737544f1c5
* add config * add cityscapes config * add default value to docstring * fix lint * add deit-s and deit-b * add readme * add eps at norm_cfg * add drop_path_rate experiment * add deit case at init_weight * add upernet result * update result and add upernet 160k config * update upernet result and fix settings * Update iters number * update result and delete some configs * fix import error * fix drop_path_rate * update result and restore config * update benchmark result * remove cityscapes exp * remove neck * neck exp * add more configs * fix init error * fix ffn setting * update result * update results * update result * update results and fill table * delete or rename configs * fix link delimiter * rename configs and fix link * rename neck to mln
59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
# model settings
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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='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth', # noqa
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backbone=dict(
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type='VisionTransformer',
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img_size=(512, 512),
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patch_size=16,
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in_channels=3,
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embed_dims=768,
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num_layers=12,
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num_heads=12,
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mlp_ratio=4,
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out_indices=(2, 5, 8, 11),
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qkv_bias=True,
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drop_rate=0.0,
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attn_drop_rate=0.0,
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drop_path_rate=0.0,
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with_cls_token=True,
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norm_cfg=dict(type='LN', eps=1e-6),
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act_cfg=dict(type='GELU'),
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norm_eval=False,
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out_shape='NCHW',
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interpolate_mode='bicubic'),
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neck=dict(
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type='MultiLevelNeck',
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in_channels=[768, 768, 768, 768],
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out_channels=768,
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scales=[4, 2, 1, 0.5]),
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decode_head=dict(
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type='UPerHead',
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in_channels=[768, 768, 768, 768],
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in_index=[0, 1, 2, 3],
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pool_scales=(1, 2, 3, 6),
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channels=512,
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dropout_ratio=0.1,
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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=dict(
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type='FCNHead',
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in_channels=768,
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in_index=3,
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channels=256,
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num_convs=1,
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concat_input=False,
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dropout_ratio=0.1,
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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=0.4)),
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# model training and testing settings
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train_cfg=dict(),
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test_cfg=dict(mode='whole')) # yapf: disable
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