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c11da07e18
* delete convert function and add instruction to README.md * unified model convert and README * remove url * fix import error * fix unittest * rename pretrain * rename vit and deit pretrain * Update upernet_deit-b16_512x512_160k_ade20k.py * Update upernet_deit-b16_512x512_80k_ade20k.py * Update upernet_deit-b16_ln_mln_512x512_160k_ade20k.py * Update upernet_deit-b16_mln_512x512_160k_ade20k.py * Update upernet_deit-s16_512x512_160k_ade20k.py * Update upernet_deit-s16_512x512_80k_ade20k.py * Update upernet_deit-s16_ln_mln_512x512_160k_ade20k.py * Update upernet_deit-s16_mln_512x512_160k_ade20k.py Co-authored-by: Jiarui XU <xvjiarui0826@gmail.com> Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
96 lines
2.8 KiB
Python
96 lines
2.8 KiB
Python
# model settings
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backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True)
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norm_cfg = dict(type='SyncBN', requires_grad=True)
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model = dict(
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type='EncoderDecoder',
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pretrained='pretrain/jx_vit_large_p16_384-b3be5167.pth',
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backbone=dict(
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type='VisionTransformer',
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img_size=(768, 768),
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patch_size=16,
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in_channels=3,
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embed_dims=1024,
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num_layers=24,
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num_heads=16,
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out_indices=(5, 11, 17, 23),
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drop_rate=0.1,
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norm_cfg=backbone_norm_cfg,
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with_cls_token=False,
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interpolate_mode='bilinear',
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),
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neck=dict(
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type='MLANeck',
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in_channels=[1024, 1024, 1024, 1024],
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out_channels=256,
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norm_cfg=norm_cfg,
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act_cfg=dict(type='ReLU'),
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),
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decode_head=dict(
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type='SETRMLAHead',
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in_channels=(256, 256, 256, 256),
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channels=512,
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in_index=(0, 1, 2, 3),
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dropout_ratio=0,
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mla_channels=128,
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num_classes=19,
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norm_cfg=norm_cfg,
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
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auxiliary_head=[
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dict(
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type='FCNHead',
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in_channels=256,
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channels=256,
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in_index=0,
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dropout_ratio=0,
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num_convs=0,
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kernel_size=1,
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concat_input=False,
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num_classes=19,
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
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dict(
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type='FCNHead',
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in_channels=256,
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channels=256,
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in_index=1,
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dropout_ratio=0,
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num_convs=0,
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kernel_size=1,
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concat_input=False,
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num_classes=19,
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
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dict(
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type='FCNHead',
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in_channels=256,
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channels=256,
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in_index=2,
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dropout_ratio=0,
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num_convs=0,
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kernel_size=1,
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concat_input=False,
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num_classes=19,
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
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dict(
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type='FCNHead',
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in_channels=256,
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channels=256,
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in_index=3,
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dropout_ratio=0,
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num_convs=0,
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kernel_size=1,
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concat_input=False,
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num_classes=19,
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
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],
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train_cfg=dict(),
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test_cfg=dict(mode='whole'))
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