| 2023/06/06 00:57:46 - 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: 1427001271 | |
| 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 00:57:50 - mmengine - INFO - Config: | |
| optim_wrapper = dict( | |
| optimizer=dict( | |
| type='SGD', | |
| lr=0.0001, | |
| momentum=0.9, | |
| weight_decay=0.0001, | |
| _scope_='mmpretrain'), | |
| clip_grad=None) | |
| param_scheduler = [ | |
| dict(type='CosineAnnealingLR', eta_min=1e-05, by_epoch=False, begin=0) | |
| ] | |
| train_cfg = dict(by_epoch=True, max_epochs=10, val_interval=1) | |
| val_cfg = dict() | |
| test_cfg = dict() | |
| auto_scale_lr = dict(base_batch_size=512) | |
| model = dict( | |
| type='ImageClassifier', | |
| backbone=dict( | |
| frozen_stages=2, | |
| type='ResNet', | |
| depth=50, | |
| num_stages=4, | |
| out_indices=(3, ), | |
| style='pytorch', | |
| init_cfg=dict( | |
| type='Pretrained', | |
| checkpoint= | |
| 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth', | |
| prefix='backbone')), | |
| neck=dict(type='GlobalAveragePooling'), | |
| head=dict( | |
| type='LinearClsHead', | |
| num_classes=2, | |
| in_channels=2048, | |
| loss=dict(type='CrossEntropyLoss', loss_weight=1.0), | |
| topk=1)) | |
| dataset_type = 'CustomDataset' | |
| data_preprocessor = dict( | |
| num_classes=2, | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True) | |
| bgr_mean = [103.53, 116.28, 123.675] | |
| bgr_std = [57.375, 57.12, 58.395] | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='JPEG', compress_val=65, prob=0.1), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.1), | |
| dict(type='PackInputs') | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='ResizeEdge', | |
| scale=256, | |
| edge='short', | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='CenterCrop', crop_size=224), | |
| dict(type='PackInputs') | |
| ] | |
| train_dataloader = dict( | |
| pin_memory=True, | |
| persistent_workers=True, | |
| collate_fn=dict(type='default_collate'), | |
| batch_size=256, | |
| num_workers=10, | |
| dataset=dict( | |
| type='ConcatDataset', | |
| datasets=[ | |
| dict( | |
| type='CustomDataset', | |
| data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/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=0.1), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.1), | |
| dict(type='PackInputs') | |
| ]), | |
| dict( | |
| type='CustomDataset', | |
| data_root='', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/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=0.1), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.1), | |
| dict(type='PackInputs') | |
| ]), | |
| dict( | |
| type='CustomDataset', | |
| data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/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=0.1), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.1), | |
| dict(type='PackInputs') | |
| ]), | |
| dict( | |
| type='CustomDataset', | |
| data_root='', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/cc1m.csv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='JPEG', compress_val=65, prob=0.1), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.1), | |
| dict(type='PackInputs') | |
| ]), | |
| 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=0.1), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.1), | |
| dict(type='PackInputs') | |
| ]) | |
| ]), | |
| sampler=dict(type='DefaultSampler', shuffle=True)) | |
| val_dataloader = dict( | |
| pin_memory=True, | |
| persistent_workers=True, | |
| collate_fn=dict(type='default_collate'), | |
| batch_size=256, | |
| num_workers=10, | |
| dataset=dict( | |
| type='ConcatDataset', | |
| datasets=[ | |
| dict( | |
| type='CustomDataset', | |
| data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV2-1-dpmsolver-25-1w.tsv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='JPEG', compress_val=65, prob=0.1), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.1), | |
| dict(type='PackInputs') | |
| ]), | |
| dict( | |
| type='CustomDataset', | |
| data_root='/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=0.1), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.1), | |
| dict(type='PackInputs') | |
| ]), | |
| dict( | |
| type='CustomDataset', | |
| data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/if-dpmsolver++-25-1w.tsv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='JPEG', compress_val=65, prob=0.1), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.1), | |
| dict(type='PackInputs') | |
| ]), | |
| dict( | |
| type='CustomDataset', | |
| data_root='', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/cc1w.csv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='JPEG', compress_val=65, prob=0.1), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.1), | |
| dict(type='PackInputs') | |
| ]) | |
| ]), | |
| sampler=dict(type='DefaultSampler', shuffle=False)) | |
| val_evaluator = dict(type='Accuracy', topk=1) | |
| test_dataloader = dict( | |
| pin_memory=True, | |
| persistent_workers=True, | |
| collate_fn=dict(type='default_collate'), | |
| batch_size=256, | |
| num_workers=10, | |
| dataset=dict( | |
| type='ConcatDataset', | |
| datasets=[ | |
| dict( | |
| type='CustomDataset', | |
| data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV2-1-dpmsolver-25-1w.tsv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='JPEG', compress_val=65, prob=0.1), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.1), | |
| dict(type='PackInputs') | |
| ]), | |
| dict( | |
| type='CustomDataset', | |
| data_root='/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=0.1), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.1), | |
| dict(type='PackInputs') | |
| ]), | |
| dict( | |
| type='CustomDataset', | |
| data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/if-dpmsolver++-25-1w.tsv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='JPEG', compress_val=65, prob=0.1), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.1), | |
| dict(type='PackInputs') | |
| ]), | |
| dict( | |
| type='CustomDataset', | |
| data_root='', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/cc1w.csv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='JPEG', compress_val=65, prob=0.1), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.1), | |
| dict(type='PackInputs') | |
| ]) | |
| ]), | |
| sampler=dict(type='DefaultSampler', shuffle=False)) | |
| test_evaluator = dict(type='Accuracy', topk=1) | |
| custom_hooks = [dict(type='EMAHook', momentum=0.0001, priority='ABOVE_NORMAL')] | |
| default_scope = 'mmpretrain' | |
| default_hooks = dict( | |
| timer=dict(type='IterTimerHook'), | |
| logger=dict(type='LoggerHook', interval=100), | |
| param_scheduler=dict(type='ParamSchedulerHook'), | |
| checkpoint=dict(type='CheckpointHook', interval=1), | |
| sampler_seed=dict(type='DistSamplerSeedHook'), | |
| visualization=dict(type='VisualizationHook', enable=True)) | |
| env_cfg = dict( | |
| cudnn_benchmark=True, | |
| mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), | |
| dist_cfg=dict(backend='nccl')) | |
| vis_backends = [dict(type='LocalVisBackend')] | |
| visualizer = dict( | |
| type='UniversalVisualizer', | |
| vis_backends=[ | |
| dict(type='LocalVisBackend'), | |
| dict(type='TensorboardVisBackend') | |
| ]) | |
| log_level = 'INFO' | |
| load_from = None | |
| resume = False | |
| randomness = dict(seed=None, deterministic=False) | |
| launcher = 'slurm' | |
| work_dir = 'workdir/resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1' | |
| 2023/06/06 00:58:02 - 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 00:58:23 - 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 00:58:24 - 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 00:58:24 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. | |
| 2023/06/06 00:58:24 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/luzeyu/workspace/fakebench/mmpretrain/workdir/resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1. | |
| 2023/06/06 00:59:44 - mmengine - INFO - Epoch(train) [1][ 100/4092] lr: 9.9999e-05 eta: 9:04:18 time: 0.8090 data_time: 0.2358 memory: 9436 loss: 0.6417 | |
| 2023/06/06 01:01:02 - mmengine - INFO - Epoch(train) [1][ 200/4092] lr: 9.9995e-05 eta: 8:56:17 time: 0.7791 data_time: 0.2187 memory: 6319 loss: 0.5971 | |
| 2023/06/06 01:02:15 - mmengine - INFO - Epoch(train) [1][ 300/4092] lr: 9.9988e-05 eta: 8:43:14 time: 0.7500 data_time: 0.0009 memory: 6319 loss: 0.5584 | |
| 2023/06/06 01:03:30 - mmengine - INFO - Epoch(train) [1][ 400/4092] lr: 9.9979e-05 eta: 8:37:10 time: 0.7361 data_time: 0.0008 memory: 6319 loss: 0.5302 | |
| 2023/06/06 01:04:47 - mmengine - INFO - Epoch(train) [1][ 500/4092] lr: 9.9967e-05 eta: 8:36:24 time: 0.7582 data_time: 0.2011 memory: 6319 loss: 0.5015 | |
| 2023/06/06 01:06:01 - mmengine - INFO - Epoch(train) [1][ 600/4092] lr: 9.9952e-05 eta: 8:32:47 time: 0.7184 data_time: 0.3321 memory: 6319 loss: 0.4688 | |
| 2023/06/06 01:07:14 - mmengine - INFO - Epoch(train) [1][ 700/4092] lr: 9.9935e-05 eta: 8:28:14 time: 0.7379 data_time: 0.0201 memory: 6319 loss: 0.4382 | |
| 2023/06/06 01:08:30 - mmengine - INFO - Epoch(train) [1][ 800/4092] lr: 9.9915e-05 eta: 8:26:42 time: 0.7372 data_time: 0.0008 memory: 6319 loss: 0.4320 | |
| 2023/06/06 01:09:43 - mmengine - INFO - Epoch(train) [1][ 900/4092] lr: 9.9893e-05 eta: 8:23:25 time: 0.7632 data_time: 0.1161 memory: 6319 loss: 0.4201 | |
| 2023/06/06 01:10:55 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 01:10:55 - mmengine - INFO - Epoch(train) [1][1000/4092] lr: 9.9868e-05 eta: 8:20:02 time: 0.7913 data_time: 0.0008 memory: 6319 loss: 0.4004 | |
| 2023/06/06 01:12:09 - mmengine - INFO - Epoch(train) [1][1100/4092] lr: 9.9840e-05 eta: 8:17:54 time: 0.8244 data_time: 0.0008 memory: 6319 loss: 0.3950 | |
| 2023/06/06 01:13:23 - mmengine - INFO - Epoch(train) [1][1200/4092] lr: 9.9809e-05 eta: 8:16:10 time: 0.7227 data_time: 0.0007 memory: 6319 loss: 0.3863 | |
| 2023/06/06 01:14:34 - mmengine - INFO - Epoch(train) [1][1300/4092] lr: 9.9776e-05 eta: 8:12:44 time: 0.7242 data_time: 0.0008 memory: 6319 loss: 0.3782 | |
| 2023/06/06 01:15:45 - mmengine - INFO - Epoch(train) [1][1400/4092] lr: 9.9741e-05 eta: 8:10:11 time: 0.7568 data_time: 0.0008 memory: 6319 loss: 0.3711 | |
| 2023/06/06 01:16:57 - mmengine - INFO - Epoch(train) [1][1500/4092] lr: 9.9702e-05 eta: 8:07:33 time: 0.7378 data_time: 0.0007 memory: 6319 loss: 0.3743 | |
| 2023/06/06 01:18:09 - mmengine - INFO - Epoch(train) [1][1600/4092] lr: 9.9661e-05 eta: 8:05:26 time: 0.6979 data_time: 0.0009 memory: 6319 loss: 0.3582 | |
| 2023/06/06 01:19:21 - mmengine - INFO - Epoch(train) [1][1700/4092] lr: 9.9618e-05 eta: 8:03:33 time: 0.7231 data_time: 0.0011 memory: 6319 loss: 0.3595 | |
| 2023/06/06 01:20:35 - mmengine - INFO - Epoch(train) [1][1800/4092] lr: 9.9571e-05 eta: 8:02:08 time: 0.7151 data_time: 0.0008 memory: 6319 loss: 0.3502 | |
| 2023/06/06 01:21:48 - mmengine - INFO - Epoch(train) [1][1900/4092] lr: 9.9523e-05 eta: 8:00:33 time: 0.7786 data_time: 0.0007 memory: 6319 loss: 0.3434 | |
| 2023/06/06 01:23:01 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 01:23:01 - mmengine - INFO - Epoch(train) [1][2000/4092] lr: 9.9471e-05 eta: 7:59:10 time: 0.7474 data_time: 0.0008 memory: 6319 loss: 0.3303 | |
| 2023/06/06 01:24:14 - mmengine - INFO - Epoch(train) [1][2100/4092] lr: 9.9417e-05 eta: 7:57:46 time: 0.7527 data_time: 0.0009 memory: 6319 loss: 0.3392 | |
| 2023/06/06 01:25:26 - mmengine - INFO - Epoch(train) [1][2200/4092] lr: 9.9360e-05 eta: 7:55:56 time: 0.7442 data_time: 0.0008 memory: 6319 loss: 0.3544 | |
| 2023/06/06 01:28:12 - mmengine - INFO - Epoch(train) [1][2300/4092] lr: 9.9301e-05 eta: 8:20:31 time: 0.7476 data_time: 0.0010 memory: 6319 loss: 0.3193 | |
| 2023/06/06 01:29:23 - mmengine - INFO - Epoch(train) [1][2400/4092] lr: 9.9239e-05 eta: 8:17:24 time: 0.7450 data_time: 0.0011 memory: 6319 loss: 0.3394 | |
| 2023/06/06 01:30:35 - mmengine - INFO - Epoch(train) [1][2500/4092] lr: 9.9174e-05 eta: 8:14:44 time: 0.7152 data_time: 0.0007 memory: 6319 loss: 0.3217 | |
| 2023/06/06 01:31:46 - mmengine - INFO - Epoch(train) [1][2600/4092] lr: 9.9107e-05 eta: 8:11:45 time: 0.6926 data_time: 0.0009 memory: 6319 loss: 0.3210 | |
| 2023/06/06 01:32:58 - mmengine - INFO - Epoch(train) [1][2700/4092] lr: 9.9037e-05 eta: 8:09:19 time: 0.6957 data_time: 0.0008 memory: 6319 loss: 0.3038 | |
| 2023/06/06 01:34:10 - mmengine - INFO - Epoch(train) [1][2800/4092] lr: 9.8965e-05 eta: 8:06:57 time: 0.7176 data_time: 0.0009 memory: 6319 loss: 0.3177 | |
| 2023/06/06 01:35:22 - mmengine - INFO - Epoch(train) [1][2900/4092] lr: 9.8890e-05 eta: 8:04:44 time: 0.7144 data_time: 0.0009 memory: 6319 loss: 0.3155 | |
| 2023/06/06 01:36:35 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 01:36:35 - mmengine - INFO - Epoch(train) [1][3000/4092] lr: 9.8812e-05 eta: 8:02:41 time: 0.6949 data_time: 0.0008 memory: 6319 loss: 0.3076 | |
| 2023/06/06 01:37:46 - mmengine - INFO - Epoch(train) [1][3100/4092] lr: 9.8732e-05 eta: 8:00:18 time: 0.7028 data_time: 0.0008 memory: 6319 loss: 0.3098 | |
| 2023/06/06 01:38:56 - mmengine - INFO - Epoch(train) [1][3200/4092] lr: 9.8650e-05 eta: 7:57:53 time: 0.7453 data_time: 0.0008 memory: 6319 loss: 0.3065 | |
| 2023/06/06 01:40:06 - mmengine - INFO - Epoch(train) [1][3300/4092] lr: 9.8564e-05 eta: 7:55:31 time: 0.6711 data_time: 0.0007 memory: 6319 loss: 0.2986 | |
| 2023/06/06 01:41:16 - mmengine - INFO - Epoch(train) [1][3400/4092] lr: 9.8476e-05 eta: 7:53:09 time: 0.7275 data_time: 0.0009 memory: 6319 loss: 0.2963 | |
| 2023/06/06 01:42:28 - mmengine - INFO - Epoch(train) [1][3500/4092] lr: 9.8386e-05 eta: 7:51:08 time: 0.6771 data_time: 0.0009 memory: 6319 loss: 0.2964 | |
| 2023/06/06 01:43:42 - mmengine - INFO - Epoch(train) [1][3600/4092] lr: 9.8293e-05 eta: 7:49:44 time: 0.7794 data_time: 0.0008 memory: 6319 loss: 0.3021 | |
| 2023/06/06 01:44:56 - mmengine - INFO - Epoch(train) [1][3700/4092] lr: 9.8198e-05 eta: 7:48:06 time: 0.8267 data_time: 0.0008 memory: 6319 loss: 0.3013 | |
| 2023/06/06 01:46:10 - mmengine - INFO - Epoch(train) [1][3800/4092] lr: 9.8099e-05 eta: 7:46:39 time: 0.7225 data_time: 0.0009 memory: 6319 loss: 0.3013 | |
| 2023/06/06 01:47:25 - mmengine - INFO - Epoch(train) [1][3900/4092] lr: 9.7999e-05 eta: 7:45:20 time: 0.7070 data_time: 0.0008 memory: 6319 loss: 0.2915 | |
| 2023/06/06 01:48:37 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 01:48:37 - mmengine - INFO - Epoch(train) [1][4000/4092] lr: 9.7896e-05 eta: 7:43:37 time: 0.7585 data_time: 0.0009 memory: 6319 loss: 0.3216 | |
| 2023/06/06 01:49:46 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 01:49:46 - mmengine - INFO - Saving checkpoint at 1 epochs | |
| 2023/06/06 01:50:30 - mmengine - INFO - Epoch(val) [1][100/119] eta: 0:00:07 time: 0.6816 data_time: 0.5936 memory: 6319 | |
| 2023/06/06 01:50:56 - mmengine - INFO - Epoch(val) [1][119/119] accuracy/top1: 83.0131 data_time: 0.3644 time: 0.4524 | |
| 2023/06/06 01:52:10 - mmengine - INFO - Epoch(train) [2][ 100/4092] lr: 9.7691e-05 eta: 7:40:49 time: 0.6733 data_time: 0.3978 memory: 6319 loss: 0.3066 | |
| 2023/06/06 01:53:20 - mmengine - INFO - Epoch(train) [2][ 200/4092] lr: 9.7580e-05 eta: 7:38:49 time: 0.6840 data_time: 0.1511 memory: 6319 loss: 0.2823 | |
| 2023/06/06 01:54:32 - mmengine - INFO - Epoch(train) [2][ 300/4092] lr: 9.7467e-05 eta: 7:37:01 time: 0.7212 data_time: 0.0024 memory: 6319 loss: 0.2794 | |
| 2023/06/06 01:55:43 - mmengine - INFO - Epoch(train) [2][ 400/4092] lr: 9.7352e-05 eta: 7:35:17 time: 0.7382 data_time: 0.0008 memory: 6319 loss: 0.2703 | |
| 2023/06/06 01:56:53 - mmengine - INFO - Epoch(train) [2][ 500/4092] lr: 9.7234e-05 eta: 7:33:23 time: 0.6471 data_time: 0.0008 memory: 6319 loss: 0.2680 | |
| 2023/06/06 01:58:06 - mmengine - INFO - Epoch(train) [2][ 600/4092] lr: 9.7113e-05 eta: 7:31:55 time: 0.8386 data_time: 0.0008 memory: 6319 loss: 0.2711 | |
| 2023/06/06 01:59:21 - mmengine - INFO - Epoch(train) [2][ 700/4092] lr: 9.6990e-05 eta: 7:30:36 time: 0.8524 data_time: 0.0009 memory: 6319 loss: 0.2783 | |
| 2023/06/06 02:00:31 - mmengine - INFO - Epoch(train) [2][ 800/4092] lr: 9.6865e-05 eta: 7:28:45 time: 0.6748 data_time: 0.0009 memory: 6319 loss: 0.2656 | |
| 2023/06/06 02:01:38 - mmengine - INFO - Epoch(train) [2][ 900/4092] lr: 9.6737e-05 eta: 7:26:40 time: 0.6461 data_time: 0.0010 memory: 6319 loss: 0.2584 | |
| 2023/06/06 02:01:45 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 02:02:48 - mmengine - INFO - Epoch(train) [2][1000/4092] lr: 9.6606e-05 eta: 7:24:52 time: 0.7332 data_time: 0.0012 memory: 6319 loss: 0.2699 | |
| 2023/06/06 02:04:00 - mmengine - INFO - Epoch(train) [2][1100/4092] lr: 9.6473e-05 eta: 7:23:19 time: 0.7268 data_time: 0.0009 memory: 6319 loss: 0.2681 | |
| 2023/06/06 02:05:12 - mmengine - INFO - Epoch(train) [2][1200/4092] lr: 9.6338e-05 eta: 7:21:45 time: 0.7184 data_time: 0.0010 memory: 6319 loss: 0.2734 | |
| 2023/06/06 02:06:22 - mmengine - INFO - Epoch(train) [2][1300/4092] lr: 9.6200e-05 eta: 7:20:05 time: 0.7383 data_time: 0.0007 memory: 6319 loss: 0.2741 | |
| 2023/06/06 02:07:32 - mmengine - INFO - Epoch(train) [2][1400/4092] lr: 9.6060e-05 eta: 7:18:19 time: 0.7033 data_time: 0.0007 memory: 6319 loss: 0.2644 | |
| 2023/06/06 02:08:39 - mmengine - INFO - Epoch(train) [2][1500/4092] lr: 9.5918e-05 eta: 7:16:24 time: 0.6655 data_time: 0.0010 memory: 6319 loss: 0.2552 | |
| 2023/06/06 02:09:47 - mmengine - INFO - Epoch(train) [2][1600/4092] lr: 9.5773e-05 eta: 7:14:28 time: 0.6554 data_time: 0.0010 memory: 6319 loss: 0.2630 | |
| 2023/06/06 02:10:56 - mmengine - INFO - Epoch(train) [2][1700/4092] lr: 9.5625e-05 eta: 7:12:43 time: 0.7198 data_time: 0.0009 memory: 6319 loss: 0.2622 | |
| 2023/06/06 02:12:01 - mmengine - INFO - Epoch(train) [2][1800/4092] lr: 9.5475e-05 eta: 7:10:37 time: 0.6761 data_time: 0.0008 memory: 6319 loss: 0.2603 | |
| 2023/06/06 02:13:14 - mmengine - INFO - Epoch(train) [2][1900/4092] lr: 9.5323e-05 eta: 7:09:20 time: 0.9657 data_time: 0.0009 memory: 6319 loss: 0.2766 | |
| 2023/06/06 02:13:21 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 02:14:23 - mmengine - INFO - Epoch(train) [2][2000/4092] lr: 9.5169e-05 eta: 7:07:37 time: 0.7123 data_time: 0.0009 memory: 6319 loss: 0.2436 | |
| 2023/06/06 02:15:30 - mmengine - INFO - Epoch(train) [2][2100/4092] lr: 9.5012e-05 eta: 7:05:49 time: 0.6767 data_time: 0.0008 memory: 6319 loss: 0.2770 | |
| 2023/06/06 02:16:41 - mmengine - INFO - Epoch(train) [2][2200/4092] lr: 9.4853e-05 eta: 7:04:18 time: 0.6891 data_time: 0.0011 memory: 6319 loss: 0.2426 | |
| 2023/06/06 02:17:51 - mmengine - INFO - Epoch(train) [2][2300/4092] lr: 9.4691e-05 eta: 7:02:49 time: 0.7354 data_time: 0.0008 memory: 6319 loss: 0.2462 | |
| 2023/06/06 02:18:59 - mmengine - INFO - Epoch(train) [2][2400/4092] lr: 9.4527e-05 eta: 7:01:07 time: 0.6790 data_time: 0.0008 memory: 6319 loss: 0.2553 | |
| 2023/06/06 02:20:08 - mmengine - INFO - Epoch(train) [2][2500/4092] lr: 9.4361e-05 eta: 6:59:31 time: 0.6513 data_time: 0.0008 memory: 6319 loss: 0.2425 | |
| 2023/06/06 02:21:18 - mmengine - INFO - Epoch(train) [2][2600/4092] lr: 9.4192e-05 eta: 6:58:00 time: 0.6749 data_time: 0.0008 memory: 6319 loss: 0.2689 | |
| 2023/06/06 02:22:27 - mmengine - INFO - Epoch(train) [2][2700/4092] lr: 9.4021e-05 eta: 6:56:25 time: 0.6558 data_time: 0.0007 memory: 6319 loss: 0.2409 | |
| 2023/06/06 02:23:36 - mmengine - INFO - Epoch(train) [2][2800/4092] lr: 9.3848e-05 eta: 6:54:52 time: 0.7019 data_time: 0.0009 memory: 6319 loss: 0.2770 | |
| 2023/06/06 02:24:45 - mmengine - INFO - Epoch(train) [2][2900/4092] lr: 9.3672e-05 eta: 6:53:19 time: 0.7396 data_time: 0.0008 memory: 6319 loss: 0.2310 | |
| 2023/06/06 02:24:52 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 02:25:55 - mmengine - INFO - Epoch(train) [2][3000/4092] lr: 9.3495e-05 eta: 6:51:50 time: 0.6743 data_time: 0.0012 memory: 6319 loss: 0.2553 | |
| 2023/06/06 02:27:04 - mmengine - INFO - Epoch(train) [2][3100/4092] lr: 9.3315e-05 eta: 6:50:17 time: 0.6631 data_time: 0.0009 memory: 6319 loss: 0.2469 | |
| 2023/06/06 02:28:14 - mmengine - INFO - Epoch(train) [2][3200/4092] lr: 9.3132e-05 eta: 6:48:48 time: 0.7029 data_time: 0.0016 memory: 6319 loss: 0.2429 | |
| 2023/06/06 02:29:24 - mmengine - INFO - Epoch(train) [2][3300/4092] lr: 9.2948e-05 eta: 6:47:26 time: 0.6996 data_time: 0.0008 memory: 6319 loss: 0.2546 | |
| 2023/06/06 02:30:35 - mmengine - INFO - Epoch(train) [2][3400/4092] lr: 9.2761e-05 eta: 6:46:04 time: 0.6885 data_time: 0.0007 memory: 6319 loss: 0.2544 | |
| 2023/06/06 02:31:46 - mmengine - INFO - Epoch(train) [2][3500/4092] lr: 9.2572e-05 eta: 6:44:40 time: 0.7154 data_time: 0.0009 memory: 6319 loss: 0.2529 | |
| 2023/06/06 02:32:57 - mmengine - INFO - Epoch(train) [2][3600/4092] lr: 9.2381e-05 eta: 6:43:20 time: 0.7095 data_time: 0.0008 memory: 6319 loss: 0.2443 | |
| 2023/06/06 02:34:14 - mmengine - INFO - Epoch(train) [2][3700/4092] lr: 9.2187e-05 eta: 6:42:24 time: 0.7587 data_time: 0.0011 memory: 6319 loss: 0.2309 | |
| 2023/06/06 02:35:26 - mmengine - INFO - Epoch(train) [2][3800/4092] lr: 9.1991e-05 eta: 6:41:08 time: 0.6877 data_time: 0.0011 memory: 6319 loss: 0.2499 | |
| 2023/06/06 02:36:39 - mmengine - INFO - Epoch(train) [2][3900/4092] lr: 9.1794e-05 eta: 6:39:55 time: 0.6867 data_time: 0.0007 memory: 6319 loss: 0.2335 | |
| 2023/06/06 02:36:46 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 02:37:57 - mmengine - INFO - Epoch(train) [2][4000/4092] lr: 9.1594e-05 eta: 6:39:04 time: 0.7114 data_time: 0.0007 memory: 6319 loss: 0.2362 | |
| 2023/06/06 02:39:02 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 02:39:02 - mmengine - INFO - Saving checkpoint at 2 epochs | |
| 2023/06/06 02:39:43 - mmengine - INFO - Epoch(val) [2][100/119] eta: 0:00:06 time: 0.6301 data_time: 0.5429 memory: 6319 | |
| 2023/06/06 02:40:10 - mmengine - INFO - Epoch(val) [2][119/119] accuracy/top1: 81.0155 data_time: 0.3427 time: 0.4298 | |
| 2023/06/06 02:41:23 - mmengine - INFO - Epoch(train) [3][ 100/4092] lr: 9.1204e-05 eta: 6:36:35 time: 0.6739 data_time: 0.4689 memory: 6319 loss: 0.2375 | |
| 2023/06/06 02:42:34 - mmengine - INFO - Epoch(train) [3][ 200/4092] lr: 9.0997e-05 eta: 6:35:13 time: 0.7003 data_time: 0.3339 memory: 6319 loss: 0.2527 | |
| 2023/06/06 02:43:43 - mmengine - INFO - Epoch(train) [3][ 300/4092] lr: 9.0789e-05 eta: 6:33:47 time: 0.6683 data_time: 0.2901 memory: 6319 loss: 0.2347 | |
| 2023/06/06 02:44:58 - mmengine - INFO - Epoch(train) [3][ 400/4092] lr: 9.0579e-05 eta: 6:32:40 time: 0.6562 data_time: 0.3168 memory: 6319 loss: 0.2416 | |
| 2023/06/06 02:46:08 - mmengine - INFO - Epoch(train) [3][ 500/4092] lr: 9.0366e-05 eta: 6:31:17 time: 0.7558 data_time: 0.5717 memory: 6319 loss: 0.2165 | |
| 2023/06/06 02:47:19 - mmengine - INFO - Epoch(train) [3][ 600/4092] lr: 9.0151e-05 eta: 6:29:59 time: 0.6772 data_time: 0.5375 memory: 6319 loss: 0.2230 | |
| 2023/06/06 02:48:29 - mmengine - INFO - Epoch(train) [3][ 700/4092] lr: 8.9935e-05 eta: 6:28:35 time: 0.6904 data_time: 0.4909 memory: 6319 loss: 0.2312 | |
| 2023/06/06 02:49:42 - mmengine - INFO - Epoch(train) [3][ 800/4092] lr: 8.9716e-05 eta: 6:27:22 time: 0.6909 data_time: 0.5512 memory: 6319 loss: 0.2282 | |
| 2023/06/06 02:49:55 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 02:50:53 - mmengine - INFO - Epoch(train) [3][ 900/4092] lr: 8.9495e-05 eta: 6:26:04 time: 0.7130 data_time: 0.5730 memory: 6319 loss: 0.2252 | |
| 2023/06/06 02:52:05 - mmengine - INFO - Epoch(train) [3][1000/4092] lr: 8.9272e-05 eta: 6:24:49 time: 0.7019 data_time: 0.5608 memory: 6319 loss: 0.2453 | |
| 2023/06/06 02:53:15 - mmengine - INFO - Epoch(train) [3][1100/4092] lr: 8.9047e-05 eta: 6:23:25 time: 0.7277 data_time: 0.5887 memory: 6319 loss: 0.2265 | |
| 2023/06/06 02:54:26 - mmengine - INFO - Epoch(train) [3][1200/4092] lr: 8.8820e-05 eta: 6:22:06 time: 0.6716 data_time: 0.5279 memory: 6319 loss: 0.2352 | |
| 2023/06/06 02:55:35 - mmengine - INFO - Epoch(train) [3][1300/4092] lr: 8.8591e-05 eta: 6:20:43 time: 0.7038 data_time: 0.5630 memory: 6319 loss: 0.2377 | |
| 2023/06/06 02:56:46 - mmengine - INFO - Epoch(train) [3][1400/4092] lr: 8.8360e-05 eta: 6:19:23 time: 0.6769 data_time: 0.5362 memory: 6319 loss: 0.2327 | |
| 2023/06/06 02:57:55 - mmengine - INFO - Epoch(train) [3][1500/4092] lr: 8.8128e-05 eta: 6:17:59 time: 0.7121 data_time: 0.5612 memory: 6319 loss: 0.2297 | |
| 2023/06/06 02:59:10 - mmengine - INFO - Epoch(train) [3][1600/4092] lr: 8.7893e-05 eta: 6:16:54 time: 0.6463 data_time: 0.5060 memory: 6319 loss: 0.2340 | |
| 2023/06/06 03:00:20 - mmengine - INFO - Epoch(train) [3][1700/4092] lr: 8.7656e-05 eta: 6:15:34 time: 0.6686 data_time: 0.5291 memory: 6319 loss: 0.2326 | |
| 2023/06/06 03:01:30 - mmengine - INFO - Epoch(train) [3][1800/4092] lr: 8.7417e-05 eta: 6:14:14 time: 0.6653 data_time: 0.5214 memory: 6319 loss: 0.2310 | |
| 2023/06/06 03:01:42 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 03:02:37 - mmengine - INFO - Epoch(train) [3][1900/4092] lr: 8.7177e-05 eta: 6:12:44 time: 0.7430 data_time: 0.4484 memory: 6319 loss: 0.2426 | |
| 2023/06/06 03:03:47 - mmengine - INFO - Epoch(train) [3][2000/4092] lr: 8.6934e-05 eta: 6:11:24 time: 0.6700 data_time: 0.3338 memory: 6319 loss: 0.2122 | |
| 2023/06/06 03:04:57 - mmengine - INFO - Epoch(train) [3][2100/4092] lr: 8.6690e-05 eta: 6:10:05 time: 0.7080 data_time: 0.1465 memory: 6319 loss: 0.2160 | |
| 2023/06/06 03:06:07 - mmengine - INFO - Epoch(train) [3][2200/4092] lr: 8.6444e-05 eta: 6:08:43 time: 0.6683 data_time: 0.0652 memory: 6319 loss: 0.2329 | |
| 2023/06/06 03:07:21 - mmengine - INFO - Epoch(train) [3][2300/4092] lr: 8.6196e-05 eta: 6:07:36 time: 0.7149 data_time: 0.1617 memory: 6319 loss: 0.2339 | |
| 2023/06/06 03:08:34 - mmengine - INFO - Epoch(train) [3][2400/4092] lr: 8.5946e-05 eta: 6:06:26 time: 0.7036 data_time: 0.1618 memory: 6319 loss: 0.2193 | |
| 2023/06/06 03:09:46 - mmengine - INFO - Epoch(train) [3][2500/4092] lr: 8.5694e-05 eta: 6:05:12 time: 0.6862 data_time: 0.0010 memory: 6319 loss: 0.2251 | |
| 2023/06/06 03:10:57 - mmengine - INFO - Epoch(train) [3][2600/4092] lr: 8.5441e-05 eta: 6:03:55 time: 0.7535 data_time: 0.0008 memory: 6319 loss: 0.2095 | |
| 2023/06/06 03:12:09 - mmengine - INFO - Epoch(train) [3][2700/4092] lr: 8.5185e-05 eta: 6:02:41 time: 0.7049 data_time: 0.0008 memory: 6319 loss: 0.2110 | |
| 2023/06/06 03:13:23 - mmengine - INFO - Epoch(train) [3][2800/4092] lr: 8.4928e-05 eta: 6:01:32 time: 0.7746 data_time: 0.0009 memory: 6319 loss: 0.2166 | |
| 2023/06/06 03:13:31 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 03:14:38 - mmengine - INFO - Epoch(train) [3][2900/4092] lr: 8.4669e-05 eta: 6:00:27 time: 0.7173 data_time: 0.0008 memory: 6319 loss: 0.2160 | |
| 2023/06/06 03:15:49 - mmengine - INFO - Epoch(train) [3][3000/4092] lr: 8.4409e-05 eta: 5:59:11 time: 0.6983 data_time: 0.0008 memory: 6319 loss: 0.2236 | |
| 2023/06/06 03:17:02 - mmengine - INFO - Epoch(train) [3][3100/4092] lr: 8.4146e-05 eta: 5:57:59 time: 0.7384 data_time: 0.0009 memory: 6319 loss: 0.2074 | |
| 2023/06/06 03:18:15 - mmengine - INFO - Epoch(train) [3][3200/4092] lr: 8.3882e-05 eta: 5:56:49 time: 0.7332 data_time: 0.0009 memory: 6319 loss: 0.2073 | |
| 2023/06/06 03:19:27 - mmengine - INFO - Epoch(train) [3][3300/4092] lr: 8.3616e-05 eta: 5:55:34 time: 0.7300 data_time: 0.0009 memory: 6319 loss: 0.2079 | |
| 2023/06/06 03:20:40 - mmengine - INFO - Epoch(train) [3][3400/4092] lr: 8.3349e-05 eta: 5:54:24 time: 0.8191 data_time: 0.0009 memory: 6319 loss: 0.1952 | |
| 2023/06/06 03:21:52 - mmengine - INFO - Epoch(train) [3][3500/4092] lr: 8.3080e-05 eta: 5:53:08 time: 0.7053 data_time: 0.0009 memory: 6319 loss: 0.2118 | |
| 2023/06/06 03:23:10 - mmengine - INFO - Epoch(train) [3][3600/4092] lr: 8.2809e-05 eta: 5:52:11 time: 1.2415 data_time: 0.0008 memory: 6319 loss: 0.2278 | |
| 2023/06/06 03:24:25 - mmengine - INFO - Epoch(train) [3][3700/4092] lr: 8.2537e-05 eta: 5:51:04 time: 0.6985 data_time: 0.0008 memory: 6319 loss: 0.2131 | |
| 2023/06/06 03:25:38 - mmengine - INFO - Epoch(train) [3][3800/4092] lr: 8.2263e-05 eta: 5:49:53 time: 0.7291 data_time: 0.0007 memory: 6319 loss: 0.2232 | |
| 2023/06/06 03:25:52 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 03:26:51 - mmengine - INFO - Epoch(train) [3][3900/4092] lr: 8.1987e-05 eta: 5:48:42 time: 0.7389 data_time: 0.0008 memory: 6319 loss: 0.2098 | |
| 2023/06/06 03:28:04 - mmengine - INFO - Epoch(train) [3][4000/4092] lr: 8.1710e-05 eta: 5:47:31 time: 0.7250 data_time: 0.0007 memory: 6319 loss: 0.2235 | |
| 2023/06/06 03:29:14 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 03:29:14 - mmengine - INFO - Saving checkpoint at 3 epochs | |
| 2023/06/06 03:29:55 - mmengine - INFO - Epoch(val) [3][100/119] eta: 0:00:06 time: 0.6817 data_time: 0.5920 memory: 6319 | |
| 2023/06/06 03:30:22 - mmengine - INFO - Epoch(val) [3][119/119] accuracy/top1: 82.5017 data_time: 0.3352 time: 0.4236 | |
| 2023/06/06 03:31:35 - mmengine - INFO - Epoch(train) [4][ 100/4092] lr: 8.1173e-05 eta: 5:45:20 time: 0.7653 data_time: 0.3452 memory: 6319 loss: 0.2124 | |
| 2023/06/06 03:32:48 - mmengine - INFO - Epoch(train) [4][ 200/4092] lr: 8.0891e-05 eta: 5:44:06 time: 0.7129 data_time: 0.1697 memory: 6319 loss: 0.2119 | |
| 2023/06/06 03:33:59 - mmengine - INFO - Epoch(train) [4][ 300/4092] lr: 8.0608e-05 eta: 5:42:51 time: 0.6693 data_time: 0.2002 memory: 6319 loss: 0.2057 | |
| 2023/06/06 03:35:14 - mmengine - INFO - Epoch(train) [4][ 400/4092] lr: 8.0323e-05 eta: 5:41:44 time: 0.7680 data_time: 0.0008 memory: 6319 loss: 0.2183 | |
| 2023/06/06 03:36:27 - mmengine - INFO - Epoch(train) [4][ 500/4092] lr: 8.0037e-05 eta: 5:40:32 time: 0.7451 data_time: 0.0007 memory: 6319 loss: 0.2009 | |
| 2023/06/06 03:37:41 - mmengine - INFO - Epoch(train) [4][ 600/4092] lr: 7.9749e-05 eta: 5:39:22 time: 0.7786 data_time: 0.0137 memory: 6319 loss: 0.2086 | |
| 2023/06/06 03:38:52 - mmengine - INFO - Epoch(train) [4][ 700/4092] lr: 7.9459e-05 eta: 5:38:07 time: 0.7002 data_time: 0.0008 memory: 6319 loss: 0.2154 | |
| 2023/06/06 03:39:07 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 03:40:09 - mmengine - INFO - Epoch(train) [4][ 800/4092] lr: 7.9169e-05 eta: 5:37:03 time: 0.7613 data_time: 0.0008 memory: 6319 loss: 0.2074 | |
| 2023/06/06 03:41:21 - mmengine - INFO - Epoch(train) [4][ 900/4092] lr: 7.8877e-05 eta: 5:35:49 time: 0.7719 data_time: 0.0010 memory: 6319 loss: 0.2295 | |
| 2023/06/06 03:42:34 - mmengine - INFO - Epoch(train) [4][1000/4092] lr: 7.8583e-05 eta: 5:34:37 time: 0.6818 data_time: 0.0007 memory: 6319 loss: 0.2154 | |
| 2023/06/06 03:43:48 - mmengine - INFO - Epoch(train) [4][1100/4092] lr: 7.8288e-05 eta: 5:33:28 time: 0.7383 data_time: 0.0008 memory: 6319 loss: 0.2053 | |
| 2023/06/06 03:45:00 - mmengine - INFO - Epoch(train) [4][1200/4092] lr: 7.7992e-05 eta: 5:32:14 time: 0.7470 data_time: 0.0008 memory: 6319 loss: 0.2038 | |
| 2023/06/06 03:46:14 - mmengine - INFO - Epoch(train) [4][1300/4092] lr: 7.7694e-05 eta: 5:31:03 time: 0.7960 data_time: 0.0008 memory: 6319 loss: 0.1935 | |
| 2023/06/06 03:47:26 - mmengine - INFO - Epoch(train) [4][1400/4092] lr: 7.7395e-05 eta: 5:29:50 time: 0.7113 data_time: 0.0009 memory: 6319 loss: 0.1868 | |
| 2023/06/06 03:48:41 - mmengine - INFO - Epoch(train) [4][1500/4092] lr: 7.7095e-05 eta: 5:28:41 time: 0.7173 data_time: 0.0008 memory: 6319 loss: 0.2074 | |
| 2023/06/06 03:49:57 - mmengine - INFO - Epoch(train) [4][1600/4092] lr: 7.6793e-05 eta: 5:27:36 time: 0.7048 data_time: 0.0008 memory: 6319 loss: 0.2050 | |
| 2023/06/06 03:51:11 - mmengine - INFO - Epoch(train) [4][1700/4092] lr: 7.6490e-05 eta: 5:26:26 time: 0.7432 data_time: 0.0011 memory: 6319 loss: 0.2100 | |
| 2023/06/06 03:51:26 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 03:52:25 - mmengine - INFO - Epoch(train) [4][1800/4092] lr: 7.6186e-05 eta: 5:25:16 time: 0.7548 data_time: 0.0008 memory: 6319 loss: 0.2036 | |
| 2023/06/06 03:53:40 - mmengine - INFO - Epoch(train) [4][1900/4092] lr: 7.5881e-05 eta: 5:24:07 time: 0.7574 data_time: 0.0009 memory: 6319 loss: 0.2252 | |
| 2023/06/06 03:54:53 - mmengine - INFO - Epoch(train) [4][2000/4092] lr: 7.5574e-05 eta: 5:22:55 time: 0.6884 data_time: 0.0010 memory: 6319 loss: 0.1938 | |
| 2023/06/06 03:56:07 - mmengine - INFO - Epoch(train) [4][2100/4092] lr: 7.5266e-05 eta: 5:21:43 time: 0.7812 data_time: 0.0008 memory: 6319 loss: 0.1937 | |
| 2023/06/06 03:57:20 - mmengine - INFO - Epoch(train) [4][2200/4092] lr: 7.4957e-05 eta: 5:20:31 time: 0.6806 data_time: 0.0008 memory: 6319 loss: 0.2127 | |
| 2023/06/06 03:58:33 - mmengine - INFO - Epoch(train) [4][2300/4092] lr: 7.4647e-05 eta: 5:19:20 time: 0.7339 data_time: 0.0008 memory: 6319 loss: 0.2008 | |
| 2023/06/06 03:59:42 - mmengine - INFO - Epoch(train) [4][2400/4092] lr: 7.4336e-05 eta: 5:18:00 time: 0.8199 data_time: 0.0009 memory: 6319 loss: 0.2070 | |
| 2023/06/06 04:00:49 - mmengine - INFO - Epoch(train) [4][2500/4092] lr: 7.4023e-05 eta: 5:16:38 time: 0.6465 data_time: 0.0008 memory: 6319 loss: 0.2020 | |
| 2023/06/06 04:01:59 - mmengine - INFO - Epoch(train) [4][2600/4092] lr: 7.3709e-05 eta: 5:15:20 time: 0.6653 data_time: 0.0007 memory: 6319 loss: 0.1918 | |
| 2023/06/06 04:03:12 - mmengine - INFO - Epoch(train) [4][2700/4092] lr: 7.3395e-05 eta: 5:14:08 time: 0.7855 data_time: 0.0009 memory: 6319 loss: 0.2002 | |
| 2023/06/06 04:03:26 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 04:04:23 - mmengine - INFO - Epoch(train) [4][2800/4092] lr: 7.3079e-05 eta: 5:12:52 time: 0.7003 data_time: 0.0008 memory: 6319 loss: 0.1922 | |
| 2023/06/06 04:05:33 - mmengine - INFO - Epoch(train) [4][2900/4092] lr: 7.2762e-05 eta: 5:11:35 time: 0.6478 data_time: 0.0011 memory: 6319 loss: 0.1829 | |
| 2023/06/06 04:06:43 - mmengine - INFO - Epoch(train) [4][3000/4092] lr: 7.2444e-05 eta: 5:10:19 time: 0.6514 data_time: 0.0009 memory: 6319 loss: 0.2042 | |
| 2023/06/06 04:07:52 - mmengine - INFO - Epoch(train) [4][3100/4092] lr: 7.2125e-05 eta: 5:09:00 time: 0.6860 data_time: 0.0008 memory: 6319 loss: 0.2087 | |
| 2023/06/06 04:09:02 - mmengine - INFO - Epoch(train) [4][3200/4092] lr: 7.1805e-05 eta: 5:07:42 time: 0.6912 data_time: 0.0009 memory: 6319 loss: 0.1959 | |
| 2023/06/06 04:10:14 - mmengine - INFO - Epoch(train) [4][3300/4092] lr: 7.1484e-05 eta: 5:06:29 time: 0.7713 data_time: 0.0008 memory: 6319 loss: 0.1932 | |
| 2023/06/06 04:11:24 - mmengine - INFO - Epoch(train) [4][3400/4092] lr: 7.1162e-05 eta: 5:05:13 time: 0.6500 data_time: 0.0008 memory: 6319 loss: 0.2022 | |
| 2023/06/06 04:12:35 - mmengine - INFO - Epoch(train) [4][3500/4092] lr: 7.0839e-05 eta: 5:03:58 time: 0.6702 data_time: 0.0008 memory: 6319 loss: 0.2044 | |
| 2023/06/06 04:13:51 - mmengine - INFO - Epoch(train) [4][3600/4092] lr: 7.0515e-05 eta: 5:02:50 time: 0.7106 data_time: 0.0008 memory: 6319 loss: 0.1811 | |
| 2023/06/06 04:15:08 - mmengine - INFO - Epoch(train) [4][3700/4092] lr: 7.0191e-05 eta: 5:01:45 time: 0.6673 data_time: 0.0008 memory: 6319 loss: 0.1854 | |
| 2023/06/06 04:15:23 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 04:16:18 - mmengine - INFO - Epoch(train) [4][3800/4092] lr: 6.9865e-05 eta: 5:00:29 time: 0.6873 data_time: 0.0008 memory: 6319 loss: 0.1951 | |
| 2023/06/06 04:17:27 - mmengine - INFO - Epoch(train) [4][3900/4092] lr: 6.9538e-05 eta: 4:59:11 time: 0.6796 data_time: 0.0008 memory: 6319 loss: 0.1844 | |
| 2023/06/06 04:18:39 - mmengine - INFO - Epoch(train) [4][4000/4092] lr: 6.9211e-05 eta: 4:57:56 time: 0.7152 data_time: 0.0008 memory: 6319 loss: 0.1930 | |
| 2023/06/06 04:19:41 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 04:19:41 - mmengine - INFO - Saving checkpoint at 4 epochs | |
| 2023/06/06 04:20:23 - mmengine - INFO - Epoch(val) [4][100/119] eta: 0:00:06 time: 0.6212 data_time: 0.5338 memory: 6319 | |
| 2023/06/06 04:20:49 - mmengine - INFO - Epoch(val) [4][119/119] accuracy/top1: 87.8523 data_time: 0.3359 time: 0.4238 | |
| 2023/06/06 04:22:03 - mmengine - INFO - Epoch(train) [5][ 100/4092] lr: 6.8580e-05 eta: 4:55:33 time: 0.7527 data_time: 0.4232 memory: 6319 loss: 0.1879 | |
| 2023/06/06 04:23:16 - mmengine - INFO - Epoch(train) [5][ 200/4092] lr: 6.8250e-05 eta: 4:54:21 time: 0.7094 data_time: 0.0855 memory: 6319 loss: 0.1848 | |
| 2023/06/06 04:24:29 - mmengine - INFO - Epoch(train) [5][ 300/4092] lr: 6.7920e-05 eta: 4:53:09 time: 0.7260 data_time: 0.0009 memory: 6319 loss: 0.1809 | |
| 2023/06/06 04:25:43 - mmengine - INFO - Epoch(train) [5][ 400/4092] lr: 6.7588e-05 eta: 4:51:58 time: 0.7914 data_time: 0.0009 memory: 6319 loss: 0.1817 | |
| 2023/06/06 04:26:55 - mmengine - INFO - Epoch(train) [5][ 500/4092] lr: 6.7256e-05 eta: 4:50:45 time: 0.7367 data_time: 0.0010 memory: 6319 loss: 0.1991 | |
| 2023/06/06 04:28:09 - mmengine - INFO - Epoch(train) [5][ 600/4092] lr: 6.6924e-05 eta: 4:49:35 time: 0.7934 data_time: 0.0008 memory: 6319 loss: 0.1778 | |
| 2023/06/06 04:28:31 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 04:29:22 - mmengine - INFO - Epoch(train) [5][ 700/4092] lr: 6.6590e-05 eta: 4:48:23 time: 0.7456 data_time: 0.0008 memory: 6319 loss: 0.1894 | |
| 2023/06/06 04:30:34 - mmengine - INFO - Epoch(train) [5][ 800/4092] lr: 6.6256e-05 eta: 4:47:10 time: 0.7498 data_time: 0.0008 memory: 6319 loss: 0.1915 | |
| 2023/06/06 04:31:47 - mmengine - INFO - Epoch(train) [5][ 900/4092] lr: 6.5921e-05 eta: 4:45:58 time: 0.7293 data_time: 0.0008 memory: 6319 loss: 0.1988 | |
| 2023/06/06 04:32:59 - mmengine - INFO - Epoch(train) [5][1000/4092] lr: 6.5586e-05 eta: 4:44:44 time: 0.7052 data_time: 0.0089 memory: 6319 loss: 0.1941 | |
| 2023/06/06 04:34:12 - mmengine - INFO - Epoch(train) [5][1100/4092] lr: 6.5250e-05 eta: 4:43:33 time: 0.7157 data_time: 0.3020 memory: 6319 loss: 0.2048 | |
| 2023/06/06 04:35:24 - mmengine - INFO - Epoch(train) [5][1200/4092] lr: 6.4913e-05 eta: 4:42:19 time: 0.7213 data_time: 0.1752 memory: 6319 loss: 0.1896 | |
| 2023/06/06 04:36:46 - mmengine - INFO - Epoch(train) [5][1300/4092] lr: 6.4576e-05 eta: 4:41:20 time: 1.1040 data_time: 0.1012 memory: 6319 loss: 0.1927 | |
| 2023/06/06 04:38:00 - mmengine - INFO - Epoch(train) [5][1400/4092] lr: 6.4238e-05 eta: 4:40:09 time: 0.7296 data_time: 0.0009 memory: 6319 loss: 0.2042 | |
| 2023/06/06 04:39:13 - mmengine - INFO - Epoch(train) [5][1500/4092] lr: 6.3899e-05 eta: 4:38:57 time: 0.7837 data_time: 0.1670 memory: 6319 loss: 0.1982 | |
| 2023/06/06 04:40:27 - mmengine - INFO - Epoch(train) [5][1600/4092] lr: 6.3560e-05 eta: 4:37:46 time: 0.7807 data_time: 0.3579 memory: 6319 loss: 0.1835 | |
| 2023/06/06 04:40:49 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 04:41:38 - mmengine - INFO - Epoch(train) [5][1700/4092] lr: 6.3221e-05 eta: 4:36:31 time: 0.7191 data_time: 0.2286 memory: 6319 loss: 0.1985 | |
| 2023/06/06 04:42:52 - mmengine - INFO - Epoch(train) [5][1800/4092] lr: 6.2881e-05 eta: 4:35:20 time: 0.7471 data_time: 0.2171 memory: 6319 loss: 0.1943 | |
| 2023/06/06 04:44:04 - mmengine - INFO - Epoch(train) [5][1900/4092] lr: 6.2541e-05 eta: 4:34:07 time: 0.6766 data_time: 0.3013 memory: 6319 loss: 0.1803 | |
| 2023/06/06 04:45:16 - mmengine - INFO - Epoch(train) [5][2000/4092] lr: 6.2200e-05 eta: 4:32:54 time: 0.8018 data_time: 0.0008 memory: 6319 loss: 0.1824 | |
| 2023/06/06 04:46:29 - mmengine - INFO - Epoch(train) [5][2100/4092] lr: 6.1859e-05 eta: 4:31:42 time: 0.7042 data_time: 0.0009 memory: 6319 loss: 0.1862 | |
| 2023/06/06 04:47:43 - mmengine - INFO - Epoch(train) [5][2200/4092] lr: 6.1517e-05 eta: 4:30:30 time: 0.7313 data_time: 0.0009 memory: 6319 loss: 0.1891 | |
| 2023/06/06 04:48:54 - mmengine - INFO - Epoch(train) [5][2300/4092] lr: 6.1175e-05 eta: 4:29:16 time: 0.6894 data_time: 0.0008 memory: 6319 loss: 0.2005 | |
| 2023/06/06 04:50:07 - mmengine - INFO - Epoch(train) [5][2400/4092] lr: 6.0833e-05 eta: 4:28:04 time: 0.7661 data_time: 0.0008 memory: 6319 loss: 0.1876 | |
| 2023/06/06 04:51:21 - mmengine - INFO - Epoch(train) [5][2500/4092] lr: 6.0490e-05 eta: 4:26:53 time: 0.7509 data_time: 0.0009 memory: 6319 loss: 0.1776 | |
| 2023/06/06 04:52:34 - mmengine - INFO - Epoch(train) [5][2600/4092] lr: 6.0147e-05 eta: 4:25:41 time: 0.7639 data_time: 0.0009 memory: 6319 loss: 0.1656 | |
| 2023/06/06 04:52:58 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 04:53:51 - mmengine - INFO - Epoch(train) [5][2700/4092] lr: 5.9803e-05 eta: 4:24:33 time: 1.1508 data_time: 0.0010 memory: 6319 loss: 0.1880 | |
| 2023/06/06 04:55:04 - mmengine - INFO - Epoch(train) [5][2800/4092] lr: 5.9460e-05 eta: 4:23:20 time: 0.6970 data_time: 0.0010 memory: 6319 loss: 0.1880 | |
| 2023/06/06 04:56:17 - mmengine - INFO - Epoch(train) [5][2900/4092] lr: 5.9116e-05 eta: 4:22:09 time: 0.6837 data_time: 0.0009 memory: 6319 loss: 0.1804 | |
| 2023/06/06 04:57:30 - mmengine - INFO - Epoch(train) [5][3000/4092] lr: 5.8772e-05 eta: 4:20:56 time: 0.7440 data_time: 0.0008 memory: 6319 loss: 0.1687 | |
| 2023/06/06 04:58:41 - mmengine - INFO - Epoch(train) [5][3100/4092] lr: 5.8427e-05 eta: 4:19:42 time: 0.6986 data_time: 0.0010 memory: 6319 loss: 0.1765 | |
| 2023/06/06 04:59:53 - mmengine - INFO - Epoch(train) [5][3200/4092] lr: 5.8083e-05 eta: 4:18:28 time: 0.7481 data_time: 0.0008 memory: 6319 loss: 0.1798 | |
| 2023/06/06 05:01:05 - mmengine - INFO - Epoch(train) [5][3300/4092] lr: 5.7738e-05 eta: 4:17:15 time: 0.7215 data_time: 0.0008 memory: 6319 loss: 0.1844 | |
| 2023/06/06 05:02:21 - mmengine - INFO - Epoch(train) [5][3400/4092] lr: 5.7393e-05 eta: 4:16:06 time: 0.7331 data_time: 0.0008 memory: 6319 loss: 0.1776 | |
| 2023/06/06 05:03:34 - mmengine - INFO - Epoch(train) [5][3500/4092] lr: 5.7048e-05 eta: 4:14:54 time: 0.7413 data_time: 0.0008 memory: 6319 loss: 0.1897 | |
| 2023/06/06 05:04:47 - mmengine - INFO - Epoch(train) [5][3600/4092] lr: 5.6703e-05 eta: 4:13:41 time: 0.7372 data_time: 0.0008 memory: 6319 loss: 0.1966 | |
| 2023/06/06 05:05:10 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 05:06:00 - mmengine - INFO - Epoch(train) [5][3700/4092] lr: 5.6358e-05 eta: 4:12:29 time: 0.7181 data_time: 0.0008 memory: 6319 loss: 0.1727 | |
| 2023/06/06 05:07:14 - mmengine - INFO - Epoch(train) [5][3800/4092] lr: 5.6012e-05 eta: 4:11:18 time: 0.7618 data_time: 0.0008 memory: 6319 loss: 0.1865 | |
| 2023/06/06 05:08:27 - mmengine - INFO - Epoch(train) [5][3900/4092] lr: 5.5667e-05 eta: 4:10:06 time: 0.7369 data_time: 0.0009 memory: 6319 loss: 0.1806 | |
| 2023/06/06 05:09:42 - mmengine - INFO - Epoch(train) [5][4000/4092] lr: 5.5321e-05 eta: 4:08:55 time: 0.7057 data_time: 0.0008 memory: 6319 loss: 0.1744 | |
| 2023/06/06 05:10:47 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 05:10:47 - mmengine - INFO - Saving checkpoint at 5 epochs | |
| 2023/06/06 05:11:28 - mmengine - INFO - Epoch(val) [5][100/119] eta: 0:00:06 time: 0.6983 data_time: 0.6109 memory: 6319 | |
| 2023/06/06 05:11:55 - mmengine - INFO - Epoch(val) [5][119/119] accuracy/top1: 90.0419 data_time: 0.3324 time: 0.4194 | |
| 2023/06/06 05:13:11 - mmengine - INFO - Epoch(train) [6][ 100/4092] lr: 5.4658e-05 eta: 4:06:37 time: 0.7909 data_time: 0.4620 memory: 6319 loss: 0.1931 | |
| 2023/06/06 05:14:25 - mmengine - INFO - Epoch(train) [6][ 200/4092] lr: 5.4313e-05 eta: 4:05:26 time: 0.7966 data_time: 0.2947 memory: 6319 loss: 0.1745 | |
| 2023/06/06 05:15:39 - mmengine - INFO - Epoch(train) [6][ 300/4092] lr: 5.3967e-05 eta: 4:04:14 time: 0.7237 data_time: 0.0064 memory: 6319 loss: 0.1741 | |
| 2023/06/06 05:16:51 - mmengine - INFO - Epoch(train) [6][ 400/4092] lr: 5.3622e-05 eta: 4:03:01 time: 0.7044 data_time: 0.0008 memory: 6319 loss: 0.1696 | |
| 2023/06/06 05:18:03 - mmengine - INFO - Epoch(train) [6][ 500/4092] lr: 5.3276e-05 eta: 4:01:49 time: 0.7209 data_time: 0.0009 memory: 6319 loss: 0.1818 | |
| 2023/06/06 05:18:37 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 05:19:24 - mmengine - INFO - Epoch(train) [6][ 600/4092] lr: 5.2931e-05 eta: 4:00:43 time: 0.6940 data_time: 0.0009 memory: 6319 loss: 0.1800 | |
| 2023/06/06 05:20:45 - mmengine - INFO - Epoch(train) [6][ 700/4092] lr: 5.2586e-05 eta: 3:59:38 time: 0.7270 data_time: 0.0012 memory: 6319 loss: 0.1700 | |
| 2023/06/06 05:21:57 - mmengine - INFO - Epoch(train) [6][ 800/4092] lr: 5.2241e-05 eta: 3:58:25 time: 0.7370 data_time: 0.0009 memory: 6319 loss: 0.1735 | |
| 2023/06/06 05:23:10 - mmengine - INFO - Epoch(train) [6][ 900/4092] lr: 5.1897e-05 eta: 3:57:12 time: 0.7386 data_time: 0.0009 memory: 6319 loss: 0.1841 | |
| 2023/06/06 05:24:30 - mmengine - INFO - Epoch(train) [6][1000/4092] lr: 5.1552e-05 eta: 3:56:06 time: 0.9761 data_time: 0.0009 memory: 6319 loss: 0.1671 | |
| 2023/06/06 05:25:50 - mmengine - INFO - Epoch(train) [6][1100/4092] lr: 5.1208e-05 eta: 3:55:00 time: 0.7023 data_time: 0.0009 memory: 6319 loss: 0.1853 | |
| 2023/06/06 05:27:00 - mmengine - INFO - Epoch(train) [6][1200/4092] lr: 5.0864e-05 eta: 3:53:45 time: 0.7260 data_time: 0.0008 memory: 6319 loss: 0.1834 | |
| 2023/06/06 05:28:12 - mmengine - INFO - Epoch(train) [6][1300/4092] lr: 5.0520e-05 eta: 3:52:31 time: 0.6930 data_time: 0.0008 memory: 6319 loss: 0.1743 | |
| 2023/06/06 05:29:19 - mmengine - INFO - Epoch(train) [6][1400/4092] lr: 5.0176e-05 eta: 3:51:13 time: 0.6507 data_time: 0.0008 memory: 6319 loss: 0.1836 | |
| 2023/06/06 05:30:34 - mmengine - INFO - Epoch(train) [6][1500/4092] lr: 4.9833e-05 eta: 3:50:02 time: 0.7906 data_time: 0.0009 memory: 6319 loss: 0.1709 | |
| 2023/06/06 05:31:02 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 05:31:45 - mmengine - INFO - Epoch(train) [6][1600/4092] lr: 4.9490e-05 eta: 3:48:48 time: 0.6766 data_time: 0.0010 memory: 6319 loss: 0.1594 | |
| 2023/06/06 05:32:56 - mmengine - INFO - Epoch(train) [6][1700/4092] lr: 4.9147e-05 eta: 3:47:33 time: 0.7720 data_time: 0.0009 memory: 6319 loss: 0.1905 | |
| 2023/06/06 05:34:07 - mmengine - INFO - Epoch(train) [6][1800/4092] lr: 4.8805e-05 eta: 3:46:19 time: 0.7351 data_time: 0.0009 memory: 6319 loss: 0.1803 | |
| 2023/06/06 05:36:49 - mmengine - INFO - Epoch(train) [6][1900/4092] lr: 4.8462e-05 eta: 3:46:21 time: 0.6649 data_time: 0.0009 memory: 6319 loss: 0.1862 | |
| 2023/06/06 05:38:02 - mmengine - INFO - Epoch(train) [6][2000/4092] lr: 4.8121e-05 eta: 3:45:07 time: 0.7321 data_time: 0.0009 memory: 6319 loss: 0.1694 | |
| 2023/06/06 05:39:24 - mmengine - INFO - Epoch(train) [6][2100/4092] lr: 4.7780e-05 eta: 3:44:01 time: 0.6986 data_time: 0.0007 memory: 6319 loss: 0.1774 | |
| 2023/06/06 05:40:36 - mmengine - INFO - Epoch(train) [6][2200/4092] lr: 4.7439e-05 eta: 3:42:47 time: 0.6732 data_time: 0.0008 memory: 6319 loss: 0.1733 | |
| 2023/06/06 05:41:47 - mmengine - INFO - Epoch(train) [6][2300/4092] lr: 4.7099e-05 eta: 3:41:32 time: 0.6546 data_time: 0.0010 memory: 6319 loss: 0.1711 | |
| 2023/06/06 05:42:58 - mmengine - INFO - Epoch(train) [6][2400/4092] lr: 4.6759e-05 eta: 3:40:17 time: 0.6798 data_time: 0.0009 memory: 6319 loss: 0.1711 | |
| 2023/06/06 05:44:08 - mmengine - INFO - Epoch(train) [6][2500/4092] lr: 4.6419e-05 eta: 3:39:02 time: 0.7296 data_time: 0.0009 memory: 6319 loss: 0.1920 | |
| 2023/06/06 05:44:37 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 05:45:20 - mmengine - INFO - Epoch(train) [6][2600/4092] lr: 4.6080e-05 eta: 3:37:47 time: 0.7309 data_time: 0.0009 memory: 6319 loss: 0.1615 | |
| 2023/06/06 05:46:29 - mmengine - INFO - Epoch(train) [6][2700/4092] lr: 4.5742e-05 eta: 3:36:31 time: 0.7305 data_time: 0.0011 memory: 6319 loss: 0.1842 | |
| 2023/06/06 05:47:40 - mmengine - INFO - Epoch(train) [6][2800/4092] lr: 4.5404e-05 eta: 3:35:16 time: 0.7066 data_time: 0.0009 memory: 6319 loss: 0.1651 | |
| 2023/06/06 05:48:50 - mmengine - INFO - Epoch(train) [6][2900/4092] lr: 4.5067e-05 eta: 3:34:00 time: 0.6536 data_time: 0.0009 memory: 6319 loss: 0.1729 | |
| 2023/06/06 05:49:59 - mmengine - INFO - Epoch(train) [6][3000/4092] lr: 4.4730e-05 eta: 3:32:45 time: 0.7351 data_time: 0.0008 memory: 6319 loss: 0.1614 | |
| 2023/06/06 05:51:09 - mmengine - INFO - Epoch(train) [6][3100/4092] lr: 4.4394e-05 eta: 3:31:29 time: 0.6639 data_time: 0.0009 memory: 6319 loss: 0.1661 | |
| 2023/06/06 05:52:21 - mmengine - INFO - Epoch(train) [6][3200/4092] lr: 4.4059e-05 eta: 3:30:15 time: 0.7033 data_time: 0.0014 memory: 6319 loss: 0.1747 | |
| 2023/06/06 05:53:31 - mmengine - INFO - Epoch(train) [6][3300/4092] lr: 4.3724e-05 eta: 3:28:59 time: 0.6926 data_time: 0.0012 memory: 6319 loss: 0.1679 | |
| 2023/06/06 05:54:52 - mmengine - INFO - Epoch(train) [6][3400/4092] lr: 4.3390e-05 eta: 3:27:52 time: 0.6704 data_time: 0.0008 memory: 6319 loss: 0.1794 | |
| 2023/06/06 05:56:03 - mmengine - INFO - Epoch(train) [6][3500/4092] lr: 4.3056e-05 eta: 3:26:38 time: 0.7032 data_time: 0.1426 memory: 6319 loss: 0.2007 | |
| 2023/06/06 05:56:32 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 05:57:14 - mmengine - INFO - Epoch(train) [6][3600/4092] lr: 4.2724e-05 eta: 3:25:23 time: 0.6976 data_time: 0.1217 memory: 6319 loss: 0.1539 | |
| 2023/06/06 05:58:23 - mmengine - INFO - Epoch(train) [6][3700/4092] lr: 4.2392e-05 eta: 3:24:07 time: 0.7112 data_time: 0.2130 memory: 6319 loss: 0.1662 | |
| 2023/06/06 05:59:34 - mmengine - INFO - Epoch(train) [6][3800/4092] lr: 4.2060e-05 eta: 3:22:53 time: 0.7096 data_time: 0.2057 memory: 6319 loss: 0.1629 | |
| 2023/06/06 06:00:40 - mmengine - INFO - Epoch(train) [6][3900/4092] lr: 4.1730e-05 eta: 3:21:35 time: 0.7004 data_time: 0.3036 memory: 6319 loss: 0.1888 | |
| 2023/06/06 06:01:46 - mmengine - INFO - Epoch(train) [6][4000/4092] lr: 4.1400e-05 eta: 3:20:17 time: 0.6331 data_time: 0.2031 memory: 6319 loss: 0.1910 | |
| 2023/06/06 06:02:45 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 06:02:45 - mmengine - INFO - Saving checkpoint at 6 epochs | |
| 2023/06/06 06:03:25 - mmengine - INFO - Epoch(val) [6][100/119] eta: 0:00:06 time: 0.6358 data_time: 0.5457 memory: 6319 | |
| 2023/06/06 06:03:51 - mmengine - INFO - Epoch(val) [6][119/119] accuracy/top1: 91.2931 data_time: 0.3151 time: 0.4053 | |
| 2023/06/06 06:05:04 - mmengine - INFO - Epoch(train) [7][ 100/4092] lr: 4.0769e-05 eta: 3:17:51 time: 0.7606 data_time: 0.6124 memory: 6319 loss: 0.1614 | |
| 2023/06/06 06:06:14 - mmengine - INFO - Epoch(train) [7][ 200/4092] lr: 4.0442e-05 eta: 3:16:36 time: 0.7394 data_time: 0.5981 memory: 6319 loss: 0.1814 | |
| 2023/06/06 06:07:24 - mmengine - INFO - Epoch(train) [7][ 300/4092] lr: 4.0116e-05 eta: 3:15:21 time: 0.7033 data_time: 0.5624 memory: 6319 loss: 0.1696 | |
| 2023/06/06 06:08:36 - mmengine - INFO - Epoch(train) [7][ 400/4092] lr: 3.9790e-05 eta: 3:14:08 time: 0.6878 data_time: 0.5472 memory: 6319 loss: 0.1757 | |
| 2023/06/06 06:09:11 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 06:09:45 - mmengine - INFO - Epoch(train) [7][ 500/4092] lr: 3.9465e-05 eta: 3:12:52 time: 0.7204 data_time: 0.5762 memory: 6319 loss: 0.1739 | |
| 2023/06/06 06:10:57 - mmengine - INFO - Epoch(train) [7][ 600/4092] lr: 3.9141e-05 eta: 3:11:39 time: 0.6916 data_time: 0.2864 memory: 6319 loss: 0.1654 | |
| 2023/06/06 06:12:06 - mmengine - INFO - Epoch(train) [7][ 700/4092] lr: 3.8819e-05 eta: 3:10:23 time: 0.6537 data_time: 0.2910 memory: 6319 loss: 0.1748 | |
| 2023/06/06 06:13:17 - mmengine - INFO - Epoch(train) [7][ 800/4092] lr: 3.8497e-05 eta: 3:09:09 time: 0.7169 data_time: 0.2875 memory: 6319 loss: 0.1674 | |
| 2023/06/06 06:14:25 - mmengine - INFO - Epoch(train) [7][ 900/4092] lr: 3.8176e-05 eta: 3:07:53 time: 0.6882 data_time: 0.4835 memory: 6319 loss: 0.1732 | |
| 2023/06/06 06:15:34 - mmengine - INFO - Epoch(train) [7][1000/4092] lr: 3.7856e-05 eta: 3:06:38 time: 0.7223 data_time: 0.4904 memory: 6319 loss: 0.1564 | |
| 2023/06/06 06:16:43 - mmengine - INFO - Epoch(train) [7][1100/4092] lr: 3.7537e-05 eta: 3:05:23 time: 0.7047 data_time: 0.5655 memory: 6319 loss: 0.1568 | |
| 2023/06/06 06:17:55 - mmengine - INFO - Epoch(train) [7][1200/4092] lr: 3.7219e-05 eta: 3:04:10 time: 0.7078 data_time: 0.5667 memory: 6319 loss: 0.1883 | |
| 2023/06/06 06:19:06 - mmengine - INFO - Epoch(train) [7][1300/4092] lr: 3.6902e-05 eta: 3:02:56 time: 0.7599 data_time: 0.6204 memory: 6319 loss: 0.1799 | |
| 2023/06/06 06:20:18 - mmengine - INFO - Epoch(train) [7][1400/4092] lr: 3.6586e-05 eta: 3:01:42 time: 0.7283 data_time: 0.5881 memory: 6319 loss: 0.1736 | |
| 2023/06/06 06:20:52 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 06:21:27 - mmengine - INFO - Epoch(train) [7][1500/4092] lr: 3.6272e-05 eta: 3:00:27 time: 0.6873 data_time: 0.5465 memory: 6319 loss: 0.1626 | |
| 2023/06/06 06:22:36 - mmengine - INFO - Epoch(train) [7][1600/4092] lr: 3.5958e-05 eta: 2:59:12 time: 0.7093 data_time: 0.5690 memory: 6319 loss: 0.1856 | |
| 2023/06/06 06:23:46 - mmengine - INFO - Epoch(train) [7][1700/4092] lr: 3.5646e-05 eta: 2:57:58 time: 0.6989 data_time: 0.5577 memory: 6319 loss: 0.1672 | |
| 2023/06/06 06:24:59 - mmengine - INFO - Epoch(train) [7][1800/4092] lr: 3.5334e-05 eta: 2:56:45 time: 0.6936 data_time: 0.5526 memory: 6319 loss: 0.1687 | |
| 2023/06/06 06:26:08 - mmengine - INFO - Epoch(train) [7][1900/4092] lr: 3.5024e-05 eta: 2:55:31 time: 0.6866 data_time: 0.5436 memory: 6319 loss: 0.1585 | |
| 2023/06/06 06:27:18 - mmengine - INFO - Epoch(train) [7][2000/4092] lr: 3.4715e-05 eta: 2:54:16 time: 0.6927 data_time: 0.3834 memory: 6319 loss: 0.1630 | |
| 2023/06/06 06:28:29 - mmengine - INFO - Epoch(train) [7][2100/4092] lr: 3.4407e-05 eta: 2:53:02 time: 0.6746 data_time: 0.2664 memory: 6319 loss: 0.1672 | |
| 2023/06/06 06:29:37 - mmengine - INFO - Epoch(train) [7][2200/4092] lr: 3.4101e-05 eta: 2:51:47 time: 0.7114 data_time: 0.4306 memory: 6319 loss: 0.1680 | |
| 2023/06/06 06:30:46 - mmengine - INFO - Epoch(train) [7][2300/4092] lr: 3.3796e-05 eta: 2:50:32 time: 0.6894 data_time: 0.4674 memory: 6319 loss: 0.1741 | |
| 2023/06/06 06:31:57 - mmengine - INFO - Epoch(train) [7][2400/4092] lr: 3.3491e-05 eta: 2:49:19 time: 0.7370 data_time: 0.5961 memory: 6319 loss: 0.1940 | |
| 2023/06/06 06:32:31 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 06:33:07 - mmengine - INFO - Epoch(train) [7][2500/4092] lr: 3.3189e-05 eta: 2:48:05 time: 0.6764 data_time: 0.5340 memory: 6319 loss: 0.1768 | |
| 2023/06/06 06:34:16 - mmengine - INFO - Epoch(train) [7][2600/4092] lr: 3.2887e-05 eta: 2:46:50 time: 0.6853 data_time: 0.5447 memory: 6319 loss: 0.1754 | |
| 2023/06/06 06:35:26 - mmengine - INFO - Epoch(train) [7][2700/4092] lr: 3.2587e-05 eta: 2:45:36 time: 0.6966 data_time: 0.5490 memory: 6319 loss: 0.1641 | |
| 2023/06/06 06:36:46 - mmengine - INFO - Epoch(train) [7][2800/4092] lr: 3.2288e-05 eta: 2:44:27 time: 0.6792 data_time: 0.5382 memory: 6319 loss: 0.1656 | |
| 2023/06/06 06:37:57 - mmengine - INFO - Epoch(train) [7][2900/4092] lr: 3.1990e-05 eta: 2:43:13 time: 0.6708 data_time: 0.5310 memory: 6319 loss: 0.1735 | |
| 2023/06/06 06:39:06 - mmengine - INFO - Epoch(train) [7][3000/4092] lr: 3.1694e-05 eta: 2:41:59 time: 0.6380 data_time: 0.4976 memory: 6319 loss: 0.1528 | |
| 2023/06/06 06:40:16 - mmengine - INFO - Epoch(train) [7][3100/4092] lr: 3.1399e-05 eta: 2:40:45 time: 0.6814 data_time: 0.5413 memory: 6319 loss: 0.1775 | |
| 2023/06/06 06:41:26 - mmengine - INFO - Epoch(train) [7][3200/4092] lr: 3.1106e-05 eta: 2:39:31 time: 0.7420 data_time: 0.5962 memory: 6319 loss: 0.1535 | |
| 2023/06/06 06:42:36 - mmengine - INFO - Epoch(train) [7][3300/4092] lr: 3.0814e-05 eta: 2:38:17 time: 0.7288 data_time: 0.5891 memory: 6319 loss: 0.1650 | |
| 2023/06/06 06:43:45 - mmengine - INFO - Epoch(train) [7][3400/4092] lr: 3.0523e-05 eta: 2:37:02 time: 0.6746 data_time: 0.5338 memory: 6319 loss: 0.1656 | |
| 2023/06/06 06:44:19 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 06:44:55 - mmengine - INFO - Epoch(train) [7][3500/4092] lr: 3.0234e-05 eta: 2:35:49 time: 0.6423 data_time: 0.5015 memory: 6319 loss: 0.1782 | |
| 2023/06/06 06:46:05 - mmengine - INFO - Epoch(train) [7][3600/4092] lr: 2.9946e-05 eta: 2:34:34 time: 0.7051 data_time: 0.5658 memory: 6319 loss: 0.1733 | |
| 2023/06/06 06:47:15 - mmengine - INFO - Epoch(train) [7][3700/4092] lr: 2.9660e-05 eta: 2:33:21 time: 0.6682 data_time: 0.5270 memory: 6319 loss: 0.1612 | |
| 2023/06/06 06:48:24 - mmengine - INFO - Epoch(train) [7][3800/4092] lr: 2.9375e-05 eta: 2:32:07 time: 0.6775 data_time: 0.5378 memory: 6319 loss: 0.1648 | |
| 2023/06/06 06:49:34 - mmengine - INFO - Epoch(train) [7][3900/4092] lr: 2.9092e-05 eta: 2:30:53 time: 0.6970 data_time: 0.5563 memory: 6319 loss: 0.1648 | |
| 2023/06/06 06:50:42 - mmengine - INFO - Epoch(train) [7][4000/4092] lr: 2.8810e-05 eta: 2:29:38 time: 0.6848 data_time: 0.5439 memory: 6319 loss: 0.1697 | |
| 2023/06/06 06:51:47 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 06:51:47 - mmengine - INFO - Saving checkpoint at 7 epochs | |
| 2023/06/06 06:52:28 - mmengine - INFO - Epoch(val) [7][100/119] eta: 0:00:06 time: 0.6404 data_time: 0.5521 memory: 6319 | |
| 2023/06/06 06:52:55 - mmengine - INFO - Epoch(val) [7][119/119] accuracy/top1: 91.7349 data_time: 0.3212 time: 0.4097 | |
| 2023/06/06 06:54:07 - mmengine - INFO - Epoch(train) [8][ 100/4092] lr: 2.8274e-05 eta: 2:27:18 time: 0.6692 data_time: 0.4350 memory: 6319 loss: 0.1593 | |
| 2023/06/06 06:55:17 - mmengine - INFO - Epoch(train) [8][ 200/4092] lr: 2.7997e-05 eta: 2:26:04 time: 0.6596 data_time: 0.4416 memory: 6319 loss: 0.1724 | |
| 2023/06/06 06:56:27 - mmengine - INFO - Epoch(train) [8][ 300/4092] lr: 2.7721e-05 eta: 2:24:50 time: 0.6673 data_time: 0.4922 memory: 6319 loss: 0.1620 | |
| 2023/06/06 06:57:08 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 06:57:38 - mmengine - INFO - Epoch(train) [8][ 400/4092] lr: 2.7447e-05 eta: 2:23:37 time: 0.7153 data_time: 0.3490 memory: 6319 loss: 0.1694 | |
| 2023/06/06 06:58:47 - mmengine - INFO - Epoch(train) [8][ 500/4092] lr: 2.7175e-05 eta: 2:22:24 time: 0.7138 data_time: 0.4309 memory: 6319 loss: 0.1526 | |
| 2023/06/06 06:59:56 - mmengine - INFO - Epoch(train) [8][ 600/4092] lr: 2.6904e-05 eta: 2:21:10 time: 0.6754 data_time: 0.1599 memory: 6319 loss: 0.1712 | |
| 2023/06/06 07:01:03 - mmengine - INFO - Epoch(train) [8][ 700/4092] lr: 2.6635e-05 eta: 2:19:55 time: 0.6670 data_time: 0.0714 memory: 6319 loss: 0.1931 | |
| 2023/06/06 07:02:13 - mmengine - INFO - Epoch(train) [8][ 800/4092] lr: 2.6368e-05 eta: 2:18:41 time: 0.7235 data_time: 0.1936 memory: 6319 loss: 0.1605 | |
| 2023/06/06 07:03:24 - mmengine - INFO - Epoch(train) [8][ 900/4092] lr: 2.6102e-05 eta: 2:17:28 time: 0.7862 data_time: 0.3424 memory: 6319 loss: 0.1638 | |
| 2023/06/06 07:04:36 - mmengine - INFO - Epoch(train) [8][1000/4092] lr: 2.5838e-05 eta: 2:16:16 time: 0.7583 data_time: 0.0010 memory: 6319 loss: 0.1731 | |
| 2023/06/06 07:05:47 - mmengine - INFO - Epoch(train) [8][1100/4092] lr: 2.5576e-05 eta: 2:15:02 time: 0.7509 data_time: 0.0009 memory: 6319 loss: 0.1605 | |
| 2023/06/06 07:07:00 - mmengine - INFO - Epoch(train) [8][1200/4092] lr: 2.5315e-05 eta: 2:13:50 time: 0.7036 data_time: 0.0010 memory: 6319 loss: 0.1636 | |
| 2023/06/06 07:08:10 - mmengine - INFO - Epoch(train) [8][1300/4092] lr: 2.5056e-05 eta: 2:12:37 time: 0.6537 data_time: 0.0011 memory: 6319 loss: 0.1692 | |
| 2023/06/06 07:08:51 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 07:09:21 - mmengine - INFO - Epoch(train) [8][1400/4092] lr: 2.4799e-05 eta: 2:11:23 time: 0.7158 data_time: 0.0011 memory: 6319 loss: 0.1660 | |
| 2023/06/06 07:10:32 - mmengine - INFO - Epoch(train) [8][1500/4092] lr: 2.4544e-05 eta: 2:10:10 time: 0.6812 data_time: 0.0008 memory: 6319 loss: 0.1564 | |
| 2023/06/06 07:11:42 - mmengine - INFO - Epoch(train) [8][1600/4092] lr: 2.4291e-05 eta: 2:08:57 time: 0.7033 data_time: 0.0010 memory: 6319 loss: 0.1701 | |
| 2023/06/06 07:12:51 - mmengine - INFO - Epoch(train) [8][1700/4092] lr: 2.4039e-05 eta: 2:07:44 time: 0.7390 data_time: 0.0010 memory: 6319 loss: 0.1743 | |
| 2023/06/06 07:14:01 - mmengine - INFO - Epoch(train) [8][1800/4092] lr: 2.3789e-05 eta: 2:06:30 time: 0.6613 data_time: 0.0008 memory: 6319 loss: 0.1634 | |
| 2023/06/06 07:15:10 - mmengine - INFO - Epoch(train) [8][1900/4092] lr: 2.3541e-05 eta: 2:05:17 time: 0.7069 data_time: 0.0012 memory: 6319 loss: 0.1791 | |
| 2023/06/06 07:16:19 - mmengine - INFO - Epoch(train) [8][2000/4092] lr: 2.3295e-05 eta: 2:04:03 time: 0.7016 data_time: 0.0008 memory: 6319 loss: 0.1675 | |
| 2023/06/06 07:17:32 - mmengine - INFO - Epoch(train) [8][2100/4092] lr: 2.3051e-05 eta: 2:02:50 time: 0.7067 data_time: 0.0009 memory: 6319 loss: 0.1815 | |
| 2023/06/06 07:18:41 - mmengine - INFO - Epoch(train) [8][2200/4092] lr: 2.2809e-05 eta: 2:01:37 time: 0.7133 data_time: 0.0010 memory: 6319 loss: 0.1514 | |
| 2023/06/06 07:19:53 - mmengine - INFO - Epoch(train) [8][2300/4092] lr: 2.2568e-05 eta: 2:00:24 time: 0.6976 data_time: 0.0009 memory: 6319 loss: 0.1597 | |
| 2023/06/06 07:20:35 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 07:21:06 - mmengine - INFO - Epoch(train) [8][2400/4092] lr: 2.2330e-05 eta: 1:59:12 time: 0.9631 data_time: 0.0009 memory: 6319 loss: 0.1851 | |
| 2023/06/06 07:22:17 - mmengine - INFO - Epoch(train) [8][2500/4092] lr: 2.2093e-05 eta: 1:57:59 time: 0.7878 data_time: 0.0009 memory: 6319 loss: 0.1673 | |
| 2023/06/06 07:23:27 - mmengine - INFO - Epoch(train) [8][2600/4092] lr: 2.1858e-05 eta: 1:56:46 time: 0.7213 data_time: 0.0009 memory: 6319 loss: 0.1604 | |
| 2023/06/06 07:24:36 - mmengine - INFO - Epoch(train) [8][2700/4092] lr: 2.1626e-05 eta: 1:55:33 time: 0.6758 data_time: 0.0011 memory: 6319 loss: 0.1625 | |
| 2023/06/06 07:25:46 - mmengine - INFO - Epoch(train) [8][2800/4092] lr: 2.1395e-05 eta: 1:54:20 time: 0.7037 data_time: 0.0010 memory: 6319 loss: 0.1778 | |
| 2023/06/06 07:26:59 - mmengine - INFO - Epoch(train) [8][2900/4092] lr: 2.1166e-05 eta: 1:53:07 time: 0.6738 data_time: 0.0010 memory: 6319 loss: 0.1790 | |
| 2023/06/06 07:28:08 - mmengine - INFO - Epoch(train) [8][3000/4092] lr: 2.0939e-05 eta: 1:51:54 time: 0.6913 data_time: 0.0009 memory: 6319 loss: 0.1610 | |
| 2023/06/06 07:29:19 - mmengine - INFO - Epoch(train) [8][3100/4092] lr: 2.0715e-05 eta: 1:50:41 time: 0.7478 data_time: 0.0007 memory: 6319 loss: 0.1729 | |
| 2023/06/06 07:30:29 - mmengine - INFO - Epoch(train) [8][3200/4092] lr: 2.0492e-05 eta: 1:49:28 time: 0.7185 data_time: 0.0010 memory: 6319 loss: 0.1998 | |
| 2023/06/06 07:31:40 - mmengine - INFO - Epoch(train) [8][3300/4092] lr: 2.0271e-05 eta: 1:48:15 time: 0.7237 data_time: 0.0009 memory: 6319 loss: 0.1761 | |
| 2023/06/06 07:32:22 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 07:32:50 - mmengine - INFO - Epoch(train) [8][3400/4092] lr: 2.0052e-05 eta: 1:47:03 time: 0.7074 data_time: 0.0008 memory: 6319 loss: 0.1756 | |
| 2023/06/06 07:34:02 - mmengine - INFO - Epoch(train) [8][3500/4092] lr: 1.9836e-05 eta: 1:45:50 time: 0.7198 data_time: 0.0008 memory: 6319 loss: 0.1502 | |
| 2023/06/06 07:35:11 - mmengine - INFO - Epoch(train) [8][3600/4092] lr: 1.9621e-05 eta: 1:44:37 time: 0.7024 data_time: 0.0009 memory: 6319 loss: 0.1591 | |
| 2023/06/06 07:36:22 - mmengine - INFO - Epoch(train) [8][3700/4092] lr: 1.9409e-05 eta: 1:43:24 time: 0.7456 data_time: 0.0009 memory: 6319 loss: 0.1811 | |
| 2023/06/06 07:37:35 - mmengine - INFO - Epoch(train) [8][3800/4092] lr: 1.9198e-05 eta: 1:42:12 time: 1.0399 data_time: 0.0009 memory: 6319 loss: 0.1697 | |
| 2023/06/06 07:38:45 - mmengine - INFO - Epoch(train) [8][3900/4092] lr: 1.8990e-05 eta: 1:40:59 time: 0.7251 data_time: 0.0009 memory: 6319 loss: 0.1785 | |
| 2023/06/06 07:39:54 - mmengine - INFO - Epoch(train) [8][4000/4092] lr: 1.8784e-05 eta: 1:39:46 time: 0.7142 data_time: 0.0008 memory: 6319 loss: 0.1625 | |
| 2023/06/06 07:40:57 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 07:40:57 - mmengine - INFO - Saving checkpoint at 8 epochs | |
| 2023/06/06 07:41:37 - mmengine - INFO - Epoch(val) [8][100/119] eta: 0:00:06 time: 0.6134 data_time: 0.5254 memory: 6319 | |
| 2023/06/06 07:42:03 - mmengine - INFO - Epoch(val) [8][119/119] accuracy/top1: 92.9067 data_time: 0.3212 time: 0.4082 | |
| 2023/06/06 07:43:14 - mmengine - INFO - Epoch(train) [9][ 100/4092] lr: 1.8394e-05 eta: 1:37:26 time: 0.7207 data_time: 0.1873 memory: 6319 loss: 0.1546 | |
| 2023/06/06 07:44:25 - mmengine - INFO - Epoch(train) [9][ 200/4092] lr: 1.8194e-05 eta: 1:36:13 time: 0.6816 data_time: 0.1899 memory: 6319 loss: 0.1688 | |
| 2023/06/06 07:45:15 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 07:45:37 - mmengine - INFO - Epoch(train) [9][ 300/4092] lr: 1.7997e-05 eta: 1:35:00 time: 0.6806 data_time: 0.3172 memory: 6319 loss: 0.1572 | |
| 2023/06/06 07:46:47 - mmengine - INFO - Epoch(train) [9][ 400/4092] lr: 1.7801e-05 eta: 1:33:48 time: 0.7137 data_time: 0.3877 memory: 6319 loss: 0.1750 | |
| 2023/06/06 07:47:57 - mmengine - INFO - Epoch(train) [9][ 500/4092] lr: 1.7608e-05 eta: 1:32:35 time: 0.6825 data_time: 0.2948 memory: 6319 loss: 0.1449 | |
| 2023/06/06 07:49:07 - mmengine - INFO - Epoch(train) [9][ 600/4092] lr: 1.7417e-05 eta: 1:31:22 time: 0.7056 data_time: 0.1645 memory: 6319 loss: 0.1794 | |
| 2023/06/06 07:50:16 - mmengine - INFO - Epoch(train) [9][ 700/4092] lr: 1.7228e-05 eta: 1:30:09 time: 0.7026 data_time: 0.0008 memory: 6319 loss: 0.1795 | |
| 2023/06/06 07:51:27 - mmengine - INFO - Epoch(train) [9][ 800/4092] lr: 1.7041e-05 eta: 1:28:57 time: 0.7037 data_time: 0.0009 memory: 6319 loss: 0.1639 | |
| 2023/06/06 07:52:38 - mmengine - INFO - Epoch(train) [9][ 900/4092] lr: 1.6857e-05 eta: 1:27:44 time: 0.7312 data_time: 0.2464 memory: 6319 loss: 0.1575 | |
| 2023/06/06 07:53:48 - mmengine - INFO - Epoch(train) [9][1000/4092] lr: 1.6675e-05 eta: 1:26:31 time: 0.6530 data_time: 0.1508 memory: 6319 loss: 0.1495 | |
| 2023/06/06 07:54:58 - mmengine - INFO - Epoch(train) [9][1100/4092] lr: 1.6495e-05 eta: 1:25:18 time: 0.6659 data_time: 0.2675 memory: 6319 loss: 0.1704 | |
| 2023/06/06 07:56:09 - mmengine - INFO - Epoch(train) [9][1200/4092] lr: 1.6317e-05 eta: 1:24:06 time: 0.7438 data_time: 0.2079 memory: 6319 loss: 0.1612 | |
| 2023/06/06 07:56:57 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 07:57:20 - mmengine - INFO - Epoch(train) [9][1300/4092] lr: 1.6142e-05 eta: 1:22:53 time: 0.7149 data_time: 0.0339 memory: 6319 loss: 0.1829 | |
| 2023/06/06 07:58:32 - mmengine - INFO - Epoch(train) [9][1400/4092] lr: 1.5969e-05 eta: 1:21:41 time: 0.7060 data_time: 0.0010 memory: 6319 loss: 0.1559 | |
| 2023/06/06 07:59:42 - mmengine - INFO - Epoch(train) [9][1500/4092] lr: 1.5798e-05 eta: 1:20:28 time: 0.6935 data_time: 0.0008 memory: 6319 loss: 0.1688 | |
| 2023/06/06 08:00:52 - mmengine - INFO - Epoch(train) [9][1600/4092] lr: 1.5629e-05 eta: 1:19:16 time: 0.6837 data_time: 0.0010 memory: 6319 loss: 0.1697 | |
| 2023/06/06 08:02:04 - mmengine - INFO - Epoch(train) [9][1700/4092] lr: 1.5463e-05 eta: 1:18:03 time: 0.7375 data_time: 0.0009 memory: 6319 loss: 0.1715 | |
| 2023/06/06 08:03:15 - mmengine - INFO - Epoch(train) [9][1800/4092] lr: 1.5299e-05 eta: 1:16:51 time: 0.7000 data_time: 0.0011 memory: 6319 loss: 0.1785 | |
| 2023/06/06 08:04:25 - mmengine - INFO - Epoch(train) [9][1900/4092] lr: 1.5138e-05 eta: 1:15:38 time: 0.7424 data_time: 0.0008 memory: 6319 loss: 0.1638 | |
| 2023/06/06 08:05:35 - mmengine - INFO - Epoch(train) [9][2000/4092] lr: 1.4979e-05 eta: 1:14:26 time: 0.6368 data_time: 0.0007 memory: 6319 loss: 0.1598 | |
| 2023/06/06 08:06:45 - mmengine - INFO - Epoch(train) [9][2100/4092] lr: 1.4822e-05 eta: 1:13:13 time: 0.6890 data_time: 0.0009 memory: 6319 loss: 0.1589 | |
| 2023/06/06 08:07:54 - mmengine - INFO - Epoch(train) [9][2200/4092] lr: 1.4668e-05 eta: 1:12:00 time: 0.7143 data_time: 0.0009 memory: 6319 loss: 0.1602 | |
| 2023/06/06 08:08:42 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 08:09:03 - mmengine - INFO - Epoch(train) [9][2300/4092] lr: 1.4515e-05 eta: 1:10:48 time: 0.7422 data_time: 0.0009 memory: 6319 loss: 0.1585 | |
| 2023/06/06 08:10:12 - mmengine - INFO - Epoch(train) [9][2400/4092] lr: 1.4366e-05 eta: 1:09:35 time: 0.7104 data_time: 0.0008 memory: 6319 loss: 0.1651 | |
| 2023/06/06 08:11:23 - mmengine - INFO - Epoch(train) [9][2500/4092] lr: 1.4219e-05 eta: 1:08:22 time: 0.7485 data_time: 0.0009 memory: 6319 loss: 0.1632 | |
| 2023/06/06 08:12:34 - mmengine - INFO - Epoch(train) [9][2600/4092] lr: 1.4074e-05 eta: 1:07:10 time: 0.6742 data_time: 0.0010 memory: 6319 loss: 0.1598 | |
| 2023/06/06 08:13:44 - mmengine - INFO - Epoch(train) [9][2700/4092] lr: 1.3931e-05 eta: 1:05:58 time: 0.6640 data_time: 0.0009 memory: 6319 loss: 0.1691 | |
| 2023/06/06 08:14:53 - mmengine - INFO - Epoch(train) [9][2800/4092] lr: 1.3791e-05 eta: 1:04:45 time: 0.6555 data_time: 0.0011 memory: 6319 loss: 0.1560 | |
| 2023/06/06 08:16:04 - mmengine - INFO - Epoch(train) [9][2900/4092] lr: 1.3654e-05 eta: 1:03:33 time: 0.6874 data_time: 0.0011 memory: 6319 loss: 0.1809 | |
| 2023/06/06 08:17:14 - mmengine - INFO - Epoch(train) [9][3000/4092] lr: 1.3519e-05 eta: 1:02:20 time: 0.7585 data_time: 0.0009 memory: 6319 loss: 0.1703 | |
| 2023/06/06 08:18:23 - mmengine - INFO - Epoch(train) [9][3100/4092] lr: 1.3386e-05 eta: 1:01:08 time: 0.7467 data_time: 0.2073 memory: 6319 loss: 0.1638 | |
| 2023/06/06 08:19:35 - mmengine - INFO - Epoch(train) [9][3200/4092] lr: 1.3256e-05 eta: 0:59:55 time: 0.7195 data_time: 0.1893 memory: 6319 loss: 0.1659 | |
| 2023/06/06 08:20:22 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 08:20:48 - mmengine - INFO - Epoch(train) [9][3300/4092] lr: 1.3128e-05 eta: 0:58:43 time: 0.7629 data_time: 0.0010 memory: 6319 loss: 0.1620 | |
| 2023/06/06 08:21:57 - mmengine - INFO - Epoch(train) [9][3400/4092] lr: 1.3003e-05 eta: 0:57:31 time: 0.7198 data_time: 0.0009 memory: 6319 loss: 0.1666 | |
| 2023/06/06 08:23:08 - mmengine - INFO - Epoch(train) [9][3500/4092] lr: 1.2880e-05 eta: 0:56:18 time: 0.7428 data_time: 0.0009 memory: 6319 loss: 0.1598 | |
| 2023/06/06 08:24:19 - mmengine - INFO - Epoch(train) [9][3600/4092] lr: 1.2759e-05 eta: 0:55:06 time: 0.7897 data_time: 0.0009 memory: 6319 loss: 0.1594 | |
| 2023/06/06 08:25:30 - mmengine - INFO - Epoch(train) [9][3700/4092] lr: 1.2641e-05 eta: 0:53:54 time: 0.6903 data_time: 0.0008 memory: 6319 loss: 0.1692 | |
| 2023/06/06 08:26:40 - mmengine - INFO - Epoch(train) [9][3800/4092] lr: 1.2526e-05 eta: 0:52:41 time: 0.6901 data_time: 0.0008 memory: 6319 loss: 0.1662 | |
| 2023/06/06 08:27:49 - mmengine - INFO - Epoch(train) [9][3900/4092] lr: 1.2413e-05 eta: 0:51:29 time: 0.6892 data_time: 0.0009 memory: 6319 loss: 0.1717 | |
| 2023/06/06 08:29:02 - mmengine - INFO - Epoch(train) [9][4000/4092] lr: 1.2303e-05 eta: 0:50:17 time: 0.7204 data_time: 0.0008 memory: 6319 loss: 0.1668 | |
| 2023/06/06 08:30:05 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 08:30:05 - mmengine - INFO - Saving checkpoint at 9 epochs | |
| 2023/06/06 08:30:45 - mmengine - INFO - Epoch(val) [9][100/119] eta: 0:00:06 time: 0.6766 data_time: 0.5872 memory: 6319 | |
| 2023/06/06 08:31:10 - mmengine - INFO - Epoch(val) [9][119/119] accuracy/top1: 93.0159 data_time: 0.3134 time: 0.4029 | |
| 2023/06/06 08:32:23 - mmengine - INFO - Epoch(train) [10][ 100/4092] lr: 1.2098e-05 eta: 0:47:58 time: 0.6929 data_time: 0.0987 memory: 6319 loss: 0.1676 | |
| 2023/06/06 08:33:14 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 08:33:33 - mmengine - INFO - Epoch(train) [10][ 200/4092] lr: 1.1995e-05 eta: 0:46:46 time: 0.7161 data_time: 0.2022 memory: 6319 loss: 0.1678 | |
| 2023/06/06 08:34:42 - mmengine - INFO - Epoch(train) [10][ 300/4092] lr: 1.1895e-05 eta: 0:45:33 time: 0.7055 data_time: 0.0009 memory: 6319 loss: 0.1426 | |
| 2023/06/06 08:35:54 - mmengine - INFO - Epoch(train) [10][ 400/4092] lr: 1.1797e-05 eta: 0:44:21 time: 0.7239 data_time: 0.0009 memory: 6319 loss: 0.1644 | |
| 2023/06/06 08:37:04 - mmengine - INFO - Epoch(train) [10][ 500/4092] lr: 1.1701e-05 eta: 0:43:09 time: 0.6705 data_time: 0.0009 memory: 6319 loss: 0.1632 | |
| 2023/06/06 08:38:15 - mmengine - INFO - Epoch(train) [10][ 600/4092] lr: 1.1608e-05 eta: 0:41:57 time: 0.6627 data_time: 0.0010 memory: 6319 loss: 0.1848 | |
| 2023/06/06 08:39:31 - mmengine - INFO - Epoch(train) [10][ 700/4092] lr: 1.1518e-05 eta: 0:40:45 time: 0.6897 data_time: 0.0009 memory: 6319 loss: 0.1533 | |
| 2023/06/06 08:40:41 - mmengine - INFO - Epoch(train) [10][ 800/4092] lr: 1.1430e-05 eta: 0:39:33 time: 0.7070 data_time: 0.0009 memory: 6319 loss: 0.1482 | |
| 2023/06/06 08:41:50 - mmengine - INFO - Epoch(train) [10][ 900/4092] lr: 1.1345e-05 eta: 0:38:20 time: 0.7723 data_time: 0.0010 memory: 6319 loss: 0.1497 | |
| 2023/06/06 08:42:59 - mmengine - INFO - Epoch(train) [10][1000/4092] lr: 1.1263e-05 eta: 0:37:08 time: 0.6892 data_time: 0.0009 memory: 6319 loss: 0.1525 | |
| 2023/06/06 08:44:09 - mmengine - INFO - Epoch(train) [10][1100/4092] lr: 1.1183e-05 eta: 0:35:56 time: 0.6578 data_time: 0.0009 memory: 6319 loss: 0.1548 | |
| 2023/06/06 08:44:58 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 08:45:19 - mmengine - INFO - Epoch(train) [10][1200/4092] lr: 1.1105e-05 eta: 0:34:44 time: 0.6491 data_time: 0.0010 memory: 6319 loss: 0.1501 | |
| 2023/06/06 08:46:29 - mmengine - INFO - Epoch(train) [10][1300/4092] lr: 1.1031e-05 eta: 0:33:31 time: 0.7208 data_time: 0.0009 memory: 6319 loss: 0.1507 | |
| 2023/06/06 08:47:42 - mmengine - INFO - Epoch(train) [10][1400/4092] lr: 1.0958e-05 eta: 0:32:19 time: 0.6935 data_time: 0.0011 memory: 6319 loss: 0.1692 | |
| 2023/06/06 08:48:52 - mmengine - INFO - Epoch(train) [10][1500/4092] lr: 1.0889e-05 eta: 0:31:07 time: 0.6928 data_time: 0.0010 memory: 6319 loss: 0.1632 | |
| 2023/06/06 08:50:04 - mmengine - INFO - Epoch(train) [10][1600/4092] lr: 1.0822e-05 eta: 0:29:55 time: 0.7008 data_time: 0.0010 memory: 6319 loss: 0.1710 | |
| 2023/06/06 08:51:15 - mmengine - INFO - Epoch(train) [10][1700/4092] lr: 1.0757e-05 eta: 0:28:43 time: 0.7213 data_time: 0.0010 memory: 6319 loss: 0.1795 | |
| 2023/06/06 08:52:25 - mmengine - INFO - Epoch(train) [10][1800/4092] lr: 1.0696e-05 eta: 0:27:31 time: 0.7137 data_time: 0.0009 memory: 6319 loss: 0.1598 | |
| 2023/06/06 08:53:37 - mmengine - INFO - Epoch(train) [10][1900/4092] lr: 1.0636e-05 eta: 0:26:19 time: 0.7228 data_time: 0.0010 memory: 6319 loss: 0.1737 | |
| 2023/06/06 08:54:49 - mmengine - INFO - Epoch(train) [10][2000/4092] lr: 1.0580e-05 eta: 0:25:07 time: 0.6470 data_time: 0.0009 memory: 6319 loss: 0.1519 | |
| 2023/06/06 08:55:58 - mmengine - INFO - Epoch(train) [10][2100/4092] lr: 1.0526e-05 eta: 0:23:55 time: 0.7224 data_time: 0.0009 memory: 6319 loss: 0.1799 | |
| 2023/06/06 08:56:49 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 08:57:11 - mmengine - INFO - Epoch(train) [10][2200/4092] lr: 1.0474e-05 eta: 0:22:43 time: 0.7347 data_time: 0.0009 memory: 6319 loss: 0.1714 | |
| 2023/06/06 08:58:23 - mmengine - INFO - Epoch(train) [10][2300/4092] lr: 1.0426e-05 eta: 0:21:31 time: 0.6977 data_time: 0.0010 memory: 6319 loss: 0.1523 | |
| 2023/06/06 08:59:36 - mmengine - INFO - Epoch(train) [10][2400/4092] lr: 1.0380e-05 eta: 0:20:19 time: 0.7016 data_time: 0.0009 memory: 6319 loss: 0.1691 | |
| 2023/06/06 09:00:45 - mmengine - INFO - Epoch(train) [10][2500/4092] lr: 1.0336e-05 eta: 0:19:06 time: 0.6834 data_time: 0.0009 memory: 6319 loss: 0.1661 | |
| 2023/06/06 09:01:54 - mmengine - INFO - Epoch(train) [10][2600/4092] lr: 1.0295e-05 eta: 0:17:54 time: 0.6715 data_time: 0.0008 memory: 6319 loss: 0.1630 | |
| 2023/06/06 09:03:06 - mmengine - INFO - Epoch(train) [10][2700/4092] lr: 1.0257e-05 eta: 0:16:42 time: 0.6889 data_time: 0.0008 memory: 6319 loss: 0.1664 | |
| 2023/06/06 09:04:17 - mmengine - INFO - Epoch(train) [10][2800/4092] lr: 1.0222e-05 eta: 0:15:30 time: 0.7031 data_time: 0.0009 memory: 6319 loss: 0.1494 | |
| 2023/06/06 09:05:28 - mmengine - INFO - Epoch(train) [10][2900/4092] lr: 1.0189e-05 eta: 0:14:18 time: 0.7960 data_time: 0.0008 memory: 6319 loss: 0.1693 | |
| 2023/06/06 09:06:37 - mmengine - INFO - Epoch(train) [10][3000/4092] lr: 1.0158e-05 eta: 0:13:06 time: 0.6756 data_time: 0.0008 memory: 6319 loss: 0.1615 | |
| 2023/06/06 09:07:47 - mmengine - INFO - Epoch(train) [10][3100/4092] lr: 1.0131e-05 eta: 0:11:54 time: 0.6999 data_time: 0.0010 memory: 6319 loss: 0.1593 | |
| 2023/06/06 09:08:36 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 09:08:56 - mmengine - INFO - Epoch(train) [10][3200/4092] lr: 1.0106e-05 eta: 0:10:42 time: 0.7180 data_time: 0.0009 memory: 6319 loss: 0.1709 | |
| 2023/06/06 09:10:09 - mmengine - INFO - Epoch(train) [10][3300/4092] lr: 1.0083e-05 eta: 0:09:30 time: 0.7259 data_time: 0.0009 memory: 6319 loss: 0.1506 | |
| 2023/06/06 09:11:18 - mmengine - INFO - Epoch(train) [10][3400/4092] lr: 1.0064e-05 eta: 0:08:18 time: 0.6662 data_time: 0.0012 memory: 6319 loss: 0.1570 | |
| 2023/06/06 09:12:31 - mmengine - INFO - Epoch(train) [10][3500/4092] lr: 1.0047e-05 eta: 0:07:06 time: 0.6967 data_time: 0.0012 memory: 6319 loss: 0.1607 | |
| 2023/06/06 09:13:44 - mmengine - INFO - Epoch(train) [10][3600/4092] lr: 1.0032e-05 eta: 0:05:54 time: 0.9085 data_time: 0.0008 memory: 6319 loss: 0.1496 | |
| 2023/06/06 09:14:55 - mmengine - INFO - Epoch(train) [10][3700/4092] lr: 1.0020e-05 eta: 0:04:42 time: 0.7271 data_time: 0.0010 memory: 6319 loss: 0.1523 | |
| 2023/06/06 09:16:09 - mmengine - INFO - Epoch(train) [10][3800/4092] lr: 1.0011e-05 eta: 0:03:30 time: 0.7483 data_time: 0.0010 memory: 6319 loss: 0.1580 | |
| 2023/06/06 09:17:19 - mmengine - INFO - Epoch(train) [10][3900/4092] lr: 1.0005e-05 eta: 0:02:18 time: 0.7160 data_time: 0.0009 memory: 6319 loss: 0.1690 | |
| 2023/06/06 09:18:28 - mmengine - INFO - Epoch(train) [10][4000/4092] lr: 1.0001e-05 eta: 0:01:06 time: 0.6836 data_time: 0.0009 memory: 6319 loss: 0.1600 | |
| 2023/06/06 09:19:36 - mmengine - INFO - Exp name: resnet50_2xb256_all2_1m_lr1e-4_aug_1e-1_20230606_005743 | |
| 2023/06/06 09:19:36 - mmengine - INFO - Saving checkpoint at 10 epochs | |
| 2023/06/06 09:20:17 - mmengine - INFO - Epoch(val) [10][100/119] eta: 0:00:06 time: 0.7365 data_time: 0.6372 memory: 6319 | |
| 2023/06/06 09:20:43 - mmengine - INFO - Epoch(val) [10][119/119] accuracy/top1: 92.8636 data_time: 0.3246 time: 0.4125 | |