| |
| norm_cfg = dict(type='SyncBN', requires_grad=True) |
| model = dict( |
| type='EncoderDecoder', |
| pretrained=None, |
| backbone=dict( |
| type='UNet', |
| in_channels=3, |
| base_channels=64, |
| num_stages=5, |
| strides=(1, 1, 1, 1, 1), |
| enc_num_convs=(2, 2, 2, 2, 2), |
| dec_num_convs=(2, 2, 2, 2), |
| downsamples=(True, True, True, True), |
| enc_dilations=(1, 1, 1, 1, 1), |
| dec_dilations=(1, 1, 1, 1), |
| with_cp=False, |
| conv_cfg=None, |
| norm_cfg=norm_cfg, |
| act_cfg=dict(type='ReLU'), |
| upsample_cfg=dict(type='InterpConv'), |
| norm_eval=False), |
| decode_head=dict( |
| type='ASPPHead', |
| in_channels=64, |
| in_index=4, |
| channels=16, |
| dilations=(1, 12, 24, 36), |
| dropout_ratio=0.1, |
| num_classes=2, |
| 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=128, |
| in_index=3, |
| channels=64, |
| num_convs=1, |
| concat_input=False, |
| dropout_ratio=0.1, |
| num_classes=2, |
| 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='slide', crop_size=256, stride=170)) |
|
|
