60 lines
1.6 KiB
Python
60 lines
1.6 KiB
Python
_base_ = [
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'../../_base_/models/swin_transformer/base_224.py',
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'../../_base_/datasets/imagenet_bs256_swin_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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backbone=dict(
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img_size=192,
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drop_path_rate=0.1,
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stage_cfgs=dict(block_cfgs=dict(window_size=6)),
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init_cfg=dict(type='Pretrained', checkpoint='', prefix='backbone.')))
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# optimizer settings
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optim_wrapper = dict(
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type='AmpOptimWrapper',
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optimizer=dict(type='AdamW', lr=5e-3, weight_decay=0.05),
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clip_grad=dict(max_norm=5.0),
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constructor='LearningRateDecayOptimWrapperConstructor',
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paramwise_cfg=dict(
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layer_decay_rate=0.9,
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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=0.0),
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'.relative_position_bias_table': 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=2.5e-7 / 1.25e-3,
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by_epoch=True,
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begin=0,
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end=20,
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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=80,
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eta_min=2.5e-7 * 2048 / 512,
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by_epoch=True,
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begin=20,
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end=100,
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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=100)
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val_cfg = dict()
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test_cfg = dict()
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default_hooks = dict(
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# save checkpoint per epoch.
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checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3),
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logger=dict(type='LoggerHook', interval=100))
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randomness = dict(seed=0)
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