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open-mmlab / mmselfsup

OpenMMLab Self-Supervised Learning Toolbox and Benchmark
https://mmselfsup.readthedocs.io/en/latest/
Apache License 2.0
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How should I pretrain movoV3 resnet50 with my own dataset? #735

Open ACbccc opened 1 year ago

ACbccc commented 1 year ago
  1. I have searched related issues but cannot get the expected help.
  2. I have read the FAQ documentation but cannot get the expected help.

Hello, I want to train MocoV3 with my own dataset,

1 how should I organize the structure of my dataset? I see the examples of imagenet but imagenet is not an unsupervised set, So what should I do? could I put every image to one folder? 2 What are the most suitable versions of pythoch and cuda for mmselfsup currently? I have installed cuda10.2+pythoch1.6 and cuda11.7+pythoch2.0, but my training has not been successful

ACbccc commented 1 year ago

My environment: cuda10.2+pythoch1.6

my config:

# >>>>>>>>>>>>>>>>>>>>> Start of Changed >>>>>>>>>>>>>>>>>>>>>>>>>
_base_ = [
    '../_base_/models/mocov3_resnet50.py',
    # '../_base_/datasets/imagenet_mocov3.py',
    '../_base_/schedules/lars_coslr-200e_in1k.py',
    '../_base_/default_runtime.py',
]

# custom dataset
dataset_type = 'mmcls.CustomDataset'

data_root = '/data/代码/mmselfsup/data/'  #I put all my unsupervised jpgs into fold  '/data/代码/mmselfsup/data/movov3'
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='RandomResizedCrop',
        size=224,
        scale=(0.2, 1.0),
        backend='pillow',
        interpolation='bicubic'),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackSelfSupInputs', meta_keys=['img_path'])
]

# dataset 8 x 512
train_dataloader = dict(
    batch_size=512,
    num_workers=8,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    collate_fn=dict(type='default_collate'),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        # ann_file='meta/train.txt', # removed if you don't have the annotation file
        data_prefix=dict(img_path='./'),
        pipeline=train_pipeline))
# <<<<<<<<<<<<<<<<<<<<<< End of Changed <<<<<<<<<<<<<<<<<<<<<<<<<<<

# optimizer
optimizer = dict(type='LARS', lr=9.6, weight_decay=1e-6, momentum=0.9)
optim_wrapper = dict(
    type='AmpOptimWrapper',
    loss_scale='dynamic',
    optimizer=optimizer,
    paramwise_cfg=dict(
        custom_keys={
            'bn': dict(decay_mult=0, lars_exclude=True),
            'bias': dict(decay_mult=0, lars_exclude=True),
            # bn layer in ResNet block downsample module
            'downsample.1': dict(decay_mult=0, lars_exclude=True),
        }),
)

# learning rate scheduler
param_scheduler = [
    dict(
        type='LinearLR',
        start_factor=1e-4,
        by_epoch=True,
        begin=0,
        end=10,
        convert_to_iter_based=True),
    dict(
        type='CosineAnnealingLR',
        T_max=90,
        by_epoch=True,
        begin=10,
        end=100,
        convert_to_iter_based=True)
]

# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=100)

# runtime settings
# only keeps the latest 3 checkpoints
default_hooks = dict(checkpoint=dict(max_keep_ckpts=3))

Error log:

(py16cu102) think@think-C246-WU4:/data/代码/mmselfsup$ CUDA_VISIBLE_DEVICES=0,1 bash tools/dist_train.sh configs/selfsup/mocov3/mocov3_resnet50_8xb512-amp-coslr-100e_in1k.py 2
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
*****************************************
/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/mmengine/model/utils.py:138: UserWarning: Cannot import torch.fx, `merge_dict` is a simple function to merge multiple dicts
  warnings.warn('Cannot import torch.fx, `merge_dict` is a simple function '
/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/mmengine/model/utils.py:138: UserWarning: Cannot import torch.fx, `merge_dict` is a simple function to merge multiple dicts
  warnings.warn('Cannot import torch.fx, `merge_dict` is a simple function '
/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
  warnings.warn(
/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/mmengine/utils/dl_utils/setup_env.py:56: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
  warnings.warn(
04/02 17:15:35 - mmengine - INFO - 
------------------------------------------------------------
System environment:
    sys.platform: linux
    Python: 3.8.16 (default, Mar  2 2023, 03:21:46) [GCC 11.2.0]
    CUDA available: True
    numpy_random_seed: 760565087
    GPU 0,1: NVIDIA GeForce RTX 2080 Ti
    CUDA_HOME: :/usr/local/cuda
    GCC: gcc (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609
    PyTorch: 1.6.0
    PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2019.0.5 Product Build 20190808 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v1.5.0 (Git Hash e2ac1fac44c5078ca927cb9b90e1b3066a0b2ed0)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.2
  - 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_70,code=sm_70;-gencode;arch=compute_75,code=sm_75
  - CuDNN 7.6.5
  - Magma 2.5.2
  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -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 -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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_STATIC_DISPATCH=OFF, 

    TorchVision: 0.7.0
    OpenCV: 4.7.0
    MMEngine: 0.7.0

Runtime environment:
    cudnn_benchmark: False
    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
    dist_cfg: {'backend': 'nccl'}
    seed: None
    Distributed launcher: pytorch
    Distributed training: True
    GPU number: 2
------------------------------------------------------------

04/02 17:15:35 - mmengine - INFO - Config:
temperature = 1.0
model = dict(
    type='MoCoV3',
    base_momentum=0.99,
    data_preprocessor=dict(
        mean=(123.675, 116.28, 103.53),
        std=(58.395, 57.12, 57.375),
        bgr_to_rgb=True),
    backbone=dict(
        type='ResNet',
        depth=50,
        in_channels=3,
        out_indices=[4],
        norm_cfg=dict(type='SyncBN')),
    neck=dict(
        type='NonLinearNeck',
        in_channels=2048,
        hid_channels=4096,
        out_channels=256,
        num_layers=2,
        with_bias=False,
        with_last_bn=True,
        with_last_bn_affine=False,
        with_last_bias=False,
        with_avg_pool=True,
        vit_backbone=False),
    head=dict(
        type='MoCoV3Head',
        predictor=dict(
            type='NonLinearNeck',
            in_channels=256,
            hid_channels=4096,
            out_channels=256,
            num_layers=2,
            with_bias=False,
            with_last_bn=False,
            with_last_bn_affine=False,
            with_last_bias=False,
            with_avg_pool=False),
        loss=dict(type='mmcls.CrossEntropyLoss', loss_weight=2.0),
        temperature=1.0))
optimizer = dict(type='LARS', lr=9.6, weight_decay=1e-06, momentum=0.9)
optim_wrapper = dict(
    type='AmpOptimWrapper',
    optimizer=dict(type='LARS', lr=9.6, weight_decay=1e-06, momentum=0.9),
    loss_scale='dynamic',
    paramwise_cfg=dict(
        custom_keys=dict({
            'bn': dict(decay_mult=0, lars_exclude=True),
            'bias': dict(decay_mult=0, lars_exclude=True),
            'downsample.1': dict(decay_mult=0, lars_exclude=True)
        })))
param_scheduler = [
    dict(
        type='LinearLR',
        start_factor=0.0001,
        by_epoch=True,
        begin=0,
        end=10,
        convert_to_iter_based=True),
    dict(
        type='CosineAnnealingLR',
        T_max=90,
        by_epoch=True,
        begin=10,
        end=100,
        convert_to_iter_based=True)
]
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=100)
default_scope = 'mmselfsup'
default_hooks = dict(
    runtime_info=dict(type='RuntimeInfoHook'),
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=50),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3),
    sampler_seed=dict(type='DistSamplerSeedHook'))
env_cfg = dict(
    cudnn_benchmark=False,
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    dist_cfg=dict(backend='nccl'))
log_processor = dict(
    window_size=10,
    custom_cfg=[dict(data_src='', method='mean', window_size='global')])
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='SelfSupVisualizer',
    vis_backends=[dict(type='LocalVisBackend')],
    name='visualizer')
log_level = 'INFO'
load_from = None
resume = False
dataset_type = 'mmcls.CustomDataset'
data_root = '/data/代码/mmselfsup/data/'
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='RandomResizedCrop',
        size=224,
        scale=(0.2, 1.0),
        backend='pillow',
        interpolation='bicubic'),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackSelfSupInputs', meta_keys=['img_path'])
]
train_dataloader = dict(
    batch_size=512,
    num_workers=8,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    collate_fn=dict(type='default_collate'),
    dataset=dict(
        type='mmcls.CustomDataset',
        data_root='/data/代码/mmselfsup/data/',
        data_prefix=dict(img_path='./'),
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='RandomResizedCrop',
                size=224,
                scale=(0.2, 1.0),
                backend='pillow',
                interpolation='bicubic'),
            dict(type='RandomFlip', prob=0.5),
            dict(type='PackSelfSupInputs', meta_keys=['img_path'])
        ]))
launcher = 'pytorch'
work_dir = './work_dirs/selfsup/mocov3_resnet50_8xb512-amp-coslr-100e_in1k'

04/02 17:15:35 - mmengine - WARNING - The "visualizer" registry in mmselfsup did not set import location. Fallback to call `mmselfsup.utils.register_all_modules` instead.
04/02 17:15:35 - mmengine - WARNING - The "vis_backend" registry in mmselfsup did not set import location. Fallback to call `mmselfsup.utils.register_all_modules` instead.
04/02 17:15:35 - mmengine - WARNING - The "model" registry in mmselfsup did not set import location. Fallback to call `mmselfsup.utils.register_all_modules` instead.
04/02 17:15:36 - mmengine - WARNING - The "model" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead.
04/02 17:15:36 - mmengine - WARNING - The value of momentum in EMA is usually a small number,which is different from the conventional notion of momentum but got 0.99. Please make sure the value is correct.
04/02 17:15:36 - mmengine - WARNING - The "hook" registry in mmselfsup did not set import location. Fallback to call `mmselfsup.utils.register_all_modules` instead.
04/02 17:15:36 - mmengine - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) RuntimeInfoHook                    
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
before_train:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_train_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(NORMAL      ) DistSamplerSeedHook                
 -------------------- 
before_train_iter:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_train_iter:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
after_train_epoch:
(NORMAL      ) IterTimerHook                      
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_val_epoch:
(NORMAL      ) IterTimerHook                      
 -------------------- 
before_val_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_val_iter:
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_val_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_test_epoch:
(NORMAL      ) IterTimerHook                      
 -------------------- 
before_test_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_test_iter:
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_test_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_run:
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
04/02 17:15:36 - mmengine - WARNING - The "loop" registry in mmselfsup did not set import location. Fallback to call `mmselfsup.utils.register_all_modules` instead.
04/02 17:15:36 - mmengine - WARNING - The "dataset" registry in mmselfsup did not set import location. Fallback to call `mmselfsup.utils.register_all_modules` instead.
04/02 17:15:36 - mmengine - WARNING - The "dataset" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead.
04/02 17:15:36 - mmengine - WARNING - The "transform" registry in mmselfsup did not set import location. Fallback to call `mmselfsup.utils.register_all_modules` instead.
04/02 17:15:36 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
04/02 17:15:36 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
04/02 17:15:36 - mmengine - WARNING - The "data sampler" registry in mmselfsup did not set import location. Fallback to call `mmselfsup.utils.register_all_modules` instead.
04/02 17:15:36 - mmengine - WARNING - `persistent_workers` is only available when pytorch version >= 1.7
04/02 17:15:36 - mmengine - WARNING - The "optimizer wrapper constructor" registry in mmselfsup did not set import location. Fallback to call `mmselfsup.utils.register_all_modules` instead.
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.bn1.weight:lr=9.6
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.bn1.weight:weight_decay=0.0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.bn1.weight:decay_mult=0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.bn1.weight:lars_exclude=True
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.bn1.bias:lr=9.6
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.bn1.bias:weight_decay=0.0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.bn1.bias:decay_mult=0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.bn1.bias:lars_exclude=True
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn1.weight:lr=9.6
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn1.weight:weight_decay=0.0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn1.weight:decay_mult=0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn1.weight:lars_exclude=True
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn1.bias:lr=9.6
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn1.bias:weight_decay=0.0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn1.bias:decay_mult=0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn1.bias:lars_exclude=True
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn2.weight:lr=9.6
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn2.weight:weight_decay=0.0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn2.weight:decay_mult=0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn2.weight:lars_exclude=True
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn2.bias:lr=9.6
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn2.bias:weight_decay=0.0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn2.bias:decay_mult=0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn2.bias:lars_exclude=True
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn3.weight:lr=9.6
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn3.weight:weight_decay=0.0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn3.weight:decay_mult=0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn3.weight:lars_exclude=True
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn3.bias:lr=9.6
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn3.bias:weight_decay=0.0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn3.bias:decay_mult=0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.bn3.bias:lars_exclude=True
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.downsample.1.weight:lr=9.6
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.downsample.1.weight:weight_decay=0.0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.downsample.1.weight:decay_mult=0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.downsample.1.weight:lars_exclude=True
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.downsample.1.bias:lr=9.6
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.downsample.1.bias:weight_decay=0.0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.downsample.1.bias:decay_mult=0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.0.downsample.1.bias:lars_exclude=True
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn1.weight:lr=9.6
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn1.weight:weight_decay=0.0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn1.weight:decay_mult=0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn1.weight:lars_exclude=True
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn1.bias:lr=9.6
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn1.bias:weight_decay=0.0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn1.bias:decay_mult=0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn1.bias:lars_exclude=True
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn2.weight:lr=9.6
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn2.weight:weight_decay=0.0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn2.weight:decay_mult=0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn2.weight:lars_exclude=True
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn2.bias:lr=9.6
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn2.bias:weight_decay=0.0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn2.bias:decay_mult=0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn2.bias:lars_exclude=True
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn3.weight:lr=9.6
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn3.weight:weight_decay=0.0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn3.weight:decay_mult=0
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn3.weight:lars_exclude=True
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn3.bias:lr=9.6
04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer1.1.bn3.bias:weight_decay=0.0
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04/02 17:15:36 - mmengine - INFO - paramwise_options -- backbone.layer4.2.bn1.weight:lr=9.6
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04/02 17:15:36 - mmengine - INFO - paramwise_options -- neck.bn0.weight:lr=9.6
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04/02 17:15:36 - mmengine - INFO - paramwise_options -- head.predictor.bn0.weight:lr=9.6
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04/02 17:15:36 - mmengine - INFO - paramwise_options -- head.predictor.bn0.bias:lr=9.6
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04/02 17:15:36 - mmengine - WARNING - The "optimizer" registry in mmselfsup did not set import location. Fallback to call `mmselfsup.utils.register_all_modules` instead.
04/02 17:15:36 - mmengine - WARNING - The "optimizer_wrapper" registry in mmselfsup did not set import location. Fallback to call `mmselfsup.utils.register_all_modules` instead.
04/02 17:15:36 - mmengine - WARNING - The "parameter scheduler" registry in mmselfsup did not set import location. Fallback to call `mmselfsup.utils.register_all_modules` instead.
04/02 17:15:36 - mmengine - WARNING - The "weight initializer" registry in mmselfsup did not set import location. Fallback to call `mmselfsup.utils.register_all_modules` instead.
04/02 17:15:36 - mmengine - INFO - Checkpoints will be saved to /data/代码/mmselfsup/work_dirs/selfsup/mocov3_resnet50_8xb512-amp-coslr-100e_in1k.
Traceback (most recent call last):
  File "tools/train.py", line 99, in <module>
    main()
  File "tools/train.py", line 95, in main
    runner.train()
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1701, in train
    model = self.train_loop.run()  # type: ignore
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/mmengine/runner/loops.py", line 96, in run
    self.run_epoch()
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/mmengine/runner/loops.py", line 112, in run_epoch
    self.run_iter(idx, data_batch)
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/mmengine/runner/loops.py", line 128, in run_iter
    outputs = self.runner.model.train_step(
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 121, in train_step
    losses = self._run_forward(data, mode='loss')
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 163, in _run_forward
    results = self(*data, mode=mode)
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 511, in forward
    output = self.module(*inputs[0], **kwargs[0])
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/data/代码/mmselfsup/mmselfsup/models/algorithms/base.py", line 127, in forward
    return self.loss(inputs, data_samples)
  File "/data/代码/mmselfsup/mmselfsup/models/algorithms/mocov3.py", line 86, in loss
    view_2 = inputs[1]
IndexError: list index out of range
Traceback (most recent call last):
  File "tools/train.py", line 99, in <module>
    main()
  File "tools/train.py", line 95, in main
    runner.train()
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1701, in train
    model = self.train_loop.run()  # type: ignore
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/mmengine/runner/loops.py", line 96, in run
    self.run_epoch()
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/mmengine/runner/loops.py", line 112, in run_epoch
    self.run_iter(idx, data_batch)
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/mmengine/runner/loops.py", line 128, in run_iter
    outputs = self.runner.model.train_step(
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 121, in train_step
    losses = self._run_forward(data, mode='loss')
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/mmengine/model/wrappers/distributed.py", line 163, in _run_forward
    results = self(*data, mode=mode)
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 511, in forward
    output = self.module(*inputs[0], **kwargs[0])
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/data/代码/mmselfsup/mmselfsup/models/algorithms/base.py", line 127, in forward
    return self.loss(inputs, data_samples)
  File "/data/代码/mmselfsup/mmselfsup/models/algorithms/mocov3.py", line 86, in loss
    view_2 = inputs[1]
IndexError: list index out of range
Traceback (most recent call last):
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/torch/distributed/launch.py", line 261, in <module>
    main()
  File "/home/think/anaconda3/envs/py16cu102/lib/python3.8/site-packages/torch/distributed/launch.py", line 256, in main
    raise subprocess.CalledProcessError(returncode=process.returncode,
subprocess.CalledProcessError: Command '['/home/think/anaconda3/envs/py16cu102/bin/python', '-u', 'tools/train.py', '--local_rank=1', 'configs/selfsup/mocov3/mocov3_resnet50_8xb512-amp-coslr-100e_in1k.py', '--launcher', 'pytorch']' returned non-zero exit status 1.
fangyixiao18 commented 1 year ago

As mocov3 is contrastive learning, you are supposed to use MultiView pipeline wrapper, you could check the original config file of mocov3

fangyixiao18 commented 1 year ago

Besides, there is some limitation of the current CustomDataset, you could try code in this branch. We unify and refactor the mmselfsup and misclassification, where you could put every image to one folder.

kalikhademi commented 1 year ago

I want to use OpenImage dataset to pretrain BYOL and SwAV and then use the weights for object detection. I will have the following data categories. OpenImages: -->Train(images) -->Valid(images)

However, I do not know how to make the meta file you mentioned in the data hierarchy. I appreciate any help.

Thanks,