 loss: 0.1213 loss_ce: 0.1213 2023/03/02 17:47:23 - mmengine - INFO - Epoch(train) [139][4300/5047] lr: 6.6076e-06 eta: 13:35:50 time: 0.8710 data_time: 0.0029 memory: 41984 loss: 0.1002 loss_ce: 0.1002 2023/03/02 17:48:50 - mmengine - INFO - Epoch(train) [139][4400/5047] lr: 6.6076e-06 eta: 13:34:22 time: 0.9029 data_time: 0.0047 memory: 40535 loss: 0.1103 loss_ce: 0.1103 2023/03/02 17:50:16 - mmengine - INFO - Epoch(train) [139][4500/5047] lr: 6.6076e-06 eta: 13:32:55 time: 0.8290 data_time: 0.0046 memory: 40241 loss: 0.1087 loss_ce: 0.1087 2023/03/02 17:50:28 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 17:51:42 - mmengine - INFO - Epoch(train) [139][4600/5047] lr: 6.6076e-06 eta: 13:31:28 time: 0.8958 data_time: 0.0044 memory: 41724 loss: 0.1058 loss_ce: 0.1058 2023/03/02 17:53:09 - mmengine - INFO - Epoch(train) [139][4700/5047] lr: 6.6076e-06 eta: 13:30:01 time: 0.8605 data_time: 0.0029 memory: 43795 loss: 0.1049 loss_ce: 0.1049 2023/03/02 17:54:36 - mmengine - INFO - Epoch(train) [139][4800/5047] lr: 6.6076e-06 eta: 13:28:34 time: 0.8993 data_time: 0.0029 memory: 42439 loss: 0.1189 loss_ce: 0.1189 2023/03/02 17:56:01 - mmengine - INFO - Epoch(train) [139][4900/5047] lr: 6.6076e-06 eta: 13:27:07 time: 0.8445 data_time: 0.0031 memory: 41419 loss: 0.1022 loss_ce: 0.1022 2023/03/02 17:57:27 - mmengine - INFO - Epoch(train) [139][5000/5047] lr: 6.6076e-06 eta: 13:25:40 time: 0.9197 data_time: 0.0031 memory: 44207 loss: 0.1017 loss_ce: 0.1017 2023/03/02 17:58:07 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 17:58:07 - mmengine - INFO - Saving checkpoint at 139 epochs 2023/03/02 17:59:39 - mmengine - INFO - Epoch(train) [140][ 100/5047] lr: 6.4066e-06 eta: 13:23:32 time: 0.9235 data_time: 0.0061 memory: 51818 loss: 0.0886 loss_ce: 0.0886 2023/03/02 18:01:05 - mmengine - INFO - Epoch(train) [140][ 200/5047] lr: 6.4066e-06 eta: 13:22:05 time: 0.8284 data_time: 0.0056 memory: 55562 loss: 0.0982 loss_ce: 0.0982 2023/03/02 18:02:30 - mmengine - INFO - Epoch(train) [140][ 300/5047] lr: 6.4066e-06 eta: 13:20:38 time: 0.8335 data_time: 0.0042 memory: 45302 loss: 0.1065 loss_ce: 0.1065 2023/03/02 18:03:57 - mmengine - INFO - Epoch(train) [140][ 400/5047] lr: 6.4066e-06 eta: 13:19:11 time: 0.8765 data_time: 0.0029 memory: 41090 loss: 0.0937 loss_ce: 0.0937 2023/03/02 18:04:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 18:05:22 - mmengine - INFO - Epoch(train) [140][ 500/5047] lr: 6.4066e-06 eta: 13:17:44 time: 0.8791 data_time: 0.0029 memory: 47813 loss: 0.1121 loss_ce: 0.1121 2023/03/02 18:06:47 - mmengine - INFO - Epoch(train) [140][ 600/5047] lr: 6.4066e-06 eta: 13:16:16 time: 0.9073 data_time: 0.0029 memory: 43289 loss: 0.1134 loss_ce: 0.1134 2023/03/02 18:08:13 - mmengine - INFO - Epoch(train) [140][ 700/5047] lr: 6.4066e-06 eta: 13:14:49 time: 0.7995 data_time: 0.0038 memory: 47132 loss: 0.1078 loss_ce: 0.1078 2023/03/02 18:09:39 - mmengine - INFO - Epoch(train) [140][ 800/5047] lr: 6.4066e-06 eta: 13:13:22 time: 0.8756 data_time: 0.0027 memory: 43880 loss: 0.1028 loss_ce: 0.1028 2023/03/02 18:11:05 - mmengine - INFO - Epoch(train) [140][ 900/5047] lr: 6.4066e-06 eta: 13:11:55 time: 0.7957 data_time: 0.0072 memory: 54205 loss: 0.1053 loss_ce: 0.1053 2023/03/02 18:12:30 - mmengine - INFO - Epoch(train) [140][1000/5047] lr: 6.4066e-06 eta: 13:10:28 time: 0.8355 data_time: 0.0027 memory: 47982 loss: 0.1141 loss_ce: 0.1141 2023/03/02 18:13:55 - mmengine - INFO - Epoch(train) [140][1100/5047] lr: 6.4066e-06 eta: 13:09:01 time: 0.9096 data_time: 0.0026 memory: 53025 loss: 0.1049 loss_ce: 0.1049 2023/03/02 18:15:20 - mmengine - INFO - Epoch(train) [140][1200/5047] lr: 6.4066e-06 eta: 13:07:34 time: 0.8289 data_time: 0.0028 memory: 55562 loss: 0.1043 loss_ce: 0.1043 2023/03/02 18:16:45 - mmengine - INFO - Epoch(train) [140][1300/5047] lr: 6.4066e-06 eta: 13:06:07 time: 0.8490 data_time: 0.0064 memory: 47136 loss: 0.1135 loss_ce: 0.1135 2023/03/02 18:18:12 - mmengine - INFO - Epoch(train) [140][1400/5047] lr: 6.4066e-06 eta: 13:04:40 time: 0.9194 data_time: 0.0027 memory: 51719 loss: 0.0980 loss_ce: 0.0980 2023/03/02 18:19:09 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 18:19:38 - mmengine - INFO - Epoch(train) [140][1500/5047] lr: 6.4066e-06 eta: 13:03:12 time: 0.8312 data_time: 0.0029 memory: 44278 loss: 0.1065 loss_ce: 0.1065 2023/03/02 18:21:02 - mmengine - INFO - Epoch(train) [140][1600/5047] lr: 6.4066e-06 eta: 13:01:45 time: 0.8703 data_time: 0.0028 memory: 44705 loss: 0.0972 loss_ce: 0.0972 2023/03/02 18:22:29 - mmengine - INFO - Epoch(train) [140][1700/5047] lr: 6.4066e-06 eta: 13:00:18 time: 0.8979 data_time: 0.0032 memory: 51719 loss: 0.1046 loss_ce: 0.1046 2023/03/02 18:23:54 - mmengine - INFO - Epoch(train) [140][1800/5047] lr: 6.4066e-06 eta: 12:58:51 time: 0.8539 data_time: 0.0030 memory: 43791 loss: 0.1100 loss_ce: 0.1100 2023/03/02 18:25:19 - mmengine - INFO - Epoch(train) [140][1900/5047] lr: 6.4066e-06 eta: 12:57:24 time: 0.7787 data_time: 0.0035 memory: 47074 loss: 0.1161 loss_ce: 0.1161 2023/03/02 18:26:44 - mmengine - INFO - Epoch(train) [140][2000/5047] lr: 6.4066e-06 eta: 12:55:57 time: 0.8514 data_time: 0.0027 memory: 46838 loss: 0.1080 loss_ce: 0.1080 2023/03/02 18:28:09 - mmengine - INFO - Epoch(train) [140][2100/5047] lr: 6.4066e-06 eta: 12:54:30 time: 0.8583 data_time: 0.0074 memory: 44539 loss: 0.1095 loss_ce: 0.1095 2023/03/02 18:29:34 - mmengine - INFO - Epoch(train) [140][2200/5047] lr: 6.4066e-06 eta: 12:53:02 time: 0.8351 data_time: 0.0030 memory: 42024 loss: 0.0943 loss_ce: 0.0943 2023/03/02 18:31:00 - mmengine - INFO - Epoch(train) [140][2300/5047] lr: 6.4066e-06 eta: 12:51:35 time: 0.8593 data_time: 0.0043 memory: 46684 loss: 0.1091 loss_ce: 0.1091 2023/03/02 18:32:26 - mmengine - INFO - Epoch(train) [140][2400/5047] lr: 6.4066e-06 eta: 12:50:08 time: 0.8420 data_time: 0.0030 memory: 43947 loss: 0.0979 loss_ce: 0.0979 2023/03/02 18:33:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 18:33:52 - mmengine - INFO - Epoch(train) [140][2500/5047] lr: 6.4066e-06 eta: 12:48:41 time: 0.8526 data_time: 0.0041 memory: 43327 loss: 0.0972 loss_ce: 0.0972 2023/03/02 18:35:18 - mmengine - INFO - Epoch(train) [140][2600/5047] lr: 6.4066e-06 eta: 12:47:14 time: 0.8712 data_time: 0.0033 memory: 47447 loss: 0.0953 loss_ce: 0.0953 2023/03/02 18:36:44 - mmengine - INFO - Epoch(train) [140][2700/5047] lr: 6.4066e-06 eta: 12:45:47 time: 0.8862 data_time: 0.0030 memory: 45302 loss: 0.1133 loss_ce: 0.1133 2023/03/02 18:38:09 - mmengine - INFO - Epoch(train) [140][2800/5047] lr: 6.4066e-06 eta: 12:44:20 time: 0.8445 data_time: 0.0031 memory: 51658 loss: 0.1245 loss_ce: 0.1245 2023/03/02 18:39:33 - mmengine - INFO - Epoch(train) [140][2900/5047] lr: 6.4066e-06 eta: 12:42:53 time: 0.8514 data_time: 0.0028 memory: 43289 loss: 0.1213 loss_ce: 0.1213 2023/03/02 18:40:59 - mmengine - INFO - Epoch(train) [140][3000/5047] lr: 6.4066e-06 eta: 12:41:26 time: 0.8244 data_time: 0.0027 memory: 50106 loss: 0.1126 loss_ce: 0.1126 2023/03/02 18:42:25 - mmengine - INFO - Epoch(train) [140][3100/5047] lr: 6.4066e-06 eta: 12:39:58 time: 0.8880 data_time: 0.0032 memory: 40535 loss: 0.1010 loss_ce: 0.1010 2023/03/02 18:43:50 - mmengine - INFO - Epoch(train) [140][3200/5047] lr: 6.4066e-06 eta: 12:38:31 time: 0.8437 data_time: 0.0045 memory: 41417 loss: 0.0966 loss_ce: 0.0966 2023/03/02 18:45:15 - mmengine - INFO - Epoch(train) [140][3300/5047] lr: 6.4066e-06 eta: 12:37:04 time: 0.8421 data_time: 0.0079 memory: 45643 loss: 0.1148 loss_ce: 0.1148 2023/03/02 18:46:41 - mmengine - INFO - Epoch(train) [140][3400/5047] lr: 6.4066e-06 eta: 12:35:37 time: 0.8548 data_time: 0.0034 memory: 46884 loss: 0.1003 loss_ce: 0.1003 2023/03/02 18:47:38 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 18:48:07 - mmengine - INFO - Epoch(train) [140][3500/5047] lr: 6.4066e-06 eta: 12:34:10 time: 0.8824 data_time: 0.0032 memory: 42467 loss: 0.1013 loss_ce: 0.1013 2023/03/02 18:49:32 - mmengine - INFO - Epoch(train) [140][3600/5047] lr: 6.4066e-06 eta: 12:32:43 time: 0.9014 data_time: 0.0028 memory: 47948 loss: 0.1206 loss_ce: 0.1206 2023/03/02 18:50:57 - mmengine - INFO - Epoch(train) [140][3700/5047] lr: 6.4066e-06 eta: 12:31:16 time: 0.8292 data_time: 0.0027 memory: 51308 loss: 0.1031 loss_ce: 0.1031 2023/03/02 18:52:24 - mmengine - INFO - Epoch(train) [140][3800/5047] lr: 6.4066e-06 eta: 12:29:49 time: 0.8988 data_time: 0.0029 memory: 46815 loss: 0.1030 loss_ce: 0.1030 2023/03/02 18:53:49 - mmengine - INFO - Epoch(train) [140][3900/5047] lr: 6.4066e-06 eta: 12:28:22 time: 0.8396 data_time: 0.0031 memory: 40535 loss: 0.0972 loss_ce: 0.0972 2023/03/02 18:55:13 - mmengine - INFO - Epoch(train) [140][4000/5047] lr: 6.4066e-06 eta: 12:26:54 time: 0.8440 data_time: 0.0029 memory: 41419 loss: 0.1000 loss_ce: 0.1000 2023/03/02 18:56:38 - mmengine - INFO - Epoch(train) [140][4100/5047] lr: 6.4066e-06 eta: 12:25:27 time: 0.8274 data_time: 0.0028 memory: 41122 loss: 0.0993 loss_ce: 0.0993 2023/03/02 18:58:04 - mmengine - INFO - Epoch(train) [140][4200/5047] lr: 6.4066e-06 eta: 12:24:00 time: 0.8105 data_time: 0.0027 memory: 47813 loss: 0.1161 loss_ce: 0.1161 2023/03/02 18:59:30 - mmengine - INFO - Epoch(train) [140][4300/5047] lr: 6.4066e-06 eta: 12:22:33 time: 0.8762 data_time: 0.0030 memory: 52503 loss: 0.1139 loss_ce: 0.1139 2023/03/02 19:00:54 - mmengine - INFO - Epoch(train) [140][4400/5047] lr: 6.4066e-06 eta: 12:21:06 time: 0.8247 data_time: 0.0032 memory: 42965 loss: 0.0992 loss_ce: 0.0992 2023/03/02 19:01:51 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 19:02:20 - mmengine - INFO - Epoch(train) [140][4500/5047] lr: 6.4066e-06 eta: 12:19:39 time: 0.8227 data_time: 0.0030 memory: 41724 loss: 0.1074 loss_ce: 0.1074 2023/03/02 19:03:47 - mmengine - INFO - Epoch(train) [140][4600/5047] lr: 6.4066e-06 eta: 12:18:12 time: 0.8603 data_time: 0.0027 memory: 49334 loss: 0.1112 loss_ce: 0.1112 2023/03/02 19:05:13 - mmengine - INFO - Epoch(train) [140][4700/5047] lr: 6.4066e-06 eta: 12:16:45 time: 0.8616 data_time: 0.0056 memory: 42394 loss: 0.1116 loss_ce: 0.1116 2023/03/02 19:06:40 - mmengine - INFO - Epoch(train) [140][4800/5047] lr: 6.4066e-06 eta: 12:15:18 time: 0.8803 data_time: 0.0027 memory: 37105 loss: 0.0965 loss_ce: 0.0965 2023/03/02 19:08:04 - mmengine - INFO - Epoch(train) [140][4900/5047] lr: 6.4066e-06 eta: 12:13:50 time: 0.8882 data_time: 0.0028 memory: 41122 loss: 0.1074 loss_ce: 0.1074 2023/03/02 19:09:30 - mmengine - INFO - Epoch(train) [140][5000/5047] lr: 6.4066e-06 eta: 12:12:23 time: 0.8349 data_time: 0.0031 memory: 48948 loss: 0.0973 loss_ce: 0.0973 2023/03/02 19:10:09 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 19:10:09 - mmengine - INFO - Saving checkpoint at 140 epochs 2023/03/02 19:11:39 - mmengine - INFO - Epoch(train) [141][ 100/5047] lr: 6.2057e-06 eta: 12:10:15 time: 0.8806 data_time: 0.0027 memory: 44956 loss: 0.1067 loss_ce: 0.1067 2023/03/02 19:13:03 - mmengine - INFO - Epoch(train) [141][ 200/5047] lr: 6.2057e-06 eta: 12:08:48 time: 0.8275 data_time: 0.0045 memory: 44724 loss: 0.1020 loss_ce: 0.1020 2023/03/02 19:14:29 - mmengine - INFO - Epoch(train) [141][ 300/5047] lr: 6.2057e-06 eta: 12:07:21 time: 0.8917 data_time: 0.0039 memory: 42336 loss: 0.1125 loss_ce: 0.1125 2023/03/02 19:15:56 - mmengine - INFO - Epoch(train) [141][ 400/5047] lr: 6.2057e-06 eta: 12:05:54 time: 0.8847 data_time: 0.0064 memory: 46874 loss: 0.1037 loss_ce: 0.1037 2023/03/02 19:16:13 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 19:17:21 - mmengine - INFO - Epoch(train) [141][ 500/5047] lr: 6.2057e-06 eta: 12:04:27 time: 0.8690 data_time: 0.0044 memory: 43289 loss: 0.1079 loss_ce: 0.1079 2023/03/02 19:18:45 - mmengine - INFO - Epoch(train) [141][ 600/5047] lr: 6.2057e-06 eta: 12:03:00 time: 0.8386 data_time: 0.0069 memory: 41122 loss: 0.1061 loss_ce: 0.1061 2023/03/02 19:20:10 - mmengine - INFO - Epoch(train) [141][ 700/5047] lr: 6.2057e-06 eta: 12:01:32 time: 0.8623 data_time: 0.0040 memory: 42455 loss: 0.1098 loss_ce: 0.1098 2023/03/02 19:21:36 - mmengine - INFO - Epoch(train) [141][ 800/5047] lr: 6.2057e-06 eta: 12:00:05 time: 0.8334 data_time: 0.0031 memory: 42965 loss: 0.0994 loss_ce: 0.0994 2023/03/02 19:23:00 - mmengine - INFO - Epoch(train) [141][ 900/5047] lr: 6.2057e-06 eta: 11:58:38 time: 0.8585 data_time: 0.0031 memory: 48129 loss: 0.1027 loss_ce: 0.1027 2023/03/02 19:24:26 - mmengine - INFO - Epoch(train) [141][1000/5047] lr: 6.2057e-06 eta: 11:57:11 time: 0.9042 data_time: 0.0030 memory: 43557 loss: 0.1038 loss_ce: 0.1038 2023/03/02 19:25:52 - mmengine - INFO - Epoch(train) [141][1100/5047] lr: 6.2057e-06 eta: 11:55:44 time: 0.8390 data_time: 0.0025 memory: 42965 loss: 0.1043 loss_ce: 0.1043 2023/03/02 19:27:18 - mmengine - INFO - Epoch(train) [141][1200/5047] lr: 6.2057e-06 eta: 11:54:17 time: 0.9020 data_time: 0.0032 memory: 55562 loss: 0.0973 loss_ce: 0.0973 2023/03/02 19:28:44 - mmengine - INFO - Epoch(train) [141][1300/5047] lr: 6.2057e-06 eta: 11:52:50 time: 0.9148 data_time: 0.0032 memory: 44278 loss: 0.1051 loss_ce: 0.1051 2023/03/02 19:30:09 - mmengine - INFO - Epoch(train) [141][1400/5047] lr: 6.2057e-06 eta: 11:51:23 time: 0.8666 data_time: 0.0028 memory: 53043 loss: 0.1004 loss_ce: 0.1004 2023/03/02 19:30:27 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 19:31:36 - mmengine - INFO - Epoch(train) [141][1500/5047] lr: 6.2057e-06 eta: 11:49:56 time: 0.8287 data_time: 0.0081 memory: 42649 loss: 0.1075 loss_ce: 0.1075 2023/03/02 19:33:02 - mmengine - INFO - Epoch(train) [141][1600/5047] lr: 6.2057e-06 eta: 11:48:29 time: 0.8469 data_time: 0.0029 memory: 47813 loss: 0.0924 loss_ce: 0.0924 2023/03/02 19:34:27 - mmengine - INFO - Epoch(train) [141][1700/5047] lr: 6.2057e-06 eta: 11:47:02 time: 0.8769 data_time: 0.0028 memory: 44617 loss: 0.0959 loss_ce: 0.0959 2023/03/02 19:35:53 - mmengine - INFO - Epoch(train) [141][1800/5047] lr: 6.2057e-06 eta: 11:45:35 time: 0.8302 data_time: 0.0031 memory: 50106 loss: 0.1011 loss_ce: 0.1011 2023/03/02 19:37:19 - mmengine - INFO - Epoch(train) [141][1900/5047] lr: 6.2057e-06 eta: 11:44:07 time: 0.8605 data_time: 0.0025 memory: 51734 loss: 0.1013 loss_ce: 0.1013 2023/03/02 19:38:44 - mmengine - INFO - Epoch(train) [141][2000/5047] lr: 6.2057e-06 eta: 11:42:40 time: 0.8840 data_time: 0.0036 memory: 42336 loss: 0.1211 loss_ce: 0.1211 2023/03/02 19:40:08 - mmengine - INFO - Epoch(train) [141][2100/5047] lr: 6.2057e-06 eta: 11:41:13 time: 0.8599 data_time: 0.0030 memory: 43947 loss: 0.1059 loss_ce: 0.1059 2023/03/02 19:41:32 - mmengine - INFO - Epoch(train) [141][2200/5047] lr: 6.2057e-06 eta: 11:39:46 time: 0.8731 data_time: 0.0053 memory: 45302 loss: 0.0965 loss_ce: 0.0965 2023/03/02 19:42:58 - mmengine - INFO - Epoch(train) [141][2300/5047] lr: 6.2057e-06 eta: 11:38:19 time: 0.8639 data_time: 0.0025 memory: 41057 loss: 0.1038 loss_ce: 0.1038 2023/03/02 19:44:26 - mmengine - INFO - Epoch(train) [141][2400/5047] lr: 6.2057e-06 eta: 11:36:52 time: 0.9140 data_time: 0.0028 memory: 44278 loss: 0.1081 loss_ce: 0.1081 2023/03/02 19:44:43 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 19:45:51 - mmengine - INFO - Epoch(train) [141][2500/5047] lr: 6.2057e-06 eta: 11:35:25 time: 0.8192 data_time: 0.0030 memory: 42273 loss: 0.1121 loss_ce: 0.1121 2023/03/02 19:47:16 - mmengine - INFO - Epoch(train) [141][2600/5047] lr: 6.2057e-06 eta: 11:33:58 time: 0.8137 data_time: 0.0032 memory: 41939 loss: 0.1205 loss_ce: 0.1205 2023/03/02 19:48:40 - mmengine - INFO - Epoch(train) [141][2700/5047] lr: 6.2057e-06 eta: 11:32:31 time: 0.9053 data_time: 0.0030 memory: 43557 loss: 0.0930 loss_ce: 0.0930 2023/03/02 19:50:07 - mmengine - INFO - Epoch(train) [141][2800/5047] lr: 6.2057e-06 eta: 11:31:04 time: 0.8586 data_time: 0.0063 memory: 44079 loss: 0.0935 loss_ce: 0.0935 2023/03/02 19:51:31 - mmengine - INFO - Epoch(train) [141][2900/5047] lr: 6.2057e-06 eta: 11:29:36 time: 0.8644 data_time: 0.0038 memory: 42965 loss: 0.1206 loss_ce: 0.1206 2023/03/02 19:52:58 - mmengine - INFO - Epoch(train) [141][3000/5047] lr: 6.2057e-06 eta: 11:28:09 time: 0.8797 data_time: 0.0028 memory: 55562 loss: 0.1033 loss_ce: 0.1033 2023/03/02 19:54:22 - mmengine - INFO - Epoch(train) [141][3100/5047] lr: 6.2057e-06 eta: 11:26:42 time: 0.8873 data_time: 0.0029 memory: 39960 loss: 0.1000 loss_ce: 0.1000 2023/03/02 19:55:47 - mmengine - INFO - Epoch(train) [141][3200/5047] lr: 6.2057e-06 eta: 11:25:15 time: 0.8176 data_time: 0.0026 memory: 43289 loss: 0.1041 loss_ce: 0.1041 2023/03/02 19:57:12 - mmengine - INFO - Epoch(train) [141][3300/5047] lr: 6.2057e-06 eta: 11:23:48 time: 0.8082 data_time: 0.0031 memory: 55562 loss: 0.0992 loss_ce: 0.0992 2023/03/02 19:58:39 - mmengine - INFO - Epoch(train) [141][3400/5047] lr: 6.2057e-06 eta: 11:22:21 time: 0.9245 data_time: 0.0032 memory: 49715 loss: 0.1001 loss_ce: 0.1001 2023/03/02 19:58:57 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 20:00:08 - mmengine - INFO - Epoch(train) [141][3500/5047] lr: 6.2057e-06 eta: 11:20:54 time: 0.9147 data_time: 0.0064 memory: 52881 loss: 0.1045 loss_ce: 0.1045 2023/03/02 20:01:34 - mmengine - INFO - Epoch(train) [141][3600/5047] lr: 6.2057e-06 eta: 11:19:27 time: 0.8608 data_time: 0.0028 memory: 43947 loss: 0.1088 loss_ce: 0.1088 2023/03/02 20:03:01 - mmengine - INFO - Epoch(train) [141][3700/5047] lr: 6.2057e-06 eta: 11:18:00 time: 0.9127 data_time: 0.0032 memory: 43289 loss: 0.1070 loss_ce: 0.1070 2023/03/02 20:04:25 - mmengine - INFO - Epoch(train) [141][3800/5047] lr: 6.2057e-06 eta: 11:16:33 time: 0.8473 data_time: 0.0057 memory: 40508 loss: 0.1057 loss_ce: 0.1057 2023/03/02 20:05:50 - mmengine - INFO - Epoch(train) [141][3900/5047] lr: 6.2057e-06 eta: 11:15:06 time: 0.8573 data_time: 0.0039 memory: 41419 loss: 0.0986 loss_ce: 0.0986 2023/03/02 20:07:15 - mmengine - INFO - Epoch(train) [141][4000/5047] lr: 6.2057e-06 eta: 11:13:39 time: 0.8204 data_time: 0.0039 memory: 42336 loss: 0.0889 loss_ce: 0.0889 2023/03/02 20:08:42 - mmengine - INFO - Epoch(train) [141][4100/5047] lr: 6.2057e-06 eta: 11:12:12 time: 0.9301 data_time: 0.0038 memory: 55562 loss: 0.0864 loss_ce: 0.0864 2023/03/02 20:10:06 - mmengine - INFO - Epoch(train) [141][4200/5047] lr: 6.2057e-06 eta: 11:10:45 time: 0.8518 data_time: 0.0054 memory: 50106 loss: 0.0980 loss_ce: 0.0980 2023/03/02 20:11:33 - mmengine - INFO - Epoch(train) [141][4300/5047] lr: 6.2057e-06 eta: 11:09:17 time: 0.8483 data_time: 0.0032 memory: 46770 loss: 0.1188 loss_ce: 0.1188 2023/03/02 20:12:59 - mmengine - INFO - Epoch(train) [141][4400/5047] lr: 6.2057e-06 eta: 11:07:50 time: 0.8610 data_time: 0.0030 memory: 44278 loss: 0.0982 loss_ce: 0.0982 2023/03/02 20:13:16 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 20:14:25 - mmengine - INFO - Epoch(train) [141][4500/5047] lr: 6.2057e-06 eta: 11:06:23 time: 0.8743 data_time: 0.0036 memory: 55562 loss: 0.1073 loss_ce: 0.1073 2023/03/02 20:15:48 - mmengine - INFO - Epoch(train) [141][4600/5047] lr: 6.2057e-06 eta: 11:04:56 time: 0.8257 data_time: 0.0034 memory: 41472 loss: 0.1094 loss_ce: 0.1094 2023/03/02 20:17:14 - mmengine - INFO - Epoch(train) [141][4700/5047] lr: 6.2057e-06 eta: 11:03:29 time: 0.8676 data_time: 0.0025 memory: 41419 loss: 0.1038 loss_ce: 0.1038 2023/03/02 20:18:40 - mmengine - INFO - Epoch(train) [141][4800/5047] lr: 6.2057e-06 eta: 11:02:02 time: 0.8359 data_time: 0.0029 memory: 43613 loss: 0.0890 loss_ce: 0.0890 2023/03/02 20:20:06 - mmengine - INFO - Epoch(train) [141][4900/5047] lr: 6.2057e-06 eta: 11:00:35 time: 0.9261 data_time: 0.0032 memory: 46638 loss: 0.1034 loss_ce: 0.1034 2023/03/02 20:21:32 - mmengine - INFO - Epoch(train) [141][5000/5047] lr: 6.2057e-06 eta: 10:59:08 time: 0.8991 data_time: 0.0030 memory: 46355 loss: 0.1264 loss_ce: 0.1264 2023/03/02 20:22:13 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 20:22:13 - mmengine - INFO - Saving checkpoint at 141 epochs 2023/03/02 20:23:43 - mmengine - INFO - Epoch(train) [142][ 100/5047] lr: 6.0047e-06 eta: 10:57:00 time: 0.8377 data_time: 0.0027 memory: 53044 loss: 0.1010 loss_ce: 0.1010 2023/03/02 20:25:08 - mmengine - INFO - Epoch(train) [142][ 200/5047] lr: 6.0047e-06 eta: 10:55:33 time: 0.8925 data_time: 0.0027 memory: 40825 loss: 0.1030 loss_ce: 0.1030 2023/03/02 20:26:34 - mmengine - INFO - Epoch(train) [142][ 300/5047] lr: 6.0047e-06 eta: 10:54:06 time: 0.9021 data_time: 0.0034 memory: 41270 loss: 0.1055 loss_ce: 0.1055 2023/03/02 20:27:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 20:27:59 - mmengine - INFO - Epoch(train) [142][ 400/5047] lr: 6.0047e-06 eta: 10:52:39 time: 0.8602 data_time: 0.0041 memory: 41348 loss: 0.0995 loss_ce: 0.0995 2023/03/02 20:29:26 - mmengine - INFO - Epoch(train) [142][ 500/5047] lr: 6.0047e-06 eta: 10:51:12 time: 0.9214 data_time: 0.0057 memory: 49147 loss: 0.1042 loss_ce: 0.1042 2023/03/02 20:30:50 - mmengine - INFO - Epoch(train) [142][ 600/5047] lr: 6.0047e-06 eta: 10:49:45 time: 0.8459 data_time: 0.0031 memory: 51211 loss: 0.1035 loss_ce: 0.1035 2023/03/02 20:32:16 - mmengine - INFO - Epoch(train) [142][ 700/5047] lr: 6.0047e-06 eta: 10:48:17 time: 0.8565 data_time: 0.0049 memory: 45302 loss: 0.1011 loss_ce: 0.1011 2023/03/02 20:33:41 - mmengine - INFO - Epoch(train) [142][ 800/5047] lr: 6.0047e-06 eta: 10:46:50 time: 0.9009 data_time: 0.0027 memory: 43140 loss: 0.1044 loss_ce: 0.1044 2023/03/02 20:35:06 - mmengine - INFO - Epoch(train) [142][ 900/5047] lr: 6.0047e-06 eta: 10:45:23 time: 0.8632 data_time: 0.0033 memory: 55562 loss: 0.0974 loss_ce: 0.0974 2023/03/02 20:36:32 - mmengine - INFO - Epoch(train) [142][1000/5047] lr: 6.0047e-06 eta: 10:43:56 time: 0.8780 data_time: 0.0029 memory: 41419 loss: 0.1093 loss_ce: 0.1093 2023/03/02 20:37:59 - mmengine - INFO - Epoch(train) [142][1100/5047] lr: 6.0047e-06 eta: 10:42:29 time: 0.8685 data_time: 0.0030 memory: 51308 loss: 0.1178 loss_ce: 0.1178 2023/03/02 20:39:24 - mmengine - INFO - Epoch(train) [142][1200/5047] lr: 6.0047e-06 eta: 10:41:02 time: 0.8540 data_time: 0.0031 memory: 45643 loss: 0.1010 loss_ce: 0.1010 2023/03/02 20:40:49 - mmengine - INFO - Epoch(train) [142][1300/5047] lr: 6.0047e-06 eta: 10:39:35 time: 0.8456 data_time: 0.0029 memory: 41419 loss: 0.0968 loss_ce: 0.0968 2023/03/02 20:41:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 20:42:16 - mmengine - INFO - Epoch(train) [142][1400/5047] lr: 6.0047e-06 eta: 10:38:08 time: 0.9190 data_time: 0.0028 memory: 41419 loss: 0.0926 loss_ce: 0.0926 2023/03/02 20:43:42 - mmengine - INFO - Epoch(train) [142][1500/5047] lr: 6.0047e-06 eta: 10:36:41 time: 0.8657 data_time: 0.0028 memory: 44956 loss: 0.1065 loss_ce: 0.1065 2023/03/02 20:45:08 - mmengine - INFO - Epoch(train) [142][1600/5047] lr: 6.0047e-06 eta: 10:35:14 time: 0.9061 data_time: 0.0034 memory: 44647 loss: 0.1151 loss_ce: 0.1151 2023/03/02 20:46:33 - mmengine - INFO - Epoch(train) [142][1700/5047] lr: 6.0047e-06 eta: 10:33:47 time: 0.8800 data_time: 0.0039 memory: 55562 loss: 0.1015 loss_ce: 0.1015 2023/03/02 20:47:58 - mmengine - INFO - Epoch(train) [142][1800/5047] lr: 6.0047e-06 eta: 10:32:20 time: 0.8557 data_time: 0.0032 memory: 43780 loss: 0.1162 loss_ce: 0.1162 2023/03/02 20:49:25 - mmengine - INFO - Epoch(train) [142][1900/5047] lr: 6.0047e-06 eta: 10:30:53 time: 0.8559 data_time: 0.0043 memory: 51312 loss: 0.1024 loss_ce: 0.1024 2023/03/02 20:50:51 - mmengine - INFO - Epoch(train) [142][2000/5047] lr: 6.0047e-06 eta: 10:29:26 time: 0.8452 data_time: 0.0026 memory: 39321 loss: 0.1008 loss_ce: 0.1008 2023/03/02 20:52:18 - mmengine - INFO - Epoch(train) [142][2100/5047] lr: 6.0047e-06 eta: 10:27:59 time: 0.8979 data_time: 0.0031 memory: 44720 loss: 0.0937 loss_ce: 0.0937 2023/03/02 20:53:44 - mmengine - INFO - Epoch(train) [142][2200/5047] lr: 6.0047e-06 eta: 10:26:32 time: 0.8849 data_time: 0.0039 memory: 44617 loss: 0.1124 loss_ce: 0.1124 2023/03/02 20:55:11 - mmengine - INFO - Epoch(train) [142][2300/5047] lr: 6.0047e-06 eta: 10:25:05 time: 0.8909 data_time: 0.0029 memory: 48188 loss: 0.1097 loss_ce: 0.1097 2023/03/02 20:56:12 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 20:56:35 - mmengine - INFO - Epoch(train) [142][2400/5047] lr: 6.0047e-06 eta: 10:23:38 time: 0.8440 data_time: 0.0035 memory: 41724 loss: 0.1168 loss_ce: 0.1168 2023/03/02 20:58:00 - mmengine - INFO - Epoch(train) [142][2500/5047] lr: 6.0047e-06 eta: 10:22:10 time: 0.8478 data_time: 0.0029 memory: 55487 loss: 0.0960 loss_ce: 0.0960 2023/03/02 20:59:26 - mmengine - INFO - Epoch(train) [142][2600/5047] lr: 6.0047e-06 eta: 10:20:43 time: 0.8792 data_time: 0.0031 memory: 45302 loss: 0.0994 loss_ce: 0.0994 2023/03/02 21:00:52 - mmengine - INFO - Epoch(train) [142][2700/5047] lr: 6.0047e-06 eta: 10:19:16 time: 0.8621 data_time: 0.0029 memory: 42965 loss: 0.0966 loss_ce: 0.0966 2023/03/02 21:02:16 - mmengine - INFO - Epoch(train) [142][2800/5047] lr: 6.0047e-06 eta: 10:17:49 time: 0.8421 data_time: 0.0025 memory: 45643 loss: 0.1001 loss_ce: 0.1001 2023/03/02 21:03:42 - mmengine - INFO - Epoch(train) [142][2900/5047] lr: 6.0047e-06 eta: 10:16:22 time: 0.8290 data_time: 0.0076 memory: 44590 loss: 0.1090 loss_ce: 0.1090 2023/03/02 21:05:07 - mmengine - INFO - Epoch(train) [142][3000/5047] lr: 6.0047e-06 eta: 10:14:55 time: 0.8468 data_time: 0.0045 memory: 45642 loss: 0.0910 loss_ce: 0.0910 2023/03/02 21:06:32 - mmengine - INFO - Epoch(train) [142][3100/5047] lr: 6.0047e-06 eta: 10:13:28 time: 0.8495 data_time: 0.0033 memory: 51896 loss: 0.1000 loss_ce: 0.1000 2023/03/02 21:07:59 - mmengine - INFO - Epoch(train) [142][3200/5047] lr: 6.0047e-06 eta: 10:12:01 time: 0.9004 data_time: 0.0034 memory: 45834 loss: 0.0968 loss_ce: 0.0968 2023/03/02 21:09:24 - mmengine - INFO - Epoch(train) [142][3300/5047] lr: 6.0047e-06 eta: 10:10:34 time: 0.8230 data_time: 0.0032 memory: 48948 loss: 0.0958 loss_ce: 0.0958 2023/03/02 21:10:25 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 21:10:48 - mmengine - INFO - Epoch(train) [142][3400/5047] lr: 6.0047e-06 eta: 10:09:07 time: 0.8443 data_time: 0.0029 memory: 43745 loss: 0.0990 loss_ce: 0.0990 2023/03/02 21:12:15 - mmengine - INFO - Epoch(train) [142][3500/5047] lr: 6.0047e-06 eta: 10:07:40 time: 0.8800 data_time: 0.0039 memory: 49219 loss: 0.0925 loss_ce: 0.0925 2023/03/02 21:13:41 - mmengine - INFO - Epoch(train) [142][3600/5047] lr: 6.0047e-06 eta: 10:06:13 time: 0.8613 data_time: 0.0029 memory: 55562 loss: 0.1054 loss_ce: 0.1054 2023/03/02 21:15:06 - mmengine - INFO - Epoch(train) [142][3700/5047] lr: 6.0047e-06 eta: 10:04:46 time: 0.8968 data_time: 0.0029 memory: 45405 loss: 0.0993 loss_ce: 0.0993 2023/03/02 21:16:31 - mmengine - INFO - Epoch(train) [142][3800/5047] lr: 6.0047e-06 eta: 10:03:19 time: 0.8069 data_time: 0.0050 memory: 46452 loss: 0.1134 loss_ce: 0.1134 2023/03/02 21:17:56 - mmengine - INFO - Epoch(train) [142][3900/5047] lr: 6.0047e-06 eta: 10:01:51 time: 0.8256 data_time: 0.0029 memory: 55562 loss: 0.1158 loss_ce: 0.1158 2023/03/02 21:19:20 - mmengine - INFO - Epoch(train) [142][4000/5047] lr: 6.0047e-06 eta: 10:00:24 time: 0.8417 data_time: 0.0032 memory: 55562 loss: 0.1056 loss_ce: 0.1056 2023/03/02 21:20:46 - mmengine - INFO - Epoch(train) [142][4100/5047] lr: 6.0047e-06 eta: 9:58:57 time: 0.8476 data_time: 0.0030 memory: 51562 loss: 0.1022 loss_ce: 0.1022 2023/03/02 21:22:11 - mmengine - INFO - Epoch(train) [142][4200/5047] lr: 6.0047e-06 eta: 9:57:30 time: 0.8713 data_time: 0.0032 memory: 41724 loss: 0.1001 loss_ce: 0.1001 2023/03/02 21:23:36 - mmengine - INFO - Epoch(train) [142][4300/5047] lr: 6.0047e-06 eta: 9:56:03 time: 0.8187 data_time: 0.0029 memory: 43865 loss: 0.0930 loss_ce: 0.0930 2023/03/02 21:24:38 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 21:25:01 - mmengine - INFO - Epoch(train) [142][4400/5047] lr: 6.0047e-06 eta: 9:54:36 time: 0.8224 data_time: 0.0087 memory: 44956 loss: 0.1050 loss_ce: 0.1050 2023/03/02 21:26:26 - mmengine - INFO - Epoch(train) [142][4500/5047] lr: 6.0047e-06 eta: 9:53:09 time: 0.8157 data_time: 0.0038 memory: 55562 loss: 0.0898 loss_ce: 0.0898 2023/03/02 21:27:50 - mmengine - INFO - Epoch(train) [142][4600/5047] lr: 6.0047e-06 eta: 9:51:42 time: 0.8725 data_time: 0.0029 memory: 44365 loss: 0.0987 loss_ce: 0.0987 2023/03/02 21:29:14 - mmengine - INFO - Epoch(train) [142][4700/5047] lr: 6.0047e-06 eta: 9:50:15 time: 0.8546 data_time: 0.0030 memory: 42336 loss: 0.1034 loss_ce: 0.1034 2023/03/02 21:30:42 - mmengine - INFO - Epoch(train) [142][4800/5047] lr: 6.0047e-06 eta: 9:48:48 time: 0.8873 data_time: 0.0028 memory: 43944 loss: 0.1023 loss_ce: 0.1023 2023/03/02 21:32:07 - mmengine - INFO - Epoch(train) [142][4900/5047] lr: 6.0047e-06 eta: 9:47:21 time: 0.8344 data_time: 0.0029 memory: 45623 loss: 0.0937 loss_ce: 0.0937 2023/03/02 21:33:32 - mmengine - INFO - Epoch(train) [142][5000/5047] lr: 6.0047e-06 eta: 9:45:54 time: 0.8636 data_time: 0.0029 memory: 40241 loss: 0.1080 loss_ce: 0.1080 2023/03/02 21:34:11 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 21:34:11 - mmengine - INFO - Saving checkpoint at 142 epochs 2023/03/02 21:35:42 - mmengine - INFO - Epoch(train) [143][ 100/5047] lr: 5.8038e-06 eta: 9:43:46 time: 0.8695 data_time: 0.0028 memory: 54229 loss: 0.0967 loss_ce: 0.0967 2023/03/02 21:37:06 - mmengine - INFO - Epoch(train) [143][ 200/5047] lr: 5.8038e-06 eta: 9:42:18 time: 0.8765 data_time: 0.0029 memory: 51563 loss: 0.0985 loss_ce: 0.0985 2023/03/02 21:38:34 - mmengine - INFO - Epoch(train) [143][ 300/5047] lr: 5.8038e-06 eta: 9:40:51 time: 0.8941 data_time: 0.0028 memory: 46437 loss: 0.0923 loss_ce: 0.0923 2023/03/02 21:38:56 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 21:39:58 - mmengine - INFO - Epoch(train) [143][ 400/5047] lr: 5.8038e-06 eta: 9:39:24 time: 0.8742 data_time: 0.0044 memory: 52882 loss: 0.1073 loss_ce: 0.1073 2023/03/02 21:41:23 - mmengine - INFO - Epoch(train) [143][ 500/5047] lr: 5.8038e-06 eta: 9:37:57 time: 0.8981 data_time: 0.0028 memory: 55562 loss: 0.1140 loss_ce: 0.1140 2023/03/02 21:42:49 - mmengine - INFO - Epoch(train) [143][ 600/5047] lr: 5.8038e-06 eta: 9:36:30 time: 0.8524 data_time: 0.0055 memory: 42096 loss: 0.1028 loss_ce: 0.1028 2023/03/02 21:44:14 - mmengine - INFO - Epoch(train) [143][ 700/5047] lr: 5.8038e-06 eta: 9:35:03 time: 0.8722 data_time: 0.0039 memory: 46182 loss: 0.1013 loss_ce: 0.1013 2023/03/02 21:45:38 - mmengine - INFO - Epoch(train) [143][ 800/5047] lr: 5.8038e-06 eta: 9:33:36 time: 0.8372 data_time: 0.0029 memory: 42149 loss: 0.0980 loss_ce: 0.0980 2023/03/02 21:47:05 - mmengine - INFO - Epoch(train) [143][ 900/5047] lr: 5.8038e-06 eta: 9:32:09 time: 0.8586 data_time: 0.0028 memory: 45643 loss: 0.1060 loss_ce: 0.1060 2023/03/02 21:48:31 - mmengine - INFO - Epoch(train) [143][1000/5047] lr: 5.8038e-06 eta: 9:30:42 time: 0.8840 data_time: 0.0044 memory: 43807 loss: 0.0893 loss_ce: 0.0893 2023/03/02 21:49:58 - mmengine - INFO - Epoch(train) [143][1100/5047] lr: 5.8038e-06 eta: 9:29:15 time: 0.8641 data_time: 0.0028 memory: 53459 loss: 0.1061 loss_ce: 0.1061 2023/03/02 21:51:25 - mmengine - INFO - Epoch(train) [143][1200/5047] lr: 5.8038e-06 eta: 9:27:48 time: 0.9290 data_time: 0.0029 memory: 55562 loss: 0.1067 loss_ce: 0.1067 2023/03/02 21:52:50 - mmengine - INFO - Epoch(train) [143][1300/5047] lr: 5.8038e-06 eta: 9:26:21 time: 0.9046 data_time: 0.0032 memory: 42477 loss: 0.1122 loss_ce: 0.1122 2023/03/02 21:53:12 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 21:54:17 - mmengine - INFO - Epoch(train) [143][1400/5047] lr: 5.8038e-06 eta: 9:24:54 time: 0.8685 data_time: 0.0049 memory: 45879 loss: 0.0981 loss_ce: 0.0981 2023/03/02 21:55:43 - mmengine - INFO - Epoch(train) [143][1500/5047] lr: 5.8038e-06 eta: 9:23:27 time: 0.8733 data_time: 0.0032 memory: 43557 loss: 0.1052 loss_ce: 0.1052 2023/03/02 21:57:09 - mmengine - INFO - Epoch(train) [143][1600/5047] lr: 5.8038e-06 eta: 9:22:00 time: 0.8694 data_time: 0.0045 memory: 41122 loss: 0.1094 loss_ce: 0.1094 2023/03/02 21:58:35 - mmengine - INFO - Epoch(train) [143][1700/5047] lr: 5.8038e-06 eta: 9:20:33 time: 0.8312 data_time: 0.0032 memory: 44208 loss: 0.0933 loss_ce: 0.0933 2023/03/02 22:00:00 - mmengine - INFO - Epoch(train) [143][1800/5047] lr: 5.8038e-06 eta: 9:19:06 time: 0.8296 data_time: 0.0030 memory: 43587 loss: 0.1073 loss_ce: 0.1073 2023/03/02 22:01:26 - mmengine - INFO - Epoch(train) [143][1900/5047] lr: 5.8038e-06 eta: 9:17:39 time: 0.8576 data_time: 0.0037 memory: 47179 loss: 0.1231 loss_ce: 0.1231 2023/03/02 22:02:52 - mmengine - INFO - Epoch(train) [143][2000/5047] lr: 5.8038e-06 eta: 9:16:12 time: 0.8403 data_time: 0.0033 memory: 42649 loss: 0.1049 loss_ce: 0.1049 2023/03/02 22:04:18 - mmengine - INFO - Epoch(train) [143][2100/5047] lr: 5.8038e-06 eta: 9:14:45 time: 0.8521 data_time: 0.0027 memory: 50446 loss: 0.1023 loss_ce: 0.1023 2023/03/02 22:05:45 - mmengine - INFO - Epoch(train) [143][2200/5047] lr: 5.8038e-06 eta: 9:13:18 time: 0.8874 data_time: 0.0029 memory: 47074 loss: 0.0912 loss_ce: 0.0912 2023/03/02 22:07:12 - mmengine - INFO - Epoch(train) [143][2300/5047] lr: 5.8038e-06 eta: 9:11:51 time: 0.8203 data_time: 0.0031 memory: 42024 loss: 0.1011 loss_ce: 0.1011 2023/03/02 22:07:34 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 22:08:36 - mmengine - INFO - Epoch(train) [143][2400/5047] lr: 5.8038e-06 eta: 9:10:24 time: 0.8750 data_time: 0.0030 memory: 44956 loss: 0.1052 loss_ce: 0.1052 2023/03/02 22:10:03 - mmengine - INFO - Epoch(train) [143][2500/5047] lr: 5.8038e-06 eta: 9:08:57 time: 0.8656 data_time: 0.0074 memory: 44467 loss: 0.1273 loss_ce: 0.1273 2023/03/02 22:11:31 - mmengine - INFO - Epoch(train) [143][2600/5047] lr: 5.8038e-06 eta: 9:07:30 time: 0.7927 data_time: 0.0077 memory: 43748 loss: 0.1034 loss_ce: 0.1034 2023/03/02 22:12:58 - mmengine - INFO - Epoch(train) [143][2700/5047] lr: 5.8038e-06 eta: 9:06:03 time: 0.8590 data_time: 0.0027 memory: 51096 loss: 0.1032 loss_ce: 0.1032 2023/03/02 22:14:23 - mmengine - INFO - Epoch(train) [143][2800/5047] lr: 5.8038e-06 eta: 9:04:36 time: 0.8326 data_time: 0.0027 memory: 42305 loss: 0.1005 loss_ce: 0.1005 2023/03/02 22:15:49 - mmengine - INFO - Epoch(train) [143][2900/5047] lr: 5.8038e-06 eta: 9:03:09 time: 0.8367 data_time: 0.0027 memory: 50505 loss: 0.0966 loss_ce: 0.0966 2023/03/02 22:17:16 - mmengine - INFO - Epoch(train) [143][3000/5047] lr: 5.8038e-06 eta: 9:01:42 time: 0.8373 data_time: 0.0032 memory: 43947 loss: 0.0972 loss_ce: 0.0972 2023/03/02 22:18:42 - mmengine - INFO - Epoch(train) [143][3100/5047] lr: 5.8038e-06 eta: 9:00:15 time: 0.8737 data_time: 0.0032 memory: 44956 loss: 0.1042 loss_ce: 0.1042 2023/03/02 22:20:07 - mmengine - INFO - Epoch(train) [143][3200/5047] lr: 5.8038e-06 eta: 8:58:48 time: 0.8574 data_time: 0.0029 memory: 38311 loss: 0.0998 loss_ce: 0.0998 2023/03/02 22:21:31 - mmengine - INFO - Epoch(train) [143][3300/5047] lr: 5.8038e-06 eta: 8:57:21 time: 0.8428 data_time: 0.0052 memory: 42214 loss: 0.1023 loss_ce: 0.1023 2023/03/02 22:21:54 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 22:22:57 - mmengine - INFO - Epoch(train) [143][3400/5047] lr: 5.8038e-06 eta: 8:55:54 time: 0.8252 data_time: 0.0027 memory: 44539 loss: 0.1064 loss_ce: 0.1064 2023/03/02 22:24:25 - mmengine - INFO - Epoch(train) [143][3500/5047] lr: 5.8038e-06 eta: 8:54:27 time: 0.8570 data_time: 0.0045 memory: 44278 loss: 0.1018 loss_ce: 0.1018 2023/03/02 22:25:49 - mmengine - INFO - Epoch(train) [143][3600/5047] lr: 5.8038e-06 eta: 8:53:00 time: 0.8357 data_time: 0.0034 memory: 40496 loss: 0.1084 loss_ce: 0.1084 2023/03/02 22:27:14 - mmengine - INFO - Epoch(train) [143][3700/5047] lr: 5.8038e-06 eta: 8:51:33 time: 0.8393 data_time: 0.0037 memory: 45392 loss: 0.0965 loss_ce: 0.0965 2023/03/02 22:28:42 - mmengine - INFO - Epoch(train) [143][3800/5047] lr: 5.8038e-06 eta: 8:50:06 time: 0.9474 data_time: 0.0030 memory: 55296 loss: 0.0992 loss_ce: 0.0992 2023/03/02 22:30:08 - mmengine - INFO - Epoch(train) [143][3900/5047] lr: 5.8038e-06 eta: 8:48:39 time: 0.8343 data_time: 0.0029 memory: 41417 loss: 0.0987 loss_ce: 0.0987 2023/03/02 22:31:33 - mmengine - INFO - Epoch(train) [143][4000/5047] lr: 5.8038e-06 eta: 8:47:12 time: 0.8348 data_time: 0.0028 memory: 50697 loss: 0.0924 loss_ce: 0.0924 2023/03/02 22:32:57 - mmengine - INFO - Epoch(train) [143][4100/5047] lr: 5.8038e-06 eta: 8:45:44 time: 0.8261 data_time: 0.0043 memory: 41323 loss: 0.1091 loss_ce: 0.1091 2023/03/02 22:34:22 - mmengine - INFO - Epoch(train) [143][4200/5047] lr: 5.8038e-06 eta: 8:44:17 time: 0.8736 data_time: 0.0027 memory: 48031 loss: 0.0946 loss_ce: 0.0946 2023/03/02 22:35:48 - mmengine - INFO - Epoch(train) [143][4300/5047] lr: 5.8038e-06 eta: 8:42:50 time: 0.8515 data_time: 0.0027 memory: 44617 loss: 0.0987 loss_ce: 0.0987 2023/03/02 22:36:10 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 22:37:14 - mmengine - INFO - Epoch(train) [143][4400/5047] lr: 5.8038e-06 eta: 8:41:23 time: 0.9071 data_time: 0.0028 memory: 43491 loss: 0.1049 loss_ce: 0.1049 2023/03/02 22:38:38 - mmengine - INFO - Epoch(train) [143][4500/5047] lr: 5.8038e-06 eta: 8:39:56 time: 0.8296 data_time: 0.0032 memory: 42024 loss: 0.1013 loss_ce: 0.1013 2023/03/02 22:40:04 - mmengine - INFO - Epoch(train) [143][4600/5047] lr: 5.8038e-06 eta: 8:38:29 time: 0.8490 data_time: 0.0030 memory: 44956 loss: 0.1037 loss_ce: 0.1037 2023/03/02 22:41:29 - mmengine - INFO - Epoch(train) [143][4700/5047] lr: 5.8038e-06 eta: 8:37:02 time: 0.8468 data_time: 0.0031 memory: 50607 loss: 0.1136 loss_ce: 0.1136 2023/03/02 22:42:54 - mmengine - INFO - Epoch(train) [143][4800/5047] lr: 5.8038e-06 eta: 8:35:35 time: 0.8888 data_time: 0.0031 memory: 42233 loss: 0.1013 loss_ce: 0.1013 2023/03/02 22:44:18 - mmengine - INFO - Epoch(train) [143][4900/5047] lr: 5.8038e-06 eta: 8:34:08 time: 0.8778 data_time: 0.0028 memory: 46958 loss: 0.0969 loss_ce: 0.0969 2023/03/02 22:45:43 - mmengine - INFO - Epoch(train) [143][5000/5047] lr: 5.8038e-06 eta: 8:32:41 time: 0.8439 data_time: 0.0028 memory: 43947 loss: 0.1065 loss_ce: 0.1065 2023/03/02 22:46:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 22:46:23 - mmengine - INFO - Saving checkpoint at 143 epochs 2023/03/02 22:47:55 - mmengine - INFO - Epoch(train) [144][ 100/5047] lr: 5.6028e-06 eta: 8:30:33 time: 0.8527 data_time: 0.0038 memory: 46355 loss: 0.1026 loss_ce: 0.1026 2023/03/02 22:49:21 - mmengine - INFO - Epoch(train) [144][ 200/5047] lr: 5.6028e-06 eta: 8:29:06 time: 0.8650 data_time: 0.0030 memory: 44956 loss: 0.0952 loss_ce: 0.0952 2023/03/02 22:50:28 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 22:50:47 - mmengine - INFO - Epoch(train) [144][ 300/5047] lr: 5.6028e-06 eta: 8:27:39 time: 0.9144 data_time: 0.0030 memory: 55562 loss: 0.1043 loss_ce: 0.1043 2023/03/02 22:52:13 - mmengine - INFO - Epoch(train) [144][ 400/5047] lr: 5.6028e-06 eta: 8:26:12 time: 0.8603 data_time: 0.0028 memory: 42120 loss: 0.1044 loss_ce: 0.1044 2023/03/02 22:53:40 - mmengine - INFO - Epoch(train) [144][ 500/5047] lr: 5.6028e-06 eta: 8:24:45 time: 0.8405 data_time: 0.0030 memory: 44061 loss: 0.1123 loss_ce: 0.1123 2023/03/02 22:55:05 - mmengine - INFO - Epoch(train) [144][ 600/5047] lr: 5.6028e-06 eta: 8:23:18 time: 0.8970 data_time: 0.0040 memory: 55562 loss: 0.0931 loss_ce: 0.0931 2023/03/02 22:56:29 - mmengine - INFO - Epoch(train) [144][ 700/5047] lr: 5.6028e-06 eta: 8:21:51 time: 0.8647 data_time: 0.0032 memory: 43115 loss: 0.0903 loss_ce: 0.0903 2023/03/02 22:57:56 - mmengine - INFO - Epoch(train) [144][ 800/5047] lr: 5.6028e-06 eta: 8:20:24 time: 0.8204 data_time: 0.0052 memory: 52791 loss: 0.1082 loss_ce: 0.1082 2023/03/02 22:59:21 - mmengine - INFO - Epoch(train) [144][ 900/5047] lr: 5.6028e-06 eta: 8:18:57 time: 0.8308 data_time: 0.0027 memory: 42336 loss: 0.0962 loss_ce: 0.0962 2023/03/02 23:00:47 - mmengine - INFO - Epoch(train) [144][1000/5047] lr: 5.6028e-06 eta: 8:17:30 time: 0.8599 data_time: 0.0027 memory: 46798 loss: 0.1123 loss_ce: 0.1123 2023/03/02 23:02:12 - mmengine - INFO - Epoch(train) [144][1100/5047] lr: 5.6028e-06 eta: 8:16:03 time: 0.8423 data_time: 0.0059 memory: 41939 loss: 0.1097 loss_ce: 0.1097 2023/03/02 23:03:39 - mmengine - INFO - Epoch(train) [144][1200/5047] lr: 5.6028e-06 eta: 8:14:36 time: 0.8603 data_time: 0.0029 memory: 55562 loss: 0.0992 loss_ce: 0.0992 2023/03/02 23:04:46 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 23:05:05 - mmengine - INFO - Epoch(train) [144][1300/5047] lr: 5.6028e-06 eta: 8:13:09 time: 0.8626 data_time: 0.0027 memory: 41834 loss: 0.0976 loss_ce: 0.0976 2023/03/02 23:06:32 - mmengine - INFO - Epoch(train) [144][1400/5047] lr: 5.6028e-06 eta: 8:11:42 time: 0.8624 data_time: 0.0049 memory: 48892 loss: 0.1136 loss_ce: 0.1136 2023/03/02 23:07:59 - mmengine - INFO - Epoch(train) [144][1500/5047] lr: 5.6028e-06 eta: 8:10:15 time: 0.8742 data_time: 0.0040 memory: 42965 loss: 0.1006 loss_ce: 0.1006 2023/03/02 23:09:25 - mmengine - INFO - Epoch(train) [144][1600/5047] lr: 5.6028e-06 eta: 8:08:48 time: 0.8200 data_time: 0.0029 memory: 45643 loss: 0.0975 loss_ce: 0.0975 2023/03/02 23:10:50 - mmengine - INFO - Epoch(train) [144][1700/5047] lr: 5.6028e-06 eta: 8:07:21 time: 0.8516 data_time: 0.0028 memory: 44587 loss: 0.0930 loss_ce: 0.0930 2023/03/02 23:12:15 - mmengine - INFO - Epoch(train) [144][1800/5047] lr: 5.6028e-06 eta: 8:05:54 time: 0.8855 data_time: 0.0030 memory: 49170 loss: 0.1253 loss_ce: 0.1253 2023/03/02 23:13:40 - mmengine - INFO - Epoch(train) [144][1900/5047] lr: 5.6028e-06 eta: 8:04:27 time: 0.8486 data_time: 0.0070 memory: 44278 loss: 0.0957 loss_ce: 0.0957 2023/03/02 23:15:05 - mmengine - INFO - Epoch(train) [144][2000/5047] lr: 5.6028e-06 eta: 8:03:00 time: 0.8098 data_time: 0.0029 memory: 46355 loss: 0.1068 loss_ce: 0.1068 2023/03/02 23:16:31 - mmengine - INFO - Epoch(train) [144][2100/5047] lr: 5.6028e-06 eta: 8:01:33 time: 0.8708 data_time: 0.0029 memory: 46158 loss: 0.0943 loss_ce: 0.0943 2023/03/02 23:17:55 - mmengine - INFO - Epoch(train) [144][2200/5047] lr: 5.6028e-06 eta: 8:00:06 time: 0.8143 data_time: 0.0064 memory: 47476 loss: 0.0879 loss_ce: 0.0879 2023/03/02 23:19:03 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 23:19:22 - mmengine - INFO - Epoch(train) [144][2300/5047] lr: 5.6028e-06 eta: 7:58:39 time: 0.9037 data_time: 0.0030 memory: 48688 loss: 0.0938 loss_ce: 0.0938 2023/03/02 23:20:48 - mmengine - INFO - Epoch(train) [144][2400/5047] lr: 5.6028e-06 eta: 7:57:12 time: 0.9122 data_time: 0.0029 memory: 48492 loss: 0.0987 loss_ce: 0.0987 2023/03/02 23:22:14 - mmengine - INFO - Epoch(train) [144][2500/5047] lr: 5.6028e-06 eta: 7:55:45 time: 0.8391 data_time: 0.0028 memory: 42024 loss: 0.1081 loss_ce: 0.1081 2023/03/02 23:23:39 - mmengine - INFO - Epoch(train) [144][2600/5047] lr: 5.6028e-06 eta: 7:54:18 time: 0.7935 data_time: 0.0128 memory: 44278 loss: 0.1036 loss_ce: 0.1036 2023/03/02 23:25:05 - mmengine - INFO - Epoch(train) [144][2700/5047] lr: 5.6028e-06 eta: 7:52:51 time: 0.8512 data_time: 0.0029 memory: 52729 loss: 0.1008 loss_ce: 0.1008 2023/03/02 23:26:32 - mmengine - INFO - Epoch(train) [144][2800/5047] lr: 5.6028e-06 eta: 7:51:24 time: 0.8913 data_time: 0.0041 memory: 45849 loss: 0.1063 loss_ce: 0.1063 2023/03/02 23:27:57 - mmengine - INFO - Epoch(train) [144][2900/5047] lr: 5.6028e-06 eta: 7:49:57 time: 0.8555 data_time: 0.0028 memory: 43289 loss: 0.0950 loss_ce: 0.0950 2023/03/02 23:29:24 - mmengine - INFO - Epoch(train) [144][3000/5047] lr: 5.6028e-06 eta: 7:48:30 time: 0.8421 data_time: 0.0041 memory: 42965 loss: 0.0990 loss_ce: 0.0990 2023/03/02 23:30:50 - mmengine - INFO - Epoch(train) [144][3100/5047] lr: 5.6028e-06 eta: 7:47:03 time: 0.8341 data_time: 0.0030 memory: 41419 loss: 0.1016 loss_ce: 0.1016 2023/03/02 23:32:16 - mmengine - INFO - Epoch(train) [144][3200/5047] lr: 5.6028e-06 eta: 7:45:36 time: 0.8470 data_time: 0.0029 memory: 44632 loss: 0.1087 loss_ce: 0.1087 2023/03/02 23:33:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 23:33:41 - mmengine - INFO - Epoch(train) [144][3300/5047] lr: 5.6028e-06 eta: 7:44:09 time: 0.8591 data_time: 0.0031 memory: 40825 loss: 0.1029 loss_ce: 0.1029 2023/03/02 23:35:07 - mmengine - INFO - Epoch(train) [144][3400/5047] lr: 5.6028e-06 eta: 7:42:42 time: 0.8377 data_time: 0.0029 memory: 53809 loss: 0.0920 loss_ce: 0.0920 2023/03/02 23:36:33 - mmengine - INFO - Epoch(train) [144][3500/5047] lr: 5.6028e-06 eta: 7:41:15 time: 0.9195 data_time: 0.0034 memory: 39798 loss: 0.1027 loss_ce: 0.1027 2023/03/02 23:38:00 - mmengine - INFO - Epoch(train) [144][3600/5047] lr: 5.6028e-06 eta: 7:39:48 time: 0.8336 data_time: 0.0030 memory: 51308 loss: 0.1141 loss_ce: 0.1141 2023/03/02 23:39:26 - mmengine - INFO - Epoch(train) [144][3700/5047] lr: 5.6028e-06 eta: 7:38:21 time: 0.8313 data_time: 0.0034 memory: 42965 loss: 0.1199 loss_ce: 0.1199 2023/03/02 23:40:50 - mmengine - INFO - Epoch(train) [144][3800/5047] lr: 5.6028e-06 eta: 7:36:54 time: 0.8528 data_time: 0.0028 memory: 55468 loss: 0.0991 loss_ce: 0.0991 2023/03/02 23:42:17 - mmengine - INFO - Epoch(train) [144][3900/5047] lr: 5.6028e-06 eta: 7:35:27 time: 0.8765 data_time: 0.0028 memory: 55366 loss: 0.1170 loss_ce: 0.1170 2023/03/02 23:43:43 - mmengine - INFO - Epoch(train) [144][4000/5047] lr: 5.6028e-06 eta: 7:34:00 time: 0.8650 data_time: 0.0032 memory: 51841 loss: 0.1124 loss_ce: 0.1124 2023/03/02 23:45:08 - mmengine - INFO - Epoch(train) [144][4100/5047] lr: 5.6028e-06 eta: 7:32:33 time: 0.8603 data_time: 0.0059 memory: 42399 loss: 0.0981 loss_ce: 0.0981 2023/03/02 23:46:34 - mmengine - INFO - Epoch(train) [144][4200/5047] lr: 5.6028e-06 eta: 7:31:06 time: 0.8547 data_time: 0.0078 memory: 54045 loss: 0.1050 loss_ce: 0.1050 2023/03/02 23:47:42 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 23:48:01 - mmengine - INFO - Epoch(train) [144][4300/5047] lr: 5.6028e-06 eta: 7:29:39 time: 0.9102 data_time: 0.0029 memory: 46005 loss: 0.1103 loss_ce: 0.1103 2023/03/02 23:49:26 - mmengine - INFO - Epoch(train) [144][4400/5047] lr: 5.6028e-06 eta: 7:28:12 time: 0.8528 data_time: 0.0028 memory: 40882 loss: 0.0963 loss_ce: 0.0963 2023/03/02 23:50:53 - mmengine - INFO - Epoch(train) [144][4500/5047] lr: 5.6028e-06 eta: 7:26:45 time: 0.8817 data_time: 0.0031 memory: 47982 loss: 0.1194 loss_ce: 0.1194 2023/03/02 23:52:19 - mmengine - INFO - Epoch(train) [144][4600/5047] lr: 5.6028e-06 eta: 7:25:18 time: 0.9042 data_time: 0.0030 memory: 48188 loss: 0.1062 loss_ce: 0.1062 2023/03/02 23:53:45 - mmengine - INFO - Epoch(train) [144][4700/5047] lr: 5.6028e-06 eta: 7:23:51 time: 0.8630 data_time: 0.0026 memory: 49443 loss: 0.1202 loss_ce: 0.1202 2023/03/02 23:55:10 - mmengine - INFO - Epoch(train) [144][4800/5047] lr: 5.6028e-06 eta: 7:22:24 time: 0.8622 data_time: 0.0028 memory: 44051 loss: 0.1024 loss_ce: 0.1024 2023/03/02 23:56:35 - mmengine - INFO - Epoch(train) [144][4900/5047] lr: 5.6028e-06 eta: 7:20:57 time: 0.8671 data_time: 0.0030 memory: 43289 loss: 0.1089 loss_ce: 0.1089 2023/03/02 23:58:01 - mmengine - INFO - Epoch(train) [144][5000/5047] lr: 5.6028e-06 eta: 7:19:30 time: 0.8235 data_time: 0.0033 memory: 44278 loss: 0.0946 loss_ce: 0.0946 2023/03/02 23:58:40 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/02 23:58:40 - mmengine - INFO - Saving checkpoint at 144 epochs 2023/03/03 00:00:13 - mmengine - INFO - Epoch(train) [145][ 100/5047] lr: 5.4019e-06 eta: 7:17:22 time: 0.9100 data_time: 0.0028 memory: 50387 loss: 0.0936 loss_ce: 0.0936 2023/03/03 00:01:40 - mmengine - INFO - Epoch(train) [145][ 200/5047] lr: 5.4019e-06 eta: 7:15:55 time: 0.8374 data_time: 0.0030 memory: 53431 loss: 0.1061 loss_ce: 0.1061 2023/03/03 00:02:07 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 00:03:04 - mmengine - INFO - Epoch(train) [145][ 300/5047] lr: 5.4019e-06 eta: 7:14:28 time: 0.8559 data_time: 0.0065 memory: 46007 loss: 0.0985 loss_ce: 0.0985 2023/03/03 00:04:30 - mmengine - INFO - Epoch(train) [145][ 400/5047] lr: 5.4019e-06 eta: 7:13:01 time: 0.8713 data_time: 0.0029 memory: 49221 loss: 0.1034 loss_ce: 0.1034 2023/03/03 00:05:57 - mmengine - INFO - Epoch(train) [145][ 500/5047] lr: 5.4019e-06 eta: 7:11:34 time: 0.8424 data_time: 0.0031 memory: 43348 loss: 0.1064 loss_ce: 0.1064 2023/03/03 00:07:22 - mmengine - INFO - Epoch(train) [145][ 600/5047] lr: 5.4019e-06 eta: 7:10:07 time: 0.8462 data_time: 0.0028 memory: 52655 loss: 0.1015 loss_ce: 0.1015 2023/03/03 00:08:48 - mmengine - INFO - Epoch(train) [145][ 700/5047] lr: 5.4019e-06 eta: 7:08:40 time: 0.8814 data_time: 0.0027 memory: 42649 loss: 0.0980 loss_ce: 0.0980 2023/03/03 00:10:13 - mmengine - INFO - Epoch(train) [145][ 800/5047] lr: 5.4019e-06 eta: 7:07:13 time: 0.8149 data_time: 0.0030 memory: 44617 loss: 0.1001 loss_ce: 0.1001 2023/03/03 00:11:39 - mmengine - INFO - Epoch(train) [145][ 900/5047] lr: 5.4019e-06 eta: 7:05:46 time: 0.8542 data_time: 0.0031 memory: 41549 loss: 0.1035 loss_ce: 0.1035 2023/03/03 00:13:06 - mmengine - INFO - Epoch(train) [145][1000/5047] lr: 5.4019e-06 eta: 7:04:19 time: 0.8867 data_time: 0.0027 memory: 42234 loss: 0.1090 loss_ce: 0.1090 2023/03/03 00:14:31 - mmengine - INFO - Epoch(train) [145][1100/5047] lr: 5.4019e-06 eta: 7:02:52 time: 0.8511 data_time: 0.0037 memory: 46355 loss: 0.0977 loss_ce: 0.0977 2023/03/03 00:15:55 - mmengine - INFO - Epoch(train) [145][1200/5047] lr: 5.4019e-06 eta: 7:01:25 time: 0.7959 data_time: 0.0032 memory: 40241 loss: 0.1177 loss_ce: 0.1177 2023/03/03 00:16:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 00:17:22 - mmengine - INFO - Epoch(train) [145][1300/5047] lr: 5.4019e-06 eta: 6:59:58 time: 0.8417 data_time: 0.0066 memory: 53809 loss: 0.0980 loss_ce: 0.0980 2023/03/03 00:18:48 - mmengine - INFO - Epoch(train) [145][1400/5047] lr: 5.4019e-06 eta: 6:58:31 time: 0.8174 data_time: 0.0028 memory: 41419 loss: 0.0995 loss_ce: 0.0995 2023/03/03 00:20:14 - mmengine - INFO - Epoch(train) [145][1500/5047] lr: 5.4019e-06 eta: 6:57:04 time: 0.8514 data_time: 0.0028 memory: 41361 loss: 0.1115 loss_ce: 0.1115 2023/03/03 00:21:42 - mmengine - INFO - Epoch(train) [145][1600/5047] lr: 5.4019e-06 eta: 6:55:37 time: 0.8863 data_time: 0.0028 memory: 49242 loss: 0.0985 loss_ce: 0.0985 2023/03/03 00:23:09 - mmengine - INFO - Epoch(train) [145][1700/5047] lr: 5.4019e-06 eta: 6:54:10 time: 0.8668 data_time: 0.0033 memory: 55114 loss: 0.1102 loss_ce: 0.1102 2023/03/03 00:24:35 - mmengine - INFO - Epoch(train) [145][1800/5047] lr: 5.4019e-06 eta: 6:52:43 time: 0.8498 data_time: 0.0029 memory: 44617 loss: 0.1092 loss_ce: 0.1092 2023/03/03 00:25:59 - mmengine - INFO - Epoch(train) [145][1900/5047] lr: 5.4019e-06 eta: 6:51:16 time: 0.9023 data_time: 0.0053 memory: 43218 loss: 0.1078 loss_ce: 0.1078 2023/03/03 00:27:26 - mmengine - INFO - Epoch(train) [145][2000/5047] lr: 5.4019e-06 eta: 6:49:49 time: 0.8562 data_time: 0.0031 memory: 44278 loss: 0.1080 loss_ce: 0.1080 2023/03/03 00:28:53 - mmengine - INFO - Epoch(train) [145][2100/5047] lr: 5.4019e-06 eta: 6:48:22 time: 0.8448 data_time: 0.0031 memory: 42164 loss: 0.0906 loss_ce: 0.0906 2023/03/03 00:30:18 - mmengine - INFO - Epoch(train) [145][2200/5047] lr: 5.4019e-06 eta: 6:46:55 time: 0.8587 data_time: 0.0031 memory: 41756 loss: 0.0978 loss_ce: 0.0978 2023/03/03 00:30:45 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 00:31:43 - mmengine - INFO - Epoch(train) [145][2300/5047] lr: 5.4019e-06 eta: 6:45:28 time: 0.8325 data_time: 0.0032 memory: 44956 loss: 0.1029 loss_ce: 0.1029 2023/03/03 00:33:09 - mmengine - INFO - Epoch(train) [145][2400/5047] lr: 5.4019e-06 eta: 6:44:01 time: 0.8638 data_time: 0.0028 memory: 54137 loss: 0.0926 loss_ce: 0.0926 2023/03/03 00:34:35 - mmengine - INFO - Epoch(train) [145][2500/5047] lr: 5.4019e-06 eta: 6:42:34 time: 0.8467 data_time: 0.0033 memory: 42024 loss: 0.1175 loss_ce: 0.1175 2023/03/03 00:36:02 - mmengine - INFO - Epoch(train) [145][2600/5047] lr: 5.4019e-06 eta: 6:41:07 time: 0.8558 data_time: 0.0041 memory: 45851 loss: 0.1170 loss_ce: 0.1170 2023/03/03 00:37:27 - mmengine - INFO - Epoch(train) [145][2700/5047] lr: 5.4019e-06 eta: 6:39:40 time: 0.8657 data_time: 0.0059 memory: 51308 loss: 0.1063 loss_ce: 0.1063 2023/03/03 00:38:53 - mmengine - INFO - Epoch(train) [145][2800/5047] lr: 5.4019e-06 eta: 6:38:13 time: 0.8891 data_time: 0.0029 memory: 43404 loss: 0.0934 loss_ce: 0.0934 2023/03/03 00:40:17 - mmengine - INFO - Epoch(train) [145][2900/5047] lr: 5.4019e-06 eta: 6:36:46 time: 0.8171 data_time: 0.0040 memory: 47638 loss: 0.1097 loss_ce: 0.1097 2023/03/03 00:41:43 - mmengine - INFO - Epoch(train) [145][3000/5047] lr: 5.4019e-06 eta: 6:35:19 time: 0.8543 data_time: 0.0042 memory: 53387 loss: 0.1054 loss_ce: 0.1054 2023/03/03 00:43:09 - mmengine - INFO - Epoch(train) [145][3100/5047] lr: 5.4019e-06 eta: 6:33:52 time: 0.8674 data_time: 0.0027 memory: 47447 loss: 0.1026 loss_ce: 0.1026 2023/03/03 00:44:33 - mmengine - INFO - Epoch(train) [145][3200/5047] lr: 5.4019e-06 eta: 6:32:25 time: 0.8606 data_time: 0.0036 memory: 44617 loss: 0.1069 loss_ce: 0.1069 2023/03/03 00:45:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 00:45:58 - mmengine - INFO - Epoch(train) [145][3300/5047] lr: 5.4019e-06 eta: 6:30:58 time: 0.8536 data_time: 0.0036 memory: 45643 loss: 0.0926 loss_ce: 0.0926 2023/03/03 00:47:24 - mmengine - INFO - Epoch(train) [145][3400/5047] lr: 5.4019e-06 eta: 6:29:31 time: 0.8684 data_time: 0.0028 memory: 45720 loss: 0.1148 loss_ce: 0.1148 2023/03/03 00:48:48 - mmengine - INFO - Epoch(train) [145][3500/5047] lr: 5.4019e-06 eta: 6:28:04 time: 0.8879 data_time: 0.0029 memory: 42649 loss: 0.1076 loss_ce: 0.1076 2023/03/03 00:50:14 - mmengine - INFO - Epoch(train) [145][3600/5047] lr: 5.4019e-06 eta: 6:26:37 time: 0.8586 data_time: 0.0031 memory: 42965 loss: 0.0910 loss_ce: 0.0910 2023/03/03 00:51:40 - mmengine - INFO - Epoch(train) [145][3700/5047] lr: 5.4019e-06 eta: 6:25:10 time: 0.8298 data_time: 0.0028 memory: 41724 loss: 0.0975 loss_ce: 0.0975 2023/03/03 00:53:06 - mmengine - INFO - Epoch(train) [145][3800/5047] lr: 5.4019e-06 eta: 6:23:43 time: 0.8733 data_time: 0.0033 memory: 42649 loss: 0.1096 loss_ce: 0.1096 2023/03/03 00:54:32 - mmengine - INFO - Epoch(train) [145][3900/5047] lr: 5.4019e-06 eta: 6:22:16 time: 0.8959 data_time: 0.0027 memory: 42024 loss: 0.0889 loss_ce: 0.0889 2023/03/03 00:55:57 - mmengine - INFO - Epoch(train) [145][4000/5047] lr: 5.4019e-06 eta: 6:20:49 time: 0.8246 data_time: 0.0053 memory: 55562 loss: 0.0957 loss_ce: 0.0957 2023/03/03 00:57:23 - mmengine - INFO - Epoch(train) [145][4100/5047] lr: 5.4019e-06 eta: 6:19:22 time: 0.8391 data_time: 0.0029 memory: 46772 loss: 0.1086 loss_ce: 0.1086 2023/03/03 00:58:49 - mmengine - INFO - Epoch(train) [145][4200/5047] lr: 5.4019e-06 eta: 6:17:55 time: 0.8065 data_time: 0.0037 memory: 55562 loss: 0.0990 loss_ce: 0.0990 2023/03/03 00:59:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 01:00:16 - mmengine - INFO - Epoch(train) [145][4300/5047] lr: 5.4019e-06 eta: 6:16:28 time: 0.8156 data_time: 0.0029 memory: 41780 loss: 0.1012 loss_ce: 0.1012 2023/03/03 01:01:42 - mmengine - INFO - Epoch(train) [145][4400/5047] lr: 5.4019e-06 eta: 6:15:01 time: 0.8426 data_time: 0.0063 memory: 42336 loss: 0.1048 loss_ce: 0.1048 2023/03/03 01:03:07 - mmengine - INFO - Epoch(train) [145][4500/5047] lr: 5.4019e-06 eta: 6:13:34 time: 0.8657 data_time: 0.0029 memory: 46005 loss: 0.0982 loss_ce: 0.0982 2023/03/03 01:04:34 - mmengine - INFO - Epoch(train) [145][4600/5047] lr: 5.4019e-06 eta: 6:12:07 time: 0.9220 data_time: 0.0041 memory: 43947 loss: 0.1006 loss_ce: 0.1006 2023/03/03 01:05:59 - mmengine - INFO - Epoch(train) [145][4700/5047] lr: 5.4019e-06 eta: 6:10:40 time: 0.8416 data_time: 0.0031 memory: 46794 loss: 0.1092 loss_ce: 0.1092 2023/03/03 01:07:25 - mmengine - INFO - Epoch(train) [145][4800/5047] lr: 5.4019e-06 eta: 6:09:13 time: 0.9048 data_time: 0.0035 memory: 49378 loss: 0.0873 loss_ce: 0.0873 2023/03/03 01:08:51 - mmengine - INFO - Epoch(train) [145][4900/5047] lr: 5.4019e-06 eta: 6:07:46 time: 0.8469 data_time: 0.0028 memory: 43289 loss: 0.1074 loss_ce: 0.1074 2023/03/03 01:10:17 - mmengine - INFO - Epoch(train) [145][5000/5047] lr: 5.4019e-06 eta: 6:06:19 time: 0.8360 data_time: 0.0027 memory: 40500 loss: 0.1023 loss_ce: 0.1023 2023/03/03 01:10:57 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 01:10:57 - mmengine - INFO - Saving checkpoint at 145 epochs 2023/03/03 01:12:27 - mmengine - INFO - Epoch(train) [146][ 100/5047] lr: 5.2009e-06 eta: 6:04:11 time: 0.8366 data_time: 0.0029 memory: 49432 loss: 0.1066 loss_ce: 0.1066 2023/03/03 01:13:38 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 01:13:51 - mmengine - INFO - Epoch(train) [146][ 200/5047] lr: 5.2009e-06 eta: 6:02:44 time: 0.8706 data_time: 0.0031 memory: 51135 loss: 0.1116 loss_ce: 0.1116 2023/03/03 01:15:18 - mmengine - INFO - Epoch(train) [146][ 300/5047] lr: 5.2009e-06 eta: 6:01:17 time: 0.8353 data_time: 0.0028 memory: 43613 loss: 0.0933 loss_ce: 0.0933 2023/03/03 01:16:43 - mmengine - INFO - Epoch(train) [146][ 400/5047] lr: 5.2009e-06 eta: 5:59:50 time: 0.8606 data_time: 0.0028 memory: 42418 loss: 0.0998 loss_ce: 0.0998 2023/03/03 01:18:09 - mmengine - INFO - Epoch(train) [146][ 500/5047] lr: 5.2009e-06 eta: 5:58:23 time: 0.8556 data_time: 0.0047 memory: 43780 loss: 0.0928 loss_ce: 0.0928 2023/03/03 01:19:34 - mmengine - INFO - Epoch(train) [146][ 600/5047] lr: 5.2009e-06 eta: 5:56:56 time: 0.7613 data_time: 0.0031 memory: 42336 loss: 0.1104 loss_ce: 0.1104 2023/03/03 01:21:01 - mmengine - INFO - Epoch(train) [146][ 700/5047] lr: 5.2009e-06 eta: 5:55:29 time: 0.8856 data_time: 0.0054 memory: 46003 loss: 0.1106 loss_ce: 0.1106 2023/03/03 01:22:28 - mmengine - INFO - Epoch(train) [146][ 800/5047] lr: 5.2009e-06 eta: 5:54:02 time: 0.8807 data_time: 0.0041 memory: 47813 loss: 0.0853 loss_ce: 0.0853 2023/03/03 01:23:55 - mmengine - INFO - Epoch(train) [146][ 900/5047] lr: 5.2009e-06 eta: 5:52:36 time: 0.8631 data_time: 0.0027 memory: 41724 loss: 0.0980 loss_ce: 0.0980 2023/03/03 01:25:22 - mmengine - INFO - Epoch(train) [146][1000/5047] lr: 5.2009e-06 eta: 5:51:09 time: 0.8471 data_time: 0.0029 memory: 55562 loss: 0.1073 loss_ce: 0.1073 2023/03/03 01:26:49 - mmengine - INFO - Epoch(train) [146][1100/5047] lr: 5.2009e-06 eta: 5:49:42 time: 0.8326 data_time: 0.0031 memory: 42305 loss: 0.1019 loss_ce: 0.1019 2023/03/03 01:28:02 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 01:28:15 - mmengine - INFO - Epoch(train) [146][1200/5047] lr: 5.2009e-06 eta: 5:48:15 time: 0.8761 data_time: 0.0029 memory: 45876 loss: 0.1048 loss_ce: 0.1048 2023/03/03 01:29:42 - mmengine - INFO - Epoch(train) [146][1300/5047] lr: 5.2009e-06 eta: 5:46:48 time: 0.8656 data_time: 0.0028 memory: 44617 loss: 0.0966 loss_ce: 0.0966 2023/03/03 01:31:08 - mmengine - INFO - Epoch(train) [146][1400/5047] lr: 5.2009e-06 eta: 5:45:21 time: 0.8749 data_time: 0.0029 memory: 46713 loss: 0.1075 loss_ce: 0.1075 2023/03/03 01:32:35 - mmengine - INFO - Epoch(train) [146][1500/5047] lr: 5.2009e-06 eta: 5:43:54 time: 0.8944 data_time: 0.0028 memory: 52127 loss: 0.0957 loss_ce: 0.0957 2023/03/03 01:34:01 - mmengine - INFO - Epoch(train) [146][1600/5047] lr: 5.2009e-06 eta: 5:42:27 time: 0.8819 data_time: 0.0033 memory: 40825 loss: 0.1099 loss_ce: 0.1099 2023/03/03 01:35:26 - mmengine - INFO - Epoch(train) [146][1700/5047] lr: 5.2009e-06 eta: 5:41:00 time: 0.9075 data_time: 0.0031 memory: 41840 loss: 0.1017 loss_ce: 0.1017 2023/03/03 01:36:54 - mmengine - INFO - Epoch(train) [146][1800/5047] lr: 5.2009e-06 eta: 5:39:33 time: 0.8649 data_time: 0.0028 memory: 43613 loss: 0.1080 loss_ce: 0.1080 2023/03/03 01:38:21 - mmengine - INFO - Epoch(train) [146][1900/5047] lr: 5.2009e-06 eta: 5:38:06 time: 0.8728 data_time: 0.0030 memory: 42649 loss: 0.1109 loss_ce: 0.1109 2023/03/03 01:39:44 - mmengine - INFO - Epoch(train) [146][2000/5047] lr: 5.2009e-06 eta: 5:36:39 time: 0.8623 data_time: 0.0033 memory: 42649 loss: 0.1154 loss_ce: 0.1154 2023/03/03 01:41:10 - mmengine - INFO - Epoch(train) [146][2100/5047] lr: 5.2009e-06 eta: 5:35:12 time: 0.8625 data_time: 0.0027 memory: 44617 loss: 0.1078 loss_ce: 0.1078 2023/03/03 01:42:23 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 01:42:35 - mmengine - INFO - Epoch(train) [146][2200/5047] lr: 5.2009e-06 eta: 5:33:45 time: 0.8110 data_time: 0.0030 memory: 42336 loss: 0.1066 loss_ce: 0.1066 2023/03/03 01:44:01 - mmengine - INFO - Epoch(train) [146][2300/5047] lr: 5.2009e-06 eta: 5:32:18 time: 0.8467 data_time: 0.0030 memory: 49235 loss: 0.0956 loss_ce: 0.0956 2023/03/03 01:45:27 - mmengine - INFO - Epoch(train) [146][2400/5047] lr: 5.2009e-06 eta: 5:30:51 time: 0.8327 data_time: 0.0028 memory: 43289 loss: 0.1096 loss_ce: 0.1096 2023/03/03 01:46:52 - mmengine - INFO - Epoch(train) [146][2500/5047] lr: 5.2009e-06 eta: 5:29:24 time: 0.8493 data_time: 0.0027 memory: 51739 loss: 0.1139 loss_ce: 0.1139 2023/03/03 01:48:18 - mmengine - INFO - Epoch(train) [146][2600/5047] lr: 5.2009e-06 eta: 5:27:57 time: 0.8816 data_time: 0.0030 memory: 55562 loss: 0.1040 loss_ce: 0.1040 2023/03/03 01:49:44 - mmengine - INFO - Epoch(train) [146][2700/5047] lr: 5.2009e-06 eta: 5:26:30 time: 0.8440 data_time: 0.0029 memory: 45787 loss: 0.1048 loss_ce: 0.1048 2023/03/03 01:51:09 - mmengine - INFO - Epoch(train) [146][2800/5047] lr: 5.2009e-06 eta: 5:25:03 time: 0.8466 data_time: 0.0029 memory: 42915 loss: 0.1072 loss_ce: 0.1072 2023/03/03 01:52:35 - mmengine - INFO - Epoch(train) [146][2900/5047] lr: 5.2009e-06 eta: 5:23:36 time: 0.8230 data_time: 0.0031 memory: 51308 loss: 0.1186 loss_ce: 0.1186 2023/03/03 01:54:02 - mmengine - INFO - Epoch(train) [146][3000/5047] lr: 5.2009e-06 eta: 5:22:09 time: 0.8808 data_time: 0.0030 memory: 55393 loss: 0.1014 loss_ce: 0.1014 2023/03/03 01:55:28 - mmengine - INFO - Epoch(train) [146][3100/5047] lr: 5.2009e-06 eta: 5:20:42 time: 0.8300 data_time: 0.0029 memory: 42150 loss: 0.1099 loss_ce: 0.1099 2023/03/03 01:56:41 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 01:56:54 - mmengine - INFO - Epoch(train) [146][3200/5047] lr: 5.2009e-06 eta: 5:19:15 time: 0.8578 data_time: 0.0031 memory: 45643 loss: 0.1031 loss_ce: 0.1031 2023/03/03 01:58:21 - mmengine - INFO - Epoch(train) [146][3300/5047] lr: 5.2009e-06 eta: 5:17:48 time: 0.8790 data_time: 0.0036 memory: 50161 loss: 0.1010 loss_ce: 0.1010 2023/03/03 01:59:46 - mmengine - INFO - Epoch(train) [146][3400/5047] lr: 5.2009e-06 eta: 5:16:21 time: 0.8963 data_time: 0.0028 memory: 42024 loss: 0.1052 loss_ce: 0.1052 2023/03/03 02:01:13 - mmengine - INFO - Epoch(train) [146][3500/5047] lr: 5.2009e-06 eta: 5:14:54 time: 0.9162 data_time: 0.0027 memory: 54232 loss: 0.1075 loss_ce: 0.1075 2023/03/03 02:02:39 - mmengine - INFO - Epoch(train) [146][3600/5047] lr: 5.2009e-06 eta: 5:13:27 time: 0.8857 data_time: 0.0094 memory: 45787 loss: 0.1242 loss_ce: 0.1242 2023/03/03 02:04:07 - mmengine - INFO - Epoch(train) [146][3700/5047] lr: 5.2009e-06 eta: 5:12:01 time: 0.8546 data_time: 0.0027 memory: 45126 loss: 0.1030 loss_ce: 0.1030 2023/03/03 02:05:32 - mmengine - INFO - Epoch(train) [146][3800/5047] lr: 5.2009e-06 eta: 5:10:34 time: 0.8286 data_time: 0.0028 memory: 47309 loss: 0.1081 loss_ce: 0.1081 2023/03/03 02:06:58 - mmengine - INFO - Epoch(train) [146][3900/5047] lr: 5.2009e-06 eta: 5:09:07 time: 0.8612 data_time: 0.0040 memory: 43289 loss: 0.1034 loss_ce: 0.1034 2023/03/03 02:08:25 - mmengine - INFO - Epoch(train) [146][4000/5047] lr: 5.2009e-06 eta: 5:07:40 time: 0.9148 data_time: 0.0027 memory: 55559 loss: 0.0964 loss_ce: 0.0964 2023/03/03 02:09:52 - mmengine - INFO - Epoch(train) [146][4100/5047] lr: 5.2009e-06 eta: 5:06:13 time: 0.8616 data_time: 0.0029 memory: 43898 loss: 0.1030 loss_ce: 0.1030 2023/03/03 02:11:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 02:11:18 - mmengine - INFO - Epoch(train) [146][4200/5047] lr: 5.2009e-06 eta: 5:04:46 time: 0.8956 data_time: 0.0027 memory: 45643 loss: 0.0976 loss_ce: 0.0976 2023/03/03 02:12:44 - mmengine - INFO - Epoch(train) [146][4300/5047] lr: 5.2009e-06 eta: 5:03:19 time: 0.8519 data_time: 0.0030 memory: 43899 loss: 0.1060 loss_ce: 0.1060 2023/03/03 02:14:10 - mmengine - INFO - Epoch(train) [146][4400/5047] lr: 5.2009e-06 eta: 5:01:52 time: 0.8701 data_time: 0.0033 memory: 52817 loss: 0.0972 loss_ce: 0.0972 2023/03/03 02:15:39 - mmengine - INFO - Epoch(train) [146][4500/5047] lr: 5.2009e-06 eta: 5:00:25 time: 0.9570 data_time: 0.0045 memory: 45851 loss: 0.1051 loss_ce: 0.1051 2023/03/03 02:17:03 - mmengine - INFO - Epoch(train) [146][4600/5047] lr: 5.2009e-06 eta: 4:58:58 time: 0.8423 data_time: 0.0028 memory: 44278 loss: 0.0888 loss_ce: 0.0888 2023/03/03 02:18:30 - mmengine - INFO - Epoch(train) [146][4700/5047] lr: 5.2009e-06 eta: 4:57:31 time: 0.8613 data_time: 0.0044 memory: 42336 loss: 0.1125 loss_ce: 0.1125 2023/03/03 02:19:56 - mmengine - INFO - Epoch(train) [146][4800/5047] lr: 5.2009e-06 eta: 4:56:04 time: 0.8491 data_time: 0.0027 memory: 41122 loss: 0.1094 loss_ce: 0.1094 2023/03/03 02:21:22 - mmengine - INFO - Epoch(train) [146][4900/5047] lr: 5.2009e-06 eta: 4:54:37 time: 0.8928 data_time: 0.0027 memory: 44956 loss: 0.1110 loss_ce: 0.1110 2023/03/03 02:22:45 - mmengine - INFO - Epoch(train) [146][5000/5047] lr: 5.2009e-06 eta: 4:53:10 time: 0.8637 data_time: 0.0029 memory: 41419 loss: 0.1190 loss_ce: 0.1190 2023/03/03 02:23:25 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 02:23:25 - mmengine - INFO - Saving checkpoint at 146 epochs 2023/03/03 02:24:55 - mmengine - INFO - Epoch(train) [147][ 100/5047] lr: 5.0000e-06 eta: 4:51:02 time: 0.7971 data_time: 0.0028 memory: 42965 loss: 0.1073 loss_ce: 0.1073 2023/03/03 02:25:28 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 02:26:20 - mmengine - INFO - Epoch(train) [147][ 200/5047] lr: 5.0000e-06 eta: 4:49:35 time: 0.8174 data_time: 0.0026 memory: 52882 loss: 0.1017 loss_ce: 0.1017 2023/03/03 02:27:47 - mmengine - INFO - Epoch(train) [147][ 300/5047] lr: 5.0000e-06 eta: 4:48:08 time: 0.8377 data_time: 0.0029 memory: 43947 loss: 0.0948 loss_ce: 0.0948 2023/03/03 02:29:13 - mmengine - INFO - Epoch(train) [147][ 400/5047] lr: 5.0000e-06 eta: 4:46:41 time: 0.8700 data_time: 0.0030 memory: 42007 loss: 0.1193 loss_ce: 0.1193 2023/03/03 02:30:39 - mmengine - INFO - Epoch(train) [147][ 500/5047] lr: 5.0000e-06 eta: 4:45:14 time: 0.8308 data_time: 0.0042 memory: 45302 loss: 0.1015 loss_ce: 0.1015 2023/03/03 02:32:03 - mmengine - INFO - Epoch(train) [147][ 600/5047] lr: 5.0000e-06 eta: 4:43:47 time: 0.8515 data_time: 0.0029 memory: 50417 loss: 0.0997 loss_ce: 0.0997 2023/03/03 02:33:29 - mmengine - INFO - Epoch(train) [147][ 700/5047] lr: 5.0000e-06 eta: 4:42:20 time: 0.8550 data_time: 0.0028 memory: 53044 loss: 0.1068 loss_ce: 0.1068 2023/03/03 02:34:54 - mmengine - INFO - Epoch(train) [147][ 800/5047] lr: 5.0000e-06 eta: 4:40:53 time: 0.8520 data_time: 0.0030 memory: 44722 loss: 0.1123 loss_ce: 0.1123 2023/03/03 02:36:18 - mmengine - INFO - Epoch(train) [147][ 900/5047] lr: 5.0000e-06 eta: 4:39:26 time: 0.8335 data_time: 0.0050 memory: 41122 loss: 0.0891 loss_ce: 0.0891 2023/03/03 02:37:42 - mmengine - INFO - Epoch(train) [147][1000/5047] lr: 5.0000e-06 eta: 4:37:59 time: 0.7957 data_time: 0.0036 memory: 41467 loss: 0.0985 loss_ce: 0.0985 2023/03/03 02:39:08 - mmengine - INFO - Epoch(train) [147][1100/5047] lr: 5.0000e-06 eta: 4:36:32 time: 0.8255 data_time: 0.0028 memory: 42024 loss: 0.1027 loss_ce: 0.1027 2023/03/03 02:39:40 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 02:40:33 - mmengine - INFO - Epoch(train) [147][1200/5047] lr: 5.0000e-06 eta: 4:35:05 time: 0.8499 data_time: 0.0048 memory: 47983 loss: 0.1118 loss_ce: 0.1118 2023/03/03 02:41:58 - mmengine - INFO - Epoch(train) [147][1300/5047] lr: 5.0000e-06 eta: 4:33:38 time: 0.8519 data_time: 0.0029 memory: 45073 loss: 0.1005 loss_ce: 0.1005 2023/03/03 02:43:24 - mmengine - INFO - Epoch(train) [147][1400/5047] lr: 5.0000e-06 eta: 4:32:11 time: 0.8106 data_time: 0.0064 memory: 40825 loss: 0.0834 loss_ce: 0.0834 2023/03/03 02:44:49 - mmengine - INFO - Epoch(train) [147][1500/5047] lr: 5.0000e-06 eta: 4:30:45 time: 0.8539 data_time: 0.0032 memory: 55562 loss: 0.1144 loss_ce: 0.1144 2023/03/03 02:46:16 - mmengine - INFO - Epoch(train) [147][1600/5047] lr: 5.0000e-06 eta: 4:29:18 time: 0.9438 data_time: 0.0028 memory: 55562 loss: 0.1070 loss_ce: 0.1070 2023/03/03 02:47:42 - mmengine - INFO - Epoch(train) [147][1700/5047] lr: 5.0000e-06 eta: 4:27:51 time: 0.8532 data_time: 0.0028 memory: 44278 loss: 0.1121 loss_ce: 0.1121 2023/03/03 02:49:08 - mmengine - INFO - Epoch(train) [147][1800/5047] lr: 5.0000e-06 eta: 4:26:24 time: 0.8523 data_time: 0.0087 memory: 44581 loss: 0.0979 loss_ce: 0.0979 2023/03/03 02:50:33 - mmengine - INFO - Epoch(train) [147][1900/5047] lr: 5.0000e-06 eta: 4:24:57 time: 0.8194 data_time: 0.0027 memory: 45643 loss: 0.1050 loss_ce: 0.1050 2023/03/03 02:52:01 - mmengine - INFO - Epoch(train) [147][2000/5047] lr: 5.0000e-06 eta: 4:23:30 time: 0.9066 data_time: 0.0057 memory: 43613 loss: 0.0910 loss_ce: 0.0910 2023/03/03 02:53:26 - mmengine - INFO - Epoch(train) [147][2100/5047] lr: 5.0000e-06 eta: 4:22:03 time: 0.8619 data_time: 0.0084 memory: 41724 loss: 0.1054 loss_ce: 0.1054 2023/03/03 02:53:59 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 02:54:52 - mmengine - INFO - Epoch(train) [147][2200/5047] lr: 5.0000e-06 eta: 4:20:36 time: 0.8758 data_time: 0.0032 memory: 45302 loss: 0.1020 loss_ce: 0.1020 2023/03/03 02:56:18 - mmengine - INFO - Epoch(train) [147][2300/5047] lr: 5.0000e-06 eta: 4:19:09 time: 0.8321 data_time: 0.0034 memory: 44278 loss: 0.1031 loss_ce: 0.1031 2023/03/03 02:57:44 - mmengine - INFO - Epoch(train) [147][2400/5047] lr: 5.0000e-06 eta: 4:17:42 time: 0.8463 data_time: 0.0033 memory: 46622 loss: 0.1200 loss_ce: 0.1200 2023/03/03 02:59:09 - mmengine - INFO - Epoch(train) [147][2500/5047] lr: 5.0000e-06 eta: 4:16:15 time: 0.8681 data_time: 0.0029 memory: 43926 loss: 0.0945 loss_ce: 0.0945 2023/03/03 03:00:34 - mmengine - INFO - Epoch(train) [147][2600/5047] lr: 5.0000e-06 eta: 4:14:48 time: 0.8773 data_time: 0.0025 memory: 41419 loss: 0.1106 loss_ce: 0.1106 2023/03/03 03:02:00 - mmengine - INFO - Epoch(train) [147][2700/5047] lr: 5.0000e-06 eta: 4:13:21 time: 0.8974 data_time: 0.0043 memory: 43068 loss: 0.1044 loss_ce: 0.1044 2023/03/03 03:03:26 - mmengine - INFO - Epoch(train) [147][2800/5047] lr: 5.0000e-06 eta: 4:11:54 time: 0.9080 data_time: 0.0030 memory: 42396 loss: 0.1117 loss_ce: 0.1117 2023/03/03 03:04:51 - mmengine - INFO - Epoch(train) [147][2900/5047] lr: 5.0000e-06 eta: 4:10:27 time: 0.8395 data_time: 0.0031 memory: 42336 loss: 0.0800 loss_ce: 0.0800 2023/03/03 03:06:17 - mmengine - INFO - Epoch(train) [147][3000/5047] lr: 5.0000e-06 eta: 4:09:00 time: 0.8278 data_time: 0.0061 memory: 42328 loss: 0.1026 loss_ce: 0.1026 2023/03/03 03:07:44 - mmengine - INFO - Epoch(train) [147][3100/5047] lr: 5.0000e-06 eta: 4:07:33 time: 0.8414 data_time: 0.0062 memory: 47813 loss: 0.1047 loss_ce: 0.1047 2023/03/03 03:08:17 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 03:09:10 - mmengine - INFO - Epoch(train) [147][3200/5047] lr: 5.0000e-06 eta: 4:06:06 time: 0.9100 data_time: 0.0035 memory: 49068 loss: 0.1133 loss_ce: 0.1133 2023/03/03 03:10:36 - mmengine - INFO - Epoch(train) [147][3300/5047] lr: 5.0000e-06 eta: 4:04:39 time: 0.8883 data_time: 0.0036 memory: 51427 loss: 0.0917 loss_ce: 0.0917 2023/03/03 03:12:03 - mmengine - INFO - Epoch(train) [147][3400/5047] lr: 5.0000e-06 eta: 4:03:12 time: 0.8993 data_time: 0.0030 memory: 48263 loss: 0.0987 loss_ce: 0.0987 2023/03/03 03:13:30 - mmengine - INFO - Epoch(train) [147][3500/5047] lr: 5.0000e-06 eta: 4:01:46 time: 0.8435 data_time: 0.0028 memory: 46005 loss: 0.1199 loss_ce: 0.1199 2023/03/03 03:14:55 - mmengine - INFO - Epoch(train) [147][3600/5047] lr: 5.0000e-06 eta: 4:00:19 time: 0.7933 data_time: 0.0028 memory: 46005 loss: 0.1177 loss_ce: 0.1177 2023/03/03 03:16:19 - mmengine - INFO - Epoch(train) [147][3700/5047] lr: 5.0000e-06 eta: 3:58:52 time: 0.7840 data_time: 0.0042 memory: 42024 loss: 0.0974 loss_ce: 0.0974 2023/03/03 03:17:45 - mmengine - INFO - Epoch(train) [147][3800/5047] lr: 5.0000e-06 eta: 3:57:25 time: 0.8481 data_time: 0.0028 memory: 48948 loss: 0.1015 loss_ce: 0.1015 2023/03/03 03:19:10 - mmengine - INFO - Epoch(train) [147][3900/5047] lr: 5.0000e-06 eta: 3:55:58 time: 0.8187 data_time: 0.0027 memory: 40241 loss: 0.1021 loss_ce: 0.1021 2023/03/03 03:20:38 - mmengine - INFO - Epoch(train) [147][4000/5047] lr: 5.0000e-06 eta: 3:54:31 time: 0.9141 data_time: 0.0028 memory: 52964 loss: 0.0902 loss_ce: 0.0902 2023/03/03 03:22:03 - mmengine - INFO - Epoch(train) [147][4100/5047] lr: 5.0000e-06 eta: 3:53:04 time: 0.8296 data_time: 0.0046 memory: 45302 loss: 0.1047 loss_ce: 0.1047 2023/03/03 03:22:36 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 03:23:30 - mmengine - INFO - Epoch(train) [147][4200/5047] lr: 5.0000e-06 eta: 3:51:37 time: 0.8620 data_time: 0.0029 memory: 45643 loss: 0.0984 loss_ce: 0.0984 2023/03/03 03:24:55 - mmengine - INFO - Epoch(train) [147][4300/5047] lr: 5.0000e-06 eta: 3:50:10 time: 0.8601 data_time: 0.0030 memory: 41298 loss: 0.1089 loss_ce: 0.1089 2023/03/03 03:26:22 - mmengine - INFO - Epoch(train) [147][4400/5047] lr: 5.0000e-06 eta: 3:48:43 time: 0.8651 data_time: 0.0031 memory: 41419 loss: 0.1014 loss_ce: 0.1014 2023/03/03 03:27:48 - mmengine - INFO - Epoch(train) [147][4500/5047] lr: 5.0000e-06 eta: 3:47:16 time: 0.8481 data_time: 0.0052 memory: 51637 loss: 0.1075 loss_ce: 0.1075 2023/03/03 03:29:14 - mmengine - INFO - Epoch(train) [147][4600/5047] lr: 5.0000e-06 eta: 3:45:49 time: 0.9038 data_time: 0.0034 memory: 52964 loss: 0.1068 loss_ce: 0.1068 2023/03/03 03:30:41 - mmengine - INFO - Epoch(train) [147][4700/5047] lr: 5.0000e-06 eta: 3:44:22 time: 0.8982 data_time: 0.0028 memory: 45302 loss: 0.1026 loss_ce: 0.1026 2023/03/03 03:32:07 - mmengine - INFO - Epoch(train) [147][4800/5047] lr: 5.0000e-06 eta: 3:42:55 time: 0.8598 data_time: 0.0028 memory: 45825 loss: 0.1063 loss_ce: 0.1063 2023/03/03 03:33:33 - mmengine - INFO - Epoch(train) [147][4900/5047] lr: 5.0000e-06 eta: 3:41:28 time: 0.8335 data_time: 0.0033 memory: 42649 loss: 0.1001 loss_ce: 0.1001 2023/03/03 03:34:59 - mmengine - INFO - Epoch(train) [147][5000/5047] lr: 5.0000e-06 eta: 3:40:01 time: 0.8330 data_time: 0.0045 memory: 44956 loss: 0.1152 loss_ce: 0.1152 2023/03/03 03:35:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 03:35:39 - mmengine - INFO - Saving checkpoint at 147 epochs 2023/03/03 03:37:03 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 03:37:11 - mmengine - INFO - Epoch(train) [148][ 100/5047] lr: 5.0000e-06 eta: 3:37:53 time: 0.8377 data_time: 0.0039 memory: 44250 loss: 0.0984 loss_ce: 0.0984 2023/03/03 03:38:36 - mmengine - INFO - Epoch(train) [148][ 200/5047] lr: 5.0000e-06 eta: 3:36:27 time: 0.8637 data_time: 0.0029 memory: 40535 loss: 0.1104 loss_ce: 0.1104 2023/03/03 03:40:01 - mmengine - INFO - Epoch(train) [148][ 300/5047] lr: 5.0000e-06 eta: 3:35:00 time: 0.8401 data_time: 0.0027 memory: 47813 loss: 0.0893 loss_ce: 0.0893 2023/03/03 03:41:27 - mmengine - INFO - Epoch(train) [148][ 400/5047] lr: 5.0000e-06 eta: 3:33:33 time: 0.8960 data_time: 0.0028 memory: 43679 loss: 0.0881 loss_ce: 0.0881 2023/03/03 03:42:51 - mmengine - INFO - Epoch(train) [148][ 500/5047] lr: 5.0000e-06 eta: 3:32:06 time: 0.8452 data_time: 0.0034 memory: 41122 loss: 0.1077 loss_ce: 0.1077 2023/03/03 03:44:17 - mmengine - INFO - Epoch(train) [148][ 600/5047] lr: 5.0000e-06 eta: 3:30:39 time: 0.8674 data_time: 0.0050 memory: 49312 loss: 0.1222 loss_ce: 0.1222 2023/03/03 03:45:41 - mmengine - INFO - Epoch(train) [148][ 700/5047] lr: 5.0000e-06 eta: 3:29:12 time: 0.8709 data_time: 0.0033 memory: 43614 loss: 0.1046 loss_ce: 0.1046 2023/03/03 03:47:07 - mmengine - INFO - Epoch(train) [148][ 800/5047] lr: 5.0000e-06 eta: 3:27:45 time: 0.8622 data_time: 0.0039 memory: 44956 loss: 0.1029 loss_ce: 0.1029 2023/03/03 03:48:35 - mmengine - INFO - Epoch(train) [148][ 900/5047] lr: 5.0000e-06 eta: 3:26:18 time: 0.8845 data_time: 0.0042 memory: 50906 loss: 0.0982 loss_ce: 0.0982 2023/03/03 03:49:58 - mmengine - INFO - Epoch(train) [148][1000/5047] lr: 5.0000e-06 eta: 3:24:51 time: 0.7815 data_time: 0.0030 memory: 47359 loss: 0.0967 loss_ce: 0.0967 2023/03/03 03:51:16 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 03:51:24 - mmengine - INFO - Epoch(train) [148][1100/5047] lr: 5.0000e-06 eta: 3:23:24 time: 0.8725 data_time: 0.0032 memory: 41942 loss: 0.1122 loss_ce: 0.1122 2023/03/03 03:52:50 - mmengine - INFO - Epoch(train) [148][1200/5047] lr: 5.0000e-06 eta: 3:21:57 time: 0.8880 data_time: 0.0032 memory: 52817 loss: 0.1160 loss_ce: 0.1160 2023/03/03 03:54:15 - mmengine - INFO - Epoch(train) [148][1300/5047] lr: 5.0000e-06 eta: 3:20:30 time: 0.8676 data_time: 0.0031 memory: 42336 loss: 0.1218 loss_ce: 0.1218 2023/03/03 03:55:42 - mmengine - INFO - Epoch(train) [148][1400/5047] lr: 5.0000e-06 eta: 3:19:03 time: 0.8754 data_time: 0.0031 memory: 55562 loss: 0.1115 loss_ce: 0.1115 2023/03/03 03:57:06 - mmengine - INFO - Epoch(train) [148][1500/5047] lr: 5.0000e-06 eta: 3:17:36 time: 0.8357 data_time: 0.0030 memory: 47447 loss: 0.0914 loss_ce: 0.0914 2023/03/03 03:58:32 - mmengine - INFO - Epoch(train) [148][1600/5047] lr: 5.0000e-06 eta: 3:16:09 time: 0.8586 data_time: 0.0036 memory: 55562 loss: 0.1056 loss_ce: 0.1056 2023/03/03 03:59:57 - mmengine - INFO - Epoch(train) [148][1700/5047] lr: 5.0000e-06 eta: 3:14:42 time: 0.9056 data_time: 0.0051 memory: 41724 loss: 0.0998 loss_ce: 0.0998 2023/03/03 04:01:24 - mmengine - INFO - Epoch(train) [148][1800/5047] lr: 5.0000e-06 eta: 3:13:15 time: 0.8781 data_time: 0.0029 memory: 42336 loss: 0.1106 loss_ce: 0.1106 2023/03/03 04:02:49 - mmengine - INFO - Epoch(train) [148][1900/5047] lr: 5.0000e-06 eta: 3:11:48 time: 0.8195 data_time: 0.0027 memory: 43808 loss: 0.1003 loss_ce: 0.1003 2023/03/03 04:04:14 - mmengine - INFO - Epoch(train) [148][2000/5047] lr: 5.0000e-06 eta: 3:10:21 time: 0.8653 data_time: 0.0030 memory: 43585 loss: 0.1075 loss_ce: 0.1075 2023/03/03 04:05:32 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 04:05:40 - mmengine - INFO - Epoch(train) [148][2100/5047] lr: 5.0000e-06 eta: 3:08:54 time: 0.8607 data_time: 0.0030 memory: 42921 loss: 0.1192 loss_ce: 0.1192 2023/03/03 04:07:06 - mmengine - INFO - Epoch(train) [148][2200/5047] lr: 5.0000e-06 eta: 3:07:28 time: 0.7983 data_time: 0.0031 memory: 43613 loss: 0.0948 loss_ce: 0.0948 2023/03/03 04:08:31 - mmengine - INFO - Epoch(train) [148][2300/5047] lr: 5.0000e-06 eta: 3:06:01 time: 0.8481 data_time: 0.0029 memory: 42024 loss: 0.1125 loss_ce: 0.1125 2023/03/03 04:09:58 - mmengine - INFO - Epoch(train) [148][2400/5047] lr: 5.0000e-06 eta: 3:04:34 time: 0.8626 data_time: 0.0028 memory: 42293 loss: 0.1091 loss_ce: 0.1091 2023/03/03 04:11:23 - mmengine - INFO - Epoch(train) [148][2500/5047] lr: 5.0000e-06 eta: 3:03:07 time: 0.8221 data_time: 0.0031 memory: 39960 loss: 0.0974 loss_ce: 0.0974 2023/03/03 04:12:49 - mmengine - INFO - Epoch(train) [148][2600/5047] lr: 5.0000e-06 eta: 3:01:40 time: 0.8517 data_time: 0.0030 memory: 47619 loss: 0.1109 loss_ce: 0.1109 2023/03/03 04:14:14 - mmengine - INFO - Epoch(train) [148][2700/5047] lr: 5.0000e-06 eta: 3:00:13 time: 0.8736 data_time: 0.0030 memory: 41419 loss: 0.1098 loss_ce: 0.1098 2023/03/03 04:15:38 - mmengine - INFO - Epoch(train) [148][2800/5047] lr: 5.0000e-06 eta: 2:58:46 time: 0.8405 data_time: 0.0055 memory: 45643 loss: 0.1094 loss_ce: 0.1094 2023/03/03 04:17:03 - mmengine - INFO - Epoch(train) [148][2900/5047] lr: 5.0000e-06 eta: 2:57:19 time: 0.8507 data_time: 0.0031 memory: 55562 loss: 0.1084 loss_ce: 0.1084 2023/03/03 04:18:28 - mmengine - INFO - Epoch(train) [148][3000/5047] lr: 5.0000e-06 eta: 2:55:52 time: 0.8172 data_time: 0.0030 memory: 40825 loss: 0.1140 loss_ce: 0.1140 2023/03/03 04:19:46 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 04:19:54 - mmengine - INFO - Epoch(train) [148][3100/5047] lr: 5.0000e-06 eta: 2:54:25 time: 0.8306 data_time: 0.0031 memory: 42024 loss: 0.0904 loss_ce: 0.0904 2023/03/03 04:21:20 - mmengine - INFO - Epoch(train) [148][3200/5047] lr: 5.0000e-06 eta: 2:52:58 time: 0.8931 data_time: 0.0029 memory: 44617 loss: 0.0949 loss_ce: 0.0949 2023/03/03 04:22:46 - mmengine - INFO - Epoch(train) [148][3300/5047] lr: 5.0000e-06 eta: 2:51:31 time: 0.8499 data_time: 0.0029 memory: 42794 loss: 0.0899 loss_ce: 0.0899 2023/03/03 04:24:10 - mmengine - INFO - Epoch(train) [148][3400/5047] lr: 5.0000e-06 eta: 2:50:04 time: 0.8092 data_time: 0.0032 memory: 45643 loss: 0.0978 loss_ce: 0.0978 2023/03/03 04:25:37 - mmengine - INFO - Epoch(train) [148][3500/5047] lr: 5.0000e-06 eta: 2:48:37 time: 0.8571 data_time: 0.0031 memory: 51561 loss: 0.0945 loss_ce: 0.0945 2023/03/03 04:27:02 - mmengine - INFO - Epoch(train) [148][3600/5047] lr: 5.0000e-06 eta: 2:47:10 time: 0.8347 data_time: 0.0029 memory: 53809 loss: 0.1018 loss_ce: 0.1018 2023/03/03 04:28:26 - mmengine - INFO - Epoch(train) [148][3700/5047] lr: 5.0000e-06 eta: 2:45:43 time: 0.8091 data_time: 0.0027 memory: 55562 loss: 0.1065 loss_ce: 0.1065 2023/03/03 04:29:52 - mmengine - INFO - Epoch(train) [148][3800/5047] lr: 5.0000e-06 eta: 2:44:16 time: 0.8677 data_time: 0.0028 memory: 51563 loss: 0.1045 loss_ce: 0.1045 2023/03/03 04:31:18 - mmengine - INFO - Epoch(train) [148][3900/5047] lr: 5.0000e-06 eta: 2:42:50 time: 0.8781 data_time: 0.0028 memory: 45643 loss: 0.0989 loss_ce: 0.0989 2023/03/03 04:32:43 - mmengine - INFO - Epoch(train) [148][4000/5047] lr: 5.0000e-06 eta: 2:41:23 time: 0.8349 data_time: 0.0028 memory: 43947 loss: 0.1107 loss_ce: 0.1107 2023/03/03 04:34:00 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 04:34:08 - mmengine - INFO - Epoch(train) [148][4100/5047] lr: 5.0000e-06 eta: 2:39:56 time: 0.8558 data_time: 0.0038 memory: 41122 loss: 0.0929 loss_ce: 0.0929 2023/03/03 04:35:33 - mmengine - INFO - Epoch(train) [148][4200/5047] lr: 5.0000e-06 eta: 2:38:29 time: 0.8539 data_time: 0.0061 memory: 41122 loss: 0.1041 loss_ce: 0.1041 2023/03/03 04:37:00 - mmengine - INFO - Epoch(train) [148][4300/5047] lr: 5.0000e-06 eta: 2:37:02 time: 0.8749 data_time: 0.0028 memory: 42628 loss: 0.1065 loss_ce: 0.1065 2023/03/03 04:38:25 - mmengine - INFO - Epoch(train) [148][4400/5047] lr: 5.0000e-06 eta: 2:35:35 time: 0.8238 data_time: 0.0029 memory: 44688 loss: 0.0962 loss_ce: 0.0962 2023/03/03 04:39:52 - mmengine - INFO - Epoch(train) [148][4500/5047] lr: 5.0000e-06 eta: 2:34:08 time: 0.8676 data_time: 0.0026 memory: 45230 loss: 0.1060 loss_ce: 0.1060 2023/03/03 04:41:17 - mmengine - INFO - Epoch(train) [148][4600/5047] lr: 5.0000e-06 eta: 2:32:41 time: 0.7970 data_time: 0.0064 memory: 50906 loss: 0.0884 loss_ce: 0.0884 2023/03/03 04:42:44 - mmengine - INFO - Epoch(train) [148][4700/5047] lr: 5.0000e-06 eta: 2:31:14 time: 0.9081 data_time: 0.0032 memory: 44477 loss: 0.1057 loss_ce: 0.1057 2023/03/03 04:44:10 - mmengine - INFO - Epoch(train) [148][4800/5047] lr: 5.0000e-06 eta: 2:29:47 time: 0.8918 data_time: 0.0117 memory: 42336 loss: 0.1262 loss_ce: 0.1262 2023/03/03 04:45:36 - mmengine - INFO - Epoch(train) [148][4900/5047] lr: 5.0000e-06 eta: 2:28:20 time: 0.8547 data_time: 0.0095 memory: 42024 loss: 0.1006 loss_ce: 0.1006 2023/03/03 04:47:01 - mmengine - INFO - Epoch(train) [148][5000/5047] lr: 5.0000e-06 eta: 2:26:53 time: 0.8280 data_time: 0.0060 memory: 40825 loss: 0.1015 loss_ce: 0.1015 2023/03/03 04:47:40 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 04:47:41 - mmengine - INFO - Saving checkpoint at 148 epochs 2023/03/03 04:48:24 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 04:49:13 - mmengine - INFO - Epoch(train) [149][ 100/5047] lr: 5.0000e-06 eta: 2:24:45 time: 0.8829 data_time: 0.0029 memory: 44278 loss: 0.0929 loss_ce: 0.0929 2023/03/03 04:50:39 - mmengine - INFO - Epoch(train) [149][ 200/5047] lr: 5.0000e-06 eta: 2:23:19 time: 0.8374 data_time: 0.0027 memory: 42336 loss: 0.0962 loss_ce: 0.0962 2023/03/03 04:52:05 - mmengine - INFO - Epoch(train) [149][ 300/5047] lr: 5.0000e-06 eta: 2:21:52 time: 0.8470 data_time: 0.0104 memory: 44411 loss: 0.0952 loss_ce: 0.0952 2023/03/03 04:53:28 - mmengine - INFO - Epoch(train) [149][ 400/5047] lr: 5.0000e-06 eta: 2:20:25 time: 0.8260 data_time: 0.0035 memory: 40118 loss: 0.1155 loss_ce: 0.1155 2023/03/03 04:54:53 - mmengine - INFO - Epoch(train) [149][ 500/5047] lr: 5.0000e-06 eta: 2:18:58 time: 0.8946 data_time: 0.0034 memory: 43585 loss: 0.1072 loss_ce: 0.1072 2023/03/03 04:56:20 - mmengine - INFO - Epoch(train) [149][ 600/5047] lr: 5.0000e-06 eta: 2:17:31 time: 0.8716 data_time: 0.0029 memory: 42649 loss: 0.0963 loss_ce: 0.0963 2023/03/03 04:57:45 - mmengine - INFO - Epoch(train) [149][ 700/5047] lr: 5.0000e-06 eta: 2:16:04 time: 0.8100 data_time: 0.0027 memory: 41724 loss: 0.1058 loss_ce: 0.1058 2023/03/03 04:59:12 - mmengine - INFO - Epoch(train) [149][ 800/5047] lr: 5.0000e-06 eta: 2:14:37 time: 0.7933 data_time: 0.0031 memory: 55562 loss: 0.0939 loss_ce: 0.0939 2023/03/03 05:00:37 - mmengine - INFO - Epoch(train) [149][ 900/5047] lr: 5.0000e-06 eta: 2:13:10 time: 0.8981 data_time: 0.0032 memory: 45689 loss: 0.1053 loss_ce: 0.1053 2023/03/03 05:02:03 - mmengine - INFO - Epoch(train) [149][1000/5047] lr: 5.0000e-06 eta: 2:11:43 time: 0.8688 data_time: 0.0030 memory: 40165 loss: 0.1144 loss_ce: 0.1144 2023/03/03 05:02:40 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 05:03:27 - mmengine - INFO - Epoch(train) [149][1100/5047] lr: 5.0000e-06 eta: 2:10:16 time: 0.8371 data_time: 0.0029 memory: 42649 loss: 0.0973 loss_ce: 0.0973 2023/03/03 05:04:52 - mmengine - INFO - Epoch(train) [149][1200/5047] lr: 5.0000e-06 eta: 2:08:49 time: 0.8318 data_time: 0.0031 memory: 52127 loss: 0.0973 loss_ce: 0.0973 2023/03/03 05:06:18 - mmengine - INFO - Epoch(train) [149][1300/5047] lr: 5.0000e-06 eta: 2:07:22 time: 0.8780 data_time: 0.0030 memory: 47074 loss: 0.1107 loss_ce: 0.1107 2023/03/03 05:07:42 - mmengine - INFO - Epoch(train) [149][1400/5047] lr: 5.0000e-06 eta: 2:05:55 time: 0.8446 data_time: 0.0099 memory: 42912 loss: 0.0977 loss_ce: 0.0977 2023/03/03 05:09:08 - mmengine - INFO - Epoch(train) [149][1500/5047] lr: 5.0000e-06 eta: 2:04:28 time: 0.8565 data_time: 0.0043 memory: 45621 loss: 0.1137 loss_ce: 0.1137 2023/03/03 05:10:33 - mmengine - INFO - Epoch(train) [149][1600/5047] lr: 5.0000e-06 eta: 2:03:02 time: 0.8753 data_time: 0.0050 memory: 42818 loss: 0.1106 loss_ce: 0.1106 2023/03/03 05:11:58 - mmengine - INFO - Epoch(train) [149][1700/5047] lr: 5.0000e-06 eta: 2:01:35 time: 0.8873 data_time: 0.0028 memory: 46355 loss: 0.1087 loss_ce: 0.1087 2023/03/03 05:13:24 - mmengine - INFO - Epoch(train) [149][1800/5047] lr: 5.0000e-06 eta: 2:00:08 time: 0.8450 data_time: 0.0046 memory: 43289 loss: 0.0968 loss_ce: 0.0968 2023/03/03 05:14:48 - mmengine - INFO - Epoch(train) [149][1900/5047] lr: 5.0000e-06 eta: 1:58:41 time: 0.7439 data_time: 0.0037 memory: 42159 loss: 0.0977 loss_ce: 0.0977 2023/03/03 05:16:14 - mmengine - INFO - Epoch(train) [149][2000/5047] lr: 5.0000e-06 eta: 1:57:14 time: 0.8248 data_time: 0.0028 memory: 51658 loss: 0.0989 loss_ce: 0.0989 2023/03/03 05:16:52 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 05:17:41 - mmengine - INFO - Epoch(train) [149][2100/5047] lr: 5.0000e-06 eta: 1:55:47 time: 0.8676 data_time: 0.0027 memory: 55562 loss: 0.1002 loss_ce: 0.1002 2023/03/03 05:19:06 - mmengine - INFO - Epoch(train) [149][2200/5047] lr: 5.0000e-06 eta: 1:54:20 time: 0.8569 data_time: 0.0029 memory: 52863 loss: 0.0962 loss_ce: 0.0962 2023/03/03 05:20:31 - mmengine - INFO - Epoch(train) [149][2300/5047] lr: 5.0000e-06 eta: 1:52:53 time: 0.8175 data_time: 0.0061 memory: 43780 loss: 0.1040 loss_ce: 0.1040 2023/03/03 05:21:57 - mmengine - INFO - Epoch(train) [149][2400/5047] lr: 5.0000e-06 eta: 1:51:26 time: 0.9081 data_time: 0.0030 memory: 43613 loss: 0.0935 loss_ce: 0.0935 2023/03/03 05:23:22 - mmengine - INFO - Epoch(train) [149][2500/5047] lr: 5.0000e-06 eta: 1:49:59 time: 0.8646 data_time: 0.0031 memory: 40163 loss: 0.1031 loss_ce: 0.1031 2023/03/03 05:24:47 - mmengine - INFO - Epoch(train) [149][2600/5047] lr: 5.0000e-06 eta: 1:48:32 time: 0.8481 data_time: 0.0028 memory: 42336 loss: 0.0949 loss_ce: 0.0949 2023/03/03 05:26:13 - mmengine - INFO - Epoch(train) [149][2700/5047] lr: 5.0000e-06 eta: 1:47:05 time: 0.8629 data_time: 0.0072 memory: 45770 loss: 0.1050 loss_ce: 0.1050 2023/03/03 05:27:37 - mmengine - INFO - Epoch(train) [149][2800/5047] lr: 5.0000e-06 eta: 1:45:38 time: 0.8551 data_time: 0.0035 memory: 47355 loss: 0.1168 loss_ce: 0.1168 2023/03/03 05:29:03 - mmengine - INFO - Epoch(train) [149][2900/5047] lr: 5.0000e-06 eta: 1:44:11 time: 0.8698 data_time: 0.0034 memory: 50290 loss: 0.1069 loss_ce: 0.1069 2023/03/03 05:30:29 - mmengine - INFO - Epoch(train) [149][3000/5047] lr: 5.0000e-06 eta: 1:42:45 time: 0.8278 data_time: 0.0064 memory: 41976 loss: 0.0976 loss_ce: 0.0976 2023/03/03 05:31:08 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 05:31:55 - mmengine - INFO - Epoch(train) [149][3100/5047] lr: 5.0000e-06 eta: 1:41:18 time: 0.8134 data_time: 0.0034 memory: 47784 loss: 0.1021 loss_ce: 0.1021 2023/03/03 05:33:21 - mmengine - INFO - Epoch(train) [149][3200/5047] lr: 5.0000e-06 eta: 1:39:51 time: 0.8647 data_time: 0.0037 memory: 41757 loss: 0.1008 loss_ce: 0.1008 2023/03/03 05:34:47 - mmengine - INFO - Epoch(train) [149][3300/5047] lr: 5.0000e-06 eta: 1:38:24 time: 0.8581 data_time: 0.0038 memory: 51308 loss: 0.1000 loss_ce: 0.1000 2023/03/03 05:36:12 - mmengine - INFO - Epoch(train) [149][3400/5047] lr: 5.0000e-06 eta: 1:36:57 time: 0.8448 data_time: 0.0029 memory: 41374 loss: 0.1133 loss_ce: 0.1133 2023/03/03 05:37:39 - mmengine - INFO - Epoch(train) [149][3500/5047] lr: 5.0000e-06 eta: 1:35:30 time: 0.8938 data_time: 0.0075 memory: 43947 loss: 0.0940 loss_ce: 0.0940 2023/03/03 05:39:03 - mmengine - INFO - Epoch(train) [149][3600/5047] lr: 5.0000e-06 eta: 1:34:03 time: 0.8388 data_time: 0.0052 memory: 46005 loss: 0.1067 loss_ce: 0.1067 2023/03/03 05:40:27 - mmengine - INFO - Epoch(train) [149][3700/5047] lr: 5.0000e-06 eta: 1:32:36 time: 0.8642 data_time: 0.0031 memory: 53684 loss: 0.0923 loss_ce: 0.0923 2023/03/03 05:41:53 - mmengine - INFO - Epoch(train) [149][3800/5047] lr: 5.0000e-06 eta: 1:31:09 time: 0.8301 data_time: 0.0031 memory: 41223 loss: 0.1162 loss_ce: 0.1162 2023/03/03 05:43:18 - mmengine - INFO - Epoch(train) [149][3900/5047] lr: 5.0000e-06 eta: 1:29:42 time: 0.8408 data_time: 0.0033 memory: 42965 loss: 0.0946 loss_ce: 0.0946 2023/03/03 05:44:43 - mmengine - INFO - Epoch(train) [149][4000/5047] lr: 5.0000e-06 eta: 1:28:15 time: 0.8376 data_time: 0.0052 memory: 41724 loss: 0.0992 loss_ce: 0.0992 2023/03/03 05:45:22 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 05:46:10 - mmengine - INFO - Epoch(train) [149][4100/5047] lr: 5.0000e-06 eta: 1:26:48 time: 0.8610 data_time: 0.0029 memory: 54673 loss: 0.1001 loss_ce: 0.1001 2023/03/03 05:47:36 - mmengine - INFO - Epoch(train) [149][4200/5047] lr: 5.0000e-06 eta: 1:25:22 time: 0.9027 data_time: 0.0039 memory: 46853 loss: 0.1069 loss_ce: 0.1069 2023/03/03 05:49:01 - mmengine - INFO - Epoch(train) [149][4300/5047] lr: 5.0000e-06 eta: 1:23:55 time: 0.8563 data_time: 0.0032 memory: 46713 loss: 0.1095 loss_ce: 0.1095 2023/03/03 05:50:26 - mmengine - INFO - Epoch(train) [149][4400/5047] lr: 5.0000e-06 eta: 1:22:28 time: 0.8193 data_time: 0.0076 memory: 43289 loss: 0.0997 loss_ce: 0.0997 2023/03/03 05:51:50 - mmengine - INFO - Epoch(train) [149][4500/5047] lr: 5.0000e-06 eta: 1:21:01 time: 0.8031 data_time: 0.0031 memory: 44617 loss: 0.1066 loss_ce: 0.1066 2023/03/03 05:53:16 - mmengine - INFO - Epoch(train) [149][4600/5047] lr: 5.0000e-06 eta: 1:19:34 time: 0.8764 data_time: 0.0056 memory: 42336 loss: 0.1063 loss_ce: 0.1063 2023/03/03 05:54:42 - mmengine - INFO - Epoch(train) [149][4700/5047] lr: 5.0000e-06 eta: 1:18:07 time: 0.8564 data_time: 0.0029 memory: 55562 loss: 0.1214 loss_ce: 0.1214 2023/03/03 05:56:09 - mmengine - INFO - Epoch(train) [149][4800/5047] lr: 5.0000e-06 eta: 1:16:40 time: 0.8479 data_time: 0.0039 memory: 42649 loss: 0.0935 loss_ce: 0.0935 2023/03/03 05:57:35 - mmengine - INFO - Epoch(train) [149][4900/5047] lr: 5.0000e-06 eta: 1:15:13 time: 0.8566 data_time: 0.0041 memory: 44952 loss: 0.1154 loss_ce: 0.1154 2023/03/03 05:59:03 - mmengine - INFO - Epoch(train) [149][5000/5047] lr: 5.0000e-06 eta: 1:13:46 time: 0.8893 data_time: 0.0046 memory: 55562 loss: 0.0869 loss_ce: 0.0869 2023/03/03 05:59:39 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 05:59:42 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 05:59:42 - mmengine - INFO - Saving checkpoint at 149 epochs 2023/03/03 06:01:13 - mmengine - INFO - Epoch(train) [150][ 100/5047] lr: 5.0000e-06 eta: 1:11:39 time: 0.8676 data_time: 0.0064 memory: 41186 loss: 0.0996 loss_ce: 0.0996 2023/03/03 06:02:40 - mmengine - INFO - Epoch(train) [150][ 200/5047] lr: 5.0000e-06 eta: 1:10:12 time: 0.8578 data_time: 0.0029 memory: 45200 loss: 0.0994 loss_ce: 0.0994 2023/03/03 06:04:06 - mmengine - INFO - Epoch(train) [150][ 300/5047] lr: 5.0000e-06 eta: 1:08:45 time: 0.8409 data_time: 0.0031 memory: 42649 loss: 0.1180 loss_ce: 0.1180 2023/03/03 06:05:32 - mmengine - INFO - Epoch(train) [150][ 400/5047] lr: 5.0000e-06 eta: 1:07:18 time: 0.8445 data_time: 0.0027 memory: 41515 loss: 0.1105 loss_ce: 0.1105 2023/03/03 06:06:58 - mmengine - INFO - Epoch(train) [150][ 500/5047] lr: 5.0000e-06 eta: 1:05:51 time: 0.8636 data_time: 0.0027 memory: 55562 loss: 0.0997 loss_ce: 0.0997 2023/03/03 06:08:23 - mmengine - INFO - Epoch(train) [150][ 600/5047] lr: 5.0000e-06 eta: 1:04:24 time: 0.8573 data_time: 0.0028 memory: 40825 loss: 0.1123 loss_ce: 0.1123 2023/03/03 06:09:49 - mmengine - INFO - Epoch(train) [150][ 700/5047] lr: 5.0000e-06 eta: 1:02:57 time: 0.8486 data_time: 0.0081 memory: 41792 loss: 0.1096 loss_ce: 0.1096 2023/03/03 06:11:15 - mmengine - INFO - Epoch(train) [150][ 800/5047] lr: 5.0000e-06 eta: 1:01:30 time: 0.8368 data_time: 0.0030 memory: 42024 loss: 0.0959 loss_ce: 0.0959 2023/03/03 06:12:40 - mmengine - INFO - Epoch(train) [150][ 900/5047] lr: 5.0000e-06 eta: 1:00:03 time: 0.8101 data_time: 0.0030 memory: 43195 loss: 0.1077 loss_ce: 0.1077 2023/03/03 06:14:04 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 06:14:07 - mmengine - INFO - Epoch(train) [150][1000/5047] lr: 5.0000e-06 eta: 0:58:36 time: 0.8831 data_time: 0.0038 memory: 42233 loss: 0.0911 loss_ce: 0.0911 2023/03/03 06:15:31 - mmengine - INFO - Epoch(train) [150][1100/5047] lr: 5.0000e-06 eta: 0:57:09 time: 0.8131 data_time: 0.0058 memory: 52964 loss: 0.1057 loss_ce: 0.1057 2023/03/03 06:16:58 - mmengine - INFO - Epoch(train) [150][1200/5047] lr: 5.0000e-06 eta: 0:55:43 time: 0.8157 data_time: 0.0031 memory: 45302 loss: 0.1012 loss_ce: 0.1012 2023/03/03 06:18:24 - mmengine - INFO - Epoch(train) [150][1300/5047] lr: 5.0000e-06 eta: 0:54:16 time: 0.8496 data_time: 0.0033 memory: 41122 loss: 0.0865 loss_ce: 0.0865 2023/03/03 06:19:50 - mmengine - INFO - Epoch(train) [150][1400/5047] lr: 5.0000e-06 eta: 0:52:49 time: 0.8745 data_time: 0.0060 memory: 51224 loss: 0.1051 loss_ce: 0.1051 2023/03/03 06:21:15 - mmengine - INFO - Epoch(train) [150][1500/5047] lr: 5.0000e-06 eta: 0:51:22 time: 0.8446 data_time: 0.0029 memory: 47447 loss: 0.0975 loss_ce: 0.0975 2023/03/03 06:22:43 - mmengine - INFO - Epoch(train) [150][1600/5047] lr: 5.0000e-06 eta: 0:49:55 time: 0.8681 data_time: 0.0031 memory: 43947 loss: 0.1020 loss_ce: 0.1020 2023/03/03 06:24:09 - mmengine - INFO - Epoch(train) [150][1700/5047] lr: 5.0000e-06 eta: 0:48:28 time: 0.8202 data_time: 0.0030 memory: 42649 loss: 0.1033 loss_ce: 0.1033 2023/03/03 06:25:37 - mmengine - INFO - Epoch(train) [150][1800/5047] lr: 5.0000e-06 eta: 0:47:01 time: 0.8066 data_time: 0.0034 memory: 44617 loss: 0.1137 loss_ce: 0.1137 2023/03/03 06:27:05 - mmengine - INFO - Epoch(train) [150][1900/5047] lr: 5.0000e-06 eta: 0:45:34 time: 0.8927 data_time: 0.0038 memory: 53249 loss: 0.1035 loss_ce: 0.1035 2023/03/03 06:28:29 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 06:28:31 - mmengine - INFO - Epoch(train) [150][2000/5047] lr: 5.0000e-06 eta: 0:44:07 time: 0.8425 data_time: 0.0027 memory: 43613 loss: 0.1002 loss_ce: 0.1002 2023/03/03 06:29:58 - mmengine - INFO - Epoch(train) [150][2100/5047] lr: 5.0000e-06 eta: 0:42:40 time: 0.8616 data_time: 0.0036 memory: 45643 loss: 0.1111 loss_ce: 0.1111 2023/03/03 06:31:22 - mmengine - INFO - Epoch(train) [150][2200/5047] lr: 5.0000e-06 eta: 0:41:14 time: 0.8645 data_time: 0.0037 memory: 41419 loss: 0.1017 loss_ce: 0.1017 2023/03/03 06:32:47 - mmengine - INFO - Epoch(train) [150][2300/5047] lr: 5.0000e-06 eta: 0:39:47 time: 0.8740 data_time: 0.0053 memory: 41724 loss: 0.1092 loss_ce: 0.1092 2023/03/03 06:34:12 - mmengine - INFO - Epoch(train) [150][2400/5047] lr: 5.0000e-06 eta: 0:38:20 time: 0.8206 data_time: 0.0031 memory: 44972 loss: 0.0934 loss_ce: 0.0934 2023/03/03 06:35:37 - mmengine - INFO - Epoch(train) [150][2500/5047] lr: 5.0000e-06 eta: 0:36:53 time: 0.8353 data_time: 0.0047 memory: 43289 loss: 0.0993 loss_ce: 0.0993 2023/03/03 06:37:02 - mmengine - INFO - Epoch(train) [150][2600/5047] lr: 5.0000e-06 eta: 0:35:26 time: 0.8291 data_time: 0.0027 memory: 42756 loss: 0.1051 loss_ce: 0.1051 2023/03/03 06:38:28 - mmengine - INFO - Epoch(train) [150][2700/5047] lr: 5.0000e-06 eta: 0:33:59 time: 0.8537 data_time: 0.0030 memory: 55562 loss: 0.1147 loss_ce: 0.1147 2023/03/03 06:39:52 - mmengine - INFO - Epoch(train) [150][2800/5047] lr: 5.0000e-06 eta: 0:32:32 time: 0.8383 data_time: 0.0040 memory: 43613 loss: 0.1007 loss_ce: 0.1007 2023/03/03 06:41:18 - mmengine - INFO - Epoch(train) [150][2900/5047] lr: 5.0000e-06 eta: 0:31:05 time: 0.8299 data_time: 0.0040 memory: 41953 loss: 0.0944 loss_ce: 0.0944 2023/03/03 06:42:42 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 06:42:44 - mmengine - INFO - Epoch(train) [150][3000/5047] lr: 5.0000e-06 eta: 0:29:38 time: 0.8737 data_time: 0.0033 memory: 50505 loss: 0.0898 loss_ce: 0.0898 2023/03/03 06:44:09 - mmengine - INFO - Epoch(train) [150][3100/5047] lr: 5.0000e-06 eta: 0:28:11 time: 0.8457 data_time: 0.0032 memory: 50505 loss: 0.0967 loss_ce: 0.0967 2023/03/03 06:45:35 - mmengine - INFO - Epoch(train) [150][3200/5047] lr: 5.0000e-06 eta: 0:26:44 time: 0.8160 data_time: 0.0033 memory: 44278 loss: 0.0865 loss_ce: 0.0865 2023/03/03 06:47:00 - mmengine - INFO - Epoch(train) [150][3300/5047] lr: 5.0000e-06 eta: 0:25:18 time: 0.8641 data_time: 0.0036 memory: 42965 loss: 0.0905 loss_ce: 0.0905 2023/03/03 06:48:26 - mmengine - INFO - Epoch(train) [150][3400/5047] lr: 5.0000e-06 eta: 0:23:51 time: 0.7901 data_time: 0.0029 memory: 40797 loss: 0.0998 loss_ce: 0.0998 2023/03/03 06:49:53 - mmengine - INFO - Epoch(train) [150][3500/5047] lr: 5.0000e-06 eta: 0:22:24 time: 0.8515 data_time: 0.0027 memory: 48565 loss: 0.1041 loss_ce: 0.1041 2023/03/03 06:51:18 - mmengine - INFO - Epoch(train) [150][3600/5047] lr: 5.0000e-06 eta: 0:20:57 time: 0.7809 data_time: 0.0030 memory: 55562 loss: 0.1117 loss_ce: 0.1117 2023/03/03 06:52:45 - mmengine - INFO - Epoch(train) [150][3700/5047] lr: 5.0000e-06 eta: 0:19:30 time: 0.8678 data_time: 0.0059 memory: 41856 loss: 0.0820 loss_ce: 0.0820 2023/03/03 06:54:09 - mmengine - INFO - Epoch(train) [150][3800/5047] lr: 5.0000e-06 eta: 0:18:03 time: 0.8426 data_time: 0.0029 memory: 40924 loss: 0.0942 loss_ce: 0.0942 2023/03/03 06:55:34 - mmengine - INFO - Epoch(train) [150][3900/5047] lr: 5.0000e-06 eta: 0:16:36 time: 0.8663 data_time: 0.0098 memory: 43404 loss: 0.1034 loss_ce: 0.1034 2023/03/03 06:56:57 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 06:57:00 - mmengine - INFO - Epoch(train) [150][4000/5047] lr: 5.0000e-06 eta: 0:15:09 time: 0.8513 data_time: 0.0029 memory: 41419 loss: 0.1053 loss_ce: 0.1053 2023/03/03 06:58:26 - mmengine - INFO - Epoch(train) [150][4100/5047] lr: 5.0000e-06 eta: 0:13:42 time: 0.8189 data_time: 0.0029 memory: 44956 loss: 0.1074 loss_ce: 0.1074 2023/03/03 06:59:53 - mmengine - INFO - Epoch(train) [150][4200/5047] lr: 5.0000e-06 eta: 0:12:16 time: 0.9141 data_time: 0.0029 memory: 44617 loss: 0.1015 loss_ce: 0.1015 2023/03/03 07:01:18 - mmengine - INFO - Epoch(train) [150][4300/5047] lr: 5.0000e-06 eta: 0:10:49 time: 0.8258 data_time: 0.0038 memory: 47074 loss: 0.1159 loss_ce: 0.1159 2023/03/03 07:02:43 - mmengine - INFO - Epoch(train) [150][4400/5047] lr: 5.0000e-06 eta: 0:09:22 time: 0.7887 data_time: 0.0029 memory: 44617 loss: 0.1122 loss_ce: 0.1122 2023/03/03 07:04:08 - mmengine - INFO - Epoch(train) [150][4500/5047] lr: 5.0000e-06 eta: 0:07:55 time: 0.8953 data_time: 0.0029 memory: 43289 loss: 0.1174 loss_ce: 0.1174 2023/03/03 07:05:37 - mmengine - INFO - Epoch(train) [150][4600/5047] lr: 5.0000e-06 eta: 0:06:28 time: 0.8683 data_time: 0.0042 memory: 46037 loss: 0.1020 loss_ce: 0.1020 2023/03/03 07:07:02 - mmengine - INFO - Epoch(train) [150][4700/5047] lr: 5.0000e-06 eta: 0:05:01 time: 0.8265 data_time: 0.0057 memory: 41721 loss: 0.0984 loss_ce: 0.0984 2023/03/03 07:08:29 - mmengine - INFO - Epoch(train) [150][4800/5047] lr: 5.0000e-06 eta: 0:03:34 time: 0.9168 data_time: 0.0094 memory: 55562 loss: 0.0981 loss_ce: 0.0981 2023/03/03 07:09:52 - mmengine - INFO - Epoch(train) [150][4900/5047] lr: 5.0000e-06 eta: 0:02:07 time: 0.8552 data_time: 0.0032 memory: 42579 loss: 0.1075 loss_ce: 0.1075 2023/03/03 07:11:14 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 07:11:17 - mmengine - INFO - Epoch(train) [150][5000/5047] lr: 5.0000e-06 eta: 0:00:40 time: 0.8418 data_time: 0.0029 memory: 49537 loss: 0.1073 loss_ce: 0.1073 2023/03/03 07:11:56 - mmengine - INFO - Exp name: spts_resnet50_150e_pretrain-spts_20230223_194550 2023/03/03 07:11:56 - mmengine - INFO - Saving checkpoint at 150 epochs