mirror of
https://github.com/open-mmlab/mmselfsup.git
synced 2025-06-03 14:59:38 +08:00
c920d976e7
* [Feature]: Add the scaffold * Code camp * Update dino.py * add loss * Update dino_neck.py * data_pipeline * Update imagenet_dino.py * [Feature]: Add dino neck * [Feature]: Add dino neck * [Feature]: Add teacher temp update hook * [Feature]: Add dino algorithm * [Feature]: Add Transform * [Feature]: Add init * [Feature]: Forward DINO * [Feature]: Add DINO * [Fix]: Delete dino dataset * [Feature]: Add docstring * [Feature]: Add readme * [Fix]: Fix reviews * [Fix]: Fix lint --------- Co-authored-by: YuanLiuuuuuu <3463423099@qq.com>
104 lines
3.1 KiB
Python
104 lines
3.1 KiB
Python
model = dict(
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type='DINO',
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data_preprocessor=dict(
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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bgr_to_rgb=True),
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backbone=dict(type='mmcls.VisionTransformer', arch='b', patch_size=16),
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neck=dict(
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type='DINONeck',
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in_channels=768,
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out_channels=65536,
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hidden_channels=2048,
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bottleneck_channels=256),
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head=dict(
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type='DINOHead',
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out_channels=65536,
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num_crops=10,
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student_temp=0.1,
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center_momentum=0.9))
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='DINOMultiCrop',
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global_crops_scale=(0.4, 1.0),
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local_crops_scale=(0.05, 0.4),
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local_crops_number=8),
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dict(type='PackSelfSupInputs', meta_keys=['img_path'])
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]
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train_dataloader = dict(
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batch_size=64,
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num_workers=16,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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collate_fn=dict(type='default_collate'),
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dataset=dict(
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type='mmcls.ImageNet',
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data_root='data/imagenet/',
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ann_file='meta/train.txt',
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data_prefix=dict(img_path='train/'),
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pipeline=train_pipeline,
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))
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optimizer = dict(type='AdamW', lr=0.0024, betas=(0.9, 0.95), weight_decay=0.05)
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optim_wrapper = dict(
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type='AmpOptimWrapper',
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optimizer=dict(
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type='AdamW', lr=0.0024, betas=(0.9, 0.95), weight_decay=0.05),
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paramwise_cfg=dict(
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custom_keys=dict(
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ln=dict(decay_mult=0.0),
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bias=dict(decay_mult=0.0),
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pos_embed=dict(decay_mult=0.0),
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mask_token=dict(decay_mult=0.0),
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cls_token=dict(decay_mult=0.0))),
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loss_scale='dynamic')
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param_scheduler = [
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dict(
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type='LinearLR',
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start_factor=1e-09,
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by_epoch=True,
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begin=0,
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end=10,
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convert_to_iter_based=True),
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dict(
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type='CosineAnnealingLR',
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T_max=90,
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by_epoch=True,
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begin=10,
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end=100,
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convert_to_iter_based=True)
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]
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train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=100)
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default_scope = 'mmselfsup'
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default_hooks = dict(
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runtime_info=dict(type='RuntimeInfoHook'),
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timer=dict(type='IterTimerHook'),
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logger=dict(type='LoggerHook', interval=100),
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param_scheduler=dict(type='ParamSchedulerHook'),
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checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=1),
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sampler_seed=dict(type='DistSamplerSeedHook'))
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env_cfg = dict(
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cudnn_benchmark=False,
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mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
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dist_cfg=dict(backend='nccl'))
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log_processor = dict(
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window_size=10,
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custom_cfg=[dict(data_src='', method='mean', window_size='global')])
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vis_backends = [dict(type='LocalVisBackend')]
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visualizer = dict(
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type='SelfSupVisualizer',
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vis_backends=[dict(type='LocalVisBackend')],
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name='visualizer')
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log_level = 'INFO'
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load_from = None
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resume = True
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randomness = dict(seed=2, diff_rank_seed=True)
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custom_hooks = [
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dict(
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type='DINOTeacherTempWarmupHook',
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warmup_teacher_temp=0.04,
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teacher_temp=0.04,
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teacher_temp_warmup_epochs=0,
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max_epochs=100)
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]
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