2023/06/06 00:58:16 - 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: 1389536065 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 00:58:21 - 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=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), 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/all_0.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=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), dict(type='PackInputs') ]), dict( type='CustomDataset', data_root='', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/all_1.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=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), dict(type='PackInputs') ]), dict( type='CustomDataset', data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/stylegan3fake8w.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=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), 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=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), dict(type='PackInputs') ]), dict( type='CustomDataset', data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/stylegan3real8w.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=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), dict(type='PackInputs') ]) ]), sampler=dict(type='DefaultSampler', shuffle=True)) val_dataloader = dict( pin_memory=True, persistent_workers=True, collate_fn=dict(type='default_collate'), batch_size=256, num_workers=10, dataset=dict( type='ConcatDataset', datasets=[ dict( type='CustomDataset', data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV2-1-dpmsolver-25-1w.tsv', pipeline=[ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='JPEG', compress_val=65, prob=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), dict(type='PackInputs') ]), dict( type='CustomDataset', data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV1-5R2-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=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), dict(type='PackInputs') ]), 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=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), 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=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), dict(type='PackInputs') ]) ]), sampler=dict(type='DefaultSampler', shuffle=False)) val_evaluator = dict(type='Accuracy', topk=1) test_dataloader = dict( pin_memory=True, persistent_workers=True, collate_fn=dict(type='default_collate'), batch_size=256, num_workers=10, dataset=dict( type='ConcatDataset', datasets=[ dict( type='CustomDataset', data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV2-1-dpmsolver-25-1w.tsv', pipeline=[ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='JPEG', compress_val=65, prob=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), dict(type='PackInputs') ]), dict( type='CustomDataset', data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV1-5R2-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=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), dict(type='PackInputs') ]), 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=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), 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=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), 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_all2_1m_lr1e-4_aug_5e-1' 2023/06/06 00:58:32 - 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 00:58:53 - mmengine - INFO - load backbone in model from: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth Name of parameter - Initialization information backbone.conv1.weight - torch.Size([64, 3, 7, 7]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.downsample.1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.downsample.1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn1.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn1.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn2.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn2.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn3.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn3.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.downsample.1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.downsample.1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn1.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn1.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn2.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn2.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn3.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn3.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn1.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn1.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn2.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn2.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn3.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn3.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.3.bn1.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.3.bn1.bias - torch.Size([128]): PretrainedInit: load from 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 https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.bn1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.bn2.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.bn2.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.bn3.weight - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.bn3.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.downsample.1.weight - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.downsample.1.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn2.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn2.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn3.weight - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn3.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn2.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn2.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn3.weight - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn3.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth head.fc.weight - torch.Size([2, 2048]): NormalInit: mean=0, std=0.01, bias=0 head.fc.bias - torch.Size([2]): NormalInit: mean=0, std=0.01, bias=0 2023/06/06 00:58:54 - 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 00:58:54 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2023/06/06 00:58:54 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/luzeyu/workspace/fakebench/mmpretrain/workdir/resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1. 2023/06/06 01:00:18 - mmengine - INFO - Epoch(train) [1][ 100/4092] lr: 9.9999e-05 eta: 9:32:51 time: 0.7276 data_time: 0.0567 memory: 9436 loss: 0.6467 2023/06/06 01:01:37 - mmengine - INFO - Epoch(train) [1][ 200/4092] lr: 9.9995e-05 eta: 9:13:48 time: 0.7685 data_time: 0.0007 memory: 6319 loss: 0.6184 2023/06/06 01:02:56 - mmengine - INFO - Epoch(train) [1][ 300/4092] lr: 9.9988e-05 eta: 9:07:10 time: 0.7909 data_time: 0.0009 memory: 6319 loss: 0.5818 2023/06/06 01:04:15 - mmengine - INFO - Epoch(train) [1][ 400/4092] lr: 9.9979e-05 eta: 9:02:34 time: 0.8350 data_time: 0.0008 memory: 6319 loss: 0.5466 2023/06/06 01:05:35 - mmengine - INFO - Epoch(train) [1][ 500/4092] lr: 9.9967e-05 eta: 9:01:06 time: 0.8036 data_time: 0.0007 memory: 6319 loss: 0.5174 2023/06/06 01:06:52 - mmengine - INFO - Epoch(train) [1][ 600/4092] lr: 9.9952e-05 eta: 8:55:32 time: 0.7820 data_time: 0.0009 memory: 6319 loss: 0.5113 2023/06/06 01:08:10 - mmengine - INFO - Epoch(train) [1][ 700/4092] lr: 9.9935e-05 eta: 8:52:31 time: 0.7548 data_time: 0.0009 memory: 6319 loss: 0.4770 2023/06/06 01:09:26 - mmengine - INFO - Epoch(train) [1][ 800/4092] lr: 9.9915e-05 eta: 8:48:34 time: 0.7602 data_time: 0.0011 memory: 6319 loss: 0.4670 2023/06/06 01:10:43 - mmengine - INFO - Epoch(train) [1][ 900/4092] lr: 9.9893e-05 eta: 8:45:45 time: 0.7635 data_time: 0.0009 memory: 6319 loss: 0.4512 2023/06/06 01:11:59 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 01:11:59 - mmengine - INFO - Epoch(train) [1][1000/4092] lr: 9.9868e-05 eta: 8:42:20 time: 0.7467 data_time: 0.0009 memory: 6319 loss: 0.4458 2023/06/06 01:13:13 - mmengine - INFO - Epoch(train) [1][1100/4092] lr: 9.9840e-05 eta: 8:38:23 time: 0.7451 data_time: 0.0015 memory: 6319 loss: 0.4359 2023/06/06 01:14:29 - mmengine - INFO - Epoch(train) [1][1200/4092] lr: 9.9809e-05 eta: 8:36:17 time: 0.8331 data_time: 0.0008 memory: 6319 loss: 0.4352 2023/06/06 01:15:46 - mmengine - INFO - Epoch(train) [1][1300/4092] lr: 9.9776e-05 eta: 8:34:02 time: 0.7362 data_time: 0.0009 memory: 6319 loss: 0.4102 2023/06/06 01:17:02 - mmengine - INFO - Epoch(train) [1][1400/4092] lr: 9.9741e-05 eta: 8:32:05 time: 0.7338 data_time: 0.0009 memory: 6319 loss: 0.4059 2023/06/06 01:18:17 - mmengine - INFO - Epoch(train) [1][1500/4092] lr: 9.9702e-05 eta: 8:29:38 time: 0.7408 data_time: 0.0008 memory: 6319 loss: 0.3949 2023/06/06 01:19:32 - mmengine - INFO - Epoch(train) [1][1600/4092] lr: 9.9661e-05 eta: 8:27:16 time: 0.7590 data_time: 0.0009 memory: 6319 loss: 0.3928 2023/06/06 01:20:48 - mmengine - INFO - Epoch(train) [1][1700/4092] lr: 9.9618e-05 eta: 8:25:13 time: 0.6609 data_time: 0.0009 memory: 6319 loss: 0.3838 2023/06/06 01:22:02 - mmengine - INFO - Epoch(train) [1][1800/4092] lr: 9.9571e-05 eta: 8:22:50 time: 0.7454 data_time: 0.0008 memory: 6319 loss: 0.3867 2023/06/06 01:23:17 - mmengine - INFO - Epoch(train) [1][1900/4092] lr: 9.9523e-05 eta: 8:20:55 time: 0.7970 data_time: 0.0008 memory: 6319 loss: 0.3863 2023/06/06 01:24:30 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 01:24:30 - mmengine - INFO - Epoch(train) [1][2000/4092] lr: 9.9471e-05 eta: 8:18:22 time: 0.7101 data_time: 0.0008 memory: 6319 loss: 0.3688 2023/06/06 01:25:45 - mmengine - INFO - Epoch(train) [1][2100/4092] lr: 9.9417e-05 eta: 8:16:19 time: 0.7135 data_time: 0.0009 memory: 6319 loss: 0.3723 2023/06/06 01:28:34 - mmengine - INFO - Epoch(train) [1][2200/4092] lr: 9.9360e-05 eta: 8:42:12 time: 0.8075 data_time: 0.0008 memory: 6319 loss: 0.3746 2023/06/06 01:29:52 - mmengine - INFO - Epoch(train) [1][2300/4092] lr: 9.9301e-05 eta: 8:39:57 time: 0.7297 data_time: 0.0010 memory: 6319 loss: 0.3827 2023/06/06 01:31:05 - mmengine - INFO - Epoch(train) [1][2400/4092] lr: 9.9239e-05 eta: 8:36:35 time: 0.7086 data_time: 0.0008 memory: 6319 loss: 0.3737 2023/06/06 01:32:17 - mmengine - INFO - Epoch(train) [1][2500/4092] lr: 9.9174e-05 eta: 8:33:14 time: 0.6826 data_time: 0.0008 memory: 6319 loss: 0.3717 2023/06/06 01:33:33 - mmengine - INFO - Epoch(train) [1][2600/4092] lr: 9.9107e-05 eta: 8:30:39 time: 0.7525 data_time: 0.0008 memory: 6319 loss: 0.3504 2023/06/06 01:34:48 - mmengine - INFO - Epoch(train) [1][2700/4092] lr: 9.9037e-05 eta: 8:28:09 time: 0.7436 data_time: 0.0009 memory: 6319 loss: 0.3462 2023/06/06 01:36:01 - mmengine - INFO - Epoch(train) [1][2800/4092] lr: 9.8965e-05 eta: 8:25:27 time: 0.6897 data_time: 0.0010 memory: 6319 loss: 0.3519 2023/06/06 01:37:16 - mmengine - INFO - Epoch(train) [1][2900/4092] lr: 9.8890e-05 eta: 8:22:58 time: 0.7586 data_time: 0.0010 memory: 6319 loss: 0.3407 2023/06/06 01:38:30 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 01:38:30 - mmengine - INFO - Epoch(train) [1][3000/4092] lr: 9.8812e-05 eta: 8:20:34 time: 0.7910 data_time: 0.0009 memory: 6319 loss: 0.3508 2023/06/06 01:39:43 - mmengine - INFO - Epoch(train) [1][3100/4092] lr: 9.8732e-05 eta: 8:18:02 time: 0.7930 data_time: 0.0010 memory: 6319 loss: 0.3420 2023/06/06 01:40:56 - mmengine - INFO - Epoch(train) [1][3200/4092] lr: 9.8650e-05 eta: 8:15:32 time: 0.6975 data_time: 0.0012 memory: 6319 loss: 0.3493 2023/06/06 01:42:08 - mmengine - INFO - Epoch(train) [1][3300/4092] lr: 9.8564e-05 eta: 8:12:59 time: 0.7505 data_time: 0.0009 memory: 6319 loss: 0.3305 2023/06/06 01:43:23 - mmengine - INFO - Epoch(train) [1][3400/4092] lr: 9.8476e-05 eta: 8:11:00 time: 0.7414 data_time: 0.0008 memory: 6319 loss: 0.3468 2023/06/06 01:44:39 - mmengine - INFO - Epoch(train) [1][3500/4092] lr: 9.8386e-05 eta: 8:09:08 time: 0.7580 data_time: 0.0010 memory: 6319 loss: 0.3498 2023/06/06 01:45:55 - mmengine - INFO - Epoch(train) [1][3600/4092] lr: 9.8293e-05 eta: 8:07:23 time: 0.7445 data_time: 0.0008 memory: 6319 loss: 0.3350 2023/06/06 01:47:14 - mmengine - INFO - Epoch(train) [1][3700/4092] lr: 9.8198e-05 eta: 8:06:17 time: 0.7830 data_time: 0.0009 memory: 6319 loss: 0.3254 2023/06/06 01:48:30 - mmengine - INFO - Epoch(train) [1][3800/4092] lr: 9.8099e-05 eta: 8:04:35 time: 0.7673 data_time: 0.0010 memory: 6319 loss: 0.3400 2023/06/06 01:49:46 - mmengine - INFO - Epoch(train) [1][3900/4092] lr: 9.7999e-05 eta: 8:02:50 time: 0.7695 data_time: 0.0009 memory: 6319 loss: 0.3286 2023/06/06 01:51:00 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 01:51:00 - mmengine - INFO - Epoch(train) [1][4000/4092] lr: 9.7896e-05 eta: 8:00:57 time: 0.7636 data_time: 0.0009 memory: 6319 loss: 0.3149 2023/06/06 01:52:10 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 01:52:10 - mmengine - INFO - Saving checkpoint at 1 epochs 2023/06/06 01:52:57 - mmengine - INFO - Epoch(val) [1][100/119] eta: 0:00:08 time: 0.8238 data_time: 0.7352 memory: 6319 2023/06/06 01:53:25 - mmengine - INFO - Epoch(val) [1][119/119] accuracy/top1: 82.7698 data_time: 0.3998 time: 0.4893 2023/06/06 01:54:40 - mmengine - INFO - Epoch(train) [2][ 100/4092] lr: 9.7691e-05 eta: 7:57:40 time: 0.7403 data_time: 0.4555 memory: 6319 loss: 0.3017 2023/06/06 01:55:55 - mmengine - INFO - Epoch(train) [2][ 200/4092] lr: 9.7580e-05 eta: 7:55:57 time: 0.7495 data_time: 0.2718 memory: 6319 loss: 0.3268 2023/06/06 01:57:08 - mmengine - INFO - Epoch(train) [2][ 300/4092] lr: 9.7467e-05 eta: 7:53:56 time: 0.7373 data_time: 0.1156 memory: 6319 loss: 0.3033 2023/06/06 01:58:21 - mmengine - INFO - Epoch(train) [2][ 400/4092] lr: 9.7352e-05 eta: 7:52:05 time: 0.8112 data_time: 0.2385 memory: 6319 loss: 0.3228 2023/06/06 01:59:35 - mmengine - INFO - Epoch(train) [2][ 500/4092] lr: 9.7234e-05 eta: 7:50:15 time: 0.7327 data_time: 0.0173 memory: 6319 loss: 0.3332 2023/06/06 02:00:52 - mmengine - INFO - Epoch(train) [2][ 600/4092] lr: 9.7113e-05 eta: 7:48:49 time: 0.7490 data_time: 0.0010 memory: 6319 loss: 0.3017 2023/06/06 02:02:02 - mmengine - INFO - Epoch(train) [2][ 700/4092] lr: 9.6990e-05 eta: 7:46:35 time: 0.7078 data_time: 0.0009 memory: 6319 loss: 0.3062 2023/06/06 02:03:16 - mmengine - INFO - Epoch(train) [2][ 800/4092] lr: 9.6865e-05 eta: 7:44:55 time: 0.7500 data_time: 0.0011 memory: 6319 loss: 0.3307 2023/06/06 02:04:31 - mmengine - INFO - Epoch(train) [2][ 900/4092] lr: 9.6737e-05 eta: 7:43:18 time: 0.7641 data_time: 0.0009 memory: 6319 loss: 0.3279 2023/06/06 02:04:39 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 02:05:45 - mmengine - INFO - Epoch(train) [2][1000/4092] lr: 9.6606e-05 eta: 7:41:36 time: 0.7436 data_time: 0.0009 memory: 6319 loss: 0.3087 2023/06/06 02:06:59 - mmengine - INFO - Epoch(train) [2][1100/4092] lr: 9.6473e-05 eta: 7:40:00 time: 0.8010 data_time: 0.0010 memory: 6319 loss: 0.2861 2023/06/06 02:08:14 - mmengine - INFO - Epoch(train) [2][1200/4092] lr: 9.6338e-05 eta: 7:38:28 time: 0.7218 data_time: 0.0010 memory: 6319 loss: 0.3208 2023/06/06 02:09:27 - mmengine - INFO - Epoch(train) [2][1300/4092] lr: 9.6200e-05 eta: 7:36:40 time: 0.7188 data_time: 0.0009 memory: 6319 loss: 0.3038 2023/06/06 02:10:39 - mmengine - INFO - Epoch(train) [2][1400/4092] lr: 9.6060e-05 eta: 7:34:49 time: 0.6924 data_time: 0.0008 memory: 6319 loss: 0.2916 2023/06/06 02:11:51 - mmengine - INFO - Epoch(train) [2][1500/4092] lr: 9.5918e-05 eta: 7:33:00 time: 0.6918 data_time: 0.0009 memory: 6319 loss: 0.3138 2023/06/06 02:13:06 - mmengine - INFO - Epoch(train) [2][1600/4092] lr: 9.5773e-05 eta: 7:31:35 time: 0.7407 data_time: 0.0009 memory: 6319 loss: 0.2937 2023/06/06 02:14:26 - mmengine - INFO - Epoch(train) [2][1700/4092] lr: 9.5625e-05 eta: 7:30:32 time: 0.8106 data_time: 0.0010 memory: 6319 loss: 0.3085 2023/06/06 02:15:41 - mmengine - INFO - Epoch(train) [2][1800/4092] lr: 9.5475e-05 eta: 7:29:07 time: 0.7120 data_time: 0.0009 memory: 6319 loss: 0.3005 2023/06/06 02:16:55 - mmengine - INFO - Epoch(train) [2][1900/4092] lr: 9.5323e-05 eta: 7:27:32 time: 0.7047 data_time: 0.0009 memory: 6319 loss: 0.3085 2023/06/06 02:17:03 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 02:18:08 - mmengine - INFO - Epoch(train) [2][2000/4092] lr: 9.5169e-05 eta: 7:25:53 time: 0.7012 data_time: 0.0010 memory: 6319 loss: 0.2957 2023/06/06 02:19:24 - mmengine - INFO - Epoch(train) [2][2100/4092] lr: 9.5012e-05 eta: 7:24:30 time: 0.7679 data_time: 0.0008 memory: 6319 loss: 0.2921 2023/06/06 02:20:37 - mmengine - INFO - Epoch(train) [2][2200/4092] lr: 9.4853e-05 eta: 7:22:54 time: 0.7100 data_time: 0.0010 memory: 6319 loss: 0.2963 2023/06/06 02:21:51 - mmengine - INFO - Epoch(train) [2][2300/4092] lr: 9.4691e-05 eta: 7:21:20 time: 0.7452 data_time: 0.0011 memory: 6319 loss: 0.2916 2023/06/06 02:23:05 - mmengine - INFO - Epoch(train) [2][2400/4092] lr: 9.4527e-05 eta: 7:19:53 time: 0.7754 data_time: 0.0009 memory: 6319 loss: 0.2812 2023/06/06 02:24:20 - mmengine - INFO - Epoch(train) [2][2500/4092] lr: 9.4361e-05 eta: 7:18:24 time: 0.8683 data_time: 0.0010 memory: 6319 loss: 0.3007 2023/06/06 02:25:34 - mmengine - INFO - Epoch(train) [2][2600/4092] lr: 9.4192e-05 eta: 7:16:54 time: 0.7002 data_time: 0.0008 memory: 6319 loss: 0.2915 2023/06/06 02:26:50 - mmengine - INFO - Epoch(train) [2][2700/4092] lr: 9.4021e-05 eta: 7:15:35 time: 0.7760 data_time: 0.0009 memory: 6319 loss: 0.3125 2023/06/06 02:28:06 - mmengine - INFO - Epoch(train) [2][2800/4092] lr: 9.3848e-05 eta: 7:14:14 time: 0.7401 data_time: 0.0009 memory: 6319 loss: 0.2726 2023/06/06 02:29:22 - mmengine - INFO - Epoch(train) [2][2900/4092] lr: 9.3672e-05 eta: 7:12:57 time: 0.7292 data_time: 0.0009 memory: 6319 loss: 0.3004 2023/06/06 02:29:30 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 02:30:39 - mmengine - INFO - Epoch(train) [2][3000/4092] lr: 9.3495e-05 eta: 7:11:41 time: 0.7777 data_time: 0.0011 memory: 6319 loss: 0.2778 2023/06/06 02:31:55 - mmengine - INFO - Epoch(train) [2][3100/4092] lr: 9.3315e-05 eta: 7:10:25 time: 0.7420 data_time: 0.0010 memory: 6319 loss: 0.3007 2023/06/06 02:33:09 - mmengine - INFO - Epoch(train) [2][3200/4092] lr: 9.3132e-05 eta: 7:08:56 time: 0.7074 data_time: 0.0008 memory: 6319 loss: 0.2931 2023/06/06 02:34:30 - mmengine - INFO - Epoch(train) [2][3300/4092] lr: 9.2948e-05 eta: 7:07:57 time: 0.8083 data_time: 0.0013 memory: 6319 loss: 0.2762 2023/06/06 02:35:45 - mmengine - INFO - Epoch(train) [2][3400/4092] lr: 9.2761e-05 eta: 7:06:36 time: 0.8479 data_time: 0.0008 memory: 6319 loss: 0.2856 2023/06/06 02:37:00 - mmengine - INFO - Epoch(train) [2][3500/4092] lr: 9.2572e-05 eta: 7:05:13 time: 0.6747 data_time: 0.0009 memory: 6319 loss: 0.2842 2023/06/06 02:38:24 - mmengine - INFO - Epoch(train) [2][3600/4092] lr: 9.2381e-05 eta: 7:04:28 time: 0.7300 data_time: 0.0009 memory: 6319 loss: 0.2812 2023/06/06 02:39:38 - mmengine - INFO - Epoch(train) [2][3700/4092] lr: 9.2187e-05 eta: 7:02:57 time: 0.7394 data_time: 0.0009 memory: 6319 loss: 0.2923 2023/06/06 02:40:56 - mmengine - INFO - Epoch(train) [2][3800/4092] lr: 9.1991e-05 eta: 7:01:47 time: 0.7227 data_time: 0.0011 memory: 6319 loss: 0.3005 2023/06/06 02:42:09 - mmengine - INFO - Epoch(train) [2][3900/4092] lr: 9.1794e-05 eta: 7:00:17 time: 0.7644 data_time: 0.0009 memory: 6319 loss: 0.2758 2023/06/06 02:42:16 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 02:43:23 - mmengine - INFO - Epoch(train) [2][4000/4092] lr: 9.1594e-05 eta: 6:58:51 time: 0.7619 data_time: 0.0010 memory: 6319 loss: 0.2709 2023/06/06 02:44:35 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 02:44:35 - mmengine - INFO - Saving checkpoint at 2 epochs 2023/06/06 02:45:20 - mmengine - INFO - Epoch(val) [2][100/119] eta: 0:00:07 time: 0.6763 data_time: 0.5850 memory: 6319 2023/06/06 02:45:47 - mmengine - INFO - Epoch(val) [2][119/119] accuracy/top1: 81.9224 data_time: 0.3713 time: 0.4613 2023/06/06 02:47:05 - mmengine - INFO - Epoch(train) [3][ 100/4092] lr: 9.1204e-05 eta: 6:56:33 time: 0.7784 data_time: 0.3623 memory: 6319 loss: 0.2805 2023/06/06 02:48:19 - mmengine - INFO - Epoch(train) [3][ 200/4092] lr: 9.0997e-05 eta: 6:55:09 time: 0.7314 data_time: 0.2476 memory: 6319 loss: 0.2813 2023/06/06 02:49:33 - mmengine - INFO - Epoch(train) [3][ 300/4092] lr: 9.0789e-05 eta: 6:53:43 time: 0.7502 data_time: 0.0113 memory: 6319 loss: 0.2848 2023/06/06 02:50:49 - mmengine - INFO - Epoch(train) [3][ 400/4092] lr: 9.0579e-05 eta: 6:52:22 time: 0.7843 data_time: 0.0010 memory: 6319 loss: 0.2834 2023/06/06 02:52:03 - mmengine - INFO - Epoch(train) [3][ 500/4092] lr: 9.0366e-05 eta: 6:50:58 time: 0.7473 data_time: 0.0008 memory: 6319 loss: 0.2887 2023/06/06 02:53:17 - mmengine - INFO - Epoch(train) [3][ 600/4092] lr: 9.0151e-05 eta: 6:49:33 time: 0.7299 data_time: 0.0008 memory: 6319 loss: 0.2831 2023/06/06 02:54:32 - mmengine - INFO - Epoch(train) [3][ 700/4092] lr: 8.9935e-05 eta: 6:48:10 time: 0.7469 data_time: 0.0009 memory: 6319 loss: 0.2758 2023/06/06 02:55:47 - mmengine - INFO - Epoch(train) [3][ 800/4092] lr: 8.9716e-05 eta: 6:46:48 time: 0.7335 data_time: 0.0008 memory: 6319 loss: 0.2611 2023/06/06 02:56:02 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 02:57:02 - mmengine - INFO - Epoch(train) [3][ 900/4092] lr: 8.9495e-05 eta: 6:45:26 time: 0.7905 data_time: 0.0007 memory: 6319 loss: 0.2864 2023/06/06 02:58:21 - mmengine - INFO - Epoch(train) [3][1000/4092] lr: 8.9272e-05 eta: 6:44:21 time: 0.8574 data_time: 0.0008 memory: 6319 loss: 0.2859 2023/06/06 02:59:37 - mmengine - INFO - Epoch(train) [3][1100/4092] lr: 8.9047e-05 eta: 6:43:01 time: 0.7644 data_time: 0.0010 memory: 6319 loss: 0.2825 2023/06/06 03:00:54 - mmengine - INFO - Epoch(train) [3][1200/4092] lr: 8.8820e-05 eta: 6:41:46 time: 0.7886 data_time: 0.0009 memory: 6319 loss: 0.2827 2023/06/06 03:02:05 - mmengine - INFO - Epoch(train) [3][1300/4092] lr: 8.8591e-05 eta: 6:40:13 time: 0.7629 data_time: 0.0009 memory: 6319 loss: 0.2766 2023/06/06 03:03:20 - mmengine - INFO - Epoch(train) [3][1400/4092] lr: 8.8360e-05 eta: 6:38:52 time: 0.7176 data_time: 0.0009 memory: 6319 loss: 0.2816 2023/06/06 03:04:36 - mmengine - INFO - Epoch(train) [3][1500/4092] lr: 8.8128e-05 eta: 6:37:33 time: 0.8579 data_time: 0.0009 memory: 6319 loss: 0.2614 2023/06/06 03:05:49 - mmengine - INFO - Epoch(train) [3][1600/4092] lr: 8.7893e-05 eta: 6:36:07 time: 0.7500 data_time: 0.0008 memory: 6319 loss: 0.2704 2023/06/06 03:07:03 - mmengine - INFO - Epoch(train) [3][1700/4092] lr: 8.7656e-05 eta: 6:34:44 time: 0.6923 data_time: 0.0008 memory: 6319 loss: 0.2542 2023/06/06 03:08:18 - mmengine - INFO - Epoch(train) [3][1800/4092] lr: 8.7417e-05 eta: 6:33:21 time: 0.7331 data_time: 0.0007 memory: 6319 loss: 0.2760 2023/06/06 03:08:32 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 03:09:33 - mmengine - INFO - Epoch(train) [3][1900/4092] lr: 8.7177e-05 eta: 6:32:04 time: 0.7779 data_time: 0.0008 memory: 6319 loss: 0.2653 2023/06/06 03:10:47 - mmengine - INFO - Epoch(train) [3][2000/4092] lr: 8.6934e-05 eta: 6:30:39 time: 0.6819 data_time: 0.0010 memory: 6319 loss: 0.2725 2023/06/06 03:12:02 - mmengine - INFO - Epoch(train) [3][2100/4092] lr: 8.6690e-05 eta: 6:29:20 time: 0.7411 data_time: 0.0010 memory: 6319 loss: 0.2684 2023/06/06 03:13:19 - mmengine - INFO - Epoch(train) [3][2200/4092] lr: 8.6444e-05 eta: 6:28:06 time: 0.7439 data_time: 0.0009 memory: 6319 loss: 0.2677 2023/06/06 03:14:38 - mmengine - INFO - Epoch(train) [3][2300/4092] lr: 8.6196e-05 eta: 6:26:57 time: 0.7392 data_time: 0.0009 memory: 6319 loss: 0.2721 2023/06/06 03:15:54 - mmengine - INFO - Epoch(train) [3][2400/4092] lr: 8.5946e-05 eta: 6:25:40 time: 0.7655 data_time: 0.0008 memory: 6319 loss: 0.2583 2023/06/06 03:17:10 - mmengine - INFO - Epoch(train) [3][2500/4092] lr: 8.5694e-05 eta: 6:24:22 time: 0.7567 data_time: 0.0009 memory: 6319 loss: 0.2638 2023/06/06 03:18:26 - mmengine - INFO - Epoch(train) [3][2600/4092] lr: 8.5441e-05 eta: 6:23:05 time: 0.7387 data_time: 0.0009 memory: 6319 loss: 0.2830 2023/06/06 03:19:40 - mmengine - INFO - Epoch(train) [3][2700/4092] lr: 8.5185e-05 eta: 6:21:44 time: 0.7503 data_time: 0.0008 memory: 6319 loss: 0.2826 2023/06/06 03:20:55 - mmengine - INFO - Epoch(train) [3][2800/4092] lr: 8.4928e-05 eta: 6:20:24 time: 0.7174 data_time: 0.0010 memory: 6319 loss: 0.2440 2023/06/06 03:21:10 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 03:22:13 - mmengine - INFO - Epoch(train) [3][2900/4092] lr: 8.4669e-05 eta: 6:19:12 time: 0.6999 data_time: 0.0011 memory: 6319 loss: 0.2680 2023/06/06 03:23:34 - mmengine - INFO - Epoch(train) [3][3000/4092] lr: 8.4409e-05 eta: 6:18:09 time: 0.7636 data_time: 0.0009 memory: 6319 loss: 0.2751 2023/06/06 03:24:50 - mmengine - INFO - Epoch(train) [3][3100/4092] lr: 8.4146e-05 eta: 6:16:51 time: 0.7459 data_time: 0.0010 memory: 6319 loss: 0.2576 2023/06/06 03:26:06 - mmengine - INFO - Epoch(train) [3][3200/4092] lr: 8.3882e-05 eta: 6:15:34 time: 0.7709 data_time: 0.0007 memory: 6319 loss: 0.2576 2023/06/06 03:27:22 - mmengine - INFO - Epoch(train) [3][3300/4092] lr: 8.3616e-05 eta: 6:14:17 time: 0.7078 data_time: 0.0008 memory: 6319 loss: 0.2428 2023/06/06 03:28:40 - mmengine - INFO - Epoch(train) [3][3400/4092] lr: 8.3349e-05 eta: 6:13:06 time: 0.7724 data_time: 0.0009 memory: 6319 loss: 0.2670 2023/06/06 03:29:57 - mmengine - INFO - Epoch(train) [3][3500/4092] lr: 8.3080e-05 eta: 6:11:50 time: 0.7585 data_time: 0.0010 memory: 6319 loss: 0.2671 2023/06/06 03:31:12 - mmengine - INFO - Epoch(train) [3][3600/4092] lr: 8.2809e-05 eta: 6:10:31 time: 0.7335 data_time: 0.0008 memory: 6319 loss: 0.2550 2023/06/06 03:32:28 - mmengine - INFO - Epoch(train) [3][3700/4092] lr: 8.2537e-05 eta: 6:09:13 time: 0.7529 data_time: 0.0010 memory: 6319 loss: 0.2653 2023/06/06 03:33:45 - mmengine - INFO - Epoch(train) [3][3800/4092] lr: 8.2263e-05 eta: 6:08:00 time: 0.7682 data_time: 0.0011 memory: 6319 loss: 0.2576 2023/06/06 03:33:59 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 03:35:00 - mmengine - INFO - Epoch(train) [3][3900/4092] lr: 8.1987e-05 eta: 6:06:39 time: 0.7986 data_time: 0.0010 memory: 6319 loss: 0.2617 2023/06/06 03:36:14 - mmengine - INFO - Epoch(train) [3][4000/4092] lr: 8.1710e-05 eta: 6:05:18 time: 0.7045 data_time: 0.0011 memory: 6319 loss: 0.2548 2023/06/06 03:37:27 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 03:37:27 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/06/06 03:38:12 - mmengine - INFO - Epoch(val) [3][100/119] eta: 0:00:07 time: 0.6277 data_time: 0.5376 memory: 6319 2023/06/06 03:38:39 - mmengine - INFO - Epoch(val) [3][119/119] accuracy/top1: 81.7387 data_time: 0.3711 time: 0.4602 2023/06/06 03:40:00 - mmengine - INFO - Epoch(train) [4][ 100/4092] lr: 8.1173e-05 eta: 6:03:09 time: 0.7065 data_time: 0.4401 memory: 6319 loss: 0.2581 2023/06/06 03:41:15 - mmengine - INFO - Epoch(train) [4][ 200/4092] lr: 8.0891e-05 eta: 6:01:50 time: 0.7446 data_time: 0.0747 memory: 6319 loss: 0.2434 2023/06/06 03:42:31 - mmengine - INFO - Epoch(train) [4][ 300/4092] lr: 8.0608e-05 eta: 6:00:32 time: 0.7453 data_time: 0.0009 memory: 6319 loss: 0.2466 2023/06/06 03:43:46 - mmengine - INFO - Epoch(train) [4][ 400/4092] lr: 8.0323e-05 eta: 5:59:14 time: 0.7624 data_time: 0.0010 memory: 6319 loss: 0.2469 2023/06/06 03:45:03 - mmengine - INFO - Epoch(train) [4][ 500/4092] lr: 8.0037e-05 eta: 5:57:59 time: 0.7806 data_time: 0.0009 memory: 6319 loss: 0.2450 2023/06/06 03:46:19 - mmengine - INFO - Epoch(train) [4][ 600/4092] lr: 7.9749e-05 eta: 5:56:42 time: 0.7400 data_time: 0.0009 memory: 6319 loss: 0.2516 2023/06/06 03:47:37 - mmengine - INFO - Epoch(train) [4][ 700/4092] lr: 7.9459e-05 eta: 5:55:29 time: 0.7678 data_time: 0.0009 memory: 6319 loss: 0.2599 2023/06/06 03:47:57 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 03:48:54 - mmengine - INFO - Epoch(train) [4][ 800/4092] lr: 7.9169e-05 eta: 5:54:13 time: 0.7357 data_time: 0.0010 memory: 6319 loss: 0.2558 2023/06/06 03:50:11 - mmengine - INFO - Epoch(train) [4][ 900/4092] lr: 7.8877e-05 eta: 5:52:59 time: 0.7497 data_time: 0.0009 memory: 6319 loss: 0.2480 2023/06/06 03:51:27 - mmengine - INFO - Epoch(train) [4][1000/4092] lr: 7.8583e-05 eta: 5:51:41 time: 0.7437 data_time: 0.0010 memory: 6319 loss: 0.2589 2023/06/06 03:52:41 - mmengine - INFO - Epoch(train) [4][1100/4092] lr: 7.8288e-05 eta: 5:50:21 time: 0.7458 data_time: 0.0009 memory: 6319 loss: 0.2309 2023/06/06 03:53:58 - mmengine - INFO - Epoch(train) [4][1200/4092] lr: 7.7992e-05 eta: 5:49:06 time: 0.7534 data_time: 0.0010 memory: 6319 loss: 0.2401 2023/06/06 03:55:13 - mmengine - INFO - Epoch(train) [4][1300/4092] lr: 7.7694e-05 eta: 5:47:47 time: 0.7990 data_time: 0.0010 memory: 6319 loss: 0.2420 2023/06/06 03:56:28 - mmengine - INFO - Epoch(train) [4][1400/4092] lr: 7.7395e-05 eta: 5:46:28 time: 0.7417 data_time: 0.0012 memory: 6319 loss: 0.2452 2023/06/06 03:57:42 - mmengine - INFO - Epoch(train) [4][1500/4092] lr: 7.7095e-05 eta: 5:45:06 time: 0.7450 data_time: 0.0010 memory: 6319 loss: 0.2393 2023/06/06 03:58:58 - mmengine - INFO - Epoch(train) [4][1600/4092] lr: 7.6793e-05 eta: 5:43:49 time: 0.7494 data_time: 0.0008 memory: 6319 loss: 0.2357 2023/06/06 04:00:12 - mmengine - INFO - Epoch(train) [4][1700/4092] lr: 7.6490e-05 eta: 5:42:30 time: 0.7087 data_time: 0.0009 memory: 6319 loss: 0.2427 2023/06/06 04:00:33 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 04:01:30 - mmengine - INFO - Epoch(train) [4][1800/4092] lr: 7.6186e-05 eta: 5:41:16 time: 0.7755 data_time: 0.0008 memory: 6319 loss: 0.2352 2023/06/06 04:02:43 - mmengine - INFO - Epoch(train) [4][1900/4092] lr: 7.5881e-05 eta: 5:39:54 time: 0.7532 data_time: 0.0009 memory: 6319 loss: 0.2549 2023/06/06 04:03:59 - mmengine - INFO - Epoch(train) [4][2000/4092] lr: 7.5574e-05 eta: 5:38:36 time: 0.7648 data_time: 0.0009 memory: 6319 loss: 0.2572 2023/06/06 04:05:14 - mmengine - INFO - Epoch(train) [4][2100/4092] lr: 7.5266e-05 eta: 5:37:19 time: 0.6891 data_time: 0.0009 memory: 6319 loss: 0.2320 2023/06/06 04:06:31 - mmengine - INFO - Epoch(train) [4][2200/4092] lr: 7.4957e-05 eta: 5:36:03 time: 0.7295 data_time: 0.0009 memory: 6319 loss: 0.2601 2023/06/06 04:07:45 - mmengine - INFO - Epoch(train) [4][2300/4092] lr: 7.4647e-05 eta: 5:34:44 time: 0.8304 data_time: 0.0008 memory: 6319 loss: 0.2433 2023/06/06 04:09:01 - mmengine - INFO - Epoch(train) [4][2400/4092] lr: 7.4336e-05 eta: 5:33:26 time: 0.7421 data_time: 0.0009 memory: 6319 loss: 0.2347 2023/06/06 04:10:16 - mmengine - INFO - Epoch(train) [4][2500/4092] lr: 7.4023e-05 eta: 5:32:08 time: 0.7426 data_time: 0.0011 memory: 6319 loss: 0.2413 2023/06/06 04:11:30 - mmengine - INFO - Epoch(train) [4][2600/4092] lr: 7.3709e-05 eta: 5:30:49 time: 0.7354 data_time: 0.0010 memory: 6319 loss: 0.2345 2023/06/06 04:12:44 - mmengine - INFO - Epoch(train) [4][2700/4092] lr: 7.3395e-05 eta: 5:29:28 time: 0.8386 data_time: 0.0010 memory: 6319 loss: 0.2358 2023/06/06 04:13:01 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 04:14:07 - mmengine - INFO - Epoch(train) [4][2800/4092] lr: 7.3079e-05 eta: 5:28:24 time: 1.4188 data_time: 0.0009 memory: 6319 loss: 0.2593 2023/06/06 04:15:25 - mmengine - INFO - Epoch(train) [4][2900/4092] lr: 7.2762e-05 eta: 5:27:10 time: 0.7593 data_time: 0.0009 memory: 6319 loss: 0.2396 2023/06/06 04:16:43 - mmengine - INFO - Epoch(train) [4][3000/4092] lr: 7.2444e-05 eta: 5:25:56 time: 0.7510 data_time: 0.0009 memory: 6319 loss: 0.2313 2023/06/06 04:17:58 - mmengine - INFO - Epoch(train) [4][3100/4092] lr: 7.2125e-05 eta: 5:24:38 time: 0.7700 data_time: 0.0008 memory: 6319 loss: 0.2398 2023/06/06 04:19:15 - mmengine - INFO - Epoch(train) [4][3200/4092] lr: 7.1805e-05 eta: 5:23:23 time: 0.8533 data_time: 0.0011 memory: 6319 loss: 0.2264 2023/06/06 04:20:32 - mmengine - INFO - Epoch(train) [4][3300/4092] lr: 7.1484e-05 eta: 5:22:09 time: 0.7617 data_time: 0.0010 memory: 6319 loss: 0.2567 2023/06/06 04:21:48 - mmengine - INFO - Epoch(train) [4][3400/4092] lr: 7.1162e-05 eta: 5:20:52 time: 0.8312 data_time: 0.0008 memory: 6319 loss: 0.2266 2023/06/06 04:23:05 - mmengine - INFO - Epoch(train) [4][3500/4092] lr: 7.0839e-05 eta: 5:19:37 time: 0.7463 data_time: 0.0010 memory: 6319 loss: 0.2386 2023/06/06 04:24:21 - mmengine - INFO - Epoch(train) [4][3600/4092] lr: 7.0515e-05 eta: 5:18:21 time: 0.7131 data_time: 0.0008 memory: 6319 loss: 0.2441 2023/06/06 04:25:34 - mmengine - INFO - Epoch(train) [4][3700/4092] lr: 7.0191e-05 eta: 5:16:59 time: 0.7135 data_time: 0.0009 memory: 6319 loss: 0.2493 2023/06/06 04:25:49 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 04:26:46 - mmengine - INFO - Epoch(train) [4][3800/4092] lr: 6.9865e-05 eta: 5:15:36 time: 0.7190 data_time: 0.0008 memory: 6319 loss: 0.2373 2023/06/06 04:28:00 - mmengine - INFO - Epoch(train) [4][3900/4092] lr: 6.9538e-05 eta: 5:14:16 time: 0.8029 data_time: 0.0009 memory: 6319 loss: 0.2515 2023/06/06 04:29:16 - mmengine - INFO - Epoch(train) [4][4000/4092] lr: 6.9211e-05 eta: 5:12:59 time: 0.7749 data_time: 0.0009 memory: 6319 loss: 0.2331 2023/06/06 04:30:24 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 04:30:24 - mmengine - INFO - Saving checkpoint at 4 epochs 2023/06/06 04:31:08 - mmengine - INFO - Epoch(val) [4][100/119] eta: 0:00:07 time: 0.4918 data_time: 0.4018 memory: 6319 2023/06/06 04:31:32 - mmengine - INFO - Epoch(val) [4][119/119] accuracy/top1: 85.1083 data_time: 0.3380 time: 0.4257 2023/06/06 04:32:48 - mmengine - INFO - Epoch(train) [5][ 100/4092] lr: 6.8580e-05 eta: 5:10:31 time: 0.7358 data_time: 0.3022 memory: 6319 loss: 0.2545 2023/06/06 04:34:04 - mmengine - INFO - Epoch(train) [5][ 200/4092] lr: 6.8250e-05 eta: 5:09:14 time: 0.7363 data_time: 0.0010 memory: 6319 loss: 0.2320 2023/06/06 04:35:19 - mmengine - INFO - Epoch(train) [5][ 300/4092] lr: 6.7920e-05 eta: 5:07:56 time: 0.7698 data_time: 0.0010 memory: 6319 loss: 0.2312 2023/06/06 04:36:39 - mmengine - INFO - Epoch(train) [5][ 400/4092] lr: 6.7588e-05 eta: 5:06:45 time: 1.3156 data_time: 0.0007 memory: 6319 loss: 0.2396 2023/06/06 04:37:58 - mmengine - INFO - Epoch(train) [5][ 500/4092] lr: 6.7256e-05 eta: 5:05:34 time: 0.7898 data_time: 0.0009 memory: 6319 loss: 0.2282 2023/06/06 04:39:14 - mmengine - INFO - Epoch(train) [5][ 600/4092] lr: 6.6924e-05 eta: 5:04:17 time: 0.8250 data_time: 0.0009 memory: 6319 loss: 0.2323 2023/06/06 04:39:42 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 04:40:27 - mmengine - INFO - Epoch(train) [5][ 700/4092] lr: 6.6590e-05 eta: 5:02:56 time: 0.7066 data_time: 0.0008 memory: 6319 loss: 0.2311 2023/06/06 04:41:42 - mmengine - INFO - Epoch(train) [5][ 800/4092] lr: 6.6256e-05 eta: 5:01:38 time: 0.7500 data_time: 0.0008 memory: 6319 loss: 0.2330 2023/06/06 04:42:58 - mmengine - INFO - Epoch(train) [5][ 900/4092] lr: 6.5921e-05 eta: 5:00:22 time: 0.7828 data_time: 0.0009 memory: 6319 loss: 0.2508 2023/06/06 04:44:14 - mmengine - INFO - Epoch(train) [5][1000/4092] lr: 6.5586e-05 eta: 4:59:05 time: 0.7278 data_time: 0.0009 memory: 6319 loss: 0.2467 2023/06/06 04:45:29 - mmengine - INFO - Epoch(train) [5][1100/4092] lr: 6.5250e-05 eta: 4:57:48 time: 0.7504 data_time: 0.0010 memory: 6319 loss: 0.2559 2023/06/06 04:46:45 - mmengine - INFO - Epoch(train) [5][1200/4092] lr: 6.4913e-05 eta: 4:56:30 time: 0.7106 data_time: 0.0010 memory: 6319 loss: 0.2270 2023/06/06 04:48:01 - mmengine - INFO - Epoch(train) [5][1300/4092] lr: 6.4576e-05 eta: 4:55:14 time: 0.7481 data_time: 0.0008 memory: 6319 loss: 0.2416 2023/06/06 04:49:17 - mmengine - INFO - Epoch(train) [5][1400/4092] lr: 6.4238e-05 eta: 4:53:57 time: 0.7690 data_time: 0.0009 memory: 6319 loss: 0.2311 2023/06/06 04:50:33 - mmengine - INFO - Epoch(train) [5][1500/4092] lr: 6.3899e-05 eta: 4:52:41 time: 0.7814 data_time: 0.0009 memory: 6319 loss: 0.2490 2023/06/06 04:51:48 - mmengine - INFO - Epoch(train) [5][1600/4092] lr: 6.3560e-05 eta: 4:51:24 time: 0.7281 data_time: 0.0009 memory: 6319 loss: 0.2329 2023/06/06 04:52:17 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 04:53:04 - mmengine - INFO - Epoch(train) [5][1700/4092] lr: 6.3221e-05 eta: 4:50:08 time: 0.7271 data_time: 0.0008 memory: 6319 loss: 0.2362 2023/06/06 04:54:24 - mmengine - INFO - Epoch(train) [5][1800/4092] lr: 6.2881e-05 eta: 4:48:56 time: 0.7365 data_time: 0.0009 memory: 6319 loss: 0.2285 2023/06/06 04:55:38 - mmengine - INFO - Epoch(train) [5][1900/4092] lr: 6.2541e-05 eta: 4:47:38 time: 0.7225 data_time: 0.0008 memory: 6319 loss: 0.2323 2023/06/06 04:56:54 - mmengine - INFO - Epoch(train) [5][2000/4092] lr: 6.2200e-05 eta: 4:46:21 time: 0.8011 data_time: 0.0010 memory: 6319 loss: 0.2440 2023/06/06 04:58:08 - mmengine - INFO - Epoch(train) [5][2100/4092] lr: 6.1859e-05 eta: 4:45:02 time: 0.7546 data_time: 0.0011 memory: 6319 loss: 0.2435 2023/06/06 04:59:23 - mmengine - INFO - Epoch(train) [5][2200/4092] lr: 6.1517e-05 eta: 4:43:44 time: 0.7521 data_time: 0.0012 memory: 6319 loss: 0.2370 2023/06/06 05:00:36 - mmengine - INFO - Epoch(train) [5][2300/4092] lr: 6.1175e-05 eta: 4:42:25 time: 0.7016 data_time: 0.0009 memory: 6319 loss: 0.2205 2023/06/06 05:01:49 - mmengine - INFO - Epoch(train) [5][2400/4092] lr: 6.0833e-05 eta: 4:41:05 time: 0.7140 data_time: 0.0008 memory: 6319 loss: 0.2285 2023/06/06 05:03:10 - mmengine - INFO - Epoch(train) [5][2500/4092] lr: 6.0490e-05 eta: 4:39:54 time: 0.6932 data_time: 0.0011 memory: 6319 loss: 0.2296 2023/06/06 05:04:25 - mmengine - INFO - Epoch(train) [5][2600/4092] lr: 6.0147e-05 eta: 4:38:37 time: 0.7555 data_time: 0.0009 memory: 6319 loss: 0.2294 2023/06/06 05:04:54 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 05:05:43 - mmengine - INFO - Epoch(train) [5][2700/4092] lr: 5.9803e-05 eta: 4:37:22 time: 0.7669 data_time: 0.0010 memory: 6319 loss: 0.2333 2023/06/06 05:06:57 - mmengine - INFO - Epoch(train) [5][2800/4092] lr: 5.9460e-05 eta: 4:36:04 time: 0.8021 data_time: 0.0009 memory: 6319 loss: 0.2551 2023/06/06 05:08:13 - mmengine - INFO - Epoch(train) [5][2900/4092] lr: 5.9116e-05 eta: 4:34:47 time: 0.7087 data_time: 0.0009 memory: 6319 loss: 0.2471 2023/06/06 05:09:27 - mmengine - INFO - Epoch(train) [5][3000/4092] lr: 5.8772e-05 eta: 4:33:29 time: 0.7371 data_time: 0.0010 memory: 6319 loss: 0.2278 2023/06/06 05:10:44 - mmengine - INFO - Epoch(train) [5][3100/4092] lr: 5.8427e-05 eta: 4:32:13 time: 0.7397 data_time: 0.0011 memory: 6319 loss: 0.2247 2023/06/06 05:12:01 - mmengine - INFO - Epoch(train) [5][3200/4092] lr: 5.8083e-05 eta: 4:30:58 time: 0.7367 data_time: 0.0009 memory: 6319 loss: 0.2403 2023/06/06 05:13:17 - mmengine - INFO - Epoch(train) [5][3300/4092] lr: 5.7738e-05 eta: 4:29:43 time: 0.7156 data_time: 0.0009 memory: 6319 loss: 0.2145 2023/06/06 05:14:34 - mmengine - INFO - Epoch(train) [5][3400/4092] lr: 5.7393e-05 eta: 4:28:27 time: 0.7647 data_time: 0.0011 memory: 6319 loss: 0.2224 2023/06/06 05:15:50 - mmengine - INFO - Epoch(train) [5][3500/4092] lr: 5.7048e-05 eta: 4:27:11 time: 0.8112 data_time: 0.0011 memory: 6319 loss: 0.2200 2023/06/06 05:17:06 - mmengine - INFO - Epoch(train) [5][3600/4092] lr: 5.6703e-05 eta: 4:25:54 time: 0.7341 data_time: 0.0009 memory: 6319 loss: 0.2186 2023/06/06 05:17:29 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 05:18:21 - mmengine - INFO - Epoch(train) [5][3700/4092] lr: 5.6358e-05 eta: 4:24:37 time: 0.7233 data_time: 0.0009 memory: 6319 loss: 0.2396 2023/06/06 05:19:41 - mmengine - INFO - Epoch(train) [5][3800/4092] lr: 5.6012e-05 eta: 4:23:25 time: 0.7516 data_time: 0.0011 memory: 6319 loss: 0.2451 2023/06/06 05:21:02 - mmengine - INFO - Epoch(train) [5][3900/4092] lr: 5.5667e-05 eta: 4:22:13 time: 0.7594 data_time: 0.0011 memory: 6319 loss: 0.2264 2023/06/06 05:22:17 - mmengine - INFO - Epoch(train) [5][4000/4092] lr: 5.5321e-05 eta: 4:20:56 time: 0.7460 data_time: 0.0010 memory: 6319 loss: 0.2093 2023/06/06 05:23:31 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 05:23:31 - mmengine - INFO - Saving checkpoint at 5 epochs 2023/06/06 05:24:16 - mmengine - INFO - Epoch(val) [5][100/119] eta: 0:00:07 time: 0.6853 data_time: 0.5955 memory: 6319 2023/06/06 05:24:43 - mmengine - INFO - Epoch(val) [5][119/119] accuracy/top1: 88.7295 data_time: 0.3710 time: 0.4601 2023/06/06 05:26:06 - mmengine - INFO - Epoch(train) [6][ 100/4092] lr: 5.4658e-05 eta: 4:18:40 time: 0.7422 data_time: 0.3618 memory: 6319 loss: 0.2210 2023/06/06 05:27:21 - mmengine - INFO - Epoch(train) [6][ 200/4092] lr: 5.4313e-05 eta: 4:17:24 time: 0.7930 data_time: 0.0892 memory: 6319 loss: 0.2218 2023/06/06 05:28:39 - mmengine - INFO - Epoch(train) [6][ 300/4092] lr: 5.3967e-05 eta: 4:16:09 time: 0.8125 data_time: 0.0008 memory: 6319 loss: 0.2273 2023/06/06 05:29:55 - mmengine - INFO - Epoch(train) [6][ 400/4092] lr: 5.3622e-05 eta: 4:14:52 time: 0.7553 data_time: 0.0008 memory: 6319 loss: 0.2373 2023/06/06 05:31:13 - mmengine - INFO - Epoch(train) [6][ 500/4092] lr: 5.3276e-05 eta: 4:13:38 time: 0.7640 data_time: 0.0010 memory: 6319 loss: 0.2372 2023/06/06 05:31:44 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 05:32:30 - mmengine - INFO - Epoch(train) [6][ 600/4092] lr: 5.2931e-05 eta: 4:12:22 time: 0.7425 data_time: 0.0011 memory: 6319 loss: 0.2217 2023/06/06 05:33:44 - mmengine - INFO - Epoch(train) [6][ 700/4092] lr: 5.2586e-05 eta: 4:11:04 time: 0.7136 data_time: 0.0010 memory: 6319 loss: 0.2179 2023/06/06 05:36:30 - mmengine - INFO - Epoch(train) [6][ 800/4092] lr: 5.2241e-05 eta: 4:11:11 time: 0.7487 data_time: 0.0016 memory: 6319 loss: 0.2180 2023/06/06 05:37:45 - mmengine - INFO - Epoch(train) [6][ 900/4092] lr: 5.1897e-05 eta: 4:09:52 time: 0.7653 data_time: 0.0009 memory: 6319 loss: 0.2441 2023/06/06 05:39:12 - mmengine - INFO - Epoch(train) [6][1000/4092] lr: 5.1552e-05 eta: 4:08:45 time: 0.8699 data_time: 0.0007 memory: 6319 loss: 0.2157 2023/06/06 05:40:31 - mmengine - INFO - Epoch(train) [6][1100/4092] lr: 5.1208e-05 eta: 4:07:30 time: 0.7318 data_time: 0.0012 memory: 6319 loss: 0.2166 2023/06/06 05:41:47 - mmengine - INFO - Epoch(train) [6][1200/4092] lr: 5.0864e-05 eta: 4:06:13 time: 0.8226 data_time: 0.0010 memory: 6319 loss: 0.2266 2023/06/06 05:43:01 - mmengine - INFO - Epoch(train) [6][1300/4092] lr: 5.0520e-05 eta: 4:04:54 time: 0.7309 data_time: 0.0009 memory: 6319 loss: 0.2154 2023/06/06 05:44:16 - mmengine - INFO - Epoch(train) [6][1400/4092] lr: 5.0176e-05 eta: 4:03:36 time: 0.7729 data_time: 0.0010 memory: 6319 loss: 0.2330 2023/06/06 05:45:32 - mmengine - INFO - Epoch(train) [6][1500/4092] lr: 4.9833e-05 eta: 4:02:18 time: 0.7289 data_time: 0.0009 memory: 6319 loss: 0.2136 2023/06/06 05:46:03 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 05:46:47 - mmengine - INFO - Epoch(train) [6][1600/4092] lr: 4.9490e-05 eta: 4:01:01 time: 0.7354 data_time: 0.0009 memory: 6319 loss: 0.2199 2023/06/06 05:48:03 - mmengine - INFO - Epoch(train) [6][1700/4092] lr: 4.9147e-05 eta: 3:59:43 time: 0.7417 data_time: 0.0008 memory: 6319 loss: 0.2292 2023/06/06 05:49:18 - mmengine - INFO - Epoch(train) [6][1800/4092] lr: 4.8805e-05 eta: 3:58:25 time: 0.7459 data_time: 0.0011 memory: 6319 loss: 0.2248 2023/06/06 05:50:33 - mmengine - INFO - Epoch(train) [6][1900/4092] lr: 4.8462e-05 eta: 3:57:07 time: 0.7584 data_time: 0.0009 memory: 6319 loss: 0.2131 2023/06/06 05:51:48 - mmengine - INFO - Epoch(train) [6][2000/4092] lr: 4.8121e-05 eta: 3:55:49 time: 0.7301 data_time: 0.0010 memory: 6319 loss: 0.2117 2023/06/06 05:53:04 - mmengine - INFO - Epoch(train) [6][2100/4092] lr: 4.7780e-05 eta: 3:54:32 time: 0.7756 data_time: 0.0014 memory: 6319 loss: 0.2200 2023/06/06 05:54:31 - mmengine - INFO - Epoch(train) [6][2200/4092] lr: 4.7439e-05 eta: 3:53:24 time: 0.7684 data_time: 0.0009 memory: 6319 loss: 0.2077 2023/06/06 05:55:46 - mmengine - INFO - Epoch(train) [6][2300/4092] lr: 4.7099e-05 eta: 3:52:05 time: 0.7135 data_time: 0.0009 memory: 6319 loss: 0.2112 2023/06/06 05:57:01 - mmengine - INFO - Epoch(train) [6][2400/4092] lr: 4.6759e-05 eta: 3:50:47 time: 0.7669 data_time: 0.0009 memory: 6319 loss: 0.2421 2023/06/06 05:58:16 - mmengine - INFO - Epoch(train) [6][2500/4092] lr: 4.6419e-05 eta: 3:49:30 time: 0.7091 data_time: 0.0011 memory: 6319 loss: 0.2262 2023/06/06 05:58:45 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 05:59:30 - mmengine - INFO - Epoch(train) [6][2600/4092] lr: 4.6080e-05 eta: 3:48:11 time: 0.7463 data_time: 0.0008 memory: 6319 loss: 0.2426 2023/06/06 06:00:41 - mmengine - INFO - Epoch(train) [6][2700/4092] lr: 4.5742e-05 eta: 3:46:50 time: 0.7600 data_time: 0.0009 memory: 6319 loss: 0.2342 2023/06/06 06:01:51 - mmengine - INFO - Epoch(train) [6][2800/4092] lr: 4.5404e-05 eta: 3:45:28 time: 0.7358 data_time: 0.0010 memory: 6319 loss: 0.2428 2023/06/06 06:03:04 - mmengine - INFO - Epoch(train) [6][2900/4092] lr: 4.5067e-05 eta: 3:44:09 time: 0.7264 data_time: 0.0011 memory: 6319 loss: 0.2073 2023/06/06 06:04:17 - mmengine - INFO - Epoch(train) [6][3000/4092] lr: 4.4730e-05 eta: 3:42:50 time: 0.7639 data_time: 0.0010 memory: 6319 loss: 0.2154 2023/06/06 06:05:32 - mmengine - INFO - Epoch(train) [6][3100/4092] lr: 4.4394e-05 eta: 3:41:32 time: 0.7721 data_time: 0.0010 memory: 6319 loss: 0.2066 2023/06/06 06:06:47 - mmengine - INFO - Epoch(train) [6][3200/4092] lr: 4.4059e-05 eta: 3:40:14 time: 0.7492 data_time: 0.0012 memory: 6319 loss: 0.2201 2023/06/06 06:08:03 - mmengine - INFO - Epoch(train) [6][3300/4092] lr: 4.3724e-05 eta: 3:38:57 time: 0.6997 data_time: 0.0010 memory: 6319 loss: 0.2286 2023/06/06 06:09:17 - mmengine - INFO - Epoch(train) [6][3400/4092] lr: 4.3390e-05 eta: 3:37:39 time: 0.7599 data_time: 0.0009 memory: 6319 loss: 0.2251 2023/06/06 06:10:32 - mmengine - INFO - Epoch(train) [6][3500/4092] lr: 4.3056e-05 eta: 3:36:21 time: 0.7302 data_time: 0.0008 memory: 6319 loss: 0.2147 2023/06/06 06:11:02 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 06:11:47 - mmengine - INFO - Epoch(train) [6][3600/4092] lr: 4.2724e-05 eta: 3:35:04 time: 0.7878 data_time: 0.0011 memory: 6319 loss: 0.2023 2023/06/06 06:13:03 - mmengine - INFO - Epoch(train) [6][3700/4092] lr: 4.2392e-05 eta: 3:33:46 time: 0.7331 data_time: 0.0008 memory: 6319 loss: 0.2047 2023/06/06 06:14:18 - mmengine - INFO - Epoch(train) [6][3800/4092] lr: 4.2060e-05 eta: 3:32:29 time: 0.7452 data_time: 0.0010 memory: 6319 loss: 0.2449 2023/06/06 06:15:35 - mmengine - INFO - Epoch(train) [6][3900/4092] lr: 4.1730e-05 eta: 3:31:12 time: 0.7946 data_time: 0.0010 memory: 6319 loss: 0.2128 2023/06/06 06:16:49 - mmengine - INFO - Epoch(train) [6][4000/4092] lr: 4.1400e-05 eta: 3:29:55 time: 0.7635 data_time: 0.0008 memory: 6319 loss: 0.2154 2023/06/06 06:17:56 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 06:17:56 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/06/06 06:18:41 - mmengine - INFO - Epoch(val) [6][100/119] eta: 0:00:07 time: 0.6478 data_time: 0.5579 memory: 6319 2023/06/06 06:19:08 - mmengine - INFO - Epoch(val) [6][119/119] accuracy/top1: 89.2938 data_time: 0.3655 time: 0.4556 2023/06/06 06:20:27 - mmengine - INFO - Epoch(train) [7][ 100/4092] lr: 4.0769e-05 eta: 3:27:27 time: 0.7437 data_time: 0.0676 memory: 6319 loss: 0.2146 2023/06/06 06:21:42 - mmengine - INFO - Epoch(train) [7][ 200/4092] lr: 4.0442e-05 eta: 3:26:10 time: 0.7826 data_time: 0.0009 memory: 6319 loss: 0.2203 2023/06/06 06:22:59 - mmengine - INFO - Epoch(train) [7][ 300/4092] lr: 4.0116e-05 eta: 3:24:53 time: 0.8765 data_time: 0.0008 memory: 6319 loss: 0.2284 2023/06/06 06:24:15 - mmengine - INFO - Epoch(train) [7][ 400/4092] lr: 3.9790e-05 eta: 3:23:36 time: 0.7793 data_time: 0.0011 memory: 6319 loss: 0.2285 2023/06/06 06:24:52 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 06:25:31 - mmengine - INFO - Epoch(train) [7][ 500/4092] lr: 3.9465e-05 eta: 3:22:20 time: 0.6905 data_time: 0.0010 memory: 6319 loss: 0.2083 2023/06/06 06:26:45 - mmengine - INFO - Epoch(train) [7][ 600/4092] lr: 3.9141e-05 eta: 3:21:01 time: 0.7519 data_time: 0.0012 memory: 6319 loss: 0.2073 2023/06/06 06:28:00 - mmengine - INFO - Epoch(train) [7][ 700/4092] lr: 3.8819e-05 eta: 3:19:44 time: 0.8323 data_time: 0.0011 memory: 6319 loss: 0.2279 2023/06/06 06:29:15 - mmengine - INFO - Epoch(train) [7][ 800/4092] lr: 3.8497e-05 eta: 3:18:27 time: 0.7189 data_time: 0.0010 memory: 6319 loss: 0.2198 2023/06/06 06:30:31 - mmengine - INFO - Epoch(train) [7][ 900/4092] lr: 3.8176e-05 eta: 3:17:10 time: 0.7632 data_time: 0.0010 memory: 6319 loss: 0.2288 2023/06/06 06:31:44 - mmengine - INFO - Epoch(train) [7][1000/4092] lr: 3.7856e-05 eta: 3:15:51 time: 0.7142 data_time: 0.0009 memory: 6319 loss: 0.2212 2023/06/06 06:32:59 - mmengine - INFO - Epoch(train) [7][1100/4092] lr: 3.7537e-05 eta: 3:14:34 time: 0.7266 data_time: 0.0012 memory: 6319 loss: 0.2185 2023/06/06 06:34:13 - mmengine - INFO - Epoch(train) [7][1200/4092] lr: 3.7219e-05 eta: 3:13:16 time: 0.7236 data_time: 0.0010 memory: 6319 loss: 0.2188 2023/06/06 06:35:28 - mmengine - INFO - Epoch(train) [7][1300/4092] lr: 3.6902e-05 eta: 3:11:59 time: 0.7414 data_time: 0.0011 memory: 6319 loss: 0.2190 2023/06/06 06:36:49 - mmengine - INFO - Epoch(train) [7][1400/4092] lr: 3.6586e-05 eta: 3:10:45 time: 0.7303 data_time: 0.0009 memory: 6319 loss: 0.2032 2023/06/06 06:37:26 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 06:38:03 - mmengine - INFO - Epoch(train) [7][1500/4092] lr: 3.6272e-05 eta: 3:09:27 time: 0.7634 data_time: 0.0009 memory: 6319 loss: 0.2278 2023/06/06 06:39:18 - mmengine - INFO - Epoch(train) [7][1600/4092] lr: 3.5958e-05 eta: 3:08:10 time: 0.7389 data_time: 0.0010 memory: 6319 loss: 0.2195 2023/06/06 06:40:34 - mmengine - INFO - Epoch(train) [7][1700/4092] lr: 3.5646e-05 eta: 3:06:53 time: 0.7473 data_time: 0.0012 memory: 6319 loss: 0.2245 2023/06/06 06:41:49 - mmengine - INFO - Epoch(train) [7][1800/4092] lr: 3.5334e-05 eta: 3:05:36 time: 0.6744 data_time: 0.0011 memory: 6319 loss: 0.2088 2023/06/06 06:43:03 - mmengine - INFO - Epoch(train) [7][1900/4092] lr: 3.5024e-05 eta: 3:04:18 time: 0.7298 data_time: 0.0011 memory: 6319 loss: 0.2193 2023/06/06 06:44:17 - mmengine - INFO - Epoch(train) [7][2000/4092] lr: 3.4715e-05 eta: 3:03:01 time: 0.7075 data_time: 0.0010 memory: 6319 loss: 0.2291 2023/06/06 06:45:36 - mmengine - INFO - Epoch(train) [7][2100/4092] lr: 3.4407e-05 eta: 3:01:45 time: 0.7866 data_time: 0.0011 memory: 6319 loss: 0.2196 2023/06/06 06:46:52 - mmengine - INFO - Epoch(train) [7][2200/4092] lr: 3.4101e-05 eta: 3:00:28 time: 0.7293 data_time: 0.0010 memory: 6319 loss: 0.2215 2023/06/06 06:48:06 - mmengine - INFO - Epoch(train) [7][2300/4092] lr: 3.3796e-05 eta: 2:59:11 time: 0.7372 data_time: 0.0010 memory: 6319 loss: 0.2180 2023/06/06 06:49:21 - mmengine - INFO - Epoch(train) [7][2400/4092] lr: 3.3491e-05 eta: 2:57:54 time: 0.7334 data_time: 0.0009 memory: 6319 loss: 0.2225 2023/06/06 06:49:53 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 06:50:35 - mmengine - INFO - Epoch(train) [7][2500/4092] lr: 3.3189e-05 eta: 2:56:36 time: 0.7129 data_time: 0.0013 memory: 6319 loss: 0.2247 2023/06/06 06:51:52 - mmengine - INFO - Epoch(train) [7][2600/4092] lr: 3.2887e-05 eta: 2:55:20 time: 0.7457 data_time: 0.0011 memory: 6319 loss: 0.2133 2023/06/06 06:53:07 - mmengine - INFO - Epoch(train) [7][2700/4092] lr: 3.2587e-05 eta: 2:54:03 time: 0.7748 data_time: 0.0010 memory: 6319 loss: 0.2101 2023/06/06 06:54:25 - mmengine - INFO - Epoch(train) [7][2800/4092] lr: 3.2288e-05 eta: 2:52:47 time: 0.7487 data_time: 0.0008 memory: 6319 loss: 0.2170 2023/06/06 06:55:39 - mmengine - INFO - Epoch(train) [7][2900/4092] lr: 3.1990e-05 eta: 2:51:30 time: 0.7505 data_time: 0.0008 memory: 6319 loss: 0.2392 2023/06/06 06:56:54 - mmengine - INFO - Epoch(train) [7][3000/4092] lr: 3.1694e-05 eta: 2:50:12 time: 0.7179 data_time: 0.0009 memory: 6319 loss: 0.2333 2023/06/06 06:58:10 - mmengine - INFO - Epoch(train) [7][3100/4092] lr: 3.1399e-05 eta: 2:48:56 time: 0.7442 data_time: 0.0008 memory: 6319 loss: 0.2169 2023/06/06 06:59:23 - mmengine - INFO - Epoch(train) [7][3200/4092] lr: 3.1106e-05 eta: 2:47:38 time: 0.7484 data_time: 0.0010 memory: 6319 loss: 0.2172 2023/06/06 07:00:35 - mmengine - INFO - Epoch(train) [7][3300/4092] lr: 3.0814e-05 eta: 2:46:19 time: 0.6734 data_time: 0.0008 memory: 6319 loss: 0.2253 2023/06/06 07:01:47 - mmengine - INFO - Epoch(train) [7][3400/4092] lr: 3.0523e-05 eta: 2:45:01 time: 0.7776 data_time: 0.0008 memory: 6319 loss: 0.2241 2023/06/06 07:02:19 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 07:03:02 - mmengine - INFO - Epoch(train) [7][3500/4092] lr: 3.0234e-05 eta: 2:43:44 time: 0.7468 data_time: 0.0008 memory: 6319 loss: 0.2086 2023/06/06 07:04:17 - mmengine - INFO - Epoch(train) [7][3600/4092] lr: 2.9946e-05 eta: 2:42:27 time: 0.7438 data_time: 0.0011 memory: 6319 loss: 0.2213 2023/06/06 07:05:32 - mmengine - INFO - Epoch(train) [7][3700/4092] lr: 2.9660e-05 eta: 2:41:10 time: 0.7352 data_time: 0.0008 memory: 6319 loss: 0.2156 2023/06/06 07:06:47 - mmengine - INFO - Epoch(train) [7][3800/4092] lr: 2.9375e-05 eta: 2:39:53 time: 0.7198 data_time: 0.0010 memory: 6319 loss: 0.2027 2023/06/06 07:08:02 - mmengine - INFO - Epoch(train) [7][3900/4092] lr: 2.9092e-05 eta: 2:38:36 time: 0.7665 data_time: 0.0010 memory: 6319 loss: 0.2086 2023/06/06 07:09:15 - mmengine - INFO - Epoch(train) [7][4000/4092] lr: 2.8810e-05 eta: 2:37:18 time: 0.7162 data_time: 0.0010 memory: 6319 loss: 0.2280 2023/06/06 07:10:22 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 07:10:22 - mmengine - INFO - Saving checkpoint at 7 epochs 2023/06/06 07:11:08 - mmengine - INFO - Epoch(val) [7][100/119] eta: 0:00:07 time: 0.7160 data_time: 0.6154 memory: 6319 2023/06/06 07:11:34 - mmengine - INFO - Epoch(val) [7][119/119] accuracy/top1: 90.8644 data_time: 0.3731 time: 0.4618 2023/06/06 07:12:51 - mmengine - INFO - Epoch(train) [8][ 100/4092] lr: 2.8274e-05 eta: 2:34:51 time: 0.7781 data_time: 0.2035 memory: 6319 loss: 0.2022 2023/06/06 07:14:06 - mmengine - INFO - Epoch(train) [8][ 200/4092] lr: 2.7997e-05 eta: 2:33:34 time: 0.7858 data_time: 0.0012 memory: 6319 loss: 0.2192 2023/06/06 07:15:21 - mmengine - INFO - Epoch(train) [8][ 300/4092] lr: 2.7721e-05 eta: 2:32:17 time: 0.7734 data_time: 0.0010 memory: 6319 loss: 0.2223 2023/06/06 07:16:04 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 07:16:35 - mmengine - INFO - Epoch(train) [8][ 400/4092] lr: 2.7447e-05 eta: 2:31:00 time: 0.7600 data_time: 0.0010 memory: 6319 loss: 0.2042 2023/06/06 07:17:53 - mmengine - INFO - Epoch(train) [8][ 500/4092] lr: 2.7175e-05 eta: 2:29:44 time: 0.7366 data_time: 0.0008 memory: 6319 loss: 0.2056 2023/06/06 07:19:07 - mmengine - INFO - Epoch(train) [8][ 600/4092] lr: 2.6904e-05 eta: 2:28:27 time: 0.7605 data_time: 0.0008 memory: 6319 loss: 0.2115 2023/06/06 07:20:24 - mmengine - INFO - Epoch(train) [8][ 700/4092] lr: 2.6635e-05 eta: 2:27:11 time: 0.8980 data_time: 0.0009 memory: 6319 loss: 0.2169 2023/06/06 07:21:41 - mmengine - INFO - Epoch(train) [8][ 800/4092] lr: 2.6368e-05 eta: 2:25:55 time: 0.7452 data_time: 0.0008 memory: 6319 loss: 0.2182 2023/06/06 07:22:56 - mmengine - INFO - Epoch(train) [8][ 900/4092] lr: 2.6102e-05 eta: 2:24:38 time: 0.7357 data_time: 0.0008 memory: 6319 loss: 0.2177 2023/06/06 07:24:10 - mmengine - INFO - Epoch(train) [8][1000/4092] lr: 2.5838e-05 eta: 2:23:21 time: 0.7183 data_time: 0.0008 memory: 6319 loss: 0.2219 2023/06/06 07:25:25 - mmengine - INFO - Epoch(train) [8][1100/4092] lr: 2.5576e-05 eta: 2:22:04 time: 0.7524 data_time: 0.0008 memory: 6319 loss: 0.2043 2023/06/06 07:26:43 - mmengine - INFO - Epoch(train) [8][1200/4092] lr: 2.5315e-05 eta: 2:20:49 time: 0.7501 data_time: 0.0009 memory: 6319 loss: 0.2100 2023/06/06 07:27:59 - mmengine - INFO - Epoch(train) [8][1300/4092] lr: 2.5056e-05 eta: 2:19:32 time: 0.7853 data_time: 0.0010 memory: 6319 loss: 0.2195 2023/06/06 07:28:43 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 07:29:17 - mmengine - INFO - Epoch(train) [8][1400/4092] lr: 2.4799e-05 eta: 2:18:16 time: 0.8273 data_time: 0.0010 memory: 6319 loss: 0.2153 2023/06/06 07:30:31 - mmengine - INFO - Epoch(train) [8][1500/4092] lr: 2.4544e-05 eta: 2:16:59 time: 0.7533 data_time: 0.0011 memory: 6319 loss: 0.2166 2023/06/06 07:31:46 - mmengine - INFO - Epoch(train) [8][1600/4092] lr: 2.4291e-05 eta: 2:15:43 time: 0.7239 data_time: 0.0009 memory: 6319 loss: 0.2020 2023/06/06 07:33:05 - mmengine - INFO - Epoch(train) [8][1700/4092] lr: 2.4039e-05 eta: 2:14:27 time: 0.7509 data_time: 0.0010 memory: 6319 loss: 0.2096 2023/06/06 07:34:21 - mmengine - INFO - Epoch(train) [8][1800/4092] lr: 2.3789e-05 eta: 2:13:11 time: 0.7533 data_time: 0.0011 memory: 6319 loss: 0.2192 2023/06/06 07:35:36 - mmengine - INFO - Epoch(train) [8][1900/4092] lr: 2.3541e-05 eta: 2:11:54 time: 0.7710 data_time: 0.0011 memory: 6319 loss: 0.2098 2023/06/06 07:36:51 - mmengine - INFO - Epoch(train) [8][2000/4092] lr: 2.3295e-05 eta: 2:10:37 time: 0.7488 data_time: 0.0009 memory: 6319 loss: 0.2031 2023/06/06 07:38:10 - mmengine - INFO - Epoch(train) [8][2100/4092] lr: 2.3051e-05 eta: 2:09:22 time: 0.7827 data_time: 0.0010 memory: 6319 loss: 0.2172 2023/06/06 07:39:24 - mmengine - INFO - Epoch(train) [8][2200/4092] lr: 2.2809e-05 eta: 2:08:05 time: 0.7382 data_time: 0.0010 memory: 6319 loss: 0.2142 2023/06/06 07:40:39 - mmengine - INFO - Epoch(train) [8][2300/4092] lr: 2.2568e-05 eta: 2:06:49 time: 0.7396 data_time: 0.0009 memory: 6319 loss: 0.2159 2023/06/06 07:41:18 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 07:41:55 - mmengine - INFO - Epoch(train) [8][2400/4092] lr: 2.2330e-05 eta: 2:05:32 time: 0.8223 data_time: 0.0009 memory: 6319 loss: 0.2172 2023/06/06 07:43:09 - mmengine - INFO - Epoch(train) [8][2500/4092] lr: 2.2093e-05 eta: 2:04:15 time: 0.7885 data_time: 0.0010 memory: 6319 loss: 0.1949 2023/06/06 07:44:24 - mmengine - INFO - Epoch(train) [8][2600/4092] lr: 2.1858e-05 eta: 2:02:58 time: 0.7555 data_time: 0.0010 memory: 6319 loss: 0.2133 2023/06/06 07:45:38 - mmengine - INFO - Epoch(train) [8][2700/4092] lr: 2.1626e-05 eta: 2:01:41 time: 0.7514 data_time: 0.0010 memory: 6319 loss: 0.2072 2023/06/06 07:46:55 - mmengine - INFO - Epoch(train) [8][2800/4092] lr: 2.1395e-05 eta: 2:00:25 time: 0.8021 data_time: 0.0012 memory: 6319 loss: 0.2115 2023/06/06 07:48:08 - mmengine - INFO - Epoch(train) [8][2900/4092] lr: 2.1166e-05 eta: 1:59:08 time: 0.7590 data_time: 0.0011 memory: 6319 loss: 0.2106 2023/06/06 07:49:24 - mmengine - INFO - Epoch(train) [8][3000/4092] lr: 2.0939e-05 eta: 1:57:52 time: 0.7446 data_time: 0.0009 memory: 6319 loss: 0.2148 2023/06/06 07:50:42 - mmengine - INFO - Epoch(train) [8][3100/4092] lr: 2.0715e-05 eta: 1:56:36 time: 0.7885 data_time: 0.0009 memory: 6319 loss: 0.2003 2023/06/06 07:51:57 - mmengine - INFO - Epoch(train) [8][3200/4092] lr: 2.0492e-05 eta: 1:55:19 time: 0.7916 data_time: 0.0012 memory: 6319 loss: 0.2210 2023/06/06 07:53:12 - mmengine - INFO - Epoch(train) [8][3300/4092] lr: 2.0271e-05 eta: 1:54:03 time: 0.7740 data_time: 0.0011 memory: 6319 loss: 0.2124 2023/06/06 07:53:50 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 07:54:27 - mmengine - INFO - Epoch(train) [8][3400/4092] lr: 2.0052e-05 eta: 1:52:46 time: 0.7203 data_time: 0.0011 memory: 6319 loss: 0.2249 2023/06/06 07:55:43 - mmengine - INFO - Epoch(train) [8][3500/4092] lr: 1.9836e-05 eta: 1:51:30 time: 0.7695 data_time: 0.0009 memory: 6319 loss: 0.2137 2023/06/06 07:56:58 - mmengine - INFO - Epoch(train) [8][3600/4092] lr: 1.9621e-05 eta: 1:50:14 time: 0.7751 data_time: 0.0010 memory: 6319 loss: 0.2158 2023/06/06 07:58:15 - mmengine - INFO - Epoch(train) [8][3700/4092] lr: 1.9409e-05 eta: 1:48:57 time: 0.7498 data_time: 0.0011 memory: 6319 loss: 0.2102 2023/06/06 07:59:30 - mmengine - INFO - Epoch(train) [8][3800/4092] lr: 1.9198e-05 eta: 1:47:41 time: 0.7471 data_time: 0.0011 memory: 6319 loss: 0.2317 2023/06/06 08:00:45 - mmengine - INFO - Epoch(train) [8][3900/4092] lr: 1.8990e-05 eta: 1:46:24 time: 0.7907 data_time: 0.0009 memory: 6319 loss: 0.2089 2023/06/06 08:02:02 - mmengine - INFO - Epoch(train) [8][4000/4092] lr: 1.8784e-05 eta: 1:45:08 time: 0.7369 data_time: 0.0008 memory: 6319 loss: 0.2050 2023/06/06 08:03:08 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 08:03:08 - mmengine - INFO - Saving checkpoint at 8 epochs 2023/06/06 08:03:53 - mmengine - INFO - Epoch(val) [8][100/119] eta: 0:00:07 time: 0.7422 data_time: 0.6513 memory: 6319 2023/06/06 08:04:20 - mmengine - INFO - Epoch(val) [8][119/119] accuracy/top1: 91.2815 data_time: 0.3766 time: 0.4667 2023/06/06 08:05:37 - mmengine - INFO - Epoch(train) [9][ 100/4092] lr: 1.8394e-05 eta: 1:42:41 time: 0.7444 data_time: 0.1491 memory: 6319 loss: 0.2149 2023/06/06 08:06:52 - mmengine - INFO - Epoch(train) [9][ 200/4092] lr: 1.8194e-05 eta: 1:41:24 time: 0.7720 data_time: 0.0010 memory: 6319 loss: 0.2059 2023/06/06 08:07:43 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 08:08:07 - mmengine - INFO - Epoch(train) [9][ 300/4092] lr: 1.7997e-05 eta: 1:40:08 time: 0.7507 data_time: 0.0010 memory: 6319 loss: 0.2010 2023/06/06 08:09:21 - mmengine - INFO - Epoch(train) [9][ 400/4092] lr: 1.7801e-05 eta: 1:38:51 time: 0.7334 data_time: 0.0011 memory: 6319 loss: 0.2070 2023/06/06 08:10:36 - mmengine - INFO - Epoch(train) [9][ 500/4092] lr: 1.7608e-05 eta: 1:37:35 time: 0.7890 data_time: 0.0011 memory: 6319 loss: 0.2013 2023/06/06 08:11:52 - mmengine - INFO - Epoch(train) [9][ 600/4092] lr: 1.7417e-05 eta: 1:36:18 time: 0.7321 data_time: 0.0009 memory: 6319 loss: 0.1978 2023/06/06 08:13:06 - mmengine - INFO - Epoch(train) [9][ 700/4092] lr: 1.7228e-05 eta: 1:35:02 time: 0.7511 data_time: 0.0009 memory: 6319 loss: 0.2191 2023/06/06 08:14:20 - mmengine - INFO - Epoch(train) [9][ 800/4092] lr: 1.7041e-05 eta: 1:33:45 time: 0.7092 data_time: 0.0011 memory: 6319 loss: 0.2122 2023/06/06 08:15:36 - mmengine - INFO - Epoch(train) [9][ 900/4092] lr: 1.6857e-05 eta: 1:32:29 time: 0.7433 data_time: 0.0010 memory: 6319 loss: 0.2143 2023/06/06 08:16:51 - mmengine - INFO - Epoch(train) [9][1000/4092] lr: 1.6675e-05 eta: 1:31:13 time: 0.7043 data_time: 0.0011 memory: 6319 loss: 0.2056 2023/06/06 08:18:06 - mmengine - INFO - Epoch(train) [9][1100/4092] lr: 1.6495e-05 eta: 1:29:56 time: 0.6958 data_time: 0.0009 memory: 6319 loss: 0.2154 2023/06/06 08:19:21 - mmengine - INFO - Epoch(train) [9][1200/4092] lr: 1.6317e-05 eta: 1:28:40 time: 0.7735 data_time: 0.0010 memory: 6319 loss: 0.2105 2023/06/06 08:20:11 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 08:20:38 - mmengine - INFO - Epoch(train) [9][1300/4092] lr: 1.6142e-05 eta: 1:27:24 time: 0.7008 data_time: 0.0011 memory: 6319 loss: 0.2146 2023/06/06 08:21:53 - mmengine - INFO - Epoch(train) [9][1400/4092] lr: 1.5969e-05 eta: 1:26:07 time: 0.7708 data_time: 0.0009 memory: 6319 loss: 0.2127 2023/06/06 08:23:09 - mmengine - INFO - Epoch(train) [9][1500/4092] lr: 1.5798e-05 eta: 1:24:51 time: 0.7452 data_time: 0.0010 memory: 6319 loss: 0.2144 2023/06/06 08:24:27 - mmengine - INFO - Epoch(train) [9][1600/4092] lr: 1.5629e-05 eta: 1:23:35 time: 0.7562 data_time: 0.0010 memory: 6319 loss: 0.2263 2023/06/06 08:25:43 - mmengine - INFO - Epoch(train) [9][1700/4092] lr: 1.5463e-05 eta: 1:22:19 time: 0.7675 data_time: 0.0010 memory: 6319 loss: 0.2165 2023/06/06 08:26:59 - mmengine - INFO - Epoch(train) [9][1800/4092] lr: 1.5299e-05 eta: 1:21:03 time: 0.7118 data_time: 0.0011 memory: 6319 loss: 0.2004 2023/06/06 08:28:16 - mmengine - INFO - Epoch(train) [9][1900/4092] lr: 1.5138e-05 eta: 1:19:47 time: 0.7517 data_time: 0.0010 memory: 6319 loss: 0.2170 2023/06/06 08:29:32 - mmengine - INFO - Epoch(train) [9][2000/4092] lr: 1.4979e-05 eta: 1:18:30 time: 0.7448 data_time: 0.0010 memory: 6319 loss: 0.2017 2023/06/06 08:30:45 - mmengine - INFO - Epoch(train) [9][2100/4092] lr: 1.4822e-05 eta: 1:17:14 time: 0.7401 data_time: 0.0010 memory: 6319 loss: 0.1936 2023/06/06 08:31:59 - mmengine - INFO - Epoch(train) [9][2200/4092] lr: 1.4668e-05 eta: 1:15:57 time: 0.7106 data_time: 0.0010 memory: 6319 loss: 0.2115 2023/06/06 08:32:46 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 08:33:15 - mmengine - INFO - Epoch(train) [9][2300/4092] lr: 1.4515e-05 eta: 1:14:41 time: 0.7421 data_time: 0.0010 memory: 6319 loss: 0.1955 2023/06/06 08:34:31 - mmengine - INFO - Epoch(train) [9][2400/4092] lr: 1.4366e-05 eta: 1:13:25 time: 0.7416 data_time: 0.0009 memory: 6319 loss: 0.2198 2023/06/06 08:35:45 - mmengine - INFO - Epoch(train) [9][2500/4092] lr: 1.4219e-05 eta: 1:12:08 time: 0.8412 data_time: 0.0012 memory: 6319 loss: 0.2145 2023/06/06 08:37:02 - mmengine - INFO - Epoch(train) [9][2600/4092] lr: 1.4074e-05 eta: 1:10:52 time: 0.7456 data_time: 0.0010 memory: 6319 loss: 0.2063 2023/06/06 08:38:17 - mmengine - INFO - Epoch(train) [9][2700/4092] lr: 1.3931e-05 eta: 1:09:36 time: 0.8046 data_time: 0.0009 memory: 6319 loss: 0.1999 2023/06/06 08:39:40 - mmengine - INFO - Epoch(train) [9][2800/4092] lr: 1.3791e-05 eta: 1:08:21 time: 0.7733 data_time: 0.0008 memory: 6319 loss: 0.2130 2023/06/06 08:40:56 - mmengine - INFO - Epoch(train) [9][2900/4092] lr: 1.3654e-05 eta: 1:07:05 time: 0.7395 data_time: 0.0008 memory: 6319 loss: 0.2052 2023/06/06 08:42:11 - mmengine - INFO - Epoch(train) [9][3000/4092] lr: 1.3519e-05 eta: 1:05:48 time: 0.7833 data_time: 0.0010 memory: 6319 loss: 0.2027 2023/06/06 08:43:27 - mmengine - INFO - Epoch(train) [9][3100/4092] lr: 1.3386e-05 eta: 1:04:32 time: 0.8056 data_time: 0.0010 memory: 6319 loss: 0.2251 2023/06/06 08:44:42 - mmengine - INFO - Epoch(train) [9][3200/4092] lr: 1.3256e-05 eta: 1:03:16 time: 0.7167 data_time: 0.0010 memory: 6319 loss: 0.2165 2023/06/06 08:45:29 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 08:46:00 - mmengine - INFO - Epoch(train) [9][3300/4092] lr: 1.3128e-05 eta: 1:02:00 time: 0.7907 data_time: 0.0010 memory: 6319 loss: 0.2154 2023/06/06 08:47:15 - mmengine - INFO - Epoch(train) [9][3400/4092] lr: 1.3003e-05 eta: 1:00:44 time: 0.7522 data_time: 0.0014 memory: 6319 loss: 0.2204 2023/06/06 08:48:33 - mmengine - INFO - Epoch(train) [9][3500/4092] lr: 1.2880e-05 eta: 0:59:28 time: 0.7013 data_time: 0.0009 memory: 6319 loss: 0.2208 2023/06/06 08:49:48 - mmengine - INFO - Epoch(train) [9][3600/4092] lr: 1.2759e-05 eta: 0:58:11 time: 0.7600 data_time: 0.0010 memory: 6319 loss: 0.2284 2023/06/06 08:51:03 - mmengine - INFO - Epoch(train) [9][3700/4092] lr: 1.2641e-05 eta: 0:56:55 time: 0.7758 data_time: 0.0008 memory: 6319 loss: 0.2244 2023/06/06 08:52:20 - mmengine - INFO - Epoch(train) [9][3800/4092] lr: 1.2526e-05 eta: 0:55:39 time: 0.8104 data_time: 0.0010 memory: 6319 loss: 0.2115 2023/06/06 08:53:34 - mmengine - INFO - Epoch(train) [9][3900/4092] lr: 1.2413e-05 eta: 0:54:22 time: 0.7465 data_time: 0.0010 memory: 6319 loss: 0.2135 2023/06/06 08:54:51 - mmengine - INFO - Epoch(train) [9][4000/4092] lr: 1.2303e-05 eta: 0:53:06 time: 0.7471 data_time: 0.0010 memory: 6319 loss: 0.2111 2023/06/06 08:55:59 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 08:55:59 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/06/06 08:56:45 - mmengine - INFO - Epoch(val) [9][100/119] eta: 0:00:07 time: 0.7141 data_time: 0.6247 memory: 6319 2023/06/06 08:57:11 - mmengine - INFO - Epoch(val) [9][119/119] accuracy/top1: 91.4784 data_time: 0.3669 time: 0.4550 2023/06/06 08:58:31 - mmengine - INFO - Epoch(train) [10][ 100/4092] lr: 1.2098e-05 eta: 0:50:40 time: 0.7676 data_time: 0.1648 memory: 6319 loss: 0.2166 2023/06/06 08:59:30 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 08:59:50 - mmengine - INFO - Epoch(train) [10][ 200/4092] lr: 1.1995e-05 eta: 0:49:24 time: 0.7874 data_time: 0.0011 memory: 6319 loss: 0.2215 2023/06/06 09:01:04 - mmengine - INFO - Epoch(train) [10][ 300/4092] lr: 1.1895e-05 eta: 0:48:08 time: 0.7119 data_time: 0.0010 memory: 6319 loss: 0.2005 2023/06/06 09:02:19 - mmengine - INFO - Epoch(train) [10][ 400/4092] lr: 1.1797e-05 eta: 0:46:52 time: 0.7177 data_time: 0.0009 memory: 6319 loss: 0.2159 2023/06/06 09:03:35 - mmengine - INFO - Epoch(train) [10][ 500/4092] lr: 1.1701e-05 eta: 0:45:36 time: 0.7312 data_time: 0.0011 memory: 6319 loss: 0.2015 2023/06/06 09:04:49 - mmengine - INFO - Epoch(train) [10][ 600/4092] lr: 1.1608e-05 eta: 0:44:19 time: 0.7109 data_time: 0.0010 memory: 6319 loss: 0.2013 2023/06/06 09:06:03 - mmengine - INFO - Epoch(train) [10][ 700/4092] lr: 1.1518e-05 eta: 0:43:03 time: 0.7603 data_time: 0.0009 memory: 6319 loss: 0.2202 2023/06/06 09:07:19 - mmengine - INFO - Epoch(train) [10][ 800/4092] lr: 1.1430e-05 eta: 0:41:47 time: 0.7416 data_time: 0.0010 memory: 6319 loss: 0.2060 2023/06/06 09:08:34 - mmengine - INFO - Epoch(train) [10][ 900/4092] lr: 1.1345e-05 eta: 0:40:30 time: 0.7928 data_time: 0.0010 memory: 6319 loss: 0.2178 2023/06/06 09:09:51 - mmengine - INFO - Epoch(train) [10][1000/4092] lr: 1.1263e-05 eta: 0:39:14 time: 0.7332 data_time: 0.0010 memory: 6319 loss: 0.2236 2023/06/06 09:11:10 - mmengine - INFO - Epoch(train) [10][1100/4092] lr: 1.1183e-05 eta: 0:37:58 time: 0.7501 data_time: 0.0013 memory: 6319 loss: 0.1999 2023/06/06 09:12:04 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 09:12:27 - mmengine - INFO - Epoch(train) [10][1200/4092] lr: 1.1105e-05 eta: 0:36:42 time: 0.7648 data_time: 0.0010 memory: 6319 loss: 0.2071 2023/06/06 09:13:43 - mmengine - INFO - Epoch(train) [10][1300/4092] lr: 1.1031e-05 eta: 0:35:26 time: 0.9441 data_time: 0.0011 memory: 6319 loss: 0.2190 2023/06/06 09:14:57 - mmengine - INFO - Epoch(train) [10][1400/4092] lr: 1.0958e-05 eta: 0:34:10 time: 0.7064 data_time: 0.0011 memory: 6319 loss: 0.2044 2023/06/06 09:16:14 - mmengine - INFO - Epoch(train) [10][1500/4092] lr: 1.0889e-05 eta: 0:32:54 time: 0.7076 data_time: 0.0011 memory: 6319 loss: 0.2198 2023/06/06 09:17:29 - mmengine - INFO - Epoch(train) [10][1600/4092] lr: 1.0822e-05 eta: 0:31:37 time: 0.7296 data_time: 0.0010 memory: 6319 loss: 0.2096 2023/06/06 09:18:45 - mmengine - INFO - Epoch(train) [10][1700/4092] lr: 1.0757e-05 eta: 0:30:21 time: 0.7686 data_time: 0.0009 memory: 6319 loss: 0.2070 2023/06/06 09:20:04 - mmengine - INFO - Epoch(train) [10][1800/4092] lr: 1.0696e-05 eta: 0:29:05 time: 0.7310 data_time: 0.0010 memory: 6319 loss: 0.2095 2023/06/06 09:21:18 - mmengine - INFO - Epoch(train) [10][1900/4092] lr: 1.0636e-05 eta: 0:27:49 time: 0.7421 data_time: 0.0016 memory: 6319 loss: 0.2009 2023/06/06 09:22:32 - mmengine - INFO - Epoch(train) [10][2000/4092] lr: 1.0580e-05 eta: 0:26:33 time: 0.7543 data_time: 0.0015 memory: 6319 loss: 0.2024 2023/06/06 09:23:52 - mmengine - INFO - Epoch(train) [10][2100/4092] lr: 1.0526e-05 eta: 0:25:17 time: 0.8326 data_time: 0.0013 memory: 6319 loss: 0.1982 2023/06/06 09:24:47 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 09:25:14 - mmengine - INFO - Epoch(train) [10][2200/4092] lr: 1.0474e-05 eta: 0:24:01 time: 1.1544 data_time: 0.0011 memory: 6319 loss: 0.2192 2023/06/06 09:26:31 - mmengine - INFO - Epoch(train) [10][2300/4092] lr: 1.0426e-05 eta: 0:22:45 time: 0.7191 data_time: 0.0009 memory: 6319 loss: 0.1905 2023/06/06 09:27:46 - mmengine - INFO - Epoch(train) [10][2400/4092] lr: 1.0380e-05 eta: 0:21:28 time: 0.7957 data_time: 0.0013 memory: 6319 loss: 0.2189 2023/06/06 09:29:02 - mmengine - INFO - Epoch(train) [10][2500/4092] lr: 1.0336e-05 eta: 0:20:12 time: 0.7244 data_time: 0.0012 memory: 6319 loss: 0.2137 2023/06/06 09:30:16 - mmengine - INFO - Epoch(train) [10][2600/4092] lr: 1.0295e-05 eta: 0:18:56 time: 0.7591 data_time: 0.0010 memory: 6319 loss: 0.1928 2023/06/06 09:31:32 - mmengine - INFO - Epoch(train) [10][2700/4092] lr: 1.0257e-05 eta: 0:17:40 time: 0.7740 data_time: 0.0010 memory: 6319 loss: 0.1990 2023/06/06 09:32:48 - mmengine - INFO - Epoch(train) [10][2800/4092] lr: 1.0222e-05 eta: 0:16:24 time: 0.7677 data_time: 0.0008 memory: 6319 loss: 0.2072 2023/06/06 09:34:02 - mmengine - INFO - Epoch(train) [10][2900/4092] lr: 1.0189e-05 eta: 0:15:07 time: 0.7200 data_time: 0.0013 memory: 6319 loss: 0.2193 2023/06/06 09:35:17 - mmengine - INFO - Epoch(train) [10][3000/4092] lr: 1.0158e-05 eta: 0:13:51 time: 0.7599 data_time: 0.0011 memory: 6319 loss: 0.2092 2023/06/06 09:36:33 - mmengine - INFO - Epoch(train) [10][3100/4092] lr: 1.0131e-05 eta: 0:12:35 time: 0.7480 data_time: 0.0011 memory: 6319 loss: 0.2111 2023/06/06 09:37:30 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 09:37:48 - mmengine - INFO - Epoch(train) [10][3200/4092] lr: 1.0106e-05 eta: 0:11:19 time: 0.7729 data_time: 0.0012 memory: 6319 loss: 0.2009 2023/06/06 09:39:03 - mmengine - INFO - Epoch(train) [10][3300/4092] lr: 1.0083e-05 eta: 0:10:03 time: 0.7603 data_time: 0.0010 memory: 6319 loss: 0.1973 2023/06/06 09:40:20 - mmengine - INFO - Epoch(train) [10][3400/4092] lr: 1.0064e-05 eta: 0:08:47 time: 0.7466 data_time: 0.0010 memory: 6319 loss: 0.2091 2023/06/06 09:41:36 - mmengine - INFO - Epoch(train) [10][3500/4092] lr: 1.0047e-05 eta: 0:07:30 time: 0.8362 data_time: 0.0009 memory: 6319 loss: 0.2217 2023/06/06 09:42:52 - mmengine - INFO - Epoch(train) [10][3600/4092] lr: 1.0032e-05 eta: 0:06:14 time: 0.7650 data_time: 0.0010 memory: 6319 loss: 0.2146 2023/06/06 09:44:07 - mmengine - INFO - Epoch(train) [10][3700/4092] lr: 1.0020e-05 eta: 0:04:58 time: 0.7336 data_time: 0.0018 memory: 6319 loss: 0.2005 2023/06/06 09:45:24 - mmengine - INFO - Epoch(train) [10][3800/4092] lr: 1.0011e-05 eta: 0:03:42 time: 0.7361 data_time: 0.0012 memory: 6319 loss: 0.1984 2023/06/06 09:46:39 - mmengine - INFO - Epoch(train) [10][3900/4092] lr: 1.0005e-05 eta: 0:02:26 time: 0.7410 data_time: 0.0008 memory: 6319 loss: 0.2112 2023/06/06 09:48:02 - mmengine - INFO - Epoch(train) [10][4000/4092] lr: 1.0001e-05 eta: 0:01:10 time: 0.7572 data_time: 0.0011 memory: 6319 loss: 0.2112 2023/06/06 09:49:10 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_5e-1_20230606_005813 2023/06/06 09:49:10 - mmengine - INFO - Saving checkpoint at 10 epochs 2023/06/06 09:49:56 - mmengine - INFO - Epoch(val) [10][100/119] eta: 0:00:07 time: 0.7298 data_time: 0.6389 memory: 6319 2023/06/06 09:50:23 - mmengine - INFO - Epoch(val) [10][119/119] accuracy/top1: 91.9004 data_time: 0.3828 time: 0.4717