| auto_scale_lr = dict(base_batch_size=4096) |
| data_preprocessor = dict( |
| mean=[ |
| 123.675, |
| 116.28, |
| 103.53, |
| ], |
| non_blocking=True, |
| std=[ |
| 58.395, |
| 57.12, |
| 57.375, |
| ], |
| to_rgb=True, |
| type='SelfSupDataPreprocessor') |
| data_root = '/workdir/ILSVRC2012/' |
| dataset_type = 'ImageNet' |
| default_hooks = dict( |
| checkpoint=dict(interval=1, max_keep_ckpts=2, type='CheckpointHook'), |
| logger=dict(interval=20, type='LoggerHook'), |
| param_scheduler=dict(type='ParamSchedulerHook'), |
| sampler_seed=dict(type='DistSamplerSeedHook'), |
| timer=dict(type='IterTimerHook'), |
| visualization=dict(enable=False, type='VisualizationHook')) |
| default_scope = 'mmpretrain' |
| env_cfg = dict( |
| cudnn_benchmark=True, |
| dist_cfg=dict(backend='nccl'), |
| mp_cfg=dict(mp_start_method='spawn', opencv_num_threads=0)) |
| launcher = 'pytorch' |
| load_from = None |
| log_level = 'INFO' |
| model = dict( |
| backbone=dict(arch='l', mask_ratio=0.75, patch_size=16, type='MAELLaMA'), |
| head=dict( |
| loss=dict(criterion='L2', type='PixelReconstructionLoss'), |
| norm_pix=True, |
| patch_size=16, |
| type='MAEPretrainHead'), |
| init_cfg=[ |
| dict(distribution='uniform', layer='Linear', type='Xavier'), |
| dict(bias=0.0, layer='LayerNorm', type='Constant', val=1.0), |
| ], |
| neck=dict( |
| decoder_depth=8, |
| decoder_embed_dim=512, |
| decoder_num_heads=16, |
| embed_dim=1024, |
| in_chans=3, |
| mlp_ratio=4.0, |
| patch_size=16, |
| type='MAEPretrainDecoder'), |
| type='MAE') |
| optim_wrapper = dict( |
| loss_scale='dynamic', |
| optimizer=dict( |
| betas=( |
| 0.9, |
| 0.95, |
| ), lr=0.0024, type='AdamW', weight_decay=0.05), |
| paramwise_cfg=dict( |
| custom_keys=dict( |
| bias=dict(decay_mult=0.0), |
| cls_token=dict(decay_mult=0.0), |
| ln=dict(decay_mult=0.0), |
| mask_token=dict(decay_mult=0.0), |
| pos_embed=dict(decay_mult=0.0))), |
| type='AmpOptimWrapper') |
| param_scheduler = [ |
| dict( |
| begin=0, |
| by_epoch=True, |
| convert_to_iter_based=True, |
| end=40, |
| start_factor=1e-09, |
| type='LinearLR'), |
| dict( |
| T_max=760, |
| begin=40, |
| by_epoch=True, |
| convert_to_iter_based=True, |
| end=800, |
| type='CosineAnnealingLR'), |
| ] |
| randomness = dict(deterministic=False, diff_rank_seed=True, seed=0) |
| resume = True |
| train_cfg = dict(max_epochs=800, type='EpochBasedTrainLoop') |
| train_dataloader = dict( |
| batch_size=256, |
| collate_fn=dict(type='default_collate'), |
| dataset=dict( |
| data_root='/workdir/ILSVRC2012/', |
| pipeline=[ |
| dict(type='LoadImageFromFile'), |
| dict( |
| backend='pillow', |
| crop_ratio_range=( |
| 0.2, |
| 1.0, |
| ), |
| interpolation='bicubic', |
| scale=224, |
| type='RandomResizedCrop'), |
| dict(prob=0.5, type='RandomFlip'), |
| dict(type='PackInputs'), |
| ], |
| split='train', |
| type='ImageNet'), |
| num_workers=8, |
| persistent_workers=True, |
| pin_memory=True, |
| sampler=dict(shuffle=True, type='DefaultSampler')) |
| train_pipeline = [ |
| dict(type='LoadImageFromFile'), |
| dict( |
| backend='pillow', |
| crop_ratio_range=( |
| 0.2, |
| 1.0, |
| ), |
| interpolation='bicubic', |
| scale=224, |
| type='RandomResizedCrop'), |
| dict(prob=0.5, type='RandomFlip'), |
| dict(type='PackInputs'), |
| ] |
| vis_backends = [ |
| dict(type='LocalVisBackend'), |
| ] |
| visualizer = dict( |
| type='UniversalVisualizer', vis_backends=[ |
| dict(type='LocalVisBackend'), |
| ]) |
| work_dir = './work_dirs/mae_lama-large-p16_8xb512-amp-coslr-800e_in1k' |
|
|
