2023/06/04 22:53:21 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.10.9 (main, Mar 8 2023, 10:47:38) [GCC 11.2.0] CUDA available: True numpy_random_seed: 708732946 GPU 0,1: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/petrelfs/share/cuda-11.6 NVCC: Cuda compilation tools, release 11.6, V11.6.124 GCC: gcc (GCC) 7.5.0 PyTorch: 1.13.1 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 11.6 - 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 2.6.1 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, 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 -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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=1.13.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.14.1 OpenCV: 4.7.0 MMEngine: 0.7.3 Runtime environment: cudnn_benchmark: True mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None deterministic: False Distributed launcher: slurm Distributed training: True GPU number: 2 ------------------------------------------------------------ 2023/06/04 22:53:26 - mmengine - INFO - Config: optim_wrapper = dict( optimizer=dict( type='SGD', lr=0.0001, momentum=0.9, weight_decay=0.0001, _scope_='mmpretrain'), clip_grad=None) param_scheduler = [ dict(type='CosineAnnealingLR', eta_min=1e-05, by_epoch=False, begin=0) ] train_cfg = dict(by_epoch=True, max_epochs=10, val_interval=1) val_cfg = dict() test_cfg = dict() auto_scale_lr = dict(base_batch_size=512) model = dict( type='ImageClassifier', backbone=dict( frozen_stages=2, type='ResNet', depth=50, num_stages=4, out_indices=(3, ), style='pytorch', init_cfg=dict( type='Pretrained', checkpoint= 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth', prefix='backbone')), neck=dict(type='GlobalAveragePooling'), head=dict( type='LinearClsHead', num_classes=2, in_channels=2048, loss=dict(type='CrossEntropyLoss', loss_weight=1.0), topk=1)) dataset_type = 'CustomDataset' data_preprocessor = dict( num_classes=2, mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) bgr_mean = [103.53, 116.28, 123.675] bgr_std = [57.375, 57.12, 58.395] train_pipeline = [ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='JPEG', compress_val=65, prob=0.1), dict(type='GaussianBlur', radius=1.5, prob=0.1), dict(type='PackInputs') ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='ResizeEdge', scale=256, edge='short', backend='pillow', interpolation='bicubic'), dict(type='CenterCrop', crop_size=224), dict(type='PackInputs') ] train_dataloader = dict( pin_memory=True, persistent_workers=True, collate_fn=dict(type='default_collate'), batch_size=256, num_workers=10, dataset=dict( type='ConcatDataset', datasets=[ dict( type='CustomDataset', data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/stablediffusionV2-1-dpmsolver-25-1m.tsv', pipeline=[ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='JPEG', compress_val=65, prob=0.1), dict(type='GaussianBlur', radius=1.5, prob=0.1), dict(type='PackInputs') ]), dict( type='CustomDataset', data_root='', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/cc1m.csv', pipeline=[ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='JPEG', compress_val=65, prob=0.1), dict(type='GaussianBlur', radius=1.5, prob=0.1), dict(type='PackInputs') ]) ]), sampler=dict(type='DefaultSampler', shuffle=True)) val_dataloader = dict( pin_memory=True, persistent_workers=True, collate_fn=dict(type='default_collate'), batch_size=256, num_workers=10, dataset=dict( type='ConcatDataset', datasets=[ dict( type='CustomDataset', data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV2-1-dpmsolver-25-1w.tsv', pipeline=[ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='JPEG', compress_val=65, prob=0.1), dict(type='GaussianBlur', radius=1.5, prob=0.1), dict(type='PackInputs') ]), dict( type='CustomDataset', data_root='', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/cc1w.csv', pipeline=[ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='JPEG', compress_val=65, prob=0.1), dict(type='GaussianBlur', radius=1.5, prob=0.1), dict(type='PackInputs') ]) ]), sampler=dict(type='DefaultSampler', shuffle=False)) val_evaluator = dict(type='Accuracy', topk=1) test_dataloader = dict( pin_memory=True, persistent_workers=True, collate_fn=dict(type='default_collate'), batch_size=256, num_workers=10, dataset=dict( type='ConcatDataset', datasets=[ dict( type='CustomDataset', data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV2-1-dpmsolver-25-1w.tsv', pipeline=[ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='JPEG', compress_val=65, prob=0.1), dict(type='GaussianBlur', radius=1.5, prob=0.1), dict(type='PackInputs') ]), dict( type='CustomDataset', data_root='', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/cc1w.csv', pipeline=[ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='JPEG', compress_val=65, prob=0.1), dict(type='GaussianBlur', radius=1.5, prob=0.1), dict(type='PackInputs') ]) ]), sampler=dict(type='DefaultSampler', shuffle=False)) test_evaluator = dict(type='Accuracy', topk=1) custom_hooks = [dict(type='EMAHook', momentum=0.0001, priority='ABOVE_NORMAL')] default_scope = 'mmpretrain' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=100), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', interval=1), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='VisualizationHook', enable=True)) env_cfg = dict( cudnn_benchmark=True, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='UniversalVisualizer', vis_backends=[ dict(type='LocalVisBackend'), dict(type='TensorboardVisBackend') ]) log_level = 'INFO' load_from = None resume = False randomness = dict(seed=None, deterministic=False) launcher = 'slurm' work_dir = 'workdir/resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1' 2023/06/04 22:53:39 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (ABOVE_NORMAL) EMAHook (BELOW_NORMAL) LoggerHook -------------------- after_load_checkpoint: (ABOVE_NORMAL) EMAHook -------------------- before_train: (VERY_HIGH ) RuntimeInfoHook (ABOVE_NORMAL) EMAHook (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 (ABOVE_NORMAL) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val_epoch: (ABOVE_NORMAL) EMAHook (NORMAL ) IterTimerHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (NORMAL ) VisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (ABOVE_NORMAL) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_save_checkpoint: (ABOVE_NORMAL) EMAHook -------------------- after_train: (VERY_LOW ) CheckpointHook -------------------- before_test_epoch: (ABOVE_NORMAL) EMAHook (NORMAL ) IterTimerHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (NORMAL ) VisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (ABOVE_NORMAL) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- 2023/06/04 22:54:01 - mmengine - INFO - load backbone in model from: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth Name of parameter - Initialization information backbone.conv1.weight - torch.Size([64, 3, 7, 7]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.downsample.1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.downsample.1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn1.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn1.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn2.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn2.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn3.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn3.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.downsample.1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.downsample.1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn1.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn1.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn2.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn2.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn3.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn3.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn1.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn1.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn2.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn2.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn3.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn3.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.3.bn1.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.3.bn1.bias - torch.Size([128]): PretrainedInit: load from 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https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.3.bn3.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.bn1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.bn1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.bn2.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.bn2.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.downsample.1.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.downsample.1.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.1.bn1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.1.bn1.bias - torch.Size([256]): PretrainedInit: 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https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.1.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.bn1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.bn1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.bn2.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.bn2.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.bn1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.bn1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.bn2.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.bn2.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.bn1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.bn1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.bn2.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.bn2.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.bn1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.bn1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.bn2.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.bn2.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.bn1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.bn1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.bn2.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.bn2.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.bn3.weight - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.bn3.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.downsample.1.weight - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.downsample.1.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn2.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn2.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn3.weight - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn3.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn2.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn2.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn3.weight - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn3.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth head.fc.weight - torch.Size([2, 2048]): NormalInit: mean=0, std=0.01, bias=0 head.fc.bias - torch.Size([2]): NormalInit: mean=0, std=0.01, bias=0 2023/06/04 22:54:01 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2023/06/04 22:54:01 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2023/06/04 22:54:01 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/luzeyu/workspace/fakebench/mmpretrain/workdir/resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1. 2023/06/04 22:55:09 - mmengine - INFO - Epoch(train) [1][ 100/3907] lr: 9.9999e-05 eta: 7:20:08 time: 0.5942 data_time: 0.0433 memory: 9436 loss: 0.6144 2023/06/04 22:56:11 - mmengine - INFO - Epoch(train) [1][ 200/3907] lr: 9.9994e-05 eta: 7:01:05 time: 0.6243 data_time: 0.0018 memory: 6319 loss: 0.5405 2023/06/04 22:57:14 - mmengine - INFO - Epoch(train) [1][ 300/3907] lr: 9.9987e-05 eta: 6:55:18 time: 0.6351 data_time: 0.0014 memory: 6319 loss: 0.4861 2023/06/04 22:58:16 - mmengine - INFO - Epoch(train) [1][ 400/3907] lr: 9.9977e-05 eta: 6:51:10 time: 0.5895 data_time: 0.0015 memory: 6319 loss: 0.4412 2023/06/04 22:59:20 - mmengine - INFO - Epoch(train) [1][ 500/3907] lr: 9.9964e-05 eta: 6:50:10 time: 0.5980 data_time: 0.0015 memory: 6319 loss: 0.3898 2023/06/04 23:00:22 - mmengine - INFO - Epoch(train) [1][ 600/3907] lr: 9.9948e-05 eta: 6:47:16 time: 0.6539 data_time: 0.0011 memory: 6319 loss: 0.3696 2023/06/04 23:01:25 - mmengine - INFO - Epoch(train) [1][ 700/3907] lr: 9.9929e-05 eta: 6:45:31 time: 0.6491 data_time: 0.0011 memory: 6319 loss: 0.3306 2023/06/04 23:02:27 - mmengine - INFO - Epoch(train) [1][ 800/3907] lr: 9.9907e-05 eta: 6:43:21 time: 0.5923 data_time: 0.0016 memory: 6319 loss: 0.3094 2023/06/04 23:03:30 - mmengine - INFO - Epoch(train) [1][ 900/3907] lr: 9.9882e-05 eta: 6:42:21 time: 0.6268 data_time: 0.0008 memory: 6319 loss: 0.2967 2023/06/04 23:04:32 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/04 23:04:32 - mmengine - INFO - Epoch(train) [1][1000/3907] lr: 9.9855e-05 eta: 6:40:26 time: 0.6295 data_time: 0.0011 memory: 6319 loss: 0.2710 2023/06/04 23:05:35 - mmengine - INFO - Epoch(train) [1][1100/3907] lr: 9.9824e-05 eta: 6:39:12 time: 0.6169 data_time: 0.0008 memory: 6319 loss: 0.2720 2023/06/04 23:06:37 - mmengine - INFO - Epoch(train) [1][1200/3907] lr: 9.9791e-05 eta: 6:37:38 time: 0.6338 data_time: 0.0009 memory: 6319 loss: 0.2541 2023/06/04 23:07:42 - mmengine - INFO - Epoch(train) [1][1300/3907] lr: 9.9755e-05 eta: 6:37:27 time: 0.7134 data_time: 0.0011 memory: 6319 loss: 0.2377 2023/06/04 23:08:42 - mmengine - INFO - Epoch(train) [1][1400/3907] lr: 9.9716e-05 eta: 6:35:17 time: 0.5884 data_time: 0.0012 memory: 6319 loss: 0.2348 2023/06/04 23:09:46 - mmengine - INFO - Epoch(train) [1][1500/3907] lr: 9.9674e-05 eta: 6:34:23 time: 0.6164 data_time: 0.0010 memory: 6319 loss: 0.2446 2023/06/04 23:10:48 - mmengine - INFO - Epoch(train) [1][1600/3907] lr: 9.9629e-05 eta: 6:33:00 time: 0.6497 data_time: 0.0011 memory: 6319 loss: 0.2131 2023/06/04 23:11:51 - mmengine - INFO - Epoch(train) [1][1700/3907] lr: 9.9581e-05 eta: 6:31:54 time: 0.6395 data_time: 0.0015 memory: 6319 loss: 0.2084 2023/06/04 23:12:54 - mmengine - INFO - Epoch(train) [1][1800/3907] lr: 9.9530e-05 eta: 6:30:53 time: 0.6245 data_time: 0.0013 memory: 6319 loss: 0.2089 2023/06/04 23:13:56 - mmengine - INFO - Epoch(train) [1][1900/3907] lr: 9.9476e-05 eta: 6:29:39 time: 0.6109 data_time: 0.0010 memory: 6319 loss: 0.2113 2023/06/04 23:14:58 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/04 23:14:58 - mmengine - INFO - Epoch(train) [1][2000/3907] lr: 9.9420e-05 eta: 6:28:14 time: 0.6053 data_time: 0.0011 memory: 6319 loss: 0.2004 2023/06/04 23:15:59 - mmengine - INFO - Epoch(train) [1][2100/3907] lr: 9.9361e-05 eta: 6:26:48 time: 0.6134 data_time: 0.0008 memory: 6319 loss: 0.1954 2023/06/04 23:17:02 - mmengine - INFO - Epoch(train) [1][2200/3907] lr: 9.9298e-05 eta: 6:25:47 time: 0.6748 data_time: 0.0012 memory: 6319 loss: 0.1850 2023/06/04 23:18:04 - mmengine - INFO - Epoch(train) [1][2300/3907] lr: 9.9233e-05 eta: 6:24:22 time: 0.6820 data_time: 0.0011 memory: 6319 loss: 0.1764 2023/06/04 23:19:10 - mmengine - INFO - Epoch(train) [1][2400/3907] lr: 9.9165e-05 eta: 6:24:11 time: 0.6188 data_time: 0.0010 memory: 6319 loss: 0.1709 2023/06/04 23:20:10 - mmengine - INFO - Epoch(train) [1][2500/3907] lr: 9.9095e-05 eta: 6:22:34 time: 0.6502 data_time: 0.0013 memory: 6319 loss: 0.1771 2023/06/04 23:21:14 - mmengine - INFO - Epoch(train) [1][2600/3907] lr: 9.9021e-05 eta: 6:21:42 time: 0.6513 data_time: 0.0010 memory: 6319 loss: 0.1946 2023/06/04 23:22:19 - mmengine - INFO - Epoch(train) [1][2700/3907] lr: 9.8944e-05 eta: 6:21:11 time: 0.6358 data_time: 0.0013 memory: 6319 loss: 0.1769 2023/06/04 23:23:23 - mmengine - INFO - Epoch(train) [1][2800/3907] lr: 9.8865e-05 eta: 6:20:28 time: 0.6681 data_time: 0.0010 memory: 6319 loss: 0.1762 2023/06/04 23:24:26 - mmengine - INFO - Epoch(train) [1][2900/3907] lr: 9.8783e-05 eta: 6:19:19 time: 0.6001 data_time: 0.0009 memory: 6319 loss: 0.1681 2023/06/04 23:25:28 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/04 23:25:28 - mmengine - INFO - Epoch(train) [1][3000/3907] lr: 9.8698e-05 eta: 6:18:03 time: 0.6313 data_time: 0.0015 memory: 6319 loss: 0.1718 2023/06/04 23:26:29 - mmengine - INFO - Epoch(train) [1][3100/3907] lr: 9.8610e-05 eta: 6:16:48 time: 0.6330 data_time: 0.0008 memory: 6319 loss: 0.1642 2023/06/04 23:27:32 - mmengine - INFO - Epoch(train) [1][3200/3907] lr: 9.8519e-05 eta: 6:15:37 time: 0.6067 data_time: 0.0012 memory: 6319 loss: 0.1780 2023/06/04 23:28:32 - mmengine - INFO - Epoch(train) [1][3300/3907] lr: 9.8426e-05 eta: 6:14:09 time: 0.6164 data_time: 0.0011 memory: 6319 loss: 0.1483 2023/06/04 23:29:34 - mmengine - INFO - Epoch(train) [1][3400/3907] lr: 9.8330e-05 eta: 6:12:54 time: 0.6566 data_time: 0.0012 memory: 6319 loss: 0.1571 2023/06/04 23:30:37 - mmengine - INFO - Epoch(train) [1][3500/3907] lr: 9.8231e-05 eta: 6:11:51 time: 0.5866 data_time: 0.0014 memory: 6319 loss: 0.1466 2023/06/04 23:31:37 - mmengine - INFO - Epoch(train) [1][3600/3907] lr: 9.8129e-05 eta: 6:10:25 time: 0.6107 data_time: 0.0011 memory: 6319 loss: 0.1623 2023/06/04 23:32:39 - mmengine - INFO - Epoch(train) [1][3700/3907] lr: 9.8024e-05 eta: 6:09:17 time: 0.6640 data_time: 0.0010 memory: 6319 loss: 0.1488 2023/06/04 23:33:40 - mmengine - INFO - Epoch(train) [1][3800/3907] lr: 9.7917e-05 eta: 6:07:56 time: 0.6098 data_time: 0.0012 memory: 6319 loss: 0.1414 2023/06/04 23:34:43 - mmengine - INFO - Epoch(train) [1][3900/3907] lr: 9.7806e-05 eta: 6:06:57 time: 0.6231 data_time: 0.0009 memory: 6319 loss: 0.1574 2023/06/04 23:34:48 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/04 23:34:48 - mmengine - INFO - Saving checkpoint at 1 epochs 2023/06/04 23:35:42 - mmengine - INFO - Epoch(val) [1][57/57] accuracy/top1: 80.4217 data_time: 0.6049 time: 0.6993 2023/06/04 23:36:46 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/04 23:36:47 - mmengine - INFO - Epoch(train) [2][ 100/3907] lr: 9.7685e-05 eta: 6:06:21 time: 0.6566 data_time: 0.0013 memory: 6319 loss: 0.1497 2023/06/04 23:37:49 - mmengine - INFO - Epoch(train) [2][ 200/3907] lr: 9.7570e-05 eta: 6:05:14 time: 0.6339 data_time: 0.0014 memory: 6319 loss: 0.1545 2023/06/04 23:38:51 - mmengine - INFO - Epoch(train) [2][ 300/3907] lr: 9.7451e-05 eta: 6:04:06 time: 0.6071 data_time: 0.0011 memory: 6319 loss: 0.1540 2023/06/04 23:39:55 - mmengine - INFO - Epoch(train) [2][ 400/3907] lr: 9.7329e-05 eta: 6:03:08 time: 0.6508 data_time: 0.0012 memory: 6319 loss: 0.1377 2023/06/04 23:40:56 - mmengine - INFO - Epoch(train) [2][ 500/3907] lr: 9.7205e-05 eta: 6:01:55 time: 0.6095 data_time: 0.0011 memory: 6319 loss: 0.1483 2023/06/04 23:41:57 - mmengine - INFO - Epoch(train) [2][ 600/3907] lr: 9.7078e-05 eta: 6:00:42 time: 0.6026 data_time: 0.0011 memory: 6319 loss: 0.1317 2023/06/04 23:42:59 - mmengine - INFO - Epoch(train) [2][ 700/3907] lr: 9.6949e-05 eta: 5:59:31 time: 0.6099 data_time: 0.0010 memory: 6319 loss: 0.1532 2023/06/04 23:44:01 - mmengine - INFO - Epoch(train) [2][ 800/3907] lr: 9.6816e-05 eta: 5:58:25 time: 0.6574 data_time: 0.0013 memory: 6319 loss: 0.1308 2023/06/04 23:45:02 - mmengine - INFO - Epoch(train) [2][ 900/3907] lr: 9.6681e-05 eta: 5:57:09 time: 0.5588 data_time: 0.0012 memory: 6319 loss: 0.1337 2023/06/04 23:46:02 - mmengine - INFO - Epoch(train) [2][1000/3907] lr: 9.6544e-05 eta: 5:55:51 time: 0.6176 data_time: 0.0013 memory: 6319 loss: 0.1303 2023/06/04 23:47:01 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/04 23:47:04 - mmengine - INFO - Epoch(train) [2][1100/3907] lr: 9.6403e-05 eta: 5:54:42 time: 0.6701 data_time: 0.0013 memory: 6319 loss: 0.1229 2023/06/04 23:48:04 - mmengine - INFO - Epoch(train) [2][1200/3907] lr: 9.6260e-05 eta: 5:53:23 time: 0.6172 data_time: 0.0011 memory: 6319 loss: 0.1405 2023/06/04 23:49:06 - mmengine - INFO - Epoch(train) [2][1300/3907] lr: 9.6114e-05 eta: 5:52:22 time: 0.6054 data_time: 0.0016 memory: 6319 loss: 0.1391 2023/06/04 23:50:08 - mmengine - INFO - Epoch(train) [2][1400/3907] lr: 9.5966e-05 eta: 5:51:14 time: 0.6270 data_time: 0.0010 memory: 6319 loss: 0.1204 2023/06/04 23:51:08 - mmengine - INFO - Epoch(train) [2][1500/3907] lr: 9.5815e-05 eta: 5:49:59 time: 0.6327 data_time: 0.0011 memory: 6319 loss: 0.1474 2023/06/04 23:52:11 - mmengine - INFO - Epoch(train) [2][1600/3907] lr: 9.5661e-05 eta: 5:49:02 time: 0.6522 data_time: 0.0015 memory: 6319 loss: 0.1289 2023/06/04 23:53:15 - mmengine - INFO - Epoch(train) [2][1700/3907] lr: 9.5505e-05 eta: 5:48:04 time: 0.6179 data_time: 0.0013 memory: 6319 loss: 0.1204 2023/06/04 23:54:17 - mmengine - INFO - Epoch(train) [2][1800/3907] lr: 9.5346e-05 eta: 5:47:02 time: 0.5668 data_time: 0.0013 memory: 6319 loss: 0.1174 2023/06/04 23:55:20 - mmengine - INFO - Epoch(train) [2][1900/3907] lr: 9.5184e-05 eta: 5:46:04 time: 0.6009 data_time: 0.0011 memory: 6319 loss: 0.1267 2023/06/04 23:56:23 - mmengine - INFO - Epoch(train) [2][2000/3907] lr: 9.5020e-05 eta: 5:45:01 time: 0.6914 data_time: 0.0011 memory: 6319 loss: 0.1327 2023/06/04 23:57:23 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/04 23:57:24 - mmengine - INFO - Epoch(train) [2][2100/3907] lr: 9.4854e-05 eta: 5:43:55 time: 0.5970 data_time: 0.0009 memory: 6319 loss: 0.1313 2023/06/04 23:58:26 - mmengine - INFO - Epoch(train) [2][2200/3907] lr: 9.4684e-05 eta: 5:42:51 time: 0.6438 data_time: 0.0010 memory: 6319 loss: 0.1189 2023/06/04 23:59:28 - mmengine - INFO - Epoch(train) [2][2300/3907] lr: 9.4512e-05 eta: 5:41:46 time: 0.6048 data_time: 0.0012 memory: 6319 loss: 0.1218 2023/06/05 00:00:30 - mmengine - INFO - Epoch(train) [2][2400/3907] lr: 9.4338e-05 eta: 5:40:38 time: 0.6326 data_time: 0.0009 memory: 6319 loss: 0.1213 2023/06/05 00:01:30 - mmengine - INFO - Epoch(train) [2][2500/3907] lr: 9.4161e-05 eta: 5:39:27 time: 0.6049 data_time: 0.0009 memory: 6319 loss: 0.1269 2023/06/05 00:02:33 - mmengine - INFO - Epoch(train) [2][2600/3907] lr: 9.3981e-05 eta: 5:38:28 time: 0.6421 data_time: 0.0010 memory: 6319 loss: 0.1067 2023/06/05 00:03:36 - mmengine - INFO - Epoch(train) [2][2700/3907] lr: 9.3799e-05 eta: 5:37:28 time: 0.6008 data_time: 0.0010 memory: 6319 loss: 0.1204 2023/06/05 00:04:37 - mmengine - INFO - Epoch(train) [2][2800/3907] lr: 9.3615e-05 eta: 5:36:19 time: 0.5981 data_time: 0.0241 memory: 6319 loss: 0.1317 2023/06/05 00:05:37 - mmengine - INFO - Epoch(train) [2][2900/3907] lr: 9.3428e-05 eta: 5:35:06 time: 0.5673 data_time: 0.0010 memory: 6319 loss: 0.1162 2023/06/05 00:06:42 - mmengine - INFO - Epoch(train) [2][3000/3907] lr: 9.3238e-05 eta: 5:34:12 time: 0.5922 data_time: 0.0013 memory: 6319 loss: 0.1247 2023/06/05 00:07:42 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 00:07:43 - mmengine - INFO - Epoch(train) [2][3100/3907] lr: 9.3046e-05 eta: 5:33:06 time: 0.6147 data_time: 0.0011 memory: 6319 loss: 0.0999 2023/06/05 00:08:44 - mmengine - INFO - Epoch(train) [2][3200/3907] lr: 9.2852e-05 eta: 5:31:58 time: 0.6372 data_time: 0.0016 memory: 6319 loss: 0.1218 2023/06/05 00:09:48 - mmengine - INFO - Epoch(train) [2][3300/3907] lr: 9.2655e-05 eta: 5:31:01 time: 0.6388 data_time: 0.0013 memory: 6319 loss: 0.1103 2023/06/05 00:10:51 - mmengine - INFO - Epoch(train) [2][3400/3907] lr: 9.2456e-05 eta: 5:30:02 time: 0.6173 data_time: 0.0012 memory: 6319 loss: 0.1180 2023/06/05 00:11:52 - mmengine - INFO - Epoch(train) [2][3500/3907] lr: 9.2254e-05 eta: 5:28:56 time: 0.6374 data_time: 0.0011 memory: 6319 loss: 0.1124 2023/06/05 00:12:55 - mmengine - INFO - Epoch(train) [2][3600/3907] lr: 9.2050e-05 eta: 5:27:54 time: 0.6131 data_time: 0.0011 memory: 6319 loss: 0.1274 2023/06/05 00:13:58 - mmengine - INFO - Epoch(train) [2][3700/3907] lr: 9.1843e-05 eta: 5:26:54 time: 0.5851 data_time: 0.0014 memory: 6319 loss: 0.1237 2023/06/05 00:15:00 - mmengine - INFO - Epoch(train) [2][3800/3907] lr: 9.1634e-05 eta: 5:25:53 time: 0.5574 data_time: 0.0009 memory: 6319 loss: 0.1099 2023/06/05 00:15:59 - mmengine - INFO - Epoch(train) [2][3900/3907] lr: 9.1423e-05 eta: 5:24:34 time: 0.5945 data_time: 0.0009 memory: 6319 loss: 0.1304 2023/06/05 00:16:04 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 00:16:04 - mmengine - INFO - Saving checkpoint at 2 epochs 2023/06/05 00:16:53 - mmengine - INFO - Epoch(val) [2][57/57] accuracy/top1: 78.6472 data_time: 0.5207 time: 0.6123 2023/06/05 00:17:57 - mmengine - INFO - Epoch(train) [3][ 100/3907] lr: 9.1194e-05 eta: 5:23:40 time: 0.6440 data_time: 0.1597 memory: 6319 loss: 0.1029 2023/06/05 00:18:53 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 00:18:59 - mmengine - INFO - Epoch(train) [3][ 200/3907] lr: 9.0978e-05 eta: 5:22:39 time: 0.5980 data_time: 0.0016 memory: 6319 loss: 0.1145 2023/06/05 00:20:01 - mmengine - INFO - Epoch(train) [3][ 300/3907] lr: 9.0759e-05 eta: 5:21:34 time: 0.6024 data_time: 0.0014 memory: 6319 loss: 0.1096 2023/06/05 00:21:02 - mmengine - INFO - Epoch(train) [3][ 400/3907] lr: 9.0539e-05 eta: 5:20:26 time: 0.5998 data_time: 0.0015 memory: 6319 loss: 0.1104 2023/06/05 00:22:04 - mmengine - INFO - Epoch(train) [3][ 500/3907] lr: 9.0315e-05 eta: 5:19:21 time: 0.6282 data_time: 0.0012 memory: 6319 loss: 0.0986 2023/06/05 00:23:05 - mmengine - INFO - Epoch(train) [3][ 600/3907] lr: 9.0090e-05 eta: 5:18:18 time: 0.6023 data_time: 0.0011 memory: 6319 loss: 0.1137 2023/06/05 00:24:09 - mmengine - INFO - Epoch(train) [3][ 700/3907] lr: 8.9862e-05 eta: 5:17:19 time: 0.6255 data_time: 0.0017 memory: 6319 loss: 0.1075 2023/06/05 00:25:11 - mmengine - INFO - Epoch(train) [3][ 800/3907] lr: 8.9632e-05 eta: 5:16:17 time: 0.5797 data_time: 0.0010 memory: 6319 loss: 0.1133 2023/06/05 00:26:14 - mmengine - INFO - Epoch(train) [3][ 900/3907] lr: 8.9400e-05 eta: 5:15:16 time: 0.5906 data_time: 0.0012 memory: 6319 loss: 0.1130 2023/06/05 00:27:14 - mmengine - INFO - Epoch(train) [3][1000/3907] lr: 8.9166e-05 eta: 5:14:07 time: 0.5974 data_time: 0.0011 memory: 6319 loss: 0.1090 2023/06/05 00:28:17 - mmengine - INFO - Epoch(train) [3][1100/3907] lr: 8.8929e-05 eta: 5:13:06 time: 0.6059 data_time: 0.0010 memory: 6319 loss: 0.1151 2023/06/05 00:29:11 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 00:29:20 - mmengine - INFO - Epoch(train) [3][1200/3907] lr: 8.8691e-05 eta: 5:12:05 time: 0.6163 data_time: 0.0012 memory: 6319 loss: 0.0966 2023/06/05 00:30:21 - mmengine - INFO - Epoch(train) [3][1300/3907] lr: 8.8450e-05 eta: 5:11:00 time: 0.6097 data_time: 0.0010 memory: 6319 loss: 0.1111 2023/06/05 00:31:25 - mmengine - INFO - Epoch(train) [3][1400/3907] lr: 8.8206e-05 eta: 5:10:01 time: 0.6459 data_time: 0.0012 memory: 6319 loss: 0.1011 2023/06/05 00:32:31 - mmengine - INFO - Epoch(train) [3][1500/3907] lr: 8.7961e-05 eta: 5:09:12 time: 0.5573 data_time: 0.0010 memory: 6319 loss: 0.0956 2023/06/05 00:33:30 - mmengine - INFO - Epoch(train) [3][1600/3907] lr: 8.7714e-05 eta: 5:07:59 time: 0.6570 data_time: 0.0016 memory: 6319 loss: 0.1045 2023/06/05 00:34:31 - mmengine - INFO - Epoch(train) [3][1700/3907] lr: 8.7464e-05 eta: 5:06:52 time: 0.6292 data_time: 0.0018 memory: 6319 loss: 0.0916 2023/06/05 00:35:33 - mmengine - INFO - Epoch(train) [3][1800/3907] lr: 8.7213e-05 eta: 5:05:49 time: 0.6127 data_time: 0.0012 memory: 6319 loss: 0.0995 2023/06/05 00:36:34 - mmengine - INFO - Epoch(train) [3][1900/3907] lr: 8.6959e-05 eta: 5:04:42 time: 0.6106 data_time: 0.0013 memory: 6319 loss: 0.0963 2023/06/05 00:37:35 - mmengine - INFO - Epoch(train) [3][2000/3907] lr: 8.6703e-05 eta: 5:03:38 time: 0.6230 data_time: 0.0013 memory: 6319 loss: 0.0992 2023/06/05 00:38:36 - mmengine - INFO - Epoch(train) [3][2100/3907] lr: 8.6445e-05 eta: 5:02:32 time: 0.6017 data_time: 0.0014 memory: 6319 loss: 0.1029 2023/06/05 00:39:30 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 00:39:37 - mmengine - INFO - Epoch(train) [3][2200/3907] lr: 8.6186e-05 eta: 5:01:26 time: 0.6403 data_time: 0.0012 memory: 6319 loss: 0.0951 2023/06/05 00:40:40 - mmengine - INFO - Epoch(train) [3][2300/3907] lr: 8.5924e-05 eta: 5:00:26 time: 0.6266 data_time: 0.0012 memory: 6319 loss: 0.1064 2023/06/05 00:41:49 - mmengine - INFO - Epoch(train) [3][2400/3907] lr: 8.5660e-05 eta: 4:59:43 time: 0.7791 data_time: 0.0016 memory: 6319 loss: 0.1105 2023/06/05 00:42:54 - mmengine - INFO - Epoch(train) [3][2500/3907] lr: 8.5394e-05 eta: 4:58:48 time: 0.5468 data_time: 0.0011 memory: 6319 loss: 0.1047 2023/06/05 00:43:53 - mmengine - INFO - Epoch(train) [3][2600/3907] lr: 8.5126e-05 eta: 4:57:35 time: 0.5611 data_time: 0.0012 memory: 6319 loss: 0.1030 2023/06/05 00:44:52 - mmengine - INFO - Epoch(train) [3][2700/3907] lr: 8.4856e-05 eta: 4:56:23 time: 0.5413 data_time: 0.0012 memory: 6319 loss: 0.1051 2023/06/05 00:45:50 - mmengine - INFO - Epoch(train) [3][2800/3907] lr: 8.4585e-05 eta: 4:55:10 time: 0.6266 data_time: 0.0011 memory: 6319 loss: 0.1068 2023/06/05 00:46:50 - mmengine - INFO - Epoch(train) [3][2900/3907] lr: 8.4311e-05 eta: 4:54:02 time: 0.6155 data_time: 0.0010 memory: 6319 loss: 0.0897 2023/06/05 00:47:51 - mmengine - INFO - Epoch(train) [3][3000/3907] lr: 8.4036e-05 eta: 4:52:57 time: 0.6194 data_time: 0.0011 memory: 6319 loss: 0.1030 2023/06/05 00:48:52 - mmengine - INFO - Epoch(train) [3][3100/3907] lr: 8.3758e-05 eta: 4:51:51 time: 0.5691 data_time: 0.0012 memory: 6319 loss: 0.0950 2023/06/05 00:49:49 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 00:49:55 - mmengine - INFO - Epoch(train) [3][3200/3907] lr: 8.3479e-05 eta: 4:50:52 time: 0.5626 data_time: 0.0012 memory: 6319 loss: 0.0938 2023/06/05 00:50:56 - mmengine - INFO - Epoch(train) [3][3300/3907] lr: 8.3198e-05 eta: 4:49:46 time: 0.5902 data_time: 0.0011 memory: 6319 loss: 0.0888 2023/06/05 00:51:59 - mmengine - INFO - Epoch(train) [3][3400/3907] lr: 8.2915e-05 eta: 4:48:45 time: 0.6066 data_time: 0.0014 memory: 6319 loss: 0.0924 2023/06/05 00:53:17 - mmengine - INFO - Epoch(train) [3][3500/3907] lr: 8.2630e-05 eta: 4:48:24 time: 0.8397 data_time: 0.0009 memory: 6319 loss: 0.1119 2023/06/05 00:54:32 - mmengine - INFO - Epoch(train) [3][3600/3907] lr: 8.2344e-05 eta: 4:47:50 time: 0.5562 data_time: 0.0009 memory: 6319 loss: 0.1063 2023/06/05 00:55:30 - mmengine - INFO - Epoch(train) [3][3700/3907] lr: 8.2056e-05 eta: 4:46:39 time: 0.6229 data_time: 0.0013 memory: 6319 loss: 0.1074 2023/06/05 00:56:30 - mmengine - INFO - Epoch(train) [3][3800/3907] lr: 8.1765e-05 eta: 4:45:30 time: 0.5701 data_time: 0.0010 memory: 6319 loss: 0.0977 2023/06/05 00:57:35 - mmengine - INFO - Epoch(train) [3][3900/3907] lr: 8.1474e-05 eta: 4:44:33 time: 0.6194 data_time: 0.0011 memory: 6319 loss: 0.0957 2023/06/05 00:57:39 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 00:57:39 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/06/05 00:58:29 - mmengine - INFO - Epoch(val) [3][57/57] accuracy/top1: 82.1823 data_time: 0.5462 time: 0.6366 2023/06/05 00:59:35 - mmengine - INFO - Epoch(train) [4][ 100/3907] lr: 8.1160e-05 eta: 4:43:34 time: 0.5875 data_time: 0.1213 memory: 6319 loss: 0.1053 2023/06/05 01:00:38 - mmengine - INFO - Epoch(train) [4][ 200/3907] lr: 8.0864e-05 eta: 4:42:32 time: 0.6172 data_time: 0.2184 memory: 6319 loss: 0.0958 2023/06/05 01:01:26 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 01:01:38 - mmengine - INFO - Epoch(train) [4][ 300/3907] lr: 8.0567e-05 eta: 4:41:26 time: 0.6325 data_time: 0.0015 memory: 6319 loss: 0.0914 2023/06/05 01:02:42 - mmengine - INFO - Epoch(train) [4][ 400/3907] lr: 8.0269e-05 eta: 4:40:26 time: 0.5816 data_time: 0.0014 memory: 6319 loss: 0.1018 2023/06/05 01:03:43 - mmengine - INFO - Epoch(train) [4][ 500/3907] lr: 7.9969e-05 eta: 4:39:21 time: 0.6351 data_time: 0.0012 memory: 6319 loss: 0.0961 2023/06/05 01:04:47 - mmengine - INFO - Epoch(train) [4][ 600/3907] lr: 7.9667e-05 eta: 4:38:20 time: 0.6687 data_time: 0.0013 memory: 6319 loss: 0.0994 2023/06/05 01:05:49 - mmengine - INFO - Epoch(train) [4][ 700/3907] lr: 7.9363e-05 eta: 4:37:17 time: 0.6186 data_time: 0.0008 memory: 6319 loss: 0.0862 2023/06/05 01:06:48 - mmengine - INFO - Epoch(train) [4][ 800/3907] lr: 7.9058e-05 eta: 4:36:09 time: 0.5616 data_time: 0.0008 memory: 6319 loss: 0.0925 2023/06/05 01:07:50 - mmengine - INFO - Epoch(train) [4][ 900/3907] lr: 7.8752e-05 eta: 4:35:04 time: 0.6511 data_time: 0.0010 memory: 6319 loss: 0.0991 2023/06/05 01:08:52 - mmengine - INFO - Epoch(train) [4][1000/3907] lr: 7.8444e-05 eta: 4:34:02 time: 0.6311 data_time: 0.0012 memory: 6319 loss: 0.0778 2023/06/05 01:09:55 - mmengine - INFO - Epoch(train) [4][1100/3907] lr: 7.8134e-05 eta: 4:33:01 time: 0.5927 data_time: 0.0009 memory: 6319 loss: 0.1054 2023/06/05 01:10:57 - mmengine - INFO - Epoch(train) [4][1200/3907] lr: 7.7823e-05 eta: 4:31:57 time: 0.6521 data_time: 0.0013 memory: 6319 loss: 0.1059 2023/06/05 01:11:47 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 01:11:59 - mmengine - INFO - Epoch(train) [4][1300/3907] lr: 7.7510e-05 eta: 4:30:54 time: 0.6131 data_time: 0.0012 memory: 6319 loss: 0.0938 2023/06/05 01:13:02 - mmengine - INFO - Epoch(train) [4][1400/3907] lr: 7.7196e-05 eta: 4:29:52 time: 0.6381 data_time: 0.0015 memory: 6319 loss: 0.0910 2023/06/05 01:14:06 - mmengine - INFO - Epoch(train) [4][1500/3907] lr: 7.6881e-05 eta: 4:28:53 time: 0.6155 data_time: 0.0010 memory: 6319 loss: 0.0993 2023/06/05 01:15:12 - mmengine - INFO - Epoch(train) [4][1600/3907] lr: 7.6564e-05 eta: 4:27:57 time: 0.6361 data_time: 0.0012 memory: 6319 loss: 0.0995 2023/06/05 01:16:16 - mmengine - INFO - Epoch(train) [4][1700/3907] lr: 7.6246e-05 eta: 4:26:59 time: 0.5898 data_time: 0.0013 memory: 6319 loss: 0.1058 2023/06/05 01:17:18 - mmengine - INFO - Epoch(train) [4][1800/3907] lr: 7.5926e-05 eta: 4:25:56 time: 0.6397 data_time: 0.0012 memory: 6319 loss: 0.0935 2023/06/05 01:18:22 - mmengine - INFO - Epoch(train) [4][1900/3907] lr: 7.5605e-05 eta: 4:24:56 time: 0.5972 data_time: 0.0009 memory: 6319 loss: 0.0946 2023/06/05 01:19:26 - mmengine - INFO - Epoch(train) [4][2000/3907] lr: 7.5283e-05 eta: 4:23:56 time: 0.6742 data_time: 0.0009 memory: 6319 loss: 0.0984 2023/06/05 01:20:29 - mmengine - INFO - Epoch(train) [4][2100/3907] lr: 7.4959e-05 eta: 4:22:55 time: 0.6965 data_time: 0.0014 memory: 6319 loss: 0.0927 2023/06/05 01:21:34 - mmengine - INFO - Epoch(train) [4][2200/3907] lr: 7.4634e-05 eta: 4:21:57 time: 0.6147 data_time: 0.0014 memory: 6319 loss: 0.0853 2023/06/05 01:22:27 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 01:22:40 - mmengine - INFO - Epoch(train) [4][2300/3907] lr: 7.4308e-05 eta: 4:21:00 time: 0.6109 data_time: 0.0013 memory: 6319 loss: 0.0944 2023/06/05 01:23:42 - mmengine - INFO - Epoch(train) [4][2400/3907] lr: 7.3980e-05 eta: 4:19:56 time: 0.6191 data_time: 0.0011 memory: 6319 loss: 0.0850 2023/06/05 01:24:46 - mmengine - INFO - Epoch(train) [4][2500/3907] lr: 7.3652e-05 eta: 4:18:57 time: 0.6604 data_time: 0.0010 memory: 6319 loss: 0.0950 2023/06/05 01:25:50 - mmengine - INFO - Epoch(train) [4][2600/3907] lr: 7.3322e-05 eta: 4:17:57 time: 0.6484 data_time: 0.0012 memory: 6319 loss: 0.1010 2023/06/05 01:26:54 - mmengine - INFO - Epoch(train) [4][2700/3907] lr: 7.2991e-05 eta: 4:16:57 time: 0.6595 data_time: 0.0013 memory: 6319 loss: 0.0707 2023/06/05 01:27:57 - mmengine - INFO - Epoch(train) [4][2800/3907] lr: 7.2659e-05 eta: 4:15:55 time: 0.6921 data_time: 0.0008 memory: 6319 loss: 0.0982 2023/06/05 01:29:02 - mmengine - INFO - Epoch(train) [4][2900/3907] lr: 7.2325e-05 eta: 4:14:57 time: 0.7043 data_time: 0.0011 memory: 6319 loss: 0.0809 2023/06/05 01:30:03 - mmengine - INFO - Epoch(train) [4][3000/3907] lr: 7.1991e-05 eta: 4:13:51 time: 0.5801 data_time: 0.0009 memory: 6319 loss: 0.0837 2023/06/05 01:31:05 - mmengine - INFO - Epoch(train) [4][3100/3907] lr: 7.1655e-05 eta: 4:12:47 time: 0.6220 data_time: 0.0011 memory: 6319 loss: 0.1000 2023/06/05 01:32:08 - mmengine - INFO - Epoch(train) [4][3200/3907] lr: 7.1318e-05 eta: 4:11:46 time: 0.6225 data_time: 0.0013 memory: 6319 loss: 0.0965 2023/06/05 01:32:57 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 01:33:10 - mmengine - INFO - Epoch(train) [4][3300/3907] lr: 7.0981e-05 eta: 4:10:43 time: 0.6231 data_time: 0.0010 memory: 6319 loss: 0.0949 2023/06/05 01:34:11 - mmengine - INFO - Epoch(train) [4][3400/3907] lr: 7.0642e-05 eta: 4:09:37 time: 0.6057 data_time: 0.0010 memory: 6319 loss: 0.1001 2023/06/05 01:35:12 - mmengine - INFO - Epoch(train) [4][3500/3907] lr: 7.0302e-05 eta: 4:08:33 time: 0.6111 data_time: 0.0014 memory: 6319 loss: 0.0895 2023/06/05 01:36:26 - mmengine - INFO - Epoch(train) [4][3600/3907] lr: 6.9961e-05 eta: 4:07:48 time: 0.6950 data_time: 0.0009 memory: 6319 loss: 0.0923 2023/06/05 01:37:25 - mmengine - INFO - Epoch(train) [4][3700/3907] lr: 6.9620e-05 eta: 4:06:40 time: 0.6230 data_time: 0.0010 memory: 6319 loss: 0.0875 2023/06/05 01:38:28 - mmengine - INFO - Epoch(train) [4][3800/3907] lr: 6.9277e-05 eta: 4:05:38 time: 0.6202 data_time: 0.0011 memory: 6319 loss: 0.0921 2023/06/05 01:39:30 - mmengine - INFO - Epoch(train) [4][3900/3907] lr: 6.8933e-05 eta: 4:04:34 time: 0.5700 data_time: 0.0006 memory: 6319 loss: 0.0955 2023/06/05 01:39:31 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 01:39:31 - mmengine - INFO - Saving checkpoint at 4 epochs 2023/06/05 01:40:19 - mmengine - INFO - Epoch(val) [4][57/57] accuracy/top1: 89.2592 data_time: 0.5374 time: 0.6265 2023/06/05 01:41:22 - mmengine - INFO - Epoch(train) [5][ 100/3907] lr: 6.8565e-05 eta: 4:03:22 time: 0.6087 data_time: 0.2433 memory: 6319 loss: 0.0906 2023/06/05 01:42:25 - mmengine - INFO - Epoch(train) [5][ 200/3907] lr: 6.8219e-05 eta: 4:02:21 time: 0.6210 data_time: 0.0011 memory: 6319 loss: 0.0801 2023/06/05 01:43:25 - mmengine - INFO - Epoch(train) [5][ 300/3907] lr: 6.7873e-05 eta: 4:01:14 time: 0.5748 data_time: 0.0012 memory: 6319 loss: 0.0855 2023/06/05 01:44:13 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 01:44:28 - mmengine - INFO - Epoch(train) [5][ 400/3907] lr: 6.7526e-05 eta: 4:00:12 time: 0.6464 data_time: 0.0013 memory: 6319 loss: 0.0860 2023/06/05 01:45:32 - mmengine - INFO - Epoch(train) [5][ 500/3907] lr: 6.7178e-05 eta: 3:59:12 time: 0.7036 data_time: 0.0012 memory: 6319 loss: 0.0851 2023/06/05 01:46:34 - mmengine - INFO - Epoch(train) [5][ 600/3907] lr: 6.6829e-05 eta: 3:58:09 time: 0.5974 data_time: 0.0013 memory: 6319 loss: 0.0835 2023/06/05 01:47:37 - mmengine - INFO - Epoch(train) [5][ 700/3907] lr: 6.6480e-05 eta: 3:57:07 time: 0.6252 data_time: 0.0014 memory: 6319 loss: 0.0940 2023/06/05 01:48:45 - mmengine - INFO - Epoch(train) [5][ 800/3907] lr: 6.6129e-05 eta: 3:56:11 time: 0.7113 data_time: 0.0012 memory: 6319 loss: 0.0816 2023/06/05 01:49:58 - mmengine - INFO - Epoch(train) [5][ 900/3907] lr: 6.5778e-05 eta: 3:55:23 time: 0.5497 data_time: 0.0011 memory: 6319 loss: 0.0895 2023/06/05 01:50:57 - mmengine - INFO - Epoch(train) [5][1000/3907] lr: 6.5427e-05 eta: 3:54:15 time: 0.6087 data_time: 0.0010 memory: 6319 loss: 0.0858 2023/06/05 01:51:57 - mmengine - INFO - Epoch(train) [5][1100/3907] lr: 6.5074e-05 eta: 3:53:09 time: 0.5784 data_time: 0.0009 memory: 6319 loss: 0.0990 2023/06/05 01:52:59 - mmengine - INFO - Epoch(train) [5][1200/3907] lr: 6.4721e-05 eta: 3:52:06 time: 0.6415 data_time: 0.0019 memory: 6319 loss: 0.0831 2023/06/05 01:54:03 - mmengine - INFO - Epoch(train) [5][1300/3907] lr: 6.4368e-05 eta: 3:51:04 time: 0.6327 data_time: 0.0011 memory: 6319 loss: 0.0898 2023/06/05 01:54:46 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 01:55:04 - mmengine - INFO - Epoch(train) [5][1400/3907] lr: 6.4014e-05 eta: 3:50:00 time: 0.5824 data_time: 0.0015 memory: 6319 loss: 0.0866 2023/06/05 01:56:07 - mmengine - INFO - Epoch(train) [5][1500/3907] lr: 6.3659e-05 eta: 3:48:58 time: 0.6218 data_time: 0.0013 memory: 6319 loss: 0.0776 2023/06/05 01:57:09 - mmengine - INFO - Epoch(train) [5][1600/3907] lr: 6.3303e-05 eta: 3:47:55 time: 0.6022 data_time: 0.0011 memory: 6319 loss: 0.0895 2023/06/05 01:58:11 - mmengine - INFO - Epoch(train) [5][1700/3907] lr: 6.2948e-05 eta: 3:46:51 time: 0.6077 data_time: 0.0014 memory: 6319 loss: 0.0736 2023/06/05 01:59:13 - mmengine - INFO - Epoch(train) [5][1800/3907] lr: 6.2591e-05 eta: 3:45:48 time: 0.6006 data_time: 0.0012 memory: 6319 loss: 0.0840 2023/06/05 02:00:13 - mmengine - INFO - Epoch(train) [5][1900/3907] lr: 6.2234e-05 eta: 3:44:43 time: 0.5905 data_time: 0.0014 memory: 6319 loss: 0.0748 2023/06/05 02:01:16 - mmengine - INFO - Epoch(train) [5][2000/3907] lr: 6.1877e-05 eta: 3:43:40 time: 0.6500 data_time: 0.0012 memory: 6319 loss: 0.0920 2023/06/05 02:02:27 - mmengine - INFO - Epoch(train) [5][2100/3907] lr: 6.1519e-05 eta: 3:42:48 time: 0.7826 data_time: 0.0010 memory: 6319 loss: 0.0937 2023/06/05 02:03:27 - mmengine - INFO - Epoch(train) [5][2200/3907] lr: 6.1161e-05 eta: 3:41:41 time: 0.5486 data_time: 0.0008 memory: 6319 loss: 0.0733 2023/06/05 02:04:27 - mmengine - INFO - Epoch(train) [5][2300/3907] lr: 6.0802e-05 eta: 3:40:36 time: 0.6194 data_time: 0.0010 memory: 6319 loss: 0.0858 2023/06/05 02:05:11 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 02:05:31 - mmengine - INFO - Epoch(train) [5][2400/3907] lr: 6.0443e-05 eta: 3:39:35 time: 0.6452 data_time: 0.0008 memory: 6319 loss: 0.0875 2023/06/05 02:06:32 - mmengine - INFO - Epoch(train) [5][2500/3907] lr: 6.0084e-05 eta: 3:38:30 time: 0.6099 data_time: 0.0008 memory: 6319 loss: 0.0854 2023/06/05 02:07:33 - mmengine - INFO - Epoch(train) [5][2600/3907] lr: 5.9724e-05 eta: 3:37:26 time: 0.6139 data_time: 0.0013 memory: 6319 loss: 0.0867 2023/06/05 02:08:36 - mmengine - INFO - Epoch(train) [5][2700/3907] lr: 5.9364e-05 eta: 3:36:24 time: 0.6391 data_time: 0.0013 memory: 6319 loss: 0.0976 2023/06/05 02:09:39 - mmengine - INFO - Epoch(train) [5][2800/3907] lr: 5.9004e-05 eta: 3:35:22 time: 0.6239 data_time: 0.0013 memory: 6319 loss: 0.0817 2023/06/05 02:10:43 - mmengine - INFO - Epoch(train) [5][2900/3907] lr: 5.8643e-05 eta: 3:34:21 time: 0.7048 data_time: 0.0012 memory: 6319 loss: 0.0827 2023/06/05 02:11:45 - mmengine - INFO - Epoch(train) [5][3000/3907] lr: 5.8283e-05 eta: 3:33:18 time: 0.6219 data_time: 0.0010 memory: 6319 loss: 0.0901 2023/06/05 02:12:45 - mmengine - INFO - Epoch(train) [5][3100/3907] lr: 5.7922e-05 eta: 3:32:12 time: 0.6343 data_time: 0.0014 memory: 6319 loss: 0.0880 2023/06/05 02:13:46 - mmengine - INFO - Epoch(train) [5][3200/3907] lr: 5.7560e-05 eta: 3:31:08 time: 0.6153 data_time: 0.0014 memory: 6319 loss: 0.0820 2023/06/05 02:14:50 - mmengine - INFO - Epoch(train) [5][3300/3907] lr: 5.7199e-05 eta: 3:30:07 time: 0.6185 data_time: 0.0013 memory: 6319 loss: 0.0713 2023/06/05 02:15:35 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 02:15:53 - mmengine - INFO - Epoch(train) [5][3400/3907] lr: 5.6838e-05 eta: 3:29:04 time: 0.5832 data_time: 0.0010 memory: 6319 loss: 0.0640 2023/06/05 02:16:55 - mmengine - INFO - Epoch(train) [5][3500/3907] lr: 5.6476e-05 eta: 3:28:01 time: 0.6259 data_time: 0.0015 memory: 6319 loss: 0.0796 2023/06/05 02:17:58 - mmengine - INFO - Epoch(train) [5][3600/3907] lr: 5.6114e-05 eta: 3:26:59 time: 0.6208 data_time: 0.0017 memory: 6319 loss: 0.0774 2023/06/05 02:19:05 - mmengine - INFO - Epoch(train) [5][3700/3907] lr: 5.5753e-05 eta: 3:26:01 time: 0.6512 data_time: 0.0452 memory: 6319 loss: 0.0744 2023/06/05 02:20:24 - mmengine - INFO - Epoch(train) [5][3800/3907] lr: 5.5391e-05 eta: 3:25:15 time: 0.6746 data_time: 0.0018 memory: 6319 loss: 0.0869 2023/06/05 02:21:21 - mmengine - INFO - Epoch(train) [5][3900/3907] lr: 5.5029e-05 eta: 3:24:07 time: 0.5811 data_time: 0.0008 memory: 6319 loss: 0.0817 2023/06/05 02:21:24 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 02:21:24 - mmengine - INFO - Saving checkpoint at 5 epochs 2023/06/05 02:22:11 - mmengine - INFO - Epoch(val) [5][57/57] accuracy/top1: 93.6850 data_time: 0.4932 time: 0.5811 2023/06/05 02:23:14 - mmengine - INFO - Epoch(train) [6][ 100/3907] lr: 5.4642e-05 eta: 3:22:58 time: 0.5858 data_time: 0.2116 memory: 6319 loss: 0.0871 2023/06/05 02:24:15 - mmengine - INFO - Epoch(train) [6][ 200/3907] lr: 5.4280e-05 eta: 3:21:54 time: 0.6000 data_time: 0.0548 memory: 6319 loss: 0.0986 2023/06/05 02:25:18 - mmengine - INFO - Epoch(train) [6][ 300/3907] lr: 5.3918e-05 eta: 3:20:51 time: 0.5921 data_time: 0.0012 memory: 6319 loss: 0.0761 2023/06/05 02:26:16 - mmengine - INFO - Epoch(train) [6][ 400/3907] lr: 5.3556e-05 eta: 3:19:44 time: 0.5497 data_time: 0.0011 memory: 6319 loss: 0.0839 2023/06/05 02:26:58 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 02:27:16 - mmengine - INFO - Epoch(train) [6][ 500/3907] lr: 5.3195e-05 eta: 3:18:39 time: 0.5999 data_time: 0.0013 memory: 6319 loss: 0.0799 2023/06/05 02:28:18 - mmengine - INFO - Epoch(train) [6][ 600/3907] lr: 5.2833e-05 eta: 3:17:36 time: 0.5771 data_time: 0.0013 memory: 6319 loss: 0.0872 2023/06/05 02:29:21 - mmengine - INFO - Epoch(train) [6][ 700/3907] lr: 5.2472e-05 eta: 3:16:33 time: 0.6362 data_time: 0.0009 memory: 6319 loss: 0.0750 2023/06/05 02:30:22 - mmengine - INFO - Epoch(train) [6][ 800/3907] lr: 5.2111e-05 eta: 3:15:29 time: 0.6204 data_time: 0.0011 memory: 6319 loss: 0.0781 2023/06/05 02:31:24 - mmengine - INFO - Epoch(train) [6][ 900/3907] lr: 5.1750e-05 eta: 3:14:26 time: 0.6397 data_time: 0.0010 memory: 6319 loss: 0.0802 2023/06/05 02:32:24 - mmengine - INFO - Epoch(train) [6][1000/3907] lr: 5.1389e-05 eta: 3:13:22 time: 0.5844 data_time: 0.0012 memory: 6319 loss: 0.0882 2023/06/05 02:33:25 - mmengine - INFO - Epoch(train) [6][1100/3907] lr: 5.1029e-05 eta: 3:12:18 time: 0.6115 data_time: 0.0010 memory: 6319 loss: 0.0808 2023/06/05 02:34:28 - mmengine - INFO - Epoch(train) [6][1200/3907] lr: 5.0668e-05 eta: 3:11:15 time: 0.6076 data_time: 0.0012 memory: 6319 loss: 0.0678 2023/06/05 02:35:30 - mmengine - INFO - Epoch(train) [6][1300/3907] lr: 5.0308e-05 eta: 3:10:12 time: 0.5770 data_time: 0.0011 memory: 6319 loss: 0.0786 2023/06/05 02:36:31 - mmengine - INFO - Epoch(train) [6][1400/3907] lr: 4.9949e-05 eta: 3:09:08 time: 0.6137 data_time: 0.0008 memory: 6319 loss: 0.0833 2023/06/05 02:37:12 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 02:37:33 - mmengine - INFO - Epoch(train) [6][1500/3907] lr: 4.9589e-05 eta: 3:08:05 time: 0.7235 data_time: 0.0014 memory: 6319 loss: 0.0815 2023/06/05 02:38:44 - mmengine - INFO - Epoch(train) [6][1600/3907] lr: 4.9230e-05 eta: 3:07:09 time: 0.6747 data_time: 0.0011 memory: 6319 loss: 0.0848 2023/06/05 02:40:21 - mmengine - INFO - Epoch(train) [6][1700/3907] lr: 4.8871e-05 eta: 3:06:36 time: 0.8727 data_time: 0.0011 memory: 6319 loss: 0.0740 2023/06/05 02:41:22 - mmengine - INFO - Epoch(train) [6][1800/3907] lr: 4.8513e-05 eta: 3:05:32 time: 0.5724 data_time: 0.0008 memory: 6319 loss: 0.0774 2023/06/05 02:42:21 - mmengine - INFO - Epoch(train) [6][1900/3907] lr: 4.8155e-05 eta: 3:04:25 time: 0.6192 data_time: 0.0012 memory: 6319 loss: 0.0956 2023/06/05 02:43:16 - mmengine - INFO - Epoch(train) [6][2000/3907] lr: 4.7798e-05 eta: 3:03:16 time: 0.5283 data_time: 0.0010 memory: 6319 loss: 0.0726 2023/06/05 02:44:13 - mmengine - INFO - Epoch(train) [6][2100/3907] lr: 4.7441e-05 eta: 3:02:09 time: 0.6080 data_time: 0.0207 memory: 6319 loss: 0.0802 2023/06/05 02:45:14 - mmengine - INFO - Epoch(train) [6][2200/3907] lr: 4.7084e-05 eta: 3:01:05 time: 0.6035 data_time: 0.0013 memory: 6319 loss: 0.0750 2023/06/05 02:46:16 - mmengine - INFO - Epoch(train) [6][2300/3907] lr: 4.6729e-05 eta: 3:00:02 time: 0.6789 data_time: 0.0012 memory: 6319 loss: 0.0635 2023/06/05 02:47:18 - mmengine - INFO - Epoch(train) [6][2400/3907] lr: 4.6373e-05 eta: 2:58:59 time: 0.6389 data_time: 0.0008 memory: 6319 loss: 0.0692 2023/06/05 02:47:56 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 02:48:19 - mmengine - INFO - Epoch(train) [6][2500/3907] lr: 4.6018e-05 eta: 2:57:55 time: 0.6203 data_time: 0.0008 memory: 6319 loss: 0.0742 2023/06/05 02:49:18 - mmengine - INFO - Epoch(train) [6][2600/3907] lr: 4.5664e-05 eta: 2:56:50 time: 0.5737 data_time: 0.0011 memory: 6319 loss: 0.0653 2023/06/05 02:50:20 - mmengine - INFO - Epoch(train) [6][2700/3907] lr: 4.5310e-05 eta: 2:55:46 time: 0.6477 data_time: 0.0013 memory: 6319 loss: 0.0740 2023/06/05 02:51:22 - mmengine - INFO - Epoch(train) [6][2800/3907] lr: 4.4957e-05 eta: 2:54:43 time: 0.6272 data_time: 0.0014 memory: 6319 loss: 0.0849 2023/06/05 02:52:24 - mmengine - INFO - Epoch(train) [6][2900/3907] lr: 4.4605e-05 eta: 2:53:40 time: 0.6080 data_time: 0.0011 memory: 6319 loss: 0.0758 2023/06/05 02:53:24 - mmengine - INFO - Epoch(train) [6][3000/3907] lr: 4.4253e-05 eta: 2:52:35 time: 0.6142 data_time: 0.0013 memory: 6319 loss: 0.0712 2023/06/05 02:54:22 - mmengine - INFO - Epoch(train) [6][3100/3907] lr: 4.3902e-05 eta: 2:51:29 time: 0.5848 data_time: 0.0010 memory: 6319 loss: 0.0815 2023/06/05 02:55:25 - mmengine - INFO - Epoch(train) [6][3200/3907] lr: 4.3552e-05 eta: 2:50:27 time: 0.6074 data_time: 0.0010 memory: 6319 loss: 0.0827 2023/06/05 02:56:25 - mmengine - INFO - Epoch(train) [6][3300/3907] lr: 4.3202e-05 eta: 2:49:23 time: 0.6092 data_time: 0.0009 memory: 6319 loss: 0.0630 2023/06/05 02:57:24 - mmengine - INFO - Epoch(train) [6][3400/3907] lr: 4.2854e-05 eta: 2:48:17 time: 0.6172 data_time: 0.0014 memory: 6319 loss: 0.0864 2023/06/05 02:58:06 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 02:58:25 - mmengine - INFO - Epoch(train) [6][3500/3907] lr: 4.2506e-05 eta: 2:47:13 time: 0.6004 data_time: 0.0011 memory: 6319 loss: 0.0703 2023/06/05 02:59:27 - mmengine - INFO - Epoch(train) [6][3600/3907] lr: 4.2158e-05 eta: 2:46:11 time: 0.5945 data_time: 0.0008 memory: 6319 loss: 0.0810 2023/06/05 03:00:28 - mmengine - INFO - Epoch(train) [6][3700/3907] lr: 4.1812e-05 eta: 2:45:07 time: 0.6618 data_time: 0.0011 memory: 6319 loss: 0.0744 2023/06/05 03:01:30 - mmengine - INFO - Epoch(train) [6][3800/3907] lr: 4.1466e-05 eta: 2:44:04 time: 0.6104 data_time: 0.0013 memory: 6319 loss: 0.0714 2023/06/05 03:02:31 - mmengine - INFO - Epoch(train) [6][3900/3907] lr: 4.1122e-05 eta: 2:43:01 time: 0.6612 data_time: 0.0009 memory: 6319 loss: 0.0888 2023/06/05 03:02:32 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 03:02:32 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/06/05 03:03:20 - mmengine - INFO - Epoch(val) [6][57/57] accuracy/top1: 94.7845 data_time: 0.5004 time: 0.5899 2023/06/05 03:04:22 - mmengine - INFO - Epoch(train) [7][ 100/3907] lr: 4.0754e-05 eta: 2:41:51 time: 0.6164 data_time: 0.4744 memory: 6319 loss: 0.0669 2023/06/05 03:05:24 - mmengine - INFO - Epoch(train) [7][ 200/3907] lr: 4.0411e-05 eta: 2:40:48 time: 0.6700 data_time: 0.5253 memory: 6319 loss: 0.0630 2023/06/05 03:06:25 - mmengine - INFO - Epoch(train) [7][ 300/3907] lr: 4.0069e-05 eta: 2:39:45 time: 0.5889 data_time: 0.4356 memory: 6319 loss: 0.0716 2023/06/05 03:07:23 - mmengine - INFO - Epoch(train) [7][ 400/3907] lr: 3.9729e-05 eta: 2:38:39 time: 0.5881 data_time: 0.4349 memory: 6319 loss: 0.0887 2023/06/05 03:08:27 - mmengine - INFO - Epoch(train) [7][ 500/3907] lr: 3.9389e-05 eta: 2:37:38 time: 0.6514 data_time: 0.4960 memory: 6319 loss: 0.0866 2023/06/05 03:09:02 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 03:09:27 - mmengine - INFO - Epoch(train) [7][ 600/3907] lr: 3.9050e-05 eta: 2:36:34 time: 0.5642 data_time: 0.4207 memory: 6319 loss: 0.0793 2023/06/05 03:10:31 - mmengine - INFO - Epoch(train) [7][ 700/3907] lr: 3.8712e-05 eta: 2:35:32 time: 0.6644 data_time: 0.5221 memory: 6319 loss: 0.0802 2023/06/05 03:11:32 - mmengine - INFO - Epoch(train) [7][ 800/3907] lr: 3.8375e-05 eta: 2:34:29 time: 0.6276 data_time: 0.4823 memory: 6319 loss: 0.0816 2023/06/05 03:12:35 - mmengine - INFO - Epoch(train) [7][ 900/3907] lr: 3.8039e-05 eta: 2:33:26 time: 0.6439 data_time: 0.4872 memory: 6319 loss: 0.0778 2023/06/05 03:13:35 - mmengine - INFO - Epoch(train) [7][1000/3907] lr: 3.7705e-05 eta: 2:32:22 time: 0.6047 data_time: 0.4618 memory: 6319 loss: 0.0706 2023/06/05 03:14:36 - mmengine - INFO - Epoch(train) [7][1100/3907] lr: 3.7371e-05 eta: 2:31:19 time: 0.6124 data_time: 0.4566 memory: 6319 loss: 0.0922 2023/06/05 03:15:34 - mmengine - INFO - Epoch(train) [7][1200/3907] lr: 3.7039e-05 eta: 2:30:14 time: 0.6098 data_time: 0.4667 memory: 6319 loss: 0.0824 2023/06/05 03:16:33 - mmengine - INFO - Epoch(train) [7][1300/3907] lr: 3.6708e-05 eta: 2:29:09 time: 0.5636 data_time: 0.4211 memory: 6319 loss: 0.0805 2023/06/05 03:17:30 - mmengine - INFO - Epoch(train) [7][1400/3907] lr: 3.6378e-05 eta: 2:28:04 time: 0.5435 data_time: 0.4015 memory: 6319 loss: 0.0690 2023/06/05 03:18:30 - mmengine - INFO - Epoch(train) [7][1500/3907] lr: 3.6049e-05 eta: 2:27:00 time: 0.6432 data_time: 0.5016 memory: 6319 loss: 0.0660 2023/06/05 03:19:08 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 03:19:33 - mmengine - INFO - Epoch(train) [7][1600/3907] lr: 3.5721e-05 eta: 2:25:58 time: 0.6021 data_time: 0.4593 memory: 6319 loss: 0.0747 2023/06/05 03:20:34 - mmengine - INFO - Epoch(train) [7][1700/3907] lr: 3.5395e-05 eta: 2:24:54 time: 0.5701 data_time: 0.4278 memory: 6319 loss: 0.0733 2023/06/05 03:21:33 - mmengine - INFO - Epoch(train) [7][1800/3907] lr: 3.5070e-05 eta: 2:23:50 time: 0.5632 data_time: 0.4210 memory: 6319 loss: 0.0741 2023/06/05 03:22:33 - mmengine - INFO - Epoch(train) [7][1900/3907] lr: 3.4746e-05 eta: 2:22:47 time: 0.6304 data_time: 0.4880 memory: 6319 loss: 0.0916 2023/06/05 03:23:34 - mmengine - INFO - Epoch(train) [7][2000/3907] lr: 3.4424e-05 eta: 2:21:43 time: 0.6336 data_time: 0.4624 memory: 6319 loss: 0.0936 2023/06/05 03:24:36 - mmengine - INFO - Epoch(train) [7][2100/3907] lr: 3.4103e-05 eta: 2:20:41 time: 0.6018 data_time: 0.4577 memory: 6319 loss: 0.0751 2023/06/05 03:25:36 - mmengine - INFO - Epoch(train) [7][2200/3907] lr: 3.3783e-05 eta: 2:19:37 time: 0.6132 data_time: 0.4712 memory: 6319 loss: 0.0968 2023/06/05 03:26:36 - mmengine - INFO - Epoch(train) [7][2300/3907] lr: 3.3465e-05 eta: 2:18:34 time: 0.5796 data_time: 0.4360 memory: 6319 loss: 0.0864 2023/06/05 03:27:39 - mmengine - INFO - Epoch(train) [7][2400/3907] lr: 3.3148e-05 eta: 2:17:31 time: 0.6460 data_time: 0.5020 memory: 6319 loss: 0.0851 2023/06/05 03:28:42 - mmengine - INFO - Epoch(train) [7][2500/3907] lr: 3.2832e-05 eta: 2:16:29 time: 0.6405 data_time: 0.4878 memory: 6319 loss: 0.0772 2023/06/05 03:29:13 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 03:29:42 - mmengine - INFO - Epoch(train) [7][2600/3907] lr: 3.2518e-05 eta: 2:15:26 time: 0.6298 data_time: 0.4859 memory: 6319 loss: 0.0843 2023/06/05 03:30:45 - mmengine - INFO - Epoch(train) [7][2700/3907] lr: 3.2205e-05 eta: 2:14:24 time: 0.6201 data_time: 0.4764 memory: 6319 loss: 0.0819 2023/06/05 03:31:47 - mmengine - INFO - Epoch(train) [7][2800/3907] lr: 3.1894e-05 eta: 2:13:21 time: 0.6483 data_time: 0.5053 memory: 6319 loss: 0.0904 2023/06/05 03:32:48 - mmengine - INFO - Epoch(train) [7][2900/3907] lr: 3.1584e-05 eta: 2:12:18 time: 0.6086 data_time: 0.4550 memory: 6319 loss: 0.0751 2023/06/05 03:33:51 - mmengine - INFO - Epoch(train) [7][3000/3907] lr: 3.1276e-05 eta: 2:11:16 time: 0.5821 data_time: 0.4395 memory: 6319 loss: 0.0826 2023/06/05 03:34:51 - mmengine - INFO - Epoch(train) [7][3100/3907] lr: 3.0969e-05 eta: 2:10:13 time: 0.6294 data_time: 0.4849 memory: 6319 loss: 0.0919 2023/06/05 03:35:51 - mmengine - INFO - Epoch(train) [7][3200/3907] lr: 3.0664e-05 eta: 2:09:09 time: 0.5464 data_time: 0.4047 memory: 6319 loss: 0.0836 2023/06/05 03:36:50 - mmengine - INFO - Epoch(train) [7][3300/3907] lr: 3.0360e-05 eta: 2:08:05 time: 0.5855 data_time: 0.4322 memory: 6319 loss: 0.0874 2023/06/05 03:37:50 - mmengine - INFO - Epoch(train) [7][3400/3907] lr: 3.0058e-05 eta: 2:07:02 time: 0.6322 data_time: 0.4880 memory: 6319 loss: 0.0883 2023/06/05 03:38:51 - mmengine - INFO - Epoch(train) [7][3500/3907] lr: 2.9758e-05 eta: 2:05:59 time: 0.5850 data_time: 0.4412 memory: 6319 loss: 0.0903 2023/06/05 03:39:23 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 03:39:53 - mmengine - INFO - Epoch(train) [7][3600/3907] lr: 2.9459e-05 eta: 2:04:56 time: 0.6256 data_time: 0.4815 memory: 6319 loss: 0.0793 2023/06/05 03:40:55 - mmengine - INFO - Epoch(train) [7][3700/3907] lr: 2.9162e-05 eta: 2:03:54 time: 0.5588 data_time: 0.4176 memory: 6319 loss: 0.0818 2023/06/05 03:41:54 - mmengine - INFO - Epoch(train) [7][3800/3907] lr: 2.8867e-05 eta: 2:02:50 time: 0.6088 data_time: 0.4661 memory: 6319 loss: 0.0839 2023/06/05 03:42:55 - mmengine - INFO - Epoch(train) [7][3900/3907] lr: 2.8573e-05 eta: 2:01:47 time: 0.5906 data_time: 0.4384 memory: 6319 loss: 0.0712 2023/06/05 03:42:58 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 03:42:58 - mmengine - INFO - Saving checkpoint at 7 epochs 2023/06/05 03:43:46 - mmengine - INFO - Epoch(val) [7][57/57] accuracy/top1: 96.3119 data_time: 0.5100 time: 0.6020 2023/06/05 03:44:50 - mmengine - INFO - Epoch(train) [8][ 100/3907] lr: 2.8261e-05 eta: 2:00:40 time: 0.5920 data_time: 0.4505 memory: 6319 loss: 0.0879 2023/06/05 03:45:51 - mmengine - INFO - Epoch(train) [8][ 200/3907] lr: 2.7971e-05 eta: 1:59:37 time: 0.6259 data_time: 0.4833 memory: 6319 loss: 0.0642 2023/06/05 03:46:52 - mmengine - INFO - Epoch(train) [8][ 300/3907] lr: 2.7682e-05 eta: 1:58:35 time: 0.6036 data_time: 0.4600 memory: 6319 loss: 0.0862 2023/06/05 03:47:54 - mmengine - INFO - Epoch(train) [8][ 400/3907] lr: 2.7395e-05 eta: 1:57:32 time: 0.6247 data_time: 0.4710 memory: 6319 loss: 0.0751 2023/06/05 03:48:53 - mmengine - INFO - Epoch(train) [8][ 500/3907] lr: 2.7111e-05 eta: 1:56:29 time: 0.5760 data_time: 0.4342 memory: 6319 loss: 0.0685 2023/06/05 03:49:51 - mmengine - INFO - Epoch(train) [8][ 600/3907] lr: 2.6828e-05 eta: 1:55:25 time: 0.5643 data_time: 0.4144 memory: 6319 loss: 0.0699 2023/06/05 03:50:22 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 03:50:52 - mmengine - INFO - Epoch(train) [8][ 700/3907] lr: 2.6546e-05 eta: 1:54:22 time: 0.5827 data_time: 0.4420 memory: 6319 loss: 0.0742 2023/06/05 03:51:51 - mmengine - INFO - Epoch(train) [8][ 800/3907] lr: 2.6267e-05 eta: 1:53:19 time: 0.6737 data_time: 0.5173 memory: 6319 loss: 0.1046 2023/06/05 03:52:53 - mmengine - INFO - Epoch(train) [8][ 900/3907] lr: 2.5989e-05 eta: 1:52:16 time: 0.6133 data_time: 0.4718 memory: 6319 loss: 0.0815 2023/06/05 03:53:55 - mmengine - INFO - Epoch(train) [8][1000/3907] lr: 2.5714e-05 eta: 1:51:14 time: 0.6003 data_time: 0.4434 memory: 6319 loss: 0.0845 2023/06/05 03:54:56 - mmengine - INFO - Epoch(train) [8][1100/3907] lr: 2.5440e-05 eta: 1:50:11 time: 0.6659 data_time: 0.5231 memory: 6319 loss: 0.0773 2023/06/05 03:55:58 - mmengine - INFO - Epoch(train) [8][1200/3907] lr: 2.5168e-05 eta: 1:49:08 time: 0.5979 data_time: 0.4535 memory: 6319 loss: 0.0728 2023/06/05 03:56:59 - mmengine - INFO - Epoch(train) [8][1300/3907] lr: 2.4898e-05 eta: 1:48:06 time: 0.6376 data_time: 0.4939 memory: 6319 loss: 0.0855 2023/06/05 03:57:58 - mmengine - INFO - Epoch(train) [8][1400/3907] lr: 2.4630e-05 eta: 1:47:03 time: 0.6086 data_time: 0.4644 memory: 6319 loss: 0.0738 2023/06/05 03:58:59 - mmengine - INFO - Epoch(train) [8][1500/3907] lr: 2.4364e-05 eta: 1:46:00 time: 0.6236 data_time: 0.4821 memory: 6319 loss: 0.0770 2023/06/05 04:00:01 - mmengine - INFO - Epoch(train) [8][1600/3907] lr: 2.4100e-05 eta: 1:44:58 time: 0.6405 data_time: 0.4964 memory: 6319 loss: 0.0698 2023/06/05 04:00:32 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 04:01:02 - mmengine - INFO - Epoch(train) [8][1700/3907] lr: 2.3838e-05 eta: 1:43:55 time: 0.5912 data_time: 0.4483 memory: 6319 loss: 0.0646 2023/06/05 04:02:04 - mmengine - INFO - Epoch(train) [8][1800/3907] lr: 2.3578e-05 eta: 1:42:53 time: 0.5862 data_time: 0.4443 memory: 6319 loss: 0.0721 2023/06/05 04:03:06 - mmengine - INFO - Epoch(train) [8][1900/3907] lr: 2.3320e-05 eta: 1:41:50 time: 0.6124 data_time: 0.4700 memory: 6319 loss: 0.0770 2023/06/05 04:04:09 - mmengine - INFO - Epoch(train) [8][2000/3907] lr: 2.3064e-05 eta: 1:40:48 time: 0.7024 data_time: 0.5597 memory: 6319 loss: 0.0865 2023/06/05 04:05:09 - mmengine - INFO - Epoch(train) [8][2100/3907] lr: 2.2810e-05 eta: 1:39:45 time: 0.5932 data_time: 0.4523 memory: 6319 loss: 0.0876 2023/06/05 04:06:09 - mmengine - INFO - Epoch(train) [8][2200/3907] lr: 2.2558e-05 eta: 1:38:42 time: 0.5839 data_time: 0.4421 memory: 6319 loss: 0.0860 2023/06/05 04:07:09 - mmengine - INFO - Epoch(train) [8][2300/3907] lr: 2.2309e-05 eta: 1:37:39 time: 0.6095 data_time: 0.4542 memory: 6319 loss: 0.0769 2023/06/05 04:08:06 - mmengine - INFO - Epoch(train) [8][2400/3907] lr: 2.2061e-05 eta: 1:36:36 time: 0.5770 data_time: 0.4348 memory: 6319 loss: 0.0752 2023/06/05 04:09:04 - mmengine - INFO - Epoch(train) [8][2500/3907] lr: 2.1816e-05 eta: 1:35:32 time: 0.5602 data_time: 0.4176 memory: 6319 loss: 0.0701 2023/06/05 04:10:03 - mmengine - INFO - Epoch(train) [8][2600/3907] lr: 2.1572e-05 eta: 1:34:29 time: 0.5870 data_time: 0.4450 memory: 6319 loss: 0.0730 2023/06/05 04:10:34 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 04:11:04 - mmengine - INFO - Epoch(train) [8][2700/3907] lr: 2.1331e-05 eta: 1:33:27 time: 0.6086 data_time: 0.4658 memory: 6319 loss: 0.0836 2023/06/05 04:12:03 - mmengine - INFO - Epoch(train) [8][2800/3907] lr: 2.1092e-05 eta: 1:32:24 time: 0.5645 data_time: 0.4225 memory: 6319 loss: 0.0875 2023/06/05 04:13:02 - mmengine - INFO - Epoch(train) [8][2900/3907] lr: 2.0855e-05 eta: 1:31:20 time: 0.6183 data_time: 0.4734 memory: 6319 loss: 0.0757 2023/06/05 04:14:04 - mmengine - INFO - Epoch(train) [8][3000/3907] lr: 2.0621e-05 eta: 1:30:18 time: 0.6540 data_time: 0.5105 memory: 6319 loss: 0.0856 2023/06/05 04:15:09 - mmengine - INFO - Epoch(train) [8][3100/3907] lr: 2.0388e-05 eta: 1:29:17 time: 0.6890 data_time: 0.5449 memory: 6319 loss: 0.0844 2023/06/05 04:16:10 - mmengine - INFO - Epoch(train) [8][3200/3907] lr: 2.0158e-05 eta: 1:28:14 time: 0.6061 data_time: 0.4630 memory: 6319 loss: 0.0747 2023/06/05 04:17:14 - mmengine - INFO - Epoch(train) [8][3300/3907] lr: 1.9930e-05 eta: 1:27:13 time: 0.6578 data_time: 0.5144 memory: 6319 loss: 0.0883 2023/06/05 04:18:15 - mmengine - INFO - Epoch(train) [8][3400/3907] lr: 1.9705e-05 eta: 1:26:10 time: 0.6399 data_time: 0.4972 memory: 6319 loss: 0.0772 2023/06/05 04:19:17 - mmengine - INFO - Epoch(train) [8][3500/3907] lr: 1.9481e-05 eta: 1:25:08 time: 0.6035 data_time: 0.4575 memory: 6319 loss: 0.0775 2023/06/05 04:20:17 - mmengine - INFO - Epoch(train) [8][3600/3907] lr: 1.9260e-05 eta: 1:24:05 time: 0.6433 data_time: 0.5013 memory: 6319 loss: 0.0758 2023/06/05 04:20:47 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 04:21:18 - mmengine - INFO - Epoch(train) [8][3700/3907] lr: 1.9042e-05 eta: 1:23:03 time: 0.7127 data_time: 0.5531 memory: 6319 loss: 0.0728 2023/06/05 04:22:19 - mmengine - INFO - Epoch(train) [8][3800/3907] lr: 1.8825e-05 eta: 1:22:01 time: 0.6105 data_time: 0.4671 memory: 6319 loss: 0.0895 2023/06/05 04:23:16 - mmengine - INFO - Epoch(train) [8][3900/3907] lr: 1.8611e-05 eta: 1:20:57 time: 0.5426 data_time: 0.3985 memory: 6319 loss: 0.0749 2023/06/05 04:23:21 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 04:23:21 - mmengine - INFO - Saving checkpoint at 8 epochs 2023/06/05 04:24:08 - mmengine - INFO - Epoch(val) [8][57/57] accuracy/top1: 96.7225 data_time: 0.4933 time: 0.5810 2023/06/05 04:25:11 - mmengine - INFO - Epoch(train) [9][ 100/3907] lr: 1.8385e-05 eta: 1:19:51 time: 0.6189 data_time: 0.4759 memory: 6319 loss: 0.0755 2023/06/05 04:26:11 - mmengine - INFO - Epoch(train) [9][ 200/3907] lr: 1.8176e-05 eta: 1:18:48 time: 0.6066 data_time: 0.4504 memory: 6319 loss: 0.0818 2023/06/05 04:27:12 - mmengine - INFO - Epoch(train) [9][ 300/3907] lr: 1.7969e-05 eta: 1:17:46 time: 0.6234 data_time: 0.4804 memory: 6319 loss: 0.0670 2023/06/05 04:28:12 - mmengine - INFO - Epoch(train) [9][ 400/3907] lr: 1.7765e-05 eta: 1:16:43 time: 0.6136 data_time: 0.4701 memory: 6319 loss: 0.0769 2023/06/05 04:29:11 - mmengine - INFO - Epoch(train) [9][ 500/3907] lr: 1.7563e-05 eta: 1:15:41 time: 0.5967 data_time: 0.4518 memory: 6319 loss: 0.0811 2023/06/05 04:30:10 - mmengine - INFO - Epoch(train) [9][ 600/3907] lr: 1.7363e-05 eta: 1:14:38 time: 0.5911 data_time: 0.4487 memory: 6319 loss: 0.0706 2023/06/05 04:31:11 - mmengine - INFO - Epoch(train) [9][ 700/3907] lr: 1.7166e-05 eta: 1:13:36 time: 0.6231 data_time: 0.4813 memory: 6319 loss: 0.0746 2023/06/05 04:31:38 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 04:32:11 - mmengine - INFO - Epoch(train) [9][ 800/3907] lr: 1.6971e-05 eta: 1:12:33 time: 0.6046 data_time: 0.4628 memory: 6319 loss: 0.0700 2023/06/05 04:33:10 - mmengine - INFO - Epoch(train) [9][ 900/3907] lr: 1.6779e-05 eta: 1:11:30 time: 0.6288 data_time: 0.4872 memory: 6319 loss: 0.0780 2023/06/05 04:34:11 - mmengine - INFO - Epoch(train) [9][1000/3907] lr: 1.6589e-05 eta: 1:10:28 time: 0.5982 data_time: 0.4406 memory: 6319 loss: 0.0653 2023/06/05 04:35:11 - mmengine - INFO - Epoch(train) [9][1100/3907] lr: 1.6402e-05 eta: 1:09:25 time: 0.5596 data_time: 0.4182 memory: 6319 loss: 0.0775 2023/06/05 04:36:10 - mmengine - INFO - Epoch(train) [9][1200/3907] lr: 1.6217e-05 eta: 1:08:23 time: 0.6044 data_time: 0.4525 memory: 6319 loss: 0.0811 2023/06/05 04:37:08 - mmengine - INFO - Epoch(train) [9][1300/3907] lr: 1.6035e-05 eta: 1:07:20 time: 0.6150 data_time: 0.4695 memory: 6319 loss: 0.0680 2023/06/05 04:38:10 - mmengine - INFO - Epoch(train) [9][1400/3907] lr: 1.5855e-05 eta: 1:06:18 time: 0.6378 data_time: 0.4838 memory: 6319 loss: 0.0654 2023/06/05 04:39:08 - mmengine - INFO - Epoch(train) [9][1500/3907] lr: 1.5678e-05 eta: 1:05:15 time: 0.5450 data_time: 0.4008 memory: 6319 loss: 0.0785 2023/06/05 04:40:05 - mmengine - INFO - Epoch(train) [9][1600/3907] lr: 1.5503e-05 eta: 1:04:12 time: 0.6424 data_time: 0.4750 memory: 6319 loss: 0.0752 2023/06/05 04:41:05 - mmengine - INFO - Epoch(train) [9][1700/3907] lr: 1.5331e-05 eta: 1:03:10 time: 0.5732 data_time: 0.4319 memory: 6319 loss: 0.0764 2023/06/05 04:41:32 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 04:42:03 - mmengine - INFO - Epoch(train) [9][1800/3907] lr: 1.5162e-05 eta: 1:02:07 time: 0.5952 data_time: 0.4367 memory: 6319 loss: 0.0812 2023/06/05 04:43:04 - mmengine - INFO - Epoch(train) [9][1900/3907] lr: 1.4995e-05 eta: 1:01:05 time: 0.5903 data_time: 0.4457 memory: 6319 loss: 0.0740 2023/06/05 04:44:03 - mmengine - INFO - Epoch(train) [9][2000/3907] lr: 1.4830e-05 eta: 1:00:02 time: 0.6237 data_time: 0.4651 memory: 6319 loss: 0.0756 2023/06/05 04:45:04 - mmengine - INFO - Epoch(train) [9][2100/3907] lr: 1.4668e-05 eta: 0:59:00 time: 0.5542 data_time: 0.4104 memory: 6319 loss: 0.0783 2023/06/05 04:46:04 - mmengine - INFO - Epoch(train) [9][2200/3907] lr: 1.4509e-05 eta: 0:57:58 time: 0.6137 data_time: 0.4575 memory: 6319 loss: 0.0677 2023/06/05 04:47:03 - mmengine - INFO - Epoch(train) [9][2300/3907] lr: 1.4353e-05 eta: 0:56:55 time: 0.5726 data_time: 0.4299 memory: 6319 loss: 0.0775 2023/06/05 04:48:03 - mmengine - INFO - Epoch(train) [9][2400/3907] lr: 1.4199e-05 eta: 0:55:53 time: 0.6064 data_time: 0.4645 memory: 6319 loss: 0.0789 2023/06/05 04:49:03 - mmengine - INFO - Epoch(train) [9][2500/3907] lr: 1.4047e-05 eta: 0:54:51 time: 0.6004 data_time: 0.4596 memory: 6319 loss: 0.0895 2023/06/05 04:50:01 - mmengine - INFO - Epoch(train) [9][2600/3907] lr: 1.3899e-05 eta: 0:53:48 time: 0.5478 data_time: 0.4055 memory: 6319 loss: 0.0702 2023/06/05 04:51:03 - mmengine - INFO - Epoch(train) [9][2700/3907] lr: 1.3753e-05 eta: 0:52:46 time: 0.5985 data_time: 0.4569 memory: 6319 loss: 0.0654 2023/06/05 04:51:27 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 04:52:03 - mmengine - INFO - Epoch(train) [9][2800/3907] lr: 1.3609e-05 eta: 0:51:44 time: 0.6840 data_time: 0.5412 memory: 6319 loss: 0.0702 2023/06/05 04:53:02 - mmengine - INFO - Epoch(train) [9][2900/3907] lr: 1.3469e-05 eta: 0:50:42 time: 0.5631 data_time: 0.4203 memory: 6319 loss: 0.0635 2023/06/05 04:54:03 - mmengine - INFO - Epoch(train) [9][3000/3907] lr: 1.3331e-05 eta: 0:49:40 time: 0.5699 data_time: 0.4271 memory: 6319 loss: 0.0938 2023/06/05 04:55:03 - mmengine - INFO - Epoch(train) [9][3100/3907] lr: 1.3196e-05 eta: 0:48:38 time: 0.6232 data_time: 0.4813 memory: 6319 loss: 0.0716 2023/06/05 04:56:01 - mmengine - INFO - Epoch(train) [9][3200/3907] lr: 1.3063e-05 eta: 0:47:35 time: 0.5956 data_time: 0.4542 memory: 6319 loss: 0.0660 2023/06/05 04:57:01 - mmengine - INFO - Epoch(train) [9][3300/3907] lr: 1.2933e-05 eta: 0:46:33 time: 0.5846 data_time: 0.4255 memory: 6319 loss: 0.0805 2023/06/05 04:58:02 - mmengine - INFO - Epoch(train) [9][3400/3907] lr: 1.2806e-05 eta: 0:45:31 time: 0.5871 data_time: 0.4452 memory: 6319 loss: 0.0723 2023/06/05 04:59:02 - mmengine - INFO - Epoch(train) [9][3500/3907] lr: 1.2682e-05 eta: 0:44:29 time: 0.5854 data_time: 0.4433 memory: 6319 loss: 0.0767 2023/06/05 05:00:01 - mmengine - INFO - Epoch(train) [9][3600/3907] lr: 1.2560e-05 eta: 0:43:27 time: 0.6187 data_time: 0.4758 memory: 6319 loss: 0.0802 2023/06/05 05:01:05 - mmengine - INFO - Epoch(train) [9][3700/3907] lr: 1.2441e-05 eta: 0:42:25 time: 0.6557 data_time: 0.5139 memory: 6319 loss: 0.0782 2023/06/05 05:01:30 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 05:02:03 - mmengine - INFO - Epoch(train) [9][3800/3907] lr: 1.2325e-05 eta: 0:41:23 time: 0.5939 data_time: 0.4509 memory: 6319 loss: 0.0830 2023/06/05 05:03:03 - mmengine - INFO - Epoch(train) [9][3900/3907] lr: 1.2211e-05 eta: 0:40:21 time: 0.6212 data_time: 0.4791 memory: 6319 loss: 0.0770 2023/06/05 05:03:05 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 05:03:05 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/06/05 05:03:52 - mmengine - INFO - Epoch(val) [9][57/57] accuracy/top1: 96.9834 data_time: 0.5080 time: 0.5982 2023/06/05 05:04:52 - mmengine - INFO - Epoch(train) [10][ 100/3907] lr: 1.2093e-05 eta: 0:39:14 time: 0.5621 data_time: 0.4199 memory: 6319 loss: 0.0748 2023/06/05 05:05:51 - mmengine - INFO - Epoch(train) [10][ 200/3907] lr: 1.1985e-05 eta: 0:38:12 time: 0.5691 data_time: 0.4259 memory: 6319 loss: 0.0837 2023/06/05 05:06:51 - mmengine - INFO - Epoch(train) [10][ 300/3907] lr: 1.1881e-05 eta: 0:37:10 time: 0.6307 data_time: 0.4882 memory: 6319 loss: 0.0876 2023/06/05 05:07:52 - mmengine - INFO - Epoch(train) [10][ 400/3907] lr: 1.1778e-05 eta: 0:36:08 time: 0.5951 data_time: 0.4422 memory: 6319 loss: 0.0720 2023/06/05 05:08:53 - mmengine - INFO - Epoch(train) [10][ 500/3907] lr: 1.1679e-05 eta: 0:35:06 time: 0.5566 data_time: 0.4146 memory: 6319 loss: 0.0775 2023/06/05 05:09:53 - mmengine - INFO - Epoch(train) [10][ 600/3907] lr: 1.1583e-05 eta: 0:34:04 time: 0.6031 data_time: 0.4592 memory: 6319 loss: 0.0647 2023/06/05 05:10:50 - mmengine - INFO - Epoch(train) [10][ 700/3907] lr: 1.1489e-05 eta: 0:33:02 time: 0.5468 data_time: 0.4054 memory: 6319 loss: 0.0781 2023/06/05 05:11:47 - mmengine - INFO - Epoch(train) [10][ 800/3907] lr: 1.1398e-05 eta: 0:32:00 time: 0.6019 data_time: 0.4425 memory: 6319 loss: 0.0828 2023/06/05 05:12:12 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 05:12:49 - mmengine - INFO - Epoch(train) [10][ 900/3907] lr: 1.1310e-05 eta: 0:30:58 time: 0.6152 data_time: 0.4726 memory: 6319 loss: 0.0835 2023/06/05 05:13:48 - mmengine - INFO - Epoch(train) [10][1000/3907] lr: 1.1225e-05 eta: 0:29:56 time: 0.6446 data_time: 0.4820 memory: 6319 loss: 0.0697 2023/06/05 05:14:49 - mmengine - INFO - Epoch(train) [10][1100/3907] lr: 1.1142e-05 eta: 0:28:54 time: 0.5972 data_time: 0.4400 memory: 6319 loss: 0.0900 2023/06/05 05:15:50 - mmengine - INFO - Epoch(train) [10][1200/3907] lr: 1.1063e-05 eta: 0:27:52 time: 0.5879 data_time: 0.4460 memory: 6319 loss: 0.0645 2023/06/05 05:16:49 - mmengine - INFO - Epoch(train) [10][1300/3907] lr: 1.0986e-05 eta: 0:26:50 time: 0.6340 data_time: 0.4896 memory: 6319 loss: 0.0746 2023/06/05 05:17:49 - mmengine - INFO - Epoch(train) [10][1400/3907] lr: 1.0912e-05 eta: 0:25:48 time: 0.5552 data_time: 0.3942 memory: 6319 loss: 0.0850 2023/06/05 05:18:45 - mmengine - INFO - Epoch(train) [10][1500/3907] lr: 1.0841e-05 eta: 0:24:46 time: 0.6212 data_time: 0.4789 memory: 6319 loss: 0.0711 2023/06/05 05:19:44 - mmengine - INFO - Epoch(train) [10][1600/3907] lr: 1.0773e-05 eta: 0:23:44 time: 0.6043 data_time: 0.4616 memory: 6319 loss: 0.0719 2023/06/05 05:20:42 - mmengine - INFO - Epoch(train) [10][1700/3907] lr: 1.0707e-05 eta: 0:22:42 time: 0.6120 data_time: 0.4548 memory: 6319 loss: 0.0703 2023/06/05 05:21:37 - mmengine - INFO - Epoch(train) [10][1800/3907] lr: 1.0645e-05 eta: 0:21:40 time: 0.5521 data_time: 0.3992 memory: 6319 loss: 0.0715 2023/06/05 05:22:00 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 05:22:35 - mmengine - INFO - Epoch(train) [10][1900/3907] lr: 1.0585e-05 eta: 0:20:38 time: 0.6041 data_time: 0.4598 memory: 6319 loss: 0.0677 2023/06/05 05:23:35 - mmengine - INFO - Epoch(train) [10][2000/3907] lr: 1.0529e-05 eta: 0:19:36 time: 0.5876 data_time: 0.4464 memory: 6319 loss: 0.0760 2023/06/05 05:24:35 - mmengine - INFO - Epoch(train) [10][2100/3907] lr: 1.0475e-05 eta: 0:18:35 time: 0.6209 data_time: 0.4786 memory: 6319 loss: 0.0667 2023/06/05 05:25:34 - mmengine - INFO - Epoch(train) [10][2200/3907] lr: 1.0424e-05 eta: 0:17:33 time: 0.5541 data_time: 0.4120 memory: 6319 loss: 0.0729 2023/06/05 05:26:31 - mmengine - INFO - Epoch(train) [10][2300/3907] lr: 1.0376e-05 eta: 0:16:31 time: 0.6067 data_time: 0.4626 memory: 6319 loss: 0.0744 2023/06/05 05:27:30 - mmengine - INFO - Epoch(train) [10][2400/3907] lr: 1.0330e-05 eta: 0:15:29 time: 0.5708 data_time: 0.4272 memory: 6319 loss: 0.0633 2023/06/05 05:28:31 - mmengine - INFO - Epoch(train) [10][2500/3907] lr: 1.0288e-05 eta: 0:14:27 time: 0.6081 data_time: 0.4651 memory: 6319 loss: 0.0665 2023/06/05 05:29:29 - mmengine - INFO - Epoch(train) [10][2600/3907] lr: 1.0249e-05 eta: 0:13:25 time: 0.5578 data_time: 0.4137 memory: 6319 loss: 0.0777 2023/06/05 05:30:27 - mmengine - INFO - Epoch(train) [10][2700/3907] lr: 1.0212e-05 eta: 0:12:24 time: 0.5640 data_time: 0.4224 memory: 6319 loss: 0.0665 2023/06/05 05:31:27 - mmengine - INFO - Epoch(train) [10][2800/3907] lr: 1.0178e-05 eta: 0:11:22 time: 0.5590 data_time: 0.4165 memory: 6319 loss: 0.0783 2023/06/05 05:31:46 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 05:32:29 - mmengine - INFO - Epoch(train) [10][2900/3907] lr: 1.0148e-05 eta: 0:10:20 time: 0.6525 data_time: 0.5093 memory: 6319 loss: 0.0665 2023/06/05 05:33:32 - mmengine - INFO - Epoch(train) [10][3000/3907] lr: 1.0120e-05 eta: 0:09:19 time: 0.5783 data_time: 0.4358 memory: 6319 loss: 0.0697 2023/06/05 05:34:33 - mmengine - INFO - Epoch(train) [10][3100/3907] lr: 1.0095e-05 eta: 0:08:17 time: 0.6042 data_time: 0.4608 memory: 6319 loss: 0.0807 2023/06/05 05:35:28 - mmengine - INFO - Epoch(train) [10][3200/3907] lr: 1.0073e-05 eta: 0:07:15 time: 0.5526 data_time: 0.4108 memory: 6319 loss: 0.0778 2023/06/05 05:36:28 - mmengine - INFO - Epoch(train) [10][3300/3907] lr: 1.0054e-05 eta: 0:06:14 time: 0.5733 data_time: 0.4312 memory: 6319 loss: 0.0591 2023/06/05 05:37:28 - mmengine - INFO - Epoch(train) [10][3400/3907] lr: 1.0038e-05 eta: 0:05:12 time: 0.6162 data_time: 0.4748 memory: 6319 loss: 0.0725 2023/06/05 05:38:28 - mmengine - INFO - Epoch(train) [10][3500/3907] lr: 1.0024e-05 eta: 0:04:10 time: 0.6028 data_time: 0.4609 memory: 6319 loss: 0.0655 2023/06/05 05:39:28 - mmengine - INFO - Epoch(train) [10][3600/3907] lr: 1.0014e-05 eta: 0:03:09 time: 0.5531 data_time: 0.4115 memory: 6319 loss: 0.0963 2023/06/05 05:40:29 - mmengine - INFO - Epoch(train) [10][3700/3907] lr: 1.0006e-05 eta: 0:02:07 time: 0.6058 data_time: 0.4628 memory: 6319 loss: 0.0800 2023/06/05 05:41:28 - mmengine - INFO - Epoch(train) [10][3800/3907] lr: 1.0002e-05 eta: 0:01:05 time: 0.6612 data_time: 0.5197 memory: 6319 loss: 0.0754 2023/06/05 05:41:50 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 05:42:26 - mmengine - INFO - Epoch(train) [10][3900/3907] lr: 1.0000e-05 eta: 0:00:04 time: 0.5697 data_time: 0.4274 memory: 6319 loss: 0.0756 2023/06/05 05:42:31 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_1e-1_20230604_225317 2023/06/05 05:42:31 - mmengine - INFO - Saving checkpoint at 10 epochs 2023/06/05 05:43:17 - mmengine - INFO - Epoch(val) [10][57/57] accuracy/top1: 97.0460 data_time: 0.4989 time: 0.5858