| norm_cfg = dict(type='SyncBN', requires_grad=True) |
| model = dict( |
| type='EncoderDecoder', |
| pretrained=None, |
| backbone=dict( |
| type='BEiT', |
| img_size=(640, 640), |
| patch_size=16, |
| in_channels=3, |
| embed_dims=768, |
| num_layers=12, |
| num_heads=12, |
| mlp_ratio=4, |
| out_indices=(3, 5, 7, 11), |
| qv_bias=True, |
| attn_drop_rate=0.0, |
| drop_path_rate=0.1, |
| norm_cfg=dict(type='LN', eps=1e-6), |
| act_cfg=dict(type='GELU'), |
| norm_eval=False, |
| init_values=0.1), |
| neck=dict(type='Feature2Pyramid', embed_dim=768, rescales=[4, 2, 1, 0.5]), |
| decode_head=dict( |
| type='UPerHead', |
| in_channels=[768, 768, 768, 768], |
| in_index=[0, 1, 2, 3], |
| pool_scales=(1, 2, 3, 6), |
| channels=768, |
| dropout_ratio=0.1, |
| num_classes=150, |
| norm_cfg=norm_cfg, |
| align_corners=False, |
| loss_decode=dict( |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), |
| auxiliary_head=dict( |
| type='FCNHead', |
| in_channels=768, |
| in_index=2, |
| channels=256, |
| num_convs=1, |
| concat_input=False, |
| dropout_ratio=0.1, |
| num_classes=150, |
| norm_cfg=norm_cfg, |
| align_corners=False, |
| loss_decode=dict( |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), |
| |
| train_cfg=dict(), |
| test_cfg=dict(mode='whole')) |
|
|
