49 lines
1.0 KiB
Python
49 lines
1.0 KiB
Python
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_base_ = [
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'../_base_/models/cae.py',
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'../_base_/datasets/imagenet_cae.py',
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'../_base_/schedules/adamw_coslr-200e_in1k.py',
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'../_base_/default_runtime.py',
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]
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# dataset
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data = dict(samples_per_gpu=64, workers_per_gpu=8)
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# optimizer
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optimizer = dict(
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lr=1.5e-3,
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paramwise_options={
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'norm': dict(weight_decay=0.),
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'bias': dict(weight_decay=0.),
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'gamma': dict(weight_decay=0.)
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},
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betas=(0.9, 0.999))
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# learning policy
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lr_config = dict(
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policy='StepFixCosineAnnealing',
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min_lr=1e-5,
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warmup='linear',
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warmup_iters=10,
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warmup_ratio=1e-4,
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warmup_by_epoch=True,
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by_epoch=False)
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# schedule
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runner = dict(max_epochs=300)
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# clip gradient
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optimizer_config = dict(grad_clip=dict(max_norm=3.0))
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# mixed precision
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fp16 = dict(loss_scale='dynamic')
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# runtime
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checkpoint_config = dict(interval=1, max_keep_ckpts=2, out_dir='')
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persistent_workers = True
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log_config = dict(
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interval=100, hooks=[
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dict(type='TextLoggerHook'),
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])
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find_unused_parameters = True
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