133 lines
3.7 KiB
Python
133 lines
3.7 KiB
Python
_base_ = '../_base_/default_runtime.py'
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# dataset settings
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dataset_type = 'ImageNet'
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data_root = 'data/imagenet/'
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data_preprocessor = dict(
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type='TwoNormDataPreprocessor',
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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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second_mean=[-31.875, -31.875, -31.875],
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second_std=[318.75, 318.75, 318.75],
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to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='ColorJitter',
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brightness=0.4,
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contrast=0.4,
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saturation=0.4,
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hue=0.),
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dict(type='RandomFlip', prob=0.5, direction='horizontal'),
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dict(
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type='RandomResizedCropAndInterpolationWithTwoPic',
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size=224,
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second_size=112,
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interpolation='bicubic',
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second_interpolation='lanczos',
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scale=(0.08, 1.0)),
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dict(
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type='BEiTMaskGenerator',
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input_size=(14, 14),
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num_masking_patches=75,
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max_num_patches=None,
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min_num_patches=16),
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dict(
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type='PackSelfSupInputs',
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algorithm_keys=['mask'],
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meta_keys=['img_path'])
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]
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train_dataloader = dict(
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batch_size=256,
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num_workers=8,
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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=dataset_type,
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data_root=data_root,
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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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# model settings
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model = dict(
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type='BEiT',
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backbone=dict(
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type='BEiTViT',
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arch='base',
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patch_size=16,
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drop_path_rate=0.1,
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final_norm=True,
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layer_scale_init_value=0.1,
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init_cfg=[
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dict(type='TruncNormal', std=0.02, layer='Linear'),
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dict(type='TruncNormal', std=0.02, layer='Conv2d'),
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dict(type='Constant', layer='LayerNorm', val=1.0, bias=0.0)
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]),
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neck=None,
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head=dict(
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type='BEiTV1Head',
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embed_dims=768,
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num_embed=8192,
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loss=dict(type='CrossEntropyLoss')),
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target_generator=dict(
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type='DALL-E',
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init_cfg=dict(
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type='Pretrained',
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checkpoint= # noqa: E251
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'https://download.openmmlab.com/mmselfsup/1.x/target_generator_ckpt/dalle_encoder.pth', # noqa: E501
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)))
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# optimizer wrapper
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optim_wrapper = dict(
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type='AmpOptimWrapper',
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loss_scale='dynamic',
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optimizer=dict(
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type='AdamW', lr=1.5e-3, betas=(0.9, 0.999), weight_decay=0.05),
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clip_grad=dict(max_norm=3.0),
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paramwise_cfg=dict(
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custom_keys={
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# the following configurations are designed for BEiT
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'.ln': dict(decay_mult=0.0),
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'.bias': dict(decay_mult=0.0),
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'q_bias': dict(decay_mult=0.0),
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'v_bias': dict(decay_mult=0.0),
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'.cls_token': dict(decay_mult=0.0),
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'.pos_embed': dict(decay_mult=0.0),
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'.gamma': dict(decay_mult=0.0),
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}))
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# learning rate scheduler
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param_scheduler = [
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dict(
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type='LinearLR',
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start_factor=1e-4,
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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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eta_min=1e-5,
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by_epoch=True,
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begin=10,
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end=300,
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convert_to_iter_based=True)
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]
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# runtime settings
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train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=300)
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default_hooks = dict(
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# only keeps the latest 3 checkpoints
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checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
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randomness = dict(seed=0, diff_rank_seed=True)
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find_unused_parameters = True
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# NOTE: `auto_scale_lr` is for automatically scaling LR
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# based on the actual training batch size.
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auto_scale_lr = dict(base_batch_size=2048)
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