85 lines
2.1 KiB
Python
85 lines
2.1 KiB
Python
_base_ = [
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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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# dataset 16 x 256
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train_dataloader = dict(batch_size=256, num_workers=8)
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# model settings, use ConvNeXt V2
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model = dict(
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type='SparK',
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input_size=224,
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downsample_raito=32,
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mask_ratio=0.6,
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enc_dec_norm_cfg=dict(type='SparseLN2d', eps=1e-6),
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enc_dec_norm_dim=768,
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backbone=dict(
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type='SparseConvNeXt',
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arch='tiny',
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drop_path_rate=0.2,
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out_indices=(0, 1, 2, 3),
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gap_before_output=False,
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layer_scale_init_value=0.,
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use_grn=True,
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),
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neck=dict(
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type='SparKLightDecoder',
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feature_dim=512,
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upsample_ratio=32, # equal to downsample_raito
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mid_channels=0,
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last_act=False),
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head=dict(
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type='SparKPretrainHead',
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loss=dict(type='PixelReconstructionLoss', criterion='L2')))
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# optimizer wrapper
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optimizer = dict(
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type='Lamb', lr=2e-4 * 4096 / 512, betas=(0.9, 0.95), weight_decay=0.04)
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optim_wrapper = dict(
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type='AmpOptimWrapper',
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optimizer=optimizer,
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clip_grad=dict(max_norm=5.0),
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paramwise_cfg=dict(
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bias_decay_mult=0.0,
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flat_decay_mult=0.0,
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custom_keys={
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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=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=780,
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by_epoch=True,
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begin=20,
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end=800,
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convert_to_iter_based=True),
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dict(
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type='CosineAnnealingWeightDecay',
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eta_min=0.2,
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T_max=800,
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by_epoch=True,
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begin=0,
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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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logger=dict(type='LoggerHook', interval=100),
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# only keeps the latest 3 checkpoints
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checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=2))
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# randomness
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randomness = dict(seed=0, diff_rank_seed=True)
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