2023/06/06 05:15:42 - 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: 1434538273 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 ------------------------------------------------------------ 2023/06/06 05:15:46 - 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/IF80w.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') ]), dict( type='CustomDataset', data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/if-dpmsolver++-50-20w.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/if-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/if-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_IF_1m_lr1e-4_aug_1e-1' 2023/06/06 05:15:57 - 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 -------------------- 2023/06/06 05:16:15 - 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 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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 https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.3.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.layer2.3.bn2.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.3.bn2.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.3.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.layer2.3.bn3.weight - torch.Size([512]): PretrainedInit: load from 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: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.1.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.layer3.1.bn2.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.1.bn2.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.1.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.layer3.1.bn3.weight - torch.Size([1024]): PretrainedInit: load from 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 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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 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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 2023/06/06 05:16:15 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2023/06/06 05:16:15 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2023/06/06 05:16:15 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/luzeyu/workspace/fakebench/mmpretrain/workdir/resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1. 2023/06/06 05:17:09 - mmengine - INFO - Epoch(train) [1][ 100/3937] lr: 9.9999e-05 eta: 5:49:41 time: 0.5047 data_time: 0.3514 memory: 9436 loss: 0.6373 2023/06/06 05:18:07 - mmengine - INFO - Epoch(train) [1][ 200/3937] lr: 9.9994e-05 eta: 6:05:34 time: 0.5414 data_time: 0.4017 memory: 6319 loss: 0.5732 2023/06/06 05:19:13 - mmengine - INFO - Epoch(train) [1][ 300/3937] lr: 9.9987e-05 eta: 6:25:38 time: 0.6411 data_time: 0.4869 memory: 6319 loss: 0.5214 2023/06/06 05:20:14 - mmengine - INFO - Epoch(train) [1][ 400/3937] lr: 9.9977e-05 eta: 6:28:07 time: 0.6815 data_time: 0.5418 memory: 6319 loss: 0.4485 2023/06/06 05:21:20 - mmengine - INFO - Epoch(train) [1][ 500/3937] lr: 9.9964e-05 eta: 6:34:51 time: 0.5462 data_time: 0.4061 memory: 6319 loss: 0.4105 2023/06/06 05:22:21 - mmengine - INFO - Epoch(train) [1][ 600/3937] lr: 9.9949e-05 eta: 6:33:26 time: 0.6187 data_time: 0.4788 memory: 6319 loss: 0.3614 2023/06/06 05:23:22 - mmengine - INFO - Epoch(train) [1][ 700/3937] lr: 9.9930e-05 eta: 6:32:17 time: 0.6050 data_time: 0.4635 memory: 6319 loss: 0.3202 2023/06/06 05:24:24 - mmengine - INFO - Epoch(train) [1][ 800/3937] lr: 9.9909e-05 eta: 6:32:24 time: 0.6451 data_time: 0.5031 memory: 6319 loss: 0.3191 2023/06/06 05:25:33 - mmengine - INFO - Epoch(train) [1][ 900/3937] lr: 9.9884e-05 eta: 6:36:55 time: 0.5433 data_time: 0.4020 memory: 6319 loss: 0.3023 2023/06/06 05:26:30 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 05:26:30 - mmengine - INFO - Epoch(train) [1][1000/3937] lr: 9.9857e-05 eta: 6:33:17 time: 0.6019 data_time: 0.4612 memory: 6319 loss: 0.2838 2023/06/06 05:27:27 - mmengine - INFO - Epoch(train) [1][1100/3937] lr: 9.9827e-05 eta: 6:29:27 time: 0.5616 data_time: 0.4169 memory: 6319 loss: 0.2604 2023/06/06 05:28:28 - mmengine - INFO - Epoch(train) [1][1200/3937] lr: 9.9794e-05 eta: 6:28:07 time: 0.5829 data_time: 0.4422 memory: 6319 loss: 0.2618 2023/06/06 05:29:25 - mmengine - INFO - Epoch(train) [1][1300/3937] lr: 9.9758e-05 eta: 6:25:32 time: 0.5849 data_time: 0.4418 memory: 6319 loss: 0.2529 2023/06/06 05:30:23 - mmengine - INFO - Epoch(train) [1][1400/3937] lr: 9.9720e-05 eta: 6:23:09 time: 0.5753 data_time: 0.4348 memory: 6319 loss: 0.2381 2023/06/06 05:31:21 - mmengine - INFO - Epoch(train) [1][1500/3937] lr: 9.9678e-05 eta: 6:21:05 time: 0.5428 data_time: 0.3910 memory: 6319 loss: 0.2377 2023/06/06 05:32:19 - mmengine - INFO - Epoch(train) [1][1600/3937] lr: 9.9634e-05 eta: 6:19:15 time: 0.5666 data_time: 0.4258 memory: 6319 loss: 0.2392 2023/06/06 05:33:16 - mmengine - INFO - Epoch(train) [1][1700/3937] lr: 9.9587e-05 eta: 6:17:01 time: 0.5770 data_time: 0.4286 memory: 6319 loss: 0.2350 2023/06/06 05:34:13 - mmengine - INFO - Epoch(train) [1][1800/3937] lr: 9.9537e-05 eta: 6:14:44 time: 0.5512 data_time: 0.4116 memory: 6319 loss: 0.2359 2023/06/06 05:36:42 - mmengine - INFO - Epoch(train) [1][1900/3937] lr: 9.9484e-05 eta: 6:43:14 time: 0.6120 data_time: 0.4719 memory: 6319 loss: 0.2265 2023/06/06 05:37:39 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 05:37:39 - mmengine - INFO - Epoch(train) [1][2000/3937] lr: 9.9429e-05 eta: 6:39:48 time: 0.6012 data_time: 0.4618 memory: 6319 loss: 0.2139 2023/06/06 05:38:44 - mmengine - INFO - Epoch(train) [1][2100/3937] lr: 9.9370e-05 eta: 6:38:52 time: 0.5164 data_time: 0.3744 memory: 6319 loss: 0.2222 2023/06/06 05:39:47 - mmengine - INFO - Epoch(train) [1][2200/3937] lr: 9.9309e-05 eta: 6:37:26 time: 0.5578 data_time: 0.4180 memory: 6319 loss: 0.2177 2023/06/06 05:40:44 - mmengine - INFO - Epoch(train) [1][2300/3937] lr: 9.9245e-05 eta: 6:34:27 time: 0.5166 data_time: 0.3758 memory: 6319 loss: 0.2220 2023/06/06 05:41:38 - mmengine - INFO - Epoch(train) [1][2400/3937] lr: 9.9178e-05 eta: 6:31:00 time: 0.5589 data_time: 0.4190 memory: 6319 loss: 0.2072 2023/06/06 05:42:36 - mmengine - INFO - Epoch(train) [1][2500/3937] lr: 9.9108e-05 eta: 6:28:24 time: 0.5872 data_time: 0.4474 memory: 6319 loss: 0.2102 2023/06/06 05:43:35 - mmengine - INFO - Epoch(train) [1][2600/3937] lr: 9.9036e-05 eta: 6:26:22 time: 0.5721 data_time: 0.4291 memory: 6319 loss: 0.2182 2023/06/06 05:44:30 - mmengine - INFO - Epoch(train) [1][2700/3937] lr: 9.8960e-05 eta: 6:23:36 time: 0.5731 data_time: 0.4330 memory: 6319 loss: 0.2115 2023/06/06 05:45:26 - mmengine - INFO - Epoch(train) [1][2800/3937] lr: 9.8882e-05 eta: 6:21:08 time: 0.5482 data_time: 0.4070 memory: 6319 loss: 0.1990 2023/06/06 05:46:23 - mmengine - INFO - Epoch(train) [1][2900/3937] lr: 9.8801e-05 eta: 6:18:56 time: 0.5595 data_time: 0.4196 memory: 6319 loss: 0.1944 2023/06/06 05:47:21 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 05:47:21 - mmengine - INFO - Epoch(train) [1][3000/3937] lr: 9.8718e-05 eta: 6:16:56 time: 0.5798 data_time: 0.4379 memory: 6319 loss: 0.1924 2023/06/06 05:48:19 - mmengine - INFO - Epoch(train) [1][3100/3937] lr: 9.8631e-05 eta: 6:15:03 time: 0.5713 data_time: 0.4307 memory: 6319 loss: 0.1904 2023/06/06 05:49:15 - mmengine - INFO - Epoch(train) [1][3200/3937] lr: 9.8542e-05 eta: 6:12:58 time: 0.5764 data_time: 0.4360 memory: 6319 loss: 0.1930 2023/06/06 05:50:13 - mmengine - INFO - Epoch(train) [1][3300/3937] lr: 9.8450e-05 eta: 6:11:07 time: 0.5986 data_time: 0.4568 memory: 6319 loss: 0.1836 2023/06/06 05:51:10 - mmengine - INFO - Epoch(train) [1][3400/3937] lr: 9.8355e-05 eta: 6:09:22 time: 0.5555 data_time: 0.4158 memory: 6319 loss: 0.1891 2023/06/06 05:52:07 - mmengine - INFO - Epoch(train) [1][3500/3937] lr: 9.8257e-05 eta: 6:07:28 time: 0.5643 data_time: 0.4242 memory: 6319 loss: 0.2057 2023/06/06 05:53:04 - mmengine - INFO - Epoch(train) [1][3600/3937] lr: 9.8157e-05 eta: 6:05:46 time: 0.5689 data_time: 0.4279 memory: 6319 loss: 0.1987 2023/06/06 05:54:01 - mmengine - INFO - Epoch(train) [1][3700/3937] lr: 9.8054e-05 eta: 6:03:58 time: 0.5294 data_time: 0.3884 memory: 6319 loss: 0.1900 2023/06/06 05:55:07 - mmengine - INFO - Epoch(train) [1][3800/3937] lr: 9.7948e-05 eta: 6:03:46 time: 0.5100 data_time: 0.3698 memory: 6319 loss: 0.1846 2023/06/06 05:56:00 - mmengine - INFO - Epoch(train) [1][3900/3937] lr: 9.7840e-05 eta: 6:01:28 time: 0.5300 data_time: 0.3885 memory: 6319 loss: 0.1877 2023/06/06 05:56:22 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 05:56:22 - mmengine - INFO - Saving checkpoint at 1 epochs 2023/06/06 05:56:58 - mmengine - INFO - Epoch(val) [1][57/57] accuracy/top1: 65.2830 data_time: 0.3456 time: 0.4378 2023/06/06 05:57:37 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 05:57:54 - mmengine - INFO - Epoch(train) [2][ 100/3937] lr: 9.7686e-05 eta: 5:59:09 time: 0.5510 data_time: 0.3870 memory: 8776 loss: 0.1776 2023/06/06 05:58:47 - mmengine - INFO - Epoch(train) [2][ 200/3937] lr: 9.7571e-05 eta: 5:57:03 time: 0.4744 data_time: 0.1604 memory: 6318 loss: 0.1698 2023/06/06 05:59:38 - mmengine - INFO - Epoch(train) [2][ 300/3937] lr: 9.7454e-05 eta: 5:54:41 time: 0.5031 data_time: 0.0548 memory: 6318 loss: 0.1841 2023/06/06 06:00:33 - mmengine - INFO - Epoch(train) [2][ 400/3937] lr: 9.7333e-05 eta: 5:52:49 time: 0.6180 data_time: 0.0845 memory: 6318 loss: 0.1695 2023/06/06 06:01:23 - mmengine - INFO - Epoch(train) [2][ 500/3937] lr: 9.7210e-05 eta: 5:50:26 time: 0.4621 data_time: 0.0924 memory: 6318 loss: 0.1613 2023/06/06 06:02:10 - mmengine - INFO - Epoch(train) [2][ 600/3937] lr: 9.7084e-05 eta: 5:47:46 time: 0.5158 data_time: 0.1673 memory: 6318 loss: 0.1570 2023/06/06 06:03:02 - mmengine - INFO - Epoch(train) [2][ 700/3937] lr: 9.6956e-05 eta: 5:45:48 time: 0.4993 data_time: 0.3292 memory: 6318 loss: 0.1773 2023/06/06 06:03:55 - mmengine - INFO - Epoch(train) [2][ 800/3937] lr: 9.6825e-05 eta: 5:43:58 time: 0.5105 data_time: 0.3632 memory: 6318 loss: 0.1792 2023/06/06 06:04:48 - mmengine - INFO - Epoch(train) [2][ 900/3937] lr: 9.6691e-05 eta: 5:42:12 time: 0.5395 data_time: 0.3273 memory: 6318 loss: 0.1745 2023/06/06 06:05:42 - mmengine - INFO - Epoch(train) [2][1000/3937] lr: 9.6554e-05 eta: 5:40:34 time: 0.5209 data_time: 0.2691 memory: 6318 loss: 0.1860 2023/06/06 06:06:18 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 06:06:35 - mmengine - INFO - Epoch(train) [2][1100/3937] lr: 9.6415e-05 eta: 5:38:50 time: 0.5503 data_time: 0.3627 memory: 6318 loss: 0.1756 2023/06/06 06:07:28 - mmengine - INFO - Epoch(train) [2][1200/3937] lr: 9.6273e-05 eta: 5:37:12 time: 0.5431 data_time: 0.2301 memory: 6318 loss: 0.1674 2023/06/06 06:08:25 - mmengine - INFO - Epoch(train) [2][1300/3937] lr: 9.6129e-05 eta: 5:35:56 time: 0.5604 data_time: 0.1979 memory: 6318 loss: 0.1689 2023/06/06 06:09:19 - mmengine - INFO - Epoch(train) [2][1400/3937] lr: 9.5982e-05 eta: 5:34:25 time: 0.5552 data_time: 0.2883 memory: 6318 loss: 0.1605 2023/06/06 06:10:11 - mmengine - INFO - Epoch(train) [2][1500/3937] lr: 9.5832e-05 eta: 5:32:45 time: 0.4835 data_time: 0.0954 memory: 6318 loss: 0.1733 2023/06/06 06:11:04 - mmengine - INFO - Epoch(train) [2][1600/3937] lr: 9.5680e-05 eta: 5:31:12 time: 0.5141 data_time: 0.1621 memory: 6318 loss: 0.1646 2023/06/06 06:11:57 - mmengine - INFO - Epoch(train) [2][1700/3937] lr: 9.5525e-05 eta: 5:29:34 time: 0.5090 data_time: 0.2315 memory: 6318 loss: 0.1661 2023/06/06 06:12:50 - mmengine - INFO - Epoch(train) [2][1800/3937] lr: 9.5368e-05 eta: 5:28:05 time: 0.5375 data_time: 0.3950 memory: 6318 loss: 0.1593 2023/06/06 06:13:44 - mmengine - INFO - Epoch(train) [2][1900/3937] lr: 9.5208e-05 eta: 5:26:40 time: 0.5412 data_time: 0.4003 memory: 6318 loss: 0.1620 2023/06/06 06:14:35 - mmengine - INFO - Epoch(train) [2][2000/3937] lr: 9.5045e-05 eta: 5:25:01 time: 0.4904 data_time: 0.3501 memory: 6318 loss: 0.1636 2023/06/06 06:15:07 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 06:15:27 - mmengine - INFO - Epoch(train) [2][2100/3937] lr: 9.4880e-05 eta: 5:23:29 time: 0.5136 data_time: 0.3732 memory: 6318 loss: 0.1564 2023/06/06 06:16:21 - mmengine - INFO - Epoch(train) [2][2200/3937] lr: 9.4713e-05 eta: 5:22:08 time: 0.5361 data_time: 0.3961 memory: 6318 loss: 0.1632 2023/06/06 06:17:14 - mmengine - INFO - Epoch(train) [2][2300/3937] lr: 9.4543e-05 eta: 5:20:40 time: 0.5368 data_time: 0.3964 memory: 6318 loss: 0.1699 2023/06/06 06:18:06 - mmengine - INFO - Epoch(train) [2][2400/3937] lr: 9.4370e-05 eta: 5:19:13 time: 0.5168 data_time: 0.3755 memory: 6318 loss: 0.1533 2023/06/06 06:18:59 - mmengine - INFO - Epoch(train) [2][2500/3937] lr: 9.4195e-05 eta: 5:17:46 time: 0.5169 data_time: 0.3764 memory: 6318 loss: 0.1366 2023/06/06 06:19:53 - mmengine - INFO - Epoch(train) [2][2600/3937] lr: 9.4017e-05 eta: 5:16:32 time: 0.7384 data_time: 0.5957 memory: 6318 loss: 0.1449 2023/06/06 06:20:47 - mmengine - INFO - Epoch(train) [2][2700/3937] lr: 9.3837e-05 eta: 5:15:14 time: 0.5579 data_time: 0.4174 memory: 6318 loss: 0.1521 2023/06/06 06:21:36 - mmengine - INFO - Epoch(train) [2][2800/3937] lr: 9.3654e-05 eta: 5:13:35 time: 0.4788 data_time: 0.3381 memory: 6318 loss: 0.1664 2023/06/06 06:22:28 - mmengine - INFO - Epoch(train) [2][2900/3937] lr: 9.3469e-05 eta: 5:12:10 time: 0.5536 data_time: 0.4131 memory: 6318 loss: 0.1545 2023/06/06 06:23:22 - mmengine - INFO - Epoch(train) [2][3000/3937] lr: 9.3282e-05 eta: 5:10:53 time: 0.5363 data_time: 0.3947 memory: 6318 loss: 0.1534 2023/06/06 06:23:54 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 06:24:18 - mmengine - INFO - Epoch(train) [2][3100/3937] lr: 9.3092e-05 eta: 5:09:48 time: 0.5498 data_time: 0.3988 memory: 6318 loss: 0.1516 2023/06/06 06:25:11 - mmengine - INFO - Epoch(train) [2][3200/3937] lr: 9.2899e-05 eta: 5:08:32 time: 0.5706 data_time: 0.4294 memory: 6318 loss: 0.1571 2023/06/06 06:26:05 - mmengine - INFO - Epoch(train) [2][3300/3937] lr: 9.2705e-05 eta: 5:07:17 time: 0.5533 data_time: 0.4121 memory: 6318 loss: 0.1340 2023/06/06 06:26:57 - mmengine - INFO - Epoch(train) [2][3400/3937] lr: 9.2507e-05 eta: 5:05:59 time: 0.5596 data_time: 0.4186 memory: 6318 loss: 0.1446 2023/06/06 06:27:51 - mmengine - INFO - Epoch(train) [2][3500/3937] lr: 9.2308e-05 eta: 5:04:46 time: 0.5405 data_time: 0.3979 memory: 6318 loss: 0.1461 2023/06/06 06:28:43 - mmengine - INFO - Epoch(train) [2][3600/3937] lr: 9.2106e-05 eta: 5:03:26 time: 0.5186 data_time: 0.3776 memory: 6318 loss: 0.1462 2023/06/06 06:29:35 - mmengine - INFO - Epoch(train) [2][3700/3937] lr: 9.1902e-05 eta: 5:02:09 time: 0.5137 data_time: 0.3727 memory: 6318 loss: 0.1502 2023/06/06 06:30:28 - mmengine - INFO - Epoch(train) [2][3800/3937] lr: 9.1695e-05 eta: 5:00:55 time: 0.5301 data_time: 0.3903 memory: 6318 loss: 0.1527 2023/06/06 06:31:20 - mmengine - INFO - Epoch(train) [2][3900/3937] lr: 9.1486e-05 eta: 4:59:38 time: 0.5244 data_time: 0.3838 memory: 6318 loss: 0.1423 2023/06/06 06:31:37 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 06:31:37 - mmengine - INFO - Saving checkpoint at 2 epochs 2023/06/06 06:32:08 - mmengine - INFO - Epoch(val) [2][57/57] accuracy/top1: 86.7993 data_time: 0.2487 time: 0.3374 2023/06/06 06:33:03 - mmengine - INFO - Epoch(train) [3][ 100/3937] lr: 9.1196e-05 eta: 4:57:55 time: 0.5260 data_time: 0.3863 memory: 6318 loss: 0.1453 2023/06/06 06:33:18 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 06:33:55 - mmengine - INFO - Epoch(train) [3][ 200/3937] lr: 9.0981e-05 eta: 4:56:40 time: 0.5138 data_time: 0.3732 memory: 6318 loss: 0.1224 2023/06/06 06:34:48 - mmengine - INFO - Epoch(train) [3][ 300/3937] lr: 9.0764e-05 eta: 4:55:30 time: 0.5260 data_time: 0.3859 memory: 6318 loss: 0.1420 2023/06/06 06:35:41 - mmengine - INFO - Epoch(train) [3][ 400/3937] lr: 9.0545e-05 eta: 4:54:16 time: 0.5375 data_time: 0.3987 memory: 6318 loss: 0.1296 2023/06/06 06:36:44 - mmengine - INFO - Epoch(train) [3][ 500/3937] lr: 9.0324e-05 eta: 4:53:42 time: 0.5341 data_time: 0.3945 memory: 6318 loss: 0.1484 2023/06/06 06:37:36 - mmengine - INFO - Epoch(train) [3][ 600/3937] lr: 9.0100e-05 eta: 4:52:29 time: 0.4678 data_time: 0.3276 memory: 6318 loss: 0.1336 2023/06/06 06:38:26 - mmengine - INFO - Epoch(train) [3][ 700/3937] lr: 8.9875e-05 eta: 4:51:09 time: 0.5399 data_time: 0.3987 memory: 6318 loss: 0.1381 2023/06/06 06:39:19 - mmengine - INFO - Epoch(train) [3][ 800/3937] lr: 8.9647e-05 eta: 4:49:56 time: 0.5374 data_time: 0.3964 memory: 6318 loss: 0.1518 2023/06/06 06:40:11 - mmengine - INFO - Epoch(train) [3][ 900/3937] lr: 8.9416e-05 eta: 4:48:45 time: 0.5084 data_time: 0.3686 memory: 6318 loss: 0.1569 2023/06/06 06:41:03 - mmengine - INFO - Epoch(train) [3][1000/3937] lr: 8.9184e-05 eta: 4:47:32 time: 0.4690 data_time: 0.3288 memory: 6318 loss: 0.1136 2023/06/06 06:41:53 - mmengine - INFO - Epoch(train) [3][1100/3937] lr: 8.8949e-05 eta: 4:46:12 time: 0.5258 data_time: 0.3855 memory: 6318 loss: 0.1454 2023/06/06 06:42:08 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 06:42:46 - mmengine - INFO - Epoch(train) [3][1200/3937] lr: 8.8712e-05 eta: 4:45:06 time: 0.5376 data_time: 0.3976 memory: 6318 loss: 0.1523 2023/06/06 06:43:39 - mmengine - INFO - Epoch(train) [3][1300/3937] lr: 8.8474e-05 eta: 4:43:58 time: 0.5290 data_time: 0.3884 memory: 6318 loss: 0.1266 2023/06/06 06:44:35 - mmengine - INFO - Epoch(train) [3][1400/3937] lr: 8.8232e-05 eta: 4:43:00 time: 0.5105 data_time: 0.3702 memory: 6318 loss: 0.1287 2023/06/06 06:45:28 - mmengine - INFO - Epoch(train) [3][1500/3937] lr: 8.7989e-05 eta: 4:41:52 time: 0.5381 data_time: 0.3972 memory: 6318 loss: 0.1386 2023/06/06 06:46:22 - mmengine - INFO - Epoch(train) [3][1600/3937] lr: 8.7744e-05 eta: 4:40:48 time: 0.5485 data_time: 0.4087 memory: 6318 loss: 0.1346 2023/06/06 06:47:15 - mmengine - INFO - Epoch(train) [3][1700/3937] lr: 8.7497e-05 eta: 4:39:40 time: 0.5403 data_time: 0.4002 memory: 6318 loss: 0.1432 2023/06/06 06:48:08 - mmengine - INFO - Epoch(train) [3][1800/3937] lr: 8.7247e-05 eta: 4:38:36 time: 0.5743 data_time: 0.4343 memory: 6318 loss: 0.1401 2023/06/06 06:49:01 - mmengine - INFO - Epoch(train) [3][1900/3937] lr: 8.6996e-05 eta: 4:37:29 time: 0.5147 data_time: 0.3745 memory: 6318 loss: 0.1292 2023/06/06 06:49:53 - mmengine - INFO - Epoch(train) [3][2000/3937] lr: 8.6742e-05 eta: 4:36:20 time: 0.5084 data_time: 0.3644 memory: 6318 loss: 0.1330 2023/06/06 06:50:47 - mmengine - INFO - Epoch(train) [3][2100/3937] lr: 8.6487e-05 eta: 4:35:16 time: 0.5392 data_time: 0.3924 memory: 6318 loss: 0.1320 2023/06/06 06:51:01 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 06:51:40 - mmengine - INFO - Epoch(train) [3][2200/3937] lr: 8.6229e-05 eta: 4:34:11 time: 0.7131 data_time: 0.5733 memory: 6318 loss: 0.1407 2023/06/06 06:52:31 - mmengine - INFO - Epoch(train) [3][2300/3937] lr: 8.5970e-05 eta: 4:33:01 time: 0.5335 data_time: 0.3915 memory: 6318 loss: 0.1336 2023/06/06 06:53:23 - mmengine - INFO - Epoch(train) [3][2400/3937] lr: 8.5708e-05 eta: 4:31:53 time: 0.5171 data_time: 0.3767 memory: 6318 loss: 0.1252 2023/06/06 06:54:17 - mmengine - INFO - Epoch(train) [3][2500/3937] lr: 8.5445e-05 eta: 4:30:51 time: 0.5400 data_time: 0.3926 memory: 6318 loss: 0.1189 2023/06/06 06:55:10 - mmengine - INFO - Epoch(train) [3][2600/3937] lr: 8.5179e-05 eta: 4:29:46 time: 0.5304 data_time: 0.3909 memory: 6318 loss: 0.1370 2023/06/06 06:56:02 - mmengine - INFO - Epoch(train) [3][2700/3937] lr: 8.4912e-05 eta: 4:28:38 time: 0.5066 data_time: 0.3662 memory: 6318 loss: 0.1270 2023/06/06 06:56:53 - mmengine - INFO - Epoch(train) [3][2800/3937] lr: 8.4643e-05 eta: 4:27:31 time: 0.4913 data_time: 0.3500 memory: 6318 loss: 0.1258 2023/06/06 06:57:47 - mmengine - INFO - Epoch(train) [3][2900/3937] lr: 8.4372e-05 eta: 4:26:28 time: 0.5241 data_time: 0.3839 memory: 6318 loss: 0.1231 2023/06/06 06:58:40 - mmengine - INFO - Epoch(train) [3][3000/3937] lr: 8.4099e-05 eta: 4:25:26 time: 0.5343 data_time: 0.3930 memory: 6318 loss: 0.1207 2023/06/06 06:59:34 - mmengine - INFO - Epoch(train) [3][3100/3937] lr: 8.3824e-05 eta: 4:24:24 time: 0.5280 data_time: 0.3880 memory: 6318 loss: 0.1270 2023/06/06 06:59:49 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 07:00:26 - mmengine - INFO - Epoch(train) [3][3200/3937] lr: 8.3547e-05 eta: 4:23:19 time: 0.5151 data_time: 0.3738 memory: 6318 loss: 0.1255 2023/06/06 07:01:14 - mmengine - INFO - Epoch(train) [3][3300/3937] lr: 8.3269e-05 eta: 4:22:03 time: 0.4971 data_time: 0.3565 memory: 6318 loss: 0.1128 2023/06/06 07:02:06 - mmengine - INFO - Epoch(train) [3][3400/3937] lr: 8.2988e-05 eta: 4:20:59 time: 0.5572 data_time: 0.4172 memory: 6318 loss: 0.1208 2023/06/06 07:03:00 - mmengine - INFO - Epoch(train) [3][3500/3937] lr: 8.2706e-05 eta: 4:19:57 time: 0.5464 data_time: 0.4059 memory: 6318 loss: 0.1126 2023/06/06 07:03:55 - mmengine - INFO - Epoch(train) [3][3600/3937] lr: 8.2423e-05 eta: 4:19:00 time: 0.5714 data_time: 0.4318 memory: 6318 loss: 0.1323 2023/06/06 07:04:47 - mmengine - INFO - Epoch(train) [3][3700/3937] lr: 8.2137e-05 eta: 4:17:55 time: 0.5590 data_time: 0.4175 memory: 6318 loss: 0.1208 2023/06/06 07:05:38 - mmengine - INFO - Epoch(train) [3][3800/3937] lr: 8.1850e-05 eta: 4:16:49 time: 0.5164 data_time: 0.3455 memory: 6318 loss: 0.1324 2023/06/06 07:06:31 - mmengine - INFO - Epoch(train) [3][3900/3937] lr: 8.1561e-05 eta: 4:15:47 time: 0.5163 data_time: 0.3638 memory: 6318 loss: 0.1253 2023/06/06 07:06:51 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 07:06:51 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/06/06 07:07:24 - mmengine - INFO - Epoch(val) [3][57/57] accuracy/top1: 91.9036 data_time: 0.3081 time: 0.3949 2023/06/06 07:08:18 - mmengine - INFO - Epoch(train) [4][ 100/3937] lr: 8.1162e-05 eta: 4:14:26 time: 0.5493 data_time: 0.4092 memory: 6318 loss: 0.1251 2023/06/06 07:09:06 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 07:09:12 - mmengine - INFO - Epoch(train) [4][ 200/3937] lr: 8.0869e-05 eta: 4:13:25 time: 0.5319 data_time: 0.3922 memory: 6318 loss: 0.1404 2023/06/06 07:10:05 - mmengine - INFO - Epoch(train) [4][ 300/3937] lr: 8.0574e-05 eta: 4:12:25 time: 0.5273 data_time: 0.3872 memory: 6318 loss: 0.1384 2023/06/06 07:10:58 - mmengine - INFO - Epoch(train) [4][ 400/3937] lr: 8.0278e-05 eta: 4:11:23 time: 0.5216 data_time: 0.3695 memory: 6318 loss: 0.1159 2023/06/06 07:11:50 - mmengine - INFO - Epoch(train) [4][ 500/3937] lr: 7.9980e-05 eta: 4:10:20 time: 0.5605 data_time: 0.4206 memory: 6318 loss: 0.1300 2023/06/06 07:12:42 - mmengine - INFO - Epoch(train) [4][ 600/3937] lr: 7.9681e-05 eta: 4:09:17 time: 0.5050 data_time: 0.3648 memory: 6318 loss: 0.1427 2023/06/06 07:13:35 - mmengine - INFO - Epoch(train) [4][ 700/3937] lr: 7.9380e-05 eta: 4:08:16 time: 0.5670 data_time: 0.4261 memory: 6318 loss: 0.1226 2023/06/06 07:14:28 - mmengine - INFO - Epoch(train) [4][ 800/3937] lr: 7.9077e-05 eta: 4:07:16 time: 0.5410 data_time: 0.4006 memory: 6318 loss: 0.1108 2023/06/06 07:15:21 - mmengine - INFO - Epoch(train) [4][ 900/3937] lr: 7.8773e-05 eta: 4:06:15 time: 0.5204 data_time: 0.3793 memory: 6318 loss: 0.1147 2023/06/06 07:16:14 - mmengine - INFO - Epoch(train) [4][1000/3937] lr: 7.8467e-05 eta: 4:05:15 time: 0.4980 data_time: 0.3544 memory: 6318 loss: 0.1201 2023/06/06 07:17:08 - mmengine - INFO - Epoch(train) [4][1100/3937] lr: 7.8160e-05 eta: 4:04:17 time: 0.5739 data_time: 0.4329 memory: 6318 loss: 0.1147 2023/06/06 07:17:57 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 07:18:01 - mmengine - INFO - Epoch(train) [4][1200/3937] lr: 7.7852e-05 eta: 4:03:16 time: 0.4707 data_time: 0.3300 memory: 6318 loss: 0.1175 2023/06/06 07:18:56 - mmengine - INFO - Epoch(train) [4][1300/3937] lr: 7.7541e-05 eta: 4:02:19 time: 0.5458 data_time: 0.4063 memory: 6318 loss: 0.1247 2023/06/06 07:19:49 - mmengine - INFO - Epoch(train) [4][1400/3937] lr: 7.7230e-05 eta: 4:01:19 time: 0.5519 data_time: 0.4119 memory: 6318 loss: 0.1383 2023/06/06 07:20:42 - mmengine - INFO - Epoch(train) [4][1500/3937] lr: 7.6917e-05 eta: 4:00:20 time: 0.4628 data_time: 0.3220 memory: 6318 loss: 0.1111 2023/06/06 07:21:37 - mmengine - INFO - Epoch(train) [4][1600/3937] lr: 7.6603e-05 eta: 3:59:23 time: 0.5125 data_time: 0.3714 memory: 6318 loss: 0.1200 2023/06/06 07:22:30 - mmengine - INFO - Epoch(train) [4][1700/3937] lr: 7.6287e-05 eta: 3:58:23 time: 0.5060 data_time: 0.3652 memory: 6318 loss: 0.1127 2023/06/06 07:23:23 - mmengine - INFO - Epoch(train) [4][1800/3937] lr: 7.5970e-05 eta: 3:57:24 time: 0.5295 data_time: 0.3892 memory: 6318 loss: 0.1079 2023/06/06 07:24:17 - mmengine - INFO - Epoch(train) [4][1900/3937] lr: 7.5652e-05 eta: 3:56:26 time: 0.5295 data_time: 0.3884 memory: 6318 loss: 0.1077 2023/06/06 07:25:08 - mmengine - INFO - Epoch(train) [4][2000/3937] lr: 7.5332e-05 eta: 3:55:23 time: 0.4793 data_time: 0.3397 memory: 6318 loss: 0.1228 2023/06/06 07:26:03 - mmengine - INFO - Epoch(train) [4][2100/3937] lr: 7.5011e-05 eta: 3:54:28 time: 0.5257 data_time: 0.3724 memory: 6318 loss: 0.1336 2023/06/06 07:26:51 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 07:26:56 - mmengine - INFO - Epoch(train) [4][2200/3937] lr: 7.4689e-05 eta: 3:53:28 time: 0.5282 data_time: 0.3885 memory: 6318 loss: 0.1253 2023/06/06 07:27:49 - mmengine - INFO - Epoch(train) [4][2300/3937] lr: 7.4365e-05 eta: 3:52:29 time: 0.5276 data_time: 0.3871 memory: 6318 loss: 0.1141 2023/06/06 07:28:42 - mmengine - INFO - Epoch(train) [4][2400/3937] lr: 7.4040e-05 eta: 3:51:29 time: 0.5059 data_time: 0.3657 memory: 6318 loss: 0.1167 2023/06/06 07:29:34 - mmengine - INFO - Epoch(train) [4][2500/3937] lr: 7.3714e-05 eta: 3:50:29 time: 0.5242 data_time: 0.3831 memory: 6318 loss: 0.1068 2023/06/06 07:30:28 - mmengine - INFO - Epoch(train) [4][2600/3937] lr: 7.3387e-05 eta: 3:49:31 time: 0.5403 data_time: 0.3998 memory: 6318 loss: 0.1111 2023/06/06 07:31:22 - mmengine - INFO - Epoch(train) [4][2700/3937] lr: 7.3059e-05 eta: 3:48:35 time: 0.5112 data_time: 0.3698 memory: 6318 loss: 0.1154 2023/06/06 07:32:16 - mmengine - INFO - Epoch(train) [4][2800/3937] lr: 7.2730e-05 eta: 3:47:38 time: 0.5330 data_time: 0.3927 memory: 6318 loss: 0.1196 2023/06/06 07:33:12 - mmengine - INFO - Epoch(train) [4][2900/3937] lr: 7.2399e-05 eta: 3:46:43 time: 0.4828 data_time: 0.3426 memory: 6318 loss: 0.1188 2023/06/06 07:34:06 - mmengine - INFO - Epoch(train) [4][3000/3937] lr: 7.2067e-05 eta: 3:45:46 time: 0.5592 data_time: 0.4177 memory: 6318 loss: 0.1068 2023/06/06 07:34:59 - mmengine - INFO - Epoch(train) [4][3100/3937] lr: 7.1734e-05 eta: 3:44:48 time: 0.5557 data_time: 0.4155 memory: 6318 loss: 0.1081 2023/06/06 07:35:47 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 07:35:53 - mmengine - INFO - Epoch(train) [4][3200/3937] lr: 7.1401e-05 eta: 3:43:50 time: 0.5145 data_time: 0.3741 memory: 6318 loss: 0.1165 2023/06/06 07:36:46 - mmengine - INFO - Epoch(train) [4][3300/3937] lr: 7.1066e-05 eta: 3:42:51 time: 0.5350 data_time: 0.3953 memory: 6318 loss: 0.1208 2023/06/06 07:37:45 - mmengine - INFO - Epoch(train) [4][3400/3937] lr: 7.0730e-05 eta: 3:42:02 time: 0.5341 data_time: 0.3927 memory: 6318 loss: 0.1159 2023/06/06 07:38:38 - mmengine - INFO - Epoch(train) [4][3500/3937] lr: 7.0393e-05 eta: 3:41:04 time: 0.5602 data_time: 0.4198 memory: 6318 loss: 0.1173 2023/06/06 07:39:32 - mmengine - INFO - Epoch(train) [4][3600/3937] lr: 7.0055e-05 eta: 3:40:07 time: 0.5138 data_time: 0.3735 memory: 6318 loss: 0.1098 2023/06/06 07:40:25 - mmengine - INFO - Epoch(train) [4][3700/3937] lr: 6.9716e-05 eta: 3:39:09 time: 0.5398 data_time: 0.3990 memory: 6318 loss: 0.1083 2023/06/06 07:41:14 - mmengine - INFO - Epoch(train) [4][3800/3937] lr: 6.9376e-05 eta: 3:38:05 time: 0.4256 data_time: 0.2859 memory: 6318 loss: 0.0960 2023/06/06 07:42:08 - mmengine - INFO - Epoch(train) [4][3900/3937] lr: 6.9035e-05 eta: 3:37:08 time: 0.5472 data_time: 0.4022 memory: 6318 loss: 0.1149 2023/06/06 07:42:27 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 07:42:27 - mmengine - INFO - Saving checkpoint at 4 epochs 2023/06/06 07:43:06 - mmengine - INFO - Epoch(val) [4][57/57] accuracy/top1: 93.9946 data_time: 0.3987 time: 0.4851 2023/06/06 07:44:02 - mmengine - INFO - Epoch(train) [5][ 100/3937] lr: 6.8567e-05 eta: 3:35:51 time: 0.5796 data_time: 0.4392 memory: 6318 loss: 0.1268 2023/06/06 07:44:54 - mmengine - INFO - Epoch(train) [5][ 200/3937] lr: 6.8224e-05 eta: 3:34:53 time: 0.5533 data_time: 0.4073 memory: 6318 loss: 0.1138 2023/06/06 07:45:25 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 07:45:48 - mmengine - INFO - Epoch(train) [5][ 300/3937] lr: 6.7881e-05 eta: 3:33:55 time: 0.5136 data_time: 0.3716 memory: 6318 loss: 0.1120 2023/06/06 07:46:43 - mmengine - INFO - Epoch(train) [5][ 400/3937] lr: 6.7536e-05 eta: 3:33:00 time: 0.5638 data_time: 0.4237 memory: 6318 loss: 0.1069 2023/06/06 07:47:35 - mmengine - INFO - Epoch(train) [5][ 500/3937] lr: 6.7191e-05 eta: 3:32:01 time: 0.5221 data_time: 0.3810 memory: 6318 loss: 0.1148 2023/06/06 07:48:28 - mmengine - INFO - Epoch(train) [5][ 600/3937] lr: 6.6845e-05 eta: 3:31:03 time: 0.5582 data_time: 0.4182 memory: 6318 loss: 0.1209 2023/06/06 07:49:20 - mmengine - INFO - Epoch(train) [5][ 700/3937] lr: 6.6498e-05 eta: 3:30:04 time: 0.5336 data_time: 0.3941 memory: 6318 loss: 0.1143 2023/06/06 07:50:13 - mmengine - INFO - Epoch(train) [5][ 800/3937] lr: 6.6151e-05 eta: 3:29:07 time: 0.5414 data_time: 0.3909 memory: 6318 loss: 0.1151 2023/06/06 07:51:07 - mmengine - INFO - Epoch(train) [5][ 900/3937] lr: 6.5802e-05 eta: 3:28:10 time: 0.5284 data_time: 0.3882 memory: 6318 loss: 0.1190 2023/06/06 07:52:01 - mmengine - INFO - Epoch(train) [5][1000/3937] lr: 6.5454e-05 eta: 3:27:14 time: 0.5280 data_time: 0.3874 memory: 6318 loss: 0.0977 2023/06/06 07:52:53 - mmengine - INFO - Epoch(train) [5][1100/3937] lr: 6.5104e-05 eta: 3:26:15 time: 0.5452 data_time: 0.4036 memory: 6318 loss: 0.1031 2023/06/06 07:53:47 - mmengine - INFO - Epoch(train) [5][1200/3937] lr: 6.4754e-05 eta: 3:25:19 time: 0.5358 data_time: 0.3943 memory: 6318 loss: 0.1205 2023/06/06 07:54:14 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 07:54:39 - mmengine - INFO - Epoch(train) [5][1300/3937] lr: 6.4403e-05 eta: 3:24:20 time: 0.5059 data_time: 0.3663 memory: 6318 loss: 0.1149 2023/06/06 07:55:30 - mmengine - INFO - Epoch(train) [5][1400/3937] lr: 6.4051e-05 eta: 3:23:20 time: 0.5050 data_time: 0.3636 memory: 6318 loss: 0.1093 2023/06/06 07:56:22 - mmengine - INFO - Epoch(train) [5][1500/3937] lr: 6.3699e-05 eta: 3:22:21 time: 0.5233 data_time: 0.3828 memory: 6318 loss: 0.1071 2023/06/06 07:57:15 - mmengine - INFO - Epoch(train) [5][1600/3937] lr: 6.3347e-05 eta: 3:21:24 time: 0.5349 data_time: 0.3948 memory: 6318 loss: 0.1112 2023/06/06 07:58:10 - mmengine - INFO - Epoch(train) [5][1700/3937] lr: 6.2994e-05 eta: 3:20:29 time: 0.5508 data_time: 0.4100 memory: 6318 loss: 0.1143 2023/06/06 07:59:04 - mmengine - INFO - Epoch(train) [5][1800/3937] lr: 6.2640e-05 eta: 3:19:33 time: 0.5132 data_time: 0.3744 memory: 6318 loss: 0.1044 2023/06/06 07:59:57 - mmengine - INFO - Epoch(train) [5][1900/3937] lr: 6.2286e-05 eta: 3:18:37 time: 0.4946 data_time: 0.3545 memory: 6318 loss: 0.1073 2023/06/06 08:00:46 - mmengine - INFO - Epoch(train) [5][2000/3937] lr: 6.1931e-05 eta: 3:17:34 time: 0.4647 data_time: 0.3237 memory: 6318 loss: 0.0993 2023/06/06 08:01:37 - mmengine - INFO - Epoch(train) [5][2100/3937] lr: 6.1576e-05 eta: 3:16:35 time: 0.4765 data_time: 0.3361 memory: 6318 loss: 0.1078 2023/06/06 08:02:28 - mmengine - INFO - Epoch(train) [5][2200/3937] lr: 6.1221e-05 eta: 3:15:35 time: 0.4753 data_time: 0.3357 memory: 6318 loss: 0.1031 2023/06/06 08:02:55 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 08:03:19 - mmengine - INFO - Epoch(train) [5][2300/3937] lr: 6.0865e-05 eta: 3:14:36 time: 0.4953 data_time: 0.3543 memory: 6318 loss: 0.1024 2023/06/06 08:04:09 - mmengine - INFO - Epoch(train) [5][2400/3937] lr: 6.0509e-05 eta: 3:13:35 time: 0.5209 data_time: 0.3811 memory: 6318 loss: 0.1015 2023/06/06 08:05:00 - mmengine - INFO - Epoch(train) [5][2500/3937] lr: 6.0152e-05 eta: 3:12:36 time: 0.5036 data_time: 0.3631 memory: 6318 loss: 0.0985 2023/06/06 08:05:51 - mmengine - INFO - Epoch(train) [5][2600/3937] lr: 5.9795e-05 eta: 3:11:38 time: 0.4994 data_time: 0.3588 memory: 6318 loss: 0.1022 2023/06/06 08:06:43 - mmengine - INFO - Epoch(train) [5][2700/3937] lr: 5.9438e-05 eta: 3:10:39 time: 0.5059 data_time: 0.3660 memory: 6318 loss: 0.0967 2023/06/06 08:07:34 - mmengine - INFO - Epoch(train) [5][2800/3937] lr: 5.9081e-05 eta: 3:09:41 time: 0.5142 data_time: 0.3740 memory: 6318 loss: 0.0941 2023/06/06 08:08:24 - mmengine - INFO - Epoch(train) [5][2900/3937] lr: 5.8723e-05 eta: 3:08:41 time: 0.5547 data_time: 0.4150 memory: 6318 loss: 0.1097 2023/06/06 08:09:15 - mmengine - INFO - Epoch(train) [5][3000/3937] lr: 5.8365e-05 eta: 3:07:43 time: 0.5186 data_time: 0.3781 memory: 6318 loss: 0.1051 2023/06/06 08:10:07 - mmengine - INFO - Epoch(train) [5][3100/3937] lr: 5.8007e-05 eta: 3:06:45 time: 0.5408 data_time: 0.4018 memory: 6318 loss: 0.1064 2023/06/06 08:10:59 - mmengine - INFO - Epoch(train) [5][3200/3937] lr: 5.7649e-05 eta: 3:05:47 time: 0.5261 data_time: 0.3858 memory: 6318 loss: 0.1119 2023/06/06 08:11:25 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 08:11:50 - mmengine - INFO - Epoch(train) [5][3300/3937] lr: 5.7290e-05 eta: 3:04:49 time: 0.4934 data_time: 0.3537 memory: 6318 loss: 0.1185 2023/06/06 08:12:41 - mmengine - INFO - Epoch(train) [5][3400/3937] lr: 5.6931e-05 eta: 3:03:51 time: 0.5185 data_time: 0.3781 memory: 6318 loss: 0.1136 2023/06/06 08:13:32 - mmengine - INFO - Epoch(train) [5][3500/3937] lr: 5.6572e-05 eta: 3:02:53 time: 0.5417 data_time: 0.3987 memory: 6318 loss: 0.1119 2023/06/06 08:14:24 - mmengine - INFO - Epoch(train) [5][3600/3937] lr: 5.6214e-05 eta: 3:01:55 time: 0.5275 data_time: 0.3866 memory: 6318 loss: 0.1118 2023/06/06 08:15:15 - mmengine - INFO - Epoch(train) [5][3700/3937] lr: 5.5855e-05 eta: 3:00:57 time: 0.5254 data_time: 0.3854 memory: 6318 loss: 0.0928 2023/06/06 08:16:05 - mmengine - INFO - Epoch(train) [5][3800/3937] lr: 5.5496e-05 eta: 2:59:59 time: 0.5167 data_time: 0.3780 memory: 6318 loss: 0.1013 2023/06/06 08:16:57 - mmengine - INFO - Epoch(train) [5][3900/3937] lr: 5.5136e-05 eta: 2:59:01 time: 0.5152 data_time: 0.3741 memory: 6318 loss: 0.1064 2023/06/06 08:17:15 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 08:17:15 - mmengine - INFO - Saving checkpoint at 5 epochs 2023/06/06 08:17:54 - mmengine - INFO - Epoch(val) [5][57/57] accuracy/top1: 94.8575 data_time: 0.3959 time: 0.4819 2023/06/06 08:18:47 - mmengine - INFO - Epoch(train) [6][ 100/3937] lr: 5.4645e-05 eta: 2:57:43 time: 0.4448 data_time: 0.3041 memory: 6318 loss: 0.1017 2023/06/06 08:19:38 - mmengine - INFO - Epoch(train) [6][ 200/3937] lr: 5.4285e-05 eta: 2:56:45 time: 0.5063 data_time: 0.3651 memory: 6318 loss: 0.1123 2023/06/06 08:20:27 - mmengine - INFO - Epoch(train) [6][ 300/3937] lr: 5.3926e-05 eta: 2:55:46 time: 0.4770 data_time: 0.3365 memory: 6318 loss: 0.0997 2023/06/06 08:20:36 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 08:21:18 - mmengine - INFO - Epoch(train) [6][ 400/3937] lr: 5.3567e-05 eta: 2:54:49 time: 0.4970 data_time: 0.3476 memory: 6318 loss: 0.1072 2023/06/06 08:22:11 - mmengine - INFO - Epoch(train) [6][ 500/3937] lr: 5.3209e-05 eta: 2:53:52 time: 0.4878 data_time: 0.3484 memory: 6318 loss: 0.0981 2023/06/06 08:23:02 - mmengine - INFO - Epoch(train) [6][ 600/3937] lr: 5.2850e-05 eta: 2:52:55 time: 0.4863 data_time: 0.3465 memory: 6318 loss: 0.1041 2023/06/06 08:23:54 - mmengine - INFO - Epoch(train) [6][ 700/3937] lr: 5.2491e-05 eta: 2:51:59 time: 0.6157 data_time: 0.4766 memory: 6318 loss: 0.1127 2023/06/06 08:24:47 - mmengine - INFO - Epoch(train) [6][ 800/3937] lr: 5.2133e-05 eta: 2:51:03 time: 0.5636 data_time: 0.4233 memory: 6318 loss: 0.0979 2023/06/06 08:25:38 - mmengine - INFO - Epoch(train) [6][ 900/3937] lr: 5.1775e-05 eta: 2:50:06 time: 0.5405 data_time: 0.4006 memory: 6318 loss: 0.1037 2023/06/06 08:26:30 - mmengine - INFO - Epoch(train) [6][1000/3937] lr: 5.1417e-05 eta: 2:49:09 time: 0.5186 data_time: 0.3783 memory: 6318 loss: 0.0983 2023/06/06 08:27:22 - mmengine - INFO - Epoch(train) [6][1100/3937] lr: 5.1059e-05 eta: 2:48:13 time: 0.5142 data_time: 0.3742 memory: 6318 loss: 0.1162 2023/06/06 08:28:15 - mmengine - INFO - Epoch(train) [6][1200/3937] lr: 5.0701e-05 eta: 2:47:17 time: 0.5352 data_time: 0.3945 memory: 6318 loss: 0.0945 2023/06/06 08:29:06 - mmengine - INFO - Epoch(train) [6][1300/3937] lr: 5.0344e-05 eta: 2:46:20 time: 0.5427 data_time: 0.4024 memory: 6318 loss: 0.1034 2023/06/06 08:29:12 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 08:29:58 - mmengine - INFO - Epoch(train) [6][1400/3937] lr: 4.9987e-05 eta: 2:45:24 time: 0.5366 data_time: 0.3955 memory: 6318 loss: 0.1004 2023/06/06 08:30:53 - mmengine - INFO - Epoch(train) [6][1500/3937] lr: 4.9630e-05 eta: 2:44:30 time: 0.7973 data_time: 0.6567 memory: 6318 loss: 0.1043 2023/06/06 08:31:46 - mmengine - INFO - Epoch(train) [6][1600/3937] lr: 4.9274e-05 eta: 2:43:34 time: 0.5309 data_time: 0.3899 memory: 6318 loss: 0.1053 2023/06/06 08:32:36 - mmengine - INFO - Epoch(train) [6][1700/3937] lr: 4.8918e-05 eta: 2:42:37 time: 0.5113 data_time: 0.3708 memory: 6318 loss: 0.1106 2023/06/06 08:33:26 - mmengine - INFO - Epoch(train) [6][1800/3937] lr: 4.8562e-05 eta: 2:41:39 time: 0.5040 data_time: 0.3638 memory: 6318 loss: 0.1211 2023/06/06 08:34:19 - mmengine - INFO - Epoch(train) [6][1900/3937] lr: 4.8207e-05 eta: 2:40:43 time: 0.5424 data_time: 0.4025 memory: 6318 loss: 0.1014 2023/06/06 08:35:11 - mmengine - INFO - Epoch(train) [6][2000/3937] lr: 4.7852e-05 eta: 2:39:47 time: 0.4983 data_time: 0.3580 memory: 6318 loss: 0.1176 2023/06/06 08:36:02 - mmengine - INFO - Epoch(train) [6][2100/3937] lr: 4.7498e-05 eta: 2:38:51 time: 0.5116 data_time: 0.3715 memory: 6318 loss: 0.0972 2023/06/06 08:36:54 - mmengine - INFO - Epoch(train) [6][2200/3937] lr: 4.7144e-05 eta: 2:37:55 time: 0.5229 data_time: 0.3824 memory: 6318 loss: 0.1076 2023/06/06 08:37:44 - mmengine - INFO - Epoch(train) [6][2300/3937] lr: 4.6791e-05 eta: 2:36:58 time: 0.5383 data_time: 0.3979 memory: 6318 loss: 0.1107 2023/06/06 08:37:51 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 08:38:36 - mmengine - INFO - Epoch(train) [6][2400/3937] lr: 4.6438e-05 eta: 2:36:01 time: 0.5162 data_time: 0.3760 memory: 6318 loss: 0.1006 2023/06/06 08:39:32 - mmengine - INFO - Epoch(train) [6][2500/3937] lr: 4.6086e-05 eta: 2:35:09 time: 0.5263 data_time: 0.3849 memory: 6318 loss: 0.1085 2023/06/06 08:40:20 - mmengine - INFO - Epoch(train) [6][2600/3937] lr: 4.5734e-05 eta: 2:34:09 time: 0.4587 data_time: 0.3120 memory: 6318 loss: 0.1082 2023/06/06 08:41:08 - mmengine - INFO - Epoch(train) [6][2700/3937] lr: 4.5383e-05 eta: 2:33:11 time: 0.4705 data_time: 0.3301 memory: 6318 loss: 0.1032 2023/06/06 08:41:56 - mmengine - INFO - Epoch(train) [6][2800/3937] lr: 4.5033e-05 eta: 2:32:12 time: 0.5014 data_time: 0.3610 memory: 6318 loss: 0.1047 2023/06/06 08:42:43 - mmengine - INFO - Epoch(train) [6][2900/3937] lr: 4.4683e-05 eta: 2:31:13 time: 0.4705 data_time: 0.3295 memory: 6318 loss: 0.1073 2023/06/06 08:43:32 - mmengine - INFO - Epoch(train) [6][3000/3937] lr: 4.4334e-05 eta: 2:30:15 time: 0.4635 data_time: 0.3223 memory: 6318 loss: 0.1008 2023/06/06 08:44:20 - mmengine - INFO - Epoch(train) [6][3100/3937] lr: 4.3985e-05 eta: 2:29:16 time: 0.4707 data_time: 0.3296 memory: 6318 loss: 0.1005 2023/06/06 08:45:08 - mmengine - INFO - Epoch(train) [6][3200/3937] lr: 4.3637e-05 eta: 2:28:18 time: 0.4723 data_time: 0.3312 memory: 6318 loss: 0.0981 2023/06/06 08:45:57 - mmengine - INFO - Epoch(train) [6][3300/3937] lr: 4.3290e-05 eta: 2:27:21 time: 0.4898 data_time: 0.3498 memory: 6318 loss: 0.0869 2023/06/06 08:46:03 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 08:46:45 - mmengine - INFO - Epoch(train) [6][3400/3937] lr: 4.2944e-05 eta: 2:26:23 time: 0.4689 data_time: 0.3280 memory: 6318 loss: 0.1017 2023/06/06 08:47:34 - mmengine - INFO - Epoch(train) [6][3500/3937] lr: 4.2598e-05 eta: 2:25:25 time: 0.4516 data_time: 0.3117 memory: 6318 loss: 0.1040 2023/06/06 08:48:23 - mmengine - INFO - Epoch(train) [6][3600/3937] lr: 4.2253e-05 eta: 2:24:28 time: 0.4694 data_time: 0.3300 memory: 6318 loss: 0.1043 2023/06/06 08:49:12 - mmengine - INFO - Epoch(train) [6][3700/3937] lr: 4.1909e-05 eta: 2:23:30 time: 0.4968 data_time: 0.2668 memory: 6318 loss: 0.1057 2023/06/06 08:49:59 - mmengine - INFO - Epoch(train) [6][3800/3937] lr: 4.1566e-05 eta: 2:22:32 time: 0.4473 data_time: 0.2770 memory: 6318 loss: 0.1044 2023/06/06 08:50:47 - mmengine - INFO - Epoch(train) [6][3900/3937] lr: 4.1224e-05 eta: 2:21:34 time: 0.4809 data_time: 0.2977 memory: 6318 loss: 0.1120 2023/06/06 08:51:06 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 08:51:06 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/06/06 08:51:43 - mmengine - INFO - Epoch(val) [6][57/57] accuracy/top1: 95.6578 data_time: 0.3769 time: 0.4641 2023/06/06 08:52:34 - mmengine - INFO - Epoch(train) [7][ 100/3937] lr: 4.0757e-05 eta: 2:20:18 time: 0.4974 data_time: 0.3529 memory: 6318 loss: 0.0987 2023/06/06 08:53:23 - mmengine - INFO - Epoch(train) [7][ 200/3937] lr: 4.0416e-05 eta: 2:19:21 time: 0.4690 data_time: 0.3285 memory: 6318 loss: 0.1043 2023/06/06 08:54:11 - mmengine - INFO - Epoch(train) [7][ 300/3937] lr: 4.0077e-05 eta: 2:18:24 time: 0.4892 data_time: 0.3483 memory: 6318 loss: 0.1056 2023/06/06 08:54:52 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 08:55:02 - mmengine - INFO - Epoch(train) [7][ 400/3937] lr: 3.9739e-05 eta: 2:17:28 time: 0.4886 data_time: 0.3486 memory: 6318 loss: 0.0884 2023/06/06 08:55:51 - mmengine - INFO - Epoch(train) [7][ 500/3937] lr: 3.9402e-05 eta: 2:16:31 time: 0.4728 data_time: 0.3242 memory: 6318 loss: 0.0926 2023/06/06 08:56:39 - mmengine - INFO - Epoch(train) [7][ 600/3937] lr: 3.9065e-05 eta: 2:15:34 time: 0.5062 data_time: 0.1736 memory: 6318 loss: 0.1043 2023/06/06 08:57:28 - mmengine - INFO - Epoch(train) [7][ 700/3937] lr: 3.8730e-05 eta: 2:14:37 time: 0.4877 data_time: 0.1162 memory: 6318 loss: 0.1075 2023/06/06 08:58:17 - mmengine - INFO - Epoch(train) [7][ 800/3937] lr: 3.8396e-05 eta: 2:13:41 time: 0.4622 data_time: 0.0617 memory: 6318 loss: 0.1010 2023/06/06 08:59:06 - mmengine - INFO - Epoch(train) [7][ 900/3937] lr: 3.8062e-05 eta: 2:12:44 time: 0.5068 data_time: 0.1352 memory: 6318 loss: 0.0981 2023/06/06 08:59:55 - mmengine - INFO - Epoch(train) [7][1000/3937] lr: 3.7730e-05 eta: 2:11:48 time: 0.4476 data_time: 0.1468 memory: 6318 loss: 0.0984 2023/06/06 09:00:44 - mmengine - INFO - Epoch(train) [7][1100/3937] lr: 3.7399e-05 eta: 2:10:51 time: 0.4717 data_time: 0.3320 memory: 6318 loss: 0.0954 2023/06/06 09:01:33 - mmengine - INFO - Epoch(train) [7][1200/3937] lr: 3.7069e-05 eta: 2:09:55 time: 0.4521 data_time: 0.3119 memory: 6318 loss: 0.0964 2023/06/06 09:02:22 - mmengine - INFO - Epoch(train) [7][1300/3937] lr: 3.6741e-05 eta: 2:08:59 time: 0.5170 data_time: 0.3764 memory: 6318 loss: 0.0928 2023/06/06 09:03:01 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 09:03:10 - mmengine - INFO - Epoch(train) [7][1400/3937] lr: 3.6413e-05 eta: 2:08:02 time: 0.4686 data_time: 0.3064 memory: 6318 loss: 0.0961 2023/06/06 09:03:58 - mmengine - INFO - Epoch(train) [7][1500/3937] lr: 3.6087e-05 eta: 2:07:06 time: 0.4651 data_time: 0.2846 memory: 6318 loss: 0.0884 2023/06/06 09:04:46 - mmengine - INFO - Epoch(train) [7][1600/3937] lr: 3.5761e-05 eta: 2:06:09 time: 0.5051 data_time: 0.3652 memory: 6318 loss: 0.0990 2023/06/06 09:05:35 - mmengine - INFO - Epoch(train) [7][1700/3937] lr: 3.5437e-05 eta: 2:05:13 time: 0.4787 data_time: 0.3387 memory: 6318 loss: 0.1021 2023/06/06 09:06:23 - mmengine - INFO - Epoch(train) [7][1800/3937] lr: 3.5115e-05 eta: 2:04:17 time: 0.4712 data_time: 0.3305 memory: 6318 loss: 0.0942 2023/06/06 09:07:13 - mmengine - INFO - Epoch(train) [7][1900/3937] lr: 3.4793e-05 eta: 2:03:21 time: 0.5552 data_time: 0.2782 memory: 6318 loss: 0.1006 2023/06/06 09:08:01 - mmengine - INFO - Epoch(train) [7][2000/3937] lr: 3.4473e-05 eta: 2:02:25 time: 0.4948 data_time: 0.1854 memory: 6318 loss: 0.1051 2023/06/06 09:08:49 - mmengine - INFO - Epoch(train) [7][2100/3937] lr: 3.4154e-05 eta: 2:01:28 time: 0.4275 data_time: 0.0934 memory: 6318 loss: 0.0947 2023/06/06 09:09:37 - mmengine - INFO - Epoch(train) [7][2200/3937] lr: 3.3836e-05 eta: 2:00:32 time: 0.4643 data_time: 0.3228 memory: 6318 loss: 0.1072 2023/06/06 09:10:25 - mmengine - INFO - Epoch(train) [7][2300/3937] lr: 3.3520e-05 eta: 1:59:36 time: 0.4775 data_time: 0.2345 memory: 6318 loss: 0.1121 2023/06/06 09:11:03 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 09:11:13 - mmengine - INFO - Epoch(train) [7][2400/3937] lr: 3.3205e-05 eta: 1:58:40 time: 0.4920 data_time: 0.2082 memory: 6318 loss: 0.1025 2023/06/06 09:12:05 - mmengine - INFO - Epoch(train) [7][2500/3937] lr: 3.2892e-05 eta: 1:57:45 time: 0.4967 data_time: 0.0349 memory: 6318 loss: 0.1035 2023/06/06 09:12:53 - mmengine - INFO - Epoch(train) [7][2600/3937] lr: 3.2580e-05 eta: 1:56:50 time: 0.4440 data_time: 0.0009 memory: 6318 loss: 0.0892 2023/06/06 09:13:43 - mmengine - INFO - Epoch(train) [7][2700/3937] lr: 3.2269e-05 eta: 1:55:55 time: 0.4798 data_time: 0.0118 memory: 6318 loss: 0.1024 2023/06/06 09:14:31 - mmengine - INFO - Epoch(train) [7][2800/3937] lr: 3.1960e-05 eta: 1:54:59 time: 0.5114 data_time: 0.0010 memory: 6318 loss: 0.0986 2023/06/06 09:15:21 - mmengine - INFO - Epoch(train) [7][2900/3937] lr: 3.1652e-05 eta: 1:54:04 time: 0.5245 data_time: 0.1300 memory: 6318 loss: 0.1037 2023/06/06 09:16:08 - mmengine - INFO - Epoch(train) [7][3000/3937] lr: 3.1346e-05 eta: 1:53:08 time: 0.4480 data_time: 0.1224 memory: 6318 loss: 0.1240 2023/06/06 09:16:56 - mmengine - INFO - Epoch(train) [7][3100/3937] lr: 3.1041e-05 eta: 1:52:12 time: 0.4890 data_time: 0.1909 memory: 6318 loss: 0.0977 2023/06/06 09:17:46 - mmengine - INFO - Epoch(train) [7][3200/3937] lr: 3.0738e-05 eta: 1:51:17 time: 0.5318 data_time: 0.3903 memory: 6318 loss: 0.0990 2023/06/06 09:18:34 - mmengine - INFO - Epoch(train) [7][3300/3937] lr: 3.0437e-05 eta: 1:50:22 time: 0.4417 data_time: 0.2983 memory: 6318 loss: 0.0883 2023/06/06 09:19:16 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 09:19:25 - mmengine - INFO - Epoch(train) [7][3400/3937] lr: 3.0136e-05 eta: 1:49:27 time: 0.4484 data_time: 0.3087 memory: 6318 loss: 0.0871 2023/06/06 09:20:14 - mmengine - INFO - Epoch(train) [7][3500/3937] lr: 2.9838e-05 eta: 1:48:32 time: 0.4788 data_time: 0.3389 memory: 6318 loss: 0.1077 2023/06/06 09:21:02 - mmengine - INFO - Epoch(train) [7][3600/3937] lr: 2.9541e-05 eta: 1:47:37 time: 0.4860 data_time: 0.3454 memory: 6318 loss: 0.1038 2023/06/06 09:21:50 - mmengine - INFO - Epoch(train) [7][3700/3937] lr: 2.9246e-05 eta: 1:46:42 time: 0.4617 data_time: 0.3214 memory: 6318 loss: 0.1015 2023/06/06 09:22:39 - mmengine - INFO - Epoch(train) [7][3800/3937] lr: 2.8952e-05 eta: 1:45:46 time: 0.4959 data_time: 0.3556 memory: 6318 loss: 0.1085 2023/06/06 09:23:33 - mmengine - INFO - Epoch(train) [7][3900/3937] lr: 2.8660e-05 eta: 1:44:54 time: 0.9999 data_time: 0.8572 memory: 6318 loss: 0.0842 2023/06/06 09:23:51 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 09:23:51 - mmengine - INFO - Saving checkpoint at 7 epochs 2023/06/06 09:24:29 - mmengine - INFO - Epoch(val) [7][57/57] accuracy/top1: 95.9779 data_time: 0.3753 time: 0.4617 2023/06/06 09:25:27 - mmengine - INFO - Epoch(train) [8][ 100/3937] lr: 2.8263e-05 eta: 1:43:42 time: 0.4893 data_time: 0.3492 memory: 6318 loss: 0.1015 2023/06/06 09:26:17 - mmengine - INFO - Epoch(train) [8][ 200/3937] lr: 2.7975e-05 eta: 1:42:48 time: 0.4971 data_time: 0.3564 memory: 6318 loss: 0.0957 2023/06/06 09:27:06 - mmengine - INFO - Epoch(train) [8][ 300/3937] lr: 2.7689e-05 eta: 1:41:53 time: 0.4953 data_time: 0.3549 memory: 6318 loss: 0.0953 2023/06/06 09:27:54 - mmengine - INFO - Epoch(train) [8][ 400/3937] lr: 2.7404e-05 eta: 1:40:58 time: 0.4518 data_time: 0.3116 memory: 6318 loss: 0.0931 2023/06/06 09:28:16 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 09:28:43 - mmengine - INFO - Epoch(train) [8][ 500/3937] lr: 2.7121e-05 eta: 1:40:03 time: 0.4707 data_time: 0.3314 memory: 6318 loss: 0.1123 2023/06/06 09:29:30 - mmengine - INFO - Epoch(train) [8][ 600/3937] lr: 2.6840e-05 eta: 1:39:08 time: 0.4655 data_time: 0.3272 memory: 6318 loss: 0.0829 2023/06/06 09:30:18 - mmengine - INFO - Epoch(train) [8][ 700/3937] lr: 2.6561e-05 eta: 1:38:13 time: 0.4717 data_time: 0.3334 memory: 6318 loss: 0.0996 2023/06/06 09:31:06 - mmengine - INFO - Epoch(train) [8][ 800/3937] lr: 2.6284e-05 eta: 1:37:18 time: 0.4751 data_time: 0.3352 memory: 6318 loss: 0.0895 2023/06/06 09:31:55 - mmengine - INFO - Epoch(train) [8][ 900/3937] lr: 2.6008e-05 eta: 1:36:23 time: 0.4863 data_time: 0.3455 memory: 6318 loss: 0.0949 2023/06/06 09:32:43 - mmengine - INFO - Epoch(train) [8][1000/3937] lr: 2.5735e-05 eta: 1:35:28 time: 0.4779 data_time: 0.3382 memory: 6318 loss: 0.0884 2023/06/06 09:33:32 - mmengine - INFO - Epoch(train) [8][1100/3937] lr: 2.5463e-05 eta: 1:34:34 time: 0.4916 data_time: 0.3517 memory: 6318 loss: 0.0989 2023/06/06 09:34:20 - mmengine - INFO - Epoch(train) [8][1200/3937] lr: 2.5193e-05 eta: 1:33:39 time: 0.4995 data_time: 0.3603 memory: 6318 loss: 0.1010 2023/06/06 09:35:08 - mmengine - INFO - Epoch(train) [8][1300/3937] lr: 2.4925e-05 eta: 1:32:44 time: 0.4772 data_time: 0.3378 memory: 6318 loss: 0.1026 2023/06/06 09:35:57 - mmengine - INFO - Epoch(train) [8][1400/3937] lr: 2.4659e-05 eta: 1:31:50 time: 0.4885 data_time: 0.3492 memory: 6318 loss: 0.1079 2023/06/06 09:36:17 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 09:36:45 - mmengine - INFO - Epoch(train) [8][1500/3937] lr: 2.4394e-05 eta: 1:30:55 time: 0.5131 data_time: 0.3725 memory: 6318 loss: 0.0903 2023/06/06 09:37:33 - mmengine - INFO - Epoch(train) [8][1600/3937] lr: 2.4132e-05 eta: 1:30:01 time: 0.4856 data_time: 0.3452 memory: 6318 loss: 0.0912 2023/06/06 09:38:22 - mmengine - INFO - Epoch(train) [8][1700/3937] lr: 2.3872e-05 eta: 1:29:06 time: 0.4990 data_time: 0.3584 memory: 6318 loss: 0.1099 2023/06/06 09:39:10 - mmengine - INFO - Epoch(train) [8][1800/3937] lr: 2.3613e-05 eta: 1:28:12 time: 0.4877 data_time: 0.3469 memory: 6318 loss: 0.1033 2023/06/06 09:39:58 - mmengine - INFO - Epoch(train) [8][1900/3937] lr: 2.3357e-05 eta: 1:27:17 time: 0.4446 data_time: 0.2944 memory: 6318 loss: 0.0841 2023/06/06 09:40:46 - mmengine - INFO - Epoch(train) [8][2000/3937] lr: 2.3103e-05 eta: 1:26:23 time: 0.4660 data_time: 0.3251 memory: 6318 loss: 0.0903 2023/06/06 09:41:36 - mmengine - INFO - Epoch(train) [8][2100/3937] lr: 2.2851e-05 eta: 1:25:29 time: 0.5002 data_time: 0.3601 memory: 6318 loss: 0.0948 2023/06/06 09:42:24 - mmengine - INFO - Epoch(train) [8][2200/3937] lr: 2.2600e-05 eta: 1:24:35 time: 0.4388 data_time: 0.2989 memory: 6318 loss: 0.0793 2023/06/06 09:43:12 - mmengine - INFO - Epoch(train) [8][2300/3937] lr: 2.2352e-05 eta: 1:23:40 time: 0.5249 data_time: 0.3846 memory: 6318 loss: 0.1010 2023/06/06 09:44:00 - mmengine - INFO - Epoch(train) [8][2400/3937] lr: 2.2106e-05 eta: 1:22:46 time: 0.4711 data_time: 0.3309 memory: 6318 loss: 0.0983 2023/06/06 09:44:19 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 09:44:48 - mmengine - INFO - Epoch(train) [8][2500/3937] lr: 2.1862e-05 eta: 1:21:52 time: 0.5036 data_time: 0.3639 memory: 6318 loss: 0.0915 2023/06/06 09:45:35 - mmengine - INFO - Epoch(train) [8][2600/3937] lr: 2.1620e-05 eta: 1:20:57 time: 0.4820 data_time: 0.3417 memory: 6318 loss: 0.0944 2023/06/06 09:46:22 - mmengine - INFO - Epoch(train) [8][2700/3937] lr: 2.1380e-05 eta: 1:20:03 time: 0.5053 data_time: 0.3642 memory: 6318 loss: 0.0903 2023/06/06 09:47:10 - mmengine - INFO - Epoch(train) [8][2800/3937] lr: 2.1143e-05 eta: 1:19:09 time: 0.4527 data_time: 0.3121 memory: 6318 loss: 0.1001 2023/06/06 09:48:08 - mmengine - INFO - Epoch(train) [8][2900/3937] lr: 2.0907e-05 eta: 1:18:18 time: 0.5498 data_time: 0.4081 memory: 6318 loss: 0.0997 2023/06/06 09:48:58 - mmengine - INFO - Epoch(train) [8][3000/3937] lr: 2.0674e-05 eta: 1:17:24 time: 0.5058 data_time: 0.3659 memory: 6318 loss: 0.1028 2023/06/06 09:49:45 - mmengine - INFO - Epoch(train) [8][3100/3937] lr: 2.0443e-05 eta: 1:16:30 time: 0.4466 data_time: 0.3062 memory: 6318 loss: 0.1055 2023/06/06 09:50:33 - mmengine - INFO - Epoch(train) [8][3200/3937] lr: 2.0214e-05 eta: 1:15:36 time: 0.4221 data_time: 0.2819 memory: 6318 loss: 0.1006 2023/06/06 09:51:21 - mmengine - INFO - Epoch(train) [8][3300/3937] lr: 1.9987e-05 eta: 1:14:42 time: 0.5357 data_time: 0.3933 memory: 6318 loss: 0.1032 2023/06/06 09:52:10 - mmengine - INFO - Epoch(train) [8][3400/3937] lr: 1.9763e-05 eta: 1:13:48 time: 0.5375 data_time: 0.3967 memory: 6318 loss: 0.0990 2023/06/06 09:52:30 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 09:52:58 - mmengine - INFO - Epoch(train) [8][3500/3937] lr: 1.9541e-05 eta: 1:12:54 time: 0.4961 data_time: 0.3552 memory: 6318 loss: 0.0946 2023/06/06 09:53:47 - mmengine - INFO - Epoch(train) [8][3600/3937] lr: 1.9321e-05 eta: 1:12:00 time: 0.4753 data_time: 0.3366 memory: 6318 loss: 0.0846 2023/06/06 09:54:35 - mmengine - INFO - Epoch(train) [8][3700/3937] lr: 1.9103e-05 eta: 1:11:07 time: 0.4752 data_time: 0.3363 memory: 6318 loss: 0.1105 2023/06/06 09:55:23 - mmengine - INFO - Epoch(train) [8][3800/3937] lr: 1.8888e-05 eta: 1:10:13 time: 0.5039 data_time: 0.3650 memory: 6318 loss: 0.0986 2023/06/06 09:56:11 - mmengine - INFO - Epoch(train) [8][3900/3937] lr: 1.8675e-05 eta: 1:09:19 time: 0.4995 data_time: 0.3594 memory: 6318 loss: 0.1031 2023/06/06 09:56:30 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 09:56:30 - mmengine - INFO - Saving checkpoint at 8 epochs 2023/06/06 09:57:08 - mmengine - INFO - Epoch(val) [8][57/57] accuracy/top1: 96.0996 data_time: 0.3809 time: 0.4695 2023/06/06 09:57:58 - mmengine - INFO - Epoch(train) [9][ 100/3937] lr: 1.8386e-05 eta: 1:08:06 time: 0.4895 data_time: 0.2757 memory: 6318 loss: 0.0985 2023/06/06 09:58:46 - mmengine - INFO - Epoch(train) [9][ 200/3937] lr: 1.8179e-05 eta: 1:07:13 time: 0.4643 data_time: 0.3203 memory: 6318 loss: 0.0889 2023/06/06 09:59:35 - mmengine - INFO - Epoch(train) [9][ 300/3937] lr: 1.7974e-05 eta: 1:06:19 time: 0.5105 data_time: 0.3589 memory: 6318 loss: 0.0767 2023/06/06 10:00:24 - mmengine - INFO - Epoch(train) [9][ 400/3937] lr: 1.7771e-05 eta: 1:05:26 time: 0.4834 data_time: 0.3182 memory: 6318 loss: 0.0912 2023/06/06 10:01:12 - mmengine - INFO - Epoch(train) [9][ 500/3937] lr: 1.7570e-05 eta: 1:04:32 time: 0.4951 data_time: 0.3545 memory: 6318 loss: 0.0979 2023/06/06 10:01:16 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 10:02:03 - mmengine - INFO - Epoch(train) [9][ 600/3937] lr: 1.7372e-05 eta: 1:03:39 time: 0.5042 data_time: 0.3643 memory: 6318 loss: 0.1015 2023/06/06 10:02:52 - mmengine - INFO - Epoch(train) [9][ 700/3937] lr: 1.7176e-05 eta: 1:02:46 time: 0.4697 data_time: 0.3254 memory: 6318 loss: 0.1068 2023/06/06 10:03:39 - mmengine - INFO - Epoch(train) [9][ 800/3937] lr: 1.6983e-05 eta: 1:01:53 time: 0.4708 data_time: 0.3297 memory: 6318 loss: 0.0952 2023/06/06 10:04:28 - mmengine - INFO - Epoch(train) [9][ 900/3937] lr: 1.6792e-05 eta: 1:00:59 time: 0.4917 data_time: 0.1874 memory: 6318 loss: 0.1064 2023/06/06 10:05:15 - mmengine - INFO - Epoch(train) [9][1000/3937] lr: 1.6604e-05 eta: 1:00:06 time: 0.4402 data_time: 0.2063 memory: 6318 loss: 0.0954 2023/06/06 10:06:03 - mmengine - INFO - Epoch(train) [9][1100/3937] lr: 1.6418e-05 eta: 0:59:12 time: 0.4709 data_time: 0.3185 memory: 6318 loss: 0.0950 2023/06/06 10:06:52 - mmengine - INFO - Epoch(train) [9][1200/3937] lr: 1.6234e-05 eta: 0:58:19 time: 0.4505 data_time: 0.1621 memory: 6318 loss: 0.1014 2023/06/06 10:07:42 - mmengine - INFO - Epoch(train) [9][1300/3937] lr: 1.6053e-05 eta: 0:57:26 time: 0.4600 data_time: 0.0015 memory: 6318 loss: 0.0885 2023/06/06 10:08:31 - mmengine - INFO - Epoch(train) [9][1400/3937] lr: 1.5874e-05 eta: 0:56:33 time: 0.4757 data_time: 0.0012 memory: 6318 loss: 0.0928 2023/06/06 10:09:20 - mmengine - INFO - Epoch(train) [9][1500/3937] lr: 1.5698e-05 eta: 0:55:40 time: 0.4927 data_time: 0.0009 memory: 6318 loss: 0.0994 2023/06/06 10:09:21 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 10:10:09 - mmengine - INFO - Epoch(train) [9][1600/3937] lr: 1.5524e-05 eta: 0:54:47 time: 0.4405 data_time: 0.0009 memory: 6318 loss: 0.0883 2023/06/06 10:10:57 - mmengine - INFO - Epoch(train) [9][1700/3937] lr: 1.5353e-05 eta: 0:53:54 time: 0.4558 data_time: 0.0009 memory: 6318 loss: 0.0957 2023/06/06 10:11:46 - mmengine - INFO - Epoch(train) [9][1800/3937] lr: 1.5185e-05 eta: 0:53:01 time: 0.4894 data_time: 0.0008 memory: 6318 loss: 0.0951 2023/06/06 10:12:36 - mmengine - INFO - Epoch(train) [9][1900/3937] lr: 1.5019e-05 eta: 0:52:08 time: 0.5259 data_time: 0.0009 memory: 6318 loss: 0.0923 2023/06/06 10:13:24 - mmengine - INFO - Epoch(train) [9][2000/3937] lr: 1.4855e-05 eta: 0:51:15 time: 0.4856 data_time: 0.0009 memory: 6318 loss: 0.1047 2023/06/06 10:14:12 - mmengine - INFO - Epoch(train) [9][2100/3937] lr: 1.4694e-05 eta: 0:50:22 time: 0.4717 data_time: 0.0011 memory: 6318 loss: 0.0939 2023/06/06 10:15:00 - mmengine - INFO - Epoch(train) [9][2200/3937] lr: 1.4536e-05 eta: 0:49:29 time: 0.4394 data_time: 0.0012 memory: 6318 loss: 0.0965 2023/06/06 10:15:48 - mmengine - INFO - Epoch(train) [9][2300/3937] lr: 1.4380e-05 eta: 0:48:36 time: 0.5093 data_time: 0.0013 memory: 6318 loss: 0.0903 2023/06/06 10:16:36 - mmengine - INFO - Epoch(train) [9][2400/3937] lr: 1.4227e-05 eta: 0:47:43 time: 0.4522 data_time: 0.0011 memory: 6318 loss: 0.0929 2023/06/06 10:17:25 - mmengine - INFO - Epoch(train) [9][2500/3937] lr: 1.4076e-05 eta: 0:46:50 time: 0.5187 data_time: 0.0015 memory: 6318 loss: 0.0882 2023/06/06 10:17:25 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 10:18:12 - mmengine - INFO - Epoch(train) [9][2600/3937] lr: 1.3928e-05 eta: 0:45:57 time: 0.4956 data_time: 0.0011 memory: 6318 loss: 0.0949 2023/06/06 10:18:59 - mmengine - INFO - Epoch(train) [9][2700/3937] lr: 1.3783e-05 eta: 0:45:04 time: 0.4372 data_time: 0.0010 memory: 6318 loss: 0.0738 2023/06/06 10:19:47 - mmengine - INFO - Epoch(train) [9][2800/3937] lr: 1.3640e-05 eta: 0:44:11 time: 0.4920 data_time: 0.0564 memory: 6318 loss: 0.1008 2023/06/06 10:20:35 - mmengine - INFO - Epoch(train) [9][2900/3937] lr: 1.3500e-05 eta: 0:43:18 time: 0.4997 data_time: 0.2096 memory: 6318 loss: 0.0955 2023/06/06 10:21:24 - mmengine - INFO - Epoch(train) [9][3000/3937] lr: 1.3362e-05 eta: 0:42:25 time: 0.4949 data_time: 0.0522 memory: 6318 loss: 0.1057 2023/06/06 10:22:12 - mmengine - INFO - Epoch(train) [9][3100/3937] lr: 1.3227e-05 eta: 0:41:32 time: 0.5162 data_time: 0.1237 memory: 6318 loss: 0.0888 2023/06/06 10:23:00 - mmengine - INFO - Epoch(train) [9][3200/3937] lr: 1.3095e-05 eta: 0:40:39 time: 0.4896 data_time: 0.1300 memory: 6318 loss: 0.0921 2023/06/06 10:23:53 - mmengine - INFO - Epoch(train) [9][3300/3937] lr: 1.2966e-05 eta: 0:39:47 time: 0.4965 data_time: 0.1501 memory: 6318 loss: 0.0902 2023/06/06 10:24:42 - mmengine - INFO - Epoch(train) [9][3400/3937] lr: 1.2839e-05 eta: 0:38:55 time: 0.5437 data_time: 0.1679 memory: 6318 loss: 0.1042 2023/06/06 10:25:37 - mmengine - INFO - Epoch(train) [9][3500/3937] lr: 1.2715e-05 eta: 0:38:03 time: 0.4753 data_time: 0.3287 memory: 6318 loss: 0.0917 2023/06/06 10:25:42 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 10:26:29 - mmengine - INFO - Epoch(train) [9][3600/3937] lr: 1.2593e-05 eta: 0:37:11 time: 0.5048 data_time: 0.3644 memory: 6318 loss: 0.0963 2023/06/06 10:27:24 - mmengine - INFO - Epoch(train) [9][3700/3937] lr: 1.2474e-05 eta: 0:36:19 time: 0.5743 data_time: 0.4333 memory: 6318 loss: 0.0851 2023/06/06 10:28:19 - mmengine - INFO - Epoch(train) [9][3800/3937] lr: 1.2358e-05 eta: 0:35:27 time: 0.5269 data_time: 0.2728 memory: 6318 loss: 0.0974 2023/06/06 10:29:10 - mmengine - INFO - Epoch(train) [9][3900/3937] lr: 1.2245e-05 eta: 0:34:35 time: 0.4858 data_time: 0.3203 memory: 6318 loss: 0.0945 2023/06/06 10:29:28 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 10:29:28 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/06/06 10:30:06 - mmengine - INFO - Epoch(val) [9][57/57] accuracy/top1: 96.6181 data_time: 0.3733 time: 0.4594 2023/06/06 10:31:00 - mmengine - INFO - Epoch(train) [10][ 100/3937] lr: 1.2094e-05 eta: 0:33:23 time: 0.4406 data_time: 0.3008 memory: 6318 loss: 0.1128 2023/06/06 10:31:50 - mmengine - INFO - Epoch(train) [10][ 200/3937] lr: 1.1987e-05 eta: 0:32:31 time: 0.5170 data_time: 0.3775 memory: 6318 loss: 0.0882 2023/06/06 10:32:38 - mmengine - INFO - Epoch(train) [10][ 300/3937] lr: 1.1883e-05 eta: 0:31:38 time: 0.4734 data_time: 0.3339 memory: 6318 loss: 0.0974 2023/06/06 10:33:27 - mmengine - INFO - Epoch(train) [10][ 400/3937] lr: 1.1781e-05 eta: 0:30:46 time: 0.4880 data_time: 0.3488 memory: 6318 loss: 0.0869 2023/06/06 10:34:16 - mmengine - INFO - Epoch(train) [10][ 500/3937] lr: 1.1683e-05 eta: 0:29:53 time: 0.4829 data_time: 0.3382 memory: 6318 loss: 0.0848 2023/06/06 10:34:50 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 10:35:05 - mmengine - INFO - Epoch(train) [10][ 600/3937] lr: 1.1587e-05 eta: 0:29:01 time: 0.4530 data_time: 0.3130 memory: 6318 loss: 0.0926 2023/06/06 10:35:53 - mmengine - INFO - Epoch(train) [10][ 700/3937] lr: 1.1494e-05 eta: 0:28:08 time: 0.4570 data_time: 0.3006 memory: 6318 loss: 0.0889 2023/06/06 10:36:43 - mmengine - INFO - Epoch(train) [10][ 800/3937] lr: 1.1403e-05 eta: 0:27:16 time: 0.4851 data_time: 0.3454 memory: 6318 loss: 0.0857 2023/06/06 10:37:31 - mmengine - INFO - Epoch(train) [10][ 900/3937] lr: 1.1316e-05 eta: 0:26:23 time: 0.4892 data_time: 0.3492 memory: 6318 loss: 0.0870 2023/06/06 10:38:20 - mmengine - INFO - Epoch(train) [10][1000/3937] lr: 1.1231e-05 eta: 0:25:31 time: 0.4914 data_time: 0.3515 memory: 6318 loss: 0.0925 2023/06/06 10:39:11 - mmengine - INFO - Epoch(train) [10][1100/3937] lr: 1.1149e-05 eta: 0:24:39 time: 0.7087 data_time: 0.5686 memory: 6318 loss: 0.0996 2023/06/06 10:40:06 - mmengine - INFO - Epoch(train) [10][1200/3937] lr: 1.1070e-05 eta: 0:23:47 time: 0.5362 data_time: 0.3947 memory: 6318 loss: 0.0914 2023/06/06 10:40:58 - mmengine - INFO - Epoch(train) [10][1300/3937] lr: 1.0993e-05 eta: 0:22:54 time: 0.4398 data_time: 0.2985 memory: 6318 loss: 0.0791 2023/06/06 10:41:49 - mmengine - INFO - Epoch(train) [10][1400/3937] lr: 1.0920e-05 eta: 0:22:02 time: 0.5093 data_time: 0.3679 memory: 6318 loss: 0.0915 2023/06/06 10:42:47 - mmengine - INFO - Epoch(train) [10][1500/3937] lr: 1.0849e-05 eta: 0:21:10 time: 1.0797 data_time: 0.7514 memory: 6318 loss: 0.0949 2023/06/06 10:43:23 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 10:43:36 - mmengine - INFO - Epoch(train) [10][1600/3937] lr: 1.0781e-05 eta: 0:20:18 time: 0.4554 data_time: 0.3007 memory: 6318 loss: 0.0859 2023/06/06 10:44:27 - mmengine - INFO - Epoch(train) [10][1700/3937] lr: 1.0716e-05 eta: 0:19:26 time: 0.4756 data_time: 0.1418 memory: 6318 loss: 0.1055 2023/06/06 10:45:15 - mmengine - INFO - Epoch(train) [10][1800/3937] lr: 1.0653e-05 eta: 0:18:34 time: 0.4653 data_time: 0.1102 memory: 6318 loss: 0.0865 2023/06/06 10:46:03 - mmengine - INFO - Epoch(train) [10][1900/3937] lr: 1.0594e-05 eta: 0:17:41 time: 0.4915 data_time: 0.2065 memory: 6318 loss: 0.0876 2023/06/06 10:46:52 - mmengine - INFO - Epoch(train) [10][2000/3937] lr: 1.0537e-05 eta: 0:16:49 time: 0.4768 data_time: 0.2327 memory: 6318 loss: 0.0855 2023/06/06 10:47:40 - mmengine - INFO - Epoch(train) [10][2100/3937] lr: 1.0483e-05 eta: 0:15:57 time: 0.5312 data_time: 0.3084 memory: 6318 loss: 0.0829 2023/06/06 10:48:30 - mmengine - INFO - Epoch(train) [10][2200/3937] lr: 1.0432e-05 eta: 0:15:04 time: 0.5082 data_time: 0.3671 memory: 6318 loss: 0.0817 2023/06/06 10:49:21 - mmengine - INFO - Epoch(train) [10][2300/3937] lr: 1.0384e-05 eta: 0:14:12 time: 0.4603 data_time: 0.3196 memory: 6318 loss: 0.0890 2023/06/06 10:50:09 - mmengine - INFO - Epoch(train) [10][2400/3937] lr: 1.0338e-05 eta: 0:13:20 time: 0.4919 data_time: 0.3505 memory: 6318 loss: 0.0840 2023/06/06 10:50:58 - mmengine - INFO - Epoch(train) [10][2500/3937] lr: 1.0296e-05 eta: 0:12:28 time: 0.4809 data_time: 0.3404 memory: 6318 loss: 0.0957 2023/06/06 10:51:31 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 10:51:47 - mmengine - INFO - Epoch(train) [10][2600/3937] lr: 1.0256e-05 eta: 0:11:36 time: 0.4657 data_time: 0.3240 memory: 6318 loss: 0.0906 2023/06/06 10:52:36 - mmengine - INFO - Epoch(train) [10][2700/3937] lr: 1.0219e-05 eta: 0:10:43 time: 0.4838 data_time: 0.3422 memory: 6318 loss: 0.0938 2023/06/06 10:53:27 - mmengine - INFO - Epoch(train) [10][2800/3937] lr: 1.0185e-05 eta: 0:09:51 time: 0.4433 data_time: 0.3036 memory: 6318 loss: 0.0948 2023/06/06 10:54:17 - mmengine - INFO - Epoch(train) [10][2900/3937] lr: 1.0154e-05 eta: 0:08:59 time: 0.5015 data_time: 0.3606 memory: 6318 loss: 0.0872 2023/06/06 10:55:07 - mmengine - INFO - Epoch(train) [10][3000/3937] lr: 1.0126e-05 eta: 0:08:07 time: 0.4892 data_time: 0.3489 memory: 6318 loss: 0.0881 2023/06/06 10:55:57 - mmengine - INFO - Epoch(train) [10][3100/3937] lr: 1.0101e-05 eta: 0:07:15 time: 0.4903 data_time: 0.3498 memory: 6318 loss: 0.1071 2023/06/06 10:56:45 - mmengine - INFO - Epoch(train) [10][3200/3937] lr: 1.0078e-05 eta: 0:06:23 time: 0.4542 data_time: 0.3144 memory: 6318 loss: 0.1080 2023/06/06 10:57:34 - mmengine - INFO - Epoch(train) [10][3300/3937] lr: 1.0058e-05 eta: 0:05:31 time: 0.4817 data_time: 0.3398 memory: 6318 loss: 0.1010 2023/06/06 10:58:24 - mmengine - INFO - Epoch(train) [10][3400/3937] lr: 1.0041e-05 eta: 0:04:39 time: 0.5132 data_time: 0.3725 memory: 6318 loss: 0.1021 2023/06/06 10:59:14 - mmengine - INFO - Epoch(train) [10][3500/3937] lr: 1.0027e-05 eta: 0:03:47 time: 0.4768 data_time: 0.3359 memory: 6318 loss: 0.0997 2023/06/06 10:59:48 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 11:00:02 - mmengine - INFO - Epoch(train) [10][3600/3937] lr: 1.0016e-05 eta: 0:02:55 time: 0.4510 data_time: 0.3103 memory: 6318 loss: 0.1002 2023/06/06 11:00:48 - mmengine - INFO - Epoch(train) [10][3700/3937] lr: 1.0008e-05 eta: 0:02:03 time: 0.4924 data_time: 0.3524 memory: 6318 loss: 0.0881 2023/06/06 11:01:37 - mmengine - INFO - Epoch(train) [10][3800/3937] lr: 1.0003e-05 eta: 0:01:11 time: 0.5142 data_time: 0.3727 memory: 6318 loss: 0.0865 2023/06/06 11:02:26 - mmengine - INFO - Epoch(train) [10][3900/3937] lr: 1.0000e-05 eta: 0:00:19 time: 0.5273 data_time: 0.3860 memory: 6318 loss: 0.0944 2023/06/06 11:02:44 - mmengine - INFO - Exp name: resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1_20230606_051539 2023/06/06 11:02:44 - mmengine - INFO - Saving checkpoint at 10 epochs 2023/06/06 11:03:23 - mmengine - INFO - Epoch(val) [10][57/57] accuracy/top1: 96.4859 data_time: 0.3758 time: 0.4634