62 lines
1.7 KiB
Python
62 lines
1.7 KiB
Python
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_base_ = [
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'../_base_/datasets/imagenet_bs256_simmim_192.py',
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'../_base_/default_runtime.py',
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]
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# model settings
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model = dict(
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type='SimMIM',
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backbone=dict(
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type='SimMIMSwinTransformer',
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arch='large',
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img_size=192,
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stage_cfgs=dict(block_cfgs=dict(window_size=12)),
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pad_small_map=True),
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neck=dict(type='SimMIMNeck', in_channels=192 * 2**3, encoder_stride=32),
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head=dict(
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type='SimMIMHead',
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patch_size=4,
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loss=dict(type='SimMIMReconstructionLoss', encoder_in_channels=3)))
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# optimizer wrapper
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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=1e-4 * 2048 / 512, betas=(0.9, 0.999), eps=1e-8),
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clip_grad=dict(max_norm=5.0),
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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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'absolute_pos_embed': dict(decay_mult=0.),
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'relative_position_bias_table': 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=5e-7 / 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='MultiStepLR',
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milestones=[700],
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by_epoch=True,
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begin=10,
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end=800,
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convert_to_iter_based=True)
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]
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# runtime
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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=10, max_keep_ckpts=3))
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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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