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* hivit added * Update hivit.py * Update hivit.py * Add files via upload * Update __init__.py * Add files via upload * Update __init__.py * Add files via upload * Update hivit.py * Add files via upload * Add files via upload * Add files via upload * Add files via upload * Update itpn.py * Add files via upload * Update __init__.py * Update mae_hivit-base-p16.py * Delete mim_itpn-base-p16.py * Add files via upload * Update itpn_hivit-base-p16.py * Update itpn.py * Update hivit.py * Update __init__.py * Update mae.py * Delete hivit.py * Update __init__.py * Delete configs/itpn directory * Add files via upload * Add files via upload * Delete configs/hivit directory * Add files via upload * refactor and add metafile and readme * update clip * add ut * update ut * update * update docstring * update model.rst --------- Co-authored-by: 田运杰 <48153283+sunsmarterjie@users.noreply.github.com>
57 lines
1.4 KiB
Python
57 lines
1.4 KiB
Python
_base_ = [
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'../_base_/models/mae_hivit-base-p16.py',
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'../_base_/datasets/imagenet_bs512_mae.py',
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'../_base_/default_runtime.py',
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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',
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lr=1.5e-4 * 4096 / 256,
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betas=(0.9, 0.95),
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weight_decay=0.05),
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paramwise_cfg=dict(
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custom_keys={
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'norm': 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.),
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'mask_token': dict(decay_mult=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=40,
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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=760,
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by_epoch=True,
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begin=40,
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end=800,
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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=800)
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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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# auto resume
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resume = 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=4096)
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