119 lines
3.3 KiB
Python
119 lines
3.3 KiB
Python
_base_ = [
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'../_base_/datasets/imagenet_bs256_beitv2.py',
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'../_base_/default_runtime.py',
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]
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# model settings
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vqkd_encoder = dict(
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arch='base',
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img_size=224,
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patch_size=16,
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in_channels=3,
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out_indices=-1,
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drop_rate=0.,
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drop_path_rate=0.,
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norm_cfg=dict(type='LN', eps=1e-6),
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final_norm=True,
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out_type='featmap',
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with_cls_token=True,
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frozen_stages=-1,
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use_abs_pos_emb=True,
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use_rel_pos_bias=False,
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use_shared_rel_pos_bias=False,
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layer_scale_init_value=0.,
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interpolate_mode='bicubic',
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patch_cfg=dict(),
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layer_cfgs=dict(),
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init_cfg=None)
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layer_scale_init_value = 0.1
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drop_path_rate = 0.1 # 0. for 300 epochs and 0.1 for 1600 epochs.
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model = dict(
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type='BEiT',
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backbone=dict(
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type='BEiTPretrainViT',
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arch='base',
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patch_size=16,
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out_indices=[-4, -1],
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drop_path_rate=drop_path_rate,
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final_norm=False,
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layer_scale_init_value=layer_scale_init_value,
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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=dict(
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type='BEiTV2Neck',
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num_layers=2,
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early_layers=9,
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backbone_arch='base',
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drop_path_rate=drop_path_rate,
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layer_scale_init_value=layer_scale_init_value,
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),
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head=dict(
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type='BEiTV2Head',
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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='VQKD',
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encoder_config=vqkd_encoder,
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init_cfg=dict(
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type='Pretrained',
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checkpoint= # noqa
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'https://download.openmmlab.com/mmselfsup/1.x/target_generator_ckpt/vqkd_encoder.pth' # noqa
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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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# betas: (0.9, 0.98) for 300 epochs and (0.9, 0.999) for 1600 epochs.
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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=1600,
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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=1600)
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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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