52 lines
1.3 KiB
Python
52 lines
1.3 KiB
Python
_base_ = './_base_.py'
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model = dict(
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backbone=dict(
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stem_channels=160,
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drop_path_rate=0.1,
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stage_blocks=[5, 5, 22, 5],
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groups=[10, 20, 40, 80],
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layer_scale=1e-5,
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offset_scale=2.0,
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post_norm=True),
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head=dict(in_channels=1920))
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='RandomResizedCrop',
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scale=384,
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backend='pillow',
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interpolation='bicubic'),
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dict(type='RandomFlip', prob=0.5, direction='horizontal'),
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dict(type='PackInputs')
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='ResizeEdge',
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scale=384,
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edge='short',
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backend='pillow',
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interpolation='bicubic'),
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dict(type='CenterCrop', crop_size=384),
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dict(type='PackInputs')
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]
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train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
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val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
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test_dataloader = val_dataloader
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optim_wrapper = dict(optimizer=dict(lr=5e-6))
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param_scheduler = [
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dict(
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type='LinearLR',
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by_epoch=True,
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begin=0,
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end=2,
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convert_to_iter_based=True),
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dict(type='CosineAnnealingLR', T_max=18, by_epoch=True, begin=2, end=20)
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]
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train_cfg = dict(by_epoch=True, max_epochs=20, val_interval=1)
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