64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
_base_ = 'knet-s3_r50-d8_upernet_8xb2-adamw-80k_ade20k-512x512.py'
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checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220308-f41b89d3.pth' # noqa
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# model settings
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norm_cfg = dict(type='SyncBN', requires_grad=True)
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num_stages = 3
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conv_kernel_size = 1
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model = dict(
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type='EncoderDecoder',
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pretrained=checkpoint_file,
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backbone=dict(
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_delete_=True,
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type='SwinTransformer',
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embed_dims=96,
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depths=[2, 2, 6, 2],
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num_heads=[3, 6, 12, 24],
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window_size=7,
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mlp_ratio=4,
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qkv_bias=True,
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qk_scale=None,
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drop_rate=0.,
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attn_drop_rate=0.,
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drop_path_rate=0.3,
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use_abs_pos_embed=False,
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patch_norm=True,
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out_indices=(0, 1, 2, 3)),
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decode_head=dict(
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kernel_generate_head=dict(in_channels=[96, 192, 384, 768])),
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auxiliary_head=dict(in_channels=384))
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optim_wrapper = dict(
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_delete_=True,
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type='OptimWrapper',
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# modify learning rate following the official implementation of Swin Transformer # noqa
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optimizer=dict(
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type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.0005),
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paramwise_cfg=dict(
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custom_keys={
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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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'norm': dict(decay_mult=0.)
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}),
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clip_grad=dict(max_norm=1, norm_type=2))
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# learning policy
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param_scheduler = [
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dict(
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type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
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end=1000),
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dict(
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type='MultiStepLR',
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begin=1000,
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end=80000,
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milestones=[60000, 72000],
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by_epoch=False,
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)
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]
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# In K-Net implementation we use batch size 2 per GPU as default
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train_dataloader = dict(batch_size=2, num_workers=2)
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val_dataloader = dict(batch_size=1, num_workers=4)
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test_dataloader = val_dataloader
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